Reviewerly/exHarmony huggingface dataset:
The Use of Hypermedia Technologies in Higher Education Institutions during Covid Lockdown

The World Wide Web continuously provides rapid access to information, and numerous information resources, virtually unlimited. The convenience and interactivity of its usage encourage people to turn to it in the educational process rather often while teaching or studying unfamiliar topics. Hypertext resources of different kinds have become a virtual educational environment. They offer new opportunities for structuring, presenting, adapting, and integrating contemporary learning materials. Besides, hypermedia technologies have made a considerable breakthrough in the educational process, particularly in self-education, combining hitherto incompatible elements. The article focuses on practical experience in the introduction of hypertext in the educational process of higher education institutions in general and during pandemic periods, in particular, the world faces nowadays. In the course of the research, it was emphasized that such technologies are pretty significant in implementing distance learning. Hypermedia technologies create especially suitable conditions for the independent acquisition and quality assimilation of the necessary information, which can be easily integrated into the educational process. The provided analysis of the implementation of e-learning materials based on hypertext and multimedia tools made it possible to point out the advantages and disadvantages of the described technologies (compared to traditional usage of printed textbooks), thus identifying their didactic potential. They became especially of great importance during the coronavirus lockdown.
Video Summarization via Label Distributions Dual-Reward

Reinforcement learning maps from perceived state representation to actions, which is adopted to solve the video summarization problem. The reward is crucial for deal with the video summarization task via reinforcement learning, since the reward signal defines the goal of video summarization. However, existing reward mechanism in reinforcement learning cannot handle the ambiguity which appears frequently in video summarization, i.e., the diverse consciousness by different people on the same video. To solve this problem, in this paper label distributions are mapped from the CNN and LSTM-based state representation to capture the subjectiveness of video summaries. The dual-reward is designed by measuring the similarity between user score distributions and the generated label distributions. Not only the average score but also the the variance of the subjective opinions are considered in summary generation. Experimental results on several benchmark datasets show that our proposed method outperforms other approaches under various settings.
Reasoning over Argument-Incomplete AAFs in the Presence of Correlations

We introduce "argument-incomplete Abstract Argumentation Frameworks with dependencies", that extend the traditional abstract argumentation reasoning to the case where some arguments are uncertain and correlated through logical dependencies (such as mutual exclusion, implication, etc.). We characterize the complexities of the problems DSAT of deciding the satisfiability of the dependencies and PDVER of verifying extensions, and show how they depend on the forms of dependencies and, for PDVER, also on the semantics of the extensions.
Differentially Private Pairwise Learning Revisited

Instead of learning with pointwise loss functions, learning with pairwise loss functions (pairwise learning) has received much attention recently as it is more capable of modeling the relative relationship between pairs of samples. However, most of the existing algorithms for pairwise learning fail to take into consideration the privacy issue in their design. To address this issue, previous work studied pairwise learning in the Differential Privacy (DP) model. However, their utilities (population errors) are far from optimal. To address the sub-optimal utility issue, in this paper, we proposed new pure or approximate DP algorithms for pairwise learning. Specifically, under the assumption that the loss functions are Lipschitz, our algorithms could achieve the optimal expected population risk for both strongly convex and general convex cases. We also conduct extensive experiments on real-world datasets to evaluate the proposed algorithms, experimental results support our theoretical analysis and show the priority of our algorithms.
Mitigation of Kernel Memory Corruption Using Multiple Kernel Memory Mechanism

Operating systems adopt kernel protection methods (e.g., mandatory access control, kernel address space layout randomization, control flow integrity, and kernel page table isolation) as essential countermeasures to reduce the likelihood of kernel vulnerability attacks. However, kernel memory corruption can still occur via the execution of malicious kernel code at the kernel layer. This is because the vulnerable kernel code and the attack target kernel code or kernel data are located in the same kernel address space. To gain complete control of a host, adversaries focus on kernel code invocations, such as function pointers that rely on the starting points of the kernel protection methods. To mitigate such subversion attacks, this paper presents multiple kernel memory (MKM), which employs an alternative design for kernel address space separation. The MKM mechanism focuses on the isolation granularity of the kernel address space during each execution of the kernel code. MKM provides two kernel address spaces, namely, i) the trampoline kernel address space, which acts as the gateway feature between user and kernel modes and ii) the security kernel address space, which utilizes the localization of the kernel protection methods (i.e., kernel observation). Additionally, MKM achieves the encapsulation of the vulnerable kernel code to prevent access to the kernel code invocations of the separated kernel address space. The evaluation results demonstrated that MKM can protect the kernel code and kernel data from a proof-of-concept kernel vulnerability that could lead to kernel memory corruption. In addition, the performance results of MKM indicate that the system call overhead latency ranges from 0.020 μs to 0.5445 μs, while the web application benchmark ranges from 196.27 μs to 6, 685.73 μs for each download access of 100,000 Hypertext Transfer Protocol sessions. MKM attained a 97.65% system benchmark score and a 99.76% kernel compilation time.
CMBF: Cross-Modal-Based Fusion Recommendation Algorithm

