Reviewerly/exHarmony huggingface dataset:
Tree-informed Bayesian multi-source domain adaptation: cross-population probabilistic cause-of-death assignment using verbal autopsy

Authors:
raw_author_name: Zhenke Wu
raw_affiliation_string: Department of Biostatistics, University of Michigan , Ann Arbor, MI 48109, United States
raw_author_name: Zehang R Li
raw_affiliation_string: Department of Statistics, University of California , Santa Cruz, CA 95064, United States
raw_author_name: Mengbing Li
raw_affiliation_string: Department of Biostatistics, University of Michigan , Ann Arbor, MI 48109, United States

Abstract: Determining causes of deaths (CODs) occurred outside of civil registration and vital statistics systems is challenging. A technique called verbal autopsy (VA) is widely adopted to gather information on deaths in practice. A VA consists of interviewing relatives of a deceased person about symptoms of the deceased in the period leading to the death, often resulting in multivariate binary responses. While statistical methods have been devised for estimating the cause-specific mortality fractions (CSMFs) for a study population, continued expansion of VA to new populations (or "domains") necessitates approaches that recognize between-domain differences while capitalizing on potential similarities. In this article, we propose such a domain-adaptive method that integrates external between-domain similarity information encoded by a prespecified rooted weighted tree. Given a cause, we use latent class models to characterize the conditional distributions of the responses that may vary by domain. We specify a logistic stick-breaking Gaussian diffusion process prior along the tree for class mixing weights with node-specific spike-and-slab priors to pool information between the domains in a data-driven way. The posterior inference is conducted via a scalable variational Bayes algorithm. Simulation studies show that the domain adaptation enabled by the proposed method improves CSMF estimation and individual COD assignment. We also illustrate and evaluate the method using a validation dataset. The article concludes with a discussion of limitations and future directions.
Incentive approaches for cloud computing: challenges and solutions

Authors:
raw_author_name: Fan Yunlong
raw_author_name: Luo Jie

Abstract: Abstract Cloud computing enables highly configurable and reliable computing resources on a rentable per-use scheme, facilitating quick and cost-effective provisioning of large-scale applications. Thanks to the fast-paced evolution of cutting-edge technologies and the rapid spread of cloud-based solutions, the cloud computing ecosystem is now part of our everyday lives. Nevertheless, cloud computing relies on highly sophisticated data centers comprising energy-consuming servers and equipment that require much energy. Stimulating cloud services for active participation and network contributions presents several challenges. Strategies based on artificial intelligence (AI), game theory, and blockchain have great potential to create an economically sustainable cloud ecosystem. This paper explores strategies grounded in AI, game theory, and blockchain to foster an economically sustainable cloud ecosystem. Informed by a survey study, our research delves into incentive approaches within cloud computing. Theoretical foundations, motivations, and enabling techniques are comprehensively examined to provide valuable insights for a broad audience. The primary contributions of this work lie in elucidating the application of AI, game theory, and blockchain to address challenges in incentivizing cloud services, paving the way for a more sustainable and efficient cloud computing landscape.
Exploring and reviewing the potential of quantum computing in enhancing cybersecurity encryption methods

Authors:
raw_author_name: None Andrew Ifesinachi Daraojimba

Abstract: As the landscape of cybersecurity continually evolves, traditional encryption methods face unprecedented challenges from the impending era of quantum computing. This paper undertakes a comprehensive exploration of the potential transformative impact that quantum computing could have on enhancing cybersecurity encryption methods. Commencing with an overview of quantum computing fundamentals, including the principles of quantum mechanics and key quantum properties, the paper delves into the disruptive power of Shor's algorithm. This algorithm, capable of exponentially faster factorization than classical counterparts, poses a significant threat to prevalent cryptographic techniques such as RSA and ECC. In response to the vulnerabilities exposed by quantum algorithms, the paper investigates the field of post-quantum cryptography, examining cryptographic algorithms designed to resist quantum attacks. Additionally, the study scrutinizes Quantum Key Distribution (QKD) as a potential solution for secure communication in a quantum environment, analyzing its strengths and limitations. The paper provides an updated survey of the current state of quantum computing, highlighting achievements, milestones, and a comparative analysis of existing quantum computing platforms. Subsequently, it assesses the potential impact of quantum computing on cybersecurity, addressing both its ability to fortify encryption and potential risks and vulnerabilities. Striking a balance between benefits and challenges, the research offers insights into the coexistence of quantum and classical cryptographic methods. Looking toward the future, the paper explores ongoing research and development in quantum computing, identifying challenges and ethical considerations. In conclusion, it synthesizes key findings, emphasizing the implications for the future of cybersecurity and advocating for continued research to ensure the development of resilient encryption methods in the quantum era.
Optimizing Blockchain Consensus: Incorporating Trust Value in the Practical Byzantine Fault Tolerance Algorithm with Boneh-Lynn-Shacham Aggregate Signature

