Reviewerly/exHarmony huggingface dataset:
Cybersecurity Issues in Crowdsourcing Engineering Initiatives

Authors:
raw_author_name: Donna Schaeffer
raw_affiliation_string: Marymount University
raw_author_name: Jillian Drake
raw_affiliation_string: Marymount University

Abstract: Abstract Cybersecurity Issues in Crowdsourcing Engineering Initiatives As crowdsourced engineering-related projects become more wide-spread and global, we must pay attention to cybersecurity issues that can emerge. Sharing data is fundamental to crowdsourcing, and that requires attention to protecting privacy. This issue is exacerbated if participants use common technologies, e.g., cell phones, that are used for other purposes and may be subject to cybersecurity vulnerabilities. Cybersecurity issues can arise during recruitment, by which social media is a prevalent tool, to projects in that bad actors can solicit input into malicious and false projects. Furthermore, the results of crowdsourced engineering projects may provide foundations for policy and legislation; thus, integrity must be maintained and bad actors prevented from manipulating data and/or results. Governance of crowdsourced projects must include attention to cybersecurity issues, regardless of their scope and scale. The exploitation of cybersecurity vulnerabilities are often result from ethical lapses. Just as engineers respect engineering professional ethics, so must citizens who participate in gathering data. In this paper, we will share a taxonomy that identifies how professional engineering ethics are represented and brought to life in 21st century crowdsourced engineering projects, which include commercial initiatives from Anheuser-Busch, Nokia, and FedEx as well as research initiatives like the Digital Humanitarian Network, the Humanitarian OpenStreetMap Team, and KeralaRescue.
Sentiment Analysis Through the Application of Machine Learning Algorithms

Authors:
raw_author_name: Dattatray G. Takale
raw_affiliation_string: Assistant Professor, Department of Computer Engineering, Vishwakarma Institute of Information Technology,  Pune, Maharashtra, India

Abstract: Sentiment analysis, also called opinion mining. It is a natural language processing (NLP) task that involves fixing the sentiment tone expressed in a piece of text including a review, tweet, post, comment, or news article, using machine learning algorithms. There are various social networking sites where people express their views, opinions, and emotions freely. All such posts are recorded and analyzed to determine emotions of the people. Present study recovers posts and finds emotions and polarity of posts. To determine emotions and polarity of posts, various techniques are used.
Comparison and optimization of different routing methods for meshed QKD networks using trusted nodes

Authors:
raw_author_name: Matthias Gunkel
raw_affiliation_string: Deutsche Telekom Technik GmbH
raw_author_name: Stephan Pachnicke

Abstract: Quantum key distribution (QKD) appears as a promising technique for encrypted communication, preserving security even in the presence of a future powerful quantum computer. At the same time, communication infrastructure becomes increasingly complex, and the exponentially increasing traffic volume makes the application of QKD a challenging task. In addition, current hardware for QKD modules is still subject to research to gain maturity, to guarantee secret key rates, and to mitigate key rate fluctuations and instabilities. Our study analyzes and optimizes five routing algorithms to efficiently use the secret keys as a resource in meshed national-wide networks. By fulfilling distinct security and performance levels, the algorithms are compared with respect to key performance indicators and optimized for blocking probabilities, load balancing, or the management traffic amount. Depending on the application, our study allows operators to choose a well-suited algorithm and gives performance estimates, including an assessment of the performance gap to globally optimized algorithms.
Hubble Meets Webb: Image-to-Image Translation in Astronomy

Authors:
raw_author_name: Vitaliy Kinakh
raw_affiliation_string: Department of Computer Science, University of Geneva, 1227 Carouge, Switzerland
raw_author_name: Yury Belousov
raw_affiliation_string: Department of Computer Science, University of Geneva, 1227 Carouge, Switzerland
raw_author_name: Mariia Drozdova
raw_affiliation_string: Department of Computer Science, University of Geneva, 1227 Carouge, Switzerland
raw_author_name: Taras Holotyak
raw_affiliation_string: Department of Computer Science, University of Geneva, 1227 Carouge, Switzerland
raw_author_name: Slava Voloshynovskiy
raw_affiliation_string: Department of Computer Science, University of Geneva, 1227 Carouge, Switzerland

