Reviewerly/exHarmony huggingface dataset:
A new similarity-based multicriteria recommendation algorithm based onautoencoders

Recommender systems provide their users an efficient way to handle information overload problem by offering personalized suggestions.Traditional recommender systems are based on two-dimensional user-item preference matrix constructed depending on the users' overall evaluations over items.However, they have begun to present their preferences under various circumstances.Thus, traditional recommendation techniques fail to process multicriteria ratings during the recommendation process.Multicriteria recommender systems are an extension of traditional recommender systems that utilize multicriteria-based user preferences.Multicriteria recommender systems provide more personalized and accurate predictions compared to traditional recommender systems.However, the increased amount of data dimension causes sparsity to be a major problem of such systems.Especially, the similarity-based multicriteria recommender systems may fail to find similar neighbors to an active user due to the lack of corated items among users.Therefore, we propose a new similarity-based multicriteria collaborative filtering approach based on autoencoders.In order to handle sparsity, the proposed method extracts nonlinear, low-dimensional, dense features from raw and sparse users'/items' preferences.Our experimental outcomes show that the proposed work can amortize the negative impacts of sparsity over the accuracy comparing with the state-of-the-art multicriteria recommendation techniques.
Lexical Analysis Using Regular Expressions for Information Retrieval from a Legal Corpus

This article presents part of the work carried out in the framework of a research that aims to optimize an Information Retrieval System, by means of its specialization for the retrieval of legal documents. One of the fundamental sub-processes in this type of system is lexical analysis, in which indexing techniques are applied. These techniques involve extracting a series of concepts representative of the topics covered in a document, and then using them as access points for retrieval. This article describes a proposal for the extraction of information and identification of dates and references to named entities, such as File No., Resolution No., Article No. of Law XXX, which refer to the legal norm in force and are widely used in different judicial documents. For the recognition of such named entities, the process employed the definition of patterns using Regular Expressions, a way of representing a language in a synthetic form, applying a set of rules. From this, the terms obtained are stored in a matrix of terms/documents. This paper also describes the algorithms used during the validation of the proposed solution and presents the experimental results that show that by applying this method a significant reduction in the size of the inputs to the matrix can be achieved.
Short-Term Prediction of Photovoltaic Power Based on Fusion Device Feature-Transfer

To attain the goal of carbon peaking and carbon neutralization, the inevitable choice is the open sharing of power data and connection to the grid of high-permeability renewable energy. However, this approach is hindered by the lack of training data for predicting new grid-connected PV power stations. To overcome this problem, this work uses open and shared power data as input for a short-term PV-power-prediction model based on feature transfer learning to facilitate the generalization of the PV-power-prediction model to multiple PV-power stations. The proposed model integrates a structure model, heat-dissipation conditions, and the loss coefficients of PV modules. Clear-Sky entropy, characterizes seasonal and weather data features, describes the main meteorological characteristics at the PV power station. Taking gate recurrent unit neural networks as the framework, the open and shared PV-power data as the source-domain training label, and a small quantity of power data from a new grid-connected PV power station as the target-domain training label, the neural network hidden layer is shared between the target domain and the source domain. The fully connected layer is established in the target domain, and the regularization constraint is introduced to fine-tune and suppress the overfitting in feature transfer. The prediction of PV power is completed by using the actual power data of PV power stations. The average measures of the normalized root mean square error (NRMSE), the normalized mean absolute percentage error (NMAPE), and the normalized maximum absolute percentage error (NLAE) for the model decrease by 15%, 12%, and 35%, respectively, which reflects a much greater adaptability than is possible with other methods. These results show that the proposed method is highly generalizable to different types of PV devices and operating environments that offer insufficient training data.
Interactive Mining of User-Preferred Co-location Patterns Based on SVM

Co-location pattern mining plays an important role in spatial data mining. With the rapid growth of spatial datasets, the usefulness of co-location patterns is strongly limited by the huge amount of the discovered patterns. To overcome this drawback, several statistics-based methods have been proposed to reduce the number of discovered co-location patterns. However, these methods cannot guarantee that their mined patterns are really user-preferred. Therefore, it is crucial to help users discover the co-location patterns they preferred through effective interactions. This paper proposes a newly interactive approach based on SVM, in order to discover user-preferred co-location patterns. First, we presented an originally interactive framework to help the user discover his/her preferred co-location patterns. Then, we designed a filtering algorithm with a small part of patterns as the training set of the SVM model for the provision of each interactive process, by which a high efficiency could be achieved by the optimization of the SVM model. Finally, the system was verified on both the real data sets and the synthetic data sets, accompanying with the 80% prediction accuracy.
Robust Home Alone Security System Using PIR Sensor and Face Recognition