A recommendation system is often used to recommend items that may be of interest to users. One of the main challenges is that the scarcity of actual interaction data between users and items restricts the performance of recommendation systems. To solve this problem, multi-modal technologies have been used for expanding available information. However, the existing multi-modal recommendation algorithms all extract the feature of single modality and simply splice the features of different modalities to predict the recommendation results. This fusion method can not completely mine the relevance of multi-modal features and lose the relationship between different modalities, which affects the prediction results. In this paper, we propose a Cross-Modal-Based Fusion Recommendation Algorithm (CMBF) that can capture both the single-modal features and the cross-modal features. Our algorithm uses a novel cross-modal fusion method to fuse the multi-modal features completely and learn the cross information between different modalities. We evaluate our algorithm on two datasets, MovieLens and Amazon. Experiments show that our method has achieved the best performance compared to other recommendation algorithms. We also design ablation study to prove that our cross-modal fusion method improves the prediction results.
Multimodal Continuous User Authentication on Mobile Devices via Interaction Patterns

Behavior-based continuous authentication is an increasingly popular methodology that utilizes behavior modeling and sensing for authentication and account access authorization. As an appearing behavioral biometric, user interaction patterns with mobile devices focus on verifying their identity in terms of their features or operating styles while interacting with devices. However, unimodal continuous authentication schemes, which are on the basis of a single source of interaction information, can only deal with a particular action or scenario. Hence, multimodal systems should be taken to suit for various environmental conditions especially in circumstances of attacks. In this paper, we propose a multimodal continuous authentication method both based on static interaction patterns and dynamic interaction patterns with mobile devices. Behavioral biometric features, HMHP, which is combined hand motion (HM) and hold posture (HP), are essentially established upon the touch screen and accelerator and capture the variation model of microhand motions and hold patterns generated in both dynamic and static scenes. By combining the features of HM and HP, the fusion feature HMHP achieves 97% accuracy with a 3.49% equal error rate.
The Nexus Between Economic Growth, Financial Development, Financial Inclusion and Financial Innovation in Africa

This research explores the connection between economic growth, financial development, financial inclusion and financial innovation in Africa by employing the panel structural vector autoregression using annual data from 2004 to 2018. The vector error correction model Granger causality test was applied to examine directional causality with financial inclusion, financial innovation and economic growth used as dependent variables, and error correction term (ECT) coefficients are negatively significant at one percent for the long-term causality. It shows that the causal association between financial inclusion and financial innovation is explained by the African economy feedback hypothesis. The short-run causality shows a causal connection between economic growth and financial inclusion as well as financial innovation. It means that additional development in any one of these variables (i.e., economic growth, financial innovation and financial inclusion) will have a vital impact on connected variables, which can be noticed in the short run. This is why policymakers and government should consider each of the facets of financial inclusion and financial innovation as they do not merely affect each other; they also affect economic activities, hence fiscal policy is capable of steering more financial inclusions, financial innovation and financial development.
Mobile vs. Non-Mobile Live-Streaming: A Comparative Analysis of Users Engagement and Interruption Using Big Data from a Large CDN Perspective

Video streaming on the Internet is constantly changing and growing. New devices and new video delivery mechanisms generate huge gaps in the understanding of how video application works. From exploratory research of one among the largest streaming services in Brazil, this work presents a comparison between mobile and non-mobile users, in large-scale lives. This work focuses on metrics such as engagement, interruption, churn, and payload. This work also presents a report-overview of mobile-users, considering the operating system, geolocation, network access, interruption, and engagement. These results might offer potential information for streaming improvement, in addition to serving as a historical mark.
Implementation of Emergency Remote Teaching in Chilean Schools due to COVID-19