Abstract: This work explores the generation of James Webb Space Telescope (JWSP) imagery via image-to-image translation from the available Hubble Space Telescope (HST) data. Comparative analysis encompasses the Pix2Pix, CycleGAN, TURBO, and DDPM-based Palette methodologies, assessing the criticality of image registration in astronomy. While the focus of this study is not on the scientific evaluation of model fairness, we note that the techniques employed may bear some limitations and the translated images could include elements that are not present in actual astronomical phenomena. To mitigate this, uncertainty estimation is integrated into our methodology, enhancing the translation's integrity and assisting astronomers in distinguishing between reliable predictions and those of questionable certainty. The evaluation was performed using metrics including MSE, SSIM, PSNR, LPIPS, and FID. The paper introduces a novel approach to quantifying uncertainty within image translation, leveraging the stochastic nature of DDPMs. This innovation not only bolsters our confidence in the translated images but also provides a valuable tool for future astronomical experiment planning. By offering predictive insights when JWST data are unavailable, our approach allows for informed preparatory strategies for making observations with the upcoming JWST, potentially optimizing its precious observational resources. To the best of our knowledge, this work is the first attempt to apply image-to-image translation for astronomical sensor-to-sensor translation.
Blind watermarking algorithm with 2D-SCCM chaotic system encryption based on SURF and RHFMs

Authors:
raw_author_name: Xiu-fang Feng
raw_author_name: Lin Teng
raw_author_name: Chun-peng Wang
raw_author_name: Hao Zhang

Abstract: Abstract In this paper, a digital watermarking algorithm was proposed based on local feature regions and radial harmonic Fourier moments. It used a quantized modulation algorithm to embed the watermark information into the frequency domain information of the image. Compared with traditional watermark directedly embedded into the pixel values, the algorithm enhanced the invisibility of the watermark. Many existing digital watermarking algorithms neglected the security of the embedding process, because of more attention on the effect of post-embedding and extraction. To overcome this problem, we proposed a two-dimensional hyperchaotic system named 2D-SCCM chaotic system, which used classical sine mapping and cubic mapping for coupling. Before embedding, the correlation among the pixels of the watermarked image was disrupted, resulting in a full process security enhancement. Combining the clustering algorithm with feature point extraction, the obtained local feature region was more stable and suitable for watermark embedding. Results showed that the improved watermark embedding algorithm was more effective for resisting geometric attacks such as cropping attacks. Moreover, the PSNR values of the final watermark-embedded images were all greater than 50 dB, indicating a good balance between imperceptibility and robustness.&amp;#xD;
The Bounded Gaussian Mechanism for Differential Privacy

Authors:
raw_author_name: Matthew Hale
raw_affiliation_string: University of Florida

Abstract: The Gaussian mechanism is one differential privacy mechanism commonly used to protect numerical data. However, it may be ill-suited to some applications because it has unbounded support and thus can produce invalid numerical answers to queries, such as negative ages or human heights in the tens of meters. One can project such private values onto valid ranges of data, though such projections lead to the accumulation of private query responses at the boundaries of such ranges, thereby harming accuracy. Motivated by the need for both privacy and accuracy over bounded domains, we present a bounded Gaussian mechanism for differential privacy, which has support only on a given region. We present both univariate and multivariate versions of this mechanism and illustrate a significant reduction in variance relative to comparable existing work.
Quantum differential privacy under noise channels

Authors:
raw_author_name: Ya-Ru Bai
raw_affiliation_string: Department of Big Data, Zhejiang University of Science and Technology, No.318, Liuhe Road, West Lake Street, Hangzhou, Zhejiang, Hangzhou, 310023, CHINA
raw_author_name: Yuanhong Tao
raw_affiliation_string: Department of Big Data, Zhejiang University of Science and Technology, No.318, Liuhe Road, West Lake Street, Hangzhou, Zhejiang, Hangzhou, 310023, CHINA
raw_author_name: Hui Zhang
raw_affiliation_string: Zhejiang University of Science and Technology, No. 318, Liuhe Road, West Lake State, Hangzhou, Zhejiang, 310023, CHINA
raw_author_name: Shao-Ming Fei
raw_affiliation_string: School of Mathematical Sciences, Capital Normal University, 105 North West Third Ring Road, Beijing, Beijing, 100037, CHINA

