Miracle-dz/UniFAR-FTUs huggingface dataset:
Recommending Third-party Library Updates with LSTM Neural Networks

During the lifecycle of a software project, oftentimes developers have the need to update third-party libraries (TPLs) from an old version to a newer one. This aims to keep their code up-to-date with the latest functionalities offered by the libraries. In practice, choosing the next version for a library is a daunting task since it is crucial to maintain a harmonious relationship with other libraries. We propose DeepLib, a novel approach to the recommendation of an upgrade plan for software projects with respect to library usage. We mine migration history to build matrices and train deep neural networks, which are eventually used to forecast the subsequent versions of the related libraries. We evaluate the framework on a dataset from the Maven Central Repository. The results show promising outcomes: DeepLib can recommend the next version for the library of interest, earning a high prediction accuracy.
Cyber Racism Detection Using Bidirectional Gated Recurrent Units and Word Embeddings

Racism is an unequal treatment based on race, color, origin, ethnicity or religion. It is often associated with rejection, inequality, and value judgment. A racist act, whether conscious or unconscious, goes beyond insult and aggression and leaves a devastating psychological effect on the victim. Although almost all laws around the world punish racist acts and speech, racist messages are on the rise on social networks. As a result, there is a strong need for reliable and accurate detectors of racist comments to identify the offenders and take appropriate punitive action against them. In this paper, we propose a model for the detection of racist statements in text messages by Bidirectional Gated Recurrent Units. For the word representation, we use different word embedding techniques, namely Word2Vec and GloVe. We show that this combination works well and provides a good level of detection. At the end of our study, we will suggest new horizons to improve the quality of our model.
Automatically Testing Containedness between Geometric Graph Classes defined by Inclusion, Exclusion and Transfer Axioms

We study classes of geometric graphs, which all correspond to the following structural characteristic. For each instance of a vertex set drawn from a universe of possible vertices, each pair of vertices is either required to be connected, forbidden to be connected, or existence or non-existence of an edge is undetermined. The conditions which require or forbid edges are universally quantified predicates defined over the vertex pair, and optionally over existence or non-existence of another edge originating at the vertex pair. We consider further a set of simple graph transformations, where the existence of an edge between two vertices is logically determined by the existence or non-existence of directed edges between both vertices in the original graph. We derive and prove the correctness of a logical expression, which is a necessary and sufficient condition for containedness relations between graph classes that are described this way. We apply the expression on classes of geometric graphs, which are used as theoretical wireless network graph models. The models are constructed from three base class types and intersection combinations of them, with some considered directly and some considered as symmetrized variants using two of the simple graph transformations. Our study then goes systematically over all possible graph classes resulting from those base classes and all possible simple graph transformations. We derive automatically containedness relations between those graph classes. Moreover, in those cases where containedness does not hold, we provide automatically derived counter examples.
SAFIR: a Semantic-Aware Neural Framework for IR

The semantic mismatch between query and document terms – i.e., the semantic gap – is a long-standing problem in Information Retrieval (IR). Two main linguistic features related to the semantic gap that can be exploited to improve retrieval are synonymy and polysemy. Recent works integrate knowledge from curated external resources into the learning process of neural language models to reduce the effect of the semantic gap. However, these knowledge-enhanced language models have been used in IR mostly for re-ranking. We propose the Semantic-Aware Neural Framework for IR (SAFIR), an unsupervised knowledge-enhanced neural framework explicitly tailored for IR. SAFIR jointly learns word, concept, and document representations from scratch. The learned representations encode both polysemy and synonymy to address the semantic gap. We investigate SAFIR application in the medical domain, where the semantic gap is prominent and there are many specialized and manually curated knowledge resources. The evaluation on shared test collections for medical retrieval shows the effectiveness of SAFIR to address the semantic gap.
Reading Songs: A Computational Analysis of Popular Songs lyrics

There is no doubt that certain songs are so easily liked by the public because of their melody. However, certain songs strike us for their lyrics, either because they convey an important (for us) meaning, or for their captivating sound when sung. Since the year 1958, the Billboard magazine held the special section Hot 100 , with a rank of the 100 most popular songs of the week. Exploiting this invaluable source regarding the musical taste of the past decades until our days, we perform an analysis over various aspects of popular songs lyrics, especially focusing on the question: what is the importance of lyrics, when classifying musical artists and genres? We find out that, as we expected, many artists are not immediately recognizable only by their lyrics; some of them, however, and especially if they belong to some specific genres (such as rap), stand out, opening to the possibility of further analysis over their styles and themes.
Understand Students Feedback Using Bi-Integrated CRF Model Based Target Extraction

