Reviewerly/exHarmony huggingface dataset:
Predicting Pulsars from Imbalanced Dataset with Hybrid Resampling Approach

Pulsar stars, usually neutron stars, are spherical and compact objects containing a large quantity of mass. Each pulsar star possesses a magnetic field and emits a slightly different pattern of electromagnetic radiation which is used to identify the potential candidates for a real pulsar star. Pulsar stars are considered an important cosmic phenomenon, and scientists use them to study nuclear physics, gravitational waves, and collisions between black holes. Defining the process of automatic detection of pulsar stars can accelerate the study of pulsar stars by scientists. This study contrives an accurate and efficient approach for true pulsar detection using supervised machine learning. For experiments, the high time-resolution (HTRU2) dataset is used in this study. To resolve the data imbalance problem and overcome model overfitting, a hybrid resampling approach is presented in this study. Experiments are performed with imbalanced and balanced datasets using well-known machine learning algorithms. Results demonstrate that the proposed hybrid resampling approach proves highly influential to avoid model overfitting and increase the prediction accuracy. With the proposed hybrid resampling approach, the extra tree classifier achieves a 0.993 accuracy score for true pulsar star prediction.
Digital Competences of Vocational Instructors with Synchronous Online Learning in Next Normal Education

This research study was conducted with the following objectives: to develop; evaluate and; investigate the vocational instructors' satisfaction with the online instructional management developed using the synchronous online learning with 2,233 vocational instructors from the Office of the Vocational Education Commission, Ministry of Education, from five regions of Thailand, namely the Central, the Northern, the Northeastern, the Southern, and the Eastern Regions and Bangkok.The research findings revealed that the vocational instructors' digital competence consisted of the following abilities: 1) analysis of course content; 2) application of video conference systems; 3) management of online classes; 4) management of online learning resources; 5) management of online learning activities; 6) development of tests; 7) development of instructional media; 8) development of instructional videos; 9) arrangement of active-learning activities; and 10) online evaluation and assessment of instruction.The vocational instructors' digital competence in online instructional management after the synchronous online learning was found to be significantly higher than before the learning, at the p-value rate of .01.Overall, 93.19% of the vocational instructors were reported to pass the evaluation of online instructional management.The vocational instructors were found to be satisfied with their own digital competence in online instructional management at the highest level.
Evolutionary vs imitation learning for neuromorphic control at the edge*

Abstract Neuromorphic computing offers the opportunity to implement extremely low power artificial intelligence at the edge. Control applications, such as autonomous vehicles and robotics, are also of great interest for neuromorphic systems at the edge. It is not clear, however, what the best neuromorphic training approaches are for control applications at the edge. In this work, we implement and compare the performance of evolutionary optimization and imitation learning approaches on an autonomous race car control task using an edge neuromorphic implementation. We show that the evolutionary approaches tend to achieve better performing smaller network sizes that are well-suited to edge deployment, but they also take significantly longer to train. We also describe a workflow to allow for future algorithmic comparisons for neuromorphic hardware on control applications at the edge.
Deep Learning Based Air-Writing Recognition with the Choice of Proper Interpolation Technique

The act of writing letters or words in free space with body movements is known as air-writing. Air-writing recognition is a special case of gesture recognition in which gestures correspond to characters and digits written in the air. Air-writing, unlike general gestures, does not require the memorization of predefined special gesture patterns. Rather, it is sensitive to the subject and language of interest. Traditional air-writing requires an extra device containing sensor(s), while the wide adoption of smart-bands eliminates the requirement of the extra device. Therefore, air-writing recognition systems are becoming more flexible day by day. However, the variability of signal duration is a key problem in developing an air-writing recognition model. Inconsistent signal duration is obvious due to the nature of the writing and data-recording process. To make the signals consistent in length, researchers attempted various strategies including padding and truncating, but these procedures result in significant data loss. Interpolation is a statistical technique that can be employed for time-series signals to ensure minimum data loss. In this paper, we extensively investigated different interpolation techniques on seven publicly available air-writing datasets and developed a method to recognize air-written characters using a 2D-CNN model. In both user-dependent and user-independent principles, our method outperformed all the state-of-the-art methods by a clear margin for all datasets.
Lattice Attacks on NTRU and LWE: A History of Refinements

