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Fourteen IP

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Barchester Towers by Anthony Trollope is part of the HackerNoon Books Series. You can jump to any chapter in this book . Fourteen Arguments in Favour of Mr. Quiverful's Claims here Fourteen Arguments in Favour of Mr. Quiverful's Claims We have most of us heard of the terrible anger of a lioness when, surrounded by her cubs, she guards her prey. Few of us wish to disturb the mother of a litter of puppies when mouthing a bone in the midst of her young family. Medea and her children are familiar to us, and so is the grief of Constance. Mrs. Quiverful, when she first heard from her husband the news which he had to impart, felt within her bosom all the rage of the lioness, the rapacity of the hound, the fury of the tragic queen, and the deep despair of the bereaved mother. Doubting, but yet hardly fearing, what might have been the tenor of Mr. Slope's discourse, she rushed back to her husband as soon as the front door was closed behind the visitor. It was well for Mr. Slope that he so escaped—the anger of such a woman, at such a moment, would have cowed even him. As a general rule, it is highly desirable that ladies should keep their temper: a woman when she storms always makes herself ugly, and usually ridiculous also. There is nothing so odious to man as a virago. Though Theseus loved an Amazon, he showed his love but roughly, and from the time of Theseus downward, no man ever wished to have his wife remarkable rather for forward prowess than retiring gentleness. A low voice "is an excellent thing in woman." Such may be laid down as a very general rule; and few women should allow themselves to deviate from it, and then only on rare occasions. But if there be a time when a woman may let her hair to the winds, when she may loose her arms, and scream out trumpet-tongued to the ears of men, it is when nature calls out within her not for her own wants, but for the wants of those whom her womb has borne, whom her breasts have suckled, for those who look to her for their daily bread as naturally as man looks to his Creator. There was nothing poetic in the nature of Mrs. Quiverful. She was neither a Medea nor a Constance. When angry, she spoke out her anger in plain words, and in a tone which might have been modulated with advantage; but she did so, at any rate, without affectation. Now, without knowing it, she rose to a tragic vein. "Well, my dear, we are not to have it." Such were the words with which her ears were greeted when she entered the parlour, still hot from the kitchen fire. And the face of her husband spoke even more plainly than his words:— E'en such a man, so faint, so spiritless, So dull, so dead in look, so woe-begone, Drew Priam's curtain in the dead of night. "What!" said she—and Mrs. Siddons could not have put more passion into a single syllable—"What! Not have it? Who says so?" And she sat opposite to her husband, with her elbows on the table, her hands clasped together, and her coarse, solid, but once handsome face stretched over it towards him. She sat as silent as death while he told his story, and very dreadful to him her silence was. He told it very lamely and badly but still in such a manner that she soon understood the whole of it. "And so you have resigned it?" said she. "I have had no opportunity of accepting it," he replied. "I had no witnesses to Mr. Slope's offer, even if that offer would bind the bishop. It was better for me, on the whole, to keep on good terms with such men than to fight for what I should never get!" "Witnesses!" she screamed, rising quickly to her feet and walking up and down the room. "Do clergymen require witnesses to their words? He made the promise in the bishop's name, and if it is to be broken, I'll know the reason why. Did he not positively say that the bishop had sent him to offer you the place?" "He did, my dear. But that is now nothing to the purpose." "It is everything to the purpose, Mr. Quiverful. Witnesses indeed! And then to talk of your honour being questioned because you wish to provide for fourteen children. It is everything to the purpose; and so they shall know, if I scream it into their ears from the town cross of Barchester." "You forget, Letitia, that the bishop has so many things in his gift. We must wait a little longer. That is all." "Wait! Shall we feed the children by waiting? Will waiting put George, and Tom, and Sam out into the world? Will it enable my poor girls to give up some of their drudgery? Will waiting make Bessy and Jane fit even to be governesses? Will waiting pay for the things we got in Barchester last week?" "It is all we can do, my dear. The disappointment is as much to me as to you; and yet, God knows, I feel it more for your sake than my own." Mrs. Quiverful was looking full into her husband's face, and saw a small hot tear appear on each of those furrowed cheeks. This was too much for her woman's heart. He also had risen, and was standing with his back to the empty grate. She rushed towards him and, seizing him in her arms, sobbed aloud upon his bosom. "You are too good, too soft, too yielding," she said at last. "These men, when they want you, they use you like a cat's paw; and when they want you no longer, they throw you aside like an old shoe. This is twice they have treated you so." "In one way this will be all for the better," argued he. "It will make the bishop feel that he is bound to do something for me." "At any rate he shall hear of it," said the lady, again reverting to her more angry mood. "At any rate he shall hear of it, and that loudly; and so shall she. She little knows Letitia Quiverful, if she thinks I will sit down quietly with the loss after all that passed between us at the palace. If there's any feeling within her, I'll make her ashamed of herself,"—and she paced the room again, stamping the floor as she went with her fat, heavy foot. "Good heavens! What a heart she must have within her to treat in such a way as this the father of fourteen unprovided children!" Mr. Quiverful proceeded to explain that he didn't think that Mrs. Proudie had had anything to do with it. "Don't tell me," said Mrs. Quiverful; "I know more about it than that. Doesn't all the world know that Mrs. Proudie is bishop of Barchester and that Mr. Slope is merely her creature? Wasn't it she that made me the promise, just as though the thing was in her own particular gift? I tell you, it was that woman who sent him over here to-day, because, for some reason of her own, she wants to go back from her word." "My dear, you're wrong—" "Now, Q., don't be so soft," she continued. "Take my word for it, the bishop knows no more about it than Jemima does." Jemima was the two-year-old. "And if you'll take my advice, you'll lose no time in going over and seeing him yourself." Soft, however, as Mr. Quiverful might be, he would not allow himself to be talked out of his opinion on this occasion, and proceeded with much minuteness to explain to his wife the tone in which Mr. Slope had spoken of Mrs. Proudie's interference in diocesan matters. As he did so, a new idea gradually instilled itself into the matron's head, and a new course of conduct presented itself to her judgement. What if, after all, Mrs. Proudie knew nothing of this visit of Mr. Slope's? In that case, might it not be possible that that lady would still be staunch to her in this matter, still stand her friend, and, perhaps, possibly carry her through in opposition to Mr. Slope? Mrs. Quiverful said nothing as this vague hope occurred to her, but listened with more than ordinary patience to what her husband had to say. While he was still explaining that in all probability the world was wrong in its estimation of Mrs. Proudie's power and authority, she had fully made up her mind as to her course of action. She did not, however, proclaim her intention. She shook her head ominously as he continued his narration, and when he had completed, she rose to go, merely observing that it was cruel, cruel treatment. She then asked him if he would mind waiting for a late dinner instead of dining at their usual hour of three; and, having received from him a concession on this point, she proceeded to carry her purpose into execution. She determined that she would at once go to the palace, that she would do so, if possible, before Mrs. Proudie could have had an interview with Mr. Slope, and that she would be either submissive, piteous, and pathetic, or else indignant, violent, and exacting, according to the manner in which she was received. She was quite confident in her own power. Strengthened as she was by the pressing wants of fourteen children, she felt that she could make her way through legions of episcopal servants and force herself, if need be, into the presence of the lady who had so wronged her. She had no shame about it, no , no dread of archdeacons. She would, as she declared to her husband, make her wail heard in the market-place if she did not get redress and justice. It might be very well for an unmarried young curate to be shamefaced in such matters; it might be all right that a snug rector, really in want of nothing, but still looking for better preferment, should carry on his affairs decently under the rose. But Mrs. Quiverful, with fourteen children, had given over being shamefaced and, in some things, had given over being decent. If it were intended that she should be ill-used in the manner proposed by Mr. Slope, it should not be done under the rose. All the world should know of it. mauvaise honte In her present mood, Mrs. Quiverful was not over-careful about her attire. She tied her bonnet under her chin, threw her shawl over her shoulders, armed herself with the old family cotton umbrella, and started for Barchester. A journey to the palace was not quite so easy a thing for Mrs. Quiverful as for our friend at Plumstead. Plumstead is nine miles from Barchester, and Puddingdale is but four. But the archdeacon could order round his brougham, and his high-trotting fast bay gelding would take him into the city within the hour. There was no brougham in the coach-house of Puddingdale Vicarage, no bay horse in the stables. There was no method of locomotion for its inhabitants but that which nature has assigned to man. Mrs. Quiverful was a broad, heavy woman, not young, nor given to walking. In her kitchen, and in the family dormitories, she was active enough, but her pace and gait were not adapted for the road. A walk into Barchester and back in the middle of an August day would be to her a terrible task, if not altogether impracticable. There was living in the parish, about half a mile from the vicarage on the road to the city, a decent, kindly farmer, well to do as regards this world and so far mindful of the next that he attended his parish church with decent regularity. To him Mrs. Quiverful had before now appealed in some of her more pressing family troubles, and had not appealed in vain. At his door she now presented herself, and, having explained to his wife that most urgent business required her to go at once to Barchester, begged that Farmer Subsoil would take her thither in his tax-cart. The farmer did not reject her plan, and, as soon as Prince could be got into his collar, they started on their journey. Mrs. Quiverful did not mention the purpose of her business, nor did the farmer alloy his kindness by any unseemly questions. She merely begged to be put down at the bridge going into the city and to be taken up again at the same place in the course of two hours. The farmer promised to be punctual to his appointment, and the lady, supported by her umbrella, took the short cut to the close and, in a few minutes, was at the bishop's door. Hitherto she had felt no dread with regard to the coming interview. She had felt nothing but an indignant longing to pour forth her claims, and declare her wrongs, if those claims were not fully admitted. But now the difficulty of her situation touched her a little. She had been at the palace once before, but then she went to give grateful thanks. Those who have thanks to return for favours received find easy admittance to the halls of the great. Such is not always the case with men, or even with women, who have favours to beg. Still less easy is access for those who demand the fulfilment of promises already made. Mrs. Quiverful had not been slow to learn the ways of the world. She knew all this, and she knew also that her cotton umbrella and all but ragged shawl would not command respect in the eyes of the palatial servants. If she were too humble, she knew well that she would never succeed. To overcome by imperious overbearing with such a shawl as hers upon her shoulders and such a bonnet on her head would have required a personal bearing very superior to that with which nature had endowed her. Of this also Mrs. Quiverful was aware. She must make it known that she was the wife of a gentleman and a clergyman, and must yet condescend to conciliate. The poor lady knew but one way to overcome these difficulties at the very threshold of her enterprise, and to this she resorted. Low as were the domestic funds at Puddingdale, she still retained possession of half a crown, and this she sacrificed to the avarice of Mrs. Proudie's metropolitan sesquipedalian serving-man. She was, she said, Mrs. Quiverful of Puddingdale, the wife of the Rev. Mr. Quiverful. She wished to see Mrs. Proudie. It was indeed quite indispensable that she should see Mrs. Proudie. James Fitzplush looked worse than dubious, did not know whether his lady were out, or engaged, or in her bedroom; thought it most probable she was subject to one of these or to some other cause that would make her invisible; but Mrs. Quiverful could sit down in the waiting-room while inquiry was being made of Mrs. Proudie's maid. "Look here, my man," said Mrs. Quiverful; "I must see her;" and she put her card and half-crown—think of it, my reader, think of it; her last half-crown—into the man's hand and sat herself down on a chair in the waiting-room. Whether the bribe carried the day, or whether the bishop's wife really chose to see the vicar's wife, it boots not now to inquire. The man returned and, begging Mrs. Quiverful to follow him, ushered her into the presence of the mistress of the diocese. Mrs. Quiverful at once saw that her patroness was in a smiling humour. Triumph sat throned upon her brow, and all the joys of dominion hovered about her curls. Her lord had that morning contested with her a great point. He had received an invitation to spend a couple of days with the archbishop. His soul longed for the gratification. Not a word, however, in his grace's note alluded to the fact of his being a married man; if he went at all, he must go alone. This necessity would have presented no insurmountable bar to the visit, or have militated much against the pleasure, had he been able to go without any reference to Mrs. Proudie. But this he could not do. He could not order his portmanteau to be packed and start with his own man, merely telling the lady of his heart that he would probably be back on Saturday. There are men—may we not rather say monsters?—who do such things, and there are wives—may we not rather say slaves?—who put up with such usage. But Dr. and Mrs. Proudie were not among the number. The bishop, with some beating about the bush, made the lady understand that he very much wished to go. The lady, without any beating about the bush, made the bishop understand that she wouldn't hear of it. It would be useless here to repeat the arguments that were used on each side, and needless to record the result. Those who are married will understand very well how the battle was lost and won, and those who are single will never understand it till they learn the lesson which experience alone can give. When Mrs. Quiverful was shown into Mrs. Proudie's room, that lady had only returned a few minutes from her lord. But before she left him she had seen the answer to the archbishop's note written and sealed. No wonder that her face was wreathed with smiles as she received Mrs. Quiverful. She instantly spoke of the subject which was so near the heart of her visitor. "Well, Mrs. Quiverful," said she, "is it decided yet when you are to move into Barchester?" "That woman," as she had an hour or two since been called, became instantly re-endowed with all the graces that can adorn a bishop's wife. Mrs. Quiverful immediately saw that her business was to be piteous, and that nothing was to be gained by indignation—nothing, indeed, unless she could be indignant in company with her patroness. "Oh, Mrs. Proudie," she began, "I fear we are not to move to Barchester at all." "Why not?" said that lady sharply, dropping at a moment's notice her smiles and condescension, and turning with her sharp quick way to business which she saw at a glance was important. And then Mrs. Quiverful told her tale. As she progressed in the history of her wrongs she perceived that the heavier she leant upon Mr. Slope the blacker became Mrs. Proudie's brow, but that such blackness was not injurious to her own case. When Mr. Slope was at Puddingdale Vicarage that morning she had regarded him as the creature of the lady-bishop; now she perceived that they were enemies. She admitted her mistake to herself without any pain or humiliation. She had but one feeling, and that was confined to her family. She cared little how she twisted and turned among these new-comers at the bishop's palace so long as she could twist her husband into the warden's house. She cared not which was her friend or which was her enemy, if only she could get this preferment which she so sorely wanted. She told her tale, and Mrs. Proudie listened to it almost in silence. She told how Mr. Slope had cozened her husband into resigning his claim, and had declared that it was the bishop's will that none but Mr. Harding should be warden. Mrs. Proudie's brow became blacker and blacker. At last she started from her chair and, begging Mrs. Quiverful to sit and wait for her return, marched out of the room. "Oh, Mrs. Proudie, it's for fourteen children—for fourteen children." Such was the burden that fell on her ear as she closed the door behind her. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Anthony Trollope (2002). Barchester Towers. Urbana, Illinois: Project Gutenberg. Retrieved October 2022 https://www.gutenberg.org/cache/epub/3409/pg3409-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html

