Reviewerly/exHarmony huggingface dataset:
Incremental software product line verification - A performance analysis with dead variable code

Abstract Verification approaches for Software Product Lines (SPL) aim at detecting variability-related defects and inconsistencies. In general, these analyses take a significant amount of time to provide complete results for an entire, complex SPL. If the SPL evolves, these results potentially become invalid, which requires a time-consuming re-verification of the entire SPL for each increment. However, in previous work we showed that variability-related changes occur rather infrequently and typically only affect small parts of a SPL. In this paper, we utilize this observation and present an incremental dead variable code analysis as an example for incremental SPL verification , which achieves significant performance improvements. It explicitly considers changes and partially updates its previous results by re-verifying changed artifacts only. We apply this approach to the Linux kernel demonstrating that our fastest incremental strategy takes only 3.20 seconds or less for most of the changes, while the non-incremental approach takes 1,020 seconds in median. We also discuss the impact of different variants of our strategy on the overall performance, providing insights into optimizations that are worthwhile.
Secure IoMT Pattern Recognition and Exploitation for Multimedia Information Processing using Private Blockchain and Fuzzy Logic

The Internet of Medical Things (IoMT) is a definite IoT connecting atmosphere that contracts with communication via intelligent medical equipment. Activity detection, motion tracking, information extraction, consumer retrieval, etc., have all been solved due to advances in the autonomous study of human behavior from multimedia information processing. Though the IoT connecting atmosphere enables and provisions our daily actions, it too has certain disadvantages. IoTgrieves from numerous safety and confidentiality challenges, such as reiteration, impersonation, man-in-the-middle, remote hijacking, privileged-insider attack, denial of service (DoS) attacks, password guessing, and malware bouts. Hence, in this paper, Private Blockchain and Fuzzy Logic based Attack Detection system (PBFL-ADS) has been proposed for secure IoMT disease prediction using Multimedia Information Processing techniques. The proposed method utilizes Bayesian inference-based trust administration to perceived malevolent nodes in Health Smartphone Structures (HSS) for PBFL-ADS. This paper focuses on the specific form of IoMT called HSS because smartphones have been widely used in the healthcare profession. Then, blockchains have been utilized to improve Bayesian trust management's efficacy in detecting hostile nodes in HSSs. The effectiveness of the suggested technique has been assessed, and test results show that blockchain technology helps identify fraudulent nodes with an acceptable workload. The proposed PBFL-ADS method increases the HS2 scenario 1.1, processor utilization at nodes 33.5%, pattern recognition ratio 92.1%.and server utilization 40.1%.
An Agent-Based Basic Educational Model for the Children with ASD Using Persuasive Technology

Children with Autism Spectrum Disorder (ASD) suffer most in communication and behavioral aspect which hampers their regular basic education and cognitive development. Technological development enhanced a lot in every sector where educational sector is no exception of that. The cognitive development of the children with ASD can be taken care of effectively by using of advanced information technology. This study has focused on the prospect of designing the digital learning platform for the children with ASD to fulfill the existing research gap for developing persuasive learning mechanism for the children with ASD. This study has proposed an agent-based model for developing a persuasive learning tool where Sanket is an agent of a touch screen based application which pursues the children with ASD regarding the learning of basic education (numbers, alphabets), which forms a basis to enable Artificial Intelligence for automation of the learning process and help the children with ASD.
Getting Rid of the Usability/Security Trade-Off: A Behavioral Approach

The usability/security trade-off indicates the inversely proportional relationship that seems to exist between usability and security. The more secure the systems, the less usable they will be. On the contrary, more usable systems will be less secure. So far, attempts to reduce the gap between usability and security have been unsuccessful. In this paper, we offer a theoretical perspective to exploit this tradeoff rather than fight it, as well as a practical approach to the use of contextual improvements in system usability to reward secure behavior. The theoretical perspective, based on the concept of reinforcement, has been successfully applied to several domains, and there is no reason to believe that the cybersecurity domain will represent an exception. Although the purpose of this article is to devise a research agenda, we also provide an example based on a single-case study where we apply the rationale underlying our proposal in a laboratory experiment.
ForTrace - A holistic forensic data set synthesis framework

