Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence (AI) applications in on-line shopping in India

Retailing in India has attracted many global players and has reached nearly 350 Billion USD according to KPMG in 2010.While the retail environment is experiencing a significant growth, Indian consumer tastes and preferences are also changing rapidly forcing retailers to grow in numbers and formats.Technological advances and expansion of internet have also paved the way for electronic retail channels.India, as an emerging economy, offers substantial opportunities for e-retailers.In addition, Internet shopping offering more flexibility, interactivity, customization and low risk during the pandemic reached remarkable popularity in the country.Particularly since the outbreak of Covid-19, major on-line sellers have been attracting more and more customers.In the light of growing on line shopping, retailers have embraced a variety of technologies to engage their customers during their on-line shopping experience.Artificial intelligence is an area which clearly facilitates this for the retailers.In this study, the aim is to better understand how use of artificial intelligence tools during on-line shopping leads to customer satisfaction and loyalty.The intent is to provide recommendations on the most effective Artificial Intelligence tools to use for decision makers at companies and retailers.The scope of the study is artificial intelligence tools used in-on line retail in India.
End-to-End Open-Set Semi-Supervised Node Classification with Out-of-Distribution Detection

Out-Of-Distribution (OOD) samples are prevalent in real-world applications. The OOD issue becomes even more severe on graph data, as the effect of OOD nodes can be potentially amplified by propagation through the graph topology. Recent works have considered the OOD detection problem, which is critical for reducing the uncertainty in learning and improving the robustness. However, no prior work considers simultaneously OOD detection and node classification on graphs in an end-to-end manner. In this paper, we study a novel problem of end-to-end open-set semi-supervised node classification (OSSNC) on graphs, which deals with node classification in the presence of OOD nodes. Given the lack of supervision on OOD nodes, we introduce a latent variable to indicate in-distribution or OOD nodes in a variational inference framework, and further propose a novel algorithm named as Learning to Mix Neighbors (LMN) which learns to dampen the influence of OOD nodes through the messaging-passing in typical graph neural networks. Extensive experiments on various datasets show that the proposed method outperforms state-of-the-art baselines in terms of both node classification and OOD detection.
Machine learning of tax avoidance detection based on hybrid metaheuristics algorithms

This paper addresses the performances of machine learning classification models for the detection of tax avoidance problems. The machine learning models employed automated features selection with hybrid two metaheuristics algorithms namely particle swarm optimization (PSO) and genetic algorithm (GA). Dealing with a real dataset on the tax avoidance cases among companies in Malaysia, has created a stumbling block for the conventional machine learning models to achieve higher accuracy in the detection process as the associations among all of the features in the datasets are extremely low. This paper presents a hybrid meta-heuristic between PSO and adaptive GA operators for the optimization of features selection in the machine learning models. The hybrid PSO-GA has been designed to employ three adaptive GA operators hence three groups of features selection will be generated. The three groups of features selection were used in random forest (RF), k-nearest neighbor (k-NN), and support vector machine (SVM). The results showed that most models that used PSO-GA hybrids have achieved better accuracy than the conventional approach (using all features from the dataset). The most accurate machine learning model was SVM, which used a PSO-GA hybrid with adaptive GA mutation.
Distance learning as an unavoidable component of higher education during the pandemic

During the short period of time, the situation has changed dramatically and demanded significant correlations of our requirements and priorities in many areas, including the system of traditional higher academic education, which in turn revealed new opportunities, prospects, challenges, and even threats. The usual system of organizing the educational process (tests/exams) which is typical for full-time education is changing now. The initial euphoria from the widespread introduction of distance learning methods is replaced by anxiety and apprehension, taking into account the duration of the changing process of digital learning, as the only alternative platform that allows continuing the implementation of higher education as it is. However, studying the consequences of online learning, most researchers consider the problem: what impact digitalization of education has on a new contingent of students who are more adapted to global digitalization and do not feel stressed implementing new online resources. We could hardly say the same about the teaching staff of universities, where frequently the state of professional and emotional burnout due to the introduced distance learning forms is diagnosed. The purpose of this research consists of determining the correlation between the ratio of e-learning in the total academic load during the coronavirus pandemic and burnout at work among teachers implementing e-learning experience to achieve the best possible results, leveling the consequences of lockdowns, thereby preserving, and possibly expanding the boundaries of students professional competencies.
Prediction System of Cloud Distribution Image Using Fully Convolutional Networks