Fourteen Arguments in Favour of Mr. Quiverful's Claims

Well, now 15. This post was originally published on allisonmidorireilly.com Toward the end of 2015, I decided to try again in 2016 to start my own and to forge my own path. Originally, I planned to write a blog post each day (or every other day) chronicling the long, hard and lonely road, especially since information about the beginning of the journey is hard to come by. Everyone talks after they’re successful, after they’ve made it and, most likely, after others hear about them and want to talk to them about what they are doing. venture Hardly anyone talks about the early days, before there’s traction or /market fit or paying customers or investments. But, I think those early days are some of the most important to talk about. I think it’s important to be open about how shitty some days are and how much work it takes to get traction and product marketing fit. I think we really ought to talk about how deep you have to dig within yourself in order to keep going when no customers show up, when your product isn’t working, when you’re running out of money and when others tell you that you can’t do it. product Then Why Didn’t I Start the Conversation? I don’t have a good reason. I wondered what I would write about on those days where I have nothing to show for the fruits of my labors. What’s there to say when I don’t get the sponsorship deal, or when no one watches the stream, besides reporting that those things happened? Perhaps I would’ve figured what to write if I actually started writing instead of making excuses. I understand now that not knowing what to write was a terrible reason, but better start now than never and start the conversation that I think should be had a little more often. It’s a much more real conversation to have in my opinion, since success isn’t overnight, no matter how much it seems to be from the outside. I also think a conversation likes this acknowledges failures and missteps as part of the process of success instead of the antitheses of success. If All We Talk about are Winners, then What Does This Say About Losing? Not everything is going to work. Not everyone wins. Not everyone wins every time. Not every step is a step in the right direction. This doesn’t mean those steps were wrong or bad. This doesn’t mean that those lonely days where no customers show up are indicative of anything personal or permanent. The days where it feels like you’re banging your head against a wall are part of forging your own path, and a part of life. In hindsight, the tough moments are easier to canonize and incorporate into the larger story of success. But, on that day, without hindsight, it just sucks. But, failures and missteps are okay. They only represent who you are if you choose to let them represent you. So, before I can fit failures and bad days neatly into a narrative of victory, I’m going to start the conversation and document the lonely road on this venture. I know it will take a lot of hustle and hard work. I know the great things won’t happen overnight. I know a lot of people will tell my I’m crazy, that I ought to quit, that I can’t do it, that it’s not possible etc. Those comments are going to come. Shrugging all of that off is easier said than done. Sticking to your guns is easier said than done. I’m not writing this post and wanting to have this conversation because I’m delusional about myself and my abilities. I’m doing this, and want to do this, to showcase how hard it is not to be.

