Reviewerly/exHarmony huggingface dataset:
The Muslim’s Poetic Dissent of Citizenship Amendment Act 2019 and Hindu’s Sloganic Resentment: A Critical Discourse Analysis

The passage of Citizenship Amendment Act (CAA) of India enacted in December 2019 basically initiated the traditional ethnic discursive tug-of-war between the Muslims and the Hindus, residing in the country. This study in hand conducted a Critical Discourse Analysis (CDA) of poems and slogans raised during the movement against CAA. Van Dijk’s Ideological Square was used to examine this two-fold discourse. First, the study examined poetic and sloganic discourse which was used by the Muslims to shape dissent against the CAA. Secondly, it examined the reverse indoctrination of the Hindus’ resentment not only shown by the slogans but also by heinous acts and atrocities committed against the Muslims in India. The poems and slogans were analyzed manually, then the sentiments of same poems and slogans were calculated by using NVivo software to apply Van Dijk’s Ideological Sqaure. The study reached the conclusion that sloganic resentments of the Hindus were mostly the reverse of the Muslim’s discourse accompanied by inhuman actions. The study found that the discourse of Hindus was face-to-face with the Muslim’s discourse.
Outdoor and Automotive Displays: Applications in Challenging Environments

Abstract Automotive displays are a highly prosperous but demanding market with an increasing presence in the industry.
A Study on the Use of Adverb ‘就’ for Chinese Education: Based on the Analysis of Example Sentences in Elementary, Middle, and Advanced Chinese Textbooks

現代漢語副詞“就”位列≪現代漢語頻率詞典≫收錄的8000個詞語中的第11位, 在副詞中使用頻率非常高, 被≪新HSK大綱(2015)≫列為HSK2級的語法項目, 是從初級階段開始學習的核心詞匯之一。本文通過對初、中、高級中文教材的例句分析, 對中文教育中副詞“就”的意義和用法進行了分析, 並提出了分析結果的探討和教育方法。結果如下：首先, 參考現有的研究, 綜合考慮了語法書和詞典中提供的副詞“就”的意義和用法, 重新將其歸類為連詞副詞、時間副詞、範圍副詞和程度副詞, 整理了其意義和用法。其次, 通過初、中、高級階段的中文教材分析, 整體上使用頻率較高的連詞副詞是時間副詞, 在中級階段是範圍副詞, 在高級階段是程度副詞。第三, 通過韓、中作者的中文教材分析, 中國作者的教材使用頻率比韓國作者的教材高近4倍。然而, 教材內的語法解釋卻是韓國作者的教材相對較詳細地解釋了多種用法, 在中國作者的教材中, 盡管出現了副詞“就”, 但語法解釋卻缺失的例子也有。第四, 通過對≪標準(2021)≫和本文重新分類的連詞副詞、時間副詞、範圍副詞和程度副詞進行對比, 提出了從初級到高級的階段性教育方法。
Graph Representation Learning via Contrasting Cluster Assignments

With the rise of contrastive learning, unsupervised graph representation learning has shown strong competitiveness. However, existing graph contrastive models typically either focus on the local view of graphs or take simple considerations of both global and local views. This may cause these models to overemphasize the importance of individual nodes and their ego-networks, or to result in poor learning of global knowledge and affect the learning of local views. Additionally, most graph representation learning models pay attention to topological proximity, assuming that nodes that are closer in graph topology are more similar. However, in real world, close nodes may be dissimilar, which makes the learned embeddings incorporate inappropriate messages and thus lack discrimination. To address these issues, we propose a novel unsupervised graph representation learning model by contrasting cluster assignments, called GRCCA. To comprehensively explore the global and local views, it combines multi-view contrastive learning and clustering algorithms with an opposite augmentation strategy. It leverages clustering algorithms to capture fine-grained global information and explore potential relevance between nodes in different augmented perspectives, while preserving high-quality global and local information through contrast between nodes and prototypes. The opposite augmentation strategy further enhances the contrast of both views, allowing the model to excavate more invariant features. Experimental results show that GRCCA has strong competitiveness compared to state-of-the-art models in different graph analysis tasks.
Automatic Facial Expression Analysis and Recognition using Zone Based Active and Salient Facial Patches

