Reviewerly/exHarmony huggingface dataset:
A method to increase video streaming speed and efficiency of storage video using multithreading and Random Access Memory

Authors:
raw_author_name: Hoa Doan Van
raw_affiliation_string: Institute of Information Technology, Academy of Military Science and Technology
raw_author_name: None Hoa Tat Thang
raw_affiliation_string: Institute of Information and Communication Technology, Military Technical Academy

Abstract: There are numerous vital applications for online video storage and network transmission today. With exceptional scalability and browser and device compatibility, Hypertext Transfer Protocol (HTTP) live streaming (HLS) is today's most popular video and audio transmission protocol. In the article, the authors proposed a method of using multithreading and Random Access Memory (RAM) to store and transmit videos to help the server achieve higher speed, improve video transmission speed and video storage efficiency. The results of this research will help multimedia server developers evaluate the appropriate balance of resource usage while still ensuring the quality of online video transmission with a large number of videos and viewers.
Threats, Attacks, and Cryptography Frameworks of Cybersecurity in Critical Infrastructures

Authors:
raw_author_name: Kyriaki Tsantikidou
raw_affiliation_string: SCYTALE Group, Computer Engineering and Informatics Department, University of Patras, 26504 Patra, Greece
raw_author_name: Nicolas Sklavos
raw_affiliation_string: SCYTALE Group, Computer Engineering and Informatics Department, University of Patras, 26504 Patra, Greece

Abstract: Critical Infrastructures (CIs), such as healthcare facilities, power grids, transportation systems, and financial institutions, are vital components of a functioning society, with the economy and safety being dependent on them. Nevertheless, they have become increasingly vulnerable to cyber threats and attacks in recent years. The main reason is their inability to quickly adapt to technological changes, employ updated cryptographic frameworks, and implement a thoroughly secure architecture based on their characteristics. In this study, the unique complexities of these systems are highlighted. Various verified cyberattacks that were executed against CIs in recent years are analyzed. Moreover, the general framework of CIs is demonstrated together with the employed technologies and cryptographic primitives. A thorough architecture of said technologies is developed to better understand the targeted components and easily identify potentially hidden threats. Afterwards, threat, adversary, and attack models that target critical systems and services are designed. The purpose is a better comprehension of the systems’ vulnerabilities, attack structures, motives, and targets for assisting CIs’ designers in creating secure frameworks and mechanisms, with the ability to mitigate such threats. Lastly, security controls and cryptography frameworks are demonstrated together with efficient mitigation architectures and implementations from the research community.
Quantum complementarity from a measurement-based perspective

Authors:
raw_author_name: Shan Huang
raw_affiliation_string: Hefei National Laboratory, University of Science and Technology of China, Hefei 230088, China; Institute for Brain Sciences and Kuang Yaming Honors School, Nanjing University, Nanjing 210023, China; National Laboratory of Solid State Microstructures and School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
raw_author_name: Wen-Bo Liu
raw_affiliation_string: National Laboratory of Solid State Microstructures and School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
raw_author_name: Hua-Lei Yin
raw_affiliation_string: Department of Physics and Beijing Key Laboratory of Opto-Electronic Functional Materials and Micro-Nano Devices, Key Laboratory of Quantum State Construction and Manipulation (Ministry of Education), Renmin University of China, Beijing 100872, China; National Laboratory of Solid State Microstructures and School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
raw_author_name: Zeng-Bing Chen
raw_affiliation_string: National Laboratory of Solid State Microstructures and School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
raw_author_name: Shengjun Wu
raw_affiliation_string: Hefei National Laboratory, University of Science and Technology of China, Hefei 230088, China; Institute for Brain Sciences and Kuang Yaming Honors School, Nanjing University, Nanjing 210023, China; National Laboratory of Solid State Microstructures and School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China

