Reviewerly/exHarmony huggingface dataset:
Effectiveness of data augmentation to predict students at risk using deep learning algorithms

Abstract The academic intervention to predict at-risk higher education (HE) students requires effective data model development. Such data modelling projects in the HE context may have common issues related to (a) adopting small-scale modelling that gives limited options for early intervention and (b) using imbalanced data that hinders capturing effective details of poorly performing students. We address the issues going beyond the distribution-based algorithm, using a multilayer perceptron classifier which shows better on confusion metric, recall, and precision measures for identifying at-risk students. Our proposed deep learning-based model, which uses data augmentation techniques to supplement the data instances and balance the dataset, aims to improve the prediction accuracy of whether the student will fail or not based on their interaction with the learning management systems to prevent struggling students from evasion.
The Case of the Jeffreys-Lindley-paradox as a Bayes-frequentist Compromise: A Perspective Based on the Rao-Lovric-Theorem

Abstract Testing a precise hypothesis can lead to substantially different results in the frequentist and Bayesian approach, a situation which is highlighted by the Jeffreys-Lindley paradox. While there exist various explanations why the paradox occurs, this article extends prior work by placing the less well-studied point-null-zero-probability paradox at the center of the analysis. The relationship between the two paradoxes is analyzed based on accepting or rejecting the existence of precise hypotheses. The perspective provided in this paper aims at demonstrating how the Bayesian and frequentist solutions can be reconciled when paying attention to the assumption of the point-null-zero-probability paradox. As a result, the Jeffreys-Lindley-paradox can be reinterpreted as a Bayes-frequentist compromise. The resolution shows that divergences between Bayesian and frequentist modes of inference stem from (a) accepting the existence of a precise hypothesis or not, (b) the assignment of positive measure to a null set and (c) the use of unstandardized p-values or p-values standardized to tail-area probabilities.
Optimization of Metformin Hydrochloride Tablet by Direct Compression Method Using Quality By Design Approach

This study investigates the feasibility of directly compressing high doses of metformin hydrochloride despite its poor compressibility. With regards to clearly outlined scientific principles, Quality by Design places a great emphasis on the systematic development of products. However, it is highly helpful and essential to understanding the causes and the interactions between the product components through a set of desired tests. Metformin hydrochloride powder was compressed using flat faced Euro B punch of 15 mm diameter, using Compaction Simulator (Stylcam 200R) at different compaction forces. Quality control tests were done on the compressed tablets and were evaluated. Utilizing the quality by design method with Umetric MODDE software, the necessity to develop direct compressible metformin hydrochloride was addressed. Tablet tensile strength, friability and disintegration time were evaluated. The effect of formulation components and variables was assessed. Binder types implemented as a singular unit or as combination showed their effect on formulation. By evaluating the variations in formulations and optimization of metformin hydrochloride, a design space was established through the identification of the critical process parameters, critical quality attributes, and the risk assessment with the aid of Modde Pro 12.1 software. Design of experiment was applied for a better understanding of the interactions between formulation variables and process parameters to achieve an optimum formulation. Metformin hydrochloride was successfully developed by direct compression and the simultaneous application of quality by design assisted in obtaining a robust formulation.
Opportunities or Challenges? Audit Risk and Blockchain Disclosures in 10-K Filings

SUMMARY This study investigates whether audit clients’ blockchain activities (including crypto-related activities) affect audit risk by examining the association between blockchain disclosures in 10-K filings and audit fees. Focusing on U.S. firms between 2013 and 2020, we empirically show that audit fees are higher for companies disclosing blockchain activities in 10-K filings. We further find that this positive association is more pronounced for companies disclosing current blockchain implementation than for companies with plans for blockchain activities. The results indicate that clients’ blockchain activities influence audit risk as assessed by external auditors. As blockchain technology and its applications are emerging, our findings have implications that should be considered by auditors, regulators, and top management.
Evaluation and analysis of civil aircraft flight test safety management capability based on matter element extension theory