Recognition of facial expression has many useful applications that have drawn researcher’s interest over the past decade. Extraction of features is a major step in the analysis of expression which leads to fast and accurate recognition of expression. Recognition of facial expressions is not an easy issue for methods of machine learning, as different people can vary in the way they show their expressions and for one expression the image of the same person can differ for brightness, background and position. Recognition of facial expression is therefore still a challenging computer vision problem. In this thesis work, we aim to design a robust technique of automatic facial expression analysis and recognition using zone based active and salient patches of the human face by combining various techniques from computer vision and pattern recognition. Expression recognition is closely related to face recognition where a lot of research has been done and a vast array of algorithms have been introduced. Facial expression recognition (FER) can also be considered as a special case of a pattern recognition problem and many techniques are available. In the designing of an FER system, we divided the system into 4 modules, i.e. preprocessing, active and salient patch extraction and classification. Voila Jones algorithm is used for face detection and after that features are extracted from the facial patches.&#x0D; The active facial patches are located on the facial regions that during different expressions undergo a major change. The active patches are located after detection of facial landmarks and hybrid features are determined from these patches. The use of small parts of face instead of the whole face for extracting features reduces the computational cost. Zoning is applied and got remarkable results. The dimensionality of the function is reduced by using linear discriminant analysis, which is further defined using the support vector machine (SVM). On the basis of classification expression is recognized. We evaluated our algorithm on Extended Cohn-Kanade (CK+) dataset.
Reverse Traceability Framework for Identifying Liability of Crashes for Self-Driving Vehicles Using Blockchains

Modern vehicles are increasingly having a higher level of technology and automation. Humans are increasingly becoming dependent on these modern technologies to take decisions related to their lives and safety. Such an increasing dependence on automation raises an important question. If an autonomous vehicle (AV) meets an accident, who will be responsible? It is not the human driver, but technology that makes those crucial decisions on the road. This question is attracting considerable attention in the insurance industry because traditional vehicle insurance is based on the liability of human drivers, but in the future, vehicle technology will replace human drivers. Therefore, the vehicle manufacturer or one of its suppliers may be held responsible for the accident. This paper presents a crash liability identification framework that can identify who is liable if there is a crash or an accident of an autonomous self-driving vehicle. The use cases demonstrate that the proposed framework can be used by regulators to efficiently identify the liable party when an AV crashes.
PENTINGNYA FIRMAN TUHAN DALAM MENGATASI PENGGUNAAN OKULTISME

ABSTRACT&#x0D; Occult (in English, occult) means things that involve actions (roles) or supernatural influences (supernatural) or that involve powers that are beyond human power or hidden knowledge (Webster, 1993). According to Baldwin, the meaning of "hidden" Occultism is not limited to things that are not seen or detected, but the substance has the purpose of manipulating or influencing other people (Elwell, 1984). God's Word says people rely on and hope in God patiently blessed (Jer. 17:5-7). If we read in Deuteronomy 18:9-13, God's Word forbids us to follow the occult. Occult issues need serious attention. Overcoming the use of the occult is a must as well as an obligation for believers to overcome it. By using qualitative methods t writer tries to explain the important role of God's Word in overcoming the dangers of using the occult. With this method, the writer tries to have a frame of mind about what is discussed in response to the dangers of using the occult, the composition of journals in research. The basic truth of God's Word, the writer has the belief that truth is very much needed in the face of the dangers of the occult.&#x0D; Keywords: Occult, Word and overcome.
Blockchain-based framework for secure and efficient exchange of electronic medical records on mobile cloud environments

The maintenance of Electronic Medical Records (EMRs) on mobile cloud platforms, in which mobile devices are incorporated with cloud computing to ease healthcare information exchange between healthcare providers and patients, has become a paradigm shift in latest days. This cutting-edge strategy makes it possible to provide medical care at a low overhead expense, with a high level of flexibility, and with electronic health record access. Furthermore, this new approach also expresses concern over the confidentiality of patient data and the safety of the networks that support e-health systems. It is a tough problem to figure out a way to effectively share EMRs amongst mobile users while also maintaining maximum security measures in mobile cloud environments. In this research, a novel framework is presented for the sharing of EMRs that integrates blockchain technology and the decentralized Interplanetary File System (DIFS) on a mobile cloud. The empirical results suggest that the concept offers a useful method for ensuring the reliability of data transfers on mobile clouds, while also preserving important patient data from any potential dangers that may arise. Comparing the results of this performance assessment and security analysis to those of previously implemented data sharing models reveals significant performance enhancements in areas such as lightweight access control method, minimal network latency, maximum security levels, and data privacy protections.
Dialect and Variant Identification as a Multi-Label Classification Task: A Proposal Based on Near-Duplicate Analysis

