Reviewerly/exHarmony huggingface dataset:
Mag-Auth: Authenticating Wireless Transmitters and Receivers on the Receiver Side via Magnetic Emissions

Device authentication over the wireless channel is still an open issue. This is especially true for low-end devices like the IoT ones, where the overhead required by traditional asymmetric cryptographic techniques can be overwhelming, or-more in general-when the crypto material might have been compromised. A robust solution for the above scenarios is Physical-Layer Authentication (PLA), which exploits the inherent intrinsic unique features of the wireless devices to achieve low-cost, crypto-less authentication. In this paper, we present Mag-Auth, a novel and lightweight authentication scheme that leverages the Electro-Magnetic (EM) emissions released at the joint connection between the wireless device and its antenna in response to an excitation signal. Specifically, Mag-Auth trains, on the collected EM emissions, an autoencoder and a Neural Network (NN). The autoencoder is employed to reject wireless devices that do not belong to the set the autoencoder and the NN have been trained over, while the NN is applied to uniquely identify the different classes of wireless transmitter-receiver pairs. Mag-Auth enjoys some unique features: it is privacy-preserving as it does not require to have access to the radio board (unlike, for instance, in-phase/quadrature (IQ)-based PLA methods); it caters to both wireless transmitter and receiver authentication scenarios; and, it sports striking performance. Indeed, our extensive experimental campaign involving 600 combinations of various wireless devices and antennas (including SDRs and IoTs) unveiled a minimum average F1-Score of 0.94 when classifying samples collected over a maximum length of 1s, proving the effectiveness and viability of using EM emissions as a lightweight, efficient, and robust authentication mechanism. Finally, we also released the collected EM emissions raw data to foster further investigations and development by Academia, Industry, and practitioners.
Support for Knowledge Management Processes with Blockchain Technology

Knowledge management facilitates the cycle of knowledge with­in organization through processes for knowledge capture/creation, storing, sharing and application. Various information technologies are already used to support those processes and their mechanisms, and new ones are con­tinually emerging. The use of blockchain technology is lately suggested in many areas, including knowledge management. This paper aims to iden­tify what are the most discussed benefits of using blockchain technology in knowledge management, considering their features, especially for specif­ic processes. It also explores possible advantages and disadvantages of the use of blockchain technology, on which depends its real adoption in organ­izations as support for knowledge management processes.
Sentiment Analysis of Semantically Interoperable Social Media Platforms Using Computational Intelligence Techniques

Competitive intelligence in social media analytics has significantly influenced behavioral finance worldwide in recent years; it is continuously emerging with a high growth rate of unpredicted variables per week. Several surveys in this large field have proved how social media involvement has made a trackless network using machine learning techniques through web applications and Android modes using interoperability. This article proposes an improved social media sentiment analytics technique to predict the individual state of mind of social media users and the ability of users to resist profound effects. The proposed estimation function tracks the counts of the aversion and satisfaction levels of each inter- and intra-linked expression. It tracks down more than one ontologically linked activity from different social media platforms with a high average success rate of 99.71%. The accuracy of the proposed solution is 97% satisfactory, which could be effectively considered in various industrial solutions such as emo-robot building, patient analysis and activity tracking, elderly care, and so on.
A Guide and Mini-Review on the Performance Evaluation Metrics in Binary Segmentation of Magnetic Resonance Images

Eight previously proposed segmentation evaluation metrics for brain magnetic resonance images (MRI), which are sensitivity (SE), specificity (SP), false-positive rate (FPR), false-negative rate (FNR), positive predicted value (PPV), accuracy (ACC), Jaccard index (JAC) and dice score (DSC) are presented and discussed in this paper. These evaluation metrics could be classified into two groups namely pixel-wise metrics and area-wise metrics. We, also, distill the most prominent previously published papers on brain MRI segmentation evaluation metrics between 2021 and 2023 in a detailed literature matrix. The identification of illness or tumor areas using brain MRI image segmentation is a large area of research. However, there is no single segmentation evaluation metric when evaluating the results of brain MRI segmentation in the current literature. Also, the pixel-wise metrics should be supported with the area-wise metrics such as DSC while evaluating the image segmentation results and each metric should be compared with other metrics for better evaluation.
Prediction of Array Antenna Assembly Accuracy Based on Auto-Encoder and Boosting-OSKELM

