Reviewerly/exHarmony huggingface dataset:
SECURING INFINIBAND TRAFFIC WITH BLUEFIELD-2 DATA PROCESSING UNITS

InfiniBand is employed in applications outside of high performance computing, including in critical infrastructure assets. This requires efforts at securing InfiniBand networks with encryption and packet inspection. Unfortunately, the performance benefits realized via the use of remote direct memory access by InfiniBand are at odds with many kernel-stack-based IP datagram encryption and network monitoring technologies. As a result, it is necessary to offload these tasks to other hardware. A promising candidate is the NVIDIA Mellanox Bluefield-2 data processing unit, which combines high-performance processors, network interfaces and flexible hardware accelerators, and runs a tailored version of Linux that provides several network management applications. This chapter characterizes the ability of Bluefield-2 data processing units to encrypt and monitor remote direct memory access traffic. The results demonstrate that the hardware accelerators of Bluefield-2 data processing units can support throughputs of nearly 86 Gbps when encrypting remote direct memory access over Converged Ethernet Version 2 traffic with Internet Protocol security (IPsec) encryption. Offloading IPsec encryption to the hardware accelerators on Bluefield-2 data processing units is a promising method for achieving confidentiality, integrity and authentication in InfiniBand networks with minimal interaction from host processors.
Game theory approach for analysing attack graphs

Many real problems involve the simultaneous security of computer networks and systems as it penetrates all areas of our daily lives. The purpose of attack graph analysis is to be able to protect computer systems and networks against attacks related to them. In this paper, we have proposed a new approach to analysing attack graphs based on game theory in order to reduce network vulnerabilities. This approach consists of turning a computer security problem into a two-player game and extracting the best strategies for each of the both of them. The purpose of this work is to help administrator to take a good decision to better secure network by using game theory methods.
An Improved RRT* Path Planning Algorithm in Dynamic Environment

With the increasingly diverse usage scenarios of mobile robots, the path planning of mobile robots in dynamic environments has become a hot issue. Aiming at the slow planning speed of RRT* algorithm in dynamic environment, this paper proposes an improved RRT* algorithm: efficient dynamic rapidly-exploring random tree star (ED-RRT*). In a static environment, the algorithm uses goal-biased sampling to reduce the randomness of the RRT* sampling method, and sets the sampling step size to be adaptive to improve the planning speed of the initial path. When the dynamic environment encounters obstacles and needs to be re-planned, the local target points are given based on the initial path and environmental information to quickly complete the local re-planning. The path information planned in the static environment is used as the prior information to improve the efficiency of the replanning algorithm. Finally, in the simulation environment based on OpenCV2, the ED-RRT* algorithm is compared with the improved RRT* algorithm. The experimental results show that ED-RRT* has faster dynamic performance and fewer nodes.
The Role of Remote Work and Telework in Libraries Moving Forward

The COVID-19 pandemic forced many employers, including libraries, to rapidly shift to full-time remote work and teleworking arrangements without a significant amount of planning. While the abrupt shift to remote work and teleworking was not ideal, many organizations were successful in keeping some or all of their core operations and services available. Many believe that there are significant benefits to remote work and telework that more than offset the drawbacks. As the world slowly returns to a state of affairs that more closely resembles pre-COVID-19 pandemic times, many people involved in all types of organizations are asking what the future will be for remote work and telework. Some are predicting that significant amounts of remote work and telework are here to say, while others think it will slowly return to the way it was over the course of time. It is too early to know what will happen in libraries and more research is needed to understand the impact of remote work and liberal telework policies on libraries.
Role Modeling as a Computing Educator in Higher Education

This paper provides insights into role modeling by educators in computing that is beyond the technical, theoretical and rational perspectives which have historically been described as dominant in computing. Surveying 199 educators in higher education, we have built on frameworks of role modeling, care, emotions, and professional competencies as a lens to see different ways of engaging in computing.
Analysis of Football Game Performance Based on Social Network