Abstract: One of the most remarkable features of quantum physics is that attributes of quantum objects, such as the wavelike and particlelike behaviors of single photons, can be complementary in the sense that they are equally real but cannot be observed simultaneously. Quantum measurements, serving as windows providing views into the abstract edifice of quantum theory, are basic tools for manifesting the intrinsic behaviors of quantum objects. However, a quantitative formulation of complementarity that highlights its manifestations in general measurement scenarios remains elusive. Here we develop a general framework for demonstrating quantum complementarity in the form of information exclusion relations (IERs), which incorporates the wave-particle duality relations as particular examples. Moreover, we explore the applications of our theory in entanglement detection and elucidate that our IERs lead to an extended form of entropic uncertainty relations, providing insights into the connection between quantum complementarity and the preparation uncertainty.
A randomized operator splitting scheme inspired by stochastic optimization methods

Authors:
raw_author_name: Monika Eisenmann
raw_affiliation_string: Centre for Mathematical Sciences, Lund University, P.O. Box 118, 22100, Lund, Sweden
raw_author_name: Tony Stillfjord
raw_affiliation_string: Centre for Mathematical Sciences, Lund University, P.O. Box 118, 22100, Lund, Sweden

Abstract: Abstract In this paper, we combine the operator splitting methodology for abstract evolution equations with that of stochastic methods for large-scale optimization problems. The combination results in a randomized splitting scheme, which in a given time step does not necessarily use all the parts of the split operator. This is in contrast to deterministic splitting schemes which always use every part at least once, and often several times. As a result, the computational cost can be significantly decreased in comparison to such methods. We rigorously define a randomized operator splitting scheme in an abstract setting and provide an error analysis where we prove that the temporal convergence order of the scheme is at least 1/2. We illustrate the theory by numerical experiments on both linear and quasilinear diffusion problems, using a randomized domain decomposition approach. We conclude that choosing the randomization in certain ways may improve the order to 1. This is as accurate as applying e.g. backward (implicit) Euler to the full problem, without splitting.
Evaluation of security framework for BYOD device in cloud environment

Authors:
raw_author_name: V. Mary Amala Bai
raw_affiliation_string: Department of Information Technology, Noorul Islam Centre for Higher Education, Kumaracoil, India

Abstract: The widespread practice of Bring your own device (BYOD) to work significantly raises cybersecurity risks. All organization’s employees and employers will greatly benefit from this trend. The growth of spyware, malware and other dubious downloads on individual devices has compelled the government to review its data security guidelines. Without user knowledge, hazardous apps are downloaded to personal devices. Both people and governments could suffer tragic consequences as a result of this. In this situation, BYODs are problematic since they can alter policies without permission and release private information. The main goal of the research was to detect fraudulent communications coming from BYOD environments. A network monitoring and managing information configuration settings of mobile devices in the network is established by providing policies and authenticating endpoint devices in the BYOD network using a mix of NAC and MDM. This framework deals with Security Manager (SaaS) as the second module is briefly described. The study's early results were positive and suggested that the framework would lessen access control-related issues.
Quantum Molecular Devices

Authors:
raw_author_name: Ronnie Kosloff
raw_affiliation_string: Institute of Chemistry, Hebrew University of Jerusalem, Jerusalem 9190401, Israel

Abstract: Miniaturization has been the driving force in contemporary technologies. However, two main obstacles limit further progress: additional reduction in size has reached its quantum limit, and lithography has reached its threshold. Future progress requires tackling three challenges: chemical synthesis of a complete device, active cooling for exploiting quantum characteristics, and quantum coherent control for operation. Chemical synthesis replaces the current top-bottom approach to manufacturing with bottom-up synthesis from elementary building blocks. New ultracold synthetic methods should be developed. An additional challenge is the active cooling of molecules, where the bottleneck is entropy removal. Notably, the current solution, namely, diffusion, is too slow. A coherent approach offers a possible solution; specifically, quantum coherent control is the method of choice for manipulating ultracold matter. Finally, the many degrees of freedom of molecules should be an asset that allows the design and implementation of complex tasks such as sensing communication and computing.
Blockchain-based deep learning in IoT, healthcare and cryptocurrency price prediction: a comprehensive review