Authors:
raw_author_name: Dayong Zhang
raw_author_name: Nur Haliza Abdul Wahab

Abstract: The consensus algorithm is the core mechanism of blockchain and is used to ensure data consistency among blockchain nodes. The PBFT consensus algorithm is widely used in alliance chains because it is resistant to Byzantine errors. However, the present PBFT (Practical Byzantine Fault Tolerance) still has issues with master node selection that is random and complicated communication. The IBFT consensus technique, which is enhanced, is proposed in this study and is based on node trust value and BLS (Boneh-Lynn-Shacham) aggregate signature. In IBFT, multi-level indicators are used to calculate the trust value of each node, and some nodes are selected to take part in network consensus as a result of this calculation. The master node is chosen from among them based on which node has the highest trust value, it transforms the BLS signature process into the information interaction process between nodes. Consequently, communication complexity is reduced, and node-to-node information exchange remains secure. The simulation experiment findings demonstrate that the IBFT consensus method enhances transaction throughput rate by 61% and reduces latency by 13% when compared to the PBFT algorithm.
A Hybrid Approach to Detect and Identify Text in Picture

Authors:
raw_author_name: Norshita Mat Nayan
raw_author_name: Riza Sulaiman
raw_author_name: Deshinta Arrova Dewi
raw_author_name: Tri Basuki Kurniawan

Abstract: In order to create computer systems that can automatically read text from images or pictures, researchers focus on detecting and recognizing text in images. This issue is particularly difficult because images often have complicated backgrounds and a wide range of properties, including color, size, shape, orientation, and texture. Our proposed approach is based on morphology, which consists of a dilation and erosion process to extract text and recognize black-and-white text areas that contain document text or images. This suggested approach has been investigated for its ability to automatically identify text aligned with text pictures, such as store names, street names, banners, and posters. The design, application, and outcomes of the device's experiments are covered in this manuscript using Optical Character Recognition (OCR) Tesseract standards and the optimized OCR Tesseract. Our result shows that the optimized OCR Tesseract performs much better compared to the standard. Image preprocessing and text processing modules comprise this device's two modules. With an Arduino Uno and drawbot/flutter for text printing, this device was created using the Raspberry Pi and a 1.2GHz processor. Doi: 10.28991/ESJ-2024-08-01-016 Full Text: PDF
Parallel Implementation of Lightweight Secure Hash Algorithm on CPU and GPU Environments

Authors:
raw_author_name: Hojin Choi
raw_affiliation_string: Department of Financial Information Security, Kookmin University, Seoul 02707, Republic of Korea
raw_author_name: SeogChung Seo
raw_affiliation_string: Department of Financial Information Security, Kookmin University, Seoul 02707, Republic of Korea

Abstract: Currently, cryptographic hash functions are widely used in various applications, including message authentication codes, cryptographic random generators, digital signatures, key derivation functions, and post-quantum algorithms. Notably, they play a vital role in establishing secure communication between servers and clients. Specifically, servers often need to compute a large number of hash functions simultaneously to provide smooth services to connected clients. In this paper, we present highly optimized parallel implementations of Lightweight Secure Hash (LSH), a hash algorithm developed in Korea, on server sides. To optimize LSH performance, we leverage two parallel architectures: AVX-512 on high-end CPUs and NVIDIA GPUs. In essence, we introduce a word-level parallel processing design suitable for AVX-512 instruction sets and a data parallel processing design appropriate for the NVIDIA CUDA platform. In the former approach, we parallelize the core functions of LSH using AVX-512 registers and instructions. As a result, our first implementation achieves a performance improvement of up to 50.37% compared to the latest LSH AVX-2 implementation. In the latter approach, we optimize the core operation of LSH with CUDA PTX assembly and apply a coalesced memory access pattern. Furthermore, we determine the optimal number of blocks/threads configuration and CUDA streams for RTX 2080Ti and RTX 3090. Consequently, in the RTX 3090 architecture, our optimized CUDA implementation achieves about a 180.62% performance improvement compared with the initially ported LSH implementation to the CUDA platform. As far as we know, this is the first work on optimizing LSH with AVX-512 and NVIDIA GPU. The proposed implementation methodologies can be used alone or together in a server environment to achieve the maximum throughput of LSH computation.
Eﬃcient Light Propagation Algorithm using Quantum Computers

