Reviewerly/exHarmony huggingface dataset:
Vision-Based Real-Time Human–Computer Interaction on Hand Gesture Recognition

Gesture recognition technology has evolved greatly over the years. It is a field in language innovation and development with the aim of deciphering human motions by the use of certain algorithms. In this age of technology, we have fabricated boundless techniques and subsequently seen their drawbacks making us aware of our own limits in terms of speed and naturalness of the human body. The intellect and inventiveness of human beings have led to the development of many tools, gesture recognition technology being one of them. They help us extend the capabilities of our senses by combining natural gestures to operate technology, thereby reducing human efforts and going beyond human abilities. Gestures can be viewed as a route for PCs to start to comprehend human non-verbal communication, in this manner fabricating a more extravagant extension among machines and people than crude content UIs or GUIs, which still breaking point most of contribution to console and mouse and connect normally with no mechanical gadgets. Utilizing the idea of motion acknowledgment, it is conceivable to point a finger now will move as needs be. With the expeditious inventions of three-dimensional applications and the upcoming hype of virtual environments in systems, there is a need of new devices that can sustain these interactions. The advancement of user interface molds and develops the human-computer interaction (HCI). The paper aims to present a review of vision-based hand gesture recognition techniques for HCI and using it to tally finger count using the same.
Efficient Password-Authenticated Key Exchange from RLWE Based on Asymmetric Key Consensus

A password-authenticated key exchange (PAKE) protocol allows two entities sharing a password to perform mutual authentication and establish a session key. Benefiting from the use of a low-entropy human-memorable password, PAKE avoids the use of PKI in the authentication process, making it more flexible and cheaper. However, with the development of quantum computing, protocols based on classical assumptions will no longer be secure, so designing a PAKE protocol capable of resisting quantum attacks has become an important research direction. In this work, we propose an efficient PAKE protocol using a new error reconciliation mechanism based on the ring learning with errors (RLWE) problem, which is considered to resist quantum attacks. Our protocol is proven security under the Bellare-Pointcheval-Rogaway (BPR) model. The protocol is implemented using the C language, which is highly portable, and is also optimized utilizing the Advanced Vector Extensions 2 (AVX2) instruction set. Compared with the C implementation of Ding's protocol, our reference C implementation is more than 12x faster, and the efficiency is doubled after AVX2 optimization. Moreover, by choosing the appropriate parameters, the security strength of our scheme is improved and the message size is reduced.
Quantum-Based Feature Selection for Multiclassification Problem in Complex Systems with Edge Computing

The complex systems with edge computing require a huge amount of multi-feature data to extract appropriate insights for their decision making, so it is important to find a feasible feature selection method to improve the computational efficiency and save the resource consumption. In this paper, a quantum-based feature selection algorithm for the multi-classification problem, namely, QReliefF, is proposed, which can effectively reduce the complexity of algorithm and improve its computational efficiency. First, all features of each sample are encoded into a quantum state by performing operations CMP and R_y, and then the amplitude estimation is applied to calculate the similarity between any two quantum states (i.e., two samples). According to the similarities, the Grover-Long method is utilized to find the nearest k neighbor samples, and then the weight vector is updated. After a certain number of iterations through the above process, the desired features can be selected with regards to the final weight vector and the threshold {\tau}. Compared with the classical ReliefF algorithm, our algorithm reduces the complexity of similarity calculation from O(MN) to O(M), the complexity of finding the nearest neighbor from O(M) to O(sqrt(M)), and resource consumption from O(MN) to O(MlogN). Meanwhile, compared with the quantum Relief algorithm, our algorithm is superior in finding the nearest neighbor, reducing the complexity from O(M) to O(sqrt(M)). Finally, in order to verify the feasibility of our algorithm, a simulation experiment based on Rigetti with a simple example is performed.
Towards a data-driven method for RGB video-based hand action quality assessment in real time

