Reviewerly/exHarmony huggingface dataset:
On Distributed Computing With Heterogeneous Communication Constraints

We consider a distributed computing framework where the distributed nodes have different communication capabilities, motivated by the heterogeneous networks in data centers and mobile edge computing systems. Following the structure of MapReduce, this framework consists of Map computation phase, Shuffle phase, and Reduce computation phase. The Shuffle phase allows distributed nodes to exchange intermediate values, in the presence of heterogeneous communication bottlenecks for different nodes (heterogeneous communication load constraints). For this setting, we characterize the minimum total computation load and the minimum worst-case computation load in some cases, under the heterogeneous communication load constraints. While the total computation load depends on the sum of the computation loads of all the nodes, the worst-case computation load depends on the computation load of a node with the heaviest job. We show an interesting insight that, for some cases, there is a tradeoff between the minimum total computation load and the minimum worst-case computation load, in the sense that both cannot be achieved at the same time. The achievability schemes are proposed with careful design on the file assignment and the data shuffling. Beyond the cut-set bound, a novel converse is proposed using the proof by contradiction. For the general case, we identify two extreme regimes in which both the scheme with coding and the scheme without coding are optimal, respectively.
Software Utility of One-Way Intuitionistic Fuzzy ANOVA

The post-COVID-19 era will bring forward a new normal - one that will accelerate digital transformation in many areas as one solution to avoid severe economic consequences. Many factors have been considered as a possible influence on the diffusion of COVID-19 around the world - one such factor being the geographic location of each country. One-way Analysis of Variance (ANOVA) studies the influence of a single factor on a variable. In a previous publication, we proposed one-way intuitionistic fuzzy ANOVA (1-D IFANOVA), based on the formalisms of Index Matrices (IMs) and Intuitionistic Fuzzy Sets (IFSs), which is a modification of classical ANOVA. In this paper, we will introduce a command-line utility for the calculation of IFANOVA results which performs the algorithm using Intuitionistic Fuzzy Pairs (IFPs). Then we will apply IFANOVA to clarify how the number of daily reported cases in European countries depends on their geographic location, using the dataset of ECDPC daily cases from January 1 to December 31, 2021. We will also analyze the data with classical ANOVA and will perform a comparative analysis of the results obtained from that and from IFANOVA.
Bidirectional Maximum Entropy Training with Word Co-occurrence for Video Captioning

Video captioning aims to generate natural language descriptions for a given video, which is a more challenging task than static image captioning since it requires a more diverse and exhaustive result. Meanwhile, it is also important that the generated captions should be consistent with the language habits of people at a fine granularity. In this work, unlike most recent works enhancing performance with additional data modalities or complex model designs, we focus on optimizing the training process of video captioning models. Firstly, to generate a more diverse video caption, we propose the bidirectional maximum entropy (BME) training, which directly optimizes the probability distribution of neighboring words under a reinforcement learning (RL) framework. Secondly, to search for more human-like captions in the larger search space created by BME, we introduce the word co-occurrence (WCO) weighting. It adaptively guides RL algorithms with co-occurrence statistics in the training corpus. Our method can be deployed on existing captioning models in a plug-and-play manner without introducing any extra parameters. Experimental results show that our method yields up to 5.8% and 7.0% improvements considering the CIDEr score on MSVD and MSR-VTT, respectively.
Learning to Maximize Speech Quality Directly Using MOS Prediction for Neural Text-to-Speech

Although recent neural text-to-speech (TTS) systems have achieved high-quality speech synthesis, there are cases where a TTS system generates low-quality speech, mainly caused by limited training data or information loss during knowledge distillation. Therefore, we propose a novel method to improve speech quality by training a TTS model under the supervision of perceptual loss, which measures the distance between the maximum possible speech quality score and the predicted one. We first pre-train a mean opinion score (MOS) prediction model and then train a TTS model to maximize the MOS of synthesized speech using the pre-trained MOS prediction model. The proposed method can be applied independently regardless of the TTS model architecture or the cause of speech quality degradation and efficiently without increasing the inference time or model complexity. The evaluation results for the MOS and phone error rate demonstrate that our proposed approach improves previous models in terms of both naturalness and intelligibility.
Impact of Clustering on a Synthetic Instance Generation in Imbalanced Data Streams Classification

