{BUG} High Validation Accuracy but rubbish decoding

Description
I implemented a new problem and while training I get high accuracy more than 80%, but during decoding I got rubbish. Is there a bug in decoding ?
Environment information
OS: Ubuntu

$ pip freeze | grep tensor
mesh-tensorflow==0.0.4
tensor2tensor==1.11.0
tensorboard==1.12.0
tensorflow==1.12.0
tensorflow-metadata==0.9.0
tensorflow-probability==0.5.0

$ python -V
Python 2.7.15 :: Anaconda, Inc.

For bugs: reproduction and error logs
Steps to reproduce:
@registry.register_hparams
def transformer_base_single_gpu_protein():
  """HParams for transformer base model for single GPU."""
  hparams = transformer_base()
  hparams.batch_size = 256
  hparams.max_length = 4096
  hparams.learning_rate_warmup_steps = 16000
  return hparams

@registry.register_problem
class TranslateAminoProtinTokensSharedVocab(text_problems.Text2TextProblem):

  @property
  def approx_vocab_size(self):
    return 2**13  # 8192

  @property
  def is_generate_per_split(self):
    return False

  @property
  def dataset_splits(self):
    return [{
        "split": problem.DatasetSplit.TRAIN,
        "shards": 9,
    }, {
        "split": problem.DatasetSplit.EVAL,
        "shards": 1,
    }]

  def eval_metrics(self):
    return [
        metrics.Metrics.ACC,
        metrics.Metrics.ACC_TOP5,
        metrics.Metrics.ACC_PER_SEQ,
        metrics.Metrics.NEG_LOG_PERPLEXITY,
        metrics.Metrics.ROUGE_L_F,
        metrics.Metrics.APPROX_BLEU,
        metrics.Metrics.APPROX_Q3
    ]

  def generate_samples(self, data_dir, tmp_dir, dataset_split):
       datasetdf = pd.read_csv(tmp_dir + '/complete_train_dataset_seperated.csv')
       for index, row in datasetdf.iterrows():
         yield {
         "inputs": row['input'],
         "targets": row['output'],
         }

import numpy as np
from sklearn.metrics import accuracy_score
import tensorflow as tf

def computeQ3ApproxAccuracy(targets, predictions):
    accs = []
    for (references, translations) in zip(targets, predictions):

        referencesLength = len(references)
        translationsLength = len(translations)
        if (referencesLength > translationsLength):
            translations += [-1] * (referencesLength - translationsLength)

        if (translationsLength > referencesLength):
            references += [-1] * (translationsLength - referencesLength)

        accs.append(accuracy_score(references, translations))
    return np.float32(np.mean(accs))

def q3_score(predictions, labels, **unused_kwargs):
  outputs = tf.to_int32(tf.argmax(predictions, axis=-1))
  # Convert the outputs and labels to a [batch_size, input_length] tensor.
  outputs = tf.squeeze(outputs, axis=[-1, -2])
  labels = tf.squeeze(labels, axis=[-1, -2])

  q3 = tf.py_func(computeQ3ApproxAccuracy, (labels, outputs), tf.float32)
  return q3, tf.constant(1.0)

t2t-trainer   --data_dir=$DATA_DIR   --problem=$PROBLEM   --model=$MODEL   --hparams_set=$HPARAMS   --output_dir=$TRAIN_DIR

t2t-decoder   --data_dir=$DATA_DIR   --problem=$PROBLEM   --model=$MODEL   --hparams_set=$HPARAMS   --output_dir=$TRAIN_DIR   --decode_hparams="beam_size=$BEAM_SIZE,alpha=$ALPHA"

Error logs:
Training results:
INFO:tensorflow:Saving dict for global step 10000: global_step = 10000, loss = 0.6671654, metrics-translate_amino_protin_tokens_shared_vocab/targets/accuracy = 0.81997406, metrics-translate_amino_protin_tokens_shared_vocab/targets/accuracy_per_sequence = 0.0, metrics-translate_a
mino_protin_tokens_shared_vocab/targets/accuracy_top5 = 0.9993136, metrics-translate_amino_protin_tokens_shared_vocab/targets/approx_bleu_score = 0.97672814, metrics-translate_amino_protin_tokens_shared_vocab/targets/approx_q3_accuracy = 0.818279, metrics-translate_amino_protin_
tokens_shared_vocab/targets/neg_log_perplexity = -0.66406167, metrics-translate_amino_protin_tokens_shared_vocab/targets/rouge_L_fscore = 0.9852302

