Reviewerly/exHarmony huggingface dataset:
Smart home Management System with Face Recognition Based on ArcFace Model in Deep Convolutional Neural Network

In recent years, artificial intelligence has proved its potential in many fields, especially in computer vision. Facial recognition is one of the most essential tasks in the field of computer vision with various prospective applications from academic research to intelligence service. In this paper, we propose an efficient deep learning approach to facial recognition. Our approach utilizes the architecture of ArcFace model based on the backbone MobileNet V2, in deep convolutional neural network (DCNN). Assistive techniques to increase highly distinguishing features in facial recognition. With the supports of the facial authentication combines with hand gestures recognition, users will be able to monitor and control his home through his mobile phone/tablet/PC. Moreover, they communicate with data and connect to smart devices easily through IoT technology. The overall proposed model is 97% of accuracy and a processing speed of 25 FPS. The interface of the smart home demonstrates the successful functions of real-time operations.
Application of computational intelligence in visual optimization tools to improve the performance of medical MIoT platforms

Access control during pandemics such as the COVID-19 pandemic is made more difficult for some public places such as cinemas, cafes, and restaurants or events. A comprehensive security system that prevents the spread of COVID-19 in instances where a large number of people try to access multiple public places simultaneously. This chapter proposes a solution consisting of medical IoT platform based on QRCode, BarCode, and RFID identification readers to check the validity of a sanitary pass associated with an intelligent facial recognition system to verify the appropriate pass owner. To gain in terms of execution time, bandwidth, storage space, energy, and memory consumptions, the face detection and recognition of the visitors deploy all deep learning routines based on artificial intelligence (AI) and also on visual quality coding that incorporates the human quality criterion. The latter applies the human visual cortex properties to efficiently reshape both the citizen payload's spectrums. Then, to communicate payloads to the server with minimal energy consumption, we apply the CoAP protocol, and aiming to secure them during communication in the MIoT platform, we deploy the Elliptic Curve Cryptography (ECC) encryption. Modeling via this MIoT platform allows access to several people with updated pass information while respecting a minimal delay for the control and provides an interesting execution time and MIoT resources.
Data Integrity and Risk

Data Integrity is a critical component of Data lifecycle management.Its importance increases even more in a complex and dynamic landscape.Actions like unauthorized access, unauthorized modifications, data manipulations, audit tampering, data backdating, data falsification, phishing and spoofing are no longer restricted to rogue individuals but in fact also prevalent in systematic organizations and states as well.Therefore, data security requires strong data integrity measures and associated technical controls in place.Without proper customized framework in place, organizations are prone to high risk of financial, reputational, revenue losses, bankruptcies, and legal penalties which we shall discuss further throughout this paper.We will also explore some of the improvised and innovative techniques in product development to better tackle the challenges and requirements of data security and integrity.
Static Analysis of Data Transformations in Jupyter Notebooks

Jupyter notebooks used to pre-process and polish raw data for data science and machine learning processes are challenging to analyze. Their data-centric code manipulates dataframes through call to library functions with complex semantics, and the properties to track over it vary widely depending on the verification task. This paper presents a novel abstract domain that simplifies writing analyses for such programs, by extracting a unique CFG from the notebook that contains all transformations applied to the data. Several properties can then be determined by analyzing such CFG, that is simpler than the original Python code. We present a first use case that exploits our analysis to infer the required shape of the dataframes manipulated by the notebook.
Deep Neural Network for Detecting Fake Profiles in Social Networks

This paper proposes a deep neural network (DNN) approach for detecting fake profiles in social networks. The DNN model is trained on a large dataset of real and fake profiles and is designed to learn complex features and patterns that distinguish between the two types of profiles. In addition, the present research aims to determine the minimum set of profile data required for recognizing fake profiles on Facebook and propose the deep convolutional neural network method for fake accounts detection on social networks, which has been developed using 16 features based on content-based and profile-based features. The results demonstrated that the proposed method could detect fake profiles with an accuracy of 99.4%, equivalent to the achieved findings based on bigger data sets and more extensive profile information. The results were obtained with the minimum available profile data. In addition, in comparison with the other methods that use the same amount and kind of data, the proposed deep neural network gives an increase in accuracy of roughly 14%. The proposed model outperforms existing methods, achieving high accuracy and F1 score in identifying fake profiles. The associated findings indicate that the proposed model attained an average accuracy of 99% while considering two distinct scenarios: one with a single theme and another with a miscellaneous one. The results demonstrate the potential of DNNs in addressing the challenging problem of detecting fake profiles, which has significant implications for maintaining the authenticity and trustworthiness of online social networks.
Hierarchical Clause Annotation: Building a Clause-Level Corpus for Semantic Parsing with Complex Sentences

