Reviewerly/exHarmony huggingface dataset:
Who Cares about the Weather? Inferring Weather Conditions for Weather-Aware Object Detection in Thermal Images

Deployments of real-world object detection systems often experience a degradation in performance over time due to concept drift. Systems that leverage thermal cameras are especially susceptible because the respective thermal signatures of objects and their surroundings are highly sensitive to environmental changes. In this study, two types of weather-aware latent conditioning methods are investigated. The proposed method aims to guide two object detectors, (YOLOv5 and Deformable DETR) to become weather-aware. This is achieved by leveraging an auxiliary branch that predicts weather-related information while conditioning intermediate layers of the object detector. While the conditioning methods proposed do not directly improve the accuracy of baseline detectors, it can be observed that conditioned networks manage to extract a weather-related signal from the thermal images, thus resulting in a decreased miss rate at the cost of increased false positives. The extracted signal appears noisy and is thus challenging to regress accurately. This is most likely a result of the qualitative nature of the thermal sensor; thus, further work is needed to identify an ideal method for optimizing the conditioning branch, as well as to further improve the accuracy of the system.
Research on the Cooperative Target State Estimation and Tracking Optimization Method of Multi-UUV

This work studied two sub-problems of the cooperative state estimation and cooperative optimization of tracking paths in multiple unmanned underwater vehicle (multi-UUV) cooperative target tracking. The mathematical model of each component of the multi-UUV cooperative target tracking system was established. According to the target bearing-only information obtained by each unmanned underwater vehicle's (UUV) detection, the extended Kalman filter algorithm based on interacting with multiple model bearing-only data was used to estimate the target state in a distributed way, and the federal fusion algorithm was used to fuse the estimated results of each UUV. The fused target state was predicted, and, based on the predicted target state, to achieve the persistent tracking of the target, the particle swarm optimization algorithm was used for the online collaborative optimization of the UUV tracking path. The simulation results showed that the multi-UUV distributed fusion filtering algorithm could obtain a better target state estimation effect, and the online path collaborative optimization method based on the prediction of the target state could achieve persistent target tracking.
Computer Interaction Using Hand Gestures

Abstract: Computer interaction using hand gestures is theprocess of automatically recognizing hand gestures that enablespeople to interact with the computer. Usually, interaction with a computer is done using input devices like keyboards, mice, etc., and the need to develop contactless human-computer interaction methods is the most desired feature during these pandemic days. Gesture recognition is a significant step towardsachieving contactless new types of human-computer interaction. A deep learning approach, convolution neural network, is used to recognize the hand gestures and mapped operation to the respective gesture is performed.
Parameterized Complexity for Finding a Perfect Phylogeny from Mixed Tumor Samples

Motivated by an application in cancer genomics, Hajirasouliha and Raphael [Proceedings of the 14th International Workshop on Algorithms in Bioinformatics, 2014, pp. 354–367] proposed the split-row problem (SR). In this problem, an binary matrix is given. A split-row operation on is defined as replacing a row by rows whose bitwise OR is equal to . The cost of the operation is the number of additional rows induced, that is, . The goal is to find a sequence of split-row operations that transforms into a matrix corresponding to a perfect phylogeny and the total cost is minimized. Recently, Hujdurović et al. [ACM Trans. Algorithms, 14 (2018), 26] proved the APX-hardness of SR and presented efficient exact and approximation algorithms. The parameterized study of SR was left as a direction for future work. Let denote the minimum total cost. This paper gives an -time exact algorithm for SR. This result indicates that SR is fixed-parameter tractable when parameterized by . In addition, in the worst case, our algorithm requires time, significantly improving the previous upper bound of . Hujdurović et al.’s exact algorithm can be modified to solve a variant of SR, called the distinct split-row problem (DSR). Our algorithm can be adapted to this variant as well. In addition, our algorithms can be extended to solve SR and DSR with the following additional constraint: only the rows in a given subset are allowed to be split.
Dephasing effects on quantum correlations and teleportation in presence of state dependent bath

Quantum information protocols are often designed in the ideal situation with no decoherence. However, in real setup, these protocols are subject to the decoherence and thus reducing fidelity of the measurement outcome. In this work, we analyze the effect of state-dependent bath on the quantum correlations and the fidelity of a single qubit teleportation. We model our system-bath interaction as qubits interacting with a common bath of bosons, and the state dependence of the bath is generated through a projective measurement on the joint state in thermal equilibrium. The analytic expressions for the time evolution of entanglement, discord and average fidelity of quantum teleportation are calculated. It is shown that due to the presence of initial system-bath correlations, the system maintains quantum correlations for long times. Furthermore, due to the presence of finite long-time entanglement of the quantum channel, the average fidelity is shown to be higher than its classical value.
A construction method of the wave–particle entanglement state of the particle system