Fourteen Days into the Venture

Comprehensive Review of AI Chatbots and ChatGPT in Education

Introduction This is a companion guide to (the "Part 1 guide"). I wrote the Part 1 guide on how to finetune Meta AI's model on Chilean Spanish. It is assumed that you have completed that guide and have generated your own finetuned XLS-R model. This guide will explain the steps to run on your finetuned XLS-R model via a . Working With wav2vec2 Part 1 - Finetuning XLS-R for Automatic Speech Recognition wav2vec2 XLS-R ("XLS-R") inference Kaggle Notebook Prerequisites and Before You Get Started To complete the guide, you will need to have: A finetuned XLS-R model for the Spanish language. An existing . Kaggle account Intermediate knowledge of Python. Intermediate knowledge of working with Kaggle Notebooks. Intermediate knowledge of ML concepts. Basic knowledge of ASR concepts. Building the Inference Notebook Step 1 - Setting Up Your Kaggle Environment Step 1.1 - Creating a New Kaggle Notebook Log in to Kaggle. Create a new Kaggle Notebook. The name of the notebook can be changed as desired. This guide uses the notebook name . spanish-asr-inference Step 1.2 - Adding the Test Datasets This guide uses the as its source for test data. Like the , the Peruvian speakers dataset also consists of two sub-datasets: 2,918 recordings of male Peruvian speakers and 2,529 recordings of female Peruvian speakers. Peruvian Spanish Speech Data Set Chilean Spanish Speech Data Set This dataset has been uploaded to Kaggle as 2 distinct datasets: Recordings of Peruvian male speakers Recordings of Peruvian female speakers Add both of these datasets to your Kaggle Notebook by clicking on . Add Input Step 1.3 - Adding the Finetuned Model You should have saved your finetuned model in of the guide as a . Step 4 Working With wav2vec2 Part 1 - Finetuning XLS-R for Automatic Speech Recognition Kaggle Model Add your finetuned model to your Kaggle Notebook by clicking on . Add Input Step 2 - Building the Inference Notebook The following 16 sub-steps build each of the inference notebook's 16 cells in order. You will note that many of the same utility methods from the Part 1 guide are used here. Step 2.1 - CELL 1: Installing Packages The first cell of the inference notebook installs dependencies. Set the first cell to: ### CELL 1: Install Packages ###
!pip install --upgrade torchaudio
!pip install jiwer Step 2.2 - CELL 2: Importing Python Packages The second cell imports required Python packages. Set the second cell to: ### CELL 2: Import Python packages ###
import re
import math
import random
import pandas as pd
import torchaudio
from datasets import load_metric
from transformers import pipeline Step 2.3 - CELL 3: Loading WER Metric The third cell imports the HuggingFace WER evaluation metric. Set the third cell to: ### CELL 3: Load WER metric ###
wer_metric = load_metric("wer") WER will be used to measure the performance of the finetuned model on test data. Step 2.4 - CELL 4: Setting Constants The fourth cell sets constants that will be used throughout the notebook. Set the fourth cell to: ### CELL 4: Constants ###
# Testing data
TEST_DATA_PATH_MALE = "/kaggle/input/google-spanish-speakers-peru-male/"
TEST_DATA_PATH_FEMALE = "/kaggle/input/google-spanish-speakers-peru-female/"
EXT = ".wav"
NUM_LOAD_FROM_EACH_SET = 3

# Special characters
SPECIAL_CHARS = r"[\d\,\-\;\!\¡\?\¿\।\‘\’\"\–\'\:\/\.\“\”\৷\…\‚\॥\\]"

# Sampling rates
ORIG_SAMPLING_RATE = 48000
TGT_SAMPLING_RATE = 16000 Step 2.5 - CELL 5: Utility Methods for Reading Index Files, Cleaning Text, and Creating Vocabulary The fifth cell defines utility methods for reading the dataset index files, as well as for cleaning transcription text and generating a random set of samples from test data. Set the fifth cell to: ### CELL 5: Utility methods for reading index files, cleaning text, random indices generator ###
def read_index_file_data(path: str, filename: str):
    data = []
    with open(path + filename, "r", encoding = "utf8") as f:
        lines = f.readlines()
        for line in lines:
            file_and_text = line.split("\t")
            data.append([path + file_and_text[0] + EXT, file_and_text[1].replace("\n", "")])
    return data

def clean_text(text: str) -> str:
    cleaned_text = re.sub(SPECIAL_CHARS, "", text)
    cleaned_text = cleaned_text.lower()
    return cleaned_text

def get_random_samples(dataset: list, num: int) -> list:
    used = []
    samples = []
    for i in range(num):
        a = -1
        while a == -1 or a in used:
            a = math.floor(len(dataset) * random.random())
        samples.append(dataset[a])
        used.append(a)
    return samples The method reads a dataset index file and produces a list of lists with audio filename and transcription data, e.g.: read_index_file_data line_index.tsv [
    ["/kaggle/input/google-spanish-speakers-chile-male/clm_08421_01719502739", "Es un viaje de negocios solamente voy por una noche"]
    ...
] The method is used to strip each text transcription of the characters specified by the regular expression assigned to in . These characters, inclusive of punctuation, can be eliminated as they don't provide any semantic value when training the model to learn mappings between audio features and text transcriptions. clean_text SPECIAL_CHARS Step 2.4 The method returns a set of random test samples with the quantity set by the constant in . get_random_samples NUM_LOAD_FROM_EACH_SET Step 2.4 Step 2.6 - CELL 6: Utility Methods for Loading and Resampling Audio Data The sixth cell defines utility methods using to load and resample audio data. Set the sixth cell to: torchaudio ### CELL 7: Utility methods for loading and resampling audio data ###
def read_audio_data(file):
    speech_array, sampling_rate = torchaudio.load(file, normalize = True)
    return speech_array, sampling_rate

def resample(waveform):
    transform = torchaudio.transforms.Resample(ORIG_SAMPLING_RATE, TGT_SAMPLING_RATE)
    waveform = transform(waveform)
    return waveform[0] The method loads a specified audio file and returns a multi-dimensional matrix of the audio data along with the sampling rate of the audio. All the audio files in the training data have a sampling rate of Hz. This "original" sampling rate is captured by the constant in . read_audio_data torch.Tensor 48000 ORIG_SAMPLING_RATE Step 2.4 The method is used to downsample audio data from a sampling rate of to the target sampling rate of . resample 48000 16000 Step 2.7 - CELL 7: Reading Test Data The seventh cell reads the test data index files for the recordings of male speakers and the recordings of female speakers using the method defined in . Set the seventh cell to: read_index_file_data Step 2.5 ### CELL 7: Read test data ###
test_data_male = read_index_file_data(TEST_DATA_PATH_MALE, "line_index.tsv")
test_data_female = read_index_file_data(TEST_DATA_PATH_FEMALE, "line_index.tsv") Step 2.8 - CELL 8: Generating Lists of Random Test Samples The eighth cell generates sets of random test samples using the method defined in . Set the eighth cell to: get_random_samples Step 2.5 ### CELL 8: Generate lists of random test samples ###
random_test_samples_male = get_random_samples(test_data_male, NUM_LOAD_FROM_EACH_SET)
random_test_samples_female = get_random_samples(test_data_female, NUM_LOAD_FROM_EACH_SET) Step 2.9 - CELL 9: Combining Test Data The ninth cell combines the male test samples and female test samples into a single list. Set the ninth cell to: ### CELL 9: Combine test data ###
all_test_samples = random_test_samples_male + random_test_samples_female Step 2.10 - CELL 10: Cleaning Transcription Test The tenth cell iterates over each test data sample and cleans the associated transcription text using the method defined in . Set the tenth cell to: clean_text Step 2.5 ### CELL 10: Clean text transcriptions ###
for index in range(len(all_test_samples)):
    all_test_samples[index][1] = clean_text(all_test_samples[index][1]) Step 2.11 - CELL 11: Loading Audio Data The eleventh cell loads each audio file specified in the list. Set the eleventh cell to: all_test_samples ### CELL 11: Load audio data ###
all_test_data = []

for index in range(len(all_test_samples)):
    speech_array, sampling_rate = read_audio_data(all_test_samples[index][0])
    all_test_data.append({
        "raw": speech_array,
        "sampling_rate": sampling_rate,
        "target_text": all_test_samples[index][1]
    }) Audio data is returned as a and stored in as a list of dictionaries. Each dictionary contains the audio data for a particular sample, the sampling rate, and the text transcription of the audio. torch.Tensor all_test_data Step 2.12 - CELL 12: Resampling Audio Data The twelfth cell resamples audio data to the target sampling rate of . Set the twelfth cell to: 16000 ### CELL 12: Resample audio data and cast to NumPy arrays ###
all_test_data = [{"raw": resample(sample["raw"]).numpy(), "sampling_rate": TGT_SAMPLING_RATE, "target_text": sample["target_text"]} for sample in all_test_data] Step 2.13 - CELL 13: Initializing Instance of Automatic Speech Recognition Pipeline The thirteenth cell initializes an instance of the HuggingFace library class. Set the thirteenth cell to: transformer pipeline ### CELL 13: Initialize instance of Automatic Speech Recognition Pipeline ###
transcriber = pipeline("automatic-speech-recognition", model = "YOUR_FINETUNED_MODEL_PATH") The parameter must be set to the path to finetuned model added to the Kaggle Notebook in , e.g.: model your Step 1.3 transcriber = pipeline("automatic-speech-recognition", model = "/kaggle/input/xls-r-300m-chilean-spanish/transformers/hardy-pine/1") Step 2.14 - CELL 14: Generating Predictions The fourteenth cell calls the initialized in the previous step on the test data to generate text predictions. Set the fourteenth cell to: transcriber ### CELL 14: Generate transcriptions ###
transcriptions = transcriber(all_test_data) Step 2.15 - CELL 15: Calculating WER Metrics The fifteenth cell calculates WER scores for each prediction as well as an overall WER score for all predictions. Set the fifteenth cell to: ### CELL 15: Calculate WER metrics ###
predictions = [transcription["text"] for transcription in transcriptions]
references = [transcription["target_text"][0] for transcription in transcriptions]

wers = []
for p in range(len(predictions)):
    wer = wer_metric.compute(predictions = [predictions[p]], references = [references[p]])
    wers.append(wer)

zipped = list(zip(predictions, references, wers))
df = pd.DataFrame(zipped, columns=["Prediction", "Reference", "WER"])
wer = wer_metric.compute(predictions = predictions, references = references) Step 2.16 - CELL 16: Printing WER Metrics The sixteenth and final cell simply prints the WER calculations from the previous step. Set the sixteenth cell to: ### CELL 16: Output WER metrics ###
pd.set_option("display.max_colwidth", None)
print(f"Overall WER: {wer}")
print(df) WER Analysis Since the notebook generates predictions on random samples of test data, the output will vary each time the notebook is run. The following output was generated on a run of the notebook with set to for a total of 6 test samples: NUM_LOAD_FROM_EACH_SET 3 Overall WER: 0.013888888888888888
                                                                              Prediction  \
0                                     quiero que me reserves el mejor asiento del teatro   
1                                         el llano en llamas es un clásico de juan rulfo   
2         el cuadro de los alcatraces es una de las pinturas más famosas de diego rivera   
3                           hay tres cafés que están abiertos hasta las once de la noche   
4  quiero que me recomiendes una dieta pero donde uno pueda comer algo no puras verduras   
5        cuántos albergues se abrieron después del terremoto del diecinueve de setiembre   