According to incomplete statistics, from 2001 to 2018, the number of Chinese civil aviation passengers has maintained an annual growth rate of 15% to 17.4%, and the national flight volume has increased by about 8% year-on-year. The rapid growth of air travel has accelerated the development of the civil aviation industry. Aviation safety has always been a focus of attention in the industry. With the continuous development of China's aviation industry, the aviation manufacturing industry in China is also growing, and the business volume of aircraft manufacturing and aircraft test flights is also rapidly increasing. How to ensure the safety of civil aircraft test flights is becoming a focus of active research in the industry. Safety is the bottom line and cornerstone of flight testing. When safety issues arise, they often lead to unimaginable disasters, and even lead to the failure of the entire model development. The test flight safety management system is an important means to ensure test flight safety. By systematically analyzing the various elements of the test flight safety management system, a test flight safety management capability evaluation model is established based on the matter element extension theory. The test flight safety management capability of the test flight unit is evaluated, and improvement strategies are sought, providing reference for airlines and test flight units to continuously improve their safety management level.
Continuous Random Variables: Joint Distributions

Learn about probability as it is used in computer science with this rigorous, yet highly accessible, undergraduate textbook. Fundamental probability concepts are explained in depth, prerequisite mathematics is summarized, and a wide range of computer science applications is described. Throughout, the material is presented in a “question and answer” style designed to encourage student engagement and understanding. Replete with almost 400 exercises, real-world computer science examples, and covering a wide range of topics from simulation with computer science workloads, to statistical inference, to randomized algorithms, to Markov models and queues, this interactive text is an invaluable learning tool whether your course covers probability with statistics, with stochastic processes, with randomized algorithms, or with simulation. The teaching package includes solutions, lecture slides, and lecture notes for students.
Quantitative and Qualitative Comparison of Decision-Map Techniques for Explaining Classification Models

Visualization techniques for understanding and explaining machine learning models have gained significant attention. One such technique is the decision map, which creates a 2D depiction of the decision behavior of classifiers trained on high-dimensional data. While several decision map techniques have been proposed recently, such as Decision Boundary Maps (DBMs), Supervised Decision Boundary Maps (SDBMs), and DeepView (DV), there is no framework for comprehensively evaluating and comparing these techniques. In this paper, we propose such a framework by combining quantitative metrics and qualitative assessment. We apply our framework to DBM, SDBM, and DV using a range of both synthetic and real-world classification techniques and datasets. Our results show that none of the evaluated decision-map techniques consistently outperforms the others in all measured aspects. Separately, our analysis exposes several previously unknown properties and limitations of decision-map techniques. To support practitioners, we also propose a workflow for selecting the most appropriate decision-map technique for given datasets, classifiers, and requirements of the application at hand.
A Comparative Study of Post-Quantum Cryptographic Algorithm Implementations for Secure and Efficient Energy Systems Monitoring

The Internet of Things (IoT) has assumed a pivotal role in the advancement of communication technology and in our daily lives. However, an IoT system such as a smart grid with poorly designed topology and weak security protocols might be vulnerable to cybercrimes. Exploits may arise from sensor data interception en route to the intended consumer within an IoT system. The increasing integration of electronic devices interconnected via the internet has galvanized the acceptance of this technology. Nonetheless, as the number of users of this technology surges, there must be an aligned concern to ensure that security measures are diligently enforced within IoT communication systems, such as in smart homes, smart cities, smart factories, smart hospitals, and smart grids. This research addresses security lacunae in the topology and configuration of IoT energy monitoring systems using post-quantum cryptographic techniques. We propose tailored implementations of the Rivest–Shamir–Adleman (RSA), N-th degree Truncated Polynomial Ring Units (NTRU), and a suite of cryptographic primitives based on Module Learning With Rounding (Saber) as post-quantum cryptographic candidate algorithms for IoT devices. These aim to secure publisher–subscriber end-to-end communication in energy system monitoring. Additionally, we offer a comparative analysis of these tailored implementations on low-resource devices, such as the Raspberry Pi, during data transmission using the Message Queuing Telemetry Transport (MQTT) protocol. Results indicate that the customized implementation of NTRU outperforms both SABER and RSA in terms of CPU and memory usage, while Light SABER emerges as the front-runner when considering encryption and decryption delays.
Design of music audio-visual archives resource management system based on random forest algorithm