Digital forensic experts are confronted with a wide variety of investigation objectives, e.g., to deal with an infected IT system. The same holds for digital forensic tools. Mostly different sources of digital traces have to be inspected including persistent storage devices (e.g., SSDs, SD cards, USB drives), volatile main memory snapshots, and network captures, respectively. In order to train experts and tools and keep their knowledge and capabilities up-to-date, a capacious amount of realistic, timely training data is necessary. However, due to different reasons like privacy, secrecy, or intellectual property rights there is a large gap in digital forensic training data. In recent years different synthesis frameworks to generate realistic digital forensic data sets have been proposed. However, none of these frameworks provides a holistic approach to generate realistic digital forensic relevant traces of different sources. In this paper we introduce ForTrace, a holistic framework for the simultaneous generation of persistent, volatile and network traces. Our approach is based on the data synthesis framework hystck. We explain our extension of hystck by defining properties of a holistic data set synthesis framework and by discussing different forensically relevant scenarios and their implementation in ForTrace. We then successfully evaluate ForTrace with respect to diverse realistic and complex scenarios. ForTrace is open source and may be adapted or extended with respect to individual needs.
Standardized Evaluation Method of Pronunciation Teaching Based on Deep Learning

With the advancement of globalization, an increasing number of people are learning and using a common language as a tool for international communication. However, there are clear distinctions between the native language and target language, especially in pronunciation, and the domestic target language, the learning environment is far from ideal, with few competent teachers. In addition, such learning cannot achieve computer-assisted language learning (CALL) technology. The efficient combination of computer technology and language teaching and learning methods provides a new solution to this problem. The core of CALL is speech recognition (SR) technology and speech evaluation technology. The development of deep learning (DL) has greatly promoted the development of speech recognition. The pronunciation resource collected from the Chinese college students, whose majors are language education or who are planning to obtain better pronunciation, shall be the research object of this paper. The study applies deep learning to the standard but of target language pronunciation and builds a standard evaluation model of pronunciation teaching based on the deep belief network (DBN). On this basis, this work improves the traditional pronunciation quality evaluation method, comprehensively considers intonation, speaking speed, rhythm, intonation, and other multi-parameter indicators and their weights, and establishes a reasonable and efficient pronunciation model. The systematic research results show that this article has theoretical and practical value in the field of phonetics education.
Behavioral Biometrics: Past, Present and Future

Behavioral biometrics are changing the way users are authenticated to access resources by adding an extra layer of security seamlessly. Behavioral biometric authentication identifies users based on a set of unique behaviors that can be observed when users perform daily activities or interact with smart devices. There are different types of behavioral biometrics that can be used to create unique profiles of users. For example, skill-based behavioral biometrics are common biometrics that is based on the instinctive, unique and stable muscle actions taken by the user. Other types include style-based behavioral biometrics, knowledge-based behavioral biometrics, strategy-based behavioral biometrics, etc. Behavioral biometrics can also be classified based on their use model. Behavioral biometrics can be used for one-time authentication or continuous authentication. One-time authentication occurs only once when a user requests access to a resource. Continuous authentication is a method of confirming the user’s identity in real-time while they are using the service. This chapter discusses the different types of behavioral biometrics and explores the various classifications of behavioral biometrics-based on their use models. The chapter highlights the most trending research directions in behavioral biometrics authentication and presents examples of current commercial solutions that are based on behavioral biometrics.
An Optimization-based Motion Planner for a Mobile Manipulator to Perform Tasks During the Motion

In this paper, we present an optimization-based motion planner to plan a locally time-optimal whole-body motion of a nonholonomic mobile manipulator, to pick up objects while simultaneously moving the manipulator and the base. The simultaneous motion further reduces the operation time of the picking tasks. What distinguishes our planner from the common motion planners, which plan the motion between two configurations, is that our planner considers performing tasks, such as grasping an object, during the motion. We formulate the time-optimal motion planning as an optimization problem. One of the major difficulties is finding an appropriate representation of the constraints for the tasks during the motion, since the time and configuration of the robot at the moment of performing the task are unknown. To address this issue, we propose a novel formulation of the optimization variables such that constraints arising from the tasks are smooth and differentiable, which is essential for obtaining a feasible solution using an NLP solver. We present the preliminary numerical result of the proposed planner, which shows that our planner can obtain a feasible trajectory that satisfies all the constraints.
GAN-Based Semantic-Aware Translation for Day-to-Night Images

Perception in autonomous driving has achieved robustness and high accuracy through deep learning. CNN-based methods require a large amount of data collection and annotation. However, most of the current datasets are built on daytime scenes, and there are few datasets for adverse conditions such as night-time. Recently, data augmentation by image-to-image translation using Generative Adversarial Networks (GANs) has attracted attention. GANs based image-to-image translation performs well for various image translation tasks. On the other hand, semantic information may be lost in problems with the significant domain gap, such as day and night. In this paper, we propose a semantic-aware image translation. This framework preserves semantic consistency by transfer learning a semantic segmentation network to GANs. Experimental results show that the proposed method achieved to generate natural night images compared to previous studies.
An automatic quality evaluator for video object segmentation masks

