Reviewerly/exHarmony huggingface dataset:
An analysis of learners’ intentions toward virtual reality online learning systems: a case study in Taiwan

The virtual reality technology, VR, features feeling of blending in, interactivity and imagination.It not only improves on the limitations of conventional learning methods but also enhances the communication of learning contents and messages via interactivity and imitation.Over the past decade, the incorporation of information technology in education has been an important research topic.Following the computer-aided teaching, interactive online learning, distance teaching and mobile learning of the earlier days, the VR technology is now regarded as a new wave of trend facilitating the incorporation of application technology into education.To explore the factors that affect users' use of virtual reality online learning systems (VROLS), this study based on the theory of cost-benefit perspective and incorporating the perceived value and the flow theories proposes an integrated research model elaborating on why people opt for VROLS services.A total of 296 valid samples were collected from online questionnaires and the data were analyzed using the structural equation modeling (SEM) approach.The findings suggest that both perceived value and flow experience are important roles in users' use of VROLS services, in which benefits (spatial presence, enjoyment, service compatibility) and costs (complexity and visual fatigue) are the critical factors affecting users' perceived value and flow experience.The implications of these findings are further discussed.
Activity Recognition and Creation of Web Service for Activity Recognition using Mobile Sensor Data using Azure Machine Learning Studio

With the increasing pervasive computation, “Activity Recognition” has become a vast and popular field of research. In the field of automated Activity Recognition, we use multiple sensors in wearable/portable devices in order to recognize the human activities such as standing still, sitting, relaxing, laying, walking, climbing stairs, knee bending cycling jogging etc. The main purpose of this paper is to discuss the field of Activity Recognition for patients and old- age persons or any person in general. This research paper can also be used for telemedicine purposes. Besides, different machine learning algorithm will be applied to achieve Activity Recognition rather precisely. Microsoft Azure ML Studio and a bench marking data set are used for creation as well as evaluation of Machine Learning Model. In addition, a Web Service for Activity Recognition is also developed by using Microsoft Azure ML Studio in order to help the developer and researcher while working on Activity Recognition.
Simpler proofs with decentralized invariants

When verifying programs where the data have some recursive structure, it is natural to make use of global invariants that are themselves recursively defined. Though this is mathematically elegant, this makes the proofs more complex, as the preservation of these invariants now requires induction. In particular, this makes the proofs less amenable to automation. An alternative is to use local invariants attached to individual components of the structure and which only involve a bounded number of elements. We call these decentralized invariants. When the structure is updated, the footprint of the modification only impacts a bounded number of invariants and reestablishing them does not require induction. In this paper, we illustrate this idea on three non-trivial programs, for which we achieve fully automated proofs.
Bitmessage Plus: A Blockchain-Based Communication Protocol With High Practicality

We are in the Internet era, when protecting the security of personal information is both vital and challenging. As the most commonly used methods of communication, centralized systems cannot meet the increasing need for information security. Blockchain, with its characteristics of openness, decentralization, and tamper resistance, is an innovative technology underlying Bitcoin. There is potential to use blockchain in developing decentralized and transparent communication systems. Bitmessage is a well-known decentralized messaging system that enables users to exchange messages and prevents accidental eavesdropping. Bitmessage achieves anonymity and privacy by relying on the blockchain flooding propagation mechanism and asymmetric encryption algorithm. Unfortunately, Bitmessage uses proof-of-work as the solution to prevent spam, which wastes computational power and makes it inefficient to be used in practice. To address this problem, we improve Bitmessage with a novel antispam mechanism based on proof-of-space, which requires the user to dedicate a certain amount of disk space to send a message. This improvement reduces the time and computing resource costs by eliminating computationally heavy hash operations. Moreover, we achieve a high level of anonymity by using the stealth address as the destination of the delivered message, which can only be identified by the intended receiver. Finally, we improve the protocol's reliability by taking the blockchain as an immutable database to store the delivered messages.
Evolutionary Optimization of Case-Based Forecasting Algorithms in Chaotic Environments

The problem of dynamic adaptation of prediction algorithms in chaotic environments based on identification of the situations-analogs in the database of retrospective observations is considered. Under conditions of symmetrical and unsymmetrical chaotic dynamics, traditional computational schemes of precedent prediction turn out to be ineffective. In this regard, a dynamic adaptation of precedent analysis algorithms based on the method of evolutionary modeling is proposed. Implementation of the computational precedent prediction scheme for chaotic processes as well as the evolutionary modeling method are described.
CDL-GAN: Contrastive Distance Learning Generative Adversarial Network for Image Generation