With the development of IT (Information Technology), the frequency of digital management is increasing. For music audio-visual archives, the traditional management mode is no longer suitable for the development of the times. Realizing digital management can better improve the management efficiency of music audio-visual materials and improve the storage safety and utilization efficiency of music audio-visual materials. Therefore, on the basis of analyzing the structure and characteristics of the digital management system of audio-visual archives, this paper constructs a music audio-visual archives resource management system based on RF (Random Forest) algorithm. RF algorithm is a statistical learning theory that includes multiple decision tree classifiers. It uses feature subspace to construct the model, which can better deal with noise and avoid over-fitting. Based on the construction process of decision forest, this paper puts forward an improved RF method. The results show that the error of the algorithm is low, and the accuracy is high, which can reach 95.47%. At the same time, the proposed method can intelligently recommend relevant music audio-visual archives resources to users according to their needs, which further improves the management efficiency and utilization efficiency of music audio-visual materials.
Image Inpainting by Mscswin Transformer Adversarial Autoencoder

Image inpainting has been researched for years. From deeper and larger models to models that focus on global information, all of them aim to obtain results closer to reality. In this paper, we combine the stripe window and line-by-line feature shift to modify the Vision Transformer (ViT) to reduce the computation cost and obtain global information from the oblique attention. In addition, we design a new loss function to enhance the texture and colors for inpainting. At last, to validate the efficacy of our proposed model, we conduct extensive experiments on commonly seen datasets (Places2 and CelebA) compared with other state-of-the-art methods. The source code and pretrained models are available at https: //github.com/bobo0303/MSCS-Net.
Graph neural network based entity augmented representation for recommendation system

A lot of rich auxiliary information can be integrated into the scoring and show good performance, which plays a good role in many recommended methods. In this paper, we use the rich semantic of knowledge graph to explore user preferences and improve the algorithm performance by using item attributes. A large number of experiments have been carried out on two data sets to prove the validity of the graph convolution recommendation model.
Hashing Algorithms

Learn about probability as it is used in computer science with this rigorous, yet highly accessible, undergraduate textbook. Fundamental probability concepts are explained in depth, prerequisite mathematics is summarized, and a wide range of computer science applications is described. Throughout, the material is presented in a “question and answer” style designed to encourage student engagement and understanding. Replete with almost 400 exercises, real-world computer science examples, and covering a wide range of topics from simulation with computer science workloads, to statistical inference, to randomized algorithms, to Markov models and queues, this interactive text is an invaluable learning tool whether your course covers probability with statistics, with stochastic processes, with randomized algorithms, or with simulation. The teaching package includes solutions, lecture slides, and lecture notes for students.
Impact of AI Affordance on AI Adoption: The Mediating Role of Self-Monitoring and Uniqueness

AbstractAlthough artificial intelligence (AI) is currently a prominent technology that has received increasing attention, the factors affecting users’ AI device adoption for healthcare were less discussed. Drawing on affordance-actualization theory and literature on AI, uniqueness theory, and healthcare, we developed a research model to explain users’ adoption intention of AI devices in the healthcare context. Data collected from 307 potential users in China were used to test the proposed model. Our findings indicated that data capture, classification, and social affordances impacted self-monitoring significantly, while data capture and social affordances were the predators of uniqueness enhancement. The self-monitoring, on the other hand, had a positive influence on uniqueness enhancement. Finally, our results revealed that self-monitoring and uniqueness enhancement have significant effects on AI adoption. From the findings, theoretical and practical implications are discussed.Keywords: AI adoptionaffordance-actualization theoryAI affordanceself-monitoringuniqueness enhancement Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsShi-Zhu LiangShi-Zhu Liang is currently an associate professor at the Department of Marketing, Yang-En University, China. Her current research interests include electronic commerce, information management and data management and application. She has published articles in Sustainability, Journal of Computer Information Systems, and others.Chun-Ming ChangChun-Ming Chang is currently a professor at the Department of International Business, Ming Chuan University, Taiwan. His current research interests include electronic commerce and knowledge management. He has published articles in Decision Support Systems, Information Systems Journal, International Journal of Human–Computer Studies, Behaviour and Information Technology and People, and others.Chiung-Hui HuangChiung-Hui Huang, a PhD student at the Department of Information Management, National Sun Yat-sen University. Her research interests include blockchain technology and marketing management.
Towards a metrics suite for evaluating cache side-channel vulnerability: Case studies on an open-source RISC-V processor

