Reviewerly/exHarmony huggingface dataset:
SCS: A Secure Cloud Storage Framework with Enhanced Integrity and Auditability Using Consortium Blockchain System

Authors:
raw_author_name: Aswani Devi Aguru
raw_affiliation_string: National Institute of Technology Warangal
raw_author_name: Suresh Babu Erukala
raw_affiliation_string: National Institute of Technology Warangal
raw_author_name: Rajesh Kaluri
raw_affiliation_string: Vellore Institute of Technology
raw_author_name: Ali Kashif Bashir
raw_affiliation_string: Manchester Metropolitan University
raw_author_name: Thippa Reddy Gadekallu
raw_affiliation_string: Lovely Professional University

Abstract: Abstract The widespread use of cloud storage enables users to access their resources remotely via a self-service model. Utilizing pay-per-use storage services provided by cloud service providers (CSPs) requires users to commit financially to their resources. This paper introduces an SCS framework that provides a secure architecture for cloud storage using a consortium blockchain network to solve the trust issue. This framework substitutes the third-party auditor with peers of a consortium blockchain network, which handles the role of data storage and verification. The storage space is segmented between uncommitted and committed storage, where uncommitted storage is designated for storing unverified documents. In contrast, committed storage is designated for the storage of committed documents. Those data documents that have undergone consensus validation by a specified threshold of peer nodes will be transferred to committed storage. The implementation of the SCS framework is carried out on Hyperledger Fabric. The security analysis proves that SCS effectively safeguards cloud storage against devastating attacks while preserving its integrity and auditability. The performance analysis reveals that the metrics for document upload time, document retrieval time, block acceptance time, execution time, and latency are optimal compared to state-of-the-art techniques.
An integrated methodology for significant wave height forecasting based on multi‐strategy random weighted grey wolf optimizer with swarm intelligence

Authors:
raw_author_name: Emrah Dokur
raw_affiliation_string: School of Engineering Bilecik Seyh Edebali University Bilecik Turkey
raw_author_name: Ugur Yuzgec
raw_affiliation_string: School of Engineering Bilecik Seyh Edebali University Bilecik Turkey

Abstract: Abstract While wave energy is regarded as one of the prominent renewable energy resources to diversify global low‐carbon generation capacity, operational reliability is the main impediment to the wide deployment of the related technology. Current experience in wave energy systems demonstrates that operation and maintenance costs are dominant in their cost structure due to unplanned maintenance resulting in energy production loss. Accurate and high performance simulation forecasting tools are required to improve the efficiency and safety of wave converters. This paper proposes a new methodology for significant wave height forecasting. It is based on incorporating swarm decomposition (SWD) and multi‐strategy random weighted grey wolf optimizer (MsRwGWO) into a multi‐layer perceptron (MLP) forecasting model. This approach takes advantage of the SWD approach to generate more stable, stationary, and regular patterns of the original signal, while the MsRwGWO optimizes the MLP model parameters efficiently. As such, forecasting accuracy has improved. Real wave datasets from three buoys in the North Atlantic Sea are used to test and validate the forecasting performance of the proposed model. Furthermore, the performance is evaluated through a comparison analysis against deep‐learning based state‐of‐the‐art forecasting models. The results show that the proposed approach significantly enhances the model's accuracy.
Neutral Electron (e°) or Neutrino (ν): e° ≡ ν

Authors:
raw_author_name: Antonio Puccini

Abstract: In order to solve the Neutron decay mass gap problem, Pauli proposed a precise solution. The brilliant idea of a 3rd particle came to Pauli (fully shared by Fermi) to compensate the energy-mass gap that emerged from the disintegration of the neutron, or negative b decay (bd-): N&amp;reg; P + e-.&#x0D; &#x0D; The basic requirements originally requested by Pauli and Fermi for the new particle, later called neutrino, are essentially three: it is electrically neutral and it must have the same mass and spin of an electron. Hence, if the mass of the neutrino (n) corresponded to that assumed by Pauli and Fermi, the &amp;beta;d- mass gap problem would be brilliantly solved.&#x0D; &#x0D; However, the current upper limits of the mass of the n are &amp;lt; 2eV.&#x0D; &#x0D; Here we show that a clear incongruity comes out: the mass attributed to the n will never be able to solve the energy gap problem of the &amp;beta;d- : it takes ≃ 250,000 n to compensate the energy-mass gap. &#x0D; &#x0D; Unless we consider, instead of n, another particle, probably still unknown, as the 3rd particle of &amp;beta;d-. To find a solution, we hypothesized the existence of an electron with no electric charge: a neutral electron (e&amp;deg;).
A Novel Framework for Image Encryption by Integrating Modified Moth Flame Optimization and Logistic Chaotic Map for Enhanced Security