                                                                               Reference  \
0                                     quiero que me reserves el mejor asiento del teatro   
1                                         el llano en llamas es un clásico de juan rulfo   
2         el cuadro de los alcatraces es una de las pinturas más famosas de diego rivera   
3                           hay tres cafés que están abiertos hasta las once de la noche   
4  quiero que me recomiendes una dieta pero donde uno pueda comer algo no puras verduras   
5       cuántos albergues se abrieron después del terremoto del diecinueve de septiembre   

        WER  
0  0.000000  
1  0.000000  
2  0.000000  
3  0.000000  
4  0.000000  
5  0.090909 As can be seen, the model did an excellent job! It only made one error with the sixth sample (index ), mis-spelling the word as . Of course, running the notebook again with different test samples and, more importantly, a larger number of test samples, will produce different and more informative results. Nonetheless, this limited data suggests the model can perform well on different dialects of Spanish - i.e. it was trained on Chilean Spanish, but appears to perform well on Peruvian Spanish. 5 septiembre setiembre Conclusion If you are just learning how to work with wav2vec2 models, I hope that the guide and this guide were useful for you. As mentioned, the finetuned model generated by the Part 1 guide is not quite state-of-the-art, but should still prove useful for many applications. Happy building! Working With wav2vec2 Part 1 - Finetuning XLS-R for Automatic Speech Recognition

Working with wav2vec2 Part 2 - Running Inference on Finetuned ASR Models

wav2vec2 পার্ট 2-এর সাথে কাজ করা - Finetuned ASR মডেলগুলিতে অনুমান চালানো

Arbeiten mit wav2vec2 Teil 2 - Ausführen von Inferenzen auf fein abgestimmten ASR-Modellen

Trabajar con wav2vec2 Parte 2: Ejecución de inferencia en modelos ASR ajustados

Travailler avec wav2vec2 Partie 2 - Exécution d'inférence sur des modèles ASR affinés

wav2vec2 के साथ कार्य करना भाग 2 - फ़ाइनट्यून्ड ASR मॉडल पर इन्फ़रेंस चलाना

wav2vec2 の操作パート 2 - 微調整された ASR モデルでの推論の実行

wav2vec2 작업 2부 - 미세 조정된 ASR 모델에서 추론 실행

Trabalhando com wav2vec2 Parte 2 - Executando inferência em modelos ASR ajustados

Работа с wav2vec2. Часть 2. Выполнение вывода на точно настроенных моделях ASR

wav2vec2 ile Çalışmak Bölüm 2 - İnce Ayarlı ASR Modellerinde Çıkarım Çalıştırmak

Làm việc với wav2vec2 Phần 2 - Chạy suy luận trên các mô hình ASR đã được tinh chỉnh

使用 wav2vec2 第 2 部分 - 在经过微调的 ASR 模型上运行推理

Working With Wav2vec2 Part 1: Finetuning XLS-R for Automatic Speech Recognition

Wav2vec2 এর সাথে কাজ করা পার্ট 1: স্বয়ংক্রিয় বক্তৃতা স্বীকৃতির জন্য XLS-R ফাইনটিউনিং

Arbeiten mit Wav2vec2 Teil 1: Feinabstimmung von XLS-R für die automatische Spracherkennung

Trabajar con Wav2vec2 Parte 1: Ajuste de XLS-R para el reconocimiento automático de voz

Travailler avec Wav2vec2 Partie 1 : Affiner XLS-R pour la reconnaissance vocale automatique

Wav2vec2 के साथ कार्य करना भाग 1: स्वचालित वाक् पहचान के लिए XLS-R को परिष्कृत करना

Wav2vec2 の操作パート 1: 自動音声認識のための XLS-R の微調整

Wav2vec2 작업 1부: 자동 음성 인식을 위한 XLS-R 미세 조정

Trabalhando com Wav2vec2 Parte 1: Ajuste fino do XLS-R para reconhecimento automático de fala

Работа с Wav2vec2. Часть 1. Точная настройка XLS-R для автоматического распознавания речи

Wav2vec2 ile Çalışmak Bölüm 1: Otomatik Konuşma Tanıma için XLS-R'ye İnce Ayar Yapmak

Làm việc với Wav2vec2 Phần 1: Tinh chỉnh XLS-R để nhận dạng giọng nói tự động

使用 Wav2vec2 第 1 部分：微调 XLS-R 以实现自动语音识别

Recommendations by Concise User Profiles from Review Text: Ethics Statement and References

