Reviewerly/exHarmony huggingface dataset:
F1 Score Based Weighted Asynchronous Federated Learning

Abstract: The domain of federated learning has observed remarkable developments in recent years, enabling collaborative model training while preserving data privacy. This paper discusses several recent advancements in the field of federated learning, particularly in asynchronous and weighted federated learning. A novel approach within the federated learning paradigm titled "F1 Score Based Weighted Asynchronous Federated Learning" is introduced. The approach addresses issues of biased aggregation and device heterogeneity by assigning weights to devices based on their F1 scores, prioritizing those with superior performance in classification. Leveraging the asynchronous nature of federated learning, this approach enhances both resource efficiency and convergence speed. By incorporating F1 scores into the weighting mechanism, a balanced emphasis on precision and recall is achieved. It can optimize resource utilization, expediting the learning process by focusing on updates from devices with more accurate and valuable information. This approach can enhance collaborative model improvement while preserving data privacy in federated learning.
Machine Learning and Cryptanalysis: An In-Depth Exploration of Current Practices and Future Potential

The rapidly evolving landscape of cryptanalysis necessitates an urgent and detailed exploration of the high-degree non-linear functions that govern the relationships between plaintext, key, and encrypted text. Historically, the complexity of these functions has posed formidable challenges to cryptanalysis. However, the advent of deep learning, supported by advanced computational resources, has revolutionized the potential for analyzing encrypted data in its raw form. This is a crucial development, given that the core principle of cryptosystem design is to eliminate discernible patterns, thereby necessitating the analysis of unprocessed encrypted data. Despite its critical importance, the integration of machine learning, and specifically deep learning, into cryptanalysis has been relatively unexplored. Deep learning algorithms stand out from traditional machine learning approaches by directly processing raw data, thus eliminating the need for predefined feature selection or extraction. This research underscores the transformative role of neural networks in aiding cryptanalysts in pinpointing vulnerabilities in ciphers by training these networks with data that accentuates inherent weaknesses alongside corresponding encryption keys. Our study represents an investigation into the feasibility and effectiveness of employing machine learning, deep learning, and innovative random optimization techniques in cryptanalysis. Furthermore, it provides a comprehensive overview of the state-of-the-art advancements in this field over the past few years. The findings of this research are not only pivotal for the field of cryptanalysis but also hold significant implications for the broader realm of data security.
Information exchange in multi-agent systems

This article analyzes the communication protocols and architecture of multi-agent systems. In the introduction, the general purpose of multi-agent systems and the principles of information exchange are described. Annotated with examples and graphs to analyze communication protocols and architectures. The article provides information on the structure, characteristics and areas of use of communication protocols. Also architectural models (SOAA, BDI, Gaia) were comparatively analyzed. This article provides information related to a specific topic of interest to professionals and industry researchers, system administrators.
POSTER: RadiK: Scalable Radix Top-K Selection on GPUs

By identifying the k largest or smallest elements in a set of data, top-k selection is critical for modern high-performance databases and machine learning systems, especially with large data volumes. However, previous studies on its GPU implementation are mostly merge-based and rely heavily on the high-speed but size-limited on-chip memory, thereby resulting in a restricted upper bound on k. This paper introduces RadiK, a highly optimized GPU-parallel radix top-k selection that is scalable with k, input length, and batch size. With a carefully designed optimization framework targeting high memory bandwidth and resource utilization, RadiK supports far larger k than the prior art, achieving up to 2.5× speedup for non-batch queries and up to 4.8× speedup for batch queries. We also propose a lightweight refinement that strengthens the robustness of RadiK against skewed distributions by adaptively scaling the input elements.
Web News Media Retrieval Analysis integrating with Knowledge Recognition of Semantic Grouping Vector Space Model

Traditional Web news media retrieval technology can only meet the specific requirements of customers. Because of its universal characteristics, it cannot meet the needs of different environments, different purposes and different times simultaneously. Researchers have proposed a search method for online news media, which is used for computing the semantic grouping vector space model. The customer's interest model is analyzed through the characteristics of the user's different classification areas. In this paper, we propose a vector space model that performs semantic grouping based on feature words. The model divides four groups that are relatively independent in the meaning of feature words in a news report: time, place, person, and event, and then form four vector spaces, and calculate the weight value and similarity of each vector space. Theoretical analysis and experimental results show that the improved model is suitable for searching Web news information, and improves the calibration rate, calibration rate and query speed.
Three-Dimensional Double Random-Phase Encryption for Simultaneous Two-Primary Data