Authors:
raw_author_name: Shefali Arora
raw_author_name: Ruchi Mittal
raw_author_name: Avinash K. Shrivastava
raw_author_name: Shivani Bali

Abstract: Purpose Deep learning (DL) is on the rise because it can make predictions and judgments based on data that is unseen. Blockchain technologies are being combined with DL frameworks in various industries to provide a safe and effective infrastructure. The review comprises literature that lists the most recent techniques used in the aforementioned application sectors. We examine the current research trends across several fields and evaluate the literature in terms of its advantages and disadvantages. Design/methodology/approach The integration of blockchain and DL has been explored in several application domains for the past five years (2018–2023). Our research is guided by five research questions, and based on these questions, we concentrate on key application domains such as the usage of Internet of Things (IoT) in several applications, healthcare and cryptocurrency price prediction. We have analyzed the main challenges and possibilities concerning blockchain technologies. We have discussed the methodologies used in the pertinent publications in these areas and contrasted the research trends during the previous five years. Additionally, we provide a comparison of the widely used blockchain frameworks that are used to create blockchain-based DL frameworks. Findings By responding to five research objectives, the study highlights and assesses the effectiveness of already published works using blockchain and DL. Our findings indicate that IoT applications, such as their use in smart cities and cars, healthcare and cryptocurrency, are the key areas of research. The primary focus of current research is the enhancement of existing systems, with data analysis, storage and sharing via decentralized systems being the main motivation for this integration. Amongst the various frameworks employed, Ethereum and Hyperledger are popular among researchers in the domain of IoT and healthcare, whereas Bitcoin is popular for research on cryptocurrency. Originality/value There is a lack of literature that summarizes the state-of-the-art methods incorporating blockchain and DL in popular domains such as healthcare, IoT and cryptocurrency price prediction. We analyze the existing research done in the past five years (2018–2023) to review the issues and emerging trends.
Automating Ground Control Point Detection in Drone Imagery: From Computer Vision to Deep Learning

Authors:
raw_author_name: Klaas Pauly
raw_affiliation_string: Remote Sensing Unit, Flemish Institute for Technological Research (VITO), Boeretang 200, B-2400 Mol, Belgium

Abstract: Drone-based photogrammetry typically requires the task of georeferencing aerial images by detecting the center of Ground Control Points (GCPs) placed in the field. Since this is a very labor-intensive task, it could benefit greatly from automation. In this study, we explore the extent to which traditional computer vision approaches can be generalized to deal with variability in real-world drone data sets and focus on training different residual neural networks (ResNet) to improve generalization. The models were trained to detect single keypoints of fixed-sized image tiles with a historic collection of drone-based Red–Green–Blue (RGB) images with black and white GCP markers in which the center was manually labeled by experienced photogrammetry operators. Different depths of ResNets and various hyperparameters (learning rate, batch size) were tested. The best results reached sub-pixel accuracy with a mean absolute error of 0.586. The paper demonstrates that this approach to drone-based mapping is a promising and effective way to reduce the human workload required for georeferencing aerial images.
Exploring semantic consistency in unpaired image translation to generate data for surgical applications

Authors:
raw_author_name: Dominik Rivoir
raw_affiliation_string: Department of Translational Surgical Oncology, National Centre for Tumor Diseases(NCT/UCC), Dresden, 01307, Germany
raw_author_name: Micha Pfeiffer
raw_affiliation_string: Department of Translational Surgical Oncology, National Centre for Tumor Diseases(NCT/UCC), Dresden, 01307, Germany
raw_author_name: Fiona Kolbinger
raw_affiliation_string: Department of Translational Surgical Oncology, National Centre for Tumor Diseases(NCT/UCC), Dresden, 01307, Germany
raw_author_name: Marius Distler
raw_affiliation_string: Department of Visceral, Thoracic and Vascular Surgery, University Hospital and Faculty of Medicine, TU Dresden, 01307, Dresden, Germany
raw_author_name: Jürgen Weitz
raw_affiliation_string: Department of Visceral, Thoracic and Vascular Surgery, University Hospital and Faculty of Medicine, TU Dresden, 01307, Dresden, Germany
raw_author_name: Stefanie Speidel
raw_affiliation_string: SECAI, TU Dresden, Dresden, Germany