Video object segmentation (VOS) has been a research hot-spot these years. However, evaluating the performance of different VOS methods requires labor-intensive and time-consuming manually labeled mask annotations, making it hard to validate the algorithm quality in field tests. In this paper, we tackle the problem of automatically measuring the mask quality for video object segmentation tasks without accessing manual annotations. We propose that with an elaborately designed network structure, we can extract quality-sensitive features to predict mask quality scores without ground-truth labels. To achieve this, we train an end-to-end convolutional neural network to capture the quality-sensitive features with both spatial reference and temporal reference. In the proposed Video Object Segmentation Evaluation Network, the VOSE-Net, the corresponding video frame and motion amplitude information are used for spatial and temporal references respectively. Instead of directly concatenating features for mask and references, we extract spatial quality cues with feature correlation, which is more rational and effective in this specific task. Taking in the segmented mask, its corresponding frame image and optical flow map, the VOSE-Net can provide an accurate quality estimation without the need for human intervention. To train and verify the proposed network, we construct a new dataset by using the DAVIS video segmentation benchmark and results from many public video object segmentation algorithms. We also demonstrate the robustness and usefulness of the proposed method on several applications, i.e. proposal selection, parameter optimization, arbitrary video mask evaluation. The experimental results and analysis show that the VOSE-Net is fast, effective and of practical use.
IT assimilation: construct, measurement, and implications in cybersecurity

Unintentional employee behaviours can be a major cause of security breaches. To mitigate the risk, employees need to habituate their new behaviours, implying a transfer of learning that may occur at ‘work’ to ‘personal’ contexts. Against this backdrop, we examine a new construct – IT Assimilation – grounded in situated learning theory by its definition as the incorporation of enterprise IT into an individual’s IT repertoire. We use a multi-phase methodological approach to conceptualise the construct and develop a validated scale to measure it. Our findings offer valuable insights for researchers and practitioners about transferring IT behaviours, particularly in the cybersecurity context..
The future of smart implants towards personalized and pervasive healthcare in Sub-Saharan Africa: Opportunities, barriers and policy recommendations

Smart implants continue to evolve, posing tremendous opportunities to move towards personalized and pervasive healthcare in Sub-Saharan Africa (SSA). However, the adoption of smart implants is still nascent in SSA despite their tremendous benefits and the increasing demand to provide pervasive and personalized healthcare. Therefore, this study aimed to provide a comprehensive analysis of opportunities and potential challenges associated with the adoption of smart implants in SSA health systems as well as policy recommendations. The study adopted the PRISMA model to search and synthesize published literature about general implants and smart implants in SSA. The study revealed that smart implants present tremendous benefits including remote monitoring of patients, data management, disease diagnosis and treatment, monitoring of drug adherence, and effective family planning methods to reduce unplanned pregnancies. However, the adoption of smart implants faces implementation challenges such as poor technological infrastructure, cultural and religious barriers, health consequences and clinical challenges, legal and policy barriers, lack of manufacturing manpower, potential resistance by regulatory authorities and users, lack of political will and funding, data privacy and security concerns. Nonetheless, there is a need for SSA countries to develop strategies that will strengthen current implant services to stimulate the manufacturing of smart implants. This can be achieved through funding, public-private partnerships, training of healthcare professionals, sensitizing communities, increasing community engagement and most importantly the development of strategies and policies that will regulate the use of smart implants in healthcare settings. There is a need to investigate the privacy and security implications of smart implants in the provision of pervasive healthcare services.
Novel Gaussian Mixture-based Video Coding for Fixed Background Video Streaming

In recent years, tremendous advances have been made in Artificial Intelligence (AI) algorithms in the field of image processing. Despite these advances, video compression using AI algorithms has always faced major challenges. These challenges often lie in two areas of higher processing load in comparison with traditional video compression methods, as well as lower visual quality in video content. Careful study and solution of these two challenges is the main motivation of this article that by focusing on them, we have introduced a new video compression based on AI. Since the challenge of processing load is often present in online systems, we have examined our AI video encoder in video streaming applications. One of the most popular applications of video streaming is traffic cameras and video surveillance in road environments which here we called it CCTVs. Our idea in this type of system goes back to fixed background images, where always occupied the bandwidth not efficiently, and the streaming video is related to duplicate background images. Our AI-based video encoder detects fixed background and caches it at the client-side by the background subtraction method. By separating the background image from the moving objects, it is only enough to send the moving objects to the destination, which can save a lot of network bandwidth. Our experimental results show that, in exchange for an acceptable reduction in visual quality assessment, the video compression processing load will be drastically reduced.
Curricula Design &amp; Pedagogy for Sketching Within HCI &amp; UX Education