CCTV-based video monitoring technology is one of the fastest growing security technologies markets. The existing video monitoring systems are, however, still not in a position to be used to prevent crime. For public safety purposes, large networks of cameras are increasingly deployed in public places like Residential Buildings, College Campus, offices, airports, railway stations, and shopping malls. Such systems are primarily dependent on human observers and are therefore limited over long periods by factors such as exhaustion and monitoring. In order to overcome this constraint, "intelligent" systems are required, which can highlight the critical data and remove normal conditions that are not a safety hazard. We propose a model utilizing machine learning techniques in order to build these smart systems. This research aims to create an application in real time, which is necessary for labs, places of work or homes where human detection and Recognition will be done for human safety
Model-based Software Defect Prediction from Software Quality Characterized Code Features by using Stacking Ensemble Learning

Software defect prediction (SDP) is critical in guaranteeing software cost reduction and quality improvement while building a software system.Thus, software defect prediction in the early stages is an essential intrigue in the software engineering discipline.We proposed a stacking ensemble learning method to improve SDP performance based on software quality-defined code characteristics.Stacking combines the base classifiers by using a meta-classifier that learns base-classifiers output.It has certain advantages, such as easy implementation and combining classifiers by investigating various inducers.The proposed method's performance has been evaluated and compared with different Machine Learning (ML) classifiers on ivy2.0,tomcat, and velocity1.6datasets available in PROMISE.The experimental findings revealed that the proposed approach has better prediction recall, accuracy, precision, AUC-ROC, and f-measure.
Hierarchical Deep Multi-task Learning for Classification of Patient Diagnoses

Recent years have witnessed an increased interest in the biomedical research community in developing machine learning models and methods that can automatically assign diagnostic codes (ICD) to patient stays based on the information in their Electronic Health Records (EHR). However, despite the recent advances, accurate automatic classification of diagnostic codes continues to face challenges, especially for low-prior diagnostic codes. To alleviate the problem, we propose to leverage information in the diagnostic hierarchy and better utilize the dependencies among diseases in this hierarchy. We develop a new hierarchical deep multi-task learning method that learns classification models for multiple diagnostic codes at the different levels of abstraction in the disease hierarchy while allowing the transfer of information from high-level nodes, more general diagnoses codes to the low-level ones, more specific diagnostic codes. After that, we refine the initial hierarchical model by utilizing the relations and information that can discriminate better between competing diseases. Our empirical results show that our new method and its refinement outperform baseline machine learning architectures that do not leverage the hierarchical structure of target diagnoses tasks or disease-disease relationships.
Sentiment Analysis of Economic Text: A Lexicon-based Approach

The goal of this paper is to propose a dictionary specifically designed for textual applications in economics. We construct the dictionary with two important characteristics: 1) to have a wide coverage of terms typically used in documents discussing economic and financial concepts, and 2) to provide a human-annotated sentiment score in the range [-1,1]. The sentiment score is a useful feature when the interest is to weight words by their sentiment content as opposed to categorizing terms in positive and negative. We use the dictionary to construct a measure of economic pessimism and show that it captures the business cycle and correlates with measures of economic and financial uncertainty.
Augmented Reality: Beyond Interaction

In Postphenomenology, various human-technology-world relations have been inve- stigated. They provide an understanding of how humans and technological artifacts shape each other and how human intentionality is mediated by technology. As discus- sed in this paper digital technology as it has developed over the last twenty years has not received the attention it deserves. The reflections on smart environments and augmented reality technology show a lack of insight into this research and are based on (premature) commercial applications that are not representative of this research. We take a closer look at this research from a human-computer interaction perspective and use this premise to comment on these relations and reflect on their values.
From Simultaneous to Streaming Machine Translation by Leveraging Streaming History