In this paper, we propose a cloud distribution prediction model in which fully convolutional networks are used to improve the prediction accuracy for photovoltaic power generation systems. The model learns the cloud distribution from meteorological satellite images and predicts the cloud image 60 min later. We examined the applicability of Day Microphysics RGB as input to the cloud image prediction model. Day Microphysics RGB is a type of RGB composite image based on the observation image of Himawari-8. It is used for daytime cloud analysis and can perform detailed cloud analysis, for example, the discrimination of cloud areas such as upper and lower clouds. The performance of the proposed method is evaluated on the basis of the root mean square error of the prediction and ground truth images.
The experience of thesis writing in terms of the methodological students' digital competence development

In the context of digital technologies' development, digital competence is a necessary prerequisite for everyone to learn, work, and actively participate in social life. Higher education institutions are faced with the mission of selecting and developing appropriate teaching strategies and cultivating digital competencies of students and academics, as well as using appropriate tools to improve the quality of education. The article highlights the experience of forming digital competencies among students majoring in the 122 Computer Science specialization while writing their thesis. Students developed a university automated system for choosing disciplines from the variable part of the curriculum. The practical results obtained by students during the research in accordance with the thesis tasks are represented by the following: examples of UML and ER diagrams (design stages), graph images (implementation stage), sections of the program code and test results of the created automated system (implementation and configuration stages of the software product). The formation and development of digital competencies during such thesis research is analyzed. The European model of digital competencies "The Digital Competence Framework for Citizens" (DigComp 2.1) has been selected as a conceptual standard. Attention is focused on three areas of DigComp 2.1: information and data literacy; digital content creation; problems solving in the digital environment. The activities that contribute to the formation of these competencies are outlined. It is concluded that the described strategy promotes the formation and development of digital competence at the eighth (highest) professional level in accordance with the DigComp 2.1 model for higher education students majoring in the 122 Computer Science specialization.
The value of superdiverse human-technology entanglements

Human-technology relations are time and place related processes. Today, it is very common to describe human-technology interaction by stating that technology is ubiquitous and permeating all aspects of our everyday lives. This is often compounded by the fact that technological development has been rapid, and it seems to be accelerating. This speed makes the understanding the effects that technology has on us and our lives challenging or even difficult to realise. These kinds of notions have been repeated for decades already. The point here is not to criticize other scholars, but to argue that to reveal the value of quotidian human-technology entanglements we need to focus on the most mundane parts of our lives, scrutinizing something we do not necessary recall nor take notice of. This has been labelled as the “secret world of doing nothing” by ethnologists Billy Ehn and Orvar Löfgeren (2010) to describe the most mundane activities of our everyday lives.
International Trade Strategy of SMEs Based on Blockchain Technology

Blockchain has the characteristics of decentralization, openness and transparency, privacy protection, and traceability. It is suitable for international logistics supervision, processing trade supervision, and cross-border payment in the international trade of small and medium-sized enterprises. At present, the application of blockchain technology is in its infancy, facing problems such as lack of universal standards, security risks, and cross-jurisdictional jurisdictions. It is necessary to further promote the true implementation of blockchain technology in international trade through technological innovation, talent cultivation, and improvement of the standard system.
Long Horizon Anomaly Prediction in Multivariate Time Series with Causal Autoencoders

Predictive maintenance is essential for complex industrial systems to foresee anomalies before major system faults or ultimate breakdown. However, the existing efforts on Industry 4.0 predictive monitoring are directed at semi-supervised anomaly detection with limited robustness for large systems, which are often accompanied by uncleaned and unlabeled data. We address the challenge of predicting anomalies through data-driven end-to-end deep learning models using early warning symptoms on multivariate time series sensor data. We introduce AnoP, a long multi-timestep anomaly prediction system based on unsupervised attention-based causal residual networks, to raise alerts for anomaly prevention. The experimental evaluation on large data sets from detector health monitoring of the Hadron Calorimeter of the CMS Experiment at LHC CERN demonstrates the promising efficacy of the proposed approach. AnoP predicted around 60% of the anomalies up to seven days ahead, and the majority of the missed anomalies are abnormalities with unpredictable noisy-like behavior. Moreover, it has discovered previously unknown anomalies in the calorimeter’s sensors.
Invariant Action Effect Model for Reinforcement Learning