Authors:
(1) Aya Kherrour, Department of Information Engineering and Computer Science University of Trento;
(2) Marco Robol, Department of Information Engineering and Computer Science University of Trento;
(3) Marco Roveri, Department of Information Engineering and Computer Science University of Trento;
(4) Paolo Giorgini, Department of Information Engineering and Computer Science University of Trento. 6 Conclusion This work aimed to evaluate the performance of several known heuristic search algorithms, such as D*, D* Lite, LPA*, LRTA*, RTAA*, and ARA* in terms of solving time, memory consumption, and path cost. The evaluation was made using different randomly generated grid environments with different characteristics alongside personalized grid environments with horizontal walls and different wall lengths as different hinders. Our experimental evaluation revealed that all the algorithms exhibit different performances with strengths and weaknesses under different grid characterizations. D* Lite consistently generated the shortest paths even in obstacle-dense grids, indicating its efficiency in dense environments. ARA* consistently provides faster solutions as the grid size increases, particularly larger than 100, while RTTA* generates faster solutions in smaller grid sizes, with the advantage of not being affected by dense environments. Our study provides valuable insights into selecting the appropriate heuristic search algorithm in the pathfinding domain. Using these insights, we propose a selection algorithm used to optimize the performance needed in a pathfinding domain, such as, solving time, path length, or memory consumption. However, our evaluation focused only on static environments, while dynamic environments may introduce additional challenges. Furthermore, we considered a limited set of experiments, not covering all possible combinations of the grid characteristics. This limitation means that the selection algorithm might not always recommend the most optimal solution, as seen in the example we introduced to evaluate our algorithm. In future work, we aim to address these limitations as follows: Firstly, we plan to extend our evaluation of the search algorithms to include dynamic environments. Secondly, we intend to explore various combinations of both domain characterization and priorities. Furthermore, we want to include additional scenario configuration, extending the random obstacles and the walls scenarios. With such additional understanding, we aim to refine our selection algorithm to automatically take decisions among the best search algorithms, based on the type of obstacles in the local search space. References [1] Nafiz Arica, Aysegul Mut, Alper Yörükçü & Kadir Alpaslan Demir (2017): An Empirical Comparison of Search Approaches for Moving Agents. Comput. Intell. 33(3), pp. 368–400, doi:10.1111/coin.12092. [2] Yngvi Björnsson, Vadim Bulitko & Nathan R. Sturtevant (2009): TBA*: Time-Bounded A*. In Craig Boutilier, editor: IJCAI 2009, Proceedings of the 21st International Joint Conference on Artificial Intelligence, Pasadena, California, USA, July 11-17, 2009, pp. 431–436. Available at http://ijcai.org/ Proceedings/09/Papers/079.pdf. [3] David Bond, Niels A. Widger, Wheeler Ruml & Xiaoxun Sun (2010): Real-Time Search in Dynamic Worlds. In Ariel Felner & Nathan R. Sturtevant, editors: Proceedings of the Third Annual Symposium on Combinatorial Search, SOCS 2010, Stone Mountain, Atlanta, Georgia, USA, July 8 10, 2010, AAAI Press, doi:10.1609/socs.v1i1.18174. Available at http://aaai.org/ocs/index.php/SOCS/SOCS10/paper/ view/2103. [4] Guillaume Brat, Ewen Denney, Dimitra Giannakopoulou, Jeremy Frank & Ari Jónsson (2006): Verification of autonomous systems for space applications. In: 2006 IEEE Aerospace Conference, IEEE, pp. 11–pp, doi:10.1109/AERO.2006.1656029. [5] Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest & Clifford Stein (2009): Introduction to Algorithms, 3rd Edition. MIT Press. Available at http://mitpress.mit.edu/books/ introduction-algorithms. [6] A. Elfes (1989): Using occupancy grids for mobile robot perception and navigation. Computer 22(6), pp. 46–57, doi:10.1109/2.30720. [7] Carlos Hernández & Jorge A. Baier (2011): Real-Time Heuristic Search with Depression Avoidance. In Toby Walsh, editor: IJCAI 2011, Proceedings of the 22nd International Joint Conference on Artificial Intelligence, Barcelona, Catalonia, Spain, July 16-22, 2011, IJCAI/AAAI, pp. 578–583, doi:10.5591/978-1-57735-516- 8/IJCAI11-104. [8] Mohd Javaid, Abid Haleem, Ravi Pratap Singh & Rajiv Suman (2021): Substantial capabilities of robotics in enhancing industry 4.0 implementation. Cognitive Robotics 1, pp. 58–75, doi:10.1016/j.cogr.2021.06.001. [9] Sven Koenig (2001): Agent-Centered Search. AI Mag. 22(4), pp. 109–132, doi:10.1609/aimag.v22i4.1596. [10] Sven Koenig (2004): A Comparison of Fast Search Methods for Real-Time Situated Agents, pp. 864–871. doi:10.1109/AAMAS.2004.10122. Available at https://doi.ieeecomputersociety.org/10.1109/ AAMAS.2004.10122. [11] Sven Koenig & Maxim Likhachev (2002): D*Lite, pp. 476–483. Available at http://www.aaai.org/ Library/AAAI/2002/aaai02-072.php. [12] Sven Koenig & Maxim Likhachev (2006): Real-time adaptive A*. In Hideyuki Nakashima, Michael P. Wellman, Gerhard Weiss & Peter Stone, editors: 5th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2006), Hakodate, Japan, May 8-12, 2006, ACM, pp. 281–288, doi:10.1145/1160633.1160682. [13] Sven Koenig & Xiaoxun Sun (2009): Comparing real-time and incremental heuristic search for real-time situated agents. Auton. Agents Multi Agent Syst. 18(3), pp. 313–341, doi:10.1007/s10458 008-9061-x. [14] Richard E Korf (1990): Real-time heuristic search. Artificial intelligence 42(2-3), pp. 189–211, doi:10.1016/0004-3702(90)90054-4. [15] Maxim Likhachev, Geoffrey J. Gordon & Sebastian Thrun (2003): ARA*: Anytime A* with Provable Bounds on Sub-Optimality, pp. 767–774. Available at https://proceedings.neurips.cc/paper/2003/hash/ ee8fe9093fbbb687bef15a38facc44d2-Abstract.html. [16] Manuel Müller, Timo Müller, Behrang Ashtari Talkhestani, Philipp Marks, Nasser Jazdi & Michael Weyrich (2021): Industrial autonomous systems: a survey on definitions, characteristics and abilities. atAutomatisierungstechnik 69(1), pp. 3–13, doi:10.1515/auto-2020-0131. [17] Michal Pechoucek, Simon G Thompson & Holger Voos (2008): Defence Industry Applications of Autonomous Agents and Multi-Agent Systems. Springer, doi:10.1007/978-3-7643-8571-2. [18] Francisco J Perez-Grau, J Ramiro Martinez-de Dios, Julio L Paneque, J Joaquin Acevedo, Arturo Torres-González, Antidio Viguria, Juan R Astorga & Anibal Ollero (2021): Introducing autonomous aerial robots in industrial manufacturing. Journal of Manufacturing Systems 60, pp. 312–324, doi:10.1016/j.jmsy.2021.06.008. [19] José Ricardo Sánchez-Ibáñez, Carlos Jesús Pérez-del-Pulgar & Alfonso García-Cerezo (2021): Path Planning for Autonomous Mobile Robots: A Review. Sensors 21(23), p. 7898, doi:10.3390/s21237898. [20] Anthony Stentz (1995): The Focussed D* Algorithm for Real-Time Replanning. In: Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, IJCAI 95, Montréal Québec, Canada, August 20-25 1995, 2 Volumes, Morgan Kaufmann, pp. 1652–1659. Available at http://ijcai.org/ Proceedings/95-2/Papers/082.pdf. [21] Dmitry S. Yershov & Steven M. LaValle (2011): Simplicial Dijkstra and A* algorithms for optimal feedback planning. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2011, San Francisco, CA, USA, September 25-30, 2011, IEEE, pp. 3862–3867, doi:10.1109/IROS.2011.6095032. This paper is available on arxiv under CC 4.0 license. Authors: (1) Aya Kherrour, Department of Information Engineering and Computer Science University of Trento; (2) Marco Robol, Department of Information Engineering and Computer Science University of Trento; (3) Marco Roveri, Department of Information Engineering and Computer Science University of Trento; (4) Paolo Giorgini, Department of Information Engineering and Computer Science University of Trento. Authors: (1) Aya Kherrour, Department of Information Engineering and Computer Science University of Trento; (2) Marco Robol, Department of Information Engineering and Computer Science University of Trento; (3) Marco Roveri, Department of Information Engineering and Computer Science University of Trento; (4) Paolo Giorgini, Department of Information Engineering and Computer Science University of Trento. 6 Conclusion This work aimed to evaluate the performance of several known heuristic search algorithms, such as D*, D* Lite, LPA*, LRTA*, RTAA*, and ARA* in terms of solving time, memory consumption, and path cost. The evaluation was made using different randomly generated grid environments with different characteristics alongside personalized grid environments with horizontal walls and different wall lengths as different hinders. Our experimental evaluation revealed that all the algorithms exhibit different performances with strengths and weaknesses under different grid characterizations. D* Lite consistently generated the shortest paths even in obstacle-dense grids, indicating its efficiency in dense environments. ARA* consistently provides faster solutions as the grid size increases, particularly larger than 100, while RTTA* generates faster solutions in smaller grid sizes, with the advantage of not being affected by dense environments. Our study provides valuable insights into selecting the appropriate heuristic search algorithm in the pathfinding domain. Using these insights, we propose a selection algorithm used to optimize the performance needed in a pathfinding domain, such as, solving time, path length, or memory consumption. However, our evaluation focused only on static environments, while dynamic environments may introduce additional challenges. Furthermore, we considered a limited set of experiments, not covering all possible combinations of the grid characteristics. This limitation means that the selection algorithm might not always recommend the most optimal solution, as seen in the example we introduced to evaluate our algorithm. In future work, we aim to address these limitations as follows: Firstly, we plan to extend our evaluation of the search algorithms to include dynamic environments. Secondly, we intend to explore various combinations of both domain characterization and priorities. Furthermore, we want to include additional scenario configuration, extending the random obstacles and the walls scenarios. With such additional understanding, we aim to refine our selection algorithm to automatically take decisions among the best search algorithms, based on the type of obstacles in the local search space. References [1] Nafiz Arica, Aysegul Mut, Alper Yörükçü & Kadir Alpaslan Demir (2017): An Empirical Comparison of Search Approaches for Moving Agents. Comput. Intell. 33(3), pp. 368–400, doi:10.1111/coin.12092. [2] Yngvi Björnsson, Vadim Bulitko & Nathan R. Sturtevant (2009): TBA*: Time-Bounded A*. In Craig Boutilier, editor: IJCAI 2009, Proceedings of the 21st International Joint Conference on Artificial Intelligence, Pasadena, California, USA, July 11-17, 2009, pp. 431–436. Available at http://ijcai.org/ Proceedings/09/Papers/079.pdf. [3] David Bond, Niels A. Widger, Wheeler Ruml & Xiaoxun Sun (2010): Real-Time Search in Dynamic Worlds. In Ariel Felner & Nathan R. Sturtevant, editors: Proceedings of the Third Annual Symposium on Combinatorial Search, SOCS 2010, Stone Mountain, Atlanta, Georgia, USA, July 8 10, 2010, AAAI Press, doi:10.1609/socs.v1i1.18174. Available at http://aaai.org/ocs/index.php/SOCS/SOCS10/paper/ view/2103. [4] Guillaume Brat, Ewen Denney, Dimitra Giannakopoulou, Jeremy Frank & Ari Jónsson (2006): Verification of autonomous systems for space applications. In: 2006 IEEE Aerospace Conference, IEEE, pp. 11–pp, doi:10.1109/AERO.2006.1656029. [5] Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest & Clifford Stein (2009): Introduction to Algorithms, 3rd Edition. MIT Press. Available at http://mitpress.mit.edu/books/ introduction-algorithms. [6] A. Elfes (1989): Using occupancy grids for mobile robot perception and navigation. Computer 22(6), pp. 46–57, doi:10.1109/2.30720. [7] Carlos Hernández & Jorge A. Baier (2011): Real-Time Heuristic Search with Depression Avoidance. In Toby Walsh, editor: IJCAI 2011, Proceedings of the 22nd International Joint Conference on Artificial Intelligence, Barcelona, Catalonia, Spain, July 16-22, 2011, IJCAI/AAAI, pp. 578–583, doi:10.5591/978-1-57735-516- 8/IJCAI11-104. [8] Mohd Javaid, Abid Haleem, Ravi Pratap Singh & Rajiv Suman (2021): Substantial capabilities of robotics in enhancing industry 4.0 implementation. Cognitive Robotics 1, pp. 58–75, doi:10.1016/j.cogr.2021.06.001. [9] Sven Koenig (2001): Agent-Centered Search. AI Mag. 22(4), pp. 109–132, doi:10.1609/aimag.v22i4.1596. [10] Sven Koenig (2004): A Comparison of Fast Search Methods for Real-Time Situated Agents, pp. 864–871. doi:10.1109/AAMAS.2004.10122. Available at https://doi.ieeecomputersociety.org/10.1109/ AAMAS.2004.10122. [11] Sven Koenig & Maxim Likhachev (2002): D*Lite, pp. 476–483. Available at http://www.aaai.org/ Library/AAAI/2002/aaai02-072.php. [12] Sven Koenig & Maxim Likhachev (2006): Real-time adaptive A*. In Hideyuki Nakashima, Michael P. Wellman, Gerhard Weiss & Peter Stone, editors: 5th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2006), Hakodate, Japan, May 8-12, 2006, ACM, pp. 281–288, doi:10.1145/1160633.1160682. [13] Sven Koenig & Xiaoxun Sun (2009): Comparing real-time and incremental heuristic search for real-time situated agents. Auton. Agents Multi Agent Syst. 18(3), pp. 313–341, doi:10.1007/s10458 008-9061-x. [14] Richard E Korf (1990): Real-time heuristic search. Artificial intelligence 42(2-3), pp. 189–211, doi:10.1016/0004-3702(90)90054-4. [15] Maxim Likhachev, Geoffrey J. Gordon & Sebastian Thrun (2003): ARA*: Anytime A* with Provable Bounds on Sub-Optimality, pp. 767–774. Available at https://proceedings.neurips.cc/paper/2003/hash/ ee8fe9093fbbb687bef15a38facc44d2-Abstract.html. [16] Manuel Müller, Timo Müller, Behrang Ashtari Talkhestani, Philipp Marks, Nasser Jazdi & Michael Weyrich (2021): Industrial autonomous systems: a survey on definitions, characteristics and abilities. atAutomatisierungstechnik 69(1), pp. 3–13, doi:10.1515/auto-2020-0131. [17] Michal Pechoucek, Simon G Thompson & Holger Voos (2008): Defence Industry Applications of Autonomous Agents and Multi-Agent Systems. Springer, doi:10.1007/978-3-7643-8571-2. [18] Francisco J Perez-Grau, J Ramiro Martinez-de Dios, Julio L Paneque, J Joaquin Acevedo, Arturo Torres-González, Antidio Viguria, Juan R Astorga & Anibal Ollero (2021): Introducing autonomous aerial robots in industrial manufacturing. Journal of Manufacturing Systems 60, pp. 312–324, doi:10.1016/j.jmsy.2021.06.008. [19] José Ricardo Sánchez-Ibáñez, Carlos Jesús Pérez-del-Pulgar & Alfonso García-Cerezo (2021): Path Planning for Autonomous Mobile Robots: A Review. Sensors 21(23), p. 7898, doi:10.3390/s21237898. [20] Anthony Stentz (1995): The Focussed D* Algorithm for Real-Time Replanning. In: Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, IJCAI 95, Montréal Québec, Canada, August 20-25 1995, 2 Volumes, Morgan Kaufmann, pp. 1652–1659. Available at http://ijcai.org/ Proceedings/95-2/Papers/082.pdf. [21] Dmitry S. Yershov & Steven M. LaValle (2011): Simplicial Dijkstra and A* algorithms for optimal feedback planning. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2011, San Francisco, CA, USA, September 25-30, 2011, IEEE, pp. 3862–3867, doi:10.1109/IROS.2011.6095032. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Future Directions in Heuristic Search

