Reviewerly/exHarmony huggingface dataset:
Synergizing cybersecurity in healthcare with novel bioprocessing for sustainable energy-centric water remediation

Authors:
raw_author_name: K. Sita Kumari
raw_affiliation_string: a Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, Andhra Pradesh, India
raw_author_name: Vasundhara Ghorpade
raw_affiliation_string: b Department of PSM, Krishna Vishwa Vidyapeeth, Karad, MS 415539, India
raw_author_name: Sulaima Lebbe Abdul Haleem
raw_affiliation_string: d Faculty of Technology, South Eastern University of Sri Lanka, Sri Lanka
raw_author_name: Sherzod Kiyosov
raw_affiliation_string: f Tashkent Institute of Finance, Tashkent, Uzbekistan

Abstract: Abstract The introduction of several novel chemicals, materials, and processes with varied levels of complexity in recent decades has been a result of the incredibly rapid technological advancement that has characterised those decades. This in turn has led to an increase in the number of pollutants released into the environment, necessitating their effective removal. The results of environmental monitoring reveal that several pollutants are exceeding the limit in ground water, which raises questions about the efficacy of the wastewater treatment methods now in use. This research proposes a novel method for sustainable smart grid (SG)-based energy analysis in water remediation and network cybersecurity analysis for healthcare application. Here the water-caused damages have been analysed based on a healthcare application using SG energy analysis and the network cyber security analysis is carried out using the federated blockchain model (SGEA_FB). Experimental analysis is carried out in terms of network integrity, throughput, scalability, and training accuracy. According to an analysis of the exergy destruction rates in every method component, power generation subsystem has the greatest exergy destruction rate at 15.4 MW. A training accuracy of 96%, throughput of 86%, network integrity of 76%, and scalability of 73% were all achieved by the suggested method.
The influence of leadership style, business ethics, and environmental performance toward corporate social performance and its impact on corporate financial performance

Authors:
raw_author_name: Harry Suharman
raw_affiliation_string: Universitas Padjadjaran, Bandung, Indonesia.
raw_author_name: Sugiono Poulus
raw_affiliation_string: Universitas Padjadjaran, Bandung, Indonesia.

Abstract: The study presents research clarifying the influence of leadership style, business ethics, and environmental performance on corporate social performance and its implications for corporate financial performance. A survey was done in manufacturing companies of Indonesia’s State Own Enterprise (SOE) and affiliates. Corporate performance is not solely directed towards shareholders or exclusively defined by financial measures; rather, it encompasses a focus on the impact of the organization on the society in which it operates. The style of leadership plays a crucial role in influencing the strategic management process, enabling the organization to implement successful strategies to realize its vision. Business ethics provide advantages to companies by fostering a favorable influence on their competitive edge in the corporate landscape. Environmental concerns are gaining growing significance among various corporate stakeholders, encompassing consumers, shareholders, prospective investors, creditors, regulatory bodies, employees, and the wider public. The finding of the study was that a leadership style had a positive influence on corporate social performance but a negative influence on corporate financial performance. Business ethics had a negative influence on financial performance but a positive influence on corporate social performance and environmental performance. Environmental performance had a negative influence on corporate social performance but positive influence on financial performance. Corporate social performance has a positive influence on corporate financial performance.
Automation of autonomous mobile robot docking based on the counter growth rapidly exploring random tree method

Authors:
raw_author_name: V. V. Golubov
raw_affiliation_string: MIREA – Russian Technological University
raw_author_name: S. V. Manko
raw_affiliation_string: MIREA – Russian Technological University

Abstract: Objectives. The article substantiates the relevance of automatic docking of autonomous mobile robots. Specific examples show that the implementation of the automatic docking functions of autonomous robots reveals the potential for creating multi-agent systems with a transformable structure. The aim of the work is to develop means for automatic docking of autonomous mobile robots in complex scenarios and an uncertain environment. Methods. The proposed approach to automating autonomous mobile robot docking is reduced to a modification of the counter-growth rapidly-exploring random tree (RRT) method. It is based on the parallel execution of a decentralized route planning algorithm with mutual coordination of distributed computing processes. The effectiveness of the complex of algorithmic and software tools developed was evaluated using computer and natural simulation methods. The final series of full-scale experiments was carried out on the example of JetBot AI kit Nvidia platforms for automatic docking of autonomous robots. This was performed using the means and methods of intelligent control, visual navigation, technical vision and wireless network communication. Results. The study analyzed the features of automatic docking as one of the tasks of group control of autonomous robots. This is part of multi-agent systems, capable of reconfiguring structures for purposeful changes to the existing set of functional properties and application possibilities. The study also proposes a decentralized modification of the counter-growth RRT method. This allows the movements of autonomous mobile robots in the course of their mutual approach and subsequent docking to be planned. A set of software-algorithmic tools was developed to automate the docking of autonomous robots. A series of model and full-scale experiments were carried out to confirm the effectiveness of the approach developed herein. Conclusions. The modification presented herein of the counter-growth RRT method, traditionally used for planning the movements of manipulators and mobile platforms, is complementary to the tasks it resolves. This enables the docking of autonomous robots to be automated. The results obtained open up the potential for universal schedulers with extended functionality for autonomous robot control systems to be designed.
Deep learning forecasting of large induced earthquakes via precursory signals