Abstract: Abstract Quantum differential privacy is a way to protect privacy in quantum computing. It perturbs the final state of quantum computing by a specific quantum noise channel and makes the output useless to the attackers, so as to achieve privacy protection. In this paper, we study the quantum differential privacy under three channels: bit flip channel, phase flip channel and depolarizing channel. We calculate the privacy parameters for these three channels based on the proportional distance. We show the privacy protection capability by calculating the decay rates of the trace distances of two arbitrary quantum states before and after passing through the channels. Under the same privacy budget, we compare the fidelities, the trace distances between the real outputs and the outputs after entering the channels, and analyze the privacy protection performance of different quantum channels in detail.
Programming Computational Electromagnetic Applications Assisted by Large Language Models [Em Programmer’s Notebook]

Authors:
raw_author_name: Leandro Carísio Fernandes
raw_affiliation_string: Federal Court of Accounts, Bras&#x00ED;lia, Brazil

Abstract: This article discusses the possibilities and limitations of using large language models (LLMs) in software development with applications in computational electromagnetics (EM). Three tasks are discussed: code translation, code generation, and code description. The tests showed that LLMs are generally very useful. Even when errors occurred, they could provide useful hints to find a solution.
Low-Cost Training of Image-to-Image Diffusion Models with Incremental Learning and Task/Domain Adaptation

Authors:
raw_author_name: Beatriz Otero
raw_affiliation_string: Department of Computer Architecture, Universitat Politècnica de Catalunya, Jordi Girona 1-3, 08034 Barcelona, Spain
raw_author_name: Ruben Tous
raw_affiliation_string: Department of Computer Architecture, Universitat Politècnica de Catalunya, Jordi Girona 1-3, 08034 Barcelona, Spain

Abstract: Diffusion models specialized in image-to-image translation tasks, like inpainting and colorization, have outperformed the state of the art, yet their computational requirements are exceptionally demanding. This study analyzes different strategies to train image-to-image diffusion models in a low-resource setting. The studied strategies include incremental learning and task/domain transfer learning. First, a base model for human face inpainting is trained from scratch with an incremental learning strategy. The resulting model achieves an FID score almost equivalent to that of its batch learning equivalent while significantly reducing the training time. Second, the base model is fine-tuned to perform a different task, image colorization, and, in a different domain, landscape images. The resulting colorization models showcase exceptional performances with a minimal number of training epochs. We examine the impact of different configurations and provide insights into the ability of image-to-image diffusion models for transfer learning across tasks and domains.
EnigmaScan: Streamlining Matrix Puzzle Solving through Image Recognition

Authors:
raw_author_name: Abhyuday Rai
raw_author_name: Aditya Mishra
raw_author_name: Vaibhav Saini
raw_author_name: Shiv Naresh Shivhare

Abstract: In an era increasingly shaped by technology, the enduring allure of word puzzles, such as word searches, crosswords, and Sudoku, remains steadfast. Yet, manually tackling these puzzles can prove to be a time-intensive and frequently exasperating endeavor. To meet this challenge, we suggest employing image recognition-based algorithms designed for puzzle-solving, specifically targeting Sudoku, word searches, and crossword puzzles. EnigmaScan is publicly accessible at https://github.com/theNewtonCode/EnigmaScan, allowing users to promptly solve their puzzles in real-time by uploading snapshots of unsolved Sudoku, word searches, or crosswords. This ground-breaking tool is designed to streamline and hasten the puzzle-solving process for users of all proficiency levels, thereby promoting a more enjoyable and accessible interaction with these timeless pastimes. The proposed algorithms exhibit remarkable efficiency, solving these puzzles within seconds and achieving an accuracy of 100%.
Positive Solution of Moving Platform Position Based on Bat-Iterative Algorithm

Authors:
raw_author_name: Lishan Jia
raw_affiliation_string: College of Electronic Information and Automation, Civil Aviation University of China, Tianjin (300300), China
raw_author_name: Haijun Xu
raw_affiliation_string: College of Aeronautical Engineering, Civil Aviation University of China, Tianjin (300300), China