In this paper, we propose a three-dimensional (3D) optical encryption technique for simultaneous two-primary data using double random-phase encryption (DRPE). In conventional DRPE, the primary data can be encrypted through two different random phase masks optically. Thus, its speed is the same as the speed of light. However, in this method, each primary dataset can be decrypted by the individual key data. For simultaneous two primary dataset such as stereo images or multi-view images, a new encryption technique is required. Therefore, in this paper, we encrypt the simultaneous two different primary datasets by DRPE. In our method, the first and second primary data are regarded as the amplitude and phase with single key data for encryption. To verify the feasibility of our method, we implement the simulation and measure the performance metrics such as thw peak signal to noise ratio (PSNR) and the peak sidelobe ratio (PSR). As a result, PSNR values of two-dimensional decryption results for the first (“LENA” text) and second (lena image) primary data by our proposed method with the correct and incorrect key data are 311.0139, 41.9609, 12.0166, and 7.4626, respectively, since the first primary data are lossless, and the second primary data are lossy. For 3D reconstruction, PSR values of the first and second primary data are 914.2644 and 774.1400, respectively.
A Conceptual Paper: Study on the Effect of Peer Relationships on the Academic Achievement among College Students

The aim of this paper is to examine the relationship between peer relation influences on the college students’ academic achievement. The study assessed peer relation influences in terms of the level of academic achievement impact they have on learners. This study will introduce peer relationships, learning engagement, and positive academic emotion as variables to analyse their effects on the academic achievement of university students. This study will adopt quantitative research methods?The respondents were selected using simple random sampling technique. Questionnaires were used to collect data, which were analyzed by multiple regressions. It is hoped that the findings of this study will be useful to teachers, principals and parents to gain more insight into the psychosocial factors that affect college students’ academic achievement and therefore help improve their academic achievement. Providing theoretical support and practical basis for improving undergraduate education.
Rewriting Archaeological Narratives: Archaeology of Archaeology through 3D Site Topography Recording

The current ubiquitous use of 3D recording technologies in archaeological fieldwork, for a large part due to the application of budget-friendly (drone) sensors and the availability of many low-cost image-based 3D modelling software packages, has exponentially increased the amount of 3D data of archaeological sites and landscapes. Various applications have advanced far beyond the experimental phase, such as the deployment of 3D surface recording for excavation interpretation, as a complementary data layer for prospection and as the basis for visualisation/presentation. Of particular attention here is the degree to which drone 3D recording can further advance the understanding of archaeological site topography. In this paper, current developments in the field of 3D recording will be discussed in the context of the ‘Archaeology of Archaeology’ approach, which is being developed at the University of Amsterdam. The paper is the result of a Round Table discussion at the CAA conference on April 5, 2023, in Amsterdam.
Computer Vision and Image Segmentation

Abstract: Image segmentation is a critical step in image processing, computer vision, and pattern recognition, which involves dividing an image into different regions or segments. Image segmentation plays an essential role in many applications, such as object recognition, medical image analysis, autonomous driving, and robotics. This paper aims to provide an overview of image segmentation techniques, including traditional and deep learning-based approaches. The paper also discusses the challenges associated with image segmentation, such as noise, illumination variations, and occlusions. Finally, the paper provides a brief discussion on the evaluation metrics used to assess the performance of image segmentation algorithms.
The Perceived Value of Implementing IFRS for SMEs: An Exploratory Study from the Point of View of Managers in Jordan

The purpose of this exploratory study is to add to the literature on the perceived value of applying IFRS for SMEs standard from the perspective of managers in Jordan’s small and medium-sized enterprises (JSMEs). Based on previous literature and the Value-Based Model Theory, which incorporates the cost-benefit analysis principle, a questionnaire was developed, and 137 valid ones were used to test data regarding the perceived value of appropriateness, possibility, cost, and benefits of implementing IFRS for SMEs standard.The study’s major conclusion found a moderate level of positive consensus about implementation perception; the results also suggested that benefits marginally outweighed implementation costs.The study’s goal is to raise policymakers’ and regulatory experts’ attention to the potential benefits, costs, and obstacles of implementing the standard.this study contributes to international accounting harmonization efforts by demonstrating how Jordanian managers assess the costs, benefits, and opportunities of IFRS for SMEs in JSMEs implementation. This study recommended the following: Expedite the application, conduct thorough IFRS for SMEs awareness and training programs at different levels, and provide financial data for these SMEs in the public interest.Future studies should test new firm variables after the application of this standard and, the reasonable costs and benefits are known.
Enhancing Transparency and Interpretability in Deep Learning Models: A Comprehensive Study on Explainable AI Techniques

