Reviewerly/exHarmony huggingface dataset:
Unmanned Aerial Vehicle Inspection Routing and Scheduling for Engineering Management

Authors:
raw_author_name: Lu Zhen
raw_affiliation_string: School of Management, Shanghai University, Shanghai 200444, China
raw_author_name: Zhiyuan Yang
raw_affiliation_string: School of Management, Shanghai University, Shanghai 200444, China
raw_author_name: Gilbert Laporte
raw_affiliation_string: Department of Decision Sciences, HEC Montréal, Montréal PQ H3T 2A7, Canada; School of Management, University of Bath, Bath BA2 7AY, UK
raw_author_name: Wen Yi
raw_affiliation_string: Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong 999077, China

Abstract: Technological advancements in unmanned aerial vehicles (UAVs) have revolutionized various industries, enabling the widespread adoption of UAV-based solutions. In engineering management, UAV-based inspection has emerged as a highly efficient method for identifying hidden risks in high-risk construction environments, surpassing traditional inspection techniques. Building on this foundation, this paper delves into the optimization of UAV inspection routing and scheduling, addressing the complexity introduced by factors such as no-fly zones, monitoring-interval time windows, and multiple monitoring rounds. To tackle this challenging problem, we propose a mixed-integer linear programming (MILP) model that optimizes inspection task assignments, monitoring sequence schedules, and charging decisions. The comprehensive consideration of these factors differentiates our problem from conventional vehicle routing problem (VRP), leading to a mathematically intractable model for commercial solvers in the case of large-scale instances. To overcome this limitation, we design a tailored variable neighborhood search (VNS) metaheuristic, customizing the algorithm to efficiently solve our model. Extensive numerical experiments are conducted to validate the efficacy of our proposed algorithm, demonstrating its scalability for both large-scale and real-scale instances. Sensitivity experiments and a case study based on an actual engineering project are also conducted, providing valuable insights for engineering managers to enhance inspection work efficiency.
Model of IoT design decision-making processes in Flow Based Programming systems

Authors:
raw_author_name: Cezary Orłowski
raw_affiliation_string: WSB University, IBM Centre for Advanced Studies, Faculty of Computer Science and New Technologies, WSB University in Gdańsk, Gdansk, Poland

Abstract: The aim of the article is to present a model of decision-making processes in the design of flow-based Internet of Things systems. This is an attempt to fill the research gap in the construction of decision-making systems for designing both infrastructure and the application development environment. Therefore, the aim of the article is to show a decision-making model for selecting infrastructure and design environment for the development of IoT systems. In order for these processes to be implemented, it is necessary to use containerization processes and Flow Based Programming systems, both at the stage of system prototyping and its subsequent implementation.
A Comparison of One- and Two-Handed Gesture User Interfaces in Virtual Reality—A Task-Based Approach

Authors:
raw_author_name: Taneli Nyyssönen
raw_affiliation_string: Department of Computing, University of Turku, 20014 Turku, Finland
raw_author_name: Seppo Helle
raw_affiliation_string: Department of Computing, University of Turku, 20014 Turku, Finland
raw_author_name: Teijo Lehtonen
raw_affiliation_string: Department of Computing, University of Turku, 20014 Turku, Finland
raw_author_name: Jouni Smed
raw_affiliation_string: Department of Computing, University of Turku, 20014 Turku, Finland

Abstract: This paper presents two gesture-based user interfaces which were designed for a 3D design review in virtual reality (VR) with inspiration drawn from the shipbuilding industry’s need to streamline and make their processes more sustainable. The user interfaces, one focusing on single-hand (unimanual) gestures and the other focusing on dual-handed (bimanual) usage, are tested as a case study using 13 tasks. The unimanual approach attempts to provide a higher degree of flexibility, while the bimanual approach seeks to provide more control over the interaction. The interfaces were developed for the Meta Quest 2 VR headset using the Unity game engine. Hand-tracking (HT) is utilized due to potential usability benefits in comparison to standard controller-based user interfaces, which lack intuitiveness regarding the controls and can cause more strain. The user interfaces were tested with 25 test users, and the results indicate a preference toward the one-handed user interface with little variation in test user categories. Additionally, the testing order, which was counterbalanced, had a statistically significant impact on the preference and performance, indicating that learning novel interaction mechanisms requires an adjustment period for reliable results. VR sickness was also strongly experienced by a few users, and there were no signs that gesture controls would significantly alleviate it.
Number detection of cylindrical objects based on improved Yolov5s algorithm

