Reviewerly/exHarmony huggingface dataset:
Supporting Cross-Session Cross-Device Search in an Academic Digital Library

Academic search tasks that are complex may require that a searcher undertake the task over the course of multiple search sessions, and possibly using multiple devices. One of the key challenges in cross-session search is resuming the previously started search task. If task resumption can be supported well in a mobile environment, we may be able to encourage academic searchers to make use of the small down-times they encounter while away from their computers (e.g., while in line at the market). If it is supported well in a desktop/laptop environment, the work that was done on a mobile device can easily be accessed and used. In this paper, we demonstrate an academic digital library search interface that supports cross-session cross-device search, using visualization approaches to support browsing and selection of past search topics, and faceted navigation features to support investigation and examination of previously saved documents.
Full-Body Motion Recognition in Immersive- Virtual-Reality-Based Exergame

Exergames have beneficial effects on the player’s motivation to exercise. However, many current games lack accurate full-body motion recognition, resulting in players not performing the physical exercise the game requires. Therefore, we aim to develop an immersive virtual reality exergame that simultaneously recognizes and reconstructs full-body movements to motivate players to learn and practice yoga. The system analyzes the entire movement execution and identifies the player’s execution errors to provide appropriate feedback so that players can then improve their movements. Such a system can be used in exergames designed for rehabilitation purposes to assist patients or to monitor their improvement. To access recognition performance, we trained and tested hidden Markov models and applied the leave-one-out cross-validation. The results show that the system achieves an F1-score of 0.79 for yoga warrior I, 0.85 for yoga warrior II, and 0.66 for extended side angle. A user study with 32 participants revealed that the game was fun and that the players enjoyed it. Moreover, performance results show that players needed fewer attempts to correctly perform a pose as the exergame progressed.
Development of an Automatic Class Attendance System using CNN-based Face Recognition

We are living in the 21 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> century which is the era of modern technology. Many traditional problems are being solved using new innovative technologies. Taking attendance daily is an indispensable part of educational institutions as well as offices. It is both exhausting and time-consuming if done manually. Biometric attendance systems through voice, iris, and fingerprint recognition require complex and expensive hardware support. An auto attendance system using face recognition, which is another biometric trait, can resolve all these problems. This paper represents the development of a face recognition based automatic student attendance system using Convolutional Neural Networks which includes data entry, dataset training, face recognition and attendance entry. The system can detect and recognize multiple person's face from video stream and automatically record daily attendance. The proposed system achieved an average recognition accuracy of about 92 %. Using this system, daily attendance can be recorded effortlessly avoiding the risk of human error.
Highly shared Convolutional Neural Networks

In order to deploy deep Convolutional Neural Networks (CNNs) on the mobile devices, many mobile CNNs are introduced. Currently, some online applications are usually re-trained because of the constantly-increasing data. However, compared with the regular models, it is not very efficient to train the present mobile models. Therefore, the purpose of this paper is to propose efficient mobile models both in the training and test processes through exploring the main causes of the current mobile CNNs’ inefficiency and the parameters’ properties. Finally, this paper introduces Highly Shared Convolutional Neural Networks (HSC-Nets). The HSC-Nets employ two shared mechanisms to reuse the filters comprehensively. Experimental results showed that, compared with the regular networks and the latest state-of-the-art group-conv mobile networks, the HSC-Nets can achieve promising performances and effectively decrease the model size. Furthermore, it is also more efficient in both the training and test processes.
Linear Regression Model for Agile Software Development Effort Estimation

Software cost estimation is always an essential task for the development management as it requires for estimating the effort and the time required for developing the software. A project manager requires software estimation for making a decision and predict the total budget. Success or failure of software development depends on the accurate estimation of cost and time. There are numerous tools and techniques have been developed for estimating the software cost. But all these techniques are best suitable for the traditional development methodology. From the past two decades, the agile methodology has been com for software development. So the traditional cost estimation techniques may not give the appropriate results for agile development. In this paper, the multiple linear regression models are proposed for comparing the best model for agile development. The correlation between the dependent and independent variables are also found out. The results showed that the proposed model outperforms from the decision tree, stochastic gradient boosting, and random forest.
Discriminant analysis based on reliability of local neighborhood