With the advent of big data and network era, the interpersonal relationship is getting closer and closer, and the advantage of teamwork is becoming more and more prominent.By analyzing the team competition rules, combining the team members' abilities, characteristics, and interactions between team members in previous competitions, team cooperation, and coordination strategy can be further optimized effectively.Based on the data samples recorded in 38 football games, this paper takes the 14th huskies vs. O14 game with a 4-0 result as an example.After data preprocessing, DBSCAN, a clustering algorithm, is introduced to simplify the data set by presenting a scatter graph.Each player's resident coordinates in each game are obtained, which provides data for social network analysis.We used a Python program to get the overall network analysis.The D6, M1, and F2 players on the Huskies form a triad configuration, the players D3 and D4, the players D1 and M2 in the O14 team form multiple dyadic configurations.To better understand the characteristics of the passing network, the individual network analysis is carried out.Through the study of particular net research degree center degree, it can be found that the Huskies coach's tactical arrangement was 4-4-2 formation, which was changed into 4-3-3 attacking formation in an actual combat operation.The tactical arrangement of the O14 coach was the same as the essential combat operation, which was 2-3-5 formation and more defensive.The in-depth analysis found that the Huskies' core was a midfielder, while the O14's core was not a midfielder.The tempo of the game and the fluidity of the passing network of the O14 team were significantly reduced, which was also the main factor leading to the failure.
Hyperparameter selection for Discrete Mumford–Shah

This work focuses on a parameter-free joint piecewise smooth image denoising and contour detection. Formulated as the minimization of a discrete Mumford–Shah functional and estimated via a theoretically grounded alternating minimization scheme, the bottleneck of such a variational approach lies in the need to fine-tune their hyperparameters, while not having access to ground truth data. To that aim, a Stein-like strategy providing optimal hyperparameters is designed, based on the minimization of an unbiased estimate of the quadratic risk. Efficient and automated minimization of the estimate of the risk crucially relies on an unbiased estimate of the gradient of the risk with respect to hyperparameters. Its practical implementation is performed using a forward differentiation of the alternating scheme minimizing the Mumford–Shah functional, requiring exact differentiation of the proximity operators involved. Intensive numerical experiments are performed on synthetic images with different geometry and noise levels, assessing the accuracy and the robustness of the proposed procedure. The resulting parameter-free piecewise-smooth estimation and contour detection procedure, not requiring prior image processing expertise nor annotated data, can then be applied to real-world images.
HoloMentor

The introduction of Augmented Reality (AR) Head Mounted Displays (HMDs) in collaboration between remote and local workers, introduces new challenges given that camera views are now mobile. We introduce HoloMentor, an AR HMD-based collaborative system designed for remote instruction over live mobile views during physical tasks. Through Actionport, we provide a world-stabilized area where remote helpers can dynamically place a pointer and annotations on the physical environment. Through Actionpad, we provide an indirect input mechanism with an absolute position to the Actionport. We show how these innovations worked for participants engaged in a remote instructional task and how they supported effective and efficient communication. Finally, we provide the next steps for addressing AR on mobile views for remote instruction.
SSR-Net: A Spatial Structural Relation Network for Vehicle Re-identification

Vehicle re-identification (Re-ID) represents the task aiming to identify the same vehicle from images captured by different cameras. Recent years have seen various feature learning-based approaches merely focusing on feature representations including global features or local features to obtain more subtle details to identify highly similar vehicles. However, few such methods consider the spatial geometrical structure relationship among local regions or between the global and local regions. By contrast, in this study, we propose a Spatial Structural Relation Network (SSR-Net) that explores the above-mentioned two kinds of relations simultaneously to learn more discriminative features by modeling the spatial structure information and global context information. In this article, we propose to adopt a Graph Convolution Network (GCN), for modeling spatial structural relationships among characteristic features. The GCN model aggregating the local and global features is shown to be more discriminative and robust to several car image transformations. To improve the performance of our proposed network, we jointly combine the classification loss with metric learning loss. Extensive experiments conducted on the public VehicleID and VeRi-776 datasets validate the effectiveness of our approach in comparison with recent works.
A Hierarchical ST-DBSCAN with Three Neighborhood Boundary Clustering Algorithm for Clustering Spatio–temporal Data

Clustering Spatio-temporal data is challenging because of the complexity of processing the spatial and temporal aspects.Various enhanced clustering approaches, such as partition-based and hierarchical-based algorithms have been proposed.However, the ST-DBSCAN density-based algorithm is commonly used to process irregularly shaped clusters.Moreover, ST-DBSCAN considers neighborhood parameters as spatial and non-spatial.The preliminary results from our experiments indicate that the ST-DBSCAN algorithm addresses temporal elements less effectively.Therefore, an improvement to the ST-DBSCAN algorithm was proposed by considering three neighborhood boundaries in neighborhood function.This experiment used the El Niño dataset from the UCI repository.The experimental results show that the proposed algorithm increased the performance indices by 27% compared to existing approaches.Further improvement using the hierarchical Ward's method (with thresholds of 0.3 and 0.1) reduced the number of clusters from 240 to 6 and increased performance indices by up to 73%.It can be concluded that ST-HDBSCAN is a suitable clustering algorithm for Spatio-temporal data.
PHN: Parallel Heterogeneous Network with Soft Gating for CTR Prediction