Entanglement of the wave–particle duality of particles has attracted enormous attention as one of fundamental feature of quantum mechanics. Herein, we systematically studied the wave–particle entanglement of the particle system composed of N particles. The results indicate that the particle system has the wave–particle entanglement after the interaction, when the wave function of any one of the system can be obtained by using same basis vectors before the interaction. Moreover, a construction method of the wave–particle entanglement state is found for the particle system. Finally, the photons can show the wave–particle entanglement in the previous experimental work, which was further confirmed by the construction method. The results provide a new understanding for the entanglement of the wave–particle duality.
Scaling Optimal Allocation of Cloud Resources Using Lagrange Relaxation

The rapid growth of Cloud Computing (CC) has increased the variety of computing resources, storage, and communication services that pose significant new challenges for the efficient use of cloud resources. The cost-efficient allocation of cloud resources has become a decisive premise for the adoption of CC services. The cost-efficient selection and scheduling of these resources to meet the demands of a scientific workflow is a challenging problem that is exacerbated by the inclusion of multiple CC providers. In this paper, we present a new strategy for the cost-efficient selection of CC resources using Lagrange relaxation. Our approach is based on preselection of resources and demand decomposition to create a series of smaller sub-problems, which allow the estimation of the best cost-structures and selection of CC service providers for a subset of the time period of the planning horizon. Decomposition of the demand is achieved through the boundary analysis of a continuous relaxation of the problem. Using the metrics defined in terms of the cost and time of completion, we demonstrate excellent performance with respect to optimal solutions. Our method reduced the computational time from hours to seconds for a representative 36-month problem and provided high-quality solutions (<0.05% relative error). Given the importance of selecting resources and scheduling complex scientific workflows, we believe that this novel strategy will be beneficial for many researchers and users of cloud computing resources.
An Efficient Approach Based on Graph Neural Networks for Predicting Wait Time in Job Schedulers

The objective of this study is to predict the wait time in job schedulers with high accuracy. Job executions in supercomputers or data centers are typically managed by job schedulers to efficiently utilize computing resources. A possible disadvantage is that, depending on resource availability and scheduling policy, the job waits for a long time before being executed. Therefore, providing the predicted wait time for individual jobs can contribute to the users’ research planning. Additionally, the job wait time potentially becomes an important input for the scheduling policy. However, the prediction of the job wait time is a challenging task because the state of the scheduling system changes dynamically by many uncertainty factors. To address this problem, a graph neural network architecture of deep learning, which is a novel approach for processing job information in the scheduler, was employed in this study. Our experiments using real historical logs confirmed that the proposed deep learning model achieved 0.3–7.9% higher prediction accuracy compared to the boosted decision tree and multi-layer perceptron models. An extensive analysis of the proposed deep learning model was performed to improve the explainability of the experimental results. In particular, the visualization of attention weights in the graph neural network expanded our understanding of the behavior of the proposed deep learning model.
A Stackelberg game framework for resource management in the CloudPoS-Based blockchain network

ABSTRACTThis paper proposes a two-stage Stackelberg game framework to study resource management in a CloudPoS-based blockchain network, analyzes multiple factors’ impact on optimal strategies and profits of cloud service provider (CSP) and miners. The results show that the proposed model can maximize the profits of CSP and miners, the fixed reward, the total supply, and the propagation delay have a monotonic impact on miners’ decisions, but a non-monotonic impact on CSP’s decisions; the number of miners has a non-monotonic impact on both decisions. Our findings emphasize that the CSP should control the number of miners participating in cloud mining and the total supply of resources.KEYWORDS: blockchaincloud computingproof-of-stakeStackelberg gameprice decision AcknowledgmentsThis research is partially funded by the grant of National Natural Science Foundation of China [No. 71971218], the National Social Science project of China “Research on Improving Logistics Service Quality and Low Carbon Governance Mechanism (21FGLB046)”.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the National Natural Science Foundation of China [71971218].
A robust human target following system in corridor environment based on wall detection

Purpose In corridor environments, human-following robot encounter difficulties when the target turning around at the corridor intersections, as walls may cause complete occlusion. This paper aims to propose a collision-free following system for robot to track humans in corridors without a prior map. Design/methodology/approach In addition to following a target and avoiding collisions robustly, the proposed system calculates the positions of walls in the environment in real-time. This allows the system to maintain a stable tracking of the target even if it is obscured after turning. The proposed solution is integrated into a four-wheeled differential drive mobile robot to follow a target in a corridor environment in real-world. Findings The experimental results demonstrate that the robot equipped with the proposed system is capable of avoiding obstacles and following a human target robustly in the corridors. Moreover, the robot achieves a 90% success rate in maintaining a stable tracking of the target after the target turns around a corner with high speed. Originality/value This paper proposes a human target following system incorporating three novel features: a path planning method based on wall positions is introduced to ensure stable tracking of the target even when it is obscured due to target turns; improvements are made to the random sample consensus (RANSAC) algorithm, enhancing its accuracy in calculating wall positions. The system is integrated into a four-wheeled differential drive mobile robot effectively demonstrates its remarkable robustness and real-time performance.
A systematic literature review on the determinants of cryptocurrency pricing

