Reviewerly/exHarmony huggingface dataset:
Classification of Book Review Sentiment in Bangla Language Using NLP, Machine Learning and LSTM

Books are said to be the best friend a person can have. Book reading culture dates back to almost a couple of thousands of years. After ancient civilizations learned to write, they stored information in tablets or walls or stones are said to be the predecessors of books. The newest form of books is called e-books, digitalization or digital printing of paper-based books. In Bangladesh, even a few years back, people had to go to the library in person to collect books but now online book stores are getting popular. With all its perks being easy, online book store comes with some penalty i.e. reader don&#x0027;t know about the books or the service of the book store itself. To avoid such, book readers tend to rely on reviews and ratings. Our goal is to analyze Bangla language reviews and give proper feedback about books and the online store so the book readers can buy desired books to read and get better services from online book stores. 6281 raw data are collected to train the machine. In this study, the Machine Learning and Deep Learning methods are used for the binary (negative, positive) analysis of sentiments. Natural Language Processing (NLP) is used for text preprocessing. Some of the popular algorithms like Support Vector Machine (SVM), Decision Tree, KNN, Random Forest, Logistic Regression and Long Short-Term Memory(LSTM) are used to classify the sentiments. Our Highest accuracy is 97.49%, achieved by LSTM.
Joint Deep Clustering: Classification and Review

Clustering is a fundamental problem in machine learning.To address this, a large number of algorithms have been developed.Some of these algorithms, such as K-means, handle the original data directly, while others, such as spectral clustering, apply linear transformation to the data.Still others, such as kernel-based algorithms, use nonlinear transformation.Since the performance of the clustering depends strongly on the quality of the data representation, representation learning approaches have been extensively researched.With the recent advances in deep learning, deep neural networks are being increasingly utilized to learn clustering-friendly representation.We provide here a review of existing algorithms that are being used to jointly optimize deep neural networks and clustering methods.
A Root of a Problem: Optimizing Single-Root Dependency Parsing

We describe two approaches to single-root dependency parsing that yield significant speed ups in such parsing.One approach has been previously used in dependency parsers in practice, but remains undocumented in the parsing literature, and is considered a heuristic.We show that this approach actually finds the optimal dependency tree.The second approach relies on simple reweighting of the inference graph being input to the dependency parser and has an optimal running time.Here, we again show that this approach is fully correct and identifies the highest-scoring parse tree.Our experiments demonstrate a manyfold speed up compared to a previous graph-based state-of-the-art parser without any loss in accuracy or optimality. 1
Translation-based Supervision for Policy Generation in Simultaneous Neural Machine Translation

In simultaneous machine translation, finding an agent with the optimal action sequence of reads and writes that maintain a high level of translation quality while minimizing the average lag in producing target tokens remains an extremely challenging problem.We propose a novel supervised learning approach for training an agent that can detect the minimum number of reads required for generating each target token by comparing simultaneous translations against full-sentence translations during training to generate oracle action sequences.These oracle sequences can then be used to train a supervised model for action generation at inference time.Our approach provides an alternative to current heuristic methods in simultaneous translation by introducing a new training objective, which is easier to train than previous attempts at training the agent using reinforcement learning techniques for this task.Our experimental results show that our novel training method for action generation produces much higher quality translations while minimizing the average lag in simultaneous translation.
Reference-Free Word- and Sentence-Level Translation Evaluation with Token-Matching Metrics

Many modern machine translation evaluation metrics like BERTScore, BLEURT, COMET, MonoTransquest or XMoverScore are based on black-box language models.Hence, it is difficult to explain why these metrics return certain scores.This year's Eval4NLP shared task tackles this challenge by searching for methods that can extract feature importance scores that correlate well with human word-level error annotations.In this paper we show that unsupervised metrics that are based on tokenmatching can intrinsically provide such scores.The submitted system interprets the similarities of the contextualized word-embeddings that are used to compute (X)BERTScore as word-level importance scores.We make our code available 1 .
The tunable 0−π qubit: Dynamics and relaxation

In this paper, we present a systematic treatment of a [Formula: see text] qubit in the presence of a time-dependent external flux. A gauge-invariant Lagrangian and the corresponding Hamiltonian are obtained. The effect of the flux noise on the qubit relaxation is obtained using the perturbation theory. Under a time-dependent drive of sinusoidal form, the survival probability, and transition probabilities have been studied for different strengths and frequencies. The driven qubit is shown to possess coherent oscillations among two distinct states for a weak to moderate strength close to resonant frequencies of the unperturbed qubit. The parameters can be chosen to prepare the system in its ground state. This feature paves the way to prolong the lifetime by combining ideas from weak measurement and quantum Zeno effect. We believe that this is an important variation of a topologically protected qubit which is tunable.
Search Trajectory Networks Applied to the Cyclic Bandwidth Sum Problem