Authors:
raw_author_name: lorcan Conlon
raw_author_name: Tobias Vogl
raw_author_name: Falk Eilenberger

Abstract: Abstract Quantum algorithms can potentially overcome the boundary of computationally hard problems. One of the cornerstones in modern optics is the beam propagation algorithm, facilitating the calculation of how waves with a particular dispersion relation propagate in time and space. This algorithm solves the wave propagation equation by Fourier transformation, multiplication with a transfer function, and subsequent back transformation. This transfer function is determined from the respective dispersion relation, which can often be expanded as a polynomial. In the case of paraxial wave propagation in free space or picosecond pulse propagation, this expansion can be truncated after the quadratic term. The classical solution to the wave propagation requires $\mathcal{O}(N log N)$ computation steps, where $N$ is the number of points into which the wave function is discretized. Here, we show that the propagation can be performed as a quantum algorithm with $\mathcal{O}((log{}N)^2)$ single-controlled phase gates, indicating exponentially reduced computational complexity. We herein demonstrate this quantum beam propagation method (QBPM) and perform such propagation in both one- and two-dimensional systems for the double-slit experiment and Gaussian beam propagation. We highlight the importance of the selection of suitable observables to retain the quantum advantage in the face of the statistical nature of the quantum measurement process, which leads to sampling errors that do not exist in classical solutions.
A Hotspot-Aware Personalized News Recommendation Mechanism Based on DistilBERT-TC-MA

Authors:
raw_author_name: Qian He
raw_affiliation_string: Sichuan University of Media and Communications, China

Abstract: Aiming at the problems of existing news recommendation methods, such as inadequate exploration of the semantic information of news, neglecting potential hotspot features of news, and challenging the balance between user preferences and hotspot features, a hotspot-aware personalized news recommendation model (DistilBERT-TC-MA) is suggested, which integrates the distilled version of BERT (DistilBERT), text convolutional neural network (TextCNN), and multilayer attention (MA). First, it takes full advantage of DistilBERT, TextCNN, and self-attention mechanism to achieve news encoding. Following this, representations of trending news are dynamically aggregated using the attention mechanism, while user preferences are mined utilizing user click history. Finally, in order to successfully accomplish the click prediction of candidate news, the hotspot features, user preferences, and candidate news are ultimately combined using a click predictor. The experimental results of the suggested DistilBERT-TC-MA model on MIND dataset are better than several other advanced methods.
Optimal Rates of Approximation by Shallow ReLU$$^k$$ Neural Networks and Applications to Nonparametric Regression

Authors:
raw_author_name: Yunfei Yang
raw_affiliation_string: Department of Mathematics, City University of Hong Kong, Kowloon, Hong Kong, China
raw_author_name: Ding-Xuan Zhou
raw_affiliation_string: School of Mathematics and Statistics, University of Sydney, Sydney, NSW, 2006, Australia

Abstract: Abstract We study the approximation capacity of some variation spaces corresponding to shallow ReLU $$^k$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mi>k</mml:mi> </mml:msup> </mml:math> neural networks. It is shown that sufficiently smooth functions are contained in these spaces with finite variation norms. For functions with less smoothness, the approximation rates in terms of the variation norm are established. Using these results, we are able to prove the optimal approximation rates in terms of the number of neurons for shallow ReLU $$^k$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mi>k</mml:mi> </mml:msup> </mml:math> neural networks. It is also shown how these results can be used to derive approximation bounds for deep neural networks and convolutional neural networks (CNNs). As applications, we study convergence rates for nonparametric regression using three ReLU neural network models: shallow neural network, over-parameterized neural network, and CNN. In particular, we show that shallow neural networks can achieve the minimax optimal rates for learning Hölder functions, which complements recent results for deep neural networks. It is also proven that over-parameterized (deep or shallow) neural networks can achieve nearly optimal rates for nonparametric regression.
Constructing an Interpretive Structural Model to Unravel the Interconnected Drivers of Teaching Quality in Higher Education

Authors:
raw_author_name: Larry Peconcillo Jr.
raw_affiliation_string: Cebu Technological University, Philippines
raw_author_name: Emerson Peteros
raw_affiliation_string: Cebu Technological University, Philippines
raw_author_name: Lanndon Ocampo
raw_affiliation_string: Cebu Technological University, Philippines

