Reviewerly/exHarmony huggingface dataset:
Yoga Posture Classification using Computer Vision

There has been over the past few years, a very increased popularity for yoga. A lot of literatures have been published that claim yoga to be beneficial in improving the overall lifestyle and health especially in rehabilitation, mental health and more. Considering the fast-paced lives that individuals live, people usually prefer to exercise or work-out from the comfort of their homes and with that a need for an instructor arises. Hence why, we have developed a self-assisted system which can be used to detect and classify yoga asanas, which is discussed in-depth in this paper. Especially now when the pandemic has taken over the world, it is not feasible to attend physical classes or have an instructor over. Using the technology of Computer Vision, a computer-assisted system such as the one discussed, comes in very handy. The technologies such as ml5.js, PoseNet and Neural Networks are made use for the human pose estimation and classification. The proposed system uses the above-mentioned technologies to take in a real-time video input and analyze the pose of an individual, and classifies the poses into yoga asanas. It also displays the name of the yoga asana that is detected along with the confidence score.
Graphical Models in Reconstructability Analysis and Bayesian Networks

Reconstructability Analysis (RA) and Bayesian Networks (BN) are both probabilistic graphical modeling methodologies used in machine learning and artificial intelligence. There are RA models that are statistically equivalent to BN models and there are also models unique to RA and models unique to BN. The primary goal of this paper is to unify these two methodologies via a lattice of structures that offers an expanded set of models to represent complex systems more accurately or more simply. The conceptualization of this lattice also offers a framework for additional innovations beyond what is presented here. Specifically, this paper integrates RA and BN by developing and visualizing: (1) a BN neutral system lattice of general and specific graphs, (2) a joint RA-BN neutral system lattice of general and specific graphs, (3) an augmented RA directed system lattice of prediction graphs, and (4) a BN directed system lattice of prediction graphs. Additionally, it (5) extends RA notation to encompass BN graphs and (6) offers an algorithm to search the joint RA-BN neutral system lattice to find the best representation of system structure from underlying system variables. All lattices shown in this paper are for four variables, but the theory and methodology presented in this paper are general and apply to any number of variables. These methodological innovations are contributions to machine learning and artificial intelligence and more generally to complex systems analysis. The paper also reviews some relevant prior work of others so that the innovations offered here can be understood in a self-contained way within the context of this paper.
Ensemble Learning with Resampling for Imbalanced Data

Imbalanced class distribution is an issue that appears in various applications. In this paper, we undertake a comprehensive study of the effects of sampling on the performance of bootstrap aggregating in the context of imbalanced data. Concretely, we carry out a comparison of sampling methods applied to single and ensemble classifiers. The experiments are conducted on simulated and real-life data using a range of sampling methods. The contributions of the paper are twofold: i) demonstrate the effectiveness of ensemble techniques based on resampled data over a single base classifier and ii) compare the effectiveness of different resampling techniques when used during the bagging stage for ensemble classifiers. The results reveal that ensemble methods overwhelmingly outperform single classifiers based on resampled data. In addition, we discover that NearMiss and random oversampling (ROS) are the optimal sampling algorithms for ensemble learning.
UNIVERSAL COMPETENCIES AS A METHODOLOGICAL BASIS OF HIGHER EDUCATION STANDARDS

Professional activity in modern society requires a specialist to have not only a certain level of professionalism in their field, but also be ready for rapid adaptation in the professional space as well as to be mobile on the labor market and pursue constant self-improvement. The activity-based methodology is changing in the direction when a specialist has an ability to successfully use the methods of organizing their work and solving professional problems with the help of modern technologies (social, information, communication, etc.). Also a specialist should be able to design and present the results of their work. Responding to the demands of modern society, Higher education provides the development of so-called “flexible” (universal) competencies of future specialists. These competences form the basis of the Federal State Educational Standard 3++ , which aim at developing systematic thinking, critical assessment of information, and the ability to use a variety of resources to solve professional problems. The article presents the experience of Omsk State Pedagogical University in organizing the process of students’ universal competencies development. The study was conducted within the framework of a research grant provided by OmSPU Science Development Fund.
Jom eLearning Unavoidable Acceptance