Authors:
(1) Ankur Nath, Department of Computer Science and Engineering, Texas A&M University;
(2) Alan Kuhnle, Department of Computer Science and Engineering, Texas A&M University. Table of Links Abstract & Introduction Related work Evaluation for Max-Cut Evaluation for SAT Summary and Outlook, References Supplementary Materials 5 SUMMARY and OUTLOOK Through our empirical evaluations, our goal is to promote an insightful comparison within the research focusing on the intersection of combinatorial optimization and machine learning. In order to provide the research community with valuable guidance, we believe it is imperative to communicate both the strengths and weaknesses of the proposed approaches. Poor instances and baseline selection may give the wrong impression about the performance of learned heuristics. Specifically, it is important to articulate the degree of improvement achieved through integrating classical heuristics with deep learning architectures and conduct a thorough comparison with classical heuristics. This will aid in elucidating the degree to which deep learning architectures enhance integrated heuristics. It assists in ascertaining whether the integration endeavor is justified and warrants the allocation of computational resources, time, and investment necessary for integrating deep learning with classical heuristics. References Albert, R. and Barab´asi, A.-L. (2002). Statistical mechanics of complex networks. Reviews of modern physics, 74(1):47. Barrett, T., Clements, W., Foerster, J., and Lvovsky, A. (2020). Exploratory combinatorial optimization with reinforcement learning. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pages 3243–3250. Barrett, T. D., Parsonson, C. W., and Laterre, A. (2022). Learning to solve combinatorial graph partitioning problems via efficient exploration. arXiv preprint arXiv:2205.14105. Battiti, R. and Protasi, M. (1997). Reactive search, a history-sensitive heuristic for max-sat. Journal of Experimental Algorithmics (JEA), 2:2–es. Battiti, R. and Tecchiolli, G. (1994). The reactive tabu search. ORSA journal on computing, 6(2):126–140. Bello, I., Pham, H., Le, Q. V., Norouzi, M., and Bengio, S. (2016). Neural combinatorial optimization with reinforcement learning. arXiv preprint arXiv:1611.09940. Bengio, Y., Lodi, A., and Prouvost, A. (2021). Machine learning for combinatorial optimization: a methodological tour d’horizon. European Journal of Operational Research, 290(2):405–421. Benlic, U. and Hao, J.-K. (2013). Breakout local search for the max-cutproblem. Engineering Applications of Artificial Intelligence, 26(3):1162–1173. Boettcher, S. and Percus, A. G. (2001). Extremal optimization for graph partitioning. Physical Review E, 64(2):026114. CPLEX (2023). Cplex. https://www.ibm.com/products/ilog-cplexoptimization-studio/cplex-optimizer. Dong, Y., Goldberg, A. V., Noe, A., Parotsidis, N., Resende, M. G., and Spaen, Q. (2021). New instances for maximum weight independent set from a vehicle routing application. In Operations Research Forum, volume 2, pages 1–6. Springer. E´en, N. and S¨orensson, N. (2003). An extensible satsolver. In International conference on theory and applications of satisfiability testing, pages 502–518. Springer. Elsokkary, N., Khan, F. S., La Torre, D., Humble, T. S., and Gottlieb, J. (2017). Financial portfolio management using d-wave quantum optimizer: The case of abu dhabi securities exchange. Technical report, Oak Ridge National Lab.(ORNL), Oak Ridge, TN (United States). Erd˝os, P., R´enyi, A., et al. (1960). On the evolution of random graphs. Publ. math. inst. hung. acad. sci, 5(1):17–60. Festa, P. and Resende, M. G. (2009). Hybrid grasp heuristics. In Foundations of Computational Intelligence Volume 3: Global Optimization, pages 75–100. Springer. Flint, A. and Blaschko, M. (2012). Perceptron learning of sat. Advances in neural information processing systems, 25. Fujishige, S. (2005). Submodular functions and optimization. Elsevier. Ganesh, V., Singh, R., Near, J. P., and Rinard, M. (2009). Avatarsat: An auto-tuning boolean sat solver. Glover, F. (1990). Tabu search: A tutorial. Interfaces, 20(4):74–94. Goemans, M. X. and Williamson, D. P. (1995). Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming. Journal of the ACM (JACM), 42(6):1115–1145. Grozea, C. and Popescu, M. (2014). Can machine learning learn a decision oracle for np problems? a test on sat. Fundamenta Informaticae, 131(3- 4):441–450. Haim, S. and Walsh, T. (2009). Restart strategy selection using machine learning techniques. In International Conference on Theory and Applications of Satisfiability Testing, pages 312–325. Springer. Helmberg, C. and Rendl, F. (2000). A spectral bundle method for semidefinite programming. SIAM Journal on Optimization, 10(3):673–696. Hoos, H. H. et al. (2002). An adaptive noise mechanism for walksat. In AAAI/IAAI, pages 655–660. Jaszczur, S., Luszczyk, M., and Michalewski, H. (2020). Neural heuristics for sat solving. arXiv preprint arXiv:2005.13406. Karp, R. M. (1972). Reducibility among Combinatorial Problems, pages 85–103. Springer US, Boston, MA. Khalil, E., Dai, H., Zhang, Y., Dilkina, B., and Song, L. (2017). Learning combinatorial optimization algorithms over graphs. Advances in neural information processing systems, 30. KhudaBukhsh, A. R., Xu, L., Hoos, H. H., and LeytonBrown, K. (2016). Satenstein: Automatically building local search sat solvers from components. Artificial Intelligence, 232:20–42. Kirkpatrick, S., Gelatt Jr, C. D., and Vecchi, M. P. (1983). Optimization by simulated annealing. science, 220(4598):671–680. Kurin, V., Godil, S., Whiteson, S., and Catanzaro, B. (2020). Can q-learning with graph networks learn a generalizable branching heuristic for a sat solver? Advances in Neural Information Processing Systems, 33:9608–9621. Lauria, M., Elffers, J., Nordstr¨om, J., and Vinyals, M. (2017). Cnfgen: A generator of crafted benchmarks. In Theory and Applications of Satisfiability Testing–SAT 2017: 20th International Conference, Melbourne, VIC, Australia, August 28–September 1, 2017, Proceedings 20, pages 464–473. Springer. Lederman, G., Rabe, M., Seshia, S., and Lee, E. A. (2019). Learning heuristics for quantified boolean formulas through reinforcement learning. In International Conference on LearningRepresentations. Leleu, T., Yamamoto, Y., McMahon, P. L., and Aihara, K. (2019). Destabilization of local minima in analog spin systems by correction of amplitude heterogeneity. Physical review letters,122(4):040607. Li, C. M., Wei, W., and Zhang, H. (2007). Combining adaptive noise and look-ahead in local search for sat. In Theory and Applications of Satisfiability Testing– SAT 2007: 10th International Conference, Lisbon, Portugal, May 28-31, 2007. Proceedings 10, pages 121–133. Springer. Liang, J. H., Ganesh, V., Poupart, P., and Czarnecki, K. (2016). Learning rate based branching heuristic for sat solvers. In Theory and Applications of Satisfiability Testing–SAT 2016: 19th International Conference, Bordeaux, France, July 5-8, 2016, Proceedings 19, pages 123–140. Springer. Manchanda, S., Mittal, A., Dhawan, A., Medya, S., Ranu, S., and Singh, A. (2019). Learning heuristics over large graphs via deep reinforcement learning. arXiv preprint arXiv:1903.03332. Mazure, B., Sais, L., and Gr´egoire, E. (1997). Tabu ´ search for sat. In Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence, pages 281–285. McAllester, D., Selman, B., Kautz, H., et al. (1997). Evidence for invariants in local search. In AAAI/IAAI, pages 321–326. Rhode Island, USA. Mitchell, D., Selman, B., Levesque, H., et al. (1992). Hard and easy distributions of sat problems. In Aaai, volume 92, pages 459–465. Perdomo-Ortiz, A., Dickson, N., Drew-Brook, M., Rose, G., and Aspuru-Guzik, A. (2012). Finding low-energy conformations of lattice protein models by quantum annealing. Scientific reports, 2(1):1–7. Selman, B., Kautz, H. A., Cohen, B., et al. (1993). Local search strategies for satisfiability testing. Cliques, coloring, and satisfiability, 26:521–532. Selman, B., Mitchell, D. G., and Levesque, H. J. (1996). Generating hard satisfiability problems. Artificial intelligence, 81(1-2):17–29. Tiunov, E. S., Ulanov, A. E., and Lvovsky, A. (2019). Annealing by simulating the coherent ising machine. Optics express, 27(7):10288–10295. Toenshoff, J., Ritzert, M., Wolf, H., and Grohe, M. (2019). Graph neural networks for maximum constraint satisfaction. Frontiers in artificial intelligence, 3:580607. Venturelli, D. and Kondratyev, A. (2019). Reverse quantum annealing approach to portfolio optimization problems. Quantum Machine Intelligence, 1(1- 2):17–30. Wolpert, D. H. and Macready, W. G. (1997). No free lunch theorems for optimization. IEEE transactions on evolutionary computation, 1(1):67–82. Ye, Y. (2003). The gset dataset. https://web.stanford.edu/ yyye/yyye/Gset/. Yolcu, E. and P´oczos, B. (2019). Learning local search heuristics for boolean satisfiability. Advances in Neural Information Processing Systems, 32. This paper is available on arxiv under CC 4.0 DEED license. Authors: (1) Ankur Nath, Department of Computer Science and Engineering, Texas A&M University; (2) Alan Kuhnle, Department of Computer Science and Engineering, Texas A&M University. Authors: Authors: (1) Ankur Nath, Department of Computer Science and Engineering, Texas A&M University; (2) Alan Kuhnle, Department of Computer Science and Engineering, Texas A&M University. Table of Links Abstract & Introduction Abstract & Introduction Related work Related work Evaluation for Max-Cut Evaluation for Max-Cut Evaluation for SAT Evaluation for SAT Summary and Outlook, References Summary and Outlook, References Supplementary Materials Supplementary Materials 5 SUMMARY and OUTLOOK Through our empirical evaluations, our goal is to promote an insightful comparison within the research focusing on the intersection of combinatorial optimization and machine learning. In order to provide the research community with valuable guidance, we believe it is imperative to communicate both the strengths and weaknesses of the proposed approaches. Poor instances and baseline selection may give the wrong impression about the performance of learned heuristics. Specifically, it is important to articulate the degree of improvement achieved through integrating classical heuristics with deep learning architectures and conduct a thorough comparison with classical heuristics. This will aid in elucidating the degree to which deep learning architectures enhance integrated heuristics. It assists in ascertaining whether the integration endeavor is justified and warrants the allocation of computational resources, time, and investment necessary for integrating deep learning with classical heuristics. References Albert, R. and Barab´asi, A.-L. (2002). Statistical mechanics of complex networks. Reviews of modern physics, 74(1):47. Barrett, T., Clements, W., Foerster, J., and Lvovsky, A. (2020). Exploratory combinatorial optimization with reinforcement learning. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pages 3243–3250. Barrett, T. D., Parsonson, C. W., and Laterre, A. (2022). Learning to solve combinatorial graph partitioning problems via efficient exploration. arXiv preprint arXiv:2205.14105. Battiti, R. and Protasi, M. (1997). Reactive search, a history-sensitive heuristic for max-sat. Journal of Experimental Algorithmics (JEA), 2:2–es. Battiti, R. and Tecchiolli, G. (1994). The reactive tabu search. ORSA journal on computing, 6(2):126–140. Bello, I., Pham, H., Le, Q. V., Norouzi, M., and Bengio, S. (2016). Neural combinatorial optimization with reinforcement learning. arXiv preprint arXiv:1611.09940. Bengio, Y., Lodi, A., and Prouvost, A. (2021). Machine learning for combinatorial optimization: a methodological tour d’horizon. European Journal of Operational Research, 290(2):405–421. Benlic, U. and Hao, J.-K. (2013). Breakout local search for the max-cutproblem. Engineering Applications of Artificial Intelligence, 26(3):1162–1173. Boettcher, S. and Percus, A. G. (2001). Extremal optimization for graph partitioning. Physical Review E, 64(2):026114. CPLEX (2023). Cplex. https://www.ibm.com/products/ilog-cplexoptimization-studio/cplex-optimizer. Dong, Y., Goldberg, A. V., Noe, A., Parotsidis, N., Resende, M. G., and Spaen, Q. (2021). New instances for maximum weight independent set from a vehicle routing application. In Operations Research Forum, volume 2, pages 1–6. Springer. E´en, N. and S¨orensson, N. (2003). An extensible satsolver. In International conference on theory and applications of satisfiability testing, pages 502–518. Springer. Elsokkary, N., Khan, F. S., La Torre, D., Humble, T. S., and Gottlieb, J. (2017). Financial portfolio management using d-wave quantum optimizer: The case of abu dhabi securities exchange. Technical report, Oak Ridge National Lab.(ORNL), Oak Ridge, TN (United States). Erd˝os, P., R´enyi, A., et al. (1960). On the evolution of random graphs. Publ. math. inst. hung. acad. sci, 5(1):17–60. Festa, P. and Resende, M. G. (2009). Hybrid grasp heuristics. In Foundations of Computational Intelligence Volume 3: Global Optimization, pages 75–100. Springer. Flint, A. and Blaschko, M. (2012). Perceptron learning of sat. Advances in neural information processing systems, 25. Fujishige, S. (2005). Submodular functions and optimization. Elsevier. Ganesh, V., Singh, R., Near, J. P., and Rinard, M. (2009). Avatarsat: An auto-tuning boolean sat solver. Glover, F. (1990). Tabu search: A tutorial. Interfaces, 20(4):74–94. Goemans, M. X. and Williamson, D. P. (1995). Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming. Journal of the ACM (JACM), 42(6):1115–1145. Grozea, C. and Popescu, M. (2014). Can machine learning learn a decision oracle for np problems? a test on sat. Fundamenta Informaticae, 131(3- 4):441–450. Haim, S. and Walsh, T. (2009). Restart strategy selection using machine learning techniques. In International Conference on Theory and Applications of Satisfiability Testing, pages 312–325. Springer. Helmberg, C. and Rendl, F. (2000). A spectral bundle method for semidefinite programming. SIAM Journal on Optimization, 10(3):673–696. Hoos, H. H. et al. (2002). An adaptive noise mechanism for walksat. In AAAI/IAAI, pages 655–660. Jaszczur, S., Luszczyk, M., and Michalewski, H. (2020). Neural heuristics for sat solving. arXiv preprint arXiv:2005.13406. Karp, R. M. (1972). Reducibility among Combinatorial Problems, pages 85–103. Springer US, Boston, MA. Khalil, E., Dai, H., Zhang, Y., Dilkina, B., and Song, L. (2017). Learning combinatorial optimization algorithms over graphs. Advances in neural information processing systems, 30. KhudaBukhsh, A. R., Xu, L., Hoos, H. H., and LeytonBrown, K. (2016). Satenstein: Automatically building local search sat solvers from components. Artificial Intelligence, 232:20–42. Kirkpatrick, S., Gelatt Jr, C. D., and Vecchi, M. P. (1983). Optimization by simulated annealing. science, 220(4598):671–680. Kurin, V., Godil, S., Whiteson, S., and Catanzaro, B. (2020). Can q-learning with graph networks learn a generalizable branching heuristic for a sat solver? Advances in Neural Information Processing Systems, 33:9608–9621. Lauria, M., Elffers, J., Nordstr¨om, J., and Vinyals, M. (2017). Cnfgen: A generator of crafted benchmarks. In Theory and Applications of Satisfiability Testing–SAT 2017: 20th International Conference, Melbourne, VIC, Australia, August 28–September 1, 2017, Proceedings 20, pages 464–473. Springer. Lederman, G., Rabe, M., Seshia, S., and Lee, E. A. (2019). Learning heuristics for quantified boolean formulas through reinforcement learning. In International Conference on LearningRepresentations. Leleu, T., Yamamoto, Y., McMahon, P. L., and Aihara, K. (2019). Destabilization of local minima in analog spin systems by correction of amplitude heterogeneity. Physical review letters,122(4):040607. Li, C. M., Wei, W., and Zhang, H. (2007). Combining adaptive noise and look-ahead in local search for sat. In Theory and Applications of Satisfiability Testing– SAT 2007: 10th International Conference, Lisbon, Portugal, May 28-31, 2007. Proceedings 10, pages 121–133. Springer. Liang, J. H., Ganesh, V., Poupart, P., and Czarnecki, K. (2016). Learning rate based branching heuristic for sat solvers. In Theory and Applications of Satisfiability Testing–SAT 2016: 19th International Conference, Bordeaux, France, July 5-8, 2016, Proceedings 19, pages 123–140. Springer. Manchanda, S., Mittal, A., Dhawan, A., Medya, S., Ranu, S., and Singh, A. (2019). Learning heuristics over large graphs via deep reinforcement learning. arXiv preprint arXiv:1903.03332. Mazure, B., Sais, L., and Gr´egoire, E. (1997). Tabu ´ search for sat. In Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence, pages 281–285. McAllester, D., Selman, B., Kautz, H., et al. (1997). Evidence for invariants in local search. In AAAI/IAAI, pages 321–326. Rhode Island, USA. Mitchell, D., Selman, B., Levesque, H., et al. (1992). Hard and easy distributions of sat problems. In Aaai, volume 92, pages 459–465. Perdomo-Ortiz, A., Dickson, N., Drew-Brook, M., Rose, G., and Aspuru-Guzik, A. (2012). Finding low-energy conformations of lattice protein models by quantum annealing. Scientific reports, 2(1):1–7. Selman, B., Kautz, H. A., Cohen, B., et al. (1993). Local search strategies for satisfiability testing. Cliques, coloring, and satisfiability, 26:521–532. Selman, B., Mitchell, D. G., and Levesque, H. J. (1996). Generating hard satisfiability problems. Artificial intelligence, 81(1-2):17–29. Tiunov, E. S., Ulanov, A. E., and Lvovsky, A. (2019). Annealing by simulating the coherent ising machine. Optics express, 27(7):10288–10295. Toenshoff, J., Ritzert, M., Wolf, H., and Grohe, M. (2019). Graph neural networks for maximum constraint satisfaction. Frontiers in artificial intelligence, 3:580607. Venturelli, D. and Kondratyev, A. (2019). Reverse quantum annealing approach to portfolio optimization problems. Quantum Machine Intelligence, 1(1- 2):17–30. Wolpert, D. H. and Macready, W. G. (1997). No free lunch theorems for optimization. IEEE transactions on evolutionary computation, 1(1):67–82. Ye, Y. (2003). The gset dataset. https://web.stanford.edu/ yyye/yyye/Gset/. Yolcu, E. and P´oczos, B. (2019). Learning local search heuristics for boolean satisfiability. Advances in Neural Information Processing Systems, 32. This paper is available on arxiv under CC 4.0 DEED license. This paper is available on arxiv under CC 4.0 DEED license. available on arxiv