Authors:
raw_author_name: Yachao Wang
raw_author_name: Dongxuan Wang
raw_author_name: Yu Liu

Abstract: As deep learning continues to advance, object detection technology holds potential and promising prospects in the recognition of cylindrical objects’ quantity, such as in industries like timber processing, construction, and pipeline engineering. The traditional manual counting methods have lower efficiency, a higher error rate, and demand a greater amount of manpower. The introduction of object detection technology can effectively address these issues, enhance work efficiency, and reduce labor costs. Therefore, this research paper introduces a novel variant of the YOLOv5s algorithm, called YOLOv5-COC, specifically designed to tackle the task of counting cylindrical objects. This paper makes the following significant contributions: Firstly, introducing the utilization of data augmentation techniques to augment the dataset, thereby enhancing the generalization ability of the model. Secondly, the K-means+⁣+ algorithm is employed as an alternative to the conventional K-means algorithm in order to enhance the initialization of anchor boxes. Thirdly, introduce distinct methodologies, including the incorporation of a coordinated attention mechanism, the amalgamation of the Bidirectional Feature Pyramid Network (BiFPN), and the substitution of the loss function, in order to further refine the model and enhance its recognition precision. Finally, employ ablation experiments to assess the optimization outcomes of the aforementioned methodologies. The experimental results reveal that the YOLOv5-COC model proposed in this study attains an mAP of 98.7%, operates at a frame rate of 60 FPS, attains a Precision of 98.3%, and boasts a Recall of 99.1%. The mAP@0.5:0.95 stands at 72.4%. In comparison to the original YOLOv5s model, the mAP value exhibits an improvement of 1.3%, the FPS experiences a remarkable surge of 27.7%, detection accuracy elevates by 1%, the recall rate advances by 1%, and the mAP@0.5:0.95 escalates by 3.5%. In summary, the YOLOv5-COC model demonstrates a sufficiently high level of accuracy in object detection tasks, mitigating instances of both false negatives and false positives. It efficiently accomplishes the task of object detection.
A recommendation model for e-commerce platforms oriented to explicit information compensation and hidden information mining

Authors:
raw_author_name: Shihong Wei
raw_affiliation_string: School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
raw_author_name: Xubin An
raw_affiliation_string: School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
raw_author_name: Qian Li
raw_affiliation_string: School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
raw_author_name: Yunpeng Xiao
raw_affiliation_string: School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

Abstract: In an e-commerce platform recommendation system, accurately mining users' preferences and hidden information has always been an important initiative. In this study, based on the potential preferences and hidden characteristics of users, we propose a recommendation model for e-commerce platforms oriented to explicit information compensation and hidden information mining. First, considering the advantages of the VGAE (variational Graph Auto-Encoder) in reconstructing data samples, we propose an EI-VGAE (Explicit Information-Variational Graph Auto-Encoder) to compensate for the lack of explicit feedback. Second, to address the problem that hidden information in e-commerce platforms is difficult to mine, an xDeepFM (eXtremely Deep Factorization Machine) is used to jointly learn higher-order interaction features explicitly and implicitly. A time decay function is also introduced to reduce the impact of higher-order interaction features on the representation of hidden information. Finally, considering the advantages of the attention mechanism for obtaining implicit relational attention and the quantification of the user's degree of preference in local representation, a GAT (Graph Attention Network)-based recommendation model for the e-commerce platform EI-GAT (Explicit and Implicit Graph Attention Network) is proposed. Our experiments show that the proposed method effectively alleviates the problem of low recommendation accuracy owing to insufficient explicit feedback from consumers and further improves recommendation accuracy by quantifying user preferences.
Integrating LSTM and NLP techniques for essay generation