The Click-though Rate (CTR) prediction task is a basic task in recommendation system. Most of the previous researches of CTR models built based on Wide & deep structure and gradually evolved into parallel structures with different modules. However, the simple accumulation of parallel structures can lead to higher structural complexity and longer training time. Based on the Sigmoid activation function of output layer, the linear addition activation value of parallel structures in the training process is easy to make the samples fall into the weak gradient interval, resulting in the phenomenon of weak gradient, and reducing the effectiveness of training. To this end, this paper proposes a Parallel Heterogeneous Network (PHN) model, which constructs a network with parallel structure through three different interaction analysis methods, and uses Soft Selection Gating (SSG) to feature heterogeneous data with different structure. Finally, residual link with trainable parameters are used in the network to mitigate the influence of weak gradient phenomenon. Furthermore, we demonstrate the effectiveness of PHN in a large number of comparative experiments, and visualize the performance of the model in training process and structure.
The Effects Health Self-Advocacy Program with School-Home Connection on Health Care and Self-Determination in Students With Developmental Disabilities

The purpose of this study is to develop the health self-advocacy program with school-home connection for students with developmental disabilities and investigate its effects on knowledge, behavior, and goal-attainment regarding health care, as well as its impact on self-determination in students with developmental disabilities. For this purpose, 14 high school students with developmental disabilities and the students’ parents, and four special education teachers participated in this study. The health self-advocacy program with school-home connection, which consisted of 15 sessions that included basic and advanced lessons per each health care domain, was implemented for five months using one-group pretest-posttest design. After the program was terminated, the differences between the pre-test and post-test on dependent variables were analyzed to examine the effects of the program. The results of the analysis showed that the program had statistically positive effects on health knowledge and health promotion behavior; furthermore, 93% of the students met their health care goals at a higher than expected level. In addition, the program had positive effects on promoting self-determination rated by the students. However, there were no statistically significant differences in self-determination rated by the parents and teachers. This study confirmed that the health self-advocacy program with school-home connection was effective in promoting health care and self-determination in students with developmental disabilities along with a necessity for future study to enhance health self-advocacy.
Developing a Novel Digital Forensics Readiness Framework for Wireless Medical Networks Using Specialised Logging

Wireless Medical Networks (WMNs) have always been a vital component for the treatment and management of chronic diseases. However, the data generated by these networks keeps growing and has become a potential target for criminals seeking to capitalise on its sensitivity and value. Wireless networks also happen to be more vulnerable to attacks compared to wired networks. In the event of such attacks, it becomes really difficult to conduct a digital Forensics investigation. This paper investigates and suggests a proactive approach of digital forensics readiness within wireless medical networks by suggesting specialised monitoring and logging mechanisms. The research first identifies threats to wireless medical networks. It then undertakes a trajectory of a systematic review of previously proposed digital forensics frameworks and identifies challenges. Finally, it proposes a conceptual framework for Digital Forensics Readiness (DFR) for wireless medical networks. The paper, therefore, makes a novel contribution to the field of digital forensics. It suggests a more streamlined, robust, and decentralised framework that is partially underpinned by blockchain technology at the evidence management layer. The framework contributes to the enforcement of evidential data integrity whilst also securing wireless medical networks.
Examining online learning platform characteristics and employee engagement relationship during Covid-19

Purpose As work-from-home policies were being implemented during Covid-19, organizations have been migrating their learning and development programs to digital platforms. This study aims to understand the role of employees’ perceptions about four key attributes of online learning platforms (OLPs), namely, online environment, ease of use, online content (OC) and online engagement anchors in their levels of work engagement (WE). Further, it also investigates how the personality attributes of the employees and financial incentives for online learning influence this relationship. Design/methodology/approach Using the snowball sampling technique, online questionnaires were distributed to about 350 employees in four IT firms in India who were working from home during the Covid-19 period. The total number of valid responses was 306 and consisted of software developers, technical leads, process specialists, testers and managers. The analysis was done using Python software (V.3.7.4) packages and Process Macro (pyprocessmacro V.1.0.8) developed for Python. Findings Perceived OLP features significantly influence employee WE. OC and ease of use are significant influencers of employee WE. The perception of OLP features indirectly affects WE through the learner’s personality characteristics. Moreover, the financial incentive to learn online shares a negative interaction effect with OLP features to influence WE. Originality/value The paper contributes to the existing literature on virtual knowledge management by identifying the driving factors that reinforce the acceptance of OLPs. Moreover, to the best of the authors’ knowledge, this is the first attempt to link online learning management system key characteristics with employee outcome as engagement. Most of the previous work has ignored exploring such a relationship in an organizational setting.
Development of an Exploratory Blockchain for Enhanced Data Security in Smart Grids