In Chapter 2, Lattice Attacks on NTRU and LWE: a History of Refinements, Martin R. Albrecht and Léo Ducas provide an overview of the advances and techniques used in the field of lattice reduction algorithms. Four decades after its invention, the LLL algorithm still plays a significant role in cryptography, not least as it has become one of the main tools to assess the security of a new wave of lattice-based cryptosystems intended for the new post-quantum cryptographic standard. The runtime of the LLL algorithm was always well understood, but the quality of its output, i.e., how short its output vectors were, could be hard to predict, even heuristically. Yet, an important aspect in the evaluation of the new lattice schemes is accurate predictions of the hardness of the underlying lattice problems, which crucially relies on estimating the 'shortness' of the vectors that can be efficiently found using lattice reduction and enumeration. Albrecht and Ducas have been on the forefront of improving such estimators and build upon their expertise in Chapter 2.
Activity Theory as a Lens for Developing and Applying Personas and Scenarios in Learning Experience Design

Theoretically-informed design is a hallmark of the field of learning and instructional design and technology (LIDT). Designing digital environments for learning on the basis of theory can lead to theoretically pure and potentially effective learning interventions, yet theory alone is insufficient to consider the myriad of issues that emerge while a learner is engaged in digitally mediated learning. As the field of LIDT shifts towards more human-centered design practice, the phenomenon of learning experience design (LXD) has emerged as a novel, multidisciplinary focus area. LXD equips designers with a range of useful methods for explicitly considering the learner within the learning context. Two methods that we argue are particularly well-suited for this are personas and scenarios. The development of personas and scenarios can be informed by activity theory, which provides a lens for holistically considering the technology usage context and the learner&rsquo;s role therein. The current article discusses the interplay of activity theory, personas, and scenarios, and illustrates how this can be potentially useful in learning experience design practice in two separate case examples. Implications are discussed.
ABE Squared: Accurately Benchmarking Efficiency of Attribute-Based Encryption

Measuring efficiency is difficult. In the last decades, several works have contributed in the quest to successfully determine and compare the efficiency of pairing-based attribute-based encryption (ABE) schemes. However, many of these works are limited: they use little to no optimizations, or use underlying pairingfriendly elliptic curves that do not provide sufficient security anymore. Hence, using these works to benchmark ABE schemes does not yield accurate results. Furthermore, most ABE design papers focus on the efficiency of one important aspect. For instance, a new scheme may aim to have a fast decryption algorithm. Upon realizing this goal, the designer compares the new scheme with existing ones, demonstrating its dominance in this particular aspect. Although this approach is intuitive and might seem fair, the way in which this comparison is done might be biased. For instance, the schemes that are compared with the new scheme may be optimized with respect to another aspect, and appear in the comparison consequently inferior.In this work, we present a framework for accurately benchmarking efficiency of ABE: ABE Squared. In particular, we focus on uncovering the multiple layers of optimization that are relevant to the implementation of ABE schemes. Moreover, we focus on making any comparison fairer by considering the influence of the potential design goals on any optimizations. On the lowest layer, we consider the available optimized arithmetic provided by state-of-the-art cryptographic libraries. On the higher layers, we consider the choice of elliptic curve, the order of the computations, and importantly, the instantiation of the scheme on the chosen curves. Additionally, we show that especially the higher-level optimizations are dependent on the goal of the designer, e.g. optimization of the decryption algorithm. To compare schemes more transparently, we develop this framework, in which ABE schemes can be justifiably optimized and compared by taking into account the possible goals of a designer. To meet these goals, we also introduce manual, heuristic type-conversion techniques where existing techniques fall short. Finally, to illustrate the effectiveness of ABE Squared, we implement several schemes and provide all relevant benchmarks. These show that the design goal influences the optimization approaches, which in turn influence the overall efficiency of the implementations. Importantly, these demonstrate that the schemes also compare differently than existing works previously suggested.
About One Approach to Using Dynamic Models to Build Digital Twins

The modern stage of technology development is characterized by the emergence of new paradigms for the construction of anthropogenic systems, such as cyber-physical systems, socio-cybernetic systems, etc. The task of data acquisition about the state of a multi-level system and managing the structure and behavior of a system consisting of many thousands of elements of different physical nature is a complex task. This article describes one of possible approaches to solving the problem of data acquisition and management of the structure of a large-scale heterogeneous system. The proposed approach is based on the idea of using dynamic digital twins, which are dynamic models of the observed system. This approach was used for the development of systems in various subject domains, in particular, in production management systems built on the Industry 4.0 principle, in the development of a technical support system for cable television networks and in the development of support systems for the construction of educational trajectories.
Path Planning in Localization Uncertaining Environment Based on Dijkstra Method