Search trajectory networks (STNs) were proposed as a tool to analyze the behavior of metaheuristics in relation to their exploration ability and the search space regions they traverse. The technique derives from the study of fitness landscapes using local optima networks (LONs). STNs are related to LONs in that both are built as graphs, modelling the transitions among solutions or group of solutions in the search space. The key difference is that STN nodes can represent solutions or groups of solutions that are not necessarily locally optimal. This work presents an STN-based study for a particular combinatorial optimization problem, the cyclic bandwidth sum minimization. STNs were employed to analyze the two leading algorithms for this problem: a memetic algorithm and a hyperheuristic memetic algorithm. We also propose a novel grouping method for STNs that can be generally applied to both continuous and combinatorial spaces.
Guessing Bits: Improved Lattice Attacks on (EC)DSA with Nonce Leakage

The lattice reduction attack on (EC)DSA (and other Schnorr-like signature schemes) with partially known nonces, originally due to Howgrave-Graham and Smart, has been at the core of many concrete cryptanalytic works, side-channel based or otherwise, in the past 20 years. The attack itself has seen limited development, however: improved analyses have been carried out, and the use of stronger lattice reduction algorithms has pushed the range of practically vulnerable parameters further, but the lattice construction based on the signatures and known nonce bits remain the same.In this paper, we propose a new idea to improve the attack based on the same data in exchange for additional computation: carry out an exhaustive search on some bits of the secret key. This turns the problem from a single bounded distance decoding (BDD) instance in a certain lattice to multiple BDD instances in a fixed lattice of larger volume but with the same bound (making the BDD problem substantially easier). Furthermore, the fact that the lattice is fixed lets us use batch/preprocessing variants of BDD solvers that are far more efficient than repeated lattice reductions on non-preprocessed lattices of the same size. As a result, our analysis suggests that our technique is competitive or outperforms the state of the art for parameter ranges corresponding to the limit of what is achievable using lattice attacks so far (around 2-bit leakage on 160-bit groups, or 3-bit leakage on 256-bit groups).We also show that variants of this idea can also be applied to bits of the nonces (leading to a similar improvement) or to filtering signature data (leading to a data-time trade-off for the lattice attack). Finally, we use our technique to obtain an improved exploitation of the TPM–FAIL dataset similar to what was achieved in the Minerva attack.
Unsupervised Relation Extraction: A Variational Autoencoder Approach

Unsupervised relation extraction works by clustering entity pairs that have the same relations in the text.Some existing variational autoencoder (VAE)-based approaches train the relation extraction model as an encoder that generates relation classifications.A decoder is trained along with the encoder to reconstruct the encoder input based on the encodergenerated relation classifications.These classifications are a latent variable so they are required to follow a pre-defined prior distribution which results in unstable training.We propose a VAE-based unsupervised relation extraction technique that overcomes this limitation by using the classifications as an intermediate variable instead of a latent variable.Specifically, classifications are conditioned on sentence input, while the latent variable is conditioned on both the classifications and the sentence input.This allows our model to connect the decoder with the encoder without putting restrictions on the classification distribution; which improves training stability.Our approach is evaluated on the NYT dataset and outperforms state-of-the-art methods.
Integrating Personalized PageRank into Neural Word Sense Disambiguation

Neural Word Sense Disambiguation (WSD) has recently been shown to benefit from the incorporation of pre-existing knowledge, such as that coming from the WordNet graph.However, state-of-the-art approaches have been successful in exploiting only the local structure of the graph, with only close neighbors of a given synset influencing the prediction.In this work, we improve a classification model by recomputing logits as a function of both the vanilla independently produced logits and the global WordNet graph.We achieve this by incorporating an online neural approximated PageRank, which enables us to refine edge weights as well.This method exploits the global graph structure while keeping space requirements linear in the number of edges.We obtain strong improvements, matching the current state of the art.
Developing a Benchmark for Reducing Data Bias in Authorship Attribution

