Reviewerly/exHarmony huggingface dataset:
An Efficient Key Generation Scheme for Secure Sharing of Patients Health Records using Attribute Based Encryption

Attribute Based Encryption that solely decrypts the cipher text's secret key attribute. Patient information is maintained on trusted third party servers in medical applications. Before sending health records to other third party servers, it is essential to protect them. Even if data are encrypted, there is always a danger of privacy violation. Scalability problems, access flexibility, and account revocation are the main security challenges. In this study, individual patient health records are encrypted utilizing a multi-authority ABE method that permits a multiple number of authorities to govern the attributes. A strong key generation approach in the classic Attribute Based Encryption is proposed in this work, which assures the robust protection of health records while also demonstrating its effectiveness. Simulation is done by using CloudSim Simulator and Statistical reports were generated using Cloud Reports. Efficiency, computation time and security of our proposed scheme are evaluated. The simulation results reveal that the proposed key generation technique is more secure and scalable.
RTSEC: Automated RTL Code Augmentation for Hardware Security Enhancement

Current hardware designs have increased in complexity, resulting in a reduced ability to perform security checks on them. Further, the addition of any security features to these designs is still largely manual which further complicates the design and integration process. In this paper, we address these shortcomings by introducing Rtsec as a framework which is capable of performing security analysis on designs as well as integrating security features directly into the HDL code, a feature that commercial EDA tools do not provide. Rtsec first breaks down HDL code into an Abstract Syntax Tree which is then used to infer the logic of the design. We demonstrate how Rtsec can be utilized to automatically include security mechanisms in RTL designs: watermarking and logic locking. We also compare the efficacy of our analysis algorithms with state of the art tools, demonstrating that Rtsec has capabilities equal or superior to those of state of the art tools while also providing the means of enhancing security features to the design.
Shallow SegNet with bilinear interpolation and weighted cross-entropy loss for Semantic segmentation of brain tissue

Semantic segmentation using deep learning techniques is now state-of-the-art in medical image segmentation, especially for brain Magnetic Resonant images (MRI). SegNet, a fully convolutional neural network architecture, is widely used for image segmentation. However, it is less frequently used than the other popular competing approach of U-Net, for brain MRI segmentation. A few researchers have proposed a fusion of SegNet and U-Net architectures to combine their desirable properties for performance gains. In this paper, a different direction of research is undertaken. A simpler yet more accurate shallow SegNet architecture is proposed to yield promising segmentation performance on brain MRI. The proposed architecture uses a bilinear interpolation upsampling mechanism instead of max unpooling. Further, a modified cross-entropy loss is employed that is weighted differently for different classes. The class imbalance problem is effectively overcome using this weighted cross-entropy loss. Performance comparison of the proposed architecture with existing works indicates that the average dice factor is enhanced to 0.83 with an improvement of 0.11 over the baseline SegNet. It is demonstrated that the proposed shallow SegNet is a simpler yet more accurate model compared to both the existing SegNet and U-Net and could serve as a baseline for fine-grained image segmentation tasks.
Developing a Cyber Incident Exercises Model to Educate Security Teams

Since cyber attacks are increasing and evolving rapidly, the need to enhance cyber-security defense is crucial. A cyber incident exercise model is a learning technique to provide knowledge about cyber security to enhance a security team’s incident response. In this research work, we proposed a cyber incident model to handle real-time security attacks in various scenarios. The proposed model consisted of three teams: (1) the black team, (2) the red team, and (3) the blue team. The black team was a group of instructors responsible for setting up the environment. They had to educate the red and blue teams about cyber security and train them on facing cyber attacks. Once the training period was completed, the members were divided into two teams to conduct a cyber-security competition in a cyber game scenario. Each of the two teams performed a different task. The red team was the offensive team that was responsible for launching cyber-security attacks. The blue team was the defensive team that was responsible for countering attacks and minimizing the damage caused by attackers; they had to conduct both cyber-security configuration and incident handling. During the scenario, the black team was responsible for guiding and monitoring both the red and the blue teams, ensuring the rules were applied throughout the competition. At the end of the competition, the members of each team changed with each other to make sure every team member was using the knowledge they gained from the training period and every participant was evaluated impartially. Finally, we showed the security team’s offensive and defensive skills via the red team and the blue team, respectively.
Hardware Acceleration of Aggregate Signature Generation and Authentication by BLS Signature over BLS12-381 curve

BLS signature is a digital signature scheme computed over elliptic curves, and it has been attracting attention with its interesting function that signatures can be aggregated. We will introduce our progress of designing two ASIC architectures to accelerate the complex computations of generating and verifying signatures respectively. The computations include mapping to elliptic curves and pairing. An important subject of our work is to adopt a relatively new curve called BLS12-381. BLS12-381 is currently one of the curves that gather the most interests and yet very few ASIC implementations are optimized for BLS12-381.
Are Negative Emotions Useful for Learning?