We argue that dialect identification should be treated as a multi-label classification problem rather than the single-class setting prevalent in existing collections and evaluations.In order to avoid extensive human re-labelling of the data, we propose an analysis of ambiguous near-duplicates in an existing collection covering four variants of French.We show how this analysis helps us provide multiple labels for a significant subset of the original data, therefore enriching the annotation with minimal human intervention.The resulting data can then be used to train dialect identifiers in a multi-label setting.Experimental results show that on the enriched dataset, the multi-label classifier produces similar accuracy to the single-label classifier on test cases that are unambiguous (single label), but it increases the macro-averaged F1-score by 0.225 absolute (71% relative gain) on ambiguous texts with multiple labels. On the original data, gains on the ambiguous test cases are smaller but still considerable (+0.077 absolute, 20% relative gain), and accuracy on non-ambiguous test cases is again similar in this case.This supports our thesis that modelling dialect identification as a multi-label problem potentially has a positive impact.
Information-Theoretic Characterization of Vowel Harmony: A Cross-Linguistic Study on Word Lists

We present a cross-linguistic study of vowel harmony that aims to quantifies this phenomenon using data-driven computational modeling. Concretely, we define an information-theoretic measure of harmonicity based on the predictability of vowels in a natural language lexicon, which we estimate using phoneme-level language models (PLMs). Prior quantitative studies have heavily relied on inflected word-forms in the analysis on vowel harmony. On the contrary, we train our models using cross-linguistically comparable lemma forms with little or no inflection, which enables us to cover more under-studied languages. Training data for our PLMs consists of word lists offering a maximum of 1000 entries per language. Despite the fact that the data we employ are substantially smaller than previously used corpora, our experiments demonstrate the neural PLMs capture vowel harmony patterns in a set of languages that exhibit this phenomenon. Our work also demonstrates that word lists are a valuable resource for typological research, and offers new possibilities for future studies on low-resource, under-studied languages.
The Solvability of Interpretability Evaluation Metrics

Feature attribution methods are popular for explaining neural network predictions, and they are often evaluated on metrics such as comprehensiveness and sufficiency. In this paper, we highlight an intriguing property of these metrics: their solvability. Concretely, we can define the problem of optimizing an explanation for a metric, which can be solved by beam search. This observation leads to the obvious yet unaddressed question: why do we use explainers (e.g., LIME) not based on solving the target metric, if the metric value represents explanation quality? We present a series of investigations showing strong performance of this beam search explainer and discuss its broader implication: a definition-evaluation duality of interpretability concepts. We implement the explainer and release the Python solvex package for models of text, image and tabular domains.
Novel Feature Discovery for Task-Oriented Dialog Systems

A novel feature represents a cluster of semantically equivalent novel user requests e.g., requests to play a song on a service or read user’s messages.Detecting and supporting novel features is crucial towards wider adoption of dialog systems by end users.Intuitively, features are represented by a combination of intents, slot types and/or their values. For example, while playing a song is a feature represented by a single intent (PlayMusic) only, playing a song on a service is another feature represented by the combination of PlayMusic intent and ServiceName slot type.Prior work on novelty detection limits the scope of features to those represented by novel single intents, leading to (1) giant clusters spanning several user-perceived fine-grained features belonging to the same intent, (2) incoherent interpretation of clusters from users’ perspective (no direct connection to some user-perceived feature), and (3) missing those features spanning several intents.In this work, we introduce feature discovery as opposed to single intent discovery, which aims at discovering novel features spanning a combination of intents and slots, and present a technique for discovering novel features from user utterances. Experiments on two datasets demonstrate the effectiveness of our approach and consistently show its ability to detect novel features.
Distinguishability Calibration to In-Context Learning

Recent years have witnessed increasing interests in prompt-based learning in which models can be trained on only a few annotated instances, making them suitable in low-resource settings. It is even challenging in fine-grained classification as the pre-trained language models tend to generate similar output embedding which makes it difficult to discriminate for the prompt-based classifier. In this work, we alleviate this information diffusion issue by proposing a calibration method based on a transformation which rotates the embedding feature into a new metric space where we adapt the ratio of each dimension to a uniform distribution to guarantee the distinguishability of learned embeddings. Furthermore, we take the advantage of hyperbolic embedding to capture the relation between dimensions by a coarse-fine metric learning strategy to enhance interpretability. Extensive experiments on the three datasets under various settings demonstrate the effectiveness of our approach.
Improving Unsupervised Out-of-domain detection through Pseudo Labeling and Learning

Unsupervised out-of-domain (OOD) detection is a task aimed at discriminating whether given samples are from the in-domain or not, without the categorical labels of in-domain instances. Unlike supervised OOD, as there are no labels for training a classifier, previous works on unsupervised OOD detection adopted the one-class classification (OCC) approach, assuming that the training samples come from a single domain. However, in-domain instances in many real world applications can have a heterogeneous distribution (i.e., across multiple domains or multiple classes). In this case, OCC methods have difficulty in reflecting the categorical information of the domain properly. To tackle this issue, we propose a two-stage framework that leverages the latent categorical information to improve representation learning for textual OOD detection. In the first stage, we train a transformer-based sentence encoder for pseudo labeling by contrastive loss and cluster loss. The second stage is pseudo label learning in which the model is re-trained with pseudo-labels obtained in the first stage. The empirical results on the three datasets show that our two-stage framework significantly outperforms baseline models in more challenging scenarios.
Performance and Risk Trade-offs for Multi-word Text Prediction at Scale

