Reviewerly/exHarmony huggingface dataset:
A Self-Adaptive and Efficient Context-Aware Healthcare Model for COPD Diseases

The emergence of pervasive computing technology has revolutionized all aspects of life and facilitated many everyday tasks. As the world fights the coronavirus pandemic, it is necessary to find new ways to use technology to fight diseases and reduce their economic burden. Distributed systems have demonstrated efficiency in the healthcare domain, not only by organizing and managing patient data but also by helping doctors and other medical experts to diagnose diseases and take measures to prevent the development of serious conditions. In the case of chronic diseases, telemonitoring systems provide a way to monitor patients’ states and biomarkers in the course of their everyday routines. We developed a Chronical Obstructive Pulmonary Disease (COPD) healthcare system to protect patients against risk factors. However, each change in the patient context initiated the execution of the system’s entire rule base, which diminished performance. In this article, we use separation of concerns to reduce the impact of contextual changes by dividing the context, rules and services into software modules (units). We combine healthcare telemonitoring with context awareness and self-adaptation to create an adaptive architecture model for COPD patients. The model’s performance is validated using COPD data, demonstrating the efficiency of the separation of concerns and adaptation techniques in context-aware systems.
PLGRIM: Hierarchical Value Learning for Large-scale Exploration in Unknown Environments

In order for an autonomous robot to efficiently explore an unknown environment, it must account for uncertainty in sensor measurements, hazard assessment, localization, and motion execution. Making decisions for maximal reward in a stochastic setting requires value learning and policy construction over a belief space, i.e., probability distribution over all possible robot-world states. However, belief space planning in a large spatial environment over long temporal horizons suffers from severe computational challenges. Moreover, constructed policies must safely adapt to unexpected changes in the belief at runtime. This work proposes a scalable value learning framework, PLGRIM (Probabilistic Local and Global Reasoning on Information roadMaps), that bridges the gap between (i) local, risk-aware resiliency and (ii) global, reward-seeking mission objectives. Leveraging hierarchical belief space planners with information-rich graph structures, PLGRIM addresses large-scale exploration problems while providing locally near-optimal coverage plans. We validate our proposed framework with high-fidelity dynamic simulations in diverse environments and on physical robots in Martian-analog lava tubes.
The Effectiveness of a Learner-Centered Pedagogical Approach with Flipped Pedagogy and Digital Learning Environment in Higher Education Feedback on a Cell Biology Course

This paper aims to examine the results of a survey of 292 first-year biology students to evaluate their perception of their learning environment, as well as their ability to handle an educational innovation such as reverse pedagogy and serious gaming integrated into a hybrid learning environment. We also present the approaches and steps that allowed us to validate our statistical study. We used a methodology to validate the instrument developed; using standard parameters, the mean and the standard deviation to describe quantitative data and percentages for qualitative data. The internal consistency of the scales was assessed using the correlation coefficient calculated after a one-factor analysis of variance as well as Cronbach's alpha coefficient (α). The scores show that the items and subscales have good internal consistency. In addition, it shows that the students are fully aware of their learning environment and the difficulties they encounter. They are motivated to embrace our pedagogical innovation. It, therefore, confirms our willingness to implement this pedagogical model for students in the coming years.
Attribute-Guided Adversarial Training for Robustness to Natural Perturbations

While existing work in robust deep learning has focused on small pixel-level norm-based perturbations, this may not account for perturbations encountered in several real-world settings. In many such cases although test data might not be available, broad specifications about the types of perturbations (such as an unknown degree of rotation) may be known. We consider a setup where robustness is expected over an unseen test domain that is not i.i.d. but deviates from the training domain. While this deviation may not be exactly known, its broad characterization is specified a priori, in terms of attributes. We propose an adversarial training approach which learns to generate new samples so as to maximize exposure of the classifier to the attributes-space, without having access to the data from the test domain. Our adversarial training solves a min-max optimization problem, with the inner maximization generating adversarial perturbations, and the outer minimization finding model parameters by optimizing the loss on adversarial perturbations generated from the inner maximization. We demonstrate the applicability of our approach on three types of naturally occurring perturbations -- object-related shifts, geometric transformations, and common image corruptions. Our approach enables deep neural networks to be robust against a wide range of naturally occurring perturbations. We demonstrate the usefulness of the proposed approach by showing the robustness gains of deep neural networks trained using our adversarial training on MNIST, CIFAR-10, and a new variant of the CLEVR dataset.
Intuitionistic Fuzzy Laplacian Twin Support Vector Machine for Semi-supervised Classification