In recent years, the research community has begun to explore Video-Based Action Quality Assessment on Human Body (VB-AQA), while few work focuses on Video-Based Action Quality Assessment on Human Hand (VH-AQA) yet. The current work on VB-AQA fails to deal with the inconsistency between captured features and the reality due to the changing angles of the camera, leaving a huge gap between VB-AQA and VH-AQA, while the computational efficiency is another critical problem. In this paper, a novel data-driven method for real-time VH-AQA is proposed. Features are formulated as spatio-temporal hand poses in this method and extracted via four steps: hand segmentation, 2D hand pose estimation, 3D hand pose estimation and hand pose organization. Based on the extracted features an assessment model is applied to evaluate the performance of actions and indicate the most promising adjustment as the feedback. We demonstrate the evaluation accuracy and computational efficiency of our method using our own Origami Video Dataset. For the latter, two new metrics are designed. It turns out that our method provides opportunities for real-time digital reconstruction of physical world activities and timely assessment.
Proposed Service Oriented Architecture for the Inheritance Web App of Bangladesh

The software creates an all-round workplace for people's lives and practices, where they progressively incorporate the increased systems of interaction. This study wants to explore the benefits of the Service-Oriented Architecture (SOA) in the daily life of the people of a nation. Cutting-edge demand to reach successful government services, they have to provide services to inhabitants, industries and government chains. To achieve the vision and dream of Digital Bangladesh, the Government of Bangladesh has launched so many e-services for the people of Bangladesh, whereas there is a web application for the family inheritance purpose named Uttarahdikar which is available as a web app and Android App. Our objective is to propose an SOA model that will permit the consumers of Uttaradhikar to use it efficiently, reliably, effectively. This study will show the real time-saving calculation for the consumers which will create the eagerness and sincerity of the Bangladeshi Nations to use it for their inheritance purpose. The integration structures of other related services to this app will also be discussed in this paper.
A Critical Analysis of Music Recommendation Systems and New Perspectives

Many businesses enhance on-line user experience using various recommender systems which have a growing innovation and research interest. Recommender systems in music streaming applications proactively suggest new selections to users by attempting to predict user preferences. While current music recommendation systems help users to efficiently discover fascinating music, challenges remain in this research area. This paper presents a critical analysis of current music recommender systems and proposes a new hybrid recommender system with efficient and enhanced prediction capabilities.
Heterogeneous Multi-Modal Sensor Fusion with Hybrid Attention for Exercise Recognition

Exercise adherence is a key component of digital behaviour change interventions for the self-management of musculoskeletal pain. Automated monitoring of exercise adherence requires sensors that can capture patients performing exercises and Machine Learning (ML) algorithms that can recognise exercises. In contrast to ambulatory activities that are recognisable with a wrist accelerometer data; exercises require multiple sensor modalities because of the complexity of movements and the settings involved. Exercise Recognition (ExR) pose many challenges to ML researchers due to the heterogeneity of the sensor modalities (e.g. image/video streams, wearables, pressure mats). We recently published MEx, a benchmark dataset for ExR, to promote the study of new and transferable HAR methods to improve ExR and benchmarked the state-of-the-art ML algorithms on 4 modalities. The results highlighted the need for fusion methods that unite the individual strengths of modalities. In this paper, we explore fusion methods with a focus on attention and propose a novel multi-modal hybrid attention fusion architecture mHAF for ExR. We achieve the best performance of 96.24% (F1-measure) with a modality combination of a pressure mat, a depth camera and an accelerometer on the thigh. mHAF significantly outperforms multiple baselines and the contribution of architecture components are verified with an ablation study. The benefits of attention fusion are clearly demonstrated by visualising attention weights; showing how mHAF learns feature importance and modality combinations suited for different exercise classes. We highlight the importance of improving deployability and minimising obtrusiveness by exploring the best performing 2 and 3 modality combinations.
Byzantine Fault Tolerance Based Multi-Block Consensus Algorithm for Throughput Scalability

Blockchain is a distributed, reliable, and secure ledger that maintains data by consensus among network participants. The consensus algorithms provide data reliability but increase the data processing time. In this paper, we propose the multi-block consensus algorithm based on Byzantine Fault Tolerance to enhance throughput. The key point of the proposed algorithm is that the primary propagates the disjoint-transaction sets to other replicas. After receiving the propagated blocks, the replicas verify the propagation part and the content part of the blocks. As sharing the verifying result, the replicas could add the valid blocks to the blockchain at a time. We evaluate the performance of the proposed algorithm comparing to the Practical Byzantine Fault Tolerance algorithm which is the most ordinary Byzantine Fault Tolerance based algorithm. By the simulation results, throughput increases as the number of users increases.
Suzzer: A Vulnerability-Guided Fuzzer Based on Deep Learning