Authors:
raw_author_name: Preeti Nagrath
raw_affiliation_string: Department of Computer Science and Engineering, Bharati Vidyapeeth's College of Engineering, New Delhi, India
raw_author_name: Kirti Gupta
raw_affiliation_string: Department of Electronics and Communications Engineering, Bharati Vidyapeeth's College of Engineering, New Delhi, India
raw_author_name: Jude D. Hemanth
raw_affiliation_string: Department of Electronics and Communications Engineering, Karunya University, Coimbatore, India

Abstract: This research explores the potential of Long Short-Term Memory (LSTM) and Natural Language Processing (NLP) for automated essay generation. The goal of the study is to create a model that can produce high-quality essays that are not only grammatically correct but also semantically meaningful and contextually relevant. The rise of NLP and Deep Learning has made it possible to generate text that is coherent and semantically sound. In this research, there is an advantage by leveraging the ability of LSTMs to capture long-term dependencies and context within the text, and combining it with NLP techniques, such as word embeddings, to process and encode textual data. The results of experiments show that the proposed model can effectively generate essays that are coherent, contextually relevant, and semantically meaningful. This is a significant advancement in the field of text generation and has potential applications in areas such as education, content creation, and language translation. In education, for example, the model could be used to generate essays for language proficiency tests or as a writing aid for students. In content creation, it could be used to generate articles, blog posts, and other written content. In language translation, the model could be used to generate essays in the target language that are semantically and contextually equivalent to the source language essay. The findings of this study contribute to the advancement of NLP and deep learning techniques in the area of text generation and open up new avenues for future research. Short after the proposed model was deployed, it was discovered that it outscored Multi-Topic Aware LSTM (MTA-LSTM), Topic-Attention LSTM (TAT-LSTM), and Topic-Averaged LSTM (TAV-LSTM) in human evaluation by 6.84 percent, 25.40 percent, and 34.94 percent, respectively. Furthermore, it enhanced automatic BLEU score evaluation scores by 11.68 percent, 26.23 percent, and 54.11 percent in MTA-LSTM, TAT-LSTM, and TAV- LSTM, respectively.
A continuous digital elevation representation model for DEM super-resolution

Authors:
raw_author_name: Mikhail G. Mozerov
raw_affiliation_string: Computer Vision Center, UAB, Barcelona, 08193, Spain; Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, Russia

Abstract: The surface of the Earth is continuous, and obtaining precise elevation data at arbitrary query positions is essential for many applications and analyses. However, existing digital elevation models suffer from a precision gap caused by discretization. Therefore, we develop a new continuous representation model (CDEM) that allows height values to be obtained at any arbitrary query position. Inspired by recent research on the implicit neural representation model, we train an encoder–decoder network to learn CDEM from discrete elevation data for DEM super-resolution tasks. The encoder targets generating latent codes from discrete elevation data, while the decoder composes these latent codes with query positions to predict corresponding elevation values. Such a learning pipeline is well-suited for DEM super-resolution tasks. To improve model accuracy, we also propose predicting the bias of elevation values between the query position and its closest known position. Real-world terrain surfaces exhibit inherent roughness with numerous small variations in localized regions, resulting in high-frequency targets that are difficult for neural networks to fit. In order to facilitate the network's ability to model data with high-frequency variations, we introduce positional encoding to map query positions into a higher-dimensional space. Compared to the Bicubic interpolation method and state-of-the-art TfaSR model, our method is demonstrated to obtain more accurate elevation values and preserve more details of terrain structure on the TFASR30, Pyrenees, and Tyrol datasets. Specifically, our EBCF-CDEM model demonstrates performance improvements over the TfaSR model, with reductions in the RMSE of elevation, slope, and aspect by 7.03%, 4.81%, and 3.07% respectively at ×4 super-resolution scale on the TFASR30 dataset, by 9.92%, 7.06%, and 6.15% at ×8 super-resolution scale on the Pyrenees dataset. Extensive experiments further validate the generalizability of our EBCF-CDEM, compared to the TfaSR model, our results in terms of RMSE of elevation, slope, and aspect are reduced by 14.78%, 12.12%, and 8.99% respectively at ×8 super-resolution scale on the Tyrol dataset, and 26.92%, 7.14% and 5.36% on the TIFASR30to10 dataset. We release our source code (including the datasets) at https://github.com/AlcibiadesTophetScipio/EBCF-CDEM to reproduce our results and encourage future research.
Supervised Stratified Subsampling for Predictive Analytics