Technology is an important component in higher education institutions in the form of LMS and e-learning to enhance the learning experience of students. The goal of the implementation of e-learning in higher educational institutions is to help save cost, improve efficiency and branding for both students and institutions. Every student has their study methods and a specific e-learning platform cannot fulfil students’ different study methods. Other than functions, user behaviour can also affect the effectiveness of e-learning. In this paper, data is being collected from students by survey and interviews from private universities about their views on e-learning. Students can freely use the time in between classes and able to view recorded classes to boost their knowledge. Students also have complaints about communication problems, easily distracted during e-learning, not motivated to study, and often encounter technical problems. Managerial and practical implication is discussed.
Entanglement Distribution and Routing in a Multi-node Quantum Network Testbed

We report a field test of distribution and reconfigurable routing of multichannel entangled photons in a quantum network testbed. Nonlocal dispersion cancellation has been leveraged to maintain high-quality entanglement in the quantum network.
A Finite Difference Fictitious Domain Wavelet Method for Solving Dirichlet Boundary Value Problem

In this paper, we introduce a Finite Difference Fictitious Domain Wavelet Method (FDFDWM) for solving two dimensional (2D) linear ellipticÂ partial differential equations (PDEs) with Dirichlet boundary conditions on regular geometric domain. The method reduces the 2D PDE into a 1D system of ordinary differential equations and applies a compactly supported wavelet to approximate the solution. The problem is embedded in a fictitious domain to aid the enforcement of the Dirichlet boundary conditions. We present numerical analysis and show that our method yields better approximation to the solution of the Dirichlet problem than traditional methods like the finite element and finite difference methods.
Intelligent Security Schema for SMS Spam Message Based on Machine Learning Algorithms

&lt;p class="0abstract"&gt;SMS spam messages represent one of the most serious threats to current traditional networks. These messages have been particularly prevalent overseas and are harmful to various types of devices. The current filtering scheme employed in conventional systems is unable to expose a large number of messages. To resolve this issue, a new intelligent security system is proposed to reduce the number of spam messages. It can detect novel spam messages that have a direct and negative impact on networks. The proposed system is heavily based on machine learning to explore various types of messages. The primary achievement of our study is the increase in the accuracy ratio as well as the reduction in the number of false alarms. According to the experimental results, it is clear that our system can realize outstanding results, detecting a massive number of massages.&lt;/p&gt;
Logic-based Robotics

This chapter explores methods for combining symbolic and sub-symbolic reasoning and learning systems to take advantage of the strengths of each approach in challenging tasks in robotics. In perception, Inductive Logic Programming (ILP) can be used to learn descriptions of classes of objects and to find relations between objects. Examples are given of perception for robots in urban search and rescue. We also describe systems for learning plans and behaviours for robots. Relational learning is used to acquire abstract model of robot actions that are then used to constrain sub-symbolic learning for low-level control. Models can be variously expressed in the classical STRIPS representation or as qualitative models. A STRIPS-like model is acquired by a robot that learns to use tools and also designs new tools. A qualitative model is constructed by a robot that learns to traverse uneven terrain in urban search and rescue. The model is refined by reinforcement learning.
Comparison Question Generation Based on Potential Compared Attributes Extraction

Question generation (QG) aims to automatically generate questions from a given passage, which is widely used in education. Existing studies on the QG task mainly focus on the answer-aware QG, which only asks an independent object related to the expected answer. However, to prompt students to develop comparative thinking skills, multiple objects need to be simultaneously focused on the QG task, which can be used to attract students to explore the differences and similarities between them. Towards this end, we consider a new task named comparison question generation (CQG). In this paper, we propose a framework that includes an attribute extractor and an attribute-attention seq2seq module. Specially, the attribute extractor is based on Stanford CoreNLP Toolkit to recognize the attributes related to the multiple objects that can be used for comparison. Then, the attribute-attention seq2seq module utilizes an attention mechanism to generate questions with the assistance of the attributes. Extensive experiments conducted on the HotpotQA dataset manifest the effectiveness of our framework, which outperforms the neural-based model and generates reliable comparison questions.
Chatbots at Work: A Taxonomy of the Use of Chatbots in the Workplace

The adoption of Artificial Intelligence (AI) applications in organisations is growing rapidly. In this study, we focus on chatbots as one type of AI applications in organisations. As an AI-enabled application, Chatbots learn from their use patterns. Therefore, the ways users adopt and use chatbots will have significant impact on the future evolvement of this technology. This study examines the use of chatbots in the workplace. It questions how employees use chatbots in the workplace. It adopts an inductive approach to examine the use of the same chatbot in the same organisation. The findings highlight the existence of different patterns of use. Based on the inductive analysis of data, we develop a taxonomy of chatbot users. The taxonomy offers four types of users based on two prominent criteria. The study presents a key step in understanding chatbot use in the workplace that could pave the way for future research.
Reservoir Transformer