Authors:
raw_author_name: Akshat Aggarwal
raw_affiliation_string: Netaji Subhas University of Technology
raw_author_name: Deepika Kukreja
raw_affiliation_string: Netaji Subhas University of Technology
raw_author_name: Indu Bala
raw_affiliation_string: Lovely Professional University

Abstract: Abstract Preserving the confidentiality of sensitive image data is of paramount importance in the digital world. Therefore, an optimized image encryption algorithm is proposed in this paper for securing image-based communication. The proposed Modified Moth Flame Optimization Algorithm (MMFO) delved along with a Logistic Chaotic Map is used to obtain improved values of evaluation parameters such as Correlation Coefficient(CC), Image Entropy (IE), Unified Average Changing Intensity (UACI) and Normalized Pixel Change Rate (NPCR) along with Uniform Histograms for the encrypted image that outperforms various state-of-the-art metaheuristic algorithms coupled with diverse chaotic maps. Using multiple plain image-dependent session keys and a Logistic Chaotic map, several encrypted images are generated which act as an initial population for the Modified algorithm. The results are presented to validate the performance of the proposed algorithm. It has been observed that the proposed algorithm has a substantial de-correlation of adjacent pixels in the encrypted image, achieving values on the order of 10 −6 .
DEThresh: Enhancing Cloud Computing Performance with Differential Evolution-Driven Datacenter Selection and Threshold-based Load Balancing Optimization

Authors:
raw_author_name: Ajay Katangur
raw_affiliation_string: Missouri State University
raw_author_name: Shusmoy Chowdhury
raw_affiliation_string: Missouri State University

Abstract: Abstract The advent of cloud computing has introduced a novel paradigm in user services, enabling on-demand access to information technology services, irrespective of time and location, with a pay-per-use model. Meeting user expectations necessitates cloud service providers to furnish an infrastructure that is efficient, reliable, and resilient to faults. Addressing the substantial volume of user requests, selecting cloud datacenters and load balancing strategies become pivotal for delivering real-time services economically and selecting appropriate datacenters and virtual machines. This research introduces DEThresh, combining a smart differential evolution-based datacenter selection method with a threshold-based load balancing mechanism aimed at enhancing resource management by focusing on cost and performance considerations. The differential evolution-based datacenter selection employs evolutionary operations to identify the most suitable DC for the userbase. Simultaneously, threshold-based load balancing ensures the reasonable distribution of tasks and optimal resource utilization, preventing task overload on virtual machines and underutilization due to task scarcity. Simulation results demonstrate that the proposed DEThresh algorithm exhibits superior response and data processing time compared to well-established popular algorithms currently in use, especially under varying userbase and datacenter conditions. Impressively, DEThresh achieves this even with fewer datacenters, ultimately reducing cloud service costs.
Predicting Agent Behavior over Repeated Trials

Authors:
raw_author_name: Markus Huff

Abstract: Agents act on an important part of an individual’s environment. An increasing amount of these agents are machines and software. In a cooperative setting, each individual needs to understand the partners’ intentions and corresponding actions to plan their own actions adequately. Misunderstandings have negative effects and diminish the efficiency of cooperation. We created a game-based experiment to study the effects of understanding a partner’s actions in a cooperative setting. In this game, the players are dependent on the ability of the other to make good decisions in the game to succeed. In two studies (N = 87, N = 281), we collected data on the understanding of an artificial agent, operationalized as the ability to predict its actions and the skill at the task itself. The participants improve at predicting the agent’s actions and at their subtask in the game over time. Following a misunderstanding, the participants’ performance at their subtask was worse, as measured by the quality, the speed, and the efficiency of their actions. Results of Study 2 suggest that the improvements in predicting the agent’s actions are likely the result of an improved understanding of the game rather than an improved understanding of the agent.
Analysis of Engineering Undergraduates’ Confidence with Hands-on Tasks – Preparation for Collaborative Manufacturing Environments in the Era of Industry 4.0

Authors:
raw_author_name: Beshoy Morkos
raw_affiliation_string: University of Georgia

Abstract: Abstract Many engineering students are expected to become skilled and experienced in solving practical hands-on projects before they graduate. For students without formal engineering experiences (co-ops, internships, full/part-time jobs) this is often achieved through courses such as capstone design, cornerstone design, and in labs. The need for engineers with hands-on exposure will only increase as we experience a revitalization of manufacturing in the United States and the emergence of Industry 4.0. However, we currently do not know students' hands-on ability as it varies between majors and students in the context of Industry 4.0. To address this, a survey of engineering students who are interested in manufacturing is necessary to assess their readiness for hands-on projects in manufacturing settings. This paper presented an analysis of engineering students' perception towards hands-on tasks in manufacturing environments. In a study with students who expressed interest in manufacturing, we collect qualitative interview data from 30 students across four engineering majors. We compare students' majors, perceptions, experience, and the courses they completed related to hands-on projects. The results suggest that many students did not feel prepared or confident to work in manufacturing environments in preparation for Industry 4.0. Further, students formal engineering experiences contributed more to their confidence in hands-on projects than curricular experiences.
Summary-Sentence Level Hierarchical Supervision for Re-Ranking Model of Two-Stage Abstractive Summarization Framework