Abstract In general, data contain noises which come from faulty instruments, flawed measurements or faulty communication. Learning with data in the context of classification or regression is inevitably affected by noises in the data. In order to remove or greatly reduce the impact of noises, we introduce the ideas of fuzzy membership functions and the Laplacian twin support vector machine (Lap-TSVM). A formulation of the linear intuitionistic fuzzy Laplacian twin support vector machine (IFLap-TSVM) is presented. Moreover, we extend the linear IFLap-TSVM to the nonlinear case by kernel function. The proposed IFLap-TSVM resolves the negative impact of noises and outliers by using fuzzy membership functions and is a more accurate reasonable classifier by using the geometric distribution information of labeled data and unlabeled data based on manifold regularization. Experiments with constructed artificial datasets, several UCI benchmark datasets and MNIST dataset show that the IFLap-TSVM has better classification accuracy than other state-of-the-art twin support vector machine (TSVM), intuitionistic fuzzy twin support vector machine (IFTSVM) and Lap-TSVM.
DeepCollaboration: Collaborative Generative and Discriminative Models for Class Incremental Learning

An important challenge for neural networks is to learn incrementally, i.e., learn new classes without catastrophic forgetting. To overcome this problem, generative replay technique has been suggested, which can generate samples belonging to learned classes while learning new ones. However, such generative models usually suffer from increased distribution mismatch between the generated and original samples along the learning process. In this work, we propose DeepCollaboration (D-Collab), a collaborative framework of deep generative and discriminative models to solve this problem effectively. We develop a discriminative learning model to incrementally update the latent feature space for continual classification. At the same time, a generative model is introduced to achieve conditional generation using the latent feature distribution produced by the discriminative model. Importantly, the generative and discriminative models are connected through bidirectional training to enforce cycle-consistency of mappings between feature and image domains. Furthermore, a domain alignment module is used to eliminate the divergence between the feature distributions of generated images and real ones. This module together with the discriminative model can perform effective sample mining to facilitate incremental learning. Extensive experiments on several visual recognition datasets show that our system can achieve state-of-the-art performance.
Authentication and secure communication by Haar Cascade Classifier, Eigen Face, LBP Histogram and variable irreducible polynomial in (2<sup>8</sup> ) finite field

One of the most common way of verification is `Face Recognition' which can also be implemented for communication between machines and humans. In industries, corporate sectors, financial exchanges etc., face detection and recognition are used for the purpose of authentication. Another area of interest is the communication of information securely. In this paper, for face detection process Haar-classifiers are used which produces an accuracy of around 99.4% for still images and 98.6% for video recordings. Using two algorithms i.e. LBPH algorithm and Eigenfaces face recognition is done. The former produces a recognition accuracy of 99.39% for still images and 99.32% for video recordings and latter produces accuracies of 99.28% and 98.87% for the same. The paper also proposes a more secure modified AES, whose modification done by 43 variable S - Box generation out of which 9 S - Box return correct inverse S - Box with the help of variable irreducible polynomial in GF (2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8</sup> ) finite field. Thus every time using the variable irreducible polynomial in GF (2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8</sup> ) a new S- Box and inverse S-Box is generated which makes it difficult for the intruders to crack modified AES.
Hybrid Course Recommendation System Design for a Real-Time Student Automation Application

Recommender systems provide personalized suggestions by processing user and item information and interactions.Personalized product recommendations make it easier for users to access products that interest them.Course recommendation systems, on the other hand, aim to guide students to fields of interest in which they can succeed.On e-learning sites, there are many courses and students from different fields.Also, students can select courses from other than the fields they are studying.However, students in educational institutions must follow a curriculum.Since each educational institution has distinct constraints on course selection, a specific approach to the problem is required to develop a course recommender system.Due to the restrictive nature of the problem, developing a recommendation system for institutions is considered challenging.Therefore, students consult a faculty member when selecting a course for enrollment.In this study, a hybrid recommender system is proposed using student and course information with collaborative filtering and content-based filtering models.The proposed system provides consistent recommendations by using explicit and implicit data, without predefined association rules.The collaborative filtering algorithms use grades as rating values.The content-based filtering algorithms utilize text-based information about students and courses by converting them into feature vectors using natural language processing methods.In the combination phase of the hybrid recommender system, only one of the collaborative filtering and one of the content-based filtering models are used with different ensembling methods.It is found that the suggested hybrid recommender system can achieve outperforming results for all evaluation metrics.The results show the values of the rank-aware metrics Precision@N, AP@N, mAP@N, and NDCG@N for the individual models and the hybrid models with different combinations.In particular, for content-based filtering with Bayesian personalized ranking, the hybrid model performs better than any algorithm in practice.
Sketch Based Image Retrieval using Deep Learning Based Machine Learning