We demonstrate that transformers obtain impressive performance even when someof the layers are randomly initialized and never updated. Inspired by old andwell-established ideas in machine learning, we explore a variety of non-linearreservoir layers interspersed with regular transformer layers, and showimprovements in wall-clock compute time until convergence, as well as overallperformance, on various machine translation and (masked) language modellingtasks.
Tambov Governorate Library and the development of librarianship in Tambov in 1920–1937

We consider the stage of development of library business in the Tambov region during the formation of Soviet power. We describe the systematization in the funds of state and trade union libraries of books nationalized from private collections, the reconstruction and expansion of the network of local (district and sub-district) libraries, reading rooms, mobile libraries that contributed to educational work among the population of the region. The activities of the new government on administrative regulation of the system of relations between libraries, libraries with other cultural and educational institutions and local authorities are studied. The work shows the activities of the management of the Central Governorate Library (CGL) to improve labor discipline, the level of education and political literacy of librarians. We describe the management of the Central Governorate Library and its policy of hiring employees, the fight against the manifestation of per-sonal conflicts of librarians in the workplace, the theft of books and careless attitude towards them. Mass events that were practiced along with the distribution of books in the library are considered: meetings of political education, excursions for pioneer children, atheistic anti-religious evenings. We describe the creation of children’s and youth departments that contributed to the development of education. It is shown that the library staff created a developed system of libraries in the region, which allowed to meet the cultural needs of the population.
The Intersection of Blockchain and 6G Technologies

The fifth generation (5G) wireless networks are on the way to be deployed around the world. The 5G technologies target to support diverse vertical applications by connecting heterogeneous devices and machines with drastic improvements in terms of high quality of service, increased network capacity and enhanced system throughput. However, 5G systems still remain a number of security challenges that have been mentioned by researchers and organizations, including decentralization, transparency, risks of data interoperability, and network privacy vulnerabilities. Furthermore, the conventional techniques may not be sufficient to deal with the security requirements of 5G. As 5G is generally deployed in heterogeneous networks with massive ubiquitous devices, it is quite necessary to provide secure and decentralized solutions. Motivated from these facts, in this paper we provide a state-of-the-art survey on the integration of blockchain with 5G networks and beyond. In this detailed survey, our primary focus is on the extensive discussions on the potential of blockchain for enabling key 5G technologies, including cloud computing, edge computing, Network Function Virtualization, Network Slicing, and D2D communications. We then explore and analyse the opportunities that blockchain potentially empowers important 5G services, ranging from spectrum management, data sharing, network virtualization, resource management to interference management, federated learning, privacy and security provision. The recent advances in the applications of blockchain in 5G Internet of Things are also surveyed in a wide range of popular use-case domains, such as smart healthcare, smart city, smart transportation, smart grid and UAVs. The main findings derived from the comprehensive survey on the cooperated blockchain-5G networks and services are then summarized, and possible research challenges with open issues are also identified. Lastly, we complete this survey by shedding new light on future directions of research on this newly emerging area.
Adversarial parameter defense by multi-step risk minimization

Previous studies demonstrate DNNs' vulnerability to adversarial examples and adversarial training can establish a defense to adversarial examples. In addition, recent studies show that deep neural networks also exhibit vulnerability to parameter corruptions. The vulnerability of model parameters is of crucial value to the study of model robustness and generalization. In this work, we introduce the concept of parameter corruption and propose to leverage the loss change indicators for measuring the flatness of the loss basin and the parameter robustness of neural network parameters. On such basis, we analyze parameter corruptions and propose the multi-step adversarial corruption algorithm. To enhance neural networks, we propose the adversarial parameter defense algorithm that minimizes the average risk of multiple adversarial parameter corruptions. Experimental results show that the proposed algorithm can improve both the parameter robustness and accuracy of neural networks.
Both sides of the story: comparing student-level data on reading performance from administrative registers to application generated data from a reading app

The use of various learning apps in school settings is growing and thus producing an increasing amount of usage generated data. However, this usage generated data has only to a very little extend been used for monitoring and promoting learning progress. We test if application usage generated data from a reading app holds potential for measuring reading ability, reading speed progress and for pointing out features in a school setting that promotes learning. We analyze new data from three different sources: (1) Usage generated data from a widely used reading app, (2) Data from a national reading ability test, and (3) Register data on student background and family characteristics. First, we find that reading app generated data to some degree tells the same story about reading ability as does the formal national reading ability test. Second, we find that the reading app data has the potential to monitor reading speed progress. Finally, we tested several models including machine learning models. Two of these were able to identify variables associated with reading speed progress with some degree of success and to point at certain conditions that promotes reading speed progress. We discuss the results and avenues for further research are presented.
Cryptanalysis for securing DICOM medical contents using multilevel encryption

