Reviewerly/exHarmony huggingface dataset:
Question Answering System with Enhancing Sentence Embedding

In order to improve the semantic understanding of the input question in the question answering system, a question answering system based on knowledge representation is constructed, which is composed of named entity recognition and question matching. The named entity recognition method based on Bert+BiLSTM+CRF is used, and the BGCNN model proposed in this paper is used for question matching. BGCNN is a model combining Bert, neural network and Siamese network. The average F1 value of the system on the financial data set is 0.9007, which is not a small improvement compared with the previous model.
Deep Residual Networks for Human Activity Recognition based on Biosignals from Wearable Devices

Wearable inertial sensors are frequently employed in the area of human activity recognition (HAR), considering they provide the most meaningful time-series data available for non-visual datasets. Nevertheless, when just motion detectors are supplied, the recognition ability of individual complex activity detection is restricted. This study examines bio-signal data in order to improve the outcomes of complex HAR. Electrocardiogram (ECG) and photoplethysmogram (PPG) data are employed separately to identify complex HAR. We employed a benchmark bio-signal dataset (PPG-DaLiA) comprising ECG, PPG, and tri-axial accelerometer (3D-ACC) signals obtained from 15 people as they performed a series of uncomplicated and complex everyday activities. After incorporating early fusion conditions, we trained and evaluated deep learning models using 5-fold cross-validation. Our findings suggest that integrating biosignal characteristics with the 3D-ACC improves the classifier's effectiveness F1-scores by 2.17% and 2.27%, respectively, for ECG and PPG.
Efficiency-reinforced Learning with Auxiliary Depth Reconstruction for Autonomous Navigation of Mobile Devices

In this paper, we take Unmanned Aerial Vehicles (UAVs) as the mobile devices to study the problem of autonomous navigation since UAVs have been adopted as intelligent vehicles for executing complex tasks such as bridge structure examination, crowd estimation, target searching, and package delivery. As Deep Reinforcement Learning (DRL) has achieved great success in many control tasks, it is envisaged to exploit DRL for autonomous navigation. Nevertheless, as the navigation path becomes distant, searching in a large number of states and action spaces becomes very challenging to DRL. In this paper, we provide a novel reinforcement learning framework to facilitate the autonomous navigation in complicated environments by jointly considering the temporal abstractions and policy efficiency to dynamically select the frequency of the action decisions with the efficiency regularization. Moreover, to bootstrap the learning procedure, we further add an auxiliary task of depth map reconstruction to accelerate the learning process. Experimental results on 3D UAV simulator and DeepMind Lab environments manifest that the proposed framework improves the state-of-the-art methods in terms of success rates in different environments.
CogAx: Early Assessment of Cognitive and Functional Impairment from Accelerometry

An individual&#x2019;s cognitive and functional abilities are commonly assessed through physical and mental status examination, observational performance measures, surveys and proxy reports of symptoms. These strategies are not ideal for early impairment detection as the individual needs to be present physically at the clinic to avail the assessments, especially for older adults who require assistance from a caregiver, and experience mobility, cognitive and functional disabilities from neurodegenerative disorders. Moreover, these strategies rely on self-reporting and proxy reports for evaluation which often leads to under-reporting of symptoms and decrease the validity of these measures. We argue that an early assessment of functional, and cognitive health impairment can be obtained from the individual&#x2019;s daily activities captured through accelerometry. In this work, we postulate to learn high-level motion related representations from accelerometer data to better correlate with underlying functional and cognitive health parameters of older adults using a contrastive and multi-task learning framework. In particular, we posit a novel indicator, Impairment Indicator using the proposed multi-task learning framework that can indicate functional or cognitive decline as neurodegenerative disease progresses. An extensive 24-hour data collection from 25 older adults with the clinician in-the-loop was carried out in a retirement community center with IRB approval. We collected the activity patterns using wearables in their homes in addition to survey-based assessments and observational performance measures recorded by a clinical evaluator to infer their current cognitive and functional impairment status. Our evaluation on the acquired dataset reveals that the representations learned using contrastive learning aids in improving the detection of activities, activity performance score, and stage of dementia to 92%, 97%, and 98%, respectively.
The Naive Bayes Algorithm in Predicting the Spread of the Omicron Variant of Covid-19 in Indonesia: Implementation and Analysis