Decoding results:
INFO:tensorflow:Inference results INPUT: G D D N N A A E V D R Q V A Q D S A E P K T G E N A A A G D S S S T N K N A E K I V A V D I S A E T E K T Y L T H V A N D M V I P A Y A D A A K Q S D L L H D L A Q K H C Q K A P V S G D E L Q A L R D Q W L V L A Q A W A S A E M V N F G P A T A S M S N L Y I N Y Y P D E R G L V H G G V A D L I T A N P A L T A E Q L A N E S A V V Q G I P G L E E A L Y A N D S L D A G Q C A Y V M S A S S A L G T R L K D I E K N W Q Q N A I K L L A I D K T A E S D Q G L N Q W F N S L L S L V E T M K S N A I E Q P L G L S G K A K G H L P A A T A G Q S R A I I N A K L A T L N K A M T D P V L T A I L G S N N E N T V A D T L S T A L A D T T A L L A Q M P E D L A T A D K A T Q Q E L Y D H L T N I T R L I K S Q L I P T L G I R V G F N S T D G D
INFO:tensorflow:Inference results OUTPUT: X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X
X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X
INFO:tensorflow:Inference results TARGET: X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X C C C C C C C H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H C C E C H H H H H H H H H H H H H H H H H H H H H C C C C C H H H H H H H H
H H H H C C C C C C C C H H H H H H H H H H H H C C C C C C H H H H H C C H H H C H H H H H H H H H H C C C C E C H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H C C H H H C C C C C H H H H H H H H H H H H H H H H H H H H H H H H H H C C C C C C C C C C C C C C C C C H H H H
H H H H H H H H H H H H C C H H H H H H H C C C C C H H H H H H H H H H H H H H H H H H H C C C C C H H H C C H H H H H H H H H H H H H H H H H H H H H H H H H C C C C C C C X X X X X X
INFO:tensorflow:Inference results INPUT: G H M V S L T L Q V E N D L K H Q L S I G A L K P G A R L I T K N L A E Q L G M S I T P V R E A L L R L V S V N A L S V A P A Q A F T V P E V G K R Q L D E I N R I R Y E L E L M A V A L A V E N L T P Q D L A E L Q E L L E K L Q Q A Q E K G D M E Q I I N V N R L F R L A I Y H R S N M P I L C E M I E Q L W V R M G P G L H Y L Y E A I N P A E L R E H I E N Y H L L L A A L K A K D K E G C R H C L A E I M Q Q N I A I L Y Q Q Y N R
INFO:tensorflow:Inference results OUTPUT: X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X
INFO:tensorflow:Inference results TARGET: X X C C C H H H H H H H H H H H H H H C C C C C C C C E Y Y Y H H H H H H H C C C H H H H H H H H H H H H H C C C C E E Y C C C Y E E C C C C C H H H H H H H H H H H H H H H H H H H H H H H C C C C H H H H H H H H H H H H H H H H H H H H C C H H H H
H H H H H H H H H H H H H C C C C H H H H H H H H H H H H H H H H H H H H H H H H C C H H H H H H H H H H H H H H H H H H H C C C H H H H H H H H H H H H H H H H H H H H Y Y Y C X
INFO:tensorflow:Inference results INPUT: G S S G S S G E K I T K V Y E L G N E P E R K L W V D R Y L T F M E E R G S P V S S L P A V G K K P L D L F R L Y V C V K E I G G L A Q V N K N K K W R E L A T N L N V G T S S S A A S S L K K Q Y I Q Y L F A F E C K I E R G E E P P P E V F S T G D T
INFO:tensorflow:Inference results OUTPUT: X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X
INFO:tensorflow:Inference results TARGET: X X X X X X C C E C C H H H H C C C C C C H H H H H H H H H H H H H H C C C C C C E C C E E C C E E C C H H H H H H H H H H H C C H H H H H H H C C H H H H H H H C C C C C C H H H H H H H H H H H H H H C H H H H H H H H H C C C C C C C C C X X X X X
INFO:tensorflow:Inference results INPUT: N T I E L F Y M P S D E E L T A N P N A L Q E A S F T E E D I N G L K G V D G V K Q V V A S A V K S M T A R Y H E E D T D I T L N G I N S G Y M D V K K L D V Q D G R T F T D N D F L S G K R A G I I S K K M A E K L F G K T S P L G K I V W A G G Q P V E V I G V L K E E S G F L S L G L S E M Y V P F N M L K T S F G T N D Y S N V S V Q T E S A D Q I K S T G K E A A R L L N D N H G T K E A Y Q V M N
INFO:tensorflow:Inference results OUTPUT: X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X
INFO:tensorflow:Inference results TARGET: C E E E E E E C C C H H H H C C C C X X X X X C C E C H H H H H H H H C C C C E E E E E E E E E E E E E E E E C C E E E E E E E E E E C H H H H H H C C C C E E E E C C C C H H H H H H C C C E E E E C H H H H H H H H C C C C C C C C E E E E C C E E E
E E E E E E C C X X X X X X X C C C E E E E E H H H H H H H C C C C C E C E E E E E E C C H H H H H H H H H H H H H H H H H H H C C C C C E E C C C
INFO:tensorflow:Inference results INPUT: M G S S H H H H H H S S G L V P R G S H M A L G S G V V P F E N L Q I E E G I I T D A E V A R F D N I R Q G L D F G Y G P D P L A F V R W H Y D K R K N R I Y A I D E L V D H K V S L K R T A D F V R K N K Y E S A R I I A D S S E P R S I D A L K L E H G I N R I E G A K K G P D S V E H G E R W L D E L D A I V I D P L R T P N I A R E F E N I D Y Q T D K N G D P I P R L E D K D N H T I D A T R Y A F E R D M K K G G V S L W G
INFO:tensorflow:Inference results OUTPUT: X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X
INFO:tensorflow:Inference results TARGET: X X X X X X X X X X X X X X X X X X X X X X X X X X C C C C C C E E E C C C C C C H H H H H H C C C E E E E E E C C E C C E C E E E E E E E E E C C C C E E E E E E E E E E C C C C H H H H H H H H H H C C C C C C C E E E C C C C H H H H H H H H H H C
C C C C E E E C C C C H H H H H H H H H H H H C C C E E E E C C C C C H H H H H H H H H C C E E E C C C C C E E E E E C C C C C H H H H H H H H H C H H H C C X X X X X X X X
INFO:tensorflow:Inference results INPUT: M R V M I T D K L R R D S E Q I W K K I F E H P F V V Q L Y S G T L P L E K F K F Y V L Q D F N Y L V G L T R A L A V I S S K A E Y P L M A E L I E L A R D E V T V E V E N Y V K L L K E L D L T L E D A I K T E P T L V N S A Y M D F M L A T A Y K G N I I E G L T A L L P C F W S Y A E I A E Y H K D K L R D N P I K I Y R E W G K V Y L S N E Y L N L V G R L R K I I D S S G H S G Y D R L R R I F I T G S K F E L A F W E M A W R G G D V F L E H H H H H H
INFO:tensorflow:Inference results OUTPUT: X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X
INFO:tensorflow:Inference results TARGET: C C C C H H H H H H H Y C H H H H H H H H C C H H H H H H H H C C C C H H H H H H H H H H H H H H H H H H H H H H H H H H H H C C C C H H H H H H H H H H H H Y C H H H H H H H H H H H H C C C C H H H H H H C C C C H H H H H H H H H H H H H H H H C C
H H H H H H H H H H H H H H H H H H H H H C H H H H Y Y C C C H H H H H H H H H H H C H H H H H H H H H H H H H H H C C C C C C H H H H H H H H H H H H H H H H H H H H H H H H C C X X X X X X X X X X X
INFO:tensorflow:Inference results INPUT: M T A F R Q R P L R L G H R G A P L K A K E N T L E S F R L A L E A G L D G V E L D V W P T R D G V F A V R H D P D T P L G P V F Q V D Y A D L K A Q E P D L P R L E E V L A L K E A F P Q A V F N V E L K S F P G L G E E A A R R L A A L L R G R E G V W V S S F D P L A L L A L R K A A P G L P L G F L M A E D H S A L L P C L G V E A V H P H H A L V T E E A V A G W R K R G L F V V A W T V N E E G E A R R L L A L G L D G L I G D R P E V L L P L G G
INFO:tensorflow:Inference results OUTPUT: X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X
INFO:tensorflow:Inference results TARGET: X X X X X X X C E E E E E C C C C C C C C C C C H H H H H H H H H C C C C E E E E E E E E C C C C C E E E C C C C E E C C E E H H H C C H H H H H H H C C C C C E H H H H H H H H H C C C C C E E E E E E C C C C C C H H H H H H H H H H H C C C C C C E
E E E E C C H H H H H H H H H H C C C C C E E E E E C C C C H H H H H H C C C C E E E E E H H H C C H H H H H H H H H C C C E E E E E C C C C H H H H H H H H H C C C C E E E E C C H H H H C C C C C
INFO:tensorflow:Inference results INPUT: A E L V S D K A L E S A P T V G W A S Q N G F T T G G A A A T S D N I Y I V T N I S E F T S A L S A G A E A K I I Q I K G T I D I S G G T P Y T D F A D Q K A R S Q I N I P A N T T V I G L G T D A K F I N G S L I I D G T D G T N N V I I R N V Y I Q T P I D V E P H Y E K G D G W N A E W D A M N I T N G A H H V W I D H V T I S D G N F T D D M Y T T K D G E T Y V Q H D G A L D I K R G S D Y V T I S N S L I D Q H D K T M L I G H S D S N G S Q D K G K L H V T L F N N V F N R V T E R A P R V R Y G S I H S F N N V F K G D A K D P V Y R Y Q Y S F G I G T S G S V L S E G N S F T I A N L S A S K A C K V V K K F N G S I F S D N G S V L N G S A V D L S G C G F S A Y T S K I P Y I Y D V Q P M T T E L A Q S I T D N A G S G K L
INFO:tensorflow:Inference results OUTPUT: X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X
X X X X X X X X X X X X X X X X X X X X X X X X X X X
INFO:tensorflow:Inference results TARGET: C C C C C Y Y Y Y C C C C C C C H H H C C C C C C C C C C C C H H H E E E E C C H H H H H H H H C C C C C C E E E E E C C E E E C C C C C C C C C H H H H H H H Y E E E C C C C E E E E E C C C C C E E E C C E E E E E H H H C C E E E E E E C C E E E C
C C C C C C E E E C C C E E E C C C C C E E E E C C C E E E E E E C C E E E C C C C C H H H C C E E C C E E C C C C C C C E E E C C C C E E E E E E C C E E E E E E E C E E E C C C Y Y Y H H H H C C C C E E E E E C C E E E E E E E C C C E E C C C E E E E E C C E E E E E C C C C C C C C C C C
E E E C C C C E E E E E C C E E E E E C C C H H H H H H H E E E C C C C E E E E E C C E E C C E E C C C C C C C C E C C C C C C C C C C C C C C C C H H H H H H H H H H C C C C C C
INFO:tensorflow:Inference results INPUT: V M N T I Q Q L M M I L N S A S D Q P S E N L I S Y F N N C T V N P K E S I L K R V K D I G Y I F K E K F A K A V G Q G C V E I G S Q R Y K L G V R L Y Y R V M E S M L K S E E E R L S I Q N F S K L L N D N I F H M S L L A C A L E V V M A T Y S R S T S Q N L D S G T D L S F P W I L N V L N L K A F D F Y K V I E S F I K A E G N L T R E M I K H L E R C E H R I M E S L A
INFO:tensorflow:Inference results OUTPUT: X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X
INFO:tensorflow:Inference results TARGET: C C C C C C H H H H H H H C C C C C C C H H H H H H H H C C C C C C H H H H H H H H H H H H H H H H H H H H H H H C C C C H H H H H H H H H H H H H H H H H H H H H H H H H H H H H H C C C C C H H H H C C H H H H H H H H H H H H H H H H H H C C C C C
C C C C C C C C C C C C C H H H H H H C C C H H H H H H H H H H H H H H C C C C C H H H H H H H H H H H H H H H H C C C
INFO:tensorflow:Inference results INPUT: M N E A L D D I D R I L V R E L A A D G R A T L S E L A T R A G L S V S A V Q S R V R R L E S R G V V Q G Y S A R I N P E A V G H L L S A F V A I T P L D P S Q P D D A P A R L E H I E E V E S C Y S V A G E A S Y V L L V R V A S A R A L E D L L Q R I R T T A N V R T R S T I I L N T F Y S D R Q H I P
INFO:tensorflow:Inference results OUTPUT: X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X
INFO:tensorflow:Inference results TARGET: X X X C C C Y Y H H H H H H H H H H C C C C C H H H H H H H H C C C H H H H H H H H H H H H H C C C E E E E E E E E C C Y Y Y C C C E E E E E E E E E C C C C C C C C H H H H Y C C C C C E E E E E E E Y C C C C E E E E E E E C C H H H H H H H H H H H H H H Y C E E E E E E E E E E E E E C C C C C C C
INFO:tensorflow:Inference results INPUT: M T D D S A V E S K Q K K S K I R K G H W I P V V A G F L R K D G K I L V G Q R P E N N S L A G Q W E F P G G K I E N G E T P E E A L A R E L N E E L G I E A E V G E L K L A C T H S Y G D V G I L I L F Y E I L Y W K G E P R A K H H M M L E W I H P E E L K H R N I P E A N R K I L H K I Y K A L G L E W R K
INFO:tensorflow:Inference results OUTPUT: X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X
INFO:tensorflow:Inference results TARGET: X X X X X X X X X X X X X X X X X C C C E E E E E E E E E E E C C E E E E E E C C C C C C C C C C E E C C E E E C C C C C C H H H H H H H H H H H H H C C E E E C C C E E E E E E E E Y C C Y E E E E E E E E E C E E E C C C C C C C C C E E E E E C H H H H H H C C C C H H H H C C H H H H H H H C C C C C X X