Authors:
raw_author_name: Ming-Chung Chang
raw_affiliation_string: Institute of Statistical Science, Academia Sinica, Taipei, Taiwan

Abstract: Predictive analytics involves the use of statistical models to make predictions; however, the power of these techniques is hindered by ever-increasing quantities of data. The richness and sheer volume of big data can have a profound effect on computation time and/or numerical stability. In the current study, we develop a novel approach to subsampling with the aim of overcoming this issue when dealing with regression problems in a supervised learning framework. The proposed method integrates stratified sampling and is model-independent. We assess the theoretical underpinnings of the proposed subsampling scheme, and demonstrate its efficacy in yielding reliable predictions with desirable robustness when applied to different statistical models. Supplementary materials for this article are available online.
Clustering dimensionless learning for multiple-physical-regime systems

Authors:
raw_author_name: Lei Zhang
raw_affiliation_string: School of Engineering Science, University of Chinese Academy of Sciences, Beijing 100049, China; The State Key Laboratory of Nonlinear Mechanics, Institute of Mechanics, Chinese Academy of Sciences, Beijing 100190, China

Abstract: The conventional physical analysis has relied on the researchers' intelligence and physical insights to establish mathematical models and analyze the dependence of physical systems on dominant parameters in different physical regimes. In this work, a novel data-driven method is proposed to identify different physical regimes without a prior knowledge of governing equations and discover the dominant dimensionless parameters. The proposed method consists of two parts: the first is a data division via cluster analysis, which is utilized to identify different physical regimes via grouping data points into clusters with the weights taken from the key features in active subspace method; the second is a data-driven analysis of dominant dimensionless parameters via the active subspace, which is utilized to discover dominant dimensionless parameters by use of clustering and its resultant information (e.g. eigenpairs of clusters). We use three example problems to demonstrate this method: pipe flows, the spread of oil slicks on a calm sea, and the eddy viscosity in turbulent channel flows. The results obtained show that the present method can identify distinct physical regimes, and discover dominant dimensionless parameters, while the data-driven dimensional analysis without clustering cannot be directly used to the physical systems of multiple physical regimes.
Accountable Malicious Entity Detection Using Re-Encryption Mechanism to Share Data

Authors:
raw_author_name: S. T. Veena
raw_affiliation_string: Mepco Schlenk Engineering College, India
raw_author_name: P. Santosh
raw_affiliation_string: Mepco Schlenk Engineering Collge, India

Abstract: Recently data sharing through the cloud is widely used and becoming a trend. But protecting data confidentiality and integrity is essential. Data confidentiality and integrity are maintained using cryptography. However, since it cannot ensure that the data hasn't been altered, there is still a trust issue. The proxy re-encryption (PRE) technique was presented as a solution to this. It includes encrypting the data twice, allowing one to determine whether or not it has been altered. The PRE system is also prone to attacks that forge re-encryption keys. Here the data is encrypted twice so that the data can be checked whether it is modified or not. But the PRE system is prone to the abuse of re-encryption keys. Therefore, accountable proxy re-encryption (APRE) is proposed. Here, if the data is altered by proxy, the system will detect whether proxy is malicious or the delegator is trying to frame proxy as malicious. Also, the authors extend the algorithm and implemented base64 encoding and decoding. This prevents many passive cyber attacks.
Enhancing Cloud Security