Authors:
raw_author_name: Vincenzo Convertito
raw_affiliation_string: Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Vesuviano, Naples, Italy
raw_author_name: Fabio Giampaolo
raw_affiliation_string: Department of Mathematics and Applications “R. Caccioppoli”, Univeristy of Naples Federico II, Naples, Italy
raw_author_name: Ortensia Amoroso
raw_affiliation_string: Department of Physics “E.R. Caianiello”, University of Salerno, Fisciano, SA, Italy
raw_author_name: Francesco Piccialli
raw_affiliation_string: Department of Mathematics and Applications “R. Caccioppoli”, Univeristy of Naples Federico II, Naples, Italy

Abstract: Abstract Precursory phenomena to earthquakes have always attracted researchers’ attention. Among the most investigated precursors, foreshocks play a key role. However, their prompt identification with respect to background seismicity still remains an issue. The task is worsened when dealing with low-magnitude earthquakes. Despite that, seismology and, in particular real-time seismology, can nowadays benefit from the use of Artificial Intelligence (AI) to face the challenge of effective precursory signals discrimination. Here, we propose a deep learning method named PreD-Net (precursor detection network) to address precursory signal identification of induced earthquakes. PreD-Net has been trained on data related to three different induced seismicity areas, namely The Geysers, located in California, USA, Cooper Basin, Australia, Hengill in Iceland. The network shows a suitable model generalization, providing considerable results on samples that were not used during the network training phase of all the sites. Tests on related samples of induced large events, with the addition of data collected from the Basel catalogue, Switzerland, assess the possibility of building a real-time warning strategy to be used to avoid adverse consequences during field operations.
High-efficiency reinforcement learning with hybrid architecture photonic integrated circuit

Authors:
raw_author_name: Jian-Xu Ma
raw_affiliation_string: TuringQ Co., Ltd., Shanghai, 200240, China
raw_author_name: Xiang-Yu Li
raw_affiliation_string: TuringQ Co., Ltd., Shanghai, 200240, China
raw_author_name: Jun-Jie Hu
raw_affiliation_string: Center for Integrated Quantum Information Technologies (IQIT), School of Physics and Astronomy and State Key Laboratory of Advanced Optical Communication Systems and Networks, Shanghai Jiao Tong University, Shanghai, 200240, China
raw_author_name: Xiao-Min Guo
raw_affiliation_string: TuringQ Co., Ltd., Shanghai, 200240, China
raw_author_name: Xi Tan
raw_affiliation_string: Center for Integrated Quantum Information Technologies (IQIT), School of Physics and Astronomy and State Key Laboratory of Advanced Optical Communication Systems and Networks, Shanghai Jiao Tong University, Shanghai, 200240, China
raw_author_name: Xian-Min Jin
raw_affiliation_string: Hefei National Laboratory, Hefei, 230088, China

Abstract: Abstract Reinforcement learning (RL) stands as one of the three fundamental paradigms within machine learning and has made a substantial leap to build general-purpose learning systems. However, using traditional electrical computers to simulate agent-environment interactions in RL models consumes tremendous computing resources, posing a significant challenge to the efficiency of RL. Here, we propose a universal framework that utilizes a photonic integrated circuit (PIC) to simulate the interactions in RL for improving the algorithm efficiency. High parallelism and precision on-chip optical interaction calculations are implemented with the assistance of link calibration in the hybrid architecture PIC. By introducing similarity information into the reward function of the RL model, PIC-RL successfully accomplishes perovskite materials synthesis task within a 3472-dimensional state space, resulting in a notable 56% improvement in efficiency. Our results validate the effectiveness of simulating RL algorithm interactions on the PIC platform, highlighting its potential to boost computing power in large-scale and sophisticated RL tasks.
Determining Intrusion Attacks Against Online Applications Using Cloud-Based Data Security

Authors:
raw_author_name: Shoba Rani P
raw_affiliation_string: R.M.D. Engineering College