Simultaneous Machine Translation is the task of incrementally translating an input sentence before it is fully available.Currently, simultaneous translation is carried out by translating each sentence independently of the previously translated text.More generally, Streaming MT can be understood as an extension of Simultaneous MT to the incremental translation of a continuous input text stream.In this work, a state-of-the-art simultaneous sentencelevel MT system is extended to the streaming setup by leveraging the streaming history.Extensive empirical results are reported on IWSLT Translation Tasks, showing that leveraging the streaming history leads to significant quality gains.In particular, the proposed system proves to compare favorably to the best performing systems.
Shattering Inequalities for Learning Optimal Decision Trees

Recently, mixed-integer programming (MIP) techniques have been applied to learn optimal decision trees. Empirical research has shown that optimal trees typically have better out-of-sample performance than heuristic approaches such as CART. However, the underlying MIP formulations often suffer from slow runtimes, due to weak linear programming (LP) relaxations. In this paper, we first propose a new MIP formulation for learning optimal decision trees with multivariate branching rules and no assumptions on the feature types. Our formulation crucially employs binary variables expressing how each observation is routed throughout the entire tree. We then introduce a new class of valid inequalities for learning optimal multivariate decision trees. Each inequality encodes an inclusion-minimal set of points that cannot be shattered by a multivariate split, and in the context of a MIP formulation, the inequalities are sparse, involving at most the number of features plus two variables. We leverage these valid inequalities within a Benders-like decomposition, where the master problem determines how to route each observation to a leaf node to minimize misclassification error, and the subproblem checks whether, for each branch node of the decision tree, it is possible to construct a multivariate split that realizes the given routing of observations; if not, the subproblem adds at least one of our valid inequalities to the master problem. We demonstrate through numerical experiments that our MIP approach outperforms (in terms of training accuracy, testing accuracy, solution time, and relative gap) two other popular MIP formulations, and is able to improve both in and out-of-sample performance, while remaining competitive in terms of solution time to a wide range of popular approaches from the literature.
Adaptation of University Students to the Educational Process During the Post Quarantine Period of Study

Students’ academic performance has always been the focus of the educational process of the university. The main idea of the proposed study is the unacceptability of lowering the educational process quality while the students are switching from online to offline classes. Actually, with the return of students to the university classrooms after a long quarantine various issues emerged referring to the the educational process that require prompt solutions. The article discusses the process of adaptation of university students after a long quarantine associated with the alternation of distance and classroom learning formats. The material of the study can be extremely relevant for numerous universities, as it allows us to look at the new didactic phenomenon such as distance learning, as well as students’ return to traditional format of education from another point of view. The research conducted by the authors of the article is based on the results of a survey of students of one of the Russian universities.
Proposition of a method to design tailor-made knowledge management systems for small and medium enterprises

The aim of this paper is to propose a knowledge management system design method for small and medium enterprises that addresses the problems encountered on the existing ones. A literature review highlighted 5 main concepts that have a crucial role in knowledge management system design. To take these recommendations into account, a knowledge management system design method was designed. It uses the small and medium enterprises characteristics, needs and problems to select adapted knowledge management tools and practices, and offers a comprehensive and tailor-made knowledge management system. This method was used to design a knowledge management system for a high-tech small and medium enterprises from the navigation sector. It allowed to choose from the adapted knowledge management tools and practices and to write a set of requirements for the knowledge management system while taking the user into account, thus easing the knowledge management system implementation and ensuring its sustainability.
Revisiting the Effects of Leakage on Dependency Parsing

Recent work by Søgaard (2020) showed that, treebank size aside, overlap between training and test graphs (termed leakage) explains more of the observed variation in dependency parsing performance than other explanations.In this work we revisit this claim, testing it on more models and languages.We find that it only holds for zero-shot cross-lingual settings.We then propose a more fine-grained measure of such leakage which, unlike the original measure, not only explains but also correlates with observed performance variation.
The gender of smart charging