While Generative Adversarial Networks (GANs) have shown promising performance in image generation, they suffer from numerous issues such as mode collapse and training instability. To stabilize GAN training and improve image synthesis quality with diversity, we propose a simple yet effective approach as Contrastive Distance Learning GAN (CDL-GAN) in this paper. Specifically, we add Consistent Contrastive Distance (CoCD) and Characteristic Contrastive Distance (ChCD) into a principled framework to improve GAN performance. The CoCD explicitly maximizes the ratio of the distance between generated images and the increment between noise vectors to strengthen image feature learning for the generator. The ChCD measures the sampling distance of the encoded images in Euler space to boost feature representations for the discriminator. We model the framework by employing Siamese Network as a module into GANs without any modification on the backbone. Both qualitative and quantitative experiments conducted on three public datasets demonstrate the effectiveness of our method.
Generalized nets and intuitionistic fuzziness as tools for modelling of data mining processes and tools

The possibilities for using the apparatuses of generalized nets and intuitionistic fuzzy sets as means for modelling and evaluation of Data Mining processes and tools are discussed and illustrated by examples.
Using IndoWordNet for Contextually Improved Machine Translation of Gujarati Idioms

Gujarati language is the Indo-Aryan language spoken by the Gujaratis, the people of the state of Gujarat of India.Gujarati is the one of the 22 official languages recognized by the Indian government.Gujarati script was adopted from Devanagari script.Approximately 3000 idioms are available in Gujarati language.Machine translation of any idiom is the challenging task because contextual information is important for the translation of a particular idiom.For the translation of Gujarati idioms into English or any other language, surrounding contextual words are considered for the translation of specific idiom in the case of ambiguity of the meaning of idiom.This paper experiments the IndoWordNet for Gujarati language for getting synonyms of surrounding contextual words.This paper uses n-gram model and experiments various window sizes surrounding the particular idiom as well as role of stop-words for correct context identification.The paper demonstrates the usefulness of context window in case of ambiguity in the meaning identification of idioms with multiple meanings.The results of this research could be consumed by any destination-independent machine translation system for Gujarati language.
Semi-Supervised Joint Learning for Hand Gesture Recognition from a Single Color Image

Hand gesture recognition and hand pose estimation are two closely correlated tasks. In this paper, we propose a deep-learning based approach which jointly learns an intermediate level shared feature for these two tasks, so that the hand gesture recognition task can be benefited from the hand pose estimation task. In the training process, a semi-supervised training scheme is designed to solve the problem of lacking proper annotation. Our approach detects the foreground hand, recognizes the hand gesture, and estimates the corresponding 3D hand pose simultaneously. To evaluate the hand gesture recognition performance of the state-of-the-arts, we propose a challenging hand gesture recognition dataset collected in unconstrained environments. Experimental results show that, the gesture recognition accuracy of ours is significantly boosted by leveraging the knowledge learned from the hand pose estimation task.
The Impact of Deploying the Internet of Things and How Will It Change Our Lives

As of the time and day starting to write this research, the total number of the Internet users are significantly increasing every minute. The popularity of connecting to and using the Internet has become natural for most if-not-all the devices and appliances manufactured nowadays. Having these devices and more connected to the Internet simply leads to what is known today as the Internet of Things (IoT); which is a super rapidly growing online network across the whole globe and eventually, we will find every “THING” is connected one way or another to the Internet. While the IoT concept is relatively not fully understood for some people, its continued applicationsare helping to improve the quality of life that many people across the world are using today. The developments revolving around the IoT concept are changing the way things are done and improving the applications of technology in the execution of activities and operations that were previously considered difficult. The current research presents a detailed definition and a clear description of the IoT concept, shedding lights on the way it has changed the nature of life that people are living today, and the expected innovations that will emerge from its development to change the lives of many people across the world in near future.Keywords: Internet of Things, IoT, IoT Applications, IoT Deployment
Deception Detection Using Machine Learning