Authors:
raw_author_name: Tarun Kumar Vashishth
raw_affiliation_string: IIMT University, India
raw_author_name: Vikas Sharma
raw_affiliation_string: IIMT University, India
raw_author_name: Kewal Krishan Sharma
raw_affiliation_string: IIMT University, India
raw_author_name: Bhupendra Kumar
raw_affiliation_string: IIMT University, India
raw_author_name: Sachin Chaudhary
raw_affiliation_string: IIMT University, India
raw_author_name: Rajneesh Panwar
raw_affiliation_string: IIMT University, India

Abstract: Cloud computing has revolutionized the way organizations store, process, and manage data, offering flexibility and scalability. However, the rise in cyber threats poses significant challenges to maintaining robust cloud security. This chapter delves into the pivotal role that Artificial Intelligence (AI) and Machine Learning (ML) play in enhancing cloud security. By harnessing the capabilities of AI and ML, organizations can proactively detect, mitigate, and respond to evolving cyber threats, ultimately fortifying their cloud infrastructure.AI-driven techniques empower security systems to recognize patterns, anomalies, and potential threats within vast datasets. ML algorithms can learn from historical attack data, enabling the prediction of future threats and the development of more effective defense mechanisms. Moreover, AI-enhanced authentication and access control mechanisms bolster identity management, reducing the risk of unauthorized access and data breaches.
A Model for Trust Decision, Data Analysis, and Evaluation to Identify Quality Web Services

Authors:
raw_author_name: Shobhana Kashyap
raw_affiliation_string: National Institute of Technology, Jalandhar, India
raw_author_name: Avtar Singh
raw_affiliation_string: National Institute of Technology, Jalandhar, India

Abstract: Cloud computing has emerged as a powerful paradigm for delivering web services, and includes scalability, flexibility, and cost efficiency. Due to functional overlap and diversity, web services form a major challenge for selecting adequate services to develop user-provider trust. To address the issue, this study presented a machine learning based trusted model to assist users in selecting trustworthy web services. In the initial stage, using K-Means clustering method the services are selected based on three clusters such as high, medium, and low trust. Next, the trust score is generated by evaluating performance parameters to identify the best services. Experiments conducted with QWS datasets demonstrate that the proposed approach efficiently predicts adequate services with a minimum error rate and high accuracy gain. This technique achieves a 99.32%, 99.36% and 99.48% accuracy rates for the low, medium, and high trust prediction, respectively. The result shows that it is more effective than existing approaches and builds a strong trust relation between users and providers.
Data Storage and Transmission Security in the Cloud

Authors:
raw_author_name: Ankita Nayak
raw_affiliation_string: KIIT University, India
raw_author_name: Ipseeta Satpathy
raw_affiliation_string: KIIT University, India
raw_author_name: B. C. M. Patnaik
raw_affiliation_string: KIIT University, India

Abstract: Cloud computing has profoundly changed the face of data management for enterprises, providing increased scalability, ease of access, and cost savings. Nonetheless, this change has highlighted the crucial need for strengthened security measures to protect sensitive data from the ever-changing spectrum of cyber threats. Following the cloud's rise as a storehouse for large datasets, the quest for sophisticated security solutions has gained traction. This motivation has resulted in the incorporation of artificial intelligence (AI) into the cloud security architecture. As cloud storage becomes increasingly popular, organizations are becoming more concerned about data security. Sensitive data is transmitted, ranging from messages and images to financial and health information. As technology advances, there is a growing threat to customer data in the cloud, making greater cloud security more important than ever. This study aims to give a comprehensive insight into the role of AI in data storage and transmission security in the cloud.