Abstract: Identifying the primary factors of teaching quality remains a pivotal agenda for informed decision making, strategic planning, and resource allocation. This study builds upon ten key factors derived from previous research and recognizes the inherent complexity within their relationships. Emphasizing the necessity for a structured model, this work employs an interpretive structural modelling (ISM) approach and Matrice d'impacts croisés multiplication appliquée á un classment (MICMAC) analysis for constructing a hierarchical model that delineates the interrelationships among the factors influencing teaching quality. The findings indicate the substantial impact of intrinsic factors, particularly teachers' individual and psychological characteristics, on other factors. Additionally, our analysis highlights the critical role of student composition in enhancing overall teaching quality. These insights significantly contribute to the literature by offering valuable guidance to decision makers for maintaining teaching quality within higher education institutions.
Detecting Ambiguities in Requirement Documents Written in Arabic Using Machine Learning Algorithms

Authors:
raw_author_name: Ahmad Althunibat
raw_affiliation_string: Al-Zaytoonah University of Jordan, Jordan
raw_author_name: Bayan Alsawareah
raw_affiliation_string: Al-Zaytoonah University of Jordan, Jordan
raw_author_name: Siti Sarah Maidin
raw_affiliation_string: INTI International University, Malaysia
raw_author_name: Iqbal Jebril
raw_affiliation_string: Al-Zaytoonah University of Jordan, Jordan
raw_author_name: Haneen A. Al-khawaja
raw_affiliation_string: Applied Science Research Center, Applied Science Private University, Jordan

Abstract: The identification of ambiguities in Arabic requirement documents plays a crucial role in requirements engineering. This is because the quality of requirements directly impacts the overall success of software development projects. Traditionally, engineers have used manual methods to evaluate requirement quality, leading to a time-consuming and subjective process that is prone to errors. This study explores the use of machine learning algorithms to automate the assessment of requirements expressed in natural language. The study aims to compare various machine learning algorithms according to their abilities in classifying requirements written in Arabic as decision tree. The findings reveal that random forest outperformed all stemmers, achieving an accuracy of 0.95 without employing a stemmer, 0.99 with the ISRI stemmer, and 0.97 with the Arabic light stemmer. These results highlight the robustness and practicality of the random forest algorithm.
Underwater image enhancement using multi-task fusion

Authors:
raw_author_name: Kaibo Liao
raw_affiliation_string: School of Computer, Central China Normal University, Wuhan, Hubei, China
raw_author_name: Xi Peng
raw_affiliation_string: School of Computer, Central China Normal University, Wuhan, Hubei, China

Abstract: Underwater images are often scattered due to suspended particles in the water, resulting in light scattering and blocking and reduced visibility and contrast. Color shifts and distortions are also caused by the absorption of different wavelengths of light in the water. This series of problems will make the underwater image quality greatly impaired, resulting in some advanced visual work can not be carried out underwater. In order to solve these problems, this paper proposes an underwater image enhancement method based on multi-task fusion, called MTF. Specifically, we first use linear constraints on the input image to achieve color correction based on the gray world assumption. The corrected image is then used to achieve visibility enhancement using an improved type-II fuzzy set-based algorithm, while the image is contrast enhanced using standard normal distribution probability density function and softplus function. However, in order to obtain more qualitative results, we propose multi-task fusion, in which we solve for similarity, then we obtain fusion weights that guarantee the best features of the image as much as possible from the obtained similarity, and finally we fuse the image with the weights to obtain the output image, and we find that multi-task fusion has excellent image enhancement and restoration capabilities, and also produces visually pleasing results. Extensive qualitative and quantitative evaluations show that MTF method achieves optimal results compared to ten state-of-the-art underwater enhancement algorithms on 2 datasets. Moreover, the method can achieve better results in application tests such as target detection and edge detection.
Understanding the dilemma of explainable artificial intelligence: a proposal for a ritual dialog framework

Authors:
raw_author_name: Yi Zeng
raw_affiliation_string: Department of Philosophy, School of Humanities, University of Chinese Academy of Sciences, Beijing, China

Abstract: Abstract This paper addresses how people understand Explainable Artificial Intelligence (XAI) in three ways: contrastive, functional, and transparent. We discuss the unique aspects and challenges of each and emphasize improving current XAI understanding frameworks. The Ritual Dialog Framework (RDF) is introduced as a solution for better dialog between AI creators and users, blending anthropological insights with current acceptance challenges. RDF focuses on building trust and a user-centered approach in XAI. By undertaking such an initiative, we aim to foster a thorough Understanding of XAI, capable of resolving the current issues of acceptance and recognition.