Fuzzing is a simple and effective way to find software bugs. Most state-of-the-art fuzzers focus on improving code coverage to enhance the possibility of causing crashes. However, a software program oftentimes has only a fairly small portion that contains vulnerabilities, leading coverage-based fuzzers to work poorly most of the time. To address this challenge, we propose Suzzer, a vulnerability-guided fuzzer, to concentrate on testing code blocks that are more likely to contain bugs. Suzzer has a light-weight static analyzer to extract ACFG vector from target programs. In order to determine which code blocks are more vulnerable, Suzzer is equipped with prediction models which get the prior probability of each ACFG vector. The prediction models will guide Suzzer to generate test inputs with higher vulnerability scores, thus improving the efficiency of finding bugs. We evaluate Suzzer using two different datasets: artificial LAVA-M dataset and a set of real-world programs. The results demonstrate that in the best case of short-term fuzzing, Suzzer saved 64.5% of the time consumed to discover vulnerabilities compared to VUzzer.
The Effect of Virtual Reality Control of a Robotic Surrogate on Presence and Social Presence in Comparison to Telecommunications Software

Telecommunication software is often used to tackle social isolation in those with restricted mobility, but lacks high-level interactions found in face-to-face conversation. This study investigated the use of an immersive control system for a robotic surrogate when compared to Skype. There was no significant difference between the presence and social presence felt between the two systems; however, Skype was found to be significantly easier to use. Future work will focus on identifying user requirements and further developing the control system.
Neural Speech Synthesis with Style Intensity Interpolation

State of the art in speech synthesis considerably reduced the gap between synthetic and human speech on the perception level. However the impact of a speech style control on the perception is not well known. In this paper, we propose a method to analyze the impact of controlling the TTS system parameters on the perception of the generated sentence. This is done through a visualization and analysis of listening test results. For this, we train a speech synthesis system with different discrete categories of speech styles. Each style is encoded using a one-hot representation in the network. After training, we interpolate between the vectors representing each style. A perception test showed that despite being trained with only discrete categories of data, the network is capable of generating intermediate intensity levels between neutral and a given speech style.
UC2Map

Many times developers start with a high-level description of a use case which is then used in place of a more formal requirement. However, such a description fails to provide rich details that are typically part of a more structured use case. Having a varied level of details and degree of formalism among use cases, it is often difficult to comprehend and visualize functional dependencies among each of them in detail. Use Case Map (UCM) elaborates such dependencies in terms of relationships and responsibilities, and act as a bridge between specifications and design artifacts. However, there is no systematic transformation approach available for deriving UCMs from use case scenarios. In this work, we present a tool-support developed to automatically generate UCMs from the use case specification by identifying relationships, responsibilities, and related functional dependencies among them.
Detecting architectural integrity violation patterns using machine learning

Recent1 years have seen a surge of research into new ways of analyzing software quality. Specifically, a set of studies has been devoted to the impact the architectural relations among files have on system maintainability and file bug-proneness. The literature has proposed a set of rules for determining recurring architectural design flaws that occur in most complex systems, are associated with bugs, and thus incur high maintenance costs. In the present paper we advocate for using machine learning as the means of refining the approach and revealing new patterns of architectural integrity violations. Having trained a machine learning model on the combination of structural and historical information acquired from the Tiki open source project, we have been able to replicate three of the six known types of architectural violations and discover one new type, the Reverse Unstable Interface pattern. The implication of our study is that machine learning can provide valuable insights into the problem and discover novel patterns which would help software analysts to pinpoint specific architectural problems that may be the root causes of elevated bug- and change-proneness.
The usage of social media for higher education purposes