Good representations can help RL agents perform concise modeling of their surroundings, and thus support effective decision-making in complex environments. Previous methods learn good representations by imposing extra constraints on dynamics. However, in the causal perspective, the causation between the action and its effect is not fully considered in those methods, which leads to the ignorance of the underlying relations among the action effects on the transitions. Based on the intuition that the same action always causes similar effects among different states, we induce such causation by taking the invariance of action effects among states as the relation. By explicitly utilizing such invariance, in this paper, we show that a better representation can be learned and potentially improves the sample efficiency and the generalization ability of the learned policy. We propose Invariant Action Effect Model (IAEM) to capture the invariance in action effects, where the effect of an action is represented as the residual of representations from neighboring states. IAEM is composed of two parts: (1) a new contrastive-based loss to capture the underlying invariance of action effects; (2) an individual action effect and provides a self-adapted weighting strategy to tackle the corner cases where the invariance does not hold. The extensive experiments on two benchmarks, i.e. Grid-World and Atari, show that the representations learned by IAEM preserve the invariance of action effects. Moreover, with the invariant action effect, IAEM can accelerate the learning process by 1.6x, rapidly generalize to new environments by fine-tuning on a few components, and outperform other dynamics-based representation methods by 1.4x in limited steps.
Controlling Underestimation Bias in Reinforcement Learning via Quasi-median Operation

How to get a good value estimation is one of the key problems in reinforcement learning (RL). Current off-policy methods, such as Maxmin Q-learning, TD3 and TADD, suffer from the underestimation problem when solving the overestimation problem. In this paper, we propose the Quasi-Median Operation, a novel way to mitigate the underestimation bias by selecting the quasi-median from multiple state-action values. Based on the quasi-median operation, we propose Quasi-Median Q-learning (QMQ) for the discrete action tasks and Quasi-Median Delayed Deep Deterministic Policy Gradient (QMD3) for the continuous action tasks. Theoretically, the underestimation bias of our method is improved while the estimation variance is significantly reduced compared to Maxmin Q-learning, TD3 and TADD. We conduct extensive experiments on the discrete and continuous action tasks, and results show that our method outperforms the state-of-the-art methods.
21. Yüzyıl Becerileri Bağlamında Kodlamaya Dayalı Eğlenceli Uygulamaların Öğretmen Görüşlerine göre Değerlendirilmesi

The purpose of this study is to assess teachers’ opinions about coding-based entertainment applications in the context of 21st-century skills. Teachers' opinions were analyzed using the Many facet Rasch measurement model. The study group of the research consists of 14 teachers working in different regions of Turkey. According to the Rasch measurement model, the study has three facets. The first of these facets is the teachers as 14 raters (judges). The other facet is six entertainment applications based on coding. The third facet consists of 11 items (criteria) established for these applications. In this study, coding-based applications such as Scratch, Code.org, CodeMonkey, CodeCombat, Mobil Kod (EBA), and mBlock were examined based on teachers' opinions. The analysis showed that the Scratch and mBlock applications had the highest quality while the Mobil Kod application had the lowest quality. In addition, it was found that through the use of coding-based applications, it may be difficult to develop students' understanding of solidarity and benevolence. On the other hand, teachers indicated that these applications arouse students' curiosity and interest in new developments and enable them to learn by doing. As a result of the analysis, it was also found that judges differ in terms of their severity and leniency. It is expected that the results of the study will contribute to this area.
CharAs-CBert: Character Assist Construction-Bert Sentence Representation Improving Sentiment Classification

In the process of semantic capture, traditional sentence representation methods tend to lose a lot of global and contextual semantics and ignore the internal structure information of words in sentences. To address these limitations, we propose a sentence representation method for character-assisted construction-Bert (CharAs-CBert) to improve the accuracy of sentiment text classification. First, based on the construction, a more effective construction vector is generated to distinguish the basic morphology of the sentence and reduce the ambiguity of the same word in different sentences. At the same time, it aims to strengthen the representation of salient words and effectively capture contextual semantics. Second, character feature vectors are introduced to explore the internal structure information of sentences and improve the representation ability of local and global semantics. Then, to make the sentence representation have better stability and robustness, character information, word information, and construction vectors are combined and used together for sentence representation. Finally, the evaluation and verification are carried out on various open-source baseline data such as ACL-14 and SemEval 2014 to demonstrate the validity and reliability of sentence representation, namely, the F1 and ACC are 87.54% and 92.88% on ACL14, respectively.
Framework for Operational Resilience in Engineering and System Test (FOREST) Part I: Methodology – Responding to “Security as a Functional Requirement”