@lukaszkaiser  as you asked, I made a small test with CNN summary problem, to check if the problem in my system configuration or not.
I trained it with the default parameters in the T2T documentation for approximetly 250K steps.
Here is the Tensorboard screenshoot:
Here is some of the decoding:
INFO:tensorflow:Inference results INPUT: (CNN)Modern women are suffering from an energy crisis. Competing in the workplace, caring for aging parents and trying teenagers, and busier than ever, we use anything we can to duct-tape our lives and ourselves together: comfort foods, alcohol, energy drinks, and an expanding array of antidepressants, anti-anxiety meds, painkillers and amphetamines. All in an effort to maintain our unnatural pace. Welcome to the increasingly altered states of America. Americans are about 5% of the world's population, yet we take half its pills, and 80% of its painkillers.This over-medication has led to record numbers of opiate overdoses and also to a growing number of women taking antidepressants -- one out of four and counting, according to a report issued by the pharmacy benefit manager, Medco. This is creating a "new normal," bringing closer the tipping point for when other women will seek chemical assistance. Like steroids in baseball, when everyone starts doing it, the players who don't are at a disadvantage. The overuse of psychiatric medication is called cosmetic psychopharmacology, and it's like cosmetic surgery: as more women get breast implants, the rest of us feel flat chested. But the new normal isn't normal at all. Certain antidepressants called SSRIs (selective serotonin reuptake inhibitors) create artificially stable and elevated levels of serotonin, the brain chemical that helps regulate mood, which can dampen empathy and emotional reactivity, while higher doses can engender apathy. Women are naturally moody. Our moods change, and this emotional fluidity helps us to be adaptable and resilient. We are dynamic and responsive to our environment. There is a biological advantage to this sensitivity; we need to know what our nonverbal babies need, what our mates are thinking and to sense danger in our surroundings. Moodiness -- being sensitive, caring deeply and occasionally being acutely dissatisfied -- is actually a natural source of power. It helps to ensure not only our survival, but that of our offspring. Women's emotionality is a sign of health, not disease, and it is our single biggest psychological asset. Yet many of us are being convinced to medicate it away. The pharmaceutical industry spends billions in payments to physicians and direct to consumer advertising. Nine out of 10 pharmaceutical companies spend less on research and development than marketing.Steady doses of antidepressant advertising aimed at women advance the question from "Should I take an antidepressant?" to "Which one should I take?" These medications can have more far-reaching effects than most people realize, blunting empathy, passion, and sensitivity. SSRIs dampen sexual desire and response, making it difficult to climax and interfering with the evaluation of potential romantic partners. SSRIs also make it hard to cry, an important signal to ourselves and those around us that something is very wrong. Tears, and the emotions beneath them, can offer a profound lesson in how people's behavior affects others. We cry when we're touched by the poignancy of humanity, or when we're angry, or frustrated by injustice. And yet, women apologize for their tears, especially in the workplace, where we've been socialized to shut down expressed emotion; it interferes with our typical agenda of forward-momentum, and it makes people uncomfortable. The problem with shutting it down is that we're stifling a piece that we need, that our partners and families need, and that society needs, particularly as women continue to ascend to the upper reaches of business and government. The world needs more compassion, sensitivity, intuition and empathy. War, rape, murder, and corporate malignancy will flourish in their absence. Change comes from discomfort -- and awareness that something is wrong. Sadness and fear are not always symptoms to be medicated. Anxiety and tension come foremost from the denial of emotions, from their repression. My patients want to feel better, but really what many of them need is to get better at feeling. The shame that for centuries has been used by our culture to separate men from their true feelings is now spreading to women, who, by their biology, are the last and much needed connection to this natural place. As we embrace new technologies, we need to be careful that we don't go too far away from nature and what's natural for us as social primates. We insist on all natural products in our baby food and household cleaners. Why don't we insist on natural moods for ourselves? If we can live in a way that is more aligned to what is genuine for us, we will be calmer, more at peace and more fulfilled. But we are drowning in artificiality. There is a "plastic vortex" of garbage the size of Texas floating in the Pacific Ocean, synthetic fillers in our breasts and faces, virtual sex partners on our computers and artificially enhanced rationality from serotonergic antidepressants. We compulsively consume this fakery without satiation or satisfaction, because it is impossible to fill up on something that is almost enough. We need a course correction, away from the synthetic, so that we can live lives that honor how we feel. But to do that, we must be able to feel, to be our moody, authentic selves. That is the first step to wellness and to wholeness. Medicating yourself into complacency may leave you unshaken, but it also may leave you unfeeling and apathetic, making it impossible to address the causes of your pain.  Don't put the alarm on mute.
INFO:tensorflow:Inference results OUTPUT: (CNN)True women are suffering from an energy crisis. Exploring in the workplace, caring for parents and aging teenagers, and trying niceier than ever, we can use anything we can to have-tape our lives and ourselves together: comfort, energy drinks, energy drinks, and an expanding array of contrapectives, anti-anxiety, nices, and spaoters. All in an effort to maintain our nicel pace. Large to the increasingly flawed states of America. Americans are about 5% of the world's population, yet we take half its pills, and 80% of its over-
INFO:tensorflow:Inference results TARGET: Julie Holland: American women taking too many anti-depressants, blunting their valuable emotional reactivity, creating new normal. She says anti-depressants dampen moodiness, but it's healthy, useful for women to be moody. Changing natural state damages society.
INFO:tensorflow:Inference results INPUT: Tech experts, analysts and customers alike have bemoaned Apple's Watch for needing an iPhone to have any real use. . But the firm has since confirmed the device will ship with 8GB of storage, and at least 2GB of this will be free for music and photos. Although this is the equivalent to just 240 songs, it does mean the timepiece can be used independently for certain tasks. Scroll down for video . Apple has confirmed that all three models of its Watch will ship with 8GB of storage, and at least 2GB will be free for music and photos. Although this is the equivalent to just 240 songs, it does mean the timepiece (pictured) can be used independently for certain tasks. The remaining storage will be used for apps and iOS. The number of tracks it can store will also vary depending on the file sizes.  . Apple confirmed the storage rumours to fan site 9to5Mac but the company has not responded to MailOnline's request for more information. . All three models of the watch will ship with 8GB of total storage. This will be used to run the Watch's iOS software and apps. . To put this into perspective, the entry-level iPhone 5C came with 8GB of storage when it launched in 2013 and rival smartwatches typically only have 4GB built-in.  . Apple will then let users store 2GB of music and 75MB of photos on the Watch as well. This music limit is the same as what the firm offers on its iPod Shuffle. . The photo limit equates to around 100 images. . To put this into perspective, the entry-level iPhone 5C came with 8GB of storage when it launched in 2013, and rival smartwatches typically only have 4GB built-in. Users can additionally store 75MB images, or 100 photos. The music limit is the same as what the firm offers on its iPod Shuffle (pictured) Apple's Watch is available in three models: the Watch, Watch Sport and Watch Edition (shown) and all models will be sold in 1.4-inch (38mm) or 1.7-inch (42mm) versions. Apple Watch is made of stainless steel, Sport is made of Aluminum and the premium Edition comes in 18-Karat Rose Gold and Yellow Gold. This music feature will appeal to runners and cyclists in particular who will be able to use the health features of the watch while exercising and listen to music without having to take their phone out with them. . The features of the Apple Watch rely heavily on pairing it with an iPhone. . To connect to a phone it either has to be within Bluetooth range or connected to the same Wi-Fi network. . It can't take photos because it doesn't have a built-in camera but it can be used to view pictures in the Photo Stream.  . With a phone:. 1. Use the phone's network to browse the web. 2. Make and receive calls. 3. Send and receive messages . 4. See notifications. 5. Use any installed app including Uber, Shazam and more . Without a phone:  . 1. Tell the time. 2. Track steps using the built-in accelerometer. 3. Pay for goods using Apple Pay. 4. Dictate messages to be sent once the phone is connected. 5. Play up to 2GB of songs . Although they will still need to wear Bluetooth headphones. . The low photo limit may be because Apple doesn't want wearers to use the device for looking at pictures. . The Watch is meant to be a companion device and the photos will be more visible on the larger screen of an iPhone and iPad. . It comes in three models: the Apple Watch, Apple Watch Sport and Apple Watch Edition, and all models are sold in either 1.4-inch (38mm) or 1.7-inch (42mm) versions. It has been designed to be tethered to an iPhone and the pair communicate over Bluetooth and Wi-Fi which gives the Watch added computing power. But this relationship means that as soon as the iPhone is out of the equation, the Watch has limited value. The Watch also falls short of its rival’s Samsung Gear S that has an optional nano SIM card slot. The Apple Watch is made of stainless steel and prices range between $549 and $1048 for the 1.4-inch model and $599 and $1099 for the larger 1.7-inch version. . Watch Sport is made of 7000 Series Silver Aluminum and ranges from $349 to $399. . The premium Edition has a similar build to the Apple Watch but comes in 18-Karat Rose Gold and Yellow Gold. Prices start at $10,000. . In the UK, the Sport starts at £299, the standard Watch starts at £479 and the Edition model will cost from £8,000. . This music feature will appeal to runners and cyclists in particular who will be able to use the health features of the watch (example app pictured) while exercising and listen to music without having to take their phone out with them. Although they will still need to wear Bluetooth headphones. Reviews of the Apple Watch have mostly been positive but while some have said it’s the best smartwatch on the market, the biggest criticism have been its lack of independent features - so this storage option will be a welcomed addition. Ernest Doku, technology expert at uSwitch.com told MailOnline: ‘The Apple reveal finally answered all of our questions on pricing and availability, but the all-important "why" remained elusive. ‘Apple is asking us to pay for a level of technical craftsmanship and prestige - but this is unchartered territory and Apple is stepping into one of the few arenas where it’s still outclassed.’
. But Stephen Ebbett from Protect Your Bubble added: ‘Sales will be driven by the novelty factor as well as the draw of brand Apple. Plus it has an exceptional track record. ‘Apple made us buy into the tablet in our millions when sceptics questioned whether there’d be enough demand. There’s no reason it can’t do the same again.’
INFO:tensorflow:Inference results OUTPUT: LONDON experts, analysts and customers alike have scuvoured Apple's Watch for an interiPhone to have any real use. . But the firm has since confirmed the device will ship with 8kg of storage, and at least 2kg of this will be free for music and photos. Although this is the equivalent to songs just, it does mean the piece can be used separately for certain tasks. Scroll down for video . Apple has confirmed that all three models of its ship will Watch
INFO:tensorflow:Inference results TARGET: Apple has confirmed all models of its Watch will ship with 8GB of storage. And at least 2GB of this will be available for storing music and photos. This is the equivalent to just 240 songs and 100 photos. But it does mean the Watch can be used independently for certain tasks. The remaining storage will be used for apps and the iOS software . To put this into perspective, Apple's iPhone 5C launched with 8GB in 2013 . And 2GB is the same storage the firm offers on its iPod Shuffle. Rival smartwatches typically only have 4GB of storage built-in   .

Looks like you're padding with X instead of
 (or rather the index its located in the vocabulary) is treated in a different way than other characters and is removed from the sequence during training. Padding with a different value would cause problems.
(If X is not padding and is intentional, the highly repetitive nature of it could cause its attention weights to overcome any degree of positional information, so its likely to always predict repeating X's. Temperature sampling could help solve this, but I find it highly unlikely that the X's are intentional since the second example you provide contains no preceding X's in the targets)

I don't pad the sequence manually or changed the default t2t setting. X is just a token that could occur at any part of the sequence. If it is a problem with padding then, there is a bug in t2t since it do it automatically.

X is not repeated a lot in the dataset, actually it occurs not much:
C: 270756
H: 262798
E:   169718
X:    54824
Y:     11243
If this is the case then C or H should be repeated.