Social media today are an inevitable part of everyday life; networking of individuals and new ways of communication and interaction have emerged in the academic world.They are available 24 hours a day, 7 days a week across a range of devices and from different locations, thus depending only on the availability of the internet and the will of their users.By enabling the creation and sharing of information, ideas, and interests through virtual communities and networks, whose purpose is to turn communication into interactive dialogue, they have influenced society, economics, politics, science, and education.Students of today are generations who have started virtual life on social media since their early age and do not know the world where there are no computers, cell phones, and social media.Social media is an example of technology students have widely adopted, but the digital gap between students and their educational institutions is evident.Numerous researches show that there is a need for integration of social media into the teaching process because students experience them as self-explanatory.There are many advantages and positive effects on the teaching process, but teachers are less likely to use them because they are more focused on their shortcomings and dangers.The use of technology can be influenced by numerous factors, and the current study is conducted to identify the current status of teachers' usage of social media in higher education and detection of factors which influence the usage of social media in higher education.The questionnaire is based on the Unified Theory of Acceptance and Use of Technology named UTAUT model as a theoretical framework, and data is collected on a sample of teachers at the Algebra University College in Zagreb and the University of Applied Sciences Baltazar Zaprešić.This research is the first research conducted among the higher education teachers in the Republic of Croatia, concerned not only with the social media usage for the higher education purposes but also with factors that influence the willingness of higher education teachers to use social media for the higher education purposes.
Multilevel secret sharing schemes arising from the normal rational curve

Multilevel secret sharing schemes are used in cryptography when a responsibility has to be delegated to qualified groups of participants rather than to a single one. In the present paper we propose a mathematical model for such a scheme based on projective geometry over a finite field. The main ingredients are normal rational curves and their osculating subspaces.
A Data Desensitization Algorithm for Privacy Protection Electric Power Industry

Abstract In view of the data security problems faced by big data technology in the development of electric power industry, this paper proposes a power big data desensitization algorithm applied to privacy protection, that is, a binary K-clustering algorithm (BKC-LDA) based on K-anonymity and L diversity. First, in order to reduce the computational complexity, a classification attribute is determined to classify the data table initially, and the equivalent class number K and the sensitive attribute value category L are limited according to the number of the original ancestor in the source data table. Then, considering the influence of the change of the internal range of the attribute value on the clustering, the equation for calculating the distance between the original ancestors with weight is established, using the idea of greedy and binary K clustering to classify the data table initially sets are clustered and generalized. In addition, this big data desensitization method determines the security level policies of different permissions by adjusting the size of K and L. Our algorithm can adapt to the desensitization of power big data with different attributes in different scenarios. It can not only fully mine the value of data, but also effectively protect the privacy of users.
Communication and Culture in the Meta-Media Era

Abstract With the rapid development of digital media technology represented by the Internet and convergent media, human society entered the “meta-media age” at the end of the twentieth century. Regarded as the “media of media,” meta-media, or the terminal screens connected by the Internet, integrate all existing media forms and their communication patterns by translating, remodeling, and even re-forming their sign-texts. Accordingly, “remediation” has become the dominant way to construct the meaning of signs in meta-media. It should be noted that the remediation of meta-media changes not only the form of the existing media, but also the way we communicate with signs. Hence, from the perspective of the semiotics of communication, this paper considers the features and cultural influence of symbolic communication in the meta-media age based on Roman Jakobson’s six factors/functions of signs model.
Augmented Reality of Android-Based Learning Media of Sun and Earth Structure

The use of Augmented Reality is an option in the delivery of information in the form of learning material because in Augmented Reality elements such as text, sound, images, and animation can be put together in its presentation, and are able to demonstrate a real object into a virtual form that can be seen from all sides. Based on the background above, the authors think that augmented reality technology can be used in conveying sun and earth material. this application will be used as a learning medium. the writer will test whether this application can be accepted as a learning medium for elementary school students.
A Semantic-Based Framework for Analyzing App Users' Feedback

The competitive market of mobile apps requires app developers to consider the users' feedback frequently. This feedback, when comes from different resources, e.g. App Stores and Twitter, will provide a broader picture of the state of the app, as the users discuss different topics on each platform. Automated tools are developed to filter the informative comments for app developers. However, to integrate the feedbacks from different platforms, one should evaluate the similarities and/or differences of the text from each one. Different meaning of the words in various context, makes this evaluation a challenging task for automated processes. For example, Request night theme and Add dark mode are two comments that are requesting the same feature. This similarity cannot be identified automatically if the semantics of the words are not embedded in the analysis. In this paper, we propose a new framework to analyze the users' feedback by embedding their semantics. As a case study, we investigate whether our approach can identify the similar/different comments from Google Play Store and Twitter, in the two well studied classes of bug reports and feature requests from literature. The initial results, validated by expert evaluation and statistical analysis, shows that this framework can automatically measure the semantic differences among users' comments in both groups. The framework can be used to build intelligent tools to integrate the users' feedback from other platforms, as well as providing ways to analyze the reviews in more detail automatically.