Abstract: In surgical computer vision applications, data privacy and expert annotation challenges impede the acquisition of labeled training data. Unpaired image-to-image translation techniques have been explored to automatically generate annotated datasets by translating synthetic images into a realistic domain. The preservation of structure and semantic consistency, i.e., per-class distribution during translation, poses a significant challenge, particularly in cases of semantic distributional mismatch.This study empirically investigates various translation methods for generating data in surgical applications, explicitly focusing on semantic consistency. Through our analysis, we introduce a novel and simple combination of effective approaches, which we call ConStructS. The defined losses within this approach operate on multiple image patches and spatial resolutions during translation.Various state-of-the-art models were extensively evaluated on two challenging surgical datasets. With two different evaluation schemes, the semantic consistency and the usefulness of the translated images on downstream semantic segmentation tasks were evaluated. The results demonstrate the effectiveness of the ConStructS method in minimizing semantic distortion, with images generated by this model showing superior utility for downstream training.In this study, we tackle semantic inconsistency in unpaired image translation for surgical applications with minimal labeled data. The simple model (ConStructS) enhances consistency during translation and serves as a practical way of generating fully labeled and semantically consistent datasets at minimal cost. Our code is available at https://gitlab.com/nct_tso_public/constructs .
Fast Digital Orthophoto Generation: A Comparative Study of Explicit and Implicit Methods

Authors:
raw_author_name: Guang Jiang
raw_affiliation_string: School of Telecommunication Engineering, Xidian University, Xi’an 710065, China

Abstract: A digital orthophoto is an image with geometric accuracy and no distortion. It is acquired through a top view of the scene and finds widespread applications in map creation, planning, and related fields. This paper classifies the algorithms for digital orthophoto generation into two groups: explicit methods and implicit methods. Explicit methods rely on traditional geometric methods, obtaining geometric structure presented with explicit parameters with Multi-View Stereo (MVS) theories, as seen in our proposed Top view constrained Dense Matching (TDM). Implicit methods rely on neural rendering, obtaining implicit neural representation of scenes through the training of neural networks, as exemplified by Neural Radiance Fields (NeRFs). Both of them obtain digital orthophotos via rendering from a top-view perspective. In addition, this paper conducts an in-depth comparative study between explicit and implicit methods. The experiments demonstrate that both algorithms meet the measurement accuracy requirements and exhibit a similar level of quality in terms of generated results. Importantly, the explicit method shows a significant advantage in terms of efficiency, with a time consumption reduction of two orders of magnitude under our latest Compute Unified Device Architecture (CUDA) version TDM algorithm. Although explicit and implicit methods differ significantly in their representation forms, they share commonalities in the implementation across algorithmic stages. These findings highlight the potential advantages of explicit methods in orthophoto generation while also providing beneficial references and practical guidance for fast digital orthophoto generation using implicit methods.
Control and Position Tracking for UAVs

Authors:
raw_author_name: Cezary Kownacki
raw_affiliation_string: Department of Industrial Processes Automation, Faculty of Mechanical Engineering, Bialystok University of Technology, Wiejska St. 45C, 15-351 Bialystok, Poland

Abstract: There has been exponential development of UAV technology and related research areas such as artificial intelligence, which will raise UAVs’ ability for autonomous flights to a higher level [...]
Solar and Wind Data Recognition: Fourier Regression for Robust Recovery