Abstract: Cloud technology makes it possible for users to access information from anywhere, all the time, on any device, and that is the major cause of the many different types of assaults. In principle, multiple dangers, including data leakage, information leakage, and unauthorized information accessibility, are active in cloud environment layering. Modern technological advancements are made accessible on a daily basis through cloud technology. In the cloud, access control and encryption solutions are more complicated. Because of this greater level, security flaws in online applications and systems are more likely to occur. Somewhere at the ends of the end nodes, a malignant insider can carry out protection assaults. Nevertheless, problems with user privacy and data protection on cloud-based social networking sites continue to exist. Such problems are not known to users. On that social networking site, they post a variety of images, videos, and private information that endures even after eradication. However, some of the data that has been made public was intended to be kept private; as a result, online social information has significantly increased the risk of personally identifiable information leaking. The context of cloud technology depends on the customer capabilities such as quick storing and retrieving offered through cloud computing environments. Dependable cloud providers use a number of methodologies to deliver various digital services, creating a variety of security risks. In this paper, the study of determining intrusive cyber-attacks over the online applications using the cloud data security. Restricting access to shared resources is essential to prevent hackers from stealing vulnerabilities in cloud computing to get unauthorised access to a user's activities as well as information. Gaining access to customer information and obstructing the use of cloud computing are the primary objectives of intrusions on cloud services.
Divergent evolutionary rates of primate brain regions as revealed by genomics and transcriptomics

Authors:
raw_author_name: Chun-Yan Chen
raw_affiliation_string: School of Ecology and Environment, Northwestern Polytechnical University , Xi’an 710072 , China; Xi'an, 710072, China
raw_author_name: Guo-Jie Zhang
raw_affiliation_string: Center of Evolutionary & Organismal Biology, and Women's Hospital at Zhejiang University School of Medicine, Zhejiang University , Hangzhou 310000 , China; Liangzhu Laboratory, Zhejiang University Medical Center , Hangzhou 310000 , China
raw_author_name: Wen Wang
raw_affiliation_string: Key Laboratory of Genetic Evolution & Animal Models, Kunming Natural History Museum of Zoology, Kunming Institute of Zoology, Chinese Academy of Sciences , Kunming 650201 , China; Kunming, 650201, China; School of Ecology and Environment, Northwestern Polytechnical University , Xi’an 710072 , China; Xi'an, 710072, China

Abstract: Although the primate brain contains numerous functionally distinct structures that have experienced diverse genetic changes during the course of evolution and development, these changes remain to be explored in detail. Here we utilize two classic metrics from evolutionary biology, the evolutionary rate index (ERI) and the transcriptome age index (TAI), to investigate the evolutionary alterations that have occurred in each area and developmental stage of the primate brain. We observed a higher evolutionary rate for those genes expressed in the non-cortical areas during primate evolution, particularly in human, with the highest rate of evolution being exhibited at brain developmental stages between late infancy and early childhood. Further, the transcriptome age of the non-cortical areas was lower than that of the cerebral cortex, with the youngest age apparent at brain developmental stages between late infancy and early childhood. Our exploration of the evolutionary patterns manifest in each brain area and developmental stage provides important reference points for further research into primate brain evolution.
Recovery of polarization entanglement in partially coherent photonic qubits

Authors:
raw_author_name: Sakshi Rao
raw_affiliation_string: Experimental Quantum Interferometry and Polarization (EQUIP), Department of Physics, IIT Delhi, Hauz Khas, New Delhi 110016, India
raw_author_name: PREETI SHARMA
raw_affiliation_string: Experimental Quantum Interferometry and Polarization (EQUIP), Department of Physics, IIT Delhi, Hauz Khas, New Delhi 110016, India
raw_author_name: bashkar kanseri
raw_affiliation_string: Experimental Quantum Interferometry and Polarization (EQUIP), Department of Physics, IIT Delhi, Hauz Khas, New Delhi 110016, India

Abstract: Partially coherent photonic qubits, owing to their robustness in propagation through random media compared to fully coherent qubits, find applications in free-space communication, quantum imaging, and quantum sensing. However, the reduction of spatial coherence degrades entanglement in qubits, adversely affecting entanglement-based applications. We report the recovery of entanglement in the partially coherent photonic qubits generated using a spontaneous parametric downconversion process despite retaining their multimode nature. This study utilizes an electron multiplying charge-coupled device (EMCCD) to perform coincidence measurements, eliminating the need for raster scanning of single-pixel detectors, which simplifies optical alignment, enhances precision, and reduces time consumption. We demonstrate that the size of apertures used to select biphotons substantially impacts the visibility and S-parameter of polarization-entangled partially coherent qubits. The entanglement is recovered with partial spatial coherence properties by choosing small sizes of the apertures in the captured image plane. This study could help in the advancement of free-space quantum communication, quantum imaging, and quantum metrology.
PMONN: an optical neural network for photonic integrated circuits based on micro-resonator