Purpose Given the cryptocurrency market boom in recent years, this study aims to identify the factors influencing cryptocurrency pricing and the major gaps for future research. Design/methodology/approach A systematic literature review was undertaken. Three databases, Scopus, Web of Science and EBSCOhost, were used for this review. The final analysis comprised 88 articles that met the eligibility criteria. Findings The influential factors were identified and categorized as supply and demand, technology, economics, market volatility, investors’ attributes and social media. This review provides a comprehensive and consolidated view of cryptocurrency pricing and maps the significant influential factors. Originality/value This paper is the first to systematically and comprehensively review the relevant literature on cryptocurrency to identify the factors of pricing fluctuation. This research contributes to cryptocurrency research as well as to consumer behaviors and marketing discipline in broad.
<scp>DevOps</scp> project management success factors: A decision‐making framework

Abstract Development and operations (DevOps) refer to the collaboration and multidisciplinary organizational effort to automate continuous delivery of information systems (IS) development project with an aim to improve the quality of the IS. The flexibility and quality production of software projects motivated the organizations to adopt DevOps paradigm. Organizations face several complexities while management of DevOps process. This study aims to explore and analyze the factors that could positively impact the management of DevOps process. Firstly, literature review was performed and identified 36 success factors that are related to 10 knowledge areas of DevOps project management. Secondly, a questionnaire survey study was conducted to get the insight of industry experts concerning the success factors of DevOps project. Finally, the fuzzy‐AHP was applied for ranking the success factors and examining the relationship between 10 knowledge areas of identified success factors. The results of this study will serve as a body of knowledge for researchers and practitioners to consider the highest priority success factors and develop the effective policies for the successful execution of DevOps project management.
Lie hypergraph and chaos‐based privacy preserving protocol for wireless sensor networks in IoT environment

Summary Wireless sensor networks (WSN) are innately resource restrained and beneficial in a wide range of applications, including smart homes, e‐health care, law, military systems, disaster management, and emergency reprieve. These applications are linked to various devices that may communicate with one another through the internet, typically known as Internet of Things (IoT). The application of WSN plays an integral role in the IoT infrastructure. In a WSN, sensors are haphazardly placed in environments where the data transmission is challenged by privacy concerns. This paper proposes a methodology termed Lie hypergraph and chaos‐based secure routing (LH‐CSR) to perform an energy‐efficient routing with secure data transmission in WSN. In the first phase, the deployed sensor nodes are transformed into a hypergraph from which cluster head (CH) is elected by hypergraph transversal property, and for secure routing, the route is formed by the Lie commutators of the Lie algebra of upper triangular matrices. The second phase emphasizes privacy preservation by introducing the novel chaotic map formulation to process the key generation. The encryption and decryption processes are maintained by key generation to prevent data loss during retrieval. In this way, the data are retained confidentially with minimal computational overhead. The performance of the LH‐CSR is evaluated through a simulation, which shows that it outperforms over compared protocols in terms of cryptographic time, network lifetime, packet delivery ratio, end‐to‐end delay, and throughput.
<i>MetaHate</i>: AI‐based hate speech detection for secured online gaming in metaverse using blockchain

Abstract The emergence of Web 3.0, blockchain technology (BC), and artificial intelligence (AI) are transforming multiplayer online gaming in the metaverse. This development has its concerns about safety and inclusivity. Hate speech, in particular, poses a significant threat to the harmony of these online communities. Traditional moderation methods struggle to cope with the immense volume of user‐generated content, necessitating innovative solutions. This article proposes a novel framework, MetaHate , that employs AI and BC to detect and combat hate speech in online gaming environments within the metaverse. Various machine learning (ML) models are applied to analyze Hindi–English code mixed datasets, with gradient boosting proving the most effective, achieving 86.01% accuracy. AI algorithms are instrumental in identifying harmful language patterns, while BC technology ensures transparency and user accountability. Moreover, a BC‐based smart contract is proposed to support the moderation of hate speech in the game chat. Integrating AI and BC can significantly enhance the safety and inclusivity of the metaverse, underscoring the importance of these technologies in the ongoing battle against hate speech and in bolstering user engagement. This research emphasizes the potential of AI and BC synergy in creating a safer metaverse, highlighting the need for continuous refinement and deployment of these technologies.
Deep Learning Algorithms in Mobile Edge with Real-Time Abnormal Event Detection for 5G-IoT Devices