Authors:
raw_author_name: Eunseok Yoo
raw_affiliation_string: School of Computing, Gachon University, 1342, Seongnam-daero, Sujeong-gu, Seongnam-si 13120, Republic of Korea
raw_author_name: Gyunyeop Kim
raw_affiliation_string: School of Computing, Gachon University, 1342, Seongnam-daero, Sujeong-gu, Seongnam-si 13120, Republic of Korea
raw_author_name: Sangwoo Kang
raw_affiliation_string: School of Computing, Gachon University, 1342, Seongnam-daero, Sujeong-gu, Seongnam-si 13120, Republic of Korea

Abstract: Fine-tuning a pre-trained sequence-to-sequence-based language model has significantly advanced the field of abstractive summarization. However, the early models of abstractive summarization were limited by the gap between training and inference, and they did not fully utilize the potential of the language model. Recent studies have introduced a two-stage framework that allows the second-stage model to re-rank the candidate summary generated by the first-stage model, to resolve these limitations. In this study, we point out that the supervision method performed in the existing re-ranking model of the two-stage abstractive summarization framework cannot learn detailed and complex information of the data. In addition, we present the problem of positional bias in the existing encoder–decoder-based re-ranking model. To address these two limitations, this study proposes a hierarchical supervision method that jointly performs summary and sentence-level supervision. For sentence-level supervision, we designed two sentence-level loss functions: intra- and inter-intra-sentence ranking losses. Compared to the existing abstractive summarization model, the proposed method exhibited a performance improvement for both the CNN/DM and XSum datasets. The proposed model outperformed the baseline model under a few-shot setting.
Implementasi Algoritma C4.5 Untuk Mengukur Tingkat Kepuasan Mahasiswa yang Berlangganan Wifi Indihome

Authors:
raw_author_name: M. Safii
raw_author_name: Sundari Retno Andani
raw_author_name: Muhammad Ridwan Lubis
raw_author_name: Renaldi Renaldi

Abstract: The urgency of research on satisfaction levels is closely related to the success and desires of a business or organization. Without a good understanding of customer satisfaction, a company or organization may lose market share, face declining sales, or even face failure. Therefore, this research must be carried out regularly and continuously. The purpose of this research is to determine the level of customer satisfaction and to determine the dominant service quality that influences the quality of service provided by PT Telkom Indihome to customers. In this research, researchers used the C4.5 Algorithm data mining technique. The research data source used was by making observations and distributing questionnaires to customers of PT Telkom Pematangsiantar City. In this case, researchers used assessment attributes, namely service quality, accessibility and product quality. This research is expected to provide information and input to PT Telkom Indihome in the form of evaluations in improving network quality. The results of the research conducted by the author obtained the following conclusion, namely that Data Mining with the C4.5 Algorithm can classify the measurement of satisfaction levels of STIKOM Tunas Bangsa students who subscribe to Indihome WiFi. The accuracy results obtained by this research were 75.00% with the average student who subscribed to Indihome WiFi stating that they were satisfied.
Perbandingan Algoritma Decision Tree dan Support Vector Machine Dalam Pemilihan Calon Mahasiswa Penerima KIP-K

Authors:
raw_author_name: Rudi Heriansyah
raw_author_name: Shinta Puspasari

Abstract: KIP Kuliah is tuition assistance from the government for high school / equivalent graduates who have good academic potential but have economic limitations. There are many things that should be considered by universities before selecting prospective students who receive KIP Lecture so that selection can be done using machine learning and classification algorithms. In this research, two machine learning algorithms will be used including: Decision Tree and Support Vector Machine (SVM). Furthermore, these two algorithms will be tested and compared the final results. Both algorithms have different results. The highest level of accuracy, precision, recall, and F1 score is 100%. This value can be achieved by the Decision Tree algorithm because the dataset used is suitable for it to solve. Therefore, the Decision Tree algorithm is recommended to be used in selecting KIP College student candidates.
On Development of English Reading Teaching in China: A Literature Review (2003-2023)