As a critical component for space exploration, navigation, and national defense, array antenna secures an indispensable position in national strategic significance. However, various parts and complex assembly processes make the array antenna hard to meet the assembly standard, which causes repeated rework and delay. To realize the accurate and efficient prediction of the assembly accuracy of array antenna, a prediction method based on an auto-encoder and online sequential kernel extreme learning machine with boosting (Boosting-OSKELM) is proposed in this paper. The method is mainly divided into two steps: Firstly, the auto-encoder with the fine-tuning trick is used for training and representation reduction of the data. Then, the data are taken as the input of Boosting-OSKELM to complete the initial training of the model. When new sample data is generated, Boosting-OSKELM can realize the online correction of the model through rapid iteration. Finally, the test shows that the average MSE of Boosting-OSKELM and ANN is 0.061 and 0.12, and the time consumption is 0.85 s and 15 s, respectively. It means that this method has strong robustness in prediction accuracy and online learning ability, which is conducive to the development of array antenna assembly.
Verifying Safety of Neural Networks from Topological Perspectives

Neural networks (NNs) are increasingly applied in safety-critical systems such as autonomous vehicles. However, they are fragile and are often ill-behaved. Consequently, their behaviors should undergo rigorous guarantees before deployment in practice. In this paper, we propose a set-boundary reachability method to investigate the safety verification problem of NNs from a topological perspective. Given an NN with an input set and a safe set, the safety verification problem is to determine whether all outputs of the NN resulting from the input set fall within the safe set. In our method, the homeomorphism property and the open map property of NNs are mainly exploited, which establish rigorous guarantees between the boundaries of the input set and the boundaries of the output set. The exploitation of these two properties facilitates reachability computations via extracting subsets of the input set rather than the entire input set, thus controlling the wrapping effect in reachability analysis and facilitating the reduction of computation burdens for safety verification. The homeomorphism property exists in some widely used NNs such as invertible residual networks (i-ResNets) and Neural ordinary differential equations (Neural ODEs), and the open map is a less strict property and easier to satisfy compared with the homeomorphism property. For NNs establishing either of these properties, our set-boundary reachability method only needs to perform reachability analysis on the boundary of the input set. Moreover, for NNs that do not feature these properties with respect to the input set, we explore subsets of the input set for establishing the local homeomorphism property and then abandon these subsets for reachability computations. Finally, some examples demonstrate the performance of the proposed method.
A comparative study of higher-order nonclassicalities of photon-added-then-subtracted and photon-subtracted-then-added quantum states

In the present paper, we have studied the higher as well as the lower-order nonclassicalities of photon-added-then-subtracted and photon-subtracted-then-added thermal and even coherent states. Different criteria such as Mandel’s function ( $$Q_M^{(l)}$$ ), higher-order antibunching ( $$d_h^{(l-1)}$$ ), sub-Poissonian photon statistics ( $$D_h^{(l-1)}$$ ), higher-order squeezing ( $$S^{(l)}$$ ), Husimi function (Q), Agarwal–Tara criteria ( $$A_3$$ ) and Klyshko’s condition (B(m)) are used to witness the nonclassical feature of these states. Many of these conditions established that the considered states are highly nonclassical. It is also realized that the non-Gaussian photon-addition-then-subtraction operation is preferred over the photon-subtraction-then-addition for developing nonclassicality.
Minimal codewords: An application of relative four-weight codes

Secret-sharing is a vital topic of cryptography and has widely used in information protection. One technique to the development of secret-sharing schemes is primarily based on error-correcting codes. In this paper, we determine the minimal codewords of relative four-weight codes and have the utility in the secret sharing schemes.
Qubit dynamics driven by dipole field in thermal noise environment