Sketch based image retrieval (SBIR) is a sub-domain of Content Based Image Retrieval(CBIR) where the user provides a drawing as an input to obtain i.e retrieve images relevant to the drawing given. The main challenge in SBIR is the subjectivity of the drawings drawn by the user as it entirely relies on the user's ability to express information in hand-drawn form. Since many of the SBIR models created aim at using singular input sketch and retrieving photos based on the given single sketch input, our project aims to enable detection and extraction of multiple sketches given together as a single input sketch image. The features are extracted from individual sketches obtained using deep learning architectures such as VGG16 , and classified to its type based on supervised machine learning using Support Vector Machines. Based on the class obtained, photos are retrieved from the database using an opencv library, CVLib , which finds the objects present in a photo image. From the number of components obtained in each photo, a ranking function is performed to rank the retrieved photos, which are then displayed to the user starting from the highest order of ranking up to the least. The system consisting of VGG16 and SVM provides 89% accuracy.
An investigation on feature and text extraction from images using image recognition in Android

In today’s era, there is a huge rise in demand for the data and information available in a printed medium. To resume time, cost, and energy, we have been developing numerous technology. One of them, convert JPG, JPEG, or PDF softcopy into text, this process calls Text extraction. Identification of characters and detection of words and texts is again an upfront challenging task. This paper is focused on an investigation of feature and text extraction from images using image recognition in Android. In this era of Research and Technology, Image processing has become one of the most critical fields. Using a number of researchers' papers, we are going to study appropriate methods or algorithm, their accuracy, a key feature of each paper. All research papers going to study in the context of Android technology.
Sample Efficient Algorithms for Learning Quantum Channels in PAC Model and the Approximate State Discrimination Problem

The probably approximately correct (PAC) model [Leslie G. Valiant, 1984] is a well studied model in classical learning theory. Here, we generalize the PAC model from concepts of Boolean functions to quantum channels, introducing PAC model for learning quantum channels, and give two sample efficient algorithms that are analogous to the classical result [Blumer et al., 1987]. The classical Occam’s razor algorithm is done trivially by excluding any concepts not compatible with the input-output pairs one gets, but such an approach is not immediately possible with a concept class of quantum channels, because the outputs are unknown quantum states from the quantum channel. To study the quantum state learning problem associated with PAC learning quantum channels, we focus on the special case where the channels all have constant output. In this special case, learning the channels reduce to a problem of learning quantum states that is similar to the well known quantum state discrimination problem [Joonwoo Bae and Leong-Chuan Kwek, 2017], but with the extra twist that we allow e-trace-distance-error in the output. We call this problem Approximate State Discrimination, which we believe is a natural problem that is of independent interest.We give two algorithms for learning quantum channels in PAC model. The first algorithm has sample complexity O((log|C| + log(1/ δ))/(e²)), but only works when the outputs are pure states, where C is the concept class, e is the error of the output, and δ is the probability of failure of the algorithm. The second algorithm has sample complexity O((log³|C|(log|C|+log(1/ δ)))/(e²)),and work for mixed state outputs. Some implications of our results are that we can PAC-learn a polynomial sized quantum circuit in polynomial samples, and approximate state discrimination can be solved in polynomial samples even when the size of the input set is exponential in the number of qubits, exponentially better than a naive state tomography.
A Blockchain-based Access Control Scheme

Abstract Access control plays an important role in protecting data privacy. However, traditional access control models such as RBAC, ABAC and UCON need to be built based on a central trusted server. Once the central trusted server is controlled by an adversary, it will pose a serious threat to data security. Therefore, this article proposes a blockchain-based access control scheme, which uses the blockchain to replace the central trusted server and uses smart contracts to complete access. The proposed scheme solves the centralization problem of the traditional access control model. Furthermore, it has been tested on representative equipment, and the experimental results show that the proposed scheme is efficient and feasible.
Does My Representation Capture X? Probe-Ably