Authorship attribution is the task of assigning an unknown document to an author from a set of candidates.In the past, studies in this field use various evaluation datasets to demonstrate the effectiveness of preprocessing steps, features, and models.However, only a small fraction of works use more than one dataset to prove claims.In this paper, we present a collection of highly diverse authorship attribution datasets, which better generalizes evaluation results from authorship attribution research.Furthermore, we implement a wide variety of previously used machine learning models and show that many approaches show vastly different performances when applied to different datasets.We include pre-trained language models, for the first time testing them in this field in a systematic way.Finally, we propose a set of aggregated scores to evaluate different aspects of the dataset collection.
Argument Mining on Twitter: A Case Study on the Planned Parenthood Debate

Twitter is a popular platform to share opinions and claims, which may be accompanied by the underlying rationale.Such information can be invaluable to policy makers, marketers and social scientists, to name a few.However, the effort to mine arguments on Twitter has been limited, mainly because a tweet is typically too short to contain an argumentboth a claim and a premise.In this paper, we propose a novel problem formulation to mine arguments from Twitter: We formulate argument mining on Twitter as a text classification task to identify tweets that serve as premises for a hashtag that represents a claim of interest.To demonstrate the efficacy of this formulation, we mine arguments for and against funding Planned Parenthood expressed in tweets.We first present a new dataset of 24,100 tweets containing hashtag #Stand-WithPP or #DefundPP, manually labeled as SUPPORT WITH REASON, SUPPORT WITH-OUT REASON, and NO EXPLICIT SUPPORT.We then train classifiers to determine the types of tweets, achieving the best performance of 71% F 1 .Our results manifest claim-specific keywords as the most informative features, which in turn reveal prominent arguments for and against funding Planned Parenthood.
Re-embedding Difficult Samples via Mutual Information Constrained Semantically Oversampling for Imbalanced Text Classification

Difficult samples of the minority class in imbalanced text classification are usually hard to be classified as they are embedded into an overlapping semantic region with the majority class.In this paper, we propose a Mutual Information constrained Semantically Oversampling framework (MISO) that can generate anchor instances to help the backbone network determine the re-embedding position of a non-overlapping representation for each difficult sample.MISO consists of (1) a semantic fusion module that learns entangled semantics among difficult and majority samples with an adaptive multi-head attention mechanism, (2) a mutual information loss that forces our model to learn new representations of entangled semantics in the non-overlapping region of the minority class, and (3) a coupled adversarial encoder-decoder that fine-tunes disentangled semantic representations to remain their correlations with the minority class, and then using these disentangled semantic representations to generate anchor instances for each difficult sample.Experiments on a variety of imbalanced text classification tasks demonstrate that anchor instances help classifiers achieve significant improvements over strong baselines.
Profiling News Discourse Structure Using Explicit Subtopic Structures Guided Critics

We present an actor-critic framework to induce subtopical structures in a news article for news discourse profiling.The model uses multiple critics that act according to known subtopic structures while the actor aims to outperform them.The content structures constitute sentences that represent latent subtopic boundaries.Then, we introduce a hierarchical neural network that uses the identified subtopic boundary sentences to model multi-level interaction between sentences, subtopics, and the document.Experimental results and analyses on the NewsDiscourse corpus show that the actor model learns to effectively segment a document into subtopics and improves the performance of the hierarchical model on the news discourse profiling task 1 .
Learning Numeracy: A Simple Yet Effective Number Embedding Approach Using Knowledge Graph

Numeracy plays a key role in natural language understanding.However, existing NLP approaches, either traditional word2vec approach or contextualized transformer-based language models, fail to learn numeracy.As the result, the performance of these models is limited when they are applied to number-intensive applications in clinical and financial domains.In this work, we propose a simple number embedding approach based on knowledge graph.We construct a knowledge graph consisting of number entities and magnitude relations.Knowledge graph embedding method is then applied to obtain number vectors.Our approach is easy to implement, and experiment results on various numeracy-related NLP tasks demonstrate the effectiveness and efficiency of our method.
CoLLIE: Continual Learning of Language Grounding from Language-Image Embeddings

This paper presents CoLLIE: a simple, yet effective model for continual learning of how language is grounded in vision. Given a pre-trained multimodal embedding model, where language and images are projected in the same semantic space (in this case CLIP by OpenAI), CoLLIE learns a transformation function that adjusts the language embeddings when needed to accommodate new language use. This is done by predicting the difference vector that needs to be applied, as well as a scaling factor for this vector, so that the adjustment is only applied when needed. Unlike traditional few-shot learning, the model does not just learn new classes and labels, but can also generalize to similar language use and leverage semantic compositionality. We verify the model's performance on two different tasks of identifying the targets of referring expressions, where it has to learn new language use. The results show that the model can efficiently learn and generalize from only a few examples, with little interference with the model's original zero-shot performance.
Quadratic Word Equations with Length Constraints, Counter Systems, and Presburger Arithmetic with Divisibility