IoT is becoming increasingly popular due to its quick expansion and variety of applications. In addition, 5G technology helps with communication and network connectivity. This work integrates C-RAN with IoT networks to provide an experimental 5G testbed. In a 5G IoT environment, this experience is utilized to enhance both perpendicular and flat localization (3D localization). DRCaG, an acronym for the proposed model, stands for a deep, complicated network with a gated layer on top. The performance of the proposed model has been demonstrated through extensive simulations in terms of learning reduction, accuracy, and matrix disorientation, with a variable signal-to-noise ratio (SNR) spanning from 20 dB to + 20 dB, which illustrates the superiority of DRCaG compared to others. An online, end-to-end solution based on deep learning techniques is presented in this study for the fast, precise, reliable, and automatic detection of diverse petty crime types. By detecting tiny crimes like hostility, bag snatching, and vandalism, the suggested system may not only identify unusual passenger behavior like vandalism and accidents but also improve passenger security. The solution performs admirably in a variety of use cases and environmental settings.
Brain-computer interaction modeling based on the stable diffusion model

This paper investigates neurotechnologies for developing brain-computer interaction (BCI) based on the generative deep learning Stable Diffusion model. An algorithm for modeling BCI is proposed and its training and testing on artificial data is described. The results are encouraging researchers and can be used in various areas of BCI, such as distance learning, remote medicine and the creation of robotic humanoids, etc.
Sentiment Analysis of IMDB Movie Reviews Using Deep Learning Techniques

Movie reviews help users to evaluate and decide if a certain movie is of their particular interest. Nowadays, there is a lot of data about movies like IMDB which is an extensive database containing thousands of movie reviews. However, analyzing each of these reviews can be time consuming and tedious, so machine learning models could be implemented for automation and analysis of these reviews. Sentiment analysis is a process that uses artificial intelligence and machine learning to find a point of view, a keyword, or a feeling in order to highlight the information of interest in the process. In this sense, an opinion can be interpreted as a dimension in the data regarding a particular topic and can be very useful in various fields of application such as data mining, web mining, and social media analytics. This paper aims to use an IMDB database that contains 50,000 reviews, and we intend to apply transformer-based language models like Bidirectional Encoder Representations from Transformers (BERT), RoBERTa, and XLNet for sentiment analysis. Moreover, we implement a TF-IDF and cluster analysis to gain insights about the topics related to both positive and negative reviews (Yasser in IMDB movie ratings sentiment analysis, 2022 [1]; Kumar et al. in Int J Interact Multimed Artif Intell 5(5), 2019 [2]; Chakraborty et al. in Soc Netw Anal Comput Res Methods Tech 7:127–147, 2018 [3]; Gadekallu et al. in Sentiment analysis and knowledge discovery in contemporary business. IGI Global, pp 77–90, 2019 [4]).
RESOLVING PARADOXES OF PHYSICS IN THE UNIVERSITY STUDY

The article substantiates the thesis about the futility of the usual interpretation of creativity as originality. In order to make our position on this issue more convincing, we have given to the originality concept an appearance of contradiction. The discussion in the article leads to the conclusion thatthe category of creative thinking will be really effective if the main characteristic of the creativity were not originality but rather the ability to independent problem solving. The article presents the optimal steps for an independent solution of research problems. Authors introduce the idea of “easy paradoxes” for developing creative thinking.
BCAP-SA—Towards a Blockchain-Based Cryptocurrency Adoption Model as a Payment Method in Saudi Arabia

Online transactions have grown to gain significant attention from various stakeholders in governments, business, and research practitioners all over the world. A decade ago, the first cryptocurrency transaction was executed to pave the way for a new era of online transactions. However, there is a relatively low adoption of blockchain-based cryptocurrency transactions as a payment method by service providers, organizations, and customers. Further, there is no study investigating the adoption of cryptocurrency technology as a payment method in Saudi Arabia. Therefore, this research proposes a new adoption model to investigate the acceptance of a blockchain-based cryptocurrency as a payment method in Saudi Arabia (BCAP-SA). A thorough research of the literature has yielded only a few studies covering external factors over a range of technical, economic, personal, and environmental aspects in different parts of the world. Hypotheses have been developed for each factor which will be examined by both qualitative (interviews) and quantitative (surveys) methods. The proposed study aims to offer service providers with insights about which factors customers would concentrate on if they were to accept blockchain-based cryptocurrency as a payment method.