The goal of the paper is to propose a new version of the Weighted Ensemble with one-class Classification and Over-sampling and Instance selection (WECOI) algorithm. This paper describes WECOI and presents the alternative approach for over-sampling, which is based on a selection of reference instances from produced clusters. This approach is flexible on applied clustering methods; however, the similarity-based clustering algorithm has been proposed as a core. For clustering, different methods may also be applied. The proposed approach has been validated experimentally using different clustering methods and shows how the clustering technique may influence synthetic instance generation and the performance of WECOI. The WECOI approach has also been compared with other algorithms dedicated to learning from imbalanced data streams. The computational experiment was carried out using several selected benchmark datasets. The computational experiment results are presented and discussed.
Fraud Identification of Financial Statements by Machine Learning Technology: Case of Listed Companies in Vietnam

The study uses data of listed non-financial companies in 2018 and 2019, combining M-Score and Z-Score models, applying ANN and SVM machine learning techniques in forecasting evidence of fraud in financial statements. Research results show that using SVM technique and M-Score index has high accuracy in predicting.
Aggregating Local and Global Text Features for Linguistic Steganalysis

Existing linguistic steganalysis methods share a similar conceptual learning paradigm: namely, learning specific features derived from a word and its surrounding words, then passing them to the classifier. However, these features are unable to deal with the impact of the association between words and the corpus, which gives rise to significant limitations. In this paper, we discover the missing link between the words and the corpus, and accordingly propose a framework of linguistic steganalysis named LS-BGAT. Specifically, we fine-tune a large-scale pre-training BERT as the local feature extraction model and employ Graph Attention Network (GAT) as the global feature extraction model. Through ensuring collaboration between the local BERT-based features and the global GAT-based features in the joint prediction layer, we combine rich semantic and syntactic information at the sentence level with underlying global information at the corpus level. Furthermore, we extend the binary classification of steganalysis to multi-category classification, thereby enhancing practicality. We empirically substantiate the effectiveness and universality of LS-BGAT on three tasks.
Language Reinforced Superposition Multimodal Fusion for Sentiment Analysis

Nowadays, BERT has been effective in context extraction and widely utilized in multimodal fusion. However, many multimodal learning systems with BERT consider different modalities equally important, which ignores the huge capability difference between the pre-trained models and others. Moreover, equal multimodal fusion might introduce noise and limit the performance of these systems. To this end, we propose a superposition multimodal fusion framework to strengthen the importance of language modality with nonverbal information transmission. With the superposition strategy, language information will fuse with nonverbal information at different stages, which allows the model to fully learn language modality information. Besides, to ensure that language information is the main content of the fusion, we propose a comparison loss to help the reinforced cross-modal attention module better transfer nonverbal information to language features. Extensive experiments are conducted to compare with baselines and the results demonstrate that our method achieves the superior performance.
How Dimensionality Reduction Affects Sentiment Analysis NLP Tasks: An Experimental Study

Dimensionality reduction is a well-known technique for limiting the size of the feature space and for discovering latent meaningful variables in the input data. It is particularly valuable when the raw data is sparse and its processing by machine learning algorithms becomes computationally very expensive. On the other hand, sentiment analysis refers to a collection of text classification methods that identify the polarity of the user opinions in blog posts, reviews, tweets, etc. However, since text is naturally very sparse, training classification models is often intractable, rendering the importance of dimensionality reduction even greater. In this paper we study the impact of dimensionality reduction in sentiment analysis classification tasks. Through extensive experimentation with traditional algorithms and benchmark datasets, we verify the general intuition that the dimensionality reduction methods significantly improve the data preprocessing times and the model training durations, while they sacrifice only small amounts of accuracy. Simultaneously, we highlight several exceptions to this rule, where the training times actually increase and the accuracy losses are significant.
Mechanisms of Temporal Quasi - Fractal Logic in Smart Systems