To obtain a compact and effective low-dimensional representation, recently, most existing discriminant manifold learning methods have integrated manifold learning into discriminant analysis (DA) for extracting the intrinsic structure of data. These methods learn two kinds of adjacency graphs, such as intrinsic graph and penalty graph, to characterize the similarity between samples from intraclass and the pseudo similarity of interclass. However, they treat every sample equally, which results in the following defects: (1) These methods cannot accurately characterize the marginal region among different classes only through penalty graphs. (2) They can not identify the noisy and outlier samples which reduce the robustness of these methods. To address these problems, we introduce an adaptive adjacency factor to perform the discriminative based reliability analysis for each sample. By integrating the adjacency factor into discriminant manifold learning methods, we propose a novel method for DA namely discriminant analysis based on reliability of local neighborhood (DA-RoLN). We mainly have three contributions in this paper: (1) By the introduction of adjacency factor, sample points can be divided into three parts: intraclass samples, marginal samples, and outliers. Therefore, DA-RoLN emphasizes the effect of valid samples and filters the influence of outliers. (2) We adaptively calculate the adjacency factor in low-dimensional space, thus, the margin between different classes in low-dimensional space is emphasized. (3) An iterative algorithm is developed to solve the objective function of DA-RoLN, and it is easy to solve with a low computational cost. Extensive experimental results show the effectiveness of DA-RoLN.
Artificial Intelligence in Education

Current educational developments in theories and practices advocate a more personalized, student-centered approach to teach 21st-century skills. However, the existing pedagogical practices cannot provide optimal student engagement as they follow a ‘one size fits all' approach. How can we provide high-quality adaptive instructions at a personalized level? Intelligent tutoring systems with embedded artificial intelligence can assist both students and teachers in providing personalized support. This chapter highlights the role of artificial intelligence in the development of intelligent tutoring systems and how these are providing personalized instructions to students with and without disabilities. This chapter gives insight into the challenges and barriers posed by the integration of intelligent tutoring systems in K-12 classrooms.
A Qualitative Study on the Perceptions and Learning Behavior of Medical Students in Online Classes

Since the emergence of coronavirus disease 2019 (COVID-19), medical schools have experienced a sudden, full-scale transition to online classes. As the COVID-19 pandemic continues, it is important to evaluate current educational programs and to assess their implications. This study explored perceptions of online classes and learning behavior among medical students. Twenty preclinical medical students were interviewed in focus groups for 2 months. They generally expressed positive perceptions about online classes, and in particular, positively assessed the ability to lead their individual lifestyles and study in comfortable environments with fewer time and space constraints. Students thought that the online environment provided a fair chance of facilitating positive interactions with the professor and considered communication with the professor to be an important factor only when it was related to the class content or directly helped with their grades and careers. Students also had negative views, such as feeling uncertain when they could not see their peers learning progress and assess themselves in comparison and feeling social isolation. Learning behaviors have also changed, as students explored their learning styles and adapted to the changed learning environment. Students expanded their learning by using online functions. However, students sometimes abused the online class format by “just playing” the lecture while not paying attention and relying on other students’ lecture transcripts to study. The results of this study are hoped to provide a useful foundation for future research on online class-based teaching and learning.
Nearest Neighbor-Based Strategy to Optimize Multi-View Triplet Network for Classification of Small-Sample Medical Imaging Data

Multi-view classification with limited sample size and data augmentation is a very common machine learning (ML) problem in medicine. With limited data, a triplet network approach for two-stage representation learning has been proposed. However, effective training and verifying the features from the representation network for their suitability in subsequent classifiers are still unsolved problems. Although typical distance-based metrics for the training capture the overall class separability of the features, the performance according to these metrics does not always lead to an optimal classification. Consequently, an exhaustive tuning with all feature-classifier combinations is required to search for the best end result. To overcome this challenge, we developed a novel nearest-neighbor (NN) validation strategy based on the triplet metric. This strategy is supported by a theoretical foundation to provide the best selection of the features with a lower bound of the highest end performance. The proposed strategy is a transparent approach to identify whether to improve the features or the classifier. This avoids the need for repeated tuning. Our evaluations on real-world medical imaging tasks (i.e., radiation therapy delivery error prediction and sarcoma survival prediction) show that our strategy is superior to other common deep representation learning baselines [i.e., autoencoder (AE) and softmax]. The strategy addresses the issue of feature's interpretability which enables more holistic feature creation such that the medical experts can focus on specifying relevant data as opposed to tedious feature engineering.
Chinese Relation Extraction on Forestry Knowledge Graph Construction

Forestry work has long been weak in data integration; its initial state will inevitably affect the forestry project development and decision-quality. Knowledge Graph (KG) can provide better abilities to organize, manage, and ... | Find, read and cite all the research you need on Tech Science Press
A Robust and Hybrid Cryptosystem for Identity Authentication