Educational institutions showing interest to find the opinion of the students about their course and the instructors to enhance the teaching-learning process. For this, most research uses sentiment analysis to track students’ behavior. Traditional sentence-level sentiment analysis focuses on the whole sentence sentiment. Previous studies show that the sentiments alone are not enough to observe the feeling of the students because different words express different sentiments in a sentence. There is a need to extract the targets in a given sentence which helps to find the sentiment towards those targets. Target extraction is the subtask of targeted sentiment analysis. In this paper, we proposed the innovative model to find the targets of the given sentence using Bi-Integrated Conditional Random Fields (CRF). A Parallel fusion neural network model is designed to perform this task. We evaluate the model using the Michigan dataset and we build a dataset for target extraction from student reviews. The experimental results show that our proposed fusion model achieves better results compared to baseline models.
Extracting Relations from Italian Wikipedia using Unsupervised Information Extraction

In this paper, we describe WikiOIE, a framework for extracting relations from Wikipedia. The framework is based on UDPipe and the Universal Dependencies project for text processing. It easily allows customizing the information extraction (IE) approach to automatically extract triples (subject, predicate, object). In this work, we propose two unsupervised IE methods to extract triples from the Italian version of Wikipedia. The former is based only on PoS-tag patterns; the latter also uses syntactic dependencies. The extraction process is provided in JSON format and is used to build a simple web interface for searching and browsing the extracted triples. A preliminary evaluation conducted on a dataset sample shows promising results, although the approach is completely unsupervised.
Organizational Data Breach: Building Conscious Care Behavior in Incident Response

Organizational and end user data breaches are highly implicated by the role of information security conscious care behavior in respective incident responses. This research study draws upon the literature in the areas of information security, incident response, theory of planned behaviour, and protection motivation theory to expand and empirically validate a modified framework of information security conscious care behaviour formation. The applicability of the theoretical framework is shown through a case study labelled as a cyber-attack of unprecedented scale and sophistication in Singapore’s history to-date, the 2018 SingHealth data breach. The single in-depth case study observed information security awareness, policy, experience, attitude, subjective norms, perceived behavioral control, threat appraisal and self-efficacy as emerging prominently in the framework’s applicability in incident handling. The data analysis did not support threat severity relationship with conscious care behaviour. The findings from the above-mentioned observations are presented as possible key drivers in the shaping information security conscious care behaviour in real-world cyber incident management.
Garbled-Word Embeddings for Jumbled Text

“Aoccdrnig to a reasrech at Cmabrigde Uinervtisy, it deosn’t mttaer in waht oredr the ltteers in a wrod are, the olny itmopnrat tihng is taht the frist and lsat ltteer be at the rghit pclae. The rset can be a toatl mses and you can sitll raed it wouthit porbelm. Tihs is bcuseae the huamn mnid deos not raed ervey lteter by istlef, but the wrod as a wlohe”. We investigate the extent to which this phenomenon applies to computers as well. Our hypothesis is that computers are able to learn distributed word representations that are resilient to character reshuffling, without incurring a significant loss in performance in tasks that use these representations. If our hypothesis is confirmed, this may form the basis for a new and more efficient way of encoding character-based representations of text in deep learning, and one that may prove especially robust to misspellings, or to corruption of text due to OCR. This paper discusses some fundamental psycho-linguistic aspects that lie at the basis of the phenomenon we investigate, and reports on a preliminary proof of concept of the above idea.
Quality and Diversity of Recommender Systems Users' Choices: a Simulation Perspective

Recommender Systems (RSs) generate personalised suggestions for items and can influence collective users’ choices behaviour. The impact of operational RSs on users’ decisions can be assessed by analysing the actual choices’ diversity, and quality, e.g., the users’ satisfaction for their choices. But, in order to estimate the potential impact of an RS in new scenarios and for not yet deployed RSs, simulating user-system interactions can be valuable. We here illustrate a simulation framework consisting of users, items, and alternative RSs. We simulate users’ choices over consecutive time intervals, by assuming that an RS influences the users’ choices with recommendations. We measure global properties of the simulated choices, such as their diversity and quality. The obtained results, and the proposed simulation framework, can be used by a system designer in order to anticipate the effect of a candidate RS in its long term usage.
Stock-Price Forecasting Based on XGBoost and LSTM