Abstract: Deep learning models have demonstrated remarkable capabilities across various domains, but their inherent complexity often leads to challenges in understanding and interpreting their decisions. The demand for transparent and interpretable artificial intelligence (AI) systems is particularly crucial in fields such as healthcare, finance, and autonomous systems. This research paper presents a comprehensive study on the application of Explainable AI (XAI) techniques to enhance transparency and interpretability in deep learning models. Keywords: Explainable AI (XAI), artificial intelligence (AI).
Read and Play Digital Music (MIDI) in R using the *fluidsynth* package

A few weeks ago, prof Matt Crump wrote a blog post in which he explores tools to handle MIDI data in R, in preparation for a cognition experiment that involves creating musical stimuli. In the article he ends up using a mix of external command line tools ffmpeg and fluidsynth and a python module.
Healthcare System Using Blockchain

The global Electronic Health Record (EHR) market is growing dramatically and is expected to reach $39.7 billion by 2022. To safeguard the security and privacy of EHR, access control is an essential mechanism for managing EHR data. This paper proposes a hybrid architecture to facilitate access control of EHR data by using both blockchain and edge nodes. Within the architecture, a blockchain-based controller manages identity and access control policies and serves as a tamper-proof log of access events. In addition, off-chain edge nodes store the EHR data and apply policies specified in Abbreviated Language For Authorization (ALFA) to enforce attribute-based access control on EHR data in collaboration with the blockchain-based access control logs. We evaluate the proposed hybrid architecture by utilizing the Hyperledger Composer Fabric blockchain to measure the performance of executing smart contracts and ACL policies in terms of transaction processing time and response time against unauthorized data retrieval. Keywords:Attribute-based Access Control, Access Control List, Blockchain, Edge Computing, Hyperledger, Smart Contract
The Effect of Students Self-Confidence on Mathematics Achievement in High School in Korea

This study looked into how students' self-confidence affected their performance in mathematics in South Korean high schools. This study employed an ex-post facto quantitative methodology with a correlational design on a sample size of 288 respondents selected using Yamane formulae. Students and math teachers from Daeyeon High School made up the sample size. To gather quantitative data, a mathematics achievement test and a confidence questionnaire in mathematics were employed. The null hypothesis, according to the Pearson product-moment correlation coefficient computation formula, is that there is no statistically significant correlation between students' self-confidence and mathematical achievement. The study discovered a statistically significant positive correlation (r=723, p=0.000) between math achievement and student confidence. Multiple linear regression analysis revealed that the independent variables in the model account for 70.8% of the variance in the dependent variable, indicating their critical role in high school mathematics achievement for students. Furthermore, it was concluded that there is a significant positive attitude towards students' mathematics achievement when it comes to their self-confidence. The study concluded that since student confidence is directly correlated with mathematical achievement, math teachers should help students solve mathematical problems in order to help them build self-confidence for better achievement
Transformative Power: The Integration of Artificial Intelligence in Education

In the 21st century, the integration of technology into education has been evolving rapidly, with Artificial Intelligence (AI) emerging as a transformative force. This article explores the multifaceted impact of AI in education, ranging from personalized learning experiences to ethical considerations.
Photovoltaic power generation prediction and optimization configuration model based on GPR and improved PSO algorithm

As the growing demand for energy as well as the strengthening of environmental awareness, photovoltaic power generation, as a clean and renewable energy source, has gradually attracted people's attention and attention. To facilitate the dispatching and planning of power system, this study uses historical data and meteorological data to build a photovoltaic power generation prediction and configuration optimization model on the ground of Gaussian process regression and improved particle swarm optimization algorithm. The simulation results show that the regression prediction curve of the Gaussian process regression prediction model is the closest to the real curve, and the prediction curve is stable and not easily disturbed by noise data. The Root-mean-square deviation and the average absolute proportional error of the model are small, and the disparity in the predicted value and the true value of the model is small; The integration of multi factor data has improved the accuracy of prediction data, and the regression prediction effect is good. The improved Particle swarm optimization algorithm could continuously enhance in the search for the optimal solution, and the Rate of convergence is fast. The Pareto solution can provide different solutions suitable for photovoltaic power generation optimization. Reasonable optimization configuration can effectively reduce active power line loss and voltage deviation, with the maximum reduction values reaching 132kW and 0.028, respectively. The research and design of predictive models and optimized configuration models can promote the formation of smart grids.
Native Market Factors for Pricing Cryptocurrencies

