Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence in 21st Century Education a Space for New Teaching Opportunities

The relevance of this study is due to the need for practical training of teachers to work in an electronic information and educational environment, which is intensively enriched by artificial intelligence technologies. The article shows that the solution of this problem requires the development of new scientifically based programs aimed at purposeful training of teachers to use artificial intelligence technologies and methods in their professional activities.
Efficient Service Selection in Multimedia Documents Adaptation Processes

Pervasive systems help access to multimedia documents at any time, from anywhere and through several devices (smart TV, laptop, tablet, etc.). Nevertheless, due to changes in users’ contexts (e.g. noisy environment, preferred language, public place, etc.), restrictions on correct access to these documents may be imposed. One possible solution is to adapt their contents using adaptation services so that they comply, as far as possible, with the current constraints. In this respect, several adaptation approaches have been proposed. However, when it comes to selecting the required adaptation services, they often carry out this task according to predefined configurations or deterministic algorithms. Actually, the efficient selection of adaptation services is one of the key-elements involved in improving the quality of service in adaptation processes. To deal with this issue (i.e. the efficient selection of adaptation services), we first provide an enriched problem formulation as well as methods that we use in problem-solving. Then, we involve standard and compact evolutionary algorithms to find efficient adaptation plans. The standard version is usually adopted in systems that are not subject to specific constraints. The compact one is used in systems for which constraints on computational resources and execution time are considered. The proposal is validated through simulation, experiments and comparisons according to performance, execution time and energy consumption. The obtained results are satisfactory and encouraging.
Cinnamon: A Domain-Specific Language for Binary Profiling and Monitoring

Binary instrumentation and rewriting frameworks provide a powerful way of implementing custom analysis and transformation techniques for applications ranging from performance profiling to security monitoring. However, using these frameworks to write even simple analyses and transformations is non-trivial. Developers often need to write framework-specific boilerplate code and work with low-level and complex programming details. This not only results in hundreds (or thousands) of lines of code, but also leaves significant room for error. To address this, we introduce Cinnamon, a domain-specific language designed to write programs for binary profiling and monitoring. Cinnamon's abstractions allow the programmer to focus on implementing their technique in a platform-independent way, without worrying about complex lower-level details. Programmers can use these abstractions to perform analysis and instrumentation at different locations and granularity levels in the binary. The flexibility of Cinnamon also enables its programs to be mapped to static, dynamic or hybrid analysis and instrumentation approaches. As a proof of concept, we target Cinnamon to three different binary frameworks by implementing a custom Cinnamon to C/C++ compiler and integrating the generated code within these frameworks. We further demonstrate the ability of Cinnamon to express a range of profiling and monitoring tools through different use-cases.
Performance of Region-Based Markov Random Field with XY Spins

Markov Random Field (MRF) models have become increasingly necessary especially in data-driven science. There are two kinds of MRF model applicable to image segmentation: edge-based and region-based. The region-based model is more easily implemented and is more robust to noise than the edge-based model. However, the region-based model often becomes trapped in a local minimum, which is sensitive to initial conditions. To overcome the issue of local minima, Okada proposed a region-based model with hidden variables formed by XY spins. This model has attracted attention regarding neuromorphic hardware implementations, but the efficacy of this model has not been thoroughly evaluated. In this paper, we verify the performance of the region-based model with XY spins, in comparison with that of the region-based model with Ising spins. To achieve this purpose, we construct variational Bayes algorithms and Markov chain Monte Carlo algorithms for both region-based models and evaluate their performances using synthetic data and real lithium-ion-battery imaging data.
Holistic Transfer to Rank for Top-N Recommendation