Caches are key microarchitectural components of modern processors to improve memory access speed. However, attackers can readily implement timing side-channel attacks by exploiting the inherent time difference between cache hits and misses, which brings serious security threats to computer systems. In recent years, cache attacks have achieved fruitful consequences regarding attack techniques, targets, and platforms. At the same time, in order to measure the cache vulnerability, researchers have been looking at verifying the security policies and have proposed a variety of evaluation metrics. However, there are certain limitations in the current metrics, such as harsh assumptions, the distinguishing ability cannot be maintained all the time, the false positive and false negative noises can not be explicitly quantified, other tools are needed, and the verification environment is difficult to reproduce. To face these issues, we proposed a set of evaluation metrics, which includes the highest score (HS), minimum rounds to disclosure (MRD), and key score scissors differential (KSSD); we give the formal expressions in practice and theoretical, respectively. In order to facilitate the accurate reproduction of the experimental environment and results, we build a dual-core RISC-V bare-metal system based on the open-source framework Chipyard. On this basis, we conduct comprehensive evaluations to exploit vulnerabilities within the RISC-V cache architecture to verify the validity of the metrics suite. Furthermore, we compared the metrics with the commonly used success rate (SR) and guessing entropy (GE). The comparison results show that our proposed metrics suite can accurately depict the effects of attacks. Moreover, KSSD can maintain discrimination and converge quickly; MRD has more practical guidance; HS can specifically describe false negative noises. Finally, we perform a theoretical evaluation of AES algorithms with different T-table implementations using the proposed metrics, analyze the system's false positive and false negative noises, and suggest how the number of cache evictions can be determined under the random replacement policy.
Kinematic simulation and verification based on six-axis tandem robotic arm

Nowadays, with the continuous development of technology, industrial robots are widely used in the manufacturing industry, where tandem robots are working with increasing capabilities. Robot kinematics aims to study in depth the laws of robot motion in a specific frame of reference and as an important function of time, as well as the correlation between the spatial structure of robot joint variables, robot end-effector positioning and pose, and their internal interactions. It takes into account not only the forces and moments of robot motion, but also the temporal characteristics of robot motion. The purpose of this paper is to study the kinematic characteristics of the robot based on the desktop-level six-axis tandem robot GLUON-6L3. First, the DH joint coordinate system of this six-degree-of-freedom robot arm is constructed by the DH parameter method, and the related basic parameters of the linkage are defined. Then, the expression of the chi-square transformation matrix is given, and finally, the kinematic solution results of the robot are verified by using Matlab software to ensure its accuracy.
Research and development of blockchain consensus algorithms

Blockchain technology is a paradigm of distributed systems, and its consistency problem is solved by consensus algorithms. This paper summarizes the existing consensus algorithms and divides them into three types: proof, voting, and Paxos-like. The implementation methods and advantages and disadvantages of some representative algorithms among the three types of consensus algorithms are introduced. Finally, the research hotspots and development directions of consensus algorithms in terms of performance and scalability are pointed out.
Identifying the Cognitive and Digital Gap in Educational Institutions Using a Technology Characterization Software

This research aims to identify the existing technological and cognitive gaps in educational institutions through the implementation of characterization software. To meet this objective, the study establishes a framework of digital competencies to validate the level of performance using the input of educational community members, which allows for establishing a diagnosis regarding the use and appropriation of technological resources. The study utilized an applied research methodology based on a validation approach using the technological acceptance method. The findings reveal educational institutions' lack of knowledge in identifying and appropriating technological resources. The research concludes that recognizing technology and technological resources can lead to the improvement of educational processes. It also provides a framework for researchers to present proposals that allow them to recognize technology's mediating role in the teaching-learning process.
A Pure Electric Driverless Crawler Construction Machinery Walking Method Based on the Fusion SLAM and Improved Pure Pursuit Algorithms

Driverless technology refers to the technology that vehicles use to drive independently with the help of driverless system under the condition of unmanned intervention. The working environment of construction machinery is bad, and the working conditions are complex. The use of driverless technology can greatly reduce the risk of driver operation, reduce labor costs and improve economic benefits.Aiming at the problem of the GPS positioning signal in the working environment of construction machinery being weak and not able to achieve accurate positioning, this paper uses the fusion SLAM algorithm based on improved NDT to realize the real-time positioning of the whole vehicle through reconstruction of the scene. Considering that the motion characteristics of crawler construction machinery are different from those of ordinary passenger cars, this paper improves the existing pure pursuit algorithm. Simulations and real vehicle tests show that the algorithm combined with the fusion SLAM algorithm can realize the motion control of driverless crawler construction machinery well, complete the tracking of the set trajectory perfectly and have high robustness. Considering that there is no mature walking method of driverless crawler construction machinery for reference, the research of this paper will lay a foundation for the development of driverless crawler construction machinery.