Studies on the impact of emotions on learning have focused primarily on using and understanding emotions close to joy and happiness to improve human experiences. However, little is said about apparently emotions, such as disgust, anger, sadness and fear and their uses and benefits within the academic teaching environment. In this study, we used a chatbot as a companion to give feedback that would evoke negative emotions. The study produced promising results for enhancing learning processes using negative emotions.
COVID-19 and Digital Economy: The Journey towards a Digital Transformation in New Normal: How to Prepare for the Future

Since the COVID-19 outbreak, the digital economy through the digital transformation of production and consumption has surged globally. In recent rapid technological revolution businesses are moving their operations and workforce to a virtual environment, where the setting and fundamental of the traditional business are changed considerably. Hence, this chapter explores further the structure of the digital supply and digital consumption, and its opportunities and challenges in promoting the digital economy in new normal. We further highlight the rise of some key concepts such as digital globalisation, digital innovation and ethical risks at individual, organisation and country level.
Affective Response Categories—Toward Personalized Reactions in Affect-Adaptive Tutoring Systems

Affect-adaptive tutoring systems detect the current emotional state of the learner and are capable of adequately responding by adapting the learning experience. Adaptations could be employed to manipulate the emotional state in a direction favorable to the learning process; for example, contextual help can be offered to mitigate frustration, or lesson plans can be accelerated to avoid boredom. Safety-critical situations, in which wrong decisions and behaviors can have fatal consequences, may particularly benefit from affect-adaptive tutoring systems, because accounting for affecting responses during training may help develop coping strategies and improve resilience. Effective adaptation, however, can only be accomplished when knowing which emotions benefit high learning performance in such systems. The results of preliminary studies indicate interindividual differences in the relationship between emotion and performance that require consideration by an affect-adaptive system. To that end, this article introduces the concept of Affective Response Categories (ARCs) that can be used to categorize learners based on their emotion-performance relationship. In an experimental study, N = 50 subjects (33% female, 19–57 years, M = 32.75, SD = 9.8) performed a simulated airspace surveillance task. Emotional valence was detected using facial expression analysis, and pupil diameters were used to indicate emotional arousal. A cluster analysis was performed to group subjects into ARCs based on their individual correlations of valence and performance as well as arousal and performance. Three different clusters were identified, one of which showed no correlations between emotion and performance. The performance of subjects in the other two clusters benefitted from negative arousal and differed only in the valence-performance correlation, which was positive or negative. Based on the identified clusters, the initial ARC model was revised. We then discuss the resulting model, outline future research, and derive implications for the larger context of the field of adaptive tutoring systems. Furthermore, potential benefits of the proposed concept are discussed and ethical issues are identified and addressed.
Some hypersurfaces over finite fields, minimal codes and secret sharing schemes

Abstract Linear error-correcting codes can be used for constructing secret sharing schemes; however, finding in general the access structures of these secret sharing schemes and, in particular, determining efficient access structures is difficult. Here we investigate the properties of certain algebraic hypersurfaces over finite fields, whose intersection numbers with any hyperplane only takes a few values; these varieties give rise to q -divisible linear codes with at most 5 weights. Furthermore, for q odd, these codes turn out to be minimal and we characterize the access structures of the secret sharing schemes based on their dual codes. Indeed, the secret sharing schemes thus obtained are democratic, that is each participant belongs to the same number of minimal access sets and can easily be described.
A Complex Java Code Generator for ACL2 Based on a Shallow Embedding of ACL2 in Java

This paper describes a code generator that translates ACL2 constructs to corresponding Java constructs, according to a shallow embedding of ACL2 in Java.Starting from purely functional ACL2 code, the generated Java code exhibits imperative and object-oriented features like destructive updates, loops, and overloading.The overall translation from ACL2 to Java is fairly elaborate, consisting of several ACL2-to-ACL2 pre-translation steps, an ACL2-to-Java proper translation step, and several Java-to-Java post-translation steps.Experiments suggest that the generated Java code is not much slower than the ACL2 code.The code generator can also recognize, and translate to Java, ACL2 representations of certain Java constructs, forerunning a code generation approach based on a shallow embedding of Java in ACL2 (i.e.going the other way).This code generator builds upon, and significantly extends, a simple Java code generator for ACL2 based on a deep embedding of ACL2 in Java.
Digital India Digital Economy Using Blockchain Technology