Recommender systems have been a valuable component in various online services such as e-commerce and entertainment. To provide an accurate top-N recommendation list of items for each target user, we have to answer a very basic question of how to model users’ feedback effectively. In this article, we focus on studying users’ explicit feedback, which is usually assumed to contain more preference information than the counterpart, i.e., implicit feedback. In particular, we follow two very recent transfer to rank algorithms by converting the original feedback to three different but related views of examinations, scores, and purchases, and then propose a novel solution called holistic transfer to rank (HoToR), which is able to address the uncertainty challenge and the inconvenience challenge in the existing works. More specifically, we take the rating scores as a weighting strategy to alleviate the uncertainty of the examinations, and we design a holistic one-stage solution to address the inconvenience of the two/three-stage training and prediction procedures in previous works. We then conduct extensive empirical studies in a direct comparison with the two closely related transfer learning algorithms and some very competitive factorization- and neighborhood-based methods on three public datasets and find that our HoToR performs significantly better than the other methods in terms of several ranking-oriented evaluation metrics.
Exploring the Prediction Consistency of Multiple Views for Transductive Visual Recognition

Although great process has been achieved to accurately classify images, many methods only use labeled images while ignoring the large quantity of unlabeled images. To make use of unlabeled images, in this letter, we propose a novel transductive visual recognition method using the prediction consistency of multiple views (T-PCMV). Both labeled and unlabeled images are used in a unified framework. The predictions of unlabeled images are learned by linearly combining the discriminative information of multiple views. We ensure the smooth constraint that visually similar images should be predicted with similar labels. To learn the classifier, we jointly minimize the classification loss and the discrepancy of predicted labels. To evaluate the usefulness of the proposed method, we conduct transductive visual recognition experiments on four image datasets. Experimental results well demonstrate the effectiveness of the proposed T-PCMV method.
Unique and Dynamic Approach to Predict Schizophrenia Disease Using Machine Learning

The society environment and its wide array of influences factors are very much connected with each phase of human life operating and development. Schizophrenia is a chronic and severe mental disease that alters human memory and mind. Mental fitness and mental disease are dictated by various and collaborating social, mental, what’s more, organic variables, similarly as well-being and sickness when all is said in done. The clearest proof identifies with the dangers of mental ailments, which in the created and creating world are related to markers of neediness, including low degrees of training. The singular goal of this proposed project is to automatically distinguish patients with schizophrenia and without schizophrenia. Detection of schizophrenia in initial periods is crucial to prevent comprehensive mental illness. Currently, identifying the schizophrenia discourse which is extensively distributed in our culture and forecasting the forthcoming phases of the mental illness has become extremely crucial in modern-day society. Normally patients express their health condition on one of the most popular social media networks called Twitter. We introduce a unique and dynamic machine learning (ML) approach to predict the schizophrenia discourse from Twitter tweets. This unique approach is extremely adaptable as practically any supervised machine learning algorithm can be utilized for prediction. The efficiency and accuracy of the machine learning algorithm are directly proportional to the excellence of feature engineering which needs a high level of domain knowledge. To overcome this problem, this paper proposes a Deep Neural Network (DNN) which is capable of automatically identifies the pattern of schizophrenia discourse and classifies the schizophrenia discourse.
Merchant Category Identification Using Credit Card Transactions

Digital payment volume has proliferated in recent years with the rapid growth of small businesses and online shops. When processing these digital transactions, recognizing each merchant’s real identity (i.e., business type) is vital to ensure the integrity of payment processing systems. Conventionally, this problem is formulated as a time series classification problem solely using the merchant transaction history. However, with the large scale of the data, and changing behaviors of merchants and consumers over time, it is extremely challenging to achieve satisfying performance from off-the-shelf classification methods. In this work, we approach this problem from a multi-modal learning perspective, where we use not only the merchant time series data but also the information of merchant-merchant relationship (i.e., affinity) to verify the self-reported business type (i.e., merchant category) of a given merchant. Specifically, we design two individual encoders, where one is responsible for encoding temporal information and the other is responsible for affinity information, and a mechanism to fuse the outputs of the two encoders to accomplish the identification task. Our experiments on real-world credit card transaction data between 71,668 merchants and 433,772,755 customers have demonstrated the effectiveness and efficiency of the proposed model.
Analysis of Learning Speaking Skills Using the WhatsApp Application in Elementary Schools