The cryptocurrency market has been growing frantically in number of cryptocurrencies, online exchanges, and market capitalization, which has amplified the need for comprehensive and robust pricing models. Using a database of all eligible cryptocurrencies listed on the CoinMarketCap website, we study the relationship between returns and several potential pricing factors, such as size (market capitalization), momentum, liquidity, and maturity. The analysis was conducted from December 27, 2013, to December 29, 2020, using weekly data for 3'667 cryptocurrencies. Results point out that portfolios of cryptocurrencies with smaller market capitalization, higher reversal, lower liquidity, and lower maturity tend to offer higher returns. The 5-factor model that additionally includes illiquidity and maturity performs better than the 3-factor model previously proposed in the literature, meaning that illiquidity and maturity significantly help capture the cross-sectional cryptocurrency risk premia. The 5-factor model presented seems robust to different procedures to construct portfolios and factors.
Dual unitaries as maximizers of the distance to local product gates

The problem of finding the resource-free, closest local unitary, to any bipartite unitary gate $U$ is addressed. Previously discussed as a measure of nonlocality, the distance ${K}_{D}(U)$ to the nearest product unitary has implications for circuit complexity and related quantities. Dual unitaries, currently of great interest in models of complex quantum many-body systems, are shown to have a preferred role as these are maximally and equally away from the set of local unitaries. This is proved here for the case of qubits, and we present strong numerical and analytical evidence that it is true in general. An analytical evaluation of ${K}_{D}(U)$ is presented for general two-qubit gates. For arbitrary local dimensions, that ${K}_{D}(U)$ is largest for dual unitaries is substantiated by its analytical evaluations for an important family of dual-unitary and for certain nondual gates. A closely allied result concerns for any bipartite unitary the existence of a pair of maximally entangled states that it connects. We give efficient numerical algorithms to find such states and to find ${K}_{D}(U)$ in general.
Path-Planning Strategy: Adaptive Ant Colony Optimization Combined with an Enhanced Dynamic Window Approach

Aiming to resolve the problems of slow convergence speed and inability to plan in real time when ant colony optimization (ACO) performs global path planning, we propose a path-planning method that improves adaptive ant colony optimization (IAACO) with the dynamic window approach (DWA). Firstly, the heuristic information function is modified, and the adaptive adjustment factor is added to speed up the algorithm’s convergence rate; secondly, elite ants and max–min ants systems are implemented to enhance the global pheromone updating process, and an adaptive pheromone volatilization factor is aimed at preventing the algorithm from enhancing its global search capabilities; then, the path optimization and withdrawal mechanism is utilized to enable smoother functioning and to avoid the deadlocks; finally, a new distance function is introduced in the evaluation function of DWA to the enhance real-time obstacle-avoidance ability. The simulation experiment results reveal that the path length of the IAACO can be shortened by 10.1% and 13.7% in contrast to the ACO. The iteration count can be decreased by 63.3% and 63.0%, respectively, leading to an enhanced optimization performance in global path planning and achieving dynamic real-time obstacle avoidance for local path planning.
ParlayANN: Scalable and Deterministic Parallel Graph-Based Approximate Nearest Neighbor Search Algorithms

Approximate nearest-neighbor search (ANNS) algorithms are a key part of the modern deep learning stack due to enabling efficient similarity search over high-dimensional vector space representations (i.e., embeddings) of data. Among various ANNS algorithms, graph-based algorithms are known to achieve the best throughput-recall tradeoffs. Despite the large scale of modern ANNS datasets, existing parallel graph-based implementations suffer from significant challenges to scale to large datasets due to heavy use of locks and other sequential bottlenecks, which 1) prevents them from efficiently scaling to a large number of processors, and 2) results in non-determinism that is undesirable in certain applications.
Evaluation of the effectiveness of search and rescue ships in rescuing the overboard personnel based on cloud model

Conducting effectiveness evaluation and analysis of the key elements of rescuing the overboard personnel by search and rescue ships, an evaluation index system for rescuing the overboard personnel is constructed. Considering the randomness and fuzziness of various evaluation indicators for rescuing the overboard personnel, a qualitative concept and quantitative calculation are transformed based on cloud models to establish an effectiveness evaluation model for rescuing the overboard personnel. Analytic Hierarchy Process is used to calculate the weights of each evaluation indicator and combined with the evaluation indicators, upper level indicators parameter of cloud model are calculated. The effectiveness evaluation conclusion of the overall goal of rescuing the overboard personnel is obtained by comparing it with the evaluation standard cloud model.