Abstract: To address the forward position problem of a 6-DOF moving platform, a Bat-iterative method leveraging the Bat algorithm was proposed as a solution. By combining the Bat algorithm with the traditional Newton-Raphson iterative algorithm, the convergence speed and accuracy of the algorithm can be improved while the convergence and global search ability are guaranteed. The concrete implementation process of the algorithm is given, and the corresponding positive solution problem of 6-DOF parallel mechanism is solved by using the algorithm, and the corresponding operation example is given. At the same time, the inverse solution method is used to obtain the data set for error analysis and comparison. In summary, the comparison results demonstrate that the Bat-iterative algorithm offers notable benefits such as fast computation speed and high precision. As a result, it proves to be well-suited for practical engineering applications, meeting the necessary requirements effectively.
Unary Quantum Finite State Automata with Control Language

Authors:
raw_author_name: Carlo Mereghetti
raw_affiliation_string: Dipartimento di Informatica “Giovanni Degli Antoni”, Università degli Studi di Milano, Via Celoria 18, 20133 Milano, Italy
raw_author_name: Beatrice Palano
raw_affiliation_string: Dipartimento di Informatica “Giovanni Degli Antoni”, Università degli Studi di Milano, Via Celoria 18, 20133 Milano, Italy

Abstract: We study quantum finite automata with control language (QFCs), a theoretical model for finite memory hybrid systems coupling a classical computational framework with a quantum component. We constructively show how to simulate measure-once, measure-many, reversible, and Latvian QFAs by QFCs, emphasizing the size cost of such simulations. Next, we prove the decidability of testing the periodicity of the stochastic events induced by a given QFC. Thanks to our QFA simulations, we can extend such a decidability result to measure-once, measure-many, reversible, and Latvian QFAs as well. Finally, we focus on comparing the size efficiency of quantum and classical finite state automata on unary regular language recognition. We show that unary regular languages can be recognized by isolated cut point QFCs for which the size is generally quadratically smaller than the size of equivalent DFAs.
Asymmetric VR Game Subgenres: Implications for Analysis and Design

Authors:
raw_author_name: Amanda Lee Hughes
raw_affiliation_string: Computer Science, Brigham Young University, Provo, UT 84602, USA
raw_author_name: Derek L. Hansen
raw_affiliation_string: Electrical & Computer Engineering, Brigham Young University, Provo, UT 84602, USA

Abstract: This paper identifies subgenres of asymmetric virtual reality (AVR) games and proposes the AVR Game Genre (AVRGG) framework for developing AVR games. We examined 66 games “in the wild” to develop the AVRGG and used it to identify 5 subgenres of AVR games including David(s) vs. Goliath, Hide and Seek, Perspective Puzzle, Order Simulation, and Lifeline. We describe these genres, which account for nearly half of the 66 games reviewed, in terms of the AVRGG framework that highlights salient asymmetries in the mechanics, dynamics, and aesthetics categories. To evaluate the usefulness of the AVRGG framework, we conducted four workshops (two with the AVRGG framework and two without) with novice game designers who generated 16 original AVR game concepts. Comparisons between the workshop groups, observations of the design sessions, focus groups, and surveys showed the promise and limitations of the AVRGG framework as a design tool. We found that novice designers were able to understand and apply the AVRGG framework after only a brief introduction. The observations indicated two primary challenges that AVR designers face: balancing the game between VR and non-VR player(s) and generating original game concepts. The AVRGG framework helped overcome the balancing concerns due to its ability to inspire novice game designers with example subgenres and draw attention to the asymmetric mechanics and competitive/cooperative nature of games. While half of those who used the AVRGG framework to design with created games that fit directly into existing subgenres, the other half viewed the subgenres as “creative constraints” useful in jumpstarting novel game designs that combined, modified, or purposefully avoided existing subgenres. Additional benefits and limitations of the AVRGG framework are outlined in the paper.
Sustainable Optimizing Performance and Energy Efficiency in Proof of Work Blockchain: A Multilinear Regression Approach

Authors:
raw_author_name: Meennapa Rukhiran
raw_affiliation_string: Faculty of Social Technology, University of Technology Tawan-ok, Chanthaburi 20110, Thailand
raw_author_name: Paniti Netinant
raw_affiliation_string: College of Digital Innovation Technology, Rangsit University, Pathum Thani 12000, Thailand