Most natural language processing (NLP) tasks suffer performance degradation when encountering long complex sentences, such as semantic parsing, syntactic parsing, machine translation, and text summarization. Previous works address the issue with an intuition of decomposing complex sentences and linking simple ones, such as RST-style discourse parsing, split-and-rephrase (SPRP), text simplification (TS), simple-sentence-decomposition (SSD), etc. However, these works are not applicable for semantic parsing like abstract meaning representation (AMR) parsing and semantic dependency parsing due to misalignments to semantic relations and unavailabilities to preserve original semantics. Following the same intuition and avoiding deficiencies of previous works, we propose a novel framework, hierarchical clause annotation (HCA), based on the linguistic research of clause hierarchy. With the HCA framework, we annotate a large HCA corpus to explore the potentialities of integrating HCA structural features into semantic parsing with complex sentences. Moreover, we decompose HCA into two subtasks, i.e., clause segmentation and clause parsing, and provide neural baseline models for more silver annotations.
Requirements engineering for sustainable software systems: a systematic mapping study

Abstract Various approaches toward the development of sustainable software systems have been proposed by the requirements engineering community over the last decade. We conducted a systematic mapping study, analyzed 55 publications, and identified 29 approaches that have been published since the year 2000. We analyzed how the approaches evolved over time and how the publications and authors are influenced by each other. Furthermore, the approaches are analyzed in terms of their supported requirements engineering activities, along with the evidence provided in the publications. Additionally, we also analyzed which sustainability definitions have been used, if an iterative application of the approaches is discussed, and if the approaches also provide a tool-support for practitioners. We noticed an increase of publications on requirements engineering approaches toward sustainability in the last years, whereas a majority discuss sustainability based on the same multi-dimensional concept. Although different case studies have been already conducted, we noticed a lack of an industrial application. Our main findings concern the need of an evaluation on how the proposed requirements engineering approaches can also be applied in agile software development processes. Additionally, we also promote the development of supporting software tools to support practitioners in adapting the proposed approaches.
Pemanfaatan Metaverse Di Bidang Pendidikan

The impact of Covid-19, the increasing number of new technologies being applied in education, as well as increasing interest in the metaverse. Closely related to this virtual environment is life in the field of education. This study aims to explain the type of metaverse, the potential and limitations of its educational application. The four metaverse categories emphasize different functions, types, or sets of Metaverse technologies. There are four spectrums of technologies and applications in the metaverse, namely augmentation, simulation, intimate technology, and external technology. The use of augmented reality in health is in the form of virtual clothes so that you can examine parts as human anatomy laboratory simulation. Spinal surgery that applies virtual technology. Utilization in the learning environment is through new forms of social communication space, freer settings for creation and sharing, and additional virtual experiences. The limitations of the metaverse are weaker social relations, and users of the metaverse who do not have an identity, and the abuse of simulation as a representative of student identity in the everyday world. Prediction of changes in daily life patterns as a negative impact of the metaverse. However, the unlimited opportunity to be a form of communication in social life is the hallmark of the metaverse. It is hoped that in the future it is recommended to use the metaverse in education, namely the teacher must conduct a careful analysis beforehand so that students can gain knowledge in the metaverse, the teacher must prepare class designs for students so that they can solve problems and carry out activities cooperatively, adaptively and creatively. Instructional designers and educators need to understand well the type of category and class metaverse.
Open innovation in SMEs: a dynamic capabilities perspective