Aniket Vashishtha, S Sai Prasad, Payal Bajaj, Vishrav Chaudhary, Kate Cook, Sandipan Dandapat, Sunayana Sitaram, Monojit Choudhury. Findings of the Association for Computational Linguistics: EACL 2023. 2023.
A Neural CRF-based Hierarchical Approach for Linear Text Segmentation

We consider the problem of segmenting unformatted text and transcripts linearly based on their topical structure. While prior approaches explicitly train to predict segment boundaries, our proposed approach solves this task by inferring the hierarchical segmentation structure associated with the input text fragment. Given the lack of a large annotated dataset for this task, we propose a data curation strategy and create a corpus of over 700K Wikipedia articles with their hierarchical structures. We then propose the first supervised approach to generating hierarchical segmentation structures based on these annotations. Our method, in particular, is based on a neural conditional random field (CRF), which explicitly models the statistical dependency between a node and its constituent child nodes. We introduce a new data augmentation scheme as part of our model training strategy, which involves sampling a variety of node aggregations, permutations, and removals, all of which help capture fine-grained and coarse topical shifts in the data and improve model performance. Extensive experiments show that our model outperforms or achieves competitive performance when compared to previous state-of-the-art algorithms in the following settings: rich-resource, cross-domain transferability, few-shot supervision, and segmentation when topic label annotations are provided.
Embedding Recycling for Language Models

Real-world applications of neural language models often involve running many different models over the same corpus. The high computational cost of these runs has led to interest in techniques that can reuse the contextualized embeddings produced in previous runs to speed training and inference of future ones. We refer to this approach as embedding recycling (ER). While multiple ER techniques have been proposed, their practical effectiveness is still unknown because existing evaluations consider very few models and do not adequately account for overhead costs. We perform an extensive evaluation of ER across eight different models (17 to 900 million parameters) and fourteen tasks in English. We show how a simple ER technique that caches activations from an intermediate layer of a pretrained model, and learns task-specific adapters on the later layers, is broadly effective. For the best-performing baseline in our experiments (DeBERTa-v2 XL), adding a precomputed cache results in a 90% speedup during training and 87-91% speedup for inference, with negligible impact on accuracy. Our analysis reveals important areas of future work.
Learning Disentangled Representations for Natural Language Definitions

Disentangling the encodings of neural models is a fundamental aspect for improving interpretability, semantic control and downstream task performance in Natural Language Processing. Currently, most disentanglement methods are unsupervised or rely on synthetic datasets with known generative factors. We argue that recurrent syntactic and semantic regularities in textual data can be used to provide the models with both structural biases and generative factors. We leverage the semantic structures present in a representative and semantically dense category of sentence types, definitional sentences, for training a Variational Autoencoder to learn disentangled representations. Our experimental results show that the proposed model outperforms unsupervised baselines on several qualitative and quantitative benchmarks for disentanglement, and it also improves the results in the downstream task of definition modeling.
Creation and evaluation of timelines for longitudinal user posts

There is increasing interest to work with user generated content in social media, especially textual posts over time. Currently there is no consistent way of segmenting user posts into timelines in a meaningful way that improves the quality and cost of manual annotation. Here we propose a set of methods for segmenting longitudinal user posts into timelines likely to contain interesting moments of change in a user’s behaviour, based on their online posting activity. We also propose a novel framework for evaluating timelines and show its applicability in the context of two different social media datasets. Finally, we present a discussion of the linguistic content of highly ranked timelines.
Improving Retrieval Augmented Neural Machine Translation by Controlling Source and Fuzzy-Match Interactions

We explore zero-shot adaptation, where a general-domain model has access to customer or domain specific parallel data at inference time, but not during training. We build on the idea of Retrieval Augmented Translation (RAT) where top-k in-domain fuzzy matches are found for the source sentence, and target-language translations of those fuzzy-matched sentences are provided to the translation model at inference time. We propose a novel architecture to control interactions between a source sentence and the top-k fuzzy target-language matches, and compare it to architectures from prior work. We conduct experiments in two language pairs (En-De and En-Fr) by training models on WMT data and testing them with five and seven multi-domain datasets, respectively. Our approach consistently outperforms the alternative architectures, improving BLEU across language pair, domain, and number k of fuzzy matches.