Path planning obtains the trajectory from one point to another with the robot's kinematics model and environment understanding. However, as the localization uncertainty through the odometry sensors is inevitably affected, the position of the moving path will deviate further and further compared to the original path, which leads to path drift in GPS denied environments. This article proposes a novel path planning algorithm based on Dijkstra to address such issues. By combining statistical characteristics of localization error caused by dead-reckoning, the replanned path with minimum cumulative error is generated with uniforming distribution in the searching space. The simulation verifies the effectiveness of the proposed algorithm. In a real scenario with measurement noise, the results of the proposed algorithm effectively reduce cumulative error compared to the results of the conventional planning algorithm.
A Semantic Reference Model for Capturing System Development and Evaluation

Reasoning over complex, diverse data requires a detailed understanding of the meaning of that data, the context in which it is valid, and the uses to which it may reasonably be put. Such is the case in the domain of certification of software for mission use in military airborne systems. Unfortunately, current practices are typically manual and hence tedious, time-consuming and susceptible to human error and biases. In this work, we describe a semantic reference model that allows us to capture the development of a system, processes followed, artifacts produced and system testing; all of which are needed for certification purposes. The semantic framework allows for an integrated view that can effectively reason over the relevant data.
Distance Education among Italian Teachers: Differences and Experiences

Abstract The successful integration of technology in teaching is a key component of education. Although prior research highlighted factors fostering the use of technology by teachers, few studies focused on whether these factors vary among teachers of different grade levels and subjects. Moreover, no studies have investigated personal experiences related to distance education among a large sample of teachers. To address these gaps, the present mixed-method study sought to examine whether factors promoting distance education varied among Italian teachers of different grade levels and subjects. A further aim was to explore experiences of teachers using distance education. The sample involved 357 Italian teachers and preservice teachers who completed an online questionnaire during the COVID-19 pandemic that also contained open-ended questions. Findings indicated that teaching self-efficacy was greater in pre-service and primary teachers, while facilitating conditions were greater in humanities and science secondary teachers. The perceived ease of use of technology and technology for pedagogy skills were more pronounced among science secondary teachers. Advanced technology skills were lower in humanities secondary teachers while the behavioural intention to use technology was greatest among pre-service teachers. Four themes emerged from the qualitative study of teachers’ insights. These included positive and negative aspects of using technology, the relationship with students, the versatility of distance education, and the quality of lessons. This study underscores the need to address training based on different teachers’ grade levels and subjects, and to focus on the emerging themes to better integrate the use of technology in schools.
The State of the Art in Integer Factoring and Breaking Public-Key Cryptography

In this column, we will review the current state of the art of cryptanalysis for three number-theoretic problems using classical (nonquantum) computers, including, in particular, our most recent computational records for integer factoring and prime-field discrete logarithms.
Design and Analysis of Clothing Catwalks Taking into Account Unity's Immersive Virtual Reality in an Artificial Intelligence Environment

In the context of the rapid development of AI area, VR technology is raising the public and researchers’ attention. This study constructs a VR clothing catwalk based on the Unity 3D game engine in the AI background. This article focuses on the study of Unity 3D to construct the scenes and costumes of the clothing catwalk and then combine the immersive experience of VR to achieve the feeling of VR. Therefore, this article designs the scenes and model costumes of the VR clothing show by analyzing the VR technology in the context of the AI environment, combined with the Unity 3D game engine. It optimizes the VR clothing show based on Unity 3D game engine designed in this paper through design performance test experiment and visual positioning comparison experiment and then investigates and analyzes the optimized VR clothing show. Based on user feedback, this article completes the function of VR clothing show and compares it with the traditional online clothing show. The experimental results show that the sensory evaluation given by users is 22.02% higher than that of users of traditional online clothing shows. In the clothing catwalk based on Unity immersive VR, the user's rating for the fluency of watching is 10.99% higher than that of the traditional online clothing catwalk.
A Deep Invertible 3-D Facial Shape Model for Interpretable Genetic Syndrome Diagnosis