I have checked if the EOS is appended correctly to the dataset and it is correct. At the end of each sample there is '1' which refers to '_' in the vocab file:
features {
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I have tested it my server with the translation problem from English to Deutsch for 250k steps using single GPU, and the decoder is producing correct results:
INFO:tensorflow:Inference results INPUT: But it was his masterful interpretation of delightfully detestable JR that led to Hagman reaching his peak of stardom.
INFO:tensorflow:Inference results OUTPUT: Aber es war seine meisterhafte Interpretation von freudig detestabilen JR, die zu Hagman führte, seinen Höhepunkt der Stärke zu erreichen.
INFO:tensorflow:Inference results TARGET: Aber es war seine meisterhafte Interpretierung des bezaubernd verabscheuungswürdigen J.R., die Hagman zu einem Superstar machte.
INFO:tensorflow:Inference results INPUT: The rocket was designed as a two-stage rocket, with a total length of 510 cm and a launch weight of 32.5 kg.
INFO:tensorflow:Inference results OUTPUT: Die Rakete wurde als zweistufige Rakete mit einer Gesamtlänge von 510 cm und einem Startgewicht von 32,5 kg konzipiert.
INFO:tensorflow:Inference results TARGET: Die Rakete war als Zweistufenrakete konzipiert, ihre Gesamtlänge betrug 510 cm und das Startgewicht war 32,5 kg.
INFO:tensorflow:Inference results INPUT: In Ligue 1, would not winning the title, like last season, be a big failure?
INFO:tensorflow:Inference results OUTPUT: In Ligue 1, würde nicht gewinnen den Titel, wie die letzte Saison, ein großes Scheitern?
INFO:tensorflow:Inference results TARGET: Wäre es ein großer Misserfolg, nicht den Titel in der Ligue 1 zu gewinnen, wie dies in der letzten Saison der Fall war?
INFO:tensorflow:Inference results INPUT: It is in this spirit that a majority of American governments have passed new laws since 2009 making the registration or voting process more difficult.

INFO:tensorflow:Inference results INPUT: I bought my first Valentino dress to wear at a party in the castle belonging to my cousin, Prince Edouard de Ligne.
INFO:tensorflow:Inference results OUTPUT: Ich kaufte mein erstes Valentino-Kleid, um bei einer Party in der Burg zu tragen, die meiner Cousin, Prinz Edouard de Ligne.
INFO:tensorflow:Inference results TARGET: Ich kaufte mir mein erstes Valentino-Kleid für ein Fest im Schloss meines Vetters, dem Prinz Edouard de Ligne.
INFO:tensorflow:Inference results INPUT: Even if the ceasefire agreed on November 21st holds, this week's fighting has strengthened the hawks on both sides.
INFO:tensorflow:Inference results OUTPUT: Selbst wenn der am 21.
INFO:tensorflow:Inference results TARGET: Selbst wenn der am 21.
INFO:tensorflow:Inference results INPUT: And yet, humanity needs to seek out some new avenues in biotechnologies, protection from radiation, creation of artificial gravity.
INFO:tensorflow:Inference results OUTPUT: Und trotzdem muss die Menschheit nach neuen Wegen in der Biotechnologie, dem Schutz vor Strahlung, der Schaffung künstlicher Gravitation suchen.
INFO:tensorflow:Inference results TARGET: Und doch muss die Menschheit nach neuen Wegen in der Biotechnologie, dem Strahlenschutz und dem Erzeugen künstlicher Gravitation suchen.
INFO:tensorflow:Inference results INPUT: In the Carlsbad region, the roads have been usable this morning, though in some places they were icy and snowy.
INFO:tensorflow:Inference results OUTPUT: In der Region Carlsbad sind die Straßen heute Morgen verwendbar, aber an einigen Orten waren sie eisig und Schnee.

INFO:tensorflow:Inference results INPUT: Circumcision, which has been proven to reduce the risk of contracting the virus by half, is barely practised by the population.
INFO:tensorflow:Inference results OUTPUT: Der Circumcision, der nachweislich das Risiko einer Halbierung des Virus vermindert, wird von der Bevölkerung kaum praktiziert.

INFO:tensorflow:Inference results INPUT: "If he calls for the referendum, we will go to his palace and overthrow him," said member of the opposition Jasser Said.
INFO:tensorflow:Inference results OUTPUT: "Wenn er das Referendum fordert, werden wir zu seinem Palast gehen und ihn stürzen", sagte Mitglied der Opposition Jasser Said.
INFO:tensorflow:Inference results TARGET: "Wenn er das Referendum ausruft, werden wir zu seinem Palast gehen und ihn stürzen", sagte der Oppositionelle Jasser Said.
INFO:tensorflow:Inference results INPUT: First of all, we want to finish top in our pool, ahead of Porto, to have home advantage in the last 16 match.
INFO:tensorflow:Inference results OUTPUT: Zunächst wollen wir in unserem Pool, vor Porto, den besten Platz im letzten 16 Spiel haben.
INFO:tensorflow:Inference results TARGET: Zunächst wollen wir Erster unserer Gruppe vor Porto werden, damit wir das Rückspiel des Achtelfinales als Heimspiel austragen können.
INFO:tensorflow:Inference results INPUT: One particularly special feature are the black book shelves, which simultaneously act as wall cladding, parapets and railings for the stairway.

INFO:tensorflow:Inference results INPUT: At Victor-Gadbois, a group of volunteers provides bodily care and help with feeding.
INFO:tensorflow:Inference results OUTPUT: Bei Victor-Gadbois bietet eine Gruppe von Freiwilligen körperliche Betreuung und Hilfe bei der Fütterung.
INFO:tensorflow:Inference results TARGET: Im Heim Victor-Gadbois bietet man mit einer Gruppe von Freiwilligen Körperpflege und Hilfestellung bei der Nahrungsaufnahme an.

@agemagician Hi, did you eventually find the error? I have a very similar problem with my custom model trained on librispeech (both clean and full). I'm using the standard librispeech problem definition and hyperparameters. Very high validation acc, but complete non-sense outputs from the decoder. Do you have any advice on how to sanity check the model?

Hi @nmoenning  ,
I was not able to find the bug yet. You can do as I did, print the values during validation within one of the metrics to make sure the training is fine.
Could you please, insert your training/test setup, so one of the admins might figure the problem:

Dataset.
Model.
Hardware.
Software.

Hi @lukaszkaiser  ,
I have checked if the problem in encoding or decoding the vocabulary and it is not the issue.

In function "_interactive_input_fn" at "decoding.py" file, after "input_ids = vocabulary.encode(input_string)", I printed the values of both "input_string" and "input_ids".
The encoding is correct and it maps the text to its corresponding vocabulary correctly.

In function "decode_interactively" at "decoding.py" file, inside the "for result in result_iter:" , I printed the "result["outputs"]".
The decoding is correct and it maps the decoded numbers to its corresponding vocabulary.

This means:

There is a real issue in reporting the performance.
There is an issue in saving/loading the checkpoints.
The estimator predictor function is not working correctly.

If I'm not mistaken, the accuracy that is computed during training is an approximation. It's computed by feeding the reference targets, shifted by one time step, instead of doing actual decoding. If you look at your predictions and targets during training, it looks like the prediction is just the targets shifted by one. This can happen if the model has learned nothing more but to always copy the input word to the output. Depending on your data, the accuracy might be high during training, as the only difference to the reference output is that it's shifted by one. On the other hand, actual decoding would just copy the input token to the output, so it would explain why you see only the token 'X' repeated during decoding.

The accuracy is approximation on the validation sub-dataset. This means if during training, I increased the number of "eval_steps" to cover all the validation dataset, then this should be the same accuracy I get during decoding the validation dataset.

The input and the output are totally different. If the model copied the input words to the output words the accuracy will never reach this performance.
Example:-
Input:                                                     G D D N N A A E V D
Ground Truth:                                       X C C E C C H H H H
Prediction During training validation: C C E C C H H H H H
Prediction During decoding:               X X X X X X X X X X
So this is not the case, the model learned the correct prediction during validation.

Ok but let's assume there's another approximation. During actual decoding, the output from each time step is used as the input of the next time step. But that's really slow. So during validation the ground truth output is used as the input of the next time step. Let's assume also that your model just learns the identity mapping. The output would be the ground truth targets shifted right by one time step. When I look at your predictions during validation, this seems to be the case:
targets:
[8 8 8 8 8 8 8 8 8 8 8 3 3 4 3 4 4 4 3 3 4 4 4 3 3 3 3 3 3 3 3 3 4 4 4 3 3 4 4 4 3 3 4 4 4 3 4 3 3 3 3 3 3 2 2 2 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 1]
prediction:
[8 8 8 8 8 8 8 8 8 8 8 8 3 3 4 3 4 4 4 3 3 4 4 4 3 3 3 3 3 3 3 3 3 4 4 4 3 3 4 4 4 3 3 4 4 4 3 4 3 3 3 3 3 3 2 2 2 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3]

Hi @senarvi  ,
I am not sure if they actually use the ground truth as an input during validation for next time step, but if this is the case I would be really surprised.
This is not a correct way to give an approximation during validation at all. I thought the approximation validation is only refers to the number of validation steps, because you might not cover all the validation dataset.
@lukaszkaiser Could you please confirm if you are using the ground truth during validation as an input for next time step or not ?
There is another issue was just opened with the same problem:
https://github.com/tensorflow/tensor2tensor/issues/1537

Do you mean the "eval_run_autoregressive" parameter which by default is "False" ?

Wasn't aware of that switch, but based on some previous mentions it seems to do just what you want.

I have enabled it :
t2t-trainer   --generate_data   --data_dir=$DATA_DIR   --tmp_dir=$TMP_DIR   --problem=$PROBLEM   --model=$MODEL   --hparams_set=$HPARAMS   --output_dir=$TRAIN_DIR   --t2t_usr_dir=$USER_DIR   --worker_gpu=6   --eval_steps=10000   --local_eval_frequency=10000 --eval_run_autoregressive=true
In this case it should give me the same results as the decoding, but I am still getting very high accuracy during validation and very low accuracy during decoding.

I tried to set it to "true", but unfortunately, there is a bug in transformer that doesn't allow it to work.
As mentioned in https://github.com/tensorflow/tensor2tensor/issues/613
So simply, I Cann't confirm 100% if this is the issue.

I will close this issue for now. Since the multi-task support is still bugy.
I hope the future t2t models will fix this bugs.

@agemagician did you ever find out the issue? I'm having rubbish output with high accuracy on a custom translation problem too.

Hi @agemagician, I have similar problem on my custom text2text problem. Anyone found out the issue?


Support for CommonJS, Node and Npm

http://www.mattpalmerlee.com/2013/04/23/create-your-first-node-module-and-publish-it-to-the-npm-registry/
http://nodejs.org/docs/latest/api/modules.html

@alexmilowski jsonld.js supports it with node, i'll try to add package.json in a fork and then help with publishing it to npm!
https://github.com/digitalbazaar/jsonld.js/blob/master/js/request.js#L117

+1

Any chance to fix that? In Scala.js, node.js is the preferred environment for testing.

FWIW, I started working on getting green-turtle to work as is in JSDOM in this pull request: https://github.com/alexmilowski/green-turtle/pull/7

Ok so I have extracted the GraphRDFaProcessor into its own commonJS module. See https://github.com/scienceai/graph-rdfa-processor. I made additional changes there so that it works both in the browser and JS-DOM.