Purpose This study aims to examine the relationships between organizational learning capabilities, open innovation and firm performance (FP) in the context of small and medium enterprises (SMEs) in the emerging economies. Design/methodology/approach Data collected from 384 manufacturing SMEs operating across the seven emirates of the UAE were statistically analyzed using SmartPLS 3 to examine the hypotheses of this study. Findings The results show that organizational learning capabilities positively influences both inbound and outbound dimensions of open innovation (OI). Inbound open innovation (IP) practice positively impacted both market effectiveness and profitability, while outbound open innovation (OP) practice only affected profitability. Findings further confirmed the mediating role of IP practice on the relationships of organizational learning capabilities with market effectiveness and profitability. In contrast, OP practice did not mediate the relationships of organizational learning capabilities with market effectiveness and profitability. Originality/value To the best of the authors knowledge, this is among the first study contributing to the extant innovation literature in terms of investigations into the significant and complex interrelations of organizational learning capabilities, OI and FP in a single study, demonstrating various theoretical implications in the context of manufacturing SMEs in emerging countries. Overall, the findings of this study confirmed that the owners/managers of the UAE’s manufacturing SMEs need to be acquainted with the need of creating a working environment fostering organizational learning processes and capabilities to enhance IP and OP activities, thereby improving their market effectiveness and profitability.
The Impact of Blockchain Technology and The Internet of Things on the Agile and Sustainable Supply Chain

In addition to being a revolutionary technology for all industries, the Internet of Things; has also shown its potential in processes such as supply chain. With the advancement of technology and the emergence of the new era, the challenges of the traditional supply chain that follows a path from top to bottom (suppliers of raw materials to final consumers) are revealed. Also, due to environmental issues and the increasing speed of the changes in the needs of customers in various industries, including the retail industry, which has undergone many changes with the emergence of innovation, the entire path of the supply chain requires the implementation of strategies such as agile and green supply chains and achieving smartness. Therefore, emerging technologies such as The Internet of Things play a significant role in the agility and greenness of the entire retail supply chain. The presence of blockchain technology with decentralized data archiving in supply chain processes based on the Internet of Things guarantees the security of big data and helps in optimal data performance. This helps the agility of supply chain processes. In this chapter, dimensions, components, and key indicators effective in the implementation of an agile smart supply chain based on transformative technologies are examined.
Compliance checking on first-order knowledge with conflicting and compensatory norms: a comparison among currently available technologies

Abstract This paper analyses and compares some of the automated reasoners that have been used in recent research for compliance checking. Although the list of the considered reasoners is not exhaustive, we believe that our analysis is representative enough to take stock of the current state of the art in the topic. We are interested here in formalizations at the first-order level. Past literature on normative reasoning mostly focuses on the propositional level. However, the propositional level is of little usefulness for concrete LegalTech applications, in which compliance checking must be enforced on (large) sets of individuals. Furthermore, we are interested in technologies that are freely available and that can be further investigated and compared by the scientific community. In other words, this paper does not consider technologies only employed in industry and/or whose source code is non-accessible. This paper formalizes a selected use case in the considered reasoners and compares the implementations, also in terms of simulations with respect to shared synthetic datasets. The comparison will highlight that lot of further research still needs to be done to integrate the benefits featured by the different reasoners into a single standardized first-order framework, suitable for LegalTech applications. All source codes are freely available at https://github.com/liviorobaldo/compliancecheckers , together with instructions to locally reproduce the simulations.
Multiple-Valued Plaintext-Checking Side-Channel Attacks on Post-Quantum KEMs

In this paper, we present a side-channel analysis (SCA) on key encapsulation mechanisms (KEMs) based on the Fujisaki–Okamoto (FO) transformation and its variants. Many post-quantum KEMs usually perform re-encryption during key decapsulation to achieve chosen-ciphertext attack (CCA) security. The side-channel leakage of re-encryption can be exploited to mount a key-recovery plaintext-checking attack (KR-PCA), even if the chosen-plaintext attack (CCA) secure decryption constructing the KEM is securely implemented. Herein, we propose an efficient side-channel-assisted KR-PCA on post-quantum KEMs, and achieve a key recovery with significantly fewer attack traces than existing ones in TCHES 2022 and 2023. The basic concept of the proposed attack is to introduce a new KR-PCA based on a multiple-valued (MV-)PC oracle and then implement a dedicated MV-PC oracle based on a multi-classification neural network (NN). The proposed attack is applicable to the NIST PQC selected algorithm Kyber and the similar lattice-based Saber, FrodoKEM and NTRU Prime, as well as SIKE. We also present how to realize a sufficiently reliable MV-PC oracle from NN model outputs that are not 100% accurate, and analyze the tradeoff between the key recovery success rate and the number of attack traces. We assess the feasibility of the proposed attack through attack experiments on three typical symmetric primitives to instantiate a random oracle (SHAKE, SHA3, and AES software). The proposed attack reduces the number of attack traces required for a reliable key recovery by up to 87% compared to the existing attacks against Kyber and other lattice-based KEMs, under the condition of 99.9999% success rate for key recovery. The proposed attack can also reduce the number of attack traces by 85% for SIKE.
Against Predictive Optimization