With the growing focus on the home as a site for life, work, and energy generation and demand, home batteries of many kinds are increasingly ubiquitous. Batteries play a dual role of everyday emerging technologies (which can be used for something) and infrastructure (which enables other things). This article considers how batteries become technologies and infrastructures both for the accomplishment of practical everyday life activities and routines in the present, and imagining future everyday life. A design anthropological and ethnographic analysis of 72 Australian households with a focus on four demographically diverse women is used to investigate the gendered discourses concerning the future of battery charging; new and emerging everyday gendered techniques and routines of battery charging and their entanglements with women’s priorities for socialities of care and wellbeing. The implications of these findings are considered through the lens of recent feminist approaches to technology and technology design, together with future charging scenarios. <em><strong>Policy relevance</strong></em> Dominant narratives in the technology and energy industries simplify the future of battery charging in two key ways. First, they assume that automated technological solutions will be able to organise battery use and charging in ways that will flatten energy supply and demand across households and other charging sites. Second, they tend to see energy consumers as typically able to perform everyday life tasks in such ways that are typically masculine. Ethnographic research suggests that both of these assumptions are misguided because they are misaligned with everyday life and gender. The implications for policy are that one needs to attend to diverse gendered experiences and uses of batteries and charging to understand how these are embedded in everyday life, and to develop plausible ways of designing inclusive future battery charging systems and technologies, with diverse gendered people in real everyday day life circumstances and for realistic, ethical futures.
Automatic Generation of Distractors for Fill-in-the-Blank Exercises with Round-Trip Neural Machine Translation

In a fill-in-the-blank exercise, a student is presented with a carrier sentence with one word hidden, and a multiple-choice list that includes the correct answer and several inappropriate options, called distractors.We propose to automatically generate distractors using roundtrip neural machine translation: the carrier sentence is translated from English into another (pivot) language and back, and distractors are produced by aligning the original sentence and its round-trip translation.We show that using hundreds of translations for a given sentence allows us to generate a rich set of challenging distractors.Further, using multiple pivot languages produces a diverse set of candidates.The distractors are evaluated against a real corpus of cloze exercises and checked manually for validity.We demonstrate that the proposed method significantly outperforms two strong baselines. 1 Carrier sentenceAre these old plates of ______ importance or can I put them into storage?
XInfoTabS: Evaluating Multilingual Tabular Natural Language Inference

The ability to reason about tabular or semistructured knowledge is a fundamental problem for today's Natural Language Processing (NLP) systems.While significant progress has been achieved in the direction of tabular reasoning, these advances are limited to English due to the absence of multilingual benchmark datasets for semi-structured data.In this paper, we use machine translation methods to construct a multilingual tabular natural language inference (TNLI) dataset, namely XINFOTABS, which expands the English TNLI dataset of INFOTABS to ten diverse languages.We also present several baselines for multilingual tabular reasoning, e.g., machine translation-based methods and cross-lingual TNLI.We discover that the XINFOTABS evaluation suite is both practical and challenging.As a result, this dataset will contribute to increased linguistic inclusion in tabular reasoning research and applications.
Phrase-aware Unsupervised Constituency Parsing

Recent studies have achieved inspiring success in unsupervised grammar induction using masked language modeling (MLM) as the proxy task.Despite their high accuracy in identifying low-level structures, prior arts tend to struggle in capturing high-level structures like clauses, since the MLM task usually only requires information from local context.In this work, we revisit LM-based constituency parsing from a phrase-centered perspective.Inspired by the natural reading process of human readers, we propose to regularize the parser with phrases extracted by an unsupervised phrase tagger to help the LM model quickly manage low-level structures.For a better understanding of high-level structures, we propose a phrase-guided masking strategy for LM to emphasize more on reconstructing nonphrase words.We show that the initial phrase regularization serves as an effective bootstrap, and phrase-guided masking improves the identification of high-level structures.Experiments on the public benchmark with two different backbone models demonstrate the effectiveness and generality of our method.
Hybrid Deep Learning Approach for Sentiment Classification of Malayalam Tweets

Social media content in regional languages is ex-panding from day to day. People use different social media platforms to express their suggestions and thoughts in their native languages. Sentiment Analysis (SA) is the known procedure for identifying the hidden sentiment present in the sentences for categorizing it as positive, negative, or neutral. The SA of Indian languages is challenging due to the unavailability of benchmark datasets and lexical resources. The analysis has been done using lexicon, Machine Learning (ML), and Deep Learning (DL) techniques. In this work, the baseline models and hybrid models of Deep Neural Network (DNN) architecture have been used for the classification of Malayalam tweets as positive, negative and neutral. Since, sentiment-tagged dataset for Malayalam is not readily available, the analysis has been done on the manually created dataset and translated Kaggle dataset. The hybrid models used in this study combine Convolutional Neural Networks (CNN) with variants of Recurrent Neural Net-works (RNN). The RNN models are Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM) and Gated Recurrent Unit (GRU). All these hybrid models improve the performance of Sentiment Classification (SC) compared to baseline models LSTM, Bi-LSTM and GRU.