Authors:
raw_author_name: Yingkun Zhang

Abstract: Reading is the foundation of mastering a language as well as a primary instrument to obtain information. Reading teaching has always been a hot topic in English language teaching (ELT). The present study sought to trace the characteristics of existing studies on English reading teaching in China. Based on research articles in the CNKI database collected by Chinese core journals and CSSCI in the past two decades, 603 studies on English reading teaching were selected and analyzed. Firstly, the Cite Space software is used to visualize the annual number of published papers, research hotspots and research trends. Subsequently, a detailed content analysis was conducted to distil and summarise the predominant research foci in English reading instruction in China over the past twenty years. The results show that the number of published papers on reading teaching research in China increases first and then fluctuates. The evolution of research topics is discerned to encompass three distinct stages and was influenced by prevailing educational policies. The research foci mainly include three aspects: English reading teaching modes, English reading teaching strategies and English reading ability. This comprehensive review aims to map out the current research landscape, shedding light on future scholarly inquiry in English reading teaching
A Privacy-Enhanced Scheme Within The Public Key Infrastructure For The Internet Of Things, Employing Elliptic Curve Diffie-Hellman (ECDH)

Authors:
raw_author_name: Abderrezzak Sebbah
raw_affiliation_string: STIC Laboratory, Department of Computer Science, Faculty of Science, University of Abou Bekr Belkaid, Tlemcen, Algeria
raw_author_name: Kadri Benamar
raw_affiliation_string: STIC Laboratory, Department of Telecommunications, Faculty of Technology, University of Abou Bekr Belkaid, Tlemcen, Algeria

Abstract: The Public Key Infrastructure (PKI) serves as the foundation for online security, particularly within the realm of the Internet of Things (IoT). It operates based on certified public keys that remain permanent but can be revoked when necessary, such as in the case of a change in ownership, compromise of the private key, or malicious activities. Although this method ensures secure key utilization with traceability, it also introduces a potential privacy risk due to the traceability and utilization of identity-based certificates. This approach is considered an innovative strategy for ensuring user confidentiality, integrity, authentication, and privacy in the context of the Internet of Things. The proposed solution integrates elliptic curves (ECDH) and traditional PKI to safeguard user privacy. It introduces two types of elliptic curve keys: long-term identity-based certified keys and dynamically generated temporary anonymous aliases. These aliases are seamlessly recorded by the certification authority, which maintains distinct directories for long-term and temporary keys. This dual-key approach enhances security while addressing the specific requirements of the Internet of Things.
Piercing All Translates of a Set of Axis-Parallel Rectangles

Authors:
raw_author_name: Adrian Dumitrescu
raw_affiliation_string: Algoresearch L.L.C., Milwaukee, USA

Abstract: For a given shape $S$ in the plane, one can ask what is the lowest possible density of a point set $P$ that pierces ("intersects", "hits") all translates of $S$. This is equivalent to determining the covering density of $S$ and as such is well studied. Here we study the analogous question for families of shapes where the connection to covering is altered. That is, we require that a single point set $P$ simultaneously pierces each translate of each shape from some family $\mathcal{F}$. We denote the lowest possible density of such an $\mathcal{F}$-piercing point set by $\pi_T(\mathcal{F})$. Specifically, we focus on families $\mathcal{F}$ consisting of axis-parallel rectangles. When $|\mathcal{F}|=2$ we exactly solve the case when one rectangle is more squarish than $2\times 1$, and give bounds (within $10\,\%$ of each other) for the remaining case when one rectangle is wide and the other one is tall. When $|\mathcal{F}|\ge 2$ we present a linear-time constant-factor approximation algorithm for computing $\pi_T(\mathcal{F})$ (with ratio $1.895$).
Realizable Learning is All You Need

Authors:
raw_author_name: Max Hopkins
raw_affiliation_string: University of California—San Diego
raw_author_name: Daniel Kane
raw_affiliation_string: University of California—San Diego
raw_author_name: Shachar Lovett
raw_affiliation_string: University of California—San Diego
raw_author_name: Gaurav Mahajan
raw_affiliation_string: University of California—San Diego

Abstract: The equivalence of realizable and agnostic learnability is a fundamental phenomenon in learning theory. With variants ranging from classical settings like PAC learning and regression to recent trends such as adversarially robust learning, it's surprising that we still lack a unified theory; traditional proofs of the equivalence tend to be disparate, and rely on strong model-specific assumptions like uniform convergence and sample compression. In this work, we give the first model-independent framework explaining the equivalence of realizable and agnostic learnability: a three-line blackbox reduction that simplifies, unifies, and extends our understanding across a wide variety of settings. This includes models with no known characterization of learnability such as learning with arbitrary distributional assumptions and more general loss functions, as well as a host of other popular settings such as robust learning, partial learning, fair learning, and the statistical query model. More generally, we argue that the equivalence of realizable and agnostic learning is actually a special case of a broader phenomenon we call property generalization: any desirable property of a learning algorithm (e.g. noise tolerance, privacy, stability) that can be satisfied over finite hypothesis classes extends (possibly in some variation) to any learnable hypothesis class.