@bergos maybe relevant for your

https://github.com/rdf-ext/rdf-parser-microdata
https://github.com/rdf-ext/rdf-parser-rdfa

@elf-pavlik I know this issue. I have already a fix for it, but I had problems getting the Green Turtle tests running, even without my changes.

I've merged your pull request.


Add support for Timber

As Timber uses .twig templates for views where gettext functions would be found, and this project uses a PHP parser, it's not currently possible to extract strings when using Timber.

I had the same issue in gulp-wp-pot which depends on wp-pot.
https://github.com/rasmusbe/gulp-wp-pot/issues/38
One way might be to parse all text elements from the php-parser to look for translation methods instead of just function calls.
But since I don't use twig myself it would be hard for me to maintain the twig-parsing.
And I really don't like the regex-method used in 1.0 of gulp-wp-pot, it was a mess to maintain.
If someone wants to add twig support, feel free to do so (as a new project or via pull request to wp-pot repository).


Feature Request: Font Colours

It would be nice if we could also change the font colour of the text for states, too.

Sure we can! But it'd be nice.


Prevent Intersection With Shapes

Hello, this is a very cool library. I am aware that the allowSelfIntersection option prevents intersection while drawing. Is there a way to prevent polygons from intersecting with each other?
You can use the snapIgnore option to ignore snapping on a shape. If a similar feature doesn't exist already, you could add allowIntersection option to prevent or allow intersection with other shapes.

I close this issue and add it to the Feature Requests. You can upvote it with a 👍

Good！I need  it


Fixed the issue for cleaning the scaffolder workspace.

Hey, I just made a Pull Request!
Screenshot 2024-07-02 at 19.42.53
Found the issue during testing the clean up of the workspace for the database implementation.
:heavy_check_mark: Checklist

[x] A changeset describing the change and affected packages. (more info)
[ ] Added or updated documentation
[ ] Tests for new functionality and regression tests for bug fixes
[ ] Screenshots attached (for UI changes)
[x] All your commits have a Signed-off-by line in the message. (more info)

Thanks :tada:


[T4A3][W11-B3] Fan Weiguang

Ready for review

Some comments added. Please close the PR after reading comments.

Comments reviewed. The isPrivate value should be private, and I will fix the indentation format. Thank you very much!


Readd the 'Rankings' section

This commit aims to readd the 'Rankings' section to the game.
The following issues should be addressed before this commit is merged:

[ ] Fix the Mii of a player not appearing on the histogram, even when the method selectSPLicense is called
[ ] Fix the crash that occurs when attempting to change the course
[ ] Fix the crash that occurs when attempting to start a Time Trial
[ ] Replace the textbox that appears when an attempt is made to download the ghost of a champion

The section loaded when attempting to start a time trial is 0x4F.

Would feature gating make sense here?

Would feature gating make sense here?

There is (untested) present functionality here, so I do not believe so.

2 issues I found while testing:

Online mode is no longer feature gated.
When selecting a track with the UpDownControl, the selection in the CourseSelectPage is not updated.

Would feature gating make sense here?

There is (untested) present functionality here, so I do not believe so.

After testing, there is no present functionality. I agree that this should be feature gated (temporary though it may be).

The online buttons are no longer visible.

The online buttons are no longer visible.

Resolved in commit ee5ca0d.

The ifdef is flipped.

The ifdef is flipped.

Resolved in commit 22533f6.


Changing license for GPL 2 compatibility

Project like buildbot would like to include AutobahnJS as part of their application can't due to GPLv2 policy.
It is even more difficult for JS, as you usually minify vendor scripts with the code of your application to minimize load latency, and distribute the resulting code as a binary package. (This is actually what buildbot www is doing)
Like for Python project, I volunteer to manage the switch.

I did not realize it is already MIT!

Yes, it's MIT .. we did the license switch long long ago when actually the only contributor was me. So no issue here luckily. I hope we get AutobahnPython moved to MIT too. Actually all the Autobahns.
Am 14.02.2015 21:37 schrieb Pierre Tardy<EMAIL_ADDRESS>Project like buildbot would like to include AutobahnJS as part of their application can't due to GPLv2 policy.
It is even more difficult for JS, as you usually minify vendor scripts with the code of your application to minimize load latency, and distribute the resulting code as a binary package. (This is actually what buildbot www is doing)
Like for Python project, I volunteer to manage the switch.
—
Reply to this email directly or view it on GitHubhttps://github.com/tavendo/AutobahnJS/issues/125.


 For you I got scolded from my GF, 1.10 you have wrong answer. See the real solution https://www.cscprogrammingtutorials.com/2015/11/program-to-calculate-average-speed-in-miles.html

Lol, my mistake, do your maths before you copy/paste from the internet.
 See the real solution
https://www.cscprogrammingtutorials.com/2015/11/program-to-calculate-average-speed-in-miles.html
—
You are receiving this because you are subscribed to this thread.
Reply to this email directly, view it on GitHub
https://github.com/jsquared21/Intro-to-Java-Programming/issues/28, or mute
the thread
https://github.com/notifications/unsubscribe-auth/AdzVsczcl7TkHMqf9qrvCMwXqgTd6NxPks5umi5HgaJpZM4XxVE9
.

Lol I must have been immature to comment like that, closing now


Can't quite figure out how the drain functionality works

I'm doing this
import ulog from 'ulog'

export const log = ulog('sample-app')

log.level = log.WARN

ulog.use({
  outputs: {
    drain: {
      log: function (...args) {
        console.log('drain...', args)
      },
    },
  },
})

ulog.set('drain', 'drain')

log('foo...')
log.warn('warn...')

In the browser, the call log('foo') gets sent to drain. But the call log.warn('warn...') doesn't and I don't understand why. How can I set this up so that all log levels get sent to drain?

Ok when I pass the query param ?log=none everything gets sent to drain. So I guess the idea here is that everything except the specified log level get's sent to drain?

Ok I think I see whats happening now. Every log type  that is equivalent to the log level and above (in terms of severity) gets logged out and everything else gets drained. If so, my question then is what is the purpose of drain?. I was assuming that if I wanted to send logs to a remote logging service, I would implement that as drain. Is that the case? And if so, does it mean then that once the log level is set, every thing that matches it will not be sent to drain? Is that how it's meant to work? I'm new to setting up logging in this manner so apologies if this is all trivial stuff.

@ifeanyiisitor
Thanks for your comments. Yes, you are correct in your assumption. Logs get sent to either the output channel or to the drain channel, depending n whether they are enabled for a certain level or not. So if enabledFor(level) returns a truthy value, messages get sent to output, otherwise to drain.
If you want to send all logging to some service, you can simply set both output and drain to the same output.
And no this is definitely not trivial stuff. And you may be new to this, but so am I. As you might have noticed, ulog v2 is still in beta and I'm still figuring stuff out so any feedback you have is welcome. Thank you for trying ulog!

log.level = log.WARN

BTW, I advice against this.

ulog.set('drain', 'drain')

And against this as well  😃
In general, I say avoid calling ulog.set and assigning to properties on the loggers unless you are writing some tests for the loggers or in very specific circumstances (like you are making a UI to control logging with)
Instead of setting values from code, try to use the configuration mechanism. This will give you much cleaner code and you will be able to control all your logging from config. If you do logger.level = logger.WARN, you have overridden the level for that logger and it will not respond to config until you remove the override with logger.level = undefined.
If you want to change a default, I suggest adding a mod that overrides the default. Suppose you want to set the default value for level to 'error', you could do this:
ulog.use({
  settings: {
    level: {
      prop: {
        default: 'error'
      }
    }
  }
})

You should include this mod in your entry point, before including any code that creates loggers. All loggers created after you added this mod will use your new default.


fix(updates/core): correct core check to improve usage when using dev env setup

As our dev environment usually involves the folder directory and the version in package.json differing the existing check was techinically wrong, this corrects it to use the package.json to make it more correct

Messages

:book:

:white_check_mark: All tests are passing
Nice one! All 15 tests are passing.

  Generated by :no_entry_sign: dangerJS against f54734289b9aed3e4b1443f428ba6f35c0f0e34d


chore: Persist initial header in local store

The buildInitialHeader method from the merkle tree db just used the current state reference to build the header and return it, so it was only valid when called if the merkle tree db was still empty.
This PR fixes the issue by computing the initial header when init is called, and persisting it to the local store, so it can be retrieved even when loading an empty db.

An alternative approach would have been something in the lines of:
if (!fromDb) {
  const initialState = await this.getStateReference(true);
-  await this.#saveInitialStateReference(initialState);
+  this.initialState = initialState;
  await this.#updateArchive(this.getInitialHeader(), true);
+ } else {
+  const emptyTrees = MerkleTrees.new(openTmpStore()).init();
+  this.initialState = emptyTrees.getStateReference(true);
+ }

However, a downside of this approach is that, if the shape of the trees state change at some point during the chain history, then we'd be returning a wrong initial hash. Though, to be fair, a lot more things would probably break.

Benchmark results
Metrics with a significant change:

protocol_circuit_simulation_time_in_ms (private-kernel-tail-to-public): 3,969 (-42%)
avm_simulation_time_ms (Token:assert_minter_and_mint): 50.6 (-44%)
protocol_circuit_proving_time_in_ms (base-parity): 1,936 (+38%)

Detailed results
All benchmarks are run on txs on the Benchmarking contract on the repository. Each tx consists of a batch call  to create_note and increment_balance, which guarantees that each tx has a private call, a nested private call, a public call, and a nested public call, as well as an emitted private note, an unencrypted log, and public storage read and write.
This benchmark source data is available in JSON format on S3 here.
Proof generation
Each column represents the number of threads used in proof generation.

Metric
1 threads
4 threads
16 threads
32 threads
64 threads

proof_construction_time_sha256_ms
5,735
1,541
715 (+1%)
768 (+1%)
768 (-1%)

proof_construction_time_sha256_30_ms
11,758
3,151
1,419
1,453
1,469

proof_construction_time_sha256_100_ms
43,710
12,080
5,473 (-1%)
5,420 (-3%)
5,409 (-3%)

proof_construction_time_poseidon_hash_ms
78.0
35.0 (+3%)
34.0
59.0
88.0 (-2%)

proof_construction_time_poseidon_hash_30_ms
1,521
416
202 (+1%)
223
267 (-1%)

proof_construction_time_poseidon_hash_100_ms
5,618
1,517
680 (-1%)
728
750

L2 block published to L1
Each column represents the number of txs on an L2 block published to L1.