Using time-series data analysis for stock-price forecasting (SPF) is complex and challenging because many factors can influence stock prices (e.g., inflation, seasonality, economic policy, societal behaviors). Such factors can be analyzed over time for SPF. Machine learning and deep learning have been shown to obtain better forecasts of stock prices than traditional approaches. This study, therefore, proposed a method to enhance the performance of an SPF system based on advanced machine learning and deep learning approaches. First, we applied extreme gradient boosting as a feature-selection technique to extract important features from high-dimensional time-series data and remove redundant features. Then, we fed selected features into a deep long short-term memory (LSTM) network to forecast stock prices. The deep LSTM network was used to reflect the temporal nature of the input time series and fully exploit future contextual information. The complex structure enables this network to capture more stochasticity within the stock price. The method does not change when applied to stock data or Forex data. Experimental results based on a Forex dataset covering 2008–2018 showed that our approach outperformed the baseline autoregressive integrated moving average approach with regard to mean absolute error, mean squared error, and root-mean-square error.
Combining Mitigation Treatments against Biases in Personalized Rankings: Use Case on Item Popularity

Historical interactions leveraged by recommender systems are often non-uniformly distributed across items. Though they are of interest for consumers, certain items end up therefore being biasedly under-recommended. Existing treatments for mitigating these biases act at a single step of the pipeline (either pre-, in, or post-processing), and it remains unanswered whether simultaneously introducing treatments throughout the pipeline leads to a better mitigation. In this paper, we analyze the impact of bias treatments along the steps of the pipeline under a use case on popularity bias. Experiments show that, with small losses in accuracy, the combination of treatments leads to better trade-offs than treatments applied separately. Our findings call for treatments rooting out bias at different steps simultaneously.
Rewards and Performance of Health Centres IV workers in Greater Mbarara District

The purpose of this paper is to study the relationship between rewards, and performance of Health Centre IV workers in greater Mbarara District. The constructs of rewards include; salary, performance bonus, recognition and promotion. This study used cross-sectional, explanatory and correlational approaches. Health center IV workers data were collected by means of a questionnaire survey from a sample of 200 health workers from 11 health centres from greater Mbarara district. Structural equation modelling was used to test the hypotheses. Results show that rewards significantly affect health workers’ performances. Therefore, once health centres IV have appropriate reward strategies for their health workers in health facilities, they are likely to work hard by meeting deadlines, being available at their work station and they would eventually enhance on job performance. It is further recommended that all health policy makers and implementators at both Ministry of health and central government should always emphasize the general wellbeing of all health workers in every health facility.
Two Might Do: A Beat-by-Beat Classification of Cardiac Abnormalities Using Deep Learning with Domain-Specific Features

This paper proposes an efficient convolutional neural network to detect 26 different classes of cardiac activities from different numbers of leads in the Phys-ionetlComputing data in the Cardiology Challenge 2021. The proposed CNN architecture is designed to utilize heart rate variation features from ECG recordings and wave-form morphologies of heartbeats simultaneously. Also, the designed architecture is flexible for the implementation of a different number of leads with a varied length of ECG recordings. The proposed algorithm achieved a score of 0.38 using only 2 channels ofECG on all recordings for the hidden test set of the challenge, placing us 21, 20, 19, 20, 20th (Team name: METU-19) out of 39 teams for 12, 6, 4, 3, and 2-leads respectively. These results show the potential of an efficient, flexible novel neural network for beat-by-beat classification of raw ECG recordings to a complex multi-class multi-label classification problem.
Speech Enhancement Using Deep Learning Methods: A Review

Speech enhancement, which aims to recover the clean speech of the corrupted signal, plays an important role in the digital speech signal processing. According to the type of degradation and noise in the speech signal, approaches to speech enhancement vary. Thus, the research topic remains challenging in practice, specifically when dealing with highly non-stationary noise and reverberation. Recent advance of deep learning technologies has provided great support for the progress in speech enhancement research field. Deep learning has been known to outperform the statistical model used in the conventional speech enhancement. Hence, it deserves a dedicated survey. In this review, we described the advantages and disadvantages of recent deep learning approaches. We also discussed challenges and trends of this field. From the reviewed works, we concluded that the trend of the deep learning architecture has shifted from the standard deep neural network (DNN) to convolutional neural network (CNN), which can efficiently learn temporal information of speech signal, and generative adversarial network (GAN), that utilize two networks training.
JPPE 24권4호_4차편집(0831).hwp