Authors:
raw_author_name: Lianqing Zhu
raw_affiliation_string: Beijing Information Science and Technology University; Guangzhou Nansha Intelligent Photonic Sensing Research Institute
raw_author_name: Mingxin Yu
raw_affiliation_string: Beijing Information Science and Technology University; Guangzhou Nansha Intelligent Photonic Sensing Research Institute
raw_author_name: Lidan Lu
raw_affiliation_string: Beijing Information Science and Technology University; Guangzhou Nansha Intelligent Photonic Sensing Research Institute
raw_author_name: Penghao Hu

Abstract: We propose an improved optical neural network (ONN) circuit architecture based on conventional micro-resonator ONNs, called the Phase-based Micro-resonator Optical Neural Network (PMONN). PMONN's core architecture features a Convolutions and Batch Normalization (CB) unit, comprising a phase-based (PB) convolutional layer, a Depth-Point-Wise (DPW) convolutional layer, and a reconstructed Batch Normalization (RBN) layer. The PB convolution kernel uses modulable phase shifts of Add-drop MRRs as learnable parameters and their optical transfer function as convolution weights. The DPW convolution kernel amplifies PB convolution weights by learning the amplification factors. To address the internal covariate shift during training, the RBN layer normalizes DPW outputs by reconstructing the BN layer of the electronic neural network, which is then merged with the DPW layer in the test stage. We employ the tunable DAs in the architecture to implement the merged layer. PMONN achieves 99.15% and 91.83% accuracy on MNIST and Fashion-MNIST datasets, respectively. This work presents a method for implementing an optical neural network on the improved architecture based on MRRs and increases the flexibility and reusability of the architecture. PMONN has potential applications as the backbone for future optical object detection neural networks.
Resource Usage and Optimization Opportunities in Workflows of GitHub Actions

Authors:
raw_author_name: Islem Bouzenia
raw_affiliation_string: University of Stuttgart, Stuttgart, Germany
raw_author_name: Michael Pradel
raw_affiliation_string: University of Stuttgart, Stuttgart, Germany

Abstract: Continuous integration and continuous delivery (CI/CD) has become a prevalent practice in software development. GitHub Actions is emerging as a popular platform for implementing CI/CD pipelines, called workflows, especially because the platform offers 2,000 minutes of computation for free to public repositories each month. To understand what these resources are used for and whether CI/CD could be more efficient, this paper presents the first comprehensive empirical study of resource usage and optimization opportunities of GitHub Action workflows. Our findings show that CI/CD imposes significant costs, e.g., $504 per year for an average paid-tier repository. The majority of the used resources is consumed by testing and building (91.2%), which is triggered by pull requests (50.7%), pushes (30.9%), and regularly scheduled workflows (15.5%). While existing optimizations, such as caching (adopted by 32.9% of paid-tier repositories), demonstrate a positive impact, they overall remain underutilized. This result underscores the need for enhanced documentation and tools to guide developers toward more resource-efficient workflows. Moreover, we show that relatively simple changes in the platform, such as deactivating scheduled workflows when repositories are inactive, could result in reductions of execution time between 1.1% and 31.6% over the impacted workflows. Overall, we envision our findings to help improve the resource efficiency of CI/CD pipelines.
Revealing Hidden Threats: An Empirical Study of Library Misuse in Smart Contracts

Authors:
raw_author_name: Jiachi Chen
raw_affiliation_string: School of software engineering, Sun Yat-sen University, Zhuhai, Guangdong, China
raw_author_name: Zigui Jiang
raw_affiliation_string: School of software engineering, Sun Yat-sen University, Zhuhai, Guangdong, China
raw_author_name: Zibin Zheng
raw_affiliation_string: School of software engineering, Sun Yat-sen University, Zhuhai, Guangdong, China

Abstract: Smart contracts are Turing-complete programs that execute on the blockchain. Developers can implement complex contracts, such as auctions and lending, on Ethereum using the Solidity programming language. As an object-oriented language, Solidity provides libraries within its syntax to facilitate code reusability and reduce development complexity. Library misuse refers to the incorrect writing or usage of libraries, resulting in unexpected results, such as introducing vulnerabilities during library development or incorporating an unsafe library during contract development. Library misuse could lead to contract defects that cause financial losses. Currently, there is a lack of research on library misuse. To fill this gap, we collected more than 500 audit reports from the official websites of five audit companies and 223,336 real-world smart contracts from Etherscan to measure library popularity and library misuse. Then, we defined eight general patterns for library misuse; three of them occurring during library development and five during library utilization, which covers the entire library lifecycle. To validate the practicality of these patterns, we manually analyzed 1,018 real-world smart contracts and publicized our dataset. We identified 905 misuse cases across 456 contracts, indicating that library misuse is a widespread issue. Three patterns of misuse are found in more than 50 contracts, primarily due to developers lacking security awareness or underestimating negative impacts. Additionally, our research revealed that vulnerable libraries on Ethereum continue to be employed even after they have been deprecated or patched. Our findings can assist contract developers in preventing library misuse and ensuring the safe use of libraries.