In healthcare, medical images are considered as important and sensitive data. The patient particulars can be identified using medical data. For transferring these data, a secure encryption algorithm is essential. Cryptographic schemes are used to enhance the security and confidentiality of medical data thereby preventing unauthorised access. Data from the contributors and storage systems leads to scalability and preservation issues. Therefore a standard is required to preserve the secrecy of medical images. Digital Imaging and Communication in Medicine (DICOM) is the universal standard for secured communication in medical files. In cryptographic technique, after encryption process, some intruders can hack the sensitive data without using precise key is known as cryptanalysis. So a typical evaluation mechanism is needed to verify the quality of cryptographic methods employed. In this paper, the results are assessed for MRI, CT, and X-ray images data. The proposed method performance is evaluated using cryptanalysis measures.
Research on the Guarantee Mechanism of Innovation and Entrepreneurship Education in Local Applied Undergraduate Universities

Although local applied undergraduate universities have launched innovation and entrepreneurship education, there are some prominent problems in innovation and entrepreneurship education, such as, vague understanding of innovation and entrepreneurship education, incomplete curriculum system for innovation and entrepreneurship education, lack of innovation and entrepreneurship education practice platforms, weak faculty, inadequate guidance and assistance for innovation and entrepreneurship education, etc.The article points out that the main problems are the ambiguous understanding of the objectives of innovation and entrepreneurship education and the imperfect development of the applied undergraduate universities.Based on the analysis of the objectives of innovation and entrepreneurship education, from the three main bodies of innovation and entrepreneurship education, the article proposes to build a guarantee mechanism which mainly includes management mechanism, curriculum system construction guarantee mechanism, teacher team guarantee mechanism, practice platform guarantee mechanism, practice training guarantee mechanism.
Persistent memory aware performance isolation with dicio

Cloud vendors increasingly support persistent memory (PM) in their cloud platform to lower the total cost of ownership (TCO). They run multiple instances on cloud nodes supporting large memory with DRAM and PM. While PM enables the low-cost in-memory computations, adding PM to a cloud raises new challenges of managing the performance interference between co-running applications. Since PM has its own unique performance characteristics, cloud vendors need to rethink the performance isolation problem beyond the conventional performance interference models on DRAM.
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms

Counterfactual explanations provide means for prescriptive model explanations by suggesting actionable feature changes (e.g., increase income) that allow individuals to achieve favorable outcomes in the future (e.g., insurance approval). Choosing an appropriate method is a crucial aspect for meaningful counterfactual explanations. As documented in recent reviews, there exists a quickly growing literature with available methods. Yet, in the absence of widely available opensource implementations, the decision in favor of certain models is primarily based on what is readily available. Going forward - to guarantee meaningful comparisons across explanation methods - we present CARLA (Counterfactual And Recourse LibrAry), a python library for benchmarking counterfactual explanation methods across both different data sets and different machine learning models. In summary, our work provides the following contributions: (i) an extensive benchmark of 11 popular counterfactual explanation methods, (ii) a benchmarking framework for research on future counterfactual explanation methods, and (iii) a standardized set of integrated evaluation measures and data sets for transparent and extensive comparisons of these methods. We have open-sourced CARLA and our experimental results on Github, making them available as competitive baselines. We welcome contributions from other research groups and practitioners.
Learning Modality-Invariant Features by Cross-Modality Adversarial Network for Visual Question Answering

Visual Question Answering (VQA) is a typical multimodal task with significant development prospect on web application. In order to answer the question based on the corresponding image, a VQA model needs to utilize the information from different modality efficiently. Although the multimodal fusion methods such as attention mechanism make significant contribution for VQA, these methods try to co-learn the multimodal features directly, ignoring the large gap between different modality and thus poor aligning the semantic. In this paper, we propose a Cross-Modality Adversarial Network (CMAN) to address this limitation. Our method combines cross-modality adversarial learning with modality-invariant attention learning aiming to learn the modality-invariant features for better semantic alignment and higher answer prediction accuracy. The accuracy of model achieves 70.81% on the test-dev split on the VQA-v2 dataset. Our results also show that the model narrows the gap between different modalities effectively and improves the alignment performance of the multimodal information.