Authors:
raw_author_name: Abdullah F. Al-Aboosi
raw_affiliation_string: Department of Multidisciplinary Engineering, Texas A&M University, College Station, TX 77843, USA
raw_author_name: Fadhil Y. Al-Aboosi
raw_affiliation_string: RAPID Manufacturing Institute-AIChE, New York, NY 10005, USA

Abstract: Accurate prediction of renewable energy output is essential for integrating sustainable energy sources into the grid, facilitating a transition towards a more resilient energy infrastructure. Novel applications of machine learning and artificial intelligence are being leveraged to enhance forecasting methodologies, enabling more accurate predictions and optimized decision-making capabilities. Integrating these novel paradigms improves forecasting accuracy, fostering a more efficient and reliable energy grid. These advancements allow better demand management, optimize resource allocation, and improve robustness to potential disruptions. The data collected from solar intensity and wind speed is often recorded through sensor-equipped instruments, which may encounter intermittent or permanent faults. Hence, this paper proposes a novel Fourier network regression model to process solar irradiance and wind speed data. The proposed approach enables accurate prediction of the underlying smooth components, facilitating effective reconstruction of missing data and enhancing the overall forecasting performance. The present study focuses on Midland, Texas, as a case study to assess direct normal irradiance (DNI), diffuse horizontal irradiance (DHI), and wind speed. Remarkably, the model exhibits a correlation of 1 with a minimal RMSE (root mean square error) of 0.0007555. This study leverages Fourier analysis for renewable energy applications, with the aim of establishing a methodology that can be applied to a novel geographic context.
VTG-GPT: Tuning-Free Zero-Shot Video Temporal Grounding with GPT

Authors:
raw_author_name: Yunzhuo Sun
raw_affiliation_string: School of Physics and Electronics, Hubei Normal University, Huangshi 435002, China;
raw_author_name: Zien Xie
raw_affiliation_string: School of Electronic Science and Engineering, Nanjing University, Nanjing 210093, China;
raw_author_name: Benxiang Zhai
raw_affiliation_string: School of Electronic Science and Engineering, Nanjing University, Nanjing 210093, China;
raw_author_name: Sidan Du
raw_affiliation_string: School of Electronic Science and Engineering, Nanjing University, Nanjing 210093, China;

Abstract: Video temporal grounding (VTG) aims to locate specific temporal segments from an untrimmed video based on a linguistic query. Most existing VTG models are trained on extensive annotated video-text pairs, a process that not only introduces human biases from the queries but also incurs significant computational costs. To tackle these challenges, we propose VTG-GPT, a GPT-based method for zero-shot VTG without training or fine-tuning. To reduce prejudice in the original query, we employ Baichuan2 to generate debiased queries. To lessen redundant information in videos, we apply MiniGPT-v2 to transform visual content into more precise captions. Finally, we devise the proposal generator and post-processing to produce accurate segments from debiased queries and image captions. Extensive experiments demonstrate that VTG-GPT significantly outperforms SOTA methods in zero-shot settings and surpasses unsupervised approaches. More notably, it achieves competitive performance comparable to supervised methods. The code is available on GitHub.
An Introduction to Reinforcement Learning and Its Application in Various Domains

Authors:
raw_author_name: Samina Amin
raw_affiliation_string: Institute of Computing, Kohat University of Science and Technology, Pakistan

Abstract: Reinforcement learning (RL) is a dynamic and evolving subfield of machine learning that focuses on training intelligent agents to learn and adapt through interactions with their environment. This introductory article provides an overview of the fundamental concepts and principles of RL, elucidating its core components, such as the agent, environment, actions, and rewards. This study aims to give readers an in-depth introduction to RL and show examples of its different uses in various domains. RL can allow agents to learn through interaction with an environment, which has led to its enormous interest. The core ideas of RL and its essential elements will be covered in this study, after which it will go into applications in industries including robotics, gaming, finance, healthcare, and more. The fundamental ideas of RL will become clearer to readers, and they will recognize how transformative it can be when used to address challenging decision-making issues. These applications demonstrate the versatility and significance of RL in shaping the future of technology and automation.