Background and objective: There is growing concern about the effects of modern society on mental health, coming with Covid-19-related caveats on depression, anxiety, and stress (DAS). This can be a subject to provide alternative methods which alleviate DAS. In line with this context, sensory gardens are widely acknowledged to stimulate the five major senses (sight, sound, taste, touch, and smell) and can have a significant (positive) impact on mental health. However, there is limited empirical evidence on the effect of these gardens with regard to alleviating DAS particularly with respect to urban society This is a gab in knowledge how such limits can be addressed. Accordingly, this present study is clearly needed in order to verify if there are any correlations between sensory gardens and (positive) effects on DAS. The aim of this study was therefore to understand current levels of DAS in a high density building with a sensory garden in a courtyard and determine correlations between experiences in the sensory courtyard and levels of DAS. Methods: The Depression Anxiety and Stress Scale (DASS-21) was employed to test the level of DAS. Results: Additionally, 13 different factors associated with experiences in the building, including the stimulation of the five major senses in the sensory courtyard, were measured to reveal their contribution to mitigating depression, anxiety, and stress. It is noted that the average levels of DAS were 7.91, 7.77 and 9.01 respectively indicating that the mental health of participants requires mental health management. However, results show that factors associated with the sensory courtyard could improve mental health. For example, new social relationships, walking with colleagues, and the stimulation of each of the five major senses in the sensory garden could improve DAS. Conclusion: This illustrates that external experiences are more effective at mitigating depression, anxiety, and stress than internal ones. Factor analysis revealed four components: stimulating the five senses; internal and external facilities; internet-based device use; and new social relationships. There was a strong positive correlation between new social relationships and walking with colleagues. There were also strong positive correlations among the stimulation of each of the five senses. In conclusion, there are strong indications that sensory courtyards can help alleviate DAS and should therefore be promoted, particularly in the current Covid-19 situation wherein the physical and mental health of the public at large are threatened. Accordingly, courtyard design should be rethought in light of the relationship between the positive impact of sensory gardens and mental health.
The use of traffic data from automatic monitoring systems to obtain day-to-day time series of vehicle traffic volumes and origin-destination flows in urban networks

The understanding of travel pattern dynamics in the urban environment is essential for the transportation systems planning and operation. Recently, the increasing availability of massive traffic data from traffic monitoring systems, including automatic number plate recognition systems (TMS-ANPR), can allow an understanding of the day-to-day variability of traffic flows in large urban network systems. However, to enhance the data quality for analysis, it is essential to carry out a previous data treatment. This work presents a method for treatment of TMS-ANPR data. The main product of this data treatment are the day-to-day time series of traffic volumes and OD flows for different periods of a typical day, allowing the analysis of the multiday dynamic of travel behavior and of the model assumptions stated in the literature about such dynamic behavior. The proposed method, which can be applied to any type of TMS-ANPR, was applied to generate time series data from the TMS-ANPR of Fortaleza city, contributing to identify suspicious and atypical data, to define representative patterns of vehicular traffic and to estimate series of OD flows.
Female entrepreneurship and evolution in SMEs in the fashion system in Colombia

The fashion industry in Colombia is one of the sectors who are showing an exponential grow in the last times with profits for more than 20.2 billion of colombian pesos, approximately 1.3 million of units sold. Under those perspective, this article will focus on how the female enterprises in the fashion system are developing in recent years with examples from highlights cases where the women have an important role as entrepreneur instead in covid times. For this study, the authors made a selection of three companies from the Colombian fashion system led by women. As a result, it was possible identify that those organizations establish the leader as a collaborator and an important part of a business, as well, justified in his/her works in the way to create and share an innovative value necessary to keep the company into the high standards of competitiveness in local and international market.
The Moderating Effect of Urbanization on the Association between Socioeconomic Status and Physical Activity in Chinese Adults: A Cross-Sectional Study

PURPOSE: Rapid urbanization has witnessed a dramatic decrease in physical activity. The purpose of this research was to explore the vulnerable inactivity group influenced by urbanization in China.METHODS: Cross-sectional data from the 2015 China Health and Nutrition Survey were utilized. A total of 7,319 households, including 20,914 individuals within 12 provinces, were pooled. Seven types of physical activity were included in statistical testing. Community-level urbanization was measured on an urbanicity scale consisting of 12 dimensions. Cross-level logistic regression analysis was conducted to estimate the direct and interactive effects of individual socioeconomic statuses and community-level urbanization on a leisure time physical activity (LTPA). After removing incomplete responses, 8,617 samples were selected for the statistical analyses.RESULTS: Community-level urbanization was positively related to participation probability in the LTPA. With an increase of one standard deviation (SD) in the level of urbanization, the odds of participating in the LTPA increased by approximately six times in the most urbanized areas compared with the least urbanized areas. The effect of employment status on the probability of participation in the LTPA differed across the levels of urbanization in each community. For white-collar workers who resided in the most urbanized communities, the odds of participating in the LTPA decreased by approximately 46% compared to their peers in the least urbanized communities.CONCLUSIONS: Community-level urbanization should be addressed when developing interventions and health policies to promote physical activity during leisure time, especially for individuals who are white-collar workers living in urbanized communities.