With the progressive development of a wide range of applications, interconnect things and internet of things (IoT) became an imperative required trend by industries and academicians. IoT became a base infrastructure for remote access or control depending on internet protocol (IP) networks, especially after the COVID-19 pandemic. The huge application domain’s infrastructure, which depends on IoT, requires a trusted connection to guarantee security and privacy while transferring data. This paper proposes a hybrid identity authentication pipeline that integrates three schemes, namely, an elliptic curve cryptography (ECC) scheme is integrated with the Ong, Schnorr, and Shamir (OSS) signature scheme and chaotic maps. The latter satisfies both security and guarantee criteria. The novelty of the proposal is in using chaotic mapping and a cyclic group to deduce a substitution box (S-Box) and a reversible matrix as a portion of the OSS signature equation. The ECC-based security part is an efficient public key cryptography mechanism with less computational cost, which makes it the most convenient to be used in IoT devices for authentication and privacy. The strength of the proposed scheme relies on combining the discrete logarithm problem (DLP) and integer factorization problem (IFP). The proposed approach was simulated using Lab-View and compared with other state-of-the art schemes. Extensive simulation results and analysis of the security and time rendering results confirmed its durability against different types of attacks, such as linear and differential attacks.
Joint Label Inference and Discriminant Embedding

Graph-based learning in semisupervised models provides an effective tool for modeling big data sets in high-dimensional spaces. It has been useful for propagating a small set of initial labels to a large set of unlabeled data. Thus, it meets the requirements of many emerging applications. However, in real-world applications, the scarcity of labeled data can negatively affect the performance of the semisupervised method. In this article, we present a new framework for semisupervised learning called joint label inference and discriminant embedding for soft label inference and linear feature extraction. The proposed criterion and its associated optimization algorithm take advantage of both labeled and unlabeled data samples in order to estimate the discriminant transformation. This type of criterion should allow learning more discriminant semisupervised models. Nine public image data sets are used in the experiments and method comparisons. These experimental results show that the performance of the proposed method is superior to that of many advanced semisupervised graph-based algorithms.
PreSim: A 3D Photo-Realistic Environment Simulator for Visual AI

Recent years have witnessed great advancement in visual artificial intelligence (AI) research based on deep learning. To take advantage of deep learning, we need to collect a large amount of data in various environments and conditions. However, collecting such data is time-consuming and labor-intensive. Apart from that, developing and testing visual AI algorithms for multisensory models is expensive and in some cases dangerous processes in the real world. We present PreSim, a 3D environment simulator which provides photo-realistic simulations using a view synthesis module and supports flexible configuration of multimodal sensors to address both of these issues. For our view synthesis module we introduce novel depth refinement, adaptive view selection and layered rendering, to provide realistic imagery. We demonstrate that PreSim has several advantages: (i) it provides a photo-realistic 3D environment which allows seamlessly integrating multisensory models in the virtual world and enables them to perceive and navigate scenes, (ii) it has an internal view synthesis module which allows transforming algorithms developed and tested in simulation to physical platforms without domain adaption, (iii) it can generate a large amount of data for vision-based applications, such as depth estimation and object pose estimation.
OpenVINO Deep Learning Workbench: Towards Analytical Platform for Neural Networks Inference Optimization

Abstract Modern neural network methods make it possible to obtain qualitative results that open the colossal potential for integrating them into industrial applications. Therefore, there is a need for systems like the OpenVINO Deep Learning Workbench to analyze and optimize neural networks’ performance on target devices. As the primary tool for inference, the OpenVINO open environment is used, which provides a wide variety of options to speed up network execution on the target hardware.
Robust hierarchical feature selection with a capped <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si32.svg"><mml:mrow><mml:msub><mml:mrow><mml:mi>ℓ</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math>-norm