Today's digital society creates an environment potentially conducive to the exchange of deceptive information. The dissemination of misleading information can have severe consequences on society. This research investigates the possibility of using shared characteristics among reviews, news articles, and emails to detect deception in text-based communication using machine learning techniques. The experiment discussed in this paper examines the use of Bag of Words and Part of Speech tag features to detect deception on the aforementioned types of communication using Neural Networks, Support Vector Machine, Naïve Bayesian, Random Forest, Logistic Regression, and Decision Tree. The contribution of this paper is two-fold. First, it provides initial insight into the identification of text communication cues useful in detecting deception across different types of text-based communication.Second, it provides a foundation for future research involving the application of machine learning algorithms to detect deception on different types of text communication.
CONCEPTUAL FOUNDATIONS OF ADVANCED PROFESSIONAL TRAINING FOR SOCIO-ECONOMIC DEVELOPMENT OF THE REGION

Рассматриваются подходы к разработке концептуальных оснований опережающей профессиональной подготовки кадров для социально-экономического развития региона. Выделяются отдельные аспекты регионализации образования в контексте деятельности учреждений среднего профессионального образования. Обосновывается актуальность и востребованность центра опережающей профессиональной подготовки, ориентированного на потребности приоритетных направлений экономического и технологического развития региона и повышение профессиональной занятости разных групп населения. Определяются цели и задачи опережающей профессиональной подготовки, ожидаемые результаты и эффекты ее реализации в практике работы центра опережающей профессиональной подготовки, созданного на базе ГПОУ «Сибирский политехнический техникум» (г. Кемерово). Продемонстрированы некоторые результаты деятельности центра опережающей профессиональной подготовки Кемеровской области за 2019 и 2020 годы. Nowadays the system of secondary vocational education must be ready to meet modern challenges and requirements for professional training and be able to cope with the contradiction between the existing quality of specialists’ training and the employers and the labor market demands for highly qualified specialists. Therefore, the issue of creating a system of advanced professional training is extremely vital. The article considers approaches to the theoretical basis for the conceptual foundations of advanced professional training of specialists required for the socio-economic development of the region. It distinguishes some certain aspects of the regionalization of education in the context of activities performed by institutions involved in the secondary vocational education. The paper also substantiates the urgency and the relevance of the advanced vocational training center, whose activity is aimed at satisfying the needs of priority directions of economic and technological development of the region and increasing the professional employment of its population. The article determines goals and objectives of advanced vocational training, specifies expected outcomes and effects of their implementation in the practice of advanced vocational training center which was created on the basis of the Siberian Polytechnic College (Kemerovo). Moreover, the author demonstrates the most significant results achieved by the center of advanced vocational training of the Kemerovo region over the period of 2019, 2020. The approbation of the designed model developed on the conceptual foundations of advanced vocational training confirmed that it contributes to the successful solution of the planned goals and objectives.
Few-shot Radiology Report Generation for Rare Diseases

Automatic radiology report generation that interprets medical images and writes their diagnostic reports is in high demand, as the manual written-report can be laborintensive and error-prone. By this context so far, some radiology report generation models have been proposed already which can hardly detect rare diseases accurately due to insufficient training data of such diseases. Radiology report generation task is therefore severely challenged while involving the rare disease. To tackle this problem, we propose a few-shot Radiology report Generation model, namely RareGen, assembled with two components for better semantic representations learning which can benefit rare disease detection and their diagnosis report generation. Specifically, a few-shot learning generative network is introduced for generating artificial medical instances for rare diseases. Moreover, a disease graph convolution is proposed to model and strengthen the intrinsic correlations among diseases, which allows knowledge transfer from regular diseases to those rare diseases. To the best of our knowledge, this is the first study that focuses on rare disease diagnosis report generation from radiology data. Extensive experiments are conducted to demonstrate the effectiveness of our model.
Dynamic Flexible Multiple-Replica Provable Data Possession in Cloud

With the rapid growth of data and development of cloud storage services, more and more individuals and organizations tend to outsource their data to the cloud environment. Cloud storage brings much convenience to people, but also brings a series of security issues, such as the integrity and reliability of data in the cloud. Based on the characteristics of multi branch tree, a dynamic flexible multiple-replica provable data possession scheme is proposed. The scheme can not only ensure the integrity and reliability of multi ple copies and realized the verification of any number of copies, but also support the dynamic update operation. In addition, the scheme also supports third party audit (TPA) and user privacy protection. The experimental results show that the scheme has high efficiency.
General Conditions for Universality of Quantum Hamiltonians