Quantum computing is a new way to process quantum information by using superposition and entanglement of the quantum system. Quantum state’s vast Hilbert space allows it to perform operations that classical computers cannot. The quantum computing has unique advantages in dealing with some complex problems, so it has attracted wide attention. Computing a single qubit is the first of seven fundamental stages needed to achieve a large-scale quantum computer that is universal, scalable and fault-tolerant. In other words, the primary task of quantum computing is the careful preparation and precise regulation of qubits. At present, the physical systems that can be used as qubits include superconducting qubits, semiconductor qubits, ion trap systems and nitrogen-vacancy (NV) color centers. These physical systems have made great progress of decoherence time and scalability. Owing to the vulnerability of qubits, ambient thermal noise can cause quantum decoherence, which greatly affects the fidelity of qubits. Improving the fidelity of qubits is therefore a key step towards large-scale quantum computing. Based on the dipole field driven qubit, the stochastic dynamic structure decomposition method is adopted and the Kubo-Einstein fluctuation-dissipation theorem is used to study the qubit control in a thermal noise environment. The dipole field has components in three directions, not just in one plane, which allows more flexible control of quantum states. Without considering the noise, the quantum state can reach the target state 100%. In the noisy environment, thermal noise will cause the deviation between the actual final state and the target final state caused by thermal fluctuation, which becomes the main factor affecting the quantum fidelity. The influence of thermal noise is related to temperature and the evolution trajectory of quantum state. Therefore, this paper proposes an optimal scheme to improve the qubit fidelity in the thermal noise environment. The feasibility of this method is verified by numerical calculation, which can provide a new solution for further guiding and evaluating the experiment. The scheme is suitable for qubit systems of various physical control fields, such as semiconductor qubits and nitrogen vacancy center qubits. This work may have more applications in the development of quantum manipulation technology and can also be extended to multi-qubit systems, the details of which will appear in the future work.
DIGITAL TALE MEDIA TO IMPROVE THE EMOTIONAL INTELLIGENCE OF ELEMENTARY SCHOOL

Abstract&#x0D; This study aims to determine the effectiveness of digital tale media and the impact of its use on students' emotional intelligence. This study used a quantitative approach with the quasi-experimental method and nonequivalent control group design. The research sample consisted of 40 students of SD Negeri Mojolegi (20 students from each class IIIA and IIIB). Both groups received a pretest and a posttest. Data collection also used a closed questionnaire. The results of this study revealed that the use of digital tale media was quite effective in improving the emotional intelligence of elementary school students, especially in the lower grades. Gradually giving fairy tales helped to improve emotional intelligence by looking at the attitudes and behaviour of the characters of the tales so students can show and develop their emotions. Thus, teachers can provide material in stages in the form of digital tales with different themes to improve students' emotional intelligence.
Critical Data Literacies

A guide to everything you need to understand to navigate a world increasingly governed by data. Data has become a defining issue of current times. Our everyday lives are shaped by the data that is produced about us (and by us) through digital technologies. In this book, Critical Data Literacies, Luci Pangrazio and Neil Selwyn introduce readers to the central concepts, ideas, and arguments required to make sense of life in the data age. The authors challenge the idea that datafication is an inevitable and inescapable condition. Drawing on emerging areas of scholarship such as data justice, data feminism, and other critical data studies approaches, they explore how individuals and communities can empower themselves to engage with data critically and creatively. Over the course of eight wide-ranging chapters, the book introduces readers to the main components of critical data literacies—from the fundamentals of identifying and understanding data to the complexities of engaging with more combative data tactics. Critical Data Literacies explores how the tradition of critical literacies can offer a powerful foundation to address the big concerns of the data age, such as issues of data justice and privacy, algorithmic bias, dataveillance, and disinformation. Bringing together cutting-edge thinking and discussion from across education, sociology, psychology, and media and communication studies, Critical Data Literacies develops a powerful argument for collectively rethinking the role that data plays in our everyday lives and re-establishing agency, free will, and the democratic public sphere.
KRITIK SOSIAL PADA NOVEL DILAN: DIA ADALAH DILANKU TAHUN 1990 KARYA PIDI BAIQ TINJAUAN SOSIOLOG

This study aims to determine the social criticism contained in the novel, and to determine the results of the analysis of social criticism contained in the novel. The method used in this study is a qualitative descriptive method. The source of the data used is the text of the novel Dilan he is Dilanku Tahun 1990 by Pidi Baiq published by Pastel Books Bandung in 2016. This study uses a sociological method to determine the social criticism contained in the novel and find results to analyze the social criticism, so as to better understand the phenomenon. social aspects contained in the novel. This research is included in the technique of literature and content analysis. The results of this study are aimed at the world of adolescent education, these problems cause various social problems in people's lives. The social criticisms included in this research are: social criticism of juvenile delinquency in school, criticism of crime, attitudes and behavior, educators (teachers), power, and injustice and law.
New 3D Visualization Technologies as a Part of Scientific Gateway and Portal

Our paper describes the use and also the great importance of new modern tools of 3D visualization technologies as one of the main components of the Science Gateway (SG), and portals. As new technologies of 3D visualization are improved and developed, so are new scientific portals and gateways, which are also created by using new 3D technologies such as Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR), etc. Our paper aims to point out and explain with several examples where we supported the use of new modern technologies of 3D visualization as part of the science gateway in the portal. Our research focuses on the creation of visualization tools in the fields of natural disasters, astronomy and astrophysics, education, water management, and many other sectors.
Can Machine Learning be a Good Teammate?