Indonesia was struck by an epidemic of the corona virus in the start of March 2020, according to official reports (covid). Indonesia continues to see a rise in the number of cases of covid-19 spreading on a daily basis. The general people are urged to engage in social distancing in order to disrupt the development of COVID-19, which has spread across Indonesia's numerous areas. For this reason, this research was undertaken as a preemptive step against the Covid-19 pandemic by estimating the extent of the Omicron variety of Covid-19's spread around the world, with a particular emphasis on Indonesia. The research methodologies employed in this study were problem analysis and literature review, as well as data gathering and execution. The Naive Bayes technique is thought to be capable of estimating the degree of COVID-19 dissemination in Indonesia. The results of the Naive Bayes method classification study revealed that 16 data from 33 data tested for Covid-19 cases per province were correctly classified with an accuracy of 46.4252 percent, while 16 data from 33 data tested for Covid-19 cases per province were misclassified with an accuracy of 46.4252 percent.
A Comprehensive Analysis of Cloud Computing Services

In today’s era, the term cloud computing is growing quickly which has purchased critical changes in different areas of India. It is a broadly spreading processing model that has changed the framework of Information Technology and the method of conveying the administration. On request, benefits are given by cloud computing. One of the essential administrations that cloud computing gives is data storing. The client can get to their information when the cloud specialist co-op moderates the information. Likewise, through cloud computing suppliers, cloud computing is generally utilized by Information Technology based new businesses which is a benefit in the advancement of their organization. In any case, its development is quickly expanding in the instruction area too. The utilization and exploration of distributed computing in training were directed through subjective philosophy. This report is an examination of the activity of cloud computing in leadership. A short overview was additionally performed to distinguish and dis-sect the benefits and hazards which distributed computing might have in the area of training. This investigation is skilled to achieve its advantages about the prologue to cloud computing in the administration of instruction.
Prediction Accuracy of Artificial Neural Networks in Thermal Management Applications Subject to Neural Network Architectures

The present study investigates the dependency of prediction accuracy of an artificial neural network (ANN) on the network architecture using 65 different neural networks from seven architecture patterns.The accuracy of the ANNs is compared based on their capability to predict heat transfer coefficients of air-cooled heat sinks operating in laminar flow.Scattered input data is used for training the networks to make the modelling more realistic and closer to practical applications.The input variables for the neural network are heat sink width, channel height, channel length, number of channels, fin thickness, and Reynolds number.The output is heat transfer coefficient.The training process for all ANNs is performed using ReLU as the activation function.The accuracy of the neural networks is evaluated by the root mean square error.It is found that the prediction accuracy of an ANN is strongly dictated by the optimization of the network architecture, which corresponds to the proper number of hidden layers and the number of neurons at each layer.The most accurate architecture in the present study predicts heat transfer coefficients of 60% and 86% of heat sinks within ±10% and ±20% of the true values, respectively.However, an ANN with an unoptimized architecture results in a substantially reduced accuracy such that it predicts heat transfer coefficients of only 19% and 30% of heat sinks within ±10% and ±20% of the true values, respectively.
PEER: A Collaborative Language Model

Textual content is often the output of a collaborative writing process: We start with an initial draft, ask for suggestions, and repeatedly make changes. Agnostic of this process, today's language models are trained to generate only the final result. As a consequence, they lack several abilities crucial for collaborative writing: They are unable to update existing texts, difficult to control and incapable of verbally planning or explaining their actions. To address these shortcomings, we introduce PEER, a collaborative language model that is trained to imitate the entire writing process itself: PEER can write drafts, add suggestions, propose edits and provide explanations for its actions. Crucially, we train multiple instances of PEER able to infill various parts of the writing process, enabling the use of self-training techniques for increasing the quality, amount and diversity of training data. This unlocks PEER's full potential by making it applicable in domains for which no edit histories are available and improving its ability to follow instructions, to write useful comments, and to explain its actions. We show that PEER achieves strong performance across various domains and editing tasks.
xVDB: A High-Coverage Approach for Constructing a Vulnerability Database

Security patches play an important role in detecting and fixing one-day vulnerabilities.However, collecting abundant security patches from diverse data sources is not a simple task.This is because (1) each data source provides vulnerability information in a different way and (2) many security patches cannot be directly collected from Common Vulnerabilities and Exposures (CVE) information (e.g., National Vulnerability Database (NVD) references).In this paper, we propose a high-coverage approach that collects known security patches by tracking multiple data sources.Specifically, we considered the following three data sources: repositories (e.g., GitHub), issue trackers (e.g., Bugzilla), and Q&A sites (e.g., Stack Overflow).From the data sources, we gather even security patches that cannot be collected by considering only CVE information (i.e., previously untracked security patches).In our experiments, we collected 12,432 CVE patches from repositories and issue trackers, and 12,458 insecure posts from Q&A sites.We could collect at least four times more CVE patches than those collected in existing approaches, which demonstrates the efficacy of our approach.The collected security patches serves as a database on a public website (i.e., IoTcube) to proceed with the detection of vulnerable code clones.
USING CANVA AS MEDIA FOR ENGLISH LANGUAGE TEACHING (ELT) IN DEVELOPING CREATIVITY FOR INFORMATICS STUDENTS’