One of the goals of digitalization is to simplify the implementation of various procedures necessary in many areas of activity, which, as a rule, require large different costs: financial, time, resource, etc. In this paper, we consider approaches to describe temporal logic from the point of view of algebra to clear up (to measure) the truth value of closed formulas of the first order language of the finite signature with equality and to clear up connections between various kinds of temporal logics. The paper is applied in nature, and one can use results received in it to construct control and planning models of smart systems. The purpose of this paper is to 1) to define the concept of a quasi-fractal algebraic system with any infinite number of levels, 2) formulate the theorem about the representation tense logic in the form of Boolean algebra with operators, and 3) use the approach to temporal logic (first order logic) from the point of view of the Category Theory [Saunders Mac Lane, Categories for the Working Mathematician, Springer (Graduate Texts in Mathematics) ISBN 0-387-98403-8] to define using this approach the notion of P – purities in the class of finite signature algebras with finite-place operations and after that in categories and in first order logics which helps one to distinguish sustainable logical systems in the sense of truth values. The paper deals with issues related to quasi - fractal temporal logic. A new operator QF is introduced for quasi - fractal algebraic systems of any infinite ordinal number of levels. So, to describe temporal logic from the point of view of algebra to clear up (to measure) the truth value of closed formulas of the first order language of the finite signature with equality and to clear up connections between various kinds of temporal logics while considering temporal logic we can define quasi - fractal time scale, or simply quasi - fractal time. Quasi - fractal temporal logic can be considered as complex system with binary connections defined by logics values - true and false, or as more complicated connections which are defined by digitalization functions. Quasi - fractal construction and fixed - point theorem helps one to highlight sustainable parts of temporal logic.
The silence of the data says a lot; pay attention: a brief exploration of visual exploratory analysis

This article proposes the use of visual exploratory analysis as an essential tool in analysing open data, qualifying it as an efficient way to overcome the epistemological obstacle of informational silence. It presents two briefcases of open data exploratory visual analysis experiments that reinforce the use of this technique as a guide for insights. It shows the methodology consisting of data pre-processing, calculation of social network analysis metrics, and visual exploratory analysis. It concludes that visual exploratory analysis is an essential guide to conjectures that will target later confirmatory quantitative methods.
A Qualitative Case Study on the Validation of Automatically Generated Multiple-Choice Questions From Science Textbooks

In a concept learning scenario, any technology-supported learning system must provide students with mechanisms that help them with the acquisition of the concepts to be learned. For the technology-supported learning systems to be successful in this task, the development of didactic material is crucial—a hard task that could be alleviated by means of an automation process. In this proposal, two systems which have been previously developed, ArikIturri and DOM-Sortze, are combined to automatically generate multiple-choice questions, based on pedagogically relevant information gathered in textbooks. Originally, the former was able to generate multiple-choice questions from plain texts; and the latter was able to elicit learning objects based on didactic material explicitly represented in electronic textbooks, i.e., definitions, examples, and exercises. This article presents an approach for the automatic generation of multiple-choice questions from learning objects extracted from textbooks. Specifically, ArikIturri uses as input the texts gathered in the learning objects elicited by DOM-Sortze and, using natural language processing techniques, generates multiple-choice questions. This way, considering domain-relevant information from the textbooks, test-type exercises which were not previously elicited by DOM-Sortze are created. In summary, this new approach is able to enrich domain modules of technology-supported learning systems. The proposal has been tested with a textbook which is written in the Basque language and the results show that the generated exercises are suitable to be used in science learning scenarios at secondary school.
Dual-Attention Based Joint Aspect Sentiment Classification Model

Aspect-Category based Sentiment Analysis (ACSA) aims to predict the aspect category and the sentiment polarity mentioned in a sentence. Most works treat it as two individual tasks: aspect category detection (ACD) and aspect category sentiment classification (ACSC), thus resulting in category missing and mismatch between sentiment words and aspect categories. This paper proposes a dual-attention based joint aspect sentiment classification model (AS-DATJM), which jointly predicts aspect category and sentiment polarity in one framework. Given a sentence, AS-DATJM firstly employs aspect aware attention in ACD to obtain the hidden aspect terms. With these terms as guidance, ACSC module aggregates relevant sentiment context over the Graph Convolutional Network. As a result, the inter-relations between aspect categories and sentiments can be captured and employed to predict both categories simultaneously. Extensive evaluations demonstrate the effctiveness of our model and results show that it outperforms the state-of-the-art methods on four benchmark datasets.
Bu-Dash: A Universal and Dynamic Graphical Password Scheme

Biometric authentication gradually replaces knowledge-based methods on mobile devices. However, Personal Identification Numbers, passcodes, and graphical password schemes such as the Android Pattern Unlock (APU) are often the primary means for authentication, or they constitute an auxiliary (or backup) method to be used in case biometrics fail. Passcodes need to be memorable to be usable, hence users tend to choose easy to guess passwords, compromising security. The APU is a great example of a popular and usable graphical password scheme which can be easily compromised, by exploiting common and predominant human behavioristic traits. Despite its vulnerabilities, the scheme's popularity has led researchers to propose adjustments and variations that enhance security but maintain its familiar user interface. Nevertheless, prior work demonstrated that improving security while preserving usability remains frequently a hard task. In this paper we propose a novel graphical password scheme built on the foundations of the well-accepted APU method, which is usable, inclusive, universal, and robust against shoulder surfing and smudge attacks. Our scheme, named Bu-Dash, features a dynamic user interface that mutates every time a user swipes the screen. Our pilot studies illustrate that Bu-Dash attracts positive user acceptance rates and maintains acceptable usability levels.
An Exploration Study on the Consequence of the COVID-19 Pandemic on Online Q&amp;A Communities