This research emerged from the need to understand educators’ working and teaching conditions during the initial period of distance teaching implementation brought about by the need to continue education in the face of the public health emergency and the suspension of on-site classes in Chilean schools. Thus, the aim of this investigation is to explore Chilean teachers’ perceptions in relation to Emergency Remote Teaching (ERT) within the COVID-19 context. The methodology includes a dynamic online questionnaire to allow a large number of educators to be included in the study. Some of the findings include the substantial curricular adaptation made by teachers, the use of teachers’ own resources to contact students, the overall low level of satisfaction with remote teaching, as well as the increased working hours, and the different connectivity conditions and levels of student participation depending on the socio-economic profile of the school. The conclusions of this research are discussed from the perspective of the hypotheses presented in this study.
Cross-Domain Self-Supervised Complete Geometric Representation Learning for Real-Scanned Point Cloud Based Pathological Gait Analysis

Accurate lower-limb pose estimation is aprerequisite of skeleton based pathological gait analysis. To achieve this goal in free-living environments for long-term monitoring, single depth sensor has been proposed in research. However, the depth map acquired from a single viewpoint encodes only partial geometric information of the lower limbs and exhibits large variations across different viewpoints. Existing off-the-shelf 3D pose tracking algorithms and public datasets for depth based human pose estimation are mainly targeted at activity recognition applications. They are relatively insensitive to skeleton estimation accuracy, especially at the foot segments. Furthermore, acquiring ground truth skeleton data for detailed biomechanics analysis also requires considerable efforts. To address these issues, we propose a novel cross-domain self-supervised complete geometric representation learning framework, with knowledge transfer from the unlabelled synthetic point clouds of full lower-limb surfaces. The proposed method can significantly reduce the number of ground truth skeletons (with only 1%) in the training phase, meanwhile ensuring accurate and precise pose estimation and capturing discriminative features across different pathological gait patterns compared to other methods.
Exploring the Value of Non-Technical Knowledge, Skills, and Abilities (KSAs) to Cybersecurity Hiring Managers

Industry's demand for cybersecurity workers with non-technical knowledge, skills, and abilities (KSAs) that complement technical prowess is not new. The purpose of this study was to connect with cybersecurity practitioners to determine which non-technical KSAs should be emphasized by educators to help meet workforce demands. This research applies a novel application of the Ground Truth Expertise Development Model (GTEDM) for exploring suitable non-technical and particularly soft KSAs necessary for cybersecurity professional development programs. This study focused on the definition and competency determination step and provided foundational KSA prioritization for further research. The field overwhelmingly agreed that non-technical skills were essential to a cybersecurity worker's success. The qualitative process produced three themes as non-technical KSA areas of the most significant import to the cybersecurity field. These KSA themes required included critically using information, communications skills, and collaboration to pursue customer/client success. The findings produce a more comprehensive list of hard, soft, and mixed non-technical skills that will benefit the public, private, and academic sector organizations as they develop cybersecurity curricula.
Let Knowledge Make Recommendations for You

The knowledge graph can make more accurate personalized recommendations for the recommendation system, but it is also interpretative and has traces to follow.The purpose of the recommendation system is to recommend a series of unobserved items for users.At present, recommendation systems based on knowledge graphs are mainly implemented in two ways: Embedding-based and pathbased.Embedding methods usually directly use information from the knowledge graph to enrich the representation of an item or user.Still, it failed to introduce multi-hop relations, and it is challenging to use semantic network information.A path-based recommendation algorithm utilizes the knowledge graph to gain multi-hop knowledge and compare the similarity between users or items to improve the recommendation effect.This paper (1) Aiming at the problem of how the recommendation algorithm effectively utilizes the semantically related information of knowledge, a self-attention-based knowledge representation learning model is designed to learn the semantic information of the entity-relationship by using the overall triplet of the entity-relationship to achieve high-quality knowledge features, Which brings more and more helpful information to the recommendation.(2) Constructing a content recommendation model with unified, embedded behavior and knowledge features, using historical user preferences combined with knowledge graphs to dynamically learn knowledge features to bring users more accurate and diverse recommendations.(3) Aiming at the problem of knowledge feature representation learning, a self-attentionbased knowledge representation learning model is proposed.Focusing on the difference in the importance of triples for determining entity semantics, the self-attention mechanism is used to learn semantics from triples to improve knowledge features.The quality of the representation provides high-quality auxiliary information for the recommendation system.The model's performance is demonstrated through link prediction and triple classification experiments to prove the feasibility of the method proposed in this article.
IntraVox: A Personalized Human Voice to Support Users with Complex Needs in Smart Homes