Probing (or diagnostic classification) has become a popular strategy for investigating whether a given set of intermediate features is present in the representations of neural models.Probing studies may have misleading results, but various recent works have suggested more reliable methodologies that compensate for the possible pitfalls of probing.However, these best practices are numerous and fast-evolving.To simplify the process of running a set of probing experiments in line with suggested methodologies, we introduce Probe-Ably: an extendable probing framework which supports and automates the application of probing methods to the user's inputs.
Influence of digital technologies on the formation of social consciousness of students

The article discusses the possibilities of infocommunications in the educational process, which promote the social consciousness of art specialty students. The analysis of scientific works showed that, despite the intensive development of information technology in higher education, there is an unsystematic use of the emotional sphere of students, which affects the formation of social consciousness. The purpose of the research is to determine the educational potential of digital technologies for the optimal use of new methods in the formation of value orientations and social attitudes of young people while learning to draw. The method of communicative design in the information and educational environment of a university is interpreted as a way of actualizing traditional values that influence the formation of students' social consciousness. In this article we consider the effectiveness of the organizational and pedagogical conditions determined by the objective factors of organizing cooperative social activities in an innovative educational environment. The orientation towards the skills and abilities of innovative critical thinking, combining science and art in the national cultural space are also considered. This contributes to the adaptation of students to professional activities as citizens of Russia and allows them to build their life and professional priorities based on value orientation. The organizational and pedagogical conditions, the method of communicative design of the formation of social consciousness of art department students described in the article can be applied to the process of training specialists in the field of architecture, design, reconstruction, and restoration, as well as painting, graphics, and sculpture.
Modeling of Human Skin by the Use of Deep Learning

Deep learning (DL) has matured well over time and resonated in various domains of computer applications. Pattern recognition gets more attention in machine learning field to take advantage of data available for modern life. Recognition by using the technology performance is worthy in terms of skin and other human features; this research tries to extract useful features from the skin and then classify these features under certain condition. The main objective of this study is to detect the skin diseases early and classify them for correct treatment. Using improved classifier (ISVM) to be adaptive with requirements of our task, many advantages can be got with this technique and it is useful in the fields of medicine, human health care, and diagnosis and life threat. Applying good classifier with best feature selection achieved good result in terms of accuracy, 95%, and recognition rate, 93%. This study concluded that adopting best strategy in selecting features and classification yields better prediction in emergency case before medicating the patient even during treatment.
Relevance and Objectives of Carrying Out Professional and Public Accreditation of Educational Programs in the Field of Human Resource Management

The article is devoted to the development of the practice of applying the procedures of professional and public accreditation of educational programs in the field of human resource management. An overview of the regulatory and methodological framework for conducting accreditation examinations is presented. The key issues of interaction between the Council for Professional Qualifications, the accrediting and educational organization are identified, their content is disclosed and recommendations for educational organizations on preparing for the accreditation examination are presented. The article presents an analysis of the main advantages that educational organizations receive when conducting professional-public accreditation of educational programs and the reasons for their resistance, among which a low level of awareness of the accreditation procedure, the benefits of its implementation in the short and long term, is highlighted. A number of prospects for the development of professional-public accreditation and the use of its results have been substantiated, which can contribute to an increase in the interest of participants in the educational services market.
Cyclic label propagation for graph semi-supervised learning

Graph neural networks (GNNs) have emerged as effective approaches for graph analysis, especially in the scenario of semi-supervised learning. Despite its success, GNN often suffers from over-smoothing and over-fitting problems, which affects its performance on node classification tasks. We analyze that an alternative method, the label propagation algorithm (LPA), avoids the aforementioned problems thus it is a promising choice for graph semi-supervised learning. Nevertheless, the intrinsic limitations of LPA on feature exploitation and relation modeling make propagating labels become less effective. To overcome these limitations, we introduce a novel framework for graph semi-supervised learning termed as Cyclic Label Propagation (CycProp for abbreviation), which integrates GNNs into the process of label propagation in a cyclic and mutually reinforcing manner to exploit the advantages of both GNNs and LPA. In particular, our proposed CycProp updates the node embeddings learned by GNN module with the augmented information by label propagation, while fine-tunes the weighted graph of label propagation with the help of node embedding in turn. After the model converges, reliably predicted labels and informative node embeddings are obtained with the LPA and GNN modules respectively. Extensive experiments on various real-world datasets are conducted, and the experimental results empirically demonstrate that the proposed CycProp model can achieve relatively significant gains over the state-of-the-art methods.