Assessing the Justification for Integrating Deep Learning in Combinatorial Optimization

This paper is available on arxiv under CC 4.0 license. Authors: (1) Muzhaffar Hazman, University of Galway, Ireland; (2) Susan McKeever, Technological University Dublin, Ireland; (3) Josephine Griffith, University of Galway, Ireland. Table of Links Abstract and Introduction Related Works Methodology Results Limitations and Future Works Conclusion, Acknowledgments, and References A Hyperparameters and Settings B Metric: Weighted F1-Score C Architectural Details D Performance Benchmarking E Contingency Table: Baseline vs. Text-STILT 6 Conclusion In this work, we addressed the challenge of training multimodal meme sentiment classifiers on a limited number of labelled memes by incorporating unimodal sentiment analysis data. We did so by proposing the first instance of STILT that applies unimodal intermediate tasks to a multimodal target task. Specifically, we tested image-only and text-only sentiment classification as intermediate tasks in training a meme sentiment classifier. We showed that this approach worked – unimodal text improved meme classification performance to a statistically significant degree. This novel approach allowed us to train a meme classifier that outperforms meme-only finetuning with only 60% as many labelled meme samples. As possible explanations for our observations, we discuss apparent similarities and differences in the roles of image and text modalities between unimodal and multimodal sentiment analysis tasks. Acknowledgments This work was conducted with the financial support of the Science Foundation Ireland Centre for Research Training in Digitally-Enhanced Reality (d-real) under Grant No. 18/CRT/6224; and the provision of computational facilities and support from the Irish Centre for High-End Computing (ICHEC). References Christian Bauckhage. 2011. Insights into internet memes. Proceedings of the International AAAI Conference on Web and Social Media, 5(1):42–49. Efrat Blaier, Itzik Malkiel, and Lior Wolf. 2021. Caption enriched samples for improving hateful memes detection. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 9350–9358, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics. Ana-Maria Bucur, Adrian Cosma, and Ioana Iordache. 2022. BLUE at memotion 2.0 2022: You have my image, my text and my transformer. In De-Factify @ AAAI 2022. First Workshop on Multimodal FactChecking and Hate Speech Detection, CEUR Workshop Proceedings. AAAI. Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, and Kristina Toutanova. 2019. BoolQ: Exploring the surprising difficulty of natural yes/no questions. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 2924–2936, Minneapolis, Minnesota. Association for Computational Linguistics. CrowdFlower. 2016. Image sentiment polarity - dataset. Available at https://data.world/ crowdflower/image-sentiment-polarity. Accessed: 2023-01-15. Baishan Duan and Yuesheng Zhu. 2022. Browallia at memotion 2.0 2022 : Multimodal memotion analysis with modified ogb strategies (short paper). In DeFactify @ AAAI 2022. First Workshop on Multimodal Fact-Checking and Hate Speech Detection, CEUR Workshop Proceedings. AAAI. Noam Gal, Limor Shifman, and Zohar Kampf. 2016. “it gets better”: Internet memes and the construction of collective identity. New Media & Society, 18(8):1698–1714. Yue Gu, Kangning Yang, Shiyu Fu, Shuhong Chen, Xinyu Li, and Ivan Marsic. 2018. Hybrid Attention based Multimodal Network for Spoken Language Classification. In Proceedings of the 27th International Conference on Computational Linguistics, pages 2379–2390, Santa Fe, New Mexico, USA. Association for Computational Linguistics. Muzhaffar Hazman, Susan McKeever, and Josephine Griffith. 2023. Meme sentiment analysis enhanced with multimodal spatial encoding and face embedding. In Artificial Intelligence and Cognitive Science, pages 318–331, Cham. Springer Nature Switzerland. Junguang Jiang, Yang Shu, Jianmin Wang, and Mingsheng Long. 2022. Transferability in deep learning: A survey. arXiv preprint, arXiv:2201.05867. Vishal Keswani, Sakshi Singh, Suryansh Agarwal, and Ashutosh Modi. 2020. IITK at SemEval-2020 task 8: Unimodal and bimodal sentiment analysis of Internet memes. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1135–1140, Barcelona (online). International Committee for Computational Linguistics. Douwe Kiela, Hamed Firooz, Aravind Mohan, Vedanuj Goswami, Amanpreet Singh, Pratik Ringshia, et al. 2020. The hateful memes challenge: Detecting hate speech in multimodal memes. Advances in Neural Information Processing Systems, 33:2611–2624. Liisi Laineste and Piret Voolaid. 2017. Laughing across borders: Intertextuality of internet memes. 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Innovative Strategies in Meme Sentiment Analysis: The Impact of Unimodal Training

This paper is under CC 4.0 license. available on arxiv Authors: (1) Shreyash Mishra has an equal contribution from IIIT Sri City, India; (2) S Suryavardan has an equal contribution from IIIT Sri City, India; (3) Megha Chakraborty, University of South Carolina, USA; (4) Parth Patwa, UCLA, USA; (5) Anku Rani, University of South Carolina, USA; (6) Aman Chadha, work does not relate to a position at Amazon from Stanford University, USA, or Amazon AI, USA; (7) Aishwarya Reganti, CMU, USA; (8) Amitava Das, University of South Carolina, USA; (9) Amit Sheth, University of South Carolina, USA; (10) Manoj Chinnakotla, Microsoft, USA; (11) Asif Ekbal, IIT Patna, India; (12) Srijan Kumar, Georgia Tech, USA. Table of Links Abstract & Introduction Related Work Task Details Participating systems Results Conclusion and Future Work and References 6. Conclusion and Future Work In this paper, we summarize the approaches used by the participants for the Memotion 3 task and analyze the results. Due to the multi-modal nature of the dataset, all teams use a pre-trained image and text embedding models. However, each team presents a novel model pipeline. The highest scores achieved in Task A, Task B and Task C of Memotion 3.0 are 34.41%, 79.77% and 59.82% respectively, which shows there is significant room for improvement. On analysis of the results and the mis-classified examples on the test set, we find that "Sarcasm" and "Humour" are difficult to identify, especially in code-mixed memes. While we address Hind-English code-mixed memes in this paper, future work could include exploring other languages/language pairs. A unified baseline model to analyze memes in multiple languages could also be an interesting possibility. References [1] R. Dawkins, The selfish gene, Granada Publishing Lim, 1979. [2] A. Marwick, Memes, Contexts 12 (2013) 12–13. [3] N. Akhther, Internet memes as form of cultural discourse: A rhetorical analysis on facebook, 2018. doi:10.31234/osf.io/sx6t7. [4] S. 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