We present IntraVox, a novel voice-based interaction system that supports users with complex needs by introducing a highly personalized, human voice command between smart home devices and sensors. Voice-enabled personal assistants (e.g., Amazon Alexa, Google Home) can be used to control smart home devices and increase the quality of life. However, people suffering from conditions such as dementia or learning disabilities and autism (LDA) can encounter verbal and memory problems in communicating with the assistants. IntraVox supports these users by verbally sending smart home control commands, based on the sensor data collected, to a voice-enabled personal assistant using a human voice. Over 12 months, we conducted 7 workshops and 9 interviews with 79 participants from multiple stakeholder categories - people with LDA, older and frail people, social and health care organisations. Results show that our solution has the potential to support people with complex needs and increase independence. Moreover, IntraVox could have the added value of reinforcing the learning of specific commands and making visible the inner workings of a home automation system, by giving users a clear cause-and-effect explanation of why certain events occur.
A DSL-Based Approach for Detecting Activities of Daily Living by Means of the AGGIR Variables

In this paper, we propose a framework for studying the AGGIR (Autonomie Gérontologique et Groupe Iso Ressources—Autonomy Gerontology Iso-Resources Groups) grid model, with the aim of assessing the level of independence of elderly people in accordance with their capabilities of performing daily activities as well as interacting with their environments. In order to model the Activities of Daily Living (ADL), we extend a previously proposed Domain Specific Language (DSL), by defining new operators to deal with constraints related to time and location of activities and event recognition. The proposed framework aims at providing an analysis tool regarding the performance of elderly/disabled people within a home environment by means of data recovered from sensors using a smart-home simulator environment. We perform an evaluation of our framework in several scenarios, considering five of the AGGIR variables (i.e., feeding, dressing, toileting, elimination, and transfers) as well as health-care devices for tracking the occurrence of elderly activities. The results demonstrate the accuracy of the proposed framework for managing the tracked records correctly and, thus, generate the appropriate event information related to the ADL.
Regularizing transformers with deep probabilistic layers

Language models (LM) have grown non-stop in the last decade, from sequence-to-sequence architectures to attention-based Transformers. However, regularization is not deeply studied in those structures. In this work, we use a Gaussian Mixture Variational Autoencoder (GMVAE) as a regularizer layer. We study its advantages regarding the depth where it is placed and prove its effectiveness in several scenarios. Experimental result demonstrates that the inclusion of deep generative models within Transformer-based architectures such as BERT, RoBERTa, or XLM-R can bring more versatile models, able to generalize better and achieve improved imputation score in tasks such as SST-2 and TREC or even impute missing/noisy words with richer text.
Learning to Drop Expensive Layers for Fast Face Recognition

Recent years have seen many advances based on Deep Convolutional Neural Networks (DCNNs) in the tasks of face recognition, most of which are developed to pursue high recognition accuracy. In this paper, we propose a novel Fast FAce Recognizer (Fast-FAR), learning to improve the speed of DCNN-based face recognition model without sacrificing recognition accuracy. Our fundamental insight is that the computation increases exponentially with the depth of a network, the easily identifiable face images can be accurately recognized by the cheap features (pixel values at shallow layers), while the challenging samples that exhibit low quality, large pose variations or occlusions need to be processed by the expensive deep layers. The major contribution of this paper is the Reinforcement Learning Agent (RLA), which is proposed to learn a decision policy determined by a reward function. The policy adaptively decides whether the recognition should be performed at an early layer with a high recognition confidence, or proceeding to the subsequent layers, thus significantly reducing feed-forward cost for the easy faces. According to the extensive experiments on the popular face recognition benchmarks, Fast-FAR reduces the inference time by 14.22%, 20.61%, and 7.84% on the dataset LFW, AgeDB-30 and CFP-FP, respectively.
Cryptocurrency Technology and Financial Reporting

Cryptocurrency technology is considered as smart technology that would transform our way of doing business in the near future. Its unique features are said to be far superior than our existing business technology which mainly based on cash and credit, especially in time, security and cost. Nevertheless, its entrance into the business arena has also turns our existing systems of financial reporting and taxing upside down as both systems were moulded, formulated and evolved just to suit the systems of cash and credit. Therefore, the financial regulators and inland revenue boards should come out with a new system that can properly and fairly adapt to the technology of cryptocurrency, in order to secure the imbalances between financial application and its law and order.
Research on the Function of Computer Aided Teaching in College Physical Education

Abstract With the rapid iteration of computer information technology and the application of CAI in college physical education teaching, the physical education teaching mode has developed from the empirical and theoretical teaching mode to the scientific and digital direction, and the physical education teaching has played a significant function in stimulating students’ learning initiative and promoting the promotion of students’ subjective status, so it has important research value. Based on this, this paper first analyses the necessity of CAI application in college physical education teaching, then studies the application function and status quo of CAI in college physical education teaching, and finally puts forward suggestions and Countermeasures for the application of CAI in college physical education.