Word equations are a crucial element in the theoretical foundation of constraint solving over strings. A word equation relates two words over string variables and constants. Its solution amounts to a function mapping variables to constant strings that equate the left and right hand sides of the equation. While the problem of solving word equations is decidable, the decidability of the problem of solving a word equation with a length constraint (i.e., a constraint relating the lengths of words in the word equation) has remained a long-standing open problem. We focus on the subclass of quadratic word equations, i.e., in which each variable occurs at most twice. We first show that the length abstractions of solutions to quadratic word equations are in general not Presburger-definable. We then describe a class of counter systems with Presburger transition relations which capture the length abstraction of a quadratic word equation with regular constraints. We provide an encoding of the effect of a simple loop of the counter systems in the existential theory of Presburger Arithmetic with divisibility (PAD). Since PAD is decidable (NP-hard and is in NEXP), we obtain a decision procedure for quadratic words equations with length constraints for which the associated counter system is flat (i.e., all nodes belong to at most one cycle). In particular, we show a decidability result (in fact, also an NP algorithm with a PAD oracle) for a recently proposed NP-complete fragment of word equations called regular-oriented word equations, when augmented with length constraints. We extend this decidability result (in fact, with a complexity upper bound of PSPACE with a PAD oracle) in the presence of regular constraints.
Graph-based Argument Quality Assessment

The paper presents a novel discourse-based approach to argument quality assessment defined as a graph classification task, where the depth of reasoning (argumentation) is evident from the number and type of detected discourse units and relations between them.We successfully applied state-of-the-art discourse parsers and machine learning models to reconstruct argument graphs with the identified and classified discourse units as nodes and relations between them as edges.Then Graph Neural Networks were trained to predict the argument quality assessing its acceptability, relevance, sufficiency and overall cogency.The obtained accuracy ranges from 74.5% to 85.0% and indicates that discourse-based argument structures reflect qualitative properties of natural language arguments.The results open many interesting prospects for future research in the field of argumentation mining.
Leveraging Bidding Graphs for Advertiser-Aware Relevance Modeling in Sponsored Search

Recently, sponsored search has become one of the most lucrative channels for marketing.As the fundamental basis of sponsored search, relevance modeling has attracted increasing attention due to the tremendous practical value.Most existing methods solely rely on the query-keyword pairs.However, keywords are usually short texts with scarce semantic information, which may not precisely reflect the underlying advertising intents.In this paper, we investigate the novel problem of advertiser-aware relevance modeling, which leverages the advertisers' information to bridge the gap between the search intents and advertising purposes.Our motivation lies in incorporating the unsupervised bidding behaviors as the complementary graphs to learn desirable advertiser representations.We further propose a Bidding-Graph augmented Triplebased Relevance model BGTR with three towers to deeply fuse the bidding graphs and semantic textual data.Empirically, we evaluate the BGTR model over a large industry dataset, and the experimental results consistently demonstrate its superiority.
Segmentation of Medical X-ray Bone Image Using Different Image Processing Techniques

Accurate medical image processing plays a crucial role in several clinical diagnoses by assisting physicians in timely treatment of wounds and mishaps.Medical doctors in the hospitals generally rely on examining bone x-ray images based on their expertise, knowledge and past experiences in determining whether a fracture exist in bone or not.Nevertheless, majority of fractures identification methods using X-rays in the hospitals is beyond human understanding due to variation in different attributes of fracture and complication of bone organization thereby making it difficult for doctors to correctly diagnose and proffer adequate treatment to patient ailments.The need for robust diagnostic image processing techniques for image segmentation for different bone structures cannot be overemphasized.This research implemented different image segmentation techniques on a bone x-ray image in order to identify the most efficient for timely medical diagnosis.Also, the strength and weaknesses of the diverse segmentation techniques were also identified.This will empowered researchers with appropriate knowledge needed to improve and build better image segmentation models which doctors can use in handling complex medical image processing problems.Also, miss rate in bone X-rays that contains multiple abnormalities can be lowered by using appropriate image segmentation techniques thereby improving some of the labor intensive work of medical personnel during bone diagnosis.MATLAB 9.7.0 programing tool was used for the implementation of the work.The results of X-ray bone segmentation revealed that active contour model using snake model showed the best performance in detecting boundaries and contours of regions of interest when used in segmenting Femur bone image than the other medical image segmentation approaches implemented in the work.