We hypothesize that successful human-machine learning teaming requires machine learning to be a good teammate. However, little is understood about what the important design factors are for creating technology that people perceive to be good teammates. In a recent survey study, data from over 1,100 users of commercially available smart technology rated characteristics of teammates. Results indicate that across several categories of technology, a good teammate must (1) be reliable, competent and communicative, (2) build human-like relationships with the user, (3) perform their own tasks, pick up the slack, and help when someone is overloaded, (4) learn to aid and support a user’s cognitive abilities, (5) offer polite explanations and be transparent in their behaviors, (6) have common, helpful goals, and (7) act in a predictable manner. Interestingly, but not surprisingly, the degree of importance given to these various characteristics varies by several individual differences in the participants, including their agreeableness, propensity to trust technology, and tendency to be an early technology adopter. In this paper, we explore the implications of these good teammate characteristics and individual differences in the design of machine learning algorithms and their user interfaces. Machine learners, particularly if coupled with interactive learning or adaptive interface design, may be able to tailor themselves or their interactions to align with what individual users perceive to be important characteristics. This has the potential to promote more reliance and common ground. While this sounds promising, it may also risk overreliance or misunderstanding between a system’s actual capabilities and the user’s perceived capabilities. We begin to lay out the possible design space considerations for building good machine learning teammates.
User experience elements of data visualization large screen: The example of electricity industry

In the era of big data, all sectors of society pay more and more attention to data value. Data visualization, as the most effective way to present data value, has been given an important position.The electricity industry has many sources of data, complex dimensions and large scale. It is necessary to present massive data clearly, scientifically and accurately through the visualization system. Therefore, it is very important to establish a set of data visualization large screen product evaluation element model based on user experience to improve user satisfaction. This paper mainly collects the user experience elements of the large screen of electricity industry data through user interviews, and uses the card sorting method to establish the element model, which is convenient to quantify the user experience of the large screen of electricity industry data visualization, and realize scientific evaluation of user experience results.
Systematic literature review in Usability Web: A tertiary study

Web usability is a topic that several researchers have studied; this is because, with new technological advances, it is possible to improve application interfaces significantly and have usable products that captivate users by being friendly, efficient and easy to use. In this sense, reviewing secondary studies reported in this field is considered appropriate. This review aims to identify the methods and/or usability evaluation and validation methodologies, in what phase of development and software domain it has been evaluated, and the problems reported in literature reviews. The applied method is the one proposed by Kitchenham and Charters (2007) for the presentation of tertiary studies, beginning with the planning phase, which includes the activities: definition of research questions, search strategies, criteria for article selection, procedure for the inclusion or exclusion of studies and, criteria to assess the quality of investigations; followed by the review phase, in which the defined planning is carried out, the activities that include: execution of the selection of studies in the digital libraries, evaluation of the quality of the studies, extraction of relevant data and synthesis of the information; and finally the reporting phase, in which the answers to the research questions posed are evidenced. Regarding the results, we found a total of 106 articles, eliminated the duplicates, reviewed the rest and selected 15 papers, in which we identified the most frequently reported evaluation methods and methodologies, such as the Heuristic method, Observational measurement / Inspection and User test/usability tests. Likewise, we found the most commonly reported problems, such as Accessibility issues, Usability measurement and Methodological problems. Also, we distinguish that usability tests have been reported with a higher incidence in Web applications and Educational. Usability evaluation is applied in the different phases of software development, but with other incidences. Finally, we conclude that there is potential for more SLR about the usability of web applications.
Digital Forensic Investigates Sexual Harassment on Telegram using Naïve Bayes

The widespread use of Telegram in Indonesia has had both positive and negative effects. While the app offers strong security features, it has also become a platform for digital crimes, including sexual harassment. This study aims to address the need for effective forensic analysis and classification methods by employing the National Institute of Justice (NIJ) methodology and Naïve Bayes classifier to analyze conversations on Telegram. The research evaluates the performance of digital forensic tools and the effectiveness of the Naïve Bayes method in identifying instances of sexual harassment conversation. The data analyzed is about conversations on the telegram application that contain sexual harassment. Data collection involves extracting relevant conversations and subjecting them to forensic analysis using MOBIL edit Forensic Express and FTK Imager. Based on the test results, the naïve Bayes algorithm can be used to classify conversations into positive and negative about sexual harassment. The value obtained from naïve Bayes testing is the accuracy value of 91.3%, Precision 100%, and Recall 90%.&#x0D;