One of the primary difficulties in treating patients with genetic syndromes is diagnosing their condition. Many syndromes are associated with characteristic facial features that can be imaged and utilized by computer-assisted diagnosis systems. In this work, we develop a novel 3D facial surface modeling approach with the objective of maximizing diagnostic model interpretability within a flexible deep learning framework. Therefore, an invertible normalizing flow architecture is introduced to enable both inferential and generative tasks in a unified and efficient manner. The proposed model can be used (1) to infer syndrome diagnosis and other demographic variables given a 3D facial surface scan and (2) to explain model inferences to non-technical users via multiple interpretability mechanisms. The model was trained and evaluated on more than 4700 facial surface scans from subjects with 47 different syndromes. For the challenging task of predicting syndrome diagnosis given a new 3D facial surface scan, age, and sex of a subject, the model achieves a competitive overall top-1 accuracy of 71%, and a mean sensitivity of 43% across all syndrome classes. We believe that invertible models such as the one presented in this work can achieve competitive inferential performance while greatly increasing model interpretability in the domain of medical diagnosis.
Spectral Angle Mapping and AI Methods Applied in Automatic Identification of Placer Deposit Magnetite Using Multispectral Camera Mounted on UAV

The use of drones in mining environments is one way in which data pertaining to the state of a site in various industries can be remotely collected. This paper proposes a combined system that employs a 6-bands multispectral image capturing camera mounted on an Unmanned Aerial Vehicle (UAV) drone, Spectral Angle Mapping (SAM), as well as Artificial Intelligence (AI). Depth possessing multispectral data were captured at different flight elevations. This was in an attempt to find the best elevation where remote identification of magnetite iron sands via the UAV drone specialized in collecting spectral information at a minimum accuracy of +/− 16 nm was possible. Data were analyzed via SAM to deduce the cosine similarity thresholds at each elevation. Using these thresholds, AI algorithms specialized in classifying imagery data were trained and tested to find the best performing model at classifying magnetite iron sand. Considering the post flight logs, the spatial area coverage of 338 m2, a global classification accuracy of 99.7%, as well the per-class precision of 99.4%, the 20 m flight elevation outputs presented the best performance ratios overall. Thus, the positive outputs of this study suggest viability in a variety of mining and mineral engineering practices.
Evaluation of teaching effect of internet of things education platform based on long-term and short-term memory network

Aiming at the problems of poor teaching effect and high energy consumption existing in traditional internet of things education platform, this paper puts forward a teaching effect evaluation method of internet of things education platform based on long-term and short-term memory network. Firstly, the status quo of the education platform of the internet of things is analysed, including the development and structure of the education platform of the internet of things. Then, the LSTM model is constructed to realise the evaluation of the teaching effect of the education platform of the internet of things. Finally, through the study of the model, the teaching effect of the internet of things education platform is evaluated. The experimental results show that the running energy consumption accounts for 12.84% of the total energy consumption of the system when the method is used to evaluate the teaching effect, which proves the effectiveness of the method.
Formal Verification of Masking Countermeasures for Arithmetic Programs

Cryptographic algorithms are widely used to protect data privacy in many aspects of daily lives from smart card to cyber-physical systems. Unfortunately, programs implementing cryptographic algorithms may be vulnerable to practical power side-channel attacks, which may infer private data via statistical analysis of the correlation between power consumptions of an electronic device and private data. To thwart these attacks, several masking schemes have been proposed, giving rise to effective countermeasures for reducing the statistical correlation between private data and power consumptions. However, programs that rely on secure masking schemes are not secure a priori. Indeed, designing effective masking programs is a labor intensive and error-prone task. Although some techniques have been proposed for formally verifying masking countermeasures and for quantifying masking strength, they are currently limited to Boolean programs and suffer from low accuracy. In this work, we propose an approach for formally verifying masking countermeasures of arithmetic programs. Our approach is more accurate for arithmetic programs and more scalable for Boolean programs comparing to the existing approaches. It is essentially a synergistic integration of type inference and model-counting based methods, armed with domain specific heuristics. The type inference system allows a fast deduction of leakage-freeness of most intermediate computations, the model-counting based methods accounts for completeness, namely, to eliminate spurious flaws, and the heuristics facilitate both type inference and model-counting based reasoning, which improve scalability and efficiency in practice. In case that the program does contain leakage, we provide a method to quantify its masking strength. A distuiguished feature of our type sytem lies in its support of compositonal reasoning when verifying programs with procedure calls, so the need of inlining procedures can be significantly reduced. We have implemented our methods in a verification tool <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">QMVerif</small> which has been extensively evaluated on cryptographic benchmarks including full AES, DES and MAC-Keccak. The experimental results demonstrate the effectiveness and efficiency of our approach, especially for compositional reasoning. In particular, our tool is able to automatically prove leakage-freeness of arithmetic programs for which only manual proofs exist so far; it is also significantly faster than the state-of-the-art tools: EasyCrypt on common arithmetic programs, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">QMSInfer</small> , SC Sniffer and maskVerif on Boolean programs.