Recent work has demonstrated the existence of universal Hamiltonians - simple spin lattice models that can simulate any other quantum many body system to any desired level of accuracy. Until now proofs of universality have relied on explicit constructions, tailored to each specific family of universal Hamiltonians. In this work we go beyond this approach, and completely classify the simulation ability of quantum Hamiltonians by their complexity classes. We do this by deriving necessary and sufficient complexity theoretic conditions characterising universal quantum Hamiltonians. Although the result concerns the theory of analogue Hamiltonian simulation - a promising application of near-term quantum technology - the proof relies on abstract complexity theoretic concepts and the theory of quantum computation. As well as providing simplified proofs of previous Hamiltonian universality results, and offering a route to new universal constructions, the results in this paper give insight into the origins of universality. For example, finally explaining the previously noted coincidences between families of universal Hamiltonian and classes of Hamiltonians appearing in complexity theory.
Identifikasi Kesulitan Belajar Mahasiswa pada Mata Kuliah Statistika Selama Masa Pandemi Covid-I9

The length of the online learning period in tertiary institutions that has lasted for approximately 10 months has made students uneasy, because the majority of students consider statistics courses to be difficult to understand, especially with the Covid-19 pandemic conditions. The purpose of this study was to determine the factors that caused student learning difficulties in the Statistics course during the Covid-19 pandemic and to determine the dominant factors causing student learning difficulties in the Statistics course during the Covid-19 pandemic. The research subjects were STIE-Indragiri students taking Statistics courses. Instruments of the research method through questionnaires and interviews with research subjects. The results obtained were the factors that made learning difficulties for students in the statistics course during the Covid-19 pandemic, namely the factors themselves, lecturers' factors, environmental factors / parents as well as facilities and infrastructure factors. The most dominant factor affecting student learning difficulties in statistics courses during the Covid-19 pandemic was the infrastructure factor with a TCR value of 64.38%.
Implications for Emotion: Using Anatomically Based Facial Coding to Compare Emoji Faces Across Platforms

Emoji faces, which are ubiquitous in our everyday communication, are thought to resemble human faces and aid emotional communication. Yet, few studies examine whether emojis are perceived as a particular emotion and whether that perception changes based on rendering differences across electronic platforms. The current paper draws upon emotion theory to evaluate whether emoji faces depict anatomical differences that are proposed to differentiate human depictions of emotion (hereafter, “facial expressions”). We modified the existing Facial Action Coding System (FACS) ( Ekman and Rosenberg, 1997 ) to apply to emoji faces. An equivalent “emoji FACS” rubric allowed us to evaluate two important questions: First, Anatomically, does the same emoji face “look” the same across platforms and versions? Second, Do emoji faces perceived as a particular emotion category resemble the proposed human facial expression for that emotion? To answer these questions, we compared the anatomically based codes for 31 emoji faces across three platforms and two version updates. We then compared those codes to the proposed human facial expression prototype for the emotion perceived within the emoji face. Overall, emoji faces across platforms and versions were not anatomically equivalent. Moreover, the majority of emoji faces did not conform to human facial expressions for an emotion, although the basic anatomical codes were shared among human and emoji faces. Some emotion categories were better predicted by the assortment of anatomical codes than others, with some individual differences among platforms. We discuss theories of emotion that help explain how emoji faces are perceived as an emotion, even when anatomical differences are not always consistent or specific to an emotion.
Conflict Management for Target Recognition Based on PPT Entropy and Entropy Distance

Conflicting evidence affects the final target recognition results. Thus, managing conflicting evidence efficiently can help to improve the belief degree of the true target. In current research, the existing approaches based on belief entropy use belief entropy itself to measure evidence conflict. However, it is not convincing to characterize the evidence conflict only through belief entropy itself. To solve this problem, we comprehensively consider the influences of the belief entropy itself and mutual belief entropy on conflict measurement, and propose a novel approach based on an improved belief entropy and entropy distance. The improved belief entropy based on pignistic probability transformation function is named pignistic probability transformation (PPT) entropy that measures the conflict between evidences from the perspective of self-belief entropy. Compared with the state-of-the-art belief entropy, it can measure the uncertainty of evidence more accurately, and make full use of the intersection information of evidence to estimate the degree of evidence conflict more reasonably. Entropy distance is a new distance measurement method and is used to measure the conflict between evidences from the perspective of mutual belief entropy. Two measures are mutually complementary in a sense. The results of numerical examples and target recognition applications demonstrate that our proposed approach has a faster convergence speed, and a higher belief degree of the true target compared with the existing methods.