In November 2016, India decided to take on probably the most herculean task that a country could face: Demonetization. This move was initiated with not one, but a wide array of motives such as ridding India of its black money, ensuring fair collection of tax for unaccounted cash and to make India a cashless economy. Millions of Indians turned towards digital payments for everything from buying groceries to paying periodic utility bills. Unified Payments Interface, or UPI, was created with the sole purpose of enabling interoperability. But as is usually the case with well-designed systems, UPI has unleashed a tsunami of economic potential along the way. While UPI is making digital payments the new normal for the citizens of India, there is one concern that stands out: the safety of people’s hard-earned money stored in a centrally monitored database. The centralization of a database is easy to monitor, but it compromises the protection of money against hackers. This article focuses on online transaction systems that make use of the blockchain technology and its features, namely distributed database, Advanced Encryption Standard (AES) and Secure Hash Algorithm 256 (SHA-256) to make the cashless payment system more robust to cyber security threats. As the outcome, a more secure and reliable model for a digital payment wallet service using blockchain technology has been developed.
BioPrivacy: a behavioral biometrics continuous authentication system based on keystroke dynamics and touch gestures

Purpose This research aims to build a system that will continuously. This paper is an extended version of SECPRE 2021 paper and presents a research on the development and validation of a behavioral biometrics continuous authentication (BBCA) system that is based on users keystroke dynamics and touch gestures on mobile devices. This paper aims to build a system that will continuously authenticate the user of a smartphone. Design/methodology/approach Session authentication schemes establish the identity of the user only at the beginning of the session, so they are vulnerable to attacks that tamper with communications after the establishment of the authenticated session. Moreover, smartphones themselves are used as authentication means, especially in two-factor authentication schemes, which are often required by several services. Whether the smartphone is in the hands of the legitimate user constitutes a great concern and correspondingly whether the legitimate user is the one who uses the services. In response to these concerns, BBCA technologies have been proposed on a large corpus of literature. This paper presents a research on the development and validation of a BBCA system (named BioPrivacy), which is based on the user’s keystroke dynamics and touch gestures, using a multi-layer perceptron (MLP). Also, this paper introduces a new BB collection tool and proposes a methodology for the selection of an appropriate set of BB. Findings The system achieved the best results for keystroke dynamics which are 97.18% accuracy, 0.02% equal error rate, 97.2% true acceptance rate and 0.02% false acceptance rate. Originality/value This paper develops a new BB collection tool, named BioPrivacy, by which behavioral data of users on mobile devices can be collected. This paper proposes a methodology for the selection of an appropriate set of BB. This paper presents the development of a BBCA system based on MLP.
COVID-19 and the rise of digitalised spoken communication

The COVID-19 pandemic triggered a swift and quasi-compulsory switch from in-person to online communication in most areas of our lives, be it professional, social or personal. It particularly engendered upheaval within the field of specialised, spoken professional genres. Alongside dialogic (that is, interactive) formats such as online meetings, conference calls and online job interviews, webinars – of which the very name underlines their digital raison d’être – present an interesting example of a novel, inherently interactive format. Indicative of the horizontal knowledge sharing that characterises web 2.0 and the “new knowledge economy”, they display a number of linguistic features which inform late capitalist society. While they are closely linked to the corporate world, webinars have been adopted by a vast array of actors in the professional sphere, serve a number of purposes and can, hence, qualify as a “hypergenre”. This chapter provides an overview of some discursive and cognitive issues raised by digitalised spoken communication, before turning to the case of webinars. It proposes a definition of webinars and analyses their specific interactive regime, as enacted via multiple technical devices and initiated via a contract of engagement which is realised by discursive and linguistic means.
The Impact of Social Customer Relationship Management Practices on Public Organizational Performance: Social Customer Relationship Management Effectiveness as Mediator Variable

Purpose:The aim of this study is to examine the impact of the Social Customer Relationship Management (Social CRM) practices on the social CRM effectiveness and the organizational performance at the Immigration Department of Malaysia.This research finding aims to help organizations to practice the best Social CRM activities to improved service and productivity for the leader.Design/Method: A descriptive-analytical method (quantitative approach has been taken in this study.A survey was conducted with 230 social CRM practitioners of immigration officers.The research hypotheses were analyzed by means of structural equation modeling (SEM) by using AMOS 22.0.Findings: This study has proved that there is a positive and significant relationship between social CRM practices and organizational performance.This study also proved an indirect effect on social CRM practices through social CRM effectiveness.The testing indicates variable SCRM Effectiveness partially mediates the relationship between Social CRM Practices and Organizational Performance.Research Limitations/ Implication: Social CRM practitioners inside the department should also focus on the best social CRM practices to meet the customer expectation and to satisfy the savvy customer as well as fulfill their needs which helps the department to improve the performance.Originality: There are few research-based studies on Social CRM practices and organizational performance.This study specifically aim to examine the role of "social CRM effectiveness" as a mediator variable between the practices of social CRM and the organizational performance in the Immigration Department in Malaysia.
Management and Monitoring of Multi-Behavior Recommendation Systems Using Graph Convolutional Neural Networks