Metric
4 txs
8 txs
16 txs

l1_rollup_calldata_size_in_bytes
708
708
708

l1_rollup_calldata_gas
6,588
6,594
6,588

l1_rollup_execution_gas
583,050
583,207
583,352

l2_block_processing_time_in_ms
756
1,430 (+1%)
2,701

l2_block_building_time_in_ms
13,184
25,741
50,951

l2_block_rollup_simulation_time_in_ms
13,184
25,741
50,950

l2_block_public_tx_process_time_in_ms
11,074
23,406
48,555

L2 chain processing
Each column represents the number of blocks on the L2 chain where each block has 8 txs.

Metric
3 blocks
5 blocks

node_history_sync_time_in_ms
7,180 (+2%)
10,009

node_database_size_in_bytes
12,709,968 (+2%)
16,871,504 (+3%)

pxe_database_size_in_bytes
16,254
26,813

Circuits stats
Stats on running time and I/O sizes collected for every kernel circuit run across all benchmarks.

Function
time_ms
bytecode_size_in_bytes

GasToken:_increase_public_balance
100
13,790

GasToken:set_portal
10.8 (+2%)
3,339

Token:constructor
129 (+2%)
23,692

FPC:constructor
91.6 (-2%)
13,592

GasToken:mint_public
82.3 (+1%)
10,158

Token:mint_public
339 (+2%)
19,034

Token:assert_minter_and_mint
:warning: 50.6 (-44%)
12,925

AuthRegistry:set_authorized
47.5 (+2%)
7,812

FPC:prepare_fee
107 (+10%)
11,068

Token:transfer_public
45.5 (+18%)
31,218

FPC:pay_refund
126 (-7%)
21,266

Benchmarking:increment_balance
1,304
11,281

Token:_increase_public_balance
84.2
15,006

FPC:pay_refund_with_shielded_rebate
107 (-3%)
22,361

Public DB Access
Time to access various public DBs.

Function
time_ms

get-nullifier-index
0.152 (-5%)

Tree insertion stats
The duration to insert a fixed batch of leaves into each tree type.

Metric
1 leaves
16 leaves
64 leaves
128 leaves
256 leaves
512 leaves
1024 leaves

batch_insert_into_append_only_tree_16_depth_ms
10.4 (+1%)
16.8 (+1%)
N/A
N/A
N/A
N/A
N/A

batch_insert_into_append_only_tree_16_depth_hash_count
16.8
31.7
N/A
N/A
N/A
N/A
N/A

batch_insert_into_append_only_tree_16_depth_hash_ms
0.605 (+1%)
0.517 (+1%)
N/A
N/A
N/A
N/A
N/A

batch_insert_into_append_only_tree_32_depth_ms
N/A
N/A
48.5
76.1 (+1%)
131
248 (+1%)
470

batch_insert_into_append_only_tree_32_depth_hash_count
N/A
N/A
95.9
159
287
543
1,055

batch_insert_into_append_only_tree_32_depth_hash_ms
N/A
N/A
0.496
0.467 (+1%)
0.450
0.448 (+1%)
0.439

batch_insert_into_indexed_tree_20_depth_ms
N/A
N/A
60.2 (+1%)
112 (+1%)
183 (+1%)
355 (+1%)
693

batch_insert_into_indexed_tree_20_depth_hash_count
N/A
N/A
109
207
355
691
1,363

batch_insert_into_indexed_tree_20_depth_hash_ms
N/A
N/A
0.509 (+1%)
0.503 (+1%)
0.480 (-1%)
0.481 (+1%)
0.477

batch_insert_into_indexed_tree_40_depth_ms
N/A
N/A
73.3 (+1%)
N/A
N/A
N/A
N/A

batch_insert_into_indexed_tree_40_depth_hash_count
N/A
N/A
133
N/A
N/A
N/A
N/A

batch_insert_into_indexed_tree_40_depth_hash_ms
N/A
N/A
0.521 (+1%)
N/A
N/A
N/A
N/A

Miscellaneous
Transaction sizes based on how many contract classes are registered in the tx.

Metric
0 registered classes
1 registered classes

tx_size_in_bytes
77,362
668,542

Transaction size based on fee payment method
| Metric |  |
| - |  |


142 1 svg can have multiple categories

You can add multiple categories to one svg

It seems that categories on a card in two lines will break the grid.

It seems that categories on a card in two lines will break the grid.

You're right 🤔, I'll look for a better way to present the categories.

I really like the idea 🥹✨

I really like the idea 🥹✨. I think you should limit how many categories each SVG can have 🤔

How many categories should each svg have? 🤔

3 maybe? 🤔

ok ok, that seems good to me 😁, I will work on it

what do you think like that?


update fail 2.0.0b40

What version of Music Assistant has the issue?
2.0.0b39
The problem
message is connection lost
How to reproduce
update
Relevant log output
No response
Additional information
No response
What version of Home Assistant Core are your running
2023.7.1
What type of installation are you running?
Home Assistant OS
On what type of hardware are you running?
Raspberry Pi

Similar error message when running update on same above versions but virtualized hardware

Same error here. Tried rolling Home Assistant Core back to 2023.6.3, but that did not fix the issue.
What version of Music Assistant has the issue?
2.0.0b39
The problem
message is connection lost
How to reproduce
update
Relevant log output
No response
Additional information
No response
What version of Home Assistant Core are your running
2023.6.3
What type of installation are you running?
Home Assistant OS
On what type of hardware are you running?
ODROID N2+

Tracking it here https://github.com/music-assistant/hass-music-assistant/issues/1343


Lecture04 Бескровный Константин

Coverage increased (+13.3%) to 62.963% when pulling d5aa3b4231df3130ccb0060d1814eb48125fc184 on Easterok:lecture04 into c0938b61d9a9c2f82f0ba2dfb4dc492717119c93 on rybalkinsd:lecture04.

+


Volunerable dependencies

@eladb  These are belongs to golang dependencies. I need to take some time to figure out how to fix it.

Refers https://github.com/containers/skopeo/commit/37d801c90bcf7b5500c828edc34e4fecd4b8c653


[release-0.5] Fix: remove workload finalizer if workload is finished

What type of PR is this?
/kind bug

What this PR does / why we need it:
Cherry-pick of #1523
Which issue(s) this PR fixes:

Fixes #
Special notes for your reviewer:
Does this PR introduce a user-facing change?

NONE

/release-note-edit
Fix a bug in the pod integration that the pod's finalizer won't be removed even though the pod is finished

/release-note-edit
Remove finalizer from Workloads that are orphaned (have no owners).

/release-note-edit
Remove finalizer from Workloads that are orphaned (have no owners).


Restic Init fails for S3 SSE enabled buckets

Output of restic version
docker run -it velero/velero:v1.7.0 restic version
restic 0.12.0 compiled with go1.15.8 on linux/amd64

How did you run restic exactly?
Ran restic through the latest version of Velero 1.7.0 in a K8s cluster v1.19. Command being run to initialize the repository:
restic init --repo=s3:<s3-repo> --password-file=/path/to/credentials --cache-dir=/scratch/.cache/restic

Ran the same restic binary version 0.12.0 locally to init the S3 repo and get access denied
restic init --repo=s3:<s3-repo>
enter password for new repository:
enter password again:
Fatal: create key in repository at s3:<s3-repo> failed: client.PutObject: Access Denied

Verified that IAM policies are correct based off of this documentation

What backend/server/service did you use to store the repository?
SSE AES256 encrypted S3 bucket
Expected behavior
Not expected, but a request to have a --sse flag or -o s3.sse="AES256" flag to enable this feature in the Minio SDK being used by Restic.

Actual behavior
Connection denied if S3 bucket has SSE enabled. If S3 bucket has SSE disabled, repo initializes.
Fatal: create key in repository at s3:<s3-repo> failed: client.PutObject: Access Denied

Steps to reproduce the behavior
Initialize restic repo to an S3 bucket that has AES265 encryption enabled
restic init --repo=s3:<s3-repo>
enter password for new repository:
enter password again:
Fatal: create key in repository at s3:<s3-repo> failed: client.PutObject: Access Denied

Do you have any idea what may have caused this?
Restic doesn't provide an option for SSE encryption. The Minio SDK does have the putObjectOption for ServerSideEncryption here
Do you have an idea how to solve the issue?
Please add a command flag to restic init or -o option to enable SSE for S3 buckets that are required to have SSE enabled.
Did restic help you today? Did it make you happy in any way?
Restic provided me some decent hours of frustration, but that doesn't take away my view that this project is awesome. I appreciate the work being done.

Passing the encryption key via a command line option isn't particularly secure. It would probably be better to use environment variables, although that is also not optimal. As the key is also necessary to read any data, I suppose it has to be passed to restic on each invocation?
The more complex problem here is to keep the complexity low enough to not create a maintenance burden for us, as already mentioned in #2008. SSE seems to be available in several variants, which makes things rather messy.

Closing based on https://github.com/restic/restic/pull/3612#issuecomment-1146869395
Please use S3 via rclone if you really need this feature.


Basic plugin system

There's a lot of interesting feature requests that require additional hardware to have setup (temperature sensors, tracking with cameras, etc).
Because of the additional hardware requirements, I don't think it makes sense to have those features in the core mariner code. So we probably need a plugin system of sorts that allows these features to be installed.

i have to agree....
Some of the thins an SLA printer requiers is a heater controled via a thermostat.
One good thing could be using a basic sensor ou a DHT11 that reads Temp and Humidity to control a relay to turn on and off a heater then a Max and Min value are reached.
Another could be a camera (pi cam as a cheap option) to control visualy if the print has faild and that way STOP the print.
I am not much of  a programer but i have all the hardware, so i will try to get "something to work" and see where it leads us.
Up so far... GOD WORK ... i am loving it ....
Kind Regards / Obrigado
Nuno Pires

I'm a programmer but don't really know much about the hardware. I'd like to help, we need to come up with a uniform interface for plugins.
Off the top of my head the things we need to hammer out are

front end we'd want some sort of paneling/homepage that displays a list of installed plugins, and you can enable, disable them.
possibly the ability to change ordering of how the plugins are displayed on homepage
define an extensible API served by the platform, maybe keep /api/plugins/* reserved for plugins? Doing a GET on /api/plugins  could poll and return a list of the plugins themselves, with information of what data is consumed? So if I have a /api/plugins/tempcheck plugin I do a GET and it returns the current temp info.