This research simulates the use of Canva as a medium for English Language Teaching (ELT) in developing the creativity of Informatics Students in English for Working Course at ITB AAS Indonesia. This research is descriptive qualitative. The result analysis shows that educators (teachers or lecturers) can use the Canva application in English Language Teaching (ELT) such as creating materials for job vacancies, cover letter/job application letter, curriculum vitae (CV) or resume in English for Working subject to the students. In creating materials for “Job Vacancy”, we can use a template or layout of brochures, flyers, or announcements. Canva is very suitable to be used for learning basic competencies about functional texts, including functional texts to make brochures/flyer/announcement, letter, and curriculum vitae (CV) or resume by editing the existing text in the Canva application and then changing it with their words according to the situation requested by the teacher. Canva is hoped can develop and grow students' creativity in the field of technology by designing their designs, adding text, adding images, or adding photos. Canva application is very easy to practice and suitable for students majoring in Informatics students who want to learn to make simple graphic designs creatively and innovatively.
Matching Theory-based Recommender Systems in Online Dating

Online dating platforms provide people with the opportunity to find a partner. Recommender systems in online dating platforms suggest one side of users to the other side of users. We discuss the potential interactions between reciprocal recommender systems (RRSs) and matching theory. We present our ongoing project to deploy a matching theory-based recommender system (MTRS) in a real-world online dating platform.
Mineral potential modelling for gold mineralization within&#x0D; the Mesozoic depressions in the Central Aldan ore-placer region&#x0D; (on the example of the Upper Yakokut ore field)

In this work, we described the patterns of formation&#x0D; and distribution of gold occurrences of various morphological&#x0D; types in the terrigenous sequence of the Upper&#x0D; Yakokutsk graben depression. We characterized the&#x0D; zones of ore-bearing dislocations with increased permeability&#x0D; within the ore region confined by regional faults.&#x0D; Such zones with a frame-block structure forming the&#x0D; nodes of conjugation of regional structures are favourable&#x0D; for their repeated resumption and correspond to the&#x0D; most prospective areas. The study of native gold from&#x0D; ores and surrounding metasomatites showed a large&#x0D; variation in its fineness, based on which we can assume&#x0D; that the gold ore zones were repeatedly renewed from&#x0D; the Middle Jurassic to the Early Cretaceous. A correlation&#x0D; was noted for several subordinate structures: zones&#x0D; of ore-bearing dislocations – structures of prospective&#x0D; areas – ore bodies of various morphologies.&#x0D; The terrigenous sequence is considered not only as a&#x0D; structure-forming unit but also as a component involved&#x0D; in hydrothermal ore formation due to the presence of&#x0D; carbonaceous siltstones layers.&#x0D; The proposed model of the gold ore region makes it&#x0D; possible to allocate the most promising prospective areas.&#x0D; The presence of similar graben depressions developed&#x0D; in prospective ore regions within the Central Aldan&#x0D; ore district indicates the likelihood of identifying&#x0D; common patterns of endogenic mineralization within&#x0D; them.
Staging listening: new methods for engaging audiences with sound in museums

This article reports on the experimental methodology and key findings of the AHRC-funded impact and engagement project Sonic Futures: Collecting, Curating and Engaging with Sound at the National Science and Media Museum (2020–21). The project undertook a series of listening-based public engagement activities – described here as staging listening – to identify new ways of engaging listening audiences with sound technology objects in museums. These activities led to the creation of three new interactive sounding exhibit prototypes created jointly with audiences. Because the project took place during periods of lockdown caused by the coronavirus pandemic in the UK in 2020–21, the exhibit prototypes were created digitally and tested via online interaction. The article argues that engaging with listening audiences can diversify and enrich museum listening scenarios, a term we use here to describe auditory situations which elicit different kinds of listening attention, interaction and learning. These listening scenarios produce divergent signatures of listening, a concept we develop here to describe the various kinds of learning and engagement we observed throughout the project.
Non-Euclidean, AntiGeometry, and NeutroGeometry Characterization

Recently, a problem is addressed about dealing the difference among Non-Euclidean, AntiGeometry and NeutroGeoemtry data sets. The problem arises while partial negation of Euclidean Geometry, full negation of Euclidean or Hybrid mode. In case of undefined geometry also many researchers raised the questions. To tackle this issue, the current paper provides some examples for Non-Euclidean, AntiGeometry, and Neutrogemoetry for better understanding.
DIGITAL TECHNOLOGIES AND LEARNING PROCESSES. THE ETERNAL GOLDEN GARLAND