We formalize predictive optimization, a category of decision-making algorithms that use machine learning (ML) to predict future outcomes of interest about \ individuals}. For example, pre-trial risk prediction algorithms such as COMPAS use ML to predict whether an individual will re-offend in the future. Our thesis is that predictive optimization raises a distinctive and serious set of normative concerns that cause it to fail on its own terms. To test this, we review 387 reports, articles, and web pages from academia, industry, non-profits, governments, and modeling contests, and find many real-world examples of predictive optimization. We select eight particularly consequential examples as case studies. Simultaneously, we develop a set of normative and technical critiques that challenge the claims made by the developers of these applications—in particular, claims of increased accuracy, efficiency, and fairness. Our key finding is that these critiques apply to each of the applications, are not easily evaded by redesigning the systems, and thus challenge whether these applications should be deployed. We argue that the burden of evidence for justifying why the deployment of predictive optimization is not harmful should rest with the developers of the tools. Based on our analysis, we provide a rubric of critical questions that can be used to deliberate or contest specific predictive optimization applications.
Computation Offloading and Resource Allocation Based on Game Theory in Symmetric MEC-Enabled Vehicular Networks

Due to the rocketing development of the Internet of Vehicles (IoV), the growth of computing-intensive and latency-sensitive applications brings a challenge to individual vehicles with limited computing resources. The computation offloading technology provides a feasible solution to this issue. In this paper, a multi-tier symmetric Vehicle-to-Everything (V2X) network framework is proposed, which consists of vehicle nodes (VNs), mobile edge computing (MEC) servers and a cloud server to provide computation offloading services for user vehicles. In this symmetric system, besides local computation, tasks can be offloaded to VNs or MEC servers or cloud servers for processing. The computation offloading problem in this network framework is considered as a game based on game theory. Then, in order to achieve the Nash equilibrium (NE) in this game, a joint optimization of computation offloading and resource allocation (JOCORA) algorithm is proposed. The numerical simulations show that the JOCORA algorithm can improve the success probability of offloading and reduce the total latency. The JOCORA algorithm has a better performance compared to other methods.
The impact of e-banking service quality on the sustainable customer satisfaction: Evidence from the Saudi Arabia commercial banking sector

The banking sector around the globe has witnessed a huge development in its services and products. The electronic banking services are considered as a competitive advantage for the banking sector. The purpose of this paper is to evaluate the effectiveness of e-banking service quality on customer satisfaction in the context of Saudi Arabian commercial banks. Both quantitative and qualitative research methods were used in the study. A sample of 308 customers from the banking sector participated in this study. The researchers have developed a self-structured questionnaire to collect the relevant data. In addition, secondary data was gathered from published sources, including websites, journal papers, and publications of the chosen commercial banks. The findings of this study show that the eight service quality dimensions; reliability, transactional efficiency, customer support, service security, ease of use, performance, satisfaction with service quality and service content have a significant impact on the level of user's satisfaction with e-banking in the Saudi Arabian commercial banks.
Equilibria, Stability, and Sensitivity for the Aerial Suspended Beam Robotic System Subject to Parameter Uncertainty

This article studies how parametric uncertainties affect the cooperative manipulation of a cable-suspended beam-shaped load by means of two aerial robots not explicitly communicating with each other. In particular, this article sheds light on the impact of the uncertain knowledge of the model parameters available to an established communicationless force-based controller. First, we find the closed-loop equilibrium configurations in the presence of the aforementioned uncertainties, and then, we study their stability. Hence, we show the fundamental role played in the robustness of the load attitude control by the internal force induced in the manipulated object by nonvertical cables. Furthermore, we formally study the sensitivity of the attitude error to such parametric variations, and we provide a method to act on the load position error in the presence of uncertainties. Eventually, we validate the results through an extensive set of numerical tests in a realistic simulation environment, including underactuated aerial vehicles and sagging-prone cables, and through hardware experiments.