In the hardware... i can help
so far i have a small Python script that read temp and humidity and prints is on screen
Temperatura = 24.0 C Humidade = 94.0%
Aguarda 2 segundos para efetuar nova leitura...n
Temperatura = 23.0 C Humidade = 94.0%
Aguarda 2 segundos para efetuar nova leitura...n
Temperatura = 23.0 C Humidade = 94.0%
Aguarda 2 segundos para efetuar nova leitura...n
Temperatura = 23.0 C Humidade = 94.0%
Aguarda 2 segundos para efetuar nova leitura...n
Temperatura = 23.0 C Humidade = 94.0%
Aguarda 2 segundos para efetuar nova leitura...n
Temperatura = 23.0 C Humidade = 94.0%
and i have hardcoded MAX limit and MIN limit to activate a relay
in other words, when printer is truned on, the temp is read and in it is under MAX value (30ºC) it activates the relay to trun on the heater.´
When MAX temp is reached, the relay is turn off (turning the heater OFF) and when it reaches MIN Temp (24ºC) it turns on the relay (heater) to start the heating process again.... and it keeps looping.
What i can't do is to convert that info (temp ºC) to a widget and i don´t know how to turn the hard coded values (Max Temp and Min Temp) in to variables that can be changed by the user.
Sample Code:
`
import Adafruit_DHT
import RPi.GPIO as GPIO
import time
sensor = Adafruit_DHT.DHT11
GPIO.setmode(GPIO.BOARD) # Activate GPIO Pins
GPIO.setup(26, GPIO.OUT) # Connect Relay Data pin to GPIO 26
print("Reading Temp and Humidity values :");
while(1):
humid, temp = Adafruit_DHT.read_retry(11, 17);
if humid is not None and temp is not None:
print ("Temperature = {0:0.1f} C Humidity = {1:0.1f}%").format(temp, humid);
print ("Wating 2 sec until next reading");
time.sleep(2) # Sleep 2 sec before next reading
if temp>= 30: # MAX TEMP SET TO 30ºC
			GPIO.output(26, 0) # Relay pin set to "0" (OFF)

	else:
			GPIO.output(26, 1) # Relay pin set to "1" (ON)

	if temp<=25: # MIN TEMP SET TO 25ºC
			GPIO.output(26, 1) # Relay pin set to "1" (ON)
	else:
			GPIO.output(26, 0) # Relay pin set to "0" (OFF)
 	# Mensagem de erro de comunicacao com o sensor
 	#		print("Falha ao ler dados do DHT11 !!!")

`
Hope this helps
Nuno Pires

for some reason cant past the script as code :(

I'm a programmer but don't really know much about the hardware. I'd like to help, we need to come up with a uniform interface for plugins.
Off the top of my head the things we need to hammer out are

front end we'd want some sort of paneling/homepage that displays a list of installed plugins, and you can enable, disable them.
possibly the ability to change ordering of how the plugins are displayed on homepage
define an extensible API served by the platform, maybe keep /api/plugins/* reserved for plugins? Doing a GET on /api/plugins  could poll and return a list of the plugins themselves, with information of what data is consumed? So if I have a /api/plugins/tempcheck plugin I do a GET and it returns the current temp info.

@hansale, this overall seems reasonable. I think I would just skip the frontend for managing plugins and UI ordering for now and focus on how the plugins would be implemented and how they would talk to mariner. We could keep things simple and just auto-discover plugins that are on a specific path like /opt/mariner/plugins or something. Using flask-plugins might be interesting too, but I haven't looked enough into it.
Plugins would have to be able to do these on the backend:

Declare which major version of mariner is supported (i.e. every time we make a breaking change on the API for plugins, we would bump the major version). We would validate this upon plugin discovery and ignore plugins that don't support the major version that is running.
Add new flask routes. I like the idea of keeping all of them under /api/plugins/<plugin-name>/ to prevent conflicts.
Add static resources to be served by mariner (so we can serve JS, etc)

On the frontend, plugins would have to be able to:

Register new cards for the homepage
Register new views
Register new entries on the drawer menu

Does this seem reasonable? Anything else that we're missing? Once we know everything that plugins need to do we can design what the plugin system could look like.

@xmodpt I saw an ingenious time lapse that uses a light sensor to tell if the resin printer is on, and the camera snaps a picture right as the UV light turns on, allowing for perfect time lapses regardless of the cure times of the device. Very simple and cool.
Are you using a temperature sensor like this one? https://www.adafruit.com/product/381
@luizribeiro As for the plugin system, I was just spitballing "ideal world" scenarios with the drag and drop, obviously a drag and drop UI for plugin ordering would be orders of magnitude more complex than having , especially considering that by virtue of running Mariner at all I'd guess the average technical proficiency of the users is substantially higher than the average.
This sounds like a good start, although it seems like #75 would need to be implemented before substantial headway could be made? As we'd want plugins to have the ability to access the printer, right?

This sounds like a good start, although it seems like #75 would need to be implemented before substantial headway could be made? As we'd want plugins to have the ability to access the printer, right?

I'm not sure if we need #75 done before we can get started on the plugin system.
Maybe for us to have a next release with the plugin system we would want both issues closed, but we can probably get the architecture of the plugin system in place without #75

@hansale
Hello m8
yes that one is the target one, however i am testing with a cheap DHT11 module this one DHT11

All of this and more can be done with Node-RED which comes pre-installed on Raspberry Pi OS (including Light). I am actually in the process of building a platform to do all of this and more.
Definitely check out Node-RED, it's free, awesome and the community is very nice and helpful.
All the best!
Dax.

Hey folks, let's keep this issue on the topic of the plugin system. If you'd like to discuss something that is unrelated to development of mariner, please use Discussions instead of Issues.


ISPN-7485 Restore REST authentication

https://issues.jboss.org/browse/ISPN-7485
This brings back REST endpoint authentication.
It supports both BASIC and CLIENT_CERT authentication methods. It still needs DIGEST and possibly SPNEGO.
To expedite the process of getting this back in, I have temporarily forked the resteasy-netty classes so that I could adapt them to a the more flexible authentication schemes we need.
It aligns the configuration schema to that of the Hot Rod endpoint by using an <authentication> element and it also works with the security-realm instead of the old security-domain.
This is preview as I haven't finished adapting the client cert test, but otherwise it should be fine.

Hm, only one related failure, but running it locally it passes every time:
[TestSuiteProgress] Test succeeded: RESTBasicSecurityIT.testSecuredReadWriteOperations
[TestSuiteProgress] Tests succeeded: 1, failed: 0, skipped: 0
Tests run: 1, Failures: 0, Errors: 0, Skipped: 0, Time elapsed: 10.795 sec - in org.infinispan.server.test.security.rest.RESTBasicSecurityIT
Results :
Tests run: 1, Failures: 0, Errors: 0, Skipped: 0

Needs rebase

Rebased

integrated, thanks @tristantarrant !


Gitpod has partnered with the Linux Foundation

Hi! 👋
Gitpod has recently partnered with the Linux Foundation. As part of this partnership, Gitpod is offering 2500 credits (or 250 hours) of free cloud development environments (CDEs) to all Linux Foundation projects, including containerd. A maintainer of containerd can submit an application at https://www.gitpod.io/discover/opensource to sign up for this offer.
Using CDEs can simplify the contribution and development process, it can also help the containerd maintainers review pull requests more easily. I have prepared an optimized Gitpod configuration for this project in my fork. Please feel free to try it out by clicking on the button below 👇

If you find it helpful, I would be glad to submit a pull request here.

Hey @fuweid! We'd love to support your project with a CDE to help improve contributor experience and more. Happy to chat if you'd like! :)

Hi @pawlean , thanks for that information. Basically, that issue is used to track real problem and feature request. So, I suggest to post this in that CNCF channel, like https://github.com/containerd/containerd?tab=readme-ov-file#communication . Thanks again.

Makes sense! Thanks @fuweid. I'll write a message :)


isPristine now differentiates between null, 0 and NaN

I realise I just submitted #1673 for something very similar just a few days ago but I've found another edge case.
This PR addresses issues where a HTML5 number field is left empty which is fed through as NaN.
I could have modified the condition on line 24 of isPristine.js but I felt that it would be easier to read as is.

Note I've now also added a check as follows;
...
} else if (Number.isNaN(initial) && Number.isNaN(data)) {
    return true;
...

I'm going to resubmit this as another PR with further changes in the near future.


Sprint 3 Planning

Issues dem Sprint 3 zuweisen
Tickets schätzen

Ticket for Sprint 4 planning already exist (#10 )


Allow loading forge credentials from project dir

We now search the .puppetforge.yml in current working dir first.
Fallback to home dir if not found.

Hello:
What does this gain us? I'm not entirely understanding.

We are running a local forge for internal puppet modules. This creates the need of pushing modules to different forge servers with different credentials.
This change allows us to check in a .puppetforge.yml for ouor local modules and have jenkins push it automatically to the local forge server.

@felixb can you rebase?

done.


Invalid SAML2 Issuer NameID format

Description
An invalid SAML response is generated when something other than an absolute URI is entered in the "Identity Provider Entity Id" field when configuring an IdP for SAML2 Web SSO.
This is the offending line from the response:
<saml2:Issuer Format="urn:oasis:names:tc:SAML:2.0:nameid-format:entity" xmlns:saml2="urn:oasis:names:tc:SAML:2.0:assertion">acme.com/samlsso</saml2:Issuer>
According to the SAML2 specs, when the "entity" NameID format is used, the value must be an absolute URI. This is causing a validation error in several SAML2 implementations we have tried.
Notes

8.3.6 Entity Identifier
URI: urn:oasis:names:tc:SAML:2.0:nameid-format:entity
Indicates that the content of the element is the identifier of an entity that provides SAML-based services (such as a SAML authority, requester, or responder) or is a participant in SAML profiles (such as a service provider supporting the browser SSO profile). Such an identifier can be used in the  element to identify the issuer of a SAML request, response, or assertion, or within the  element to make assertions about system entities that can issue SAML requests, responses, and assertions. It can also be used in other elements and attributes whose purpose is to identify a system entity in various protocol exchanges.
The syntax of such an identifier is a URI of not more than 1024 characters in length. It is RECOMMENDED that a system entity use a URL containing its own domain name to identify itself

The recommendation as per the specification is that the entity uses a URL containing its own domain name to identify itself. The use of a URI is mandatory. A URI has been defined in the SAML 2.0 specifications as follows:

1.3.2 URI Values
All SAML URI reference values have the type xs:anyURI, which is built in to the W3C XML Schema Datatypes specification [Schema2].
Unless otherwise indicated in this specification, all URI reference values used within SAML-defined elements or attributes MUST consist of at least one non-whitespace character, and are REQUIRED to be absolute [RFC 2396].
Note that the SAML specification makes extensive use of URI references as identifiers, such as status codes, format types, attribute and system entity names, etc. In such cases, it is essential that the values be both unique and consistent, such that the same URI is never used at different times to represent different underlying information.