Beyond any possible rhetorical division between those who are for or against a more or less massive use of technologies, it is undeniable that technologies always have effects on cognitive, relational, and autonomy processes of individuals, in every season of life and by virtue of the quantity and quality of the use to which we are exposed. The experience of the pandemic caused by Covid19 definitely amplified and highlighted this fact, making the advantages and disadvantages of online life, to which many were forced, immediately apparent. The data on the learning process, in particular on the exclusions that online education has generated, is evident, although very patchy. The difference was determined by the technological skills of learners and teachers, by the possibilities of access to appropriate infrastructures and devices, and by the style of conducting teaching. With regard to this last point, pre-Covid teaching methods were an important factor: digital teaching has often amplified, for better or for worse, what was already being done in the traditional way, with the evidence, however, that some changes that were less evident in frontal teaching were urgent in mediated teaching. The speed of the lesson, for example, the didactic rhythm, the anchorage to the concrete and the levels of personalization were some of the conditions that compromised the success of online training. Technological teaching has also concerned the population of pupils in difficult, disabled, and disadvantaged situations, for whom the impact with the ICT world has been accompanied, as research has shown, by other risk factors (Ianes, 2020), again linked mainly to pre-Covid elements, and referring above all too experienced integration, practiced as a cultural model for schools or as a routine afterthought. The United Nations Convention on the Rights of Persons with Disabilities, recognizes new technologies as an essential contextual element for the promotion of the person's functioning, becoming tools capable of compensating for deficits, facilitating independent living, or, conversely, an obstacle and depriving environment/tool. For this reason, they find a specific place within the Environmental Factors of the ICF (WHO, 2001) and the assessment that must be made of them in view of individualized educational planning.
Analysis of the efficiency of support programs of Russian universities (2010–2020)

The article provides a comparative analysis of the scientometric achievements of universities of the “5–100” group and the two leading universities of the country, Moscow State University and St Petersburg State University. Issues of linking the financing of higher education and scientometric achievements of educational institutions are considered. Particular attention is paid to qualitative indicators, in particular, publications in high-ranking journals. The fastest effect of promotion in ratings is achieved, among other things, using unscrupulous methods and techniques, which in the long run have serious reputational costs. The content of individual coefficients is disclosed, which it is advisable to use to compare the scientometric achievements of universities. A qualitative description of the comparative parameters in the field of scientometrics, the features of their calculation and interpretation, as well as a logical interpretation of the comparison results are given. The purpose of the study is to identify the most objective indicators for comparing the scientific value of the publication activity of university staff, which can later be used to comparatively characterize the effectiveness of universities in terms of promotion in international rankings. The most qualitative comparison is possible for a number of scientometric indicators, which are based on the division of publication sources into quartiles. The problem of “garbage” (“predatory”) journals is considered in the context of university ratings. The trends in higher education in terms of achieving the planned scientometric parameters are characterized, and new indicators are identified that can be used to obtain a reliable and verifiable qualimetric assessment of the “value” of the university or the scientific community. The indicator “fair citation” is introduced, which is supposed to be used to assess the quality of the published material.
Bayesian Estimation of Presidential Elections in Ghana: A Validation Approach

Elections are one of the barometers through which electorates measure the performance of governments and decide whether to renew their mandate or not. The success of every election goes a long way to strengthen the frontiers of a country's democracy and provide legitimacy for those who hold political power. However, the electoral process of many African countries has been challenged in courts or allegations of fraud and vote rigging are leveled against the winning party or candidate. Therefore, there is the need for a statistical method for checking and validating election results to ascertain fraud and vote rigging claims. Existing validation methods include the Parallel Vote Tabulation methodology. However, some significant disadvantages of this approach are issues of cost, sampling techniques and sample size determination. To overcome these, this study resorts to using the Dirichlet multinomial Bayesian model to compute posterior probabilities of valid votes cast and Bayesian credible intervals to ascertain the legitimacy of the votes cast. Using the Ghana general elections in 2020, the fitted Bayesian model accurately predicted approximately 99% of the proportion of votes obtained by New Patriotic Party, National Democratic Congress and all Other Political Parties. Also, the valid votes received by all the political parties fall within the Bayesian credible intervals indicating that the credibility of the 2020 presidential elections held in Ghana may not be in doubt.
Face Recognition Algorithms: A Review

Facial recognition is the method by which an individual's identity is determined by a facial image. With the support of this method, it is potential to use the face image of the person to document it in any safety system. Facial recognition methods for static images can approximately be classified into comprehensive approaches to comprehensive and feature-based approaches. The comprehensive systems use the whole raw face image as input, while feature-based approaches utilize limited face features and use their regular and decorative properties [1]. A huge number of facial recognition systems have been advanced in the past periods. In this paper, we review a varied range of approaches used to identify facial recognition, which contains LDA, PCA, SVM, and ICA.