ABSTRACT An end‐to‐end methodology for addressing cyber resilience as a development and test philosophy in a system is described. Although focused on cybersecurity, the methodology applies to any resilience concerns and features of a system. Resilience is a functional characteristic of a system, requiring a process to evaluate the function of different aspects of a system under attack or disruption. The result of this process is a set of functional requirements and functional views of cyber resilience processes in a model‐based systems engineering tool. The methodology consists of a meta‐process model called the Framework for Operational Resilience in Engineering and System Test (FOREST) and a reference architecture metamodel called Mission Aware. In practice these are used to make security and related resilience decisions in capability development using a standard, risk‐based approach for cybersecurity requirements development. Part I of this article describes the methodology and Part II presents its use in a case study of a fictional weapon system called Silverfish.
Denoising Time Cycle Modeling for Recommendation

Recently, modeling temporal patterns of user-item interactions have attracted much attention in recommender systems. We argue that existing methods ignore the variety of temporal patterns of user behaviors. We define the subset of user behaviors that are ir- relevant to the target item as noises, which limits the performance of target-related time cycle modeling and affect the recommendation performance. In this paper, we propose Denoising Time Cycle Modeling (DiCycle), a novel approach to denoise user behaviors and select the subset of user behaviors that are highly related to the target item. DiCycle is able to explicitly model diverse time cycle patterns for recommendation. Extensive experiments are conducted on both public benchmarks and a real-world dataset, demonstrating the superior performance of DiCycle over the state-of-the-art recommendation methods.
Pedagogical conditions for the implementation of distance learning (using the example of online platforms Zoom and Microsoft Teams)

Distance learning has become an integral part of modern life. In the conditions of the coronavirus pandemic, in which many countries are still today, online platforms have been and remain one of the most important means of organizing the learning process. In our country, education using distance learning technologies has been implemented in educational institutions. The subject of the study is the study of pedagogical conditions for the implementation of distance learning based on the online platforms Zoom and Microsoft Teams. In order to identify the optimal conditions for the organization of distance learning, a written survey in the form of a questionnaire was conducted within the framework of the study. The main research methods include: review of scientific literature, observation, survey. Currently, the problems of integrating various training courses in synchronous and asynchronous formats, developing scientific and methodological support in the form of manuals, recommendations for working with online platforms are becoming increasingly relevant. The novelty of the research lies in the study of optimal pedagogical conditions for the implementation of distance learning based on online platforms. The authors come to the conclusion that learning based on distance learning technologies contributes to the implementation of modern educational paradigms such as personality-oriented education, individualization of the trajectory of educational activity, competence approach, self-education and self-development of students, project and research training.
Decoloniality in computing

This interview explores the relationship between social computing technology and decolonization and the relationship between coloniality and computing research.
Quantum annealing with special drivers for circuit fault diagnostics

We present a very general construction for quantum annealing protocols to solve Combinational Circuit Fault Diagnosis problems that restricts the evolution to the space of valid diagnoses. This is accomplished by using special local drivers that induce a transition graph on the space of feasible configurations that is regular and instance independent for each given circuit topology. Analysis of small instances shows that the energy gap has a generic form, and that the minimum gap occurs in the last third of the evolution. We used these features to construct an improved annealing schedule and benchmarked its performance through closed system simulations. We found that degeneracy can help the performance of quantum annealing, especially for instances with a higher number of faults in their minimum fault diagnosis. This contrasts with the performance of classical approaches based on brute force search that are used in industry for large scale circuits.
Research on artificial intelligence of accounting information processing based on image processing

&lt;abstract&gt; &lt;p&gt;The rapid development and wide application of artificial intelligence is deeply affecting all aspects of human society. Combine artificial intelligence with the accounting industry, use computers to efficiently and automatically process accounting information, and let the accounting industry move towards the intelligent era. This can help people reduce the workload and speed up work efficiency. In recent years, with the rapid development of economy and technology, the use of financial instrument vouchers has exploded, but the processing requirements of financial instrument vouchers have become more and more efficient. Traditional accounting information processing methods, due to the staff's energy and ability, it is often difficult to quickly and accurately handle accounting information. This makes the processing of accounting information lack of timeliness, the degree of utilization of accounting information by enterprises is relatively low, and the demand for intelligent processing of accounting information is constantly pressing. In view of the above problems, this paper uses image processing technology to intelligently identify the content of accounting information to achieve automatic ticket input, improve work efficiency, reduce error rate and reduce labor costs. By simulating the actual 230 invoice images, the results show that the recognition accuracy rate is as high as 98.7%. The results show that the method is effective and has great application value, which is of great significance to the artificial intelligence of accounting information processing.&lt;/p&gt; &lt;/abstract&gt;