Abstract: The energy-intensive characteristics of the computations performed by graphics processing units (GPUs) in proof-of-work (PoW) blockchain technology are readily apparent. The optimization of GPU feature configuration is a complex subject that significantly impacts a system’s energy consumption and performance efficiency. The primary objectives of this study are to examine and improve the energy consumption characteristics of GPUs, which play a crucial role in the functioning of blockchains and the mining of cryptocurrencies. This study examines the complex relationship between GPU configurations and system architecture components and their effects on energy efficiency and sustainability. The methodology of this study conducts experiments involving various GPU models and mining software, evaluating their effectiveness across various configurations and environments. Multilinear regression analysis is used to study the complex relationships between critical performance indicators like power consumption, thermal dynamics, core speed, and hash rate and their effects on energy efficiency and performance. The results reveal that strategically adjusting GPU hardware, software, and configuration can preserve substantial energy while preserving computational efficiency. GPU core speed, temperature, core memory speed, ETASH algorithms, fan speed, and energy usage significantly affected the dependent computational-efficiency variable (p = 0.000 and R2 = 0.962) using multilinear regression analysis. GPU core speed, temperature, core memory speed, fan speed, and energy usage significantly affected efficient energy usage (p = 0.000 and R2 = 0.989). The contributions of this study offer practical recommendations for optimizing the feature configurations of GPUs to reduce energy consumption, mitigate the environmental impacts of blockchain operations, and contribute to the current research on performance in PoW blockchain applications.
An In-Depth Analysis of Collision Avoidance Path Planning Algorithms in Autonomous Vehicles

Authors:
raw_author_name: Ramesh Chandra Poonia
raw_affiliation_string: Department of
Computer Science, CHRIST (Deemed to be University), Delhi-NCR, 201003, India

Abstract: Abstract: Path planning is a way to define the motion of an autonomous surface vehicle (ASV) in any existing obstacle environment to enable the vehicle's movement by setting directions to avoid that can react to the obstacles in the vehicle's path. A good, planned path perceives the environment to the extent of uncertainty and tries to build or adapt its change in the path of motion. Efficient path planning algorithms are needed to alleviate deficiencies, which are to be modified using the deterministic path that leads the ASV to reach a goal or a desired location while finding an optimal solution has become a challenge in the field of optimization along with a collision-free path, making path planning a critical thinker. The traditional algorithms have a lot of training and computation, making it difficult in a realistic environment. This review paper explores the different techniques available in path planning and collision avoidance of ASV in a dynamic environment. The objective of good path planning and collision avoidance for a dynamic environment is compared effectively with the existing obstacle’s movement of different vehicles. Different path planning technical approaches are compared with their performance and collision avoidance for unmanned vehicles in marine environments by early researchers. This paper gives us a clear idea for developing an effective path planning technique to overcome marine accidents in the dynamic ocean environment while choosing the shortest, obstacle-free path for Autonomous Surface Vehicles that can reduce risk and enhance the safety of unmanned vehicle movement in a harsh ocean environment.
ENHANCING SENTIMENT ANALYSIS ACCURACY IN DELIVERY SERVICES THROUGH SYNTHETIC MINORITY OVERSAMPLING TECHNIQUE

Authors:
raw_author_name: Septi Andryana
raw_affiliation_string: Universitas Nasional

Abstract: Paxel is one of the delivery services that use the application. On Google Play, there are more than 10 thousand users leaving reviews. From this review data, a sentiment analysis was then carried out to determine the level of user satisfaction with Paxel's services. The methods used in this study are Random Forest (RF) and Support Vector Machine (SVM), as well as applying Synthetic Minority Oversampling Technique (SMOTE) to overcome data imbalance. The results showed that the method testing by dividing the data into two, namely training data and testing data by 80:20, stated that by applying the SMOTE, a higher accuracy value was obtained, where the accuracy of the RF method reached 91%, and the SVM method reached 87%. The level of user satisfaction with Paxel services tends to be neutral. This can be seen in the classification of the RF method with F1-Score values for the Positive class 89%, Neutral class 93%, and Negative class 92%.