Different recommendation algorithms, which often use only a single type of user-item engagement, are plagued by imbalanced datasets and cold start problems. Multi-behavior recommendations, which takes advantage of a variety of customer interaction including click and favorites, can be a good option. Early attempts at multi-behavior suggestion tried to consider the varying levels of effect each behavior has on the target behavior. They also disregard the meanings of behaviors, which are implicit in multi-behavior information. Because of these two flaws, the information isn’t being completely utilized to improve suggestion performance on the specific behavior. In this paper, we take a novel response to the situation by creating a unified network to capture multi-behavior information and displaying the MBGCNNN model (Multi-Behavior Graph Convolutional Neural Network). MBGCNN may effectively overcome the constraints of prior studies by learning behavior intensity via the user-item dissemination level and collecting behavior interpretation via the items dissemination level. Practical derives from various data sets back up our model’s order to leverage multi-behavior data. On real methods, our approach beats the average background by 25.02 percent and 6.51 percent, respectively. Additional research on cold-start consumers supports the viability of our suggested approach.
A Systematic Bibliometric Analysis of Hate Speech Detection on Social Media Sites

With the increasing availability of internet facilities for everyone across the globe, the internet plays an integral part in modern communication. People have the ease of contacting others and sharing thoughts and ideas quickly. This has raised an enormous amount of spread of Hate Speech on Online Social Media Sites. This paper aims to provide systematic bibliometric analysis and mappings of existing literature for Hate Speech Detection and to identify the existence of Hate speech-related research. Bibliometric Analysis of Machine Learning and Deep Learning articles in Hate, hostile, and abusive speech is considered. This is accomplished using the SCOPUS database, with tools like VOSViewer, Biblioshiny, and ScienceScape. Explored parameters consist of the document type, most active countries, top journals, relevant affiliations, trending topics, etc. It is observed that the current literature on hate speech is concentrated on a specific philosophy. An unexpected need to rectify this situation was evident from this bibliometric analysis due to recent occurrences of hate speech in the digital world.
Improving Performance of ELM with RAO Optimization for Brain CT Image Classification

the study uses a RAO multi-objective optimization algorithm with an extreme learning machine (ELM) to detect the stroke in CT brain image. The RAO optimization algorithm is a newly developed optimization technique that needs no hyper-parameters to update and lead to global optima. Using the heuristic technique with ELM makes a hyper-parameter less classification model. The proposed model is validated for Brain stroke detection from CT images. The proposed model is tested on reduced version of CQ500 dataset and achieves 85.00% of testing accuracy.
Extracting Searching as Learning Tasks Based on IBRT Approach

With the rapid development of the World Wide Web and information retrieval technology, learning supported by searching engines (such as making travel plans) has boomed over the past years. With the help of search engines, learners can easily retrieve and find large amounts of information on the web. Recent research in the searching as learning (SAL) area has associated web searching with learning. In SAL processes, web learners recursively plan tasks, formulate search queries, obtain information from web pages, and change knowledge structures, to gradually complete their learning goals. To improve the experiences of web learners, it is important to accurately present and extract tasks. Using learning styles and similarity metrics, we first proposed an IBRT model to implement structured representations of the SAL process for each learner. SAL tasks were then extracted from the structures of IBRT. In this study, a series of experiments were carried out against assignment datasets from the Northeastern University (China) UWP Programming Course. Comparison results show that the proposed method can significantly improve the performance of SAL task extraction.
Object servoing of differential-drive service robots using switched control

Object servoing is becoming more and more important for service robots. Because of the nonholonomic constraints of a differential-drive service robot and the possibly changed pose of a movable object, it is challenging to design an object servoing scheme for the differential-drive service robot such that it can asymptotically park at a predefined relative pose to that of the movable object. In this paper, a novel object servoing scheme is proposed for the differential-drive service robots using switched control. Each relative online pose is first estimated by using features of the movable object, the estimated pose is an input of an object servoing friendly parking controller. The linear velocity and angular speed are then determined by the proposed controller. Simulation results validate the performance of the proposed object servoing scheme. Due to its low online computational cost, the proposed scheme can be applied for the real-time tasks of differential-drive service robots to movable objects.