Abstract The Covid-19 pandemic approaching the 21st century has had a significant impact in all fields, including education. Finally, all learning activities are carried out online or online. This study aims to analyze the implementation process of learning speaking skills using the WhatsApp group application, the obstacles faced, and the teacher’s efforts to improve speaking skills. This research was conducted at SD Negeri Katikan 1 and SD Negeri Katikan 2, Kedunggalar District, Ngawi Regency, East Java. This research uses descriptive qualitative research methods. The subjects used in this study were teachers of class V SD Negeri Katikan 1 and SD Negeri Katikan 2. The sampling technique used was purposive sampling. Data collection techniques used in this study were in-depth interviews and document analysis. Data analysis used interactive analysis techniques by Miles and Huberman. The result of this research is that the Covid-19 pandemic has had a huge impact on the learning process, learning elementary school speaking skills which are usually carried out offline has now been converted into online or online learning via WhatsApp. Learning of speaking skills is carried out through the WhatsApp feature, video calls, learning videos, pictures, and voice notes. Barriers to online learning are network problems, ownership of an android cellphone, and student discipline. The efforts made by the teacher to improve students’ speaking skills in online learning through WhatsApp is to familiarize students with speaking in every learning activity, such as apperception activities, core activities (presentation of material and assignments), and other activities (leading prayer, presentation).
Fast and Effective Object Classification for Big Image Data

Classification for big image data is an essential field of big data technology. Big image data classification has a wide range of applications, such as face recognition, object detection, and human posture recognition. Traditional methods for big image data classification usually ignore the close connection between different levels of features in neural networks. In this paper, we propose a new way for big image data classification that improves the previous method through the Multi-Level Feature Connection Network (MFC-Net). MFC-Net presents a multi-level feature connection module to mine more useful features for classification. Experiments show that MFC-Net significantly improves its baseline SSD (Single Shot Multibox Detector) and other advanced classification neural networks in the classic datasets Pascal VOC and Microsoft COCO.
The hybridised indexing method for research-based information retrieval

An information retrieval system (IRS) is used to retrieve documents based on an information need. The IRS makes relevance judgements by attempting to match a query to a document. As IRS capabilities are indexing design dependent, the hybrid indexing method (IRS-H) is introduced. The objectives of this article are to examine IRS-H (as an alternative indexing method that performs exact phrase matching) and IRS-I, regarding retrieval usefulness, identification of relevant documents, and the quality of rejecting irrelevant documents by conducting three experiments and by analysing the related data. Three experiments took place where a collection of 100 research documents and 75 queries were presented to: (1) five participants answering a questionnaire, (2) IRS-I to generate data and (3) IRS-H to generate data. The data generated during the experiments were statistically analysed using the performance measurements of Precision, Recall and Specificity, and one-tailed Student’s t-tests. The results reveal that IRS-H (1) increased the retrieval of relevant documents, (2) reduced incorrect identification of relevant documents and (3) increased the quality of rejecting irrelevant documents. The research found that the hybrid indexing method, using a small closed document collection of a hundred documents, produced the required outputs and that it may be used as an alternative IRS indexing method.
Learning Continuous Cost-to-Go Functions for Non-holonomic Systems

This paper presents a supervised learning method to generate continuous cost-to-go functions of non-holonomic systems directly from the workspace description. Supervision from informative examples reduces training time and improves network performance. The manifold representing the optimal trajectories of a non-holonomic system has high-curvature regions which can not be efficiently captured with uniform sampling. To address this challenge, we present an adaptive sampling method which makes use of sampling based planners along with local, closed-form solutions to generate training samples. The cost-to-go function over a specific workspace is represented as a neural network whose weights are generated by a second, higher order network. The networks are trained in an end-to-end fashion. In our previous work, this architecture was shown to successfully learn to generate the cost-to-go functions of holonomic systems using uniform sampling. In this work, we show that uniform sampling fails for non-holonomic systems. However, with the proposed adaptive sampling methodology, our network can generate near-optimal trajectories for non-holonomic systems while avoiding obstacles. Experiments show that our method is two orders of magnitude faster compared to traditional approaches in cluttered environments.
Evaluation of Alignment: Precision, Recall, Weighting and Limitations