This work aims to document a two-sided impact of the COVID-19 pandemic on online question and answer communities. It implements empirical analyses on subsidiary communities affiliating to the Stack Exchange network. Using a difference-in-difference approach to identify the impact of the pandemic on community volume (the counts of questions and answers) and responsiveness (the likelihood of questions to be answered), this work has the following discoveries. First, the community volume grows, both in questions and answers, driven by a prolonged time of staying at home during the pandemic. Second, the community responsiveness declines, driven by an influx of new community members during the pandemic, which is also a result of the prolonged time of staying at home. Theoretical and practical implications are discussed.
Mood-Based Movie Recommendation System

Mood-based movie recommendation system provides a platform which makes a simple task of choosing a movie, more efficient. Here, the person has to choose the mood according to which the system can recommend a movie based on their choices. As we all know, searching and deciding which movie to watch take much more time than actually watching the movie. So, through this application, they are able to make a better movie choice in much less time. This movie recommendation system is the one that considers the mood of the user for extracting out few specific genres for further selection. The system then asks for user’s some favorite movies in the chosen genre so that it can recommend a movie based on their choices. Here, we have used Python and flask Web framework (and different libraries) to build the Web application, and the dataset used is scrapped from IMDb’s Web site and combined from different dataset available on Kaggle.
Continuous Variable Quantum MNIST Classifiers&amp;lt;br/&amp;gt;—Classical-Quantum Hybrid Quantum Neural Networks

In this paper, classical and continuous variable (CV) quantum neural network hybrid multi-classifiers are presented using the MNIST dataset.Currently available classifiers can classify only up to two classes.The proposed architecture allows networks to classify classes up to n m classes, where n represents cutoff dimension and m the number of qumodes on photonic quantum computers.The combination of cutoff dimension and probability measurement method in the CV model allows a quantum circuit to produce output vectors of size n m .They are then interpreted as one-hot encoded labels, padded with n m -10 zeros.The total of seven different classifiers is built using 2, 3, …, 6, and 8-qumodes on photonic quantum computing simulators, based on the binary classifier architecture proposed in "Continuous variable quantum neural networks" [1].They are composed of a classical feed-forward neural network, a quantum data encoding circuit, and a CV quantum neural network circuit.On a truncated MNIST dataset of 600 samples, a 4-qumode hybrid classifier achieves 100% training accuracy.
OntoINT: A Framework for Ontology Integration Based on Entity Linking from Heterogeneous Knowledge Sources

In artificial intelligence, knowledge representation can be a crucial field of work, particularly in the development of the query answering system. Ontology is used to talk about a particular space for query answering structure of shared knowledge. Ontology integration is necessary to fathom this issue of blended information. In the proposed OntoINT framework, the ontologies are subjected to spectral clustering and ANOVA–Jaccard similarity index under sunflower optimization as similarity measurement for Ontology matching. The performance of the proposed OntoINT is analysed and associated with other iterations of the OntoINT and baseline models. It was found that our approach is superior in terms of performance. It can be observed precision to be 93.97%, recall to be 96.02%, accuracy to be 94.89%, F-measure to be 94.98% and percentage of new and yet relevant concepts discovered to be 88.48% for OntoINT network for dataset 1 and precision to be 91.89%, recall to be 93.02%, accuracy to be 92.89%, F-measure to be 92.45% and percentage of new and yet relevant concepts discovered to be 84.78% for dataset 2.
Synopsis of Video Files Using Neural Networks

The following paper examines the development of a framework that allows the generation of video synopsis. That is a video file obtained by overlaying the main moving objects in a single scene. This allows for file length reduction thus optimization of the analysis and storage of video surveillance footage. The proposed framework is based on modern methods in the field of machine learning for the automatic recognition and localization of objects in the video frames, their segmentation, tracking, and merging on the extracted background. Machine learning models based on convolutional neural networks were used for this purpose.
Blockchain Technology and Its Applications: A Systematic Review of the Literature

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