Data integrity is of paramount importance to the operation and analysis of Microgrids.This paper has developed a functional blockchain system incorporating digital file hashing.The system provides the means to detect file tampering contributing to the advancement of data transparency and security.Opportunities for improvement to the current system have been provided, along with suggestions for future research.
Improved Underwater Image Enhancement Model Based on Atomization Images Model and Deep Learning

Underwater image enhancement has been widely used and received increasing attention. However, visual perception is affected by environmental factors, which reduces the visual quality. These images often suffer from color distortion, low contrast, and lack of detail, and the color of the image turns green or blue. This paper proposes an underwater image enhancement method based on deep learning and establishes a neural network based on dilated convolution and parameter correction. Firstly, multi-scale features are extracted, and then global features are used to enhance local features at each scale. In addition, the innovative CBAM attention mechanism is introduced. The algorithm is significantly faster than ICM, RGHS, UCM, CLAHE, HE, etc., in terms of operation speed. Besides, better computational performance, color correction, and detail retention are obtained.
Research Data Management: A Case Study Examining Aerospace, Industrial and Mechanical Science Engineering Faculty Research Practices

This study explores data management perspectives of aerospace, industrial and mechanical science engineering faculty affiliated with (University of Illinois Urbana-Champaign (UIUC)). The author conducted fourteen semi-structured interviews and analyzed them using a qualitative inductive coding method. This study builds upon previous work and seeks to explore how the elements of data management planning align with researchers’ workflow, challenges, and awareness to research data services. Overall, the goal of this study is to gain a better understanding of these researcher’s data management practices and enhance the research data services provided to faculty and research groups. All these responses inform existing research data management services.
Visualizing the datasphere: Representations of old bodies and their data in promotional images of smart sensor technologies for aging at home

Technologies for people aging at home are increasingly prevalent and include ambient monitoring devices that work together with wearables to remotely track and monitor older adults' biometric data and activities of daily living. There is, however, little research into the promotional and speculative images of technology-in-use. Our paper examines the ways in which the datafication of aging is offered up visually by technology companies to promote their products. Specifically, we ask: how are data visualized in promotional images of smart sensor technologies for aging at home? And in these visualizations, what happens to the aging body and relations of care? We include in our definition of smart sensor technologies both wearable and ambient monitoring devices, so long as they are used for the in-home passive monitoring of the inhabitant by a caregiver, excluding those devices targeted for institutional settings or those used for self-monitoring purposes. Our sample consists of 221 images collected between January and July of 2021 from the websites of 14 English-language companies that offer smart sensor technology for aging at home. Following a visual semiotic analysis, we present 3 themes on the visual representation of old bodies and their data: (1) Captured Data, (2) Spatialized Data, and (3) Networked Data. Each, we argue, contribute to a broader visualization of the "datasphere". We conclude by highlighting the underlying assumptions of old bodies in the co-constitution of aging and technologies in which the fleshy and lived corporeality of bodies is more often lost, reduced to data points and automated care scenarios, and further disentangled from other bodies, contexts and things.
Learning a brand-new language through Duolingo: A case study of a gifted student

This article describes a case study of a gifted student who spent about two months learning a new language through the use of a mobile application. The purpose of this study is to investigate how a gifted student perceives the effectiveness of Duolingo, as well as how the participant improved his language skills. The participant was a 14-year-old Turkish male who began using Duolingo knowing nothing about French. Data was gathered through interviews, self-reports, and language tests. According to the findings, Duolingo provided a user-friendly, enjoyable, and competitive learning environment for the participant, but it was not regarded as an all-in-one language-learning tool. The participant appeared to gain various language skills, particularly reading and writing skills, without referring to other sources. When considering the improvement level in this case, Duolingo should be a reciprocal learning tool as an addition to language courses rather than a replacement for regular language courses.