Requirement
URI validation should be implemented to the field for ensuring validity of the provided URI
Type
Bug
Priority
High
Affected version
5.1.0, 5.8.0
Component/s
saml2-sso
Severity
Major
Estimated Complexity
Novice
Label/s
Type/Bug, Component/SAML, Affected/5.1.0, Affected/5.8.0, Severity/Major, Complexity/Low, Priority/Normal
Moved from https://wso2.org/jira/browse/IDENTITY-4992

This issue is being closed due to extended inactivity. Please feel free to reopen it if further attention is needed. Thank you for helping us keep the issue list relevant and focused!


Is it possible to link from one slide to another?

I see examples of external hyperlinks but I am wondering if you have an example of linking between slides?

Hi @heavysixer,
Unfortunately, linking between slides is not currently supported.

Hi @gitbrent looking through the code it looks like createHyperlinkRels might allow for relative links between slides. Is this something that could be exposed as a public function instead of being only used internally?

Actually, you CAN link to other slides easily. Any element that supports a hyperlink in its options object, just put "#3", "#4", etc. in the URL to link to that slide. For example:
slide.addImage({path: 'images/world.jpg', x: 1.0, y: 1.0, w: 2.0, h: 2.0,
hyperlink: {
url: '#4',
tooltip: 'Go to slide #4',
});

@gregdolley: Holy cow, that worked! Today I Learned...
@heavysixer: Does this work for you?
var pptx = new PptxGenJS();

var slide = pptx.addNewSlide();
slide.addImage({
    data: LOGO_STARLABS, x: 1.0, y: 1.0, w: 2.0, h: 2.0,
    hyperlink: { url:'#2', tooltip:'Go to slide #4' }
});

var slide = pptx.addNewSlide();
slide.addText('Link goes here', { x:0.5, y:0.7, font_size:18 });

pptx.save('PptxGenJS-Links');

@gitbrent We're using node, so I had to change the first line and LOGO_STARLABS wasn't defined, so I used an image on my machine. But, yeah, this version worked fine:
`var pptx = require('pptxgenjs');
var slide = pptx.addNewSlide();
slide.addImage({
path: 'test/images/world.jpg', x: 1.0, y: 1.0, w: 2.0, h: 2.0,
hyperlink: { url:'#2', tooltip:'Go to slide #2' }
});
var slide = pptx.addNewSlide();
slide.addText('Link goes here', { x:0.5, y:0.7, font_size:18 });
pptx.save('PptxGenJS-Links');`

@gitbrent unfortunately it does not work for me, but I am on a Mac using the official Microsoft version of Powerpoint. I will check on a PC to see if it works there.

I've noticed that MS generates the XML slightly different than what this tool produces.
This lib creates a link like this:
<a:r>
              <a:rPr lang="en-US" u="sng" dirty="0" smtClean="0">
                <a:solidFill><a:srgbClr val="CC0000"/></a:solidFill><a:hlinkClick r:id="rId2" tooltip="Go to slide 3"/></a:rPr>
              <a:t>This is slide 1. Click to go to slide 3</a:t>
            </a:r>

MS generates it like this:
<a:r>
              <a:rPr lang="en-US" dirty="0" smtClean="0">
                <a:solidFill><a:srgbClr val="00CD00"/></a:solidFill><a:hlinkClick r:id="rId3" action="ppaction://hlinksldjump"/></a:rPr>
              <a:t>Linked By MS</a:t>
            </a:r>

The key differences appears to be the action="ppaction://hlinksldjump" attributes.

@gitbrent Is there a way to pass arbitrary attributes from the options hash to the link? If so we could easily add the support for action.

Hi @heavysixer,
Having looked at the XML PowerPoint generates, I can see that while a hyperlink can be targeted to a slide may work, the correct code is what you've shown.
Therefore, i'll add a new "slide" option to hyperlinks so that slide links can be properly supported.
Slide XML:
<a:r>
  <a:rPr lang="en-US" dirty="0" smtClean="0">
    <a:hlinkClick r:id="rId3" action="ppaction://hlinksldjump" />
  </a:rPr>
  <a:t>Link to slide #2!</a:t>
</a:r>

Slide Ref:
<Relationship Id="rId3" Type="http://schemas.openxmlformats.org/officeDocument/2006/relationships/slide" Target="slide2.xml"/>

Links to Slides within a Presentation are now supported in the main branch.  Readme updated.
// EX: Hyperlink: Web
slide.addText(
    [{
        text: 'PptxGenJS Project',
        options: { hyperlink:{ url:'https://github.com/gitbrent/pptxgenjs', tooltip:'Visit Homepage' } }
    }],
    { x:1.0, y:1.0, w:5, h:1 }
);
// EX: Hyperlink: Slide in Presentation
slide.addText(
    [{
        text: 'Slide #2',
        options: { hyperlink:{ slide:'2', tooltip:'Go to Summary Slide' } }
    }],
    { x:1.0, y:2.5, w:5, h:1 }
);

great stuff @gitbrent !


configuration bug

Hi Roman! I just updated the templates, and to created a new solution. The generated configurations seem like they are messed up. Is it just me?
https://i.imgur.com/GRmceDb.png
https://i.imgur.com/jJpXM1g.png

Sorry for the issue, a fix has been released in version 3.0.1
dotnet new install Nice3point.Revit.Templates::3.0.1


Please state in which files the preprocessing is performed:?

Please state in which files the preprocessing is performed:?  I am a newbie....So i was curious how you actually ran the classifer.....Maybe if you tell some detail about file...how you did POS tagging means in which file,location of Riad's Dataset for speeling correction.....I mean the whole preprocessing part is a bit difficult to understand as i am unable to understand the sequence in which the files are run??

Hi, astro6026,
Firstly, Riad's work is not included in this repository. If you're interested in details of his dataset like spelling correction, you have to ask him at Kaggle directly.
As for preprocessing, the main work is in clean_data.ipynb. Before running this notebook, you must download the dataset of this competition from Kaggle and the pre-trained word embeddings I list in my readme file.
Besides, you might find that I do not upload the file cleanwords.txt. I guess it was ignored by the git setting and I'm sorry for my carelessness. You can rebuild this file by yourself, the format should be:
typo, corrected word

For example:
wikibreak,wiki break
wikidata,wiki data

This file should be placed in the folder features. By the way, the pre-trained wordvec should also be placed in this folder.
If pre-processing work properly, cleaned dataset should be generated at the folder Dataset and by that time you can run train_cleaned_word_level.ipynb to train our models.
POS tagging was written in (dirty_pos.ipynb)[https://github.com/zake7749/DeepToxic/blob/master/tools/dirty_pos.ipynb]. As you can see it, the codes and structures are so messy that I still have no time to refactor this part. Because we tried this idea at a very late moment, the solution is very difficult to understand. I don't recommend to reuse our POS strategy.
I am currently busy with personal affairs, so I can not recheck this project before this weekend. If you still have problems at that time, please let me know.

Thanks... I hope I will be able to able to understand it........

The steps are as follows:

Download the dataset and pre-trained word embeddings, and put them into the corresponding directories.
Execute clean_data.ipynb.
Execute train_cleaned_word_level.ipynb. The predictions would be generated under the path submit_path_prefix .

Please note that predictions generated by train_cleaned_word_level.ipynb are based on the LAST checkpoint rather than the BEST checkpoint.
I evaluate the performance of my models by cross-validation.

i have a request from u can u please upload the clean text file............and give a little explanation about the get_av_rnn function used in model_zoo file of soo toxic and the AttentionWeight class in the same file

What's the clean text file? Is the cleanwords.txt or the cleaned dataset?
If you're talking about the former, you can create your own mapping arbitrarily (just have to follow the format I mentioned before) since this file is not the key part in our solution. You still can reach the same performance without this file.
If you're talking about the latter, due to the restriction of competition rules, I cannot upload this dataset.
The detail of get_av_rnn please check our solution. As for AttentionWeight, it basically is a weighted sum pooling which estimates the importance of hidden output from each timestep by applying dot product with a trainable weight matrix.

I am trying to understand only the word level part of the AV_RNN model.............Please Help......How are the features being extracted from the text......does it has to do with word embeddings??... Please answer my question

I determined hyper-parameters by evaluating model performance with cross-validation.
av stands for all views. I think summarizing the hidden outputs with multiple pooling methods would be more generalized.
Sure, there are many methods for hyper-parameters selection while I do not use any of them in this competition.
I don't quite understand your question. I used the pre-trained word vectors and aggregated them by a rnn.

As I said, I determined hyper-parameters by cross-validation.
The term feature maps are the outputs of filters. It is just a hyper-parameter of CNN.

Can you tell me about the GlobalMaxPooling1D, GlobalAvgPooling1D and SpatialDropout1D layer.........Actually, I can't figure out the form in which the data is travelling

between layer......

I would recommend you read the Keras Documentation that has detailed descriptions of all layers.


Remove now-unused workflow_status ModalWorkflow view

Follow up to https://github.com/wagtail/wagtail/pull/8538. The workflow_status view is now unused: https://github.com/wagtail/wagtail/blob/main/wagtail/admin/views/pages/workflow.py#L205.
It needs to be removed, as well as its corresponding URL patterns, and any test still relying on it being a ModalWorkflow implementation.

Completed in #10720


Cannot find protocol declaration for nsobject

Hi Richard,
I dragged .h and .m files into my directory and an error said "cannot find protocol declaration for NSObject". I checked that the @protocol RKSwipeBetweenViewControllersDelegate following with a <NSObject>. After deleting that everything was fine.

interesting, thanks for letting me know i'll look into it

move #import <UIKit/UIKit.h> above the @protocol RKSwipeBetweenViewControllersDelegate <NSObject>

done