Sketching is recognised as an important tool in the journey of research and practical processes of Human Computer Interaction (HCI) and User Experience Design (UX). However, it is not always included in higher education curriculum, in which HCI and UX is often a single module in one year group amongst more “traditional” approaches in computer science. The benefits of sketching and visualisation practice can be used by students across the board in computing degrees, but especially so within HCI and UX, where novel approaches and ideation are valued and practiced. By the time learners leave higher education, they may or may not have engaged with this valuable skill. HCI has a lot in common with UX, and the two are commonly conflated to be the same thing, though despite this, there is not a focus on practical sketching and visualisation skills. In comparison, within the UX workplace environment, sketching is part of design thinking and vital for the structuring of ideas, storyboards, user journey maps and more. We focus on the incorporation and exploration of sketching as an educational tool, technique and output within HCI, and how this learning is given and received over a number of contexts. This paper outlines case studies where sketching has been included in both formal and informal learning with both undergraduate, postgraduate, and post education populations, and how this knowledge exchange has been both enhanced and changed by the recent compulsory move to online teaching during the COVID-19 pandemic. We discuss practice and learning in the context of four case studies: Data-Sketching in a First Year Minor; Sketching in a 2nd Year HCI Cohort; Sketching as a Foundational Tool for MSc User Experience Design; and, Sketching in HCI for Peer-to-Peer Learning . Further, we make recommendations for incorporating sketching practice and theory into both undergraduate and postgraduate university programs, as well as for peer-to-peer learning in both public and private contexts.
Human activity recognition by wireless body area networks through multi‐objective feature selection with deep learning

Abstract Wireless body area network (WBAN) is a novel technology with the incorporation of numerous types of devices, which is also employed in health monitoring applications. Human activity recognition (HAR) receives more interest in recent times along with wearable sensors. HAR system provides information about a person's identity, personality, and psychological state. In the present scenario, it becomes important to model the active learning paradigms with the help of wearable sensors for analysing human activities. Although various deep learning models and existing algorithms secure better outcomes through the sensor data analysis regarding HAR, the decision‐making evaluation seems to be a complex one. The main intent of this paper is to implement the WBAN‐based HAR system using the improved deep learning model. By connecting the wearable sensors, the signals are gathered regarding human activity from diverse benchmark sources. After collecting the required signals, the pre‐processing of the input signals is done using artefact removal and median filtering. Further, the feature extraction is performed, which intends to extract a set of features by utilizing short‐time Fourier transform (STFT) and statistical features. For reducing the feature‐length, a multi‐objective‐based optimal feature selection is adopted. Human activities such as ‘walking, walking upstairs, walking downstairs, sitting, standing, lying, and jogging’ are recognized with help of selected optimal features. The optimized probabilistic neural network (PNN) and convolutional neural network (CNN) are combined and named as adaptive probabilistic‐based CNN (AP‐CNN). The effective performance of optimal feature selection and recognition is accomplished by incorporating the developed backward updating position‐based sea lion optimization algorithm (BU‐SLnO). Finally, the performance of the BU‐SLnO‐AP‐CNN‐based suggested model is analysed, which shows 1.001%, 1.237%, 0.811%, and 0.859% advanced than SLnO‐AP‐CNN, feed‐forward‐AP‐CNN (FF‐AP‐CNN), grey wolf optimization‐AP‐CNN (GWO‐AP‐CNN), particle swarm optimization‐AP‐CNN (PSO‐AP‐CNN) when observing the dataset 1. The experimental outcomes from comparison with various classification techniques demonstrate the efficiency of the developed technique.
Deep learning on microcontrollers

Microcontrollers are an attractive deployment target due to their low cost, modest power usage and abundance in the wild. However, deploying models to such hardware is non-trivial due to a small amount of on-chip RAM (often < 512KB) and limited compute capabilities. In this work, we delve into the requirements and challenges of fast DNN inference on MCUs: we describe how the memory hierarchy influences the architecture of the model, expose often under-reported costs of compression and quantization techniques, and highlight issues that become critical when deploying to MCUs compared to mobiles. Our findings and experiences are also distilled into a set of guidelines that should ease the future deployment of DNN-based applications on microcontrollers.