Feature selection methods face new challenges in large-scale classification tasks because massive categories are managed in a hierarchical structure. Hierarchical feature selection can take full advantage of the dependencies among hierarchically structured classes. However, most of the existing hierarchical feature selection methods are not robust for dealing with the inevitable data outliers, resulting in a serious inter-level error propagation problem in the following classification process. In this paper, we propose a robust hierarchical feature selection method with a capped ℓ2-norm (HFSCN), which can reduce the adverse effects of data outliers and learn relatively robust and discriminative feature subsets for the hierarchical classification process. Firstly, a large-scale global classification task is split into several small local sub-classification tasks according to the hierarchical class structure and the divide-and-conquer strategy, which makes it easy for feature selection modeling. Secondly, a capped ℓ2-norm based loss function is used in the feature selection process of each local sub-classification task to eliminate the data outliers, which can alleviate the negative effects outliers and improve the robustness of the learned feature weighted matrix. Finally, an inter-level relation constraint based on the similarity between the parent and child classes is added to the feature selection model, which can enhance the discriminative ability of the selected feature subset for each sub-classification task with the learned robust feature weighted matrix. Compared with seven traditional and state-of-art hierarchical feature selection methods, the superior performance of HFSCN is verified on 16 real and synthetic datasets.
A study of fall detection monitoring system for elderly people through IOT and mobile based application devices in indoor environment

This research presents the structure and framework for identifying falls by remote observing of old individuals in indoor environments by taking advantages of the Internet of things as well as mobile-based applications. This smart framework identifies fall occurred to older individuals who are living alone or living in residential nursing homes. To monitor the fall it uses real-time monitoring by use of open source camera and wearable devices. The system is carried out by pose recognition and object detection method for identifying the object taken by an open-source camera.A systematic review was performed using the Primo Search tool for finding eBooks, articles, and journals from the CSU library database. To provide a high efficiency using this information all inclusion criteria were meet by choosing the journal article which was closely related to the topic. The proposed study of fall detection monitoring system for older people living in geriatric residents allows data for caregivers and clinicians to provide better control in monitoring the health status of older patients and allows closer communication with the patients' family members and relatives. This study can be used as an approach for improving the cost of care in elderly population.
DIGITAL HUMANITARIAN PROJECT AS A COMPONENT OF DIGITAL HUMANITIES

Introduction. Digitalization and innovative sphere of scholarly research, "digital humanities", integrates the methodological apparatus and develops the potential of the humanities and engineering sciences under the condition of their effective interaction. One of the elements of the infrastructure of the digital humanities is research projects to create resources and databases of humanities knowledge. Problem Statement. Defining the concept of digital humanitarian project and its features is important for the development of interdisciplinary methodology of humanities and information technology. Purpose. The purpose is to substantiate the properties of Digital Humanitarian Project (DHP) as innovative research product, which is important in modern scientific communication and should be an officially recognized type of scholarly research electronic publication. Materials and Methods. Methods for historiographical and structural analysis and synthesis of DHP research concepts and completion of humanitarian digital projects at the NAS of Ukraine, and several other methods have been used. Results. For the first time the research has summarized the concept of DHP as interdisciplinary research product based on the analysis of modern concepts of digital humanities. Conclusions. The digital humanitarian project as a component of the digital humanities is an innovative research result and a type of scholarly research publication. The specific criteria are as follows: intellectual and innovative contribution of the interdisciplinary team to the development of scientific knowledge; importance for the development of educational and research infrastructure; influence on other DHP. The new model of scientific communication provides for the introduction and improvement of digital technologies in the processes of research and procedures for obtaining, processing, publishing, managing, and using scientific data, forming relevant humanities knowledge bases using achievements of socio-humanities, library and information activities etc. The criterion of new knowledge is not only a new content, but also new means of organization, classification, and interaction with this content.
PowerScout: A Security-Oriented Power Delivery Network Modeling Framework for Cross-Domain Side-Channel Analysis

The growing complexity of modern electronic systems often leads to the design of more sophisticated power delivery networks (PDNs). Similar to other system-level shared resources, the on-board PDN unintentionally introduces side channels across design layers and voltage domains, despite the fact that PDNs are not part of the functional design. Recent work have demonstrated that exploitation of the side channel can compromise the system security (i.e. information leakage and fault injection). In this work, we systematically investigate the PDN-based side channel as well as the countermeasures. To facilitate our goal, we develop PowerScout, a security-oriented PDN simulation framework that unifies the modeling of different PDN-based side-channel attacks. PowerScout performs fast nodal analysis of complex PDNs at the system level to quantitatively evaluate the severity of side-channel vulnerabilities. With the support of PowerScout, for the first time, we validate PDN side-channel attacks in literature through simulation results. Further, we are able to quantitatively measure the security impact of PDN parameters and configurations. For example, towards information leakage, removing near-chip capacitors can increase intra-chip information leakage by a maximum of 23.23dB at mid-frequency and inter-chip leakage by an average of 31.68dB at mid- and high-frequencies. Similarly, the optimal toggling frequency and duty cycle are derived to achieve fault injection attacks with higher success rate and more precise control.