In the real world, data does not come neatly packaged. Instead, it typically comes as small updates from many sources with different conventions. Building a single, cohesive knowledge-base to work from requires merging small updates from many different sources. This paper outlines methods we have investigated for scoring merging routines. Given a challenge problem consisting of a large knowledge-base and a set of smaller documents, algorithms are asked to identify alignment points between the smaller document and the knowledge base. This paper surveys options for evaluating such algorithms, providing notes on strengths, weaknesses and considerations for interpretation.
Challenges and Issues of Recommender System for Big Data Applications

The knowledge overload is a big problem in today’s world. In reality, the information overload implies the availability of so much data or knowledge that goes beyond the user’s manageable limits and causes a great difficulty in all kinds of decision taking. The main reason we need a recommender system in modern society is that because of the proliferation of the Internet, people have so many options to choose from. A recommender system refers to a system that can predict a user’s future preference for a set of items and recommends the top items. It is a knowledge retrieval application that enhances accessibility as well as efficiently and effectively suggests relevant items to users by considering the user interests and preferences. A recommendation framework tries to tackle the problem of overloading information. There are so many other instances such as these, where we have plenty of data, but we can't decide what we want. Even though volume of information has increased, a new problem has arisen as people have had difficulty selecting the items they actually want to see. Recommenders systems have the ability to change the way websites interact with users and allow businesses to optimize their Return on Investment (ROI); based on the information, they can collect on the preferences and purchases of each customer. A traditional recommendation system cannot do its work without sufficient information, and big data offers plenty of user data such as past transactions, browsing history, and reviews for recommendation systems in order to provide accurate and efficient recommendations. In short, even the most advanced recommenders without big data can’t be successful. This work primarily discusses and reflects on current issues, challenges, and research gaps in the production of high-quality recommender systems. Such problems and challenges will present new paths for study, and the target can be accomplished for high-quality recommender systems. The entire research is broken into major tasks including the study of state-of-the-art approaches for recommender system and big data applications; overcoming the problem of cold start, scalability, and building a proactive recommender system; this research considers the process of development for a generic intelligent recommender system that can be work on more than one domain; it also expands the basic recommender program definition.
HOME BIAS AS A KEY BEHAVIORAL DEVIATION OF PORTFOLIO INVESTORS DECISIONS

The comparative analysis of the main approaches to the reasons for the home bias is explored. The main factors of home bias are identified. The reasons for the existence of home bias are determined on the basis of systematization of the existing discourse. The main stages of the evolution of approaches to determining the reasons for home bias are identified. It was found that the most promising approach is the behaviourist approach, as well as the institutional approach and the transaction costs approach. The historical and current dynamics of the home bias in the investment portfolios of different countries is studied. A comparative analysis of the home bias for different groups of countries according to their level of economic development is carried out. It was found that developed countries have the lowest share of local market assets in the portfolio. The evolution of approaches to the home bias has been studied. The main scientific publications on the issue of home bias in the historical perspective are analysed and systematized. The main quantitative dimensions of the expression of home bias are highlighted. The main forms of quantitative expression of home bias, which received relative expression in the form of indices, are studied. The qualitative and quantitative composition of modern investment portfolios of the countries with the largest shares in the world market capitalization is studied and analysed. It has been found that there are countries with traditionally low and high levels of international diversification, such as China and Luxembourg, respectively.
Adaptive Starting Points in Video Learning Environments for New Learners Based on Video and Topic Tree Relations

Teaching and learning complex fields of knowledge, including engineering, is rarely easy, particularly because of rapid changes and high interdependences among different topics. In addition, learners are increasingly heterogeneous in terms of different experiences and levels of knowledge. Not dealing with increasingly heterogeneous learners appropriately may result in high dropout rates and poor training quality. A possible way out is addressing learners more individually. Hopefully, digital learning offers, such as video platforms, will help to convey knowledge better and more individually through a broader learning offer. But learners with different levels of knowledge need different starting points into a course based on their level of knowledge. Thus, a crucial problem is finding the most appropriate starting point for learners in such environments. This paper presents a novel approach to identify individual starting points in online video courses for learners with different levels of knowledge. The underlying algorithm identifies topics that are most useful for the concrete learner at the beginning of the course in order to introduce a new field of knowledge appropriately.