Reviewerly/exHarmony huggingface dataset:
Forensic Audio and Voice Analysis: TV Series Reinforce False Popular Beliefs

People’s perception of forensic evidence is greatly influenced by crime TV series. The analysis of the human voice is no exception. However, unlike fingerprints—with which fiction and popular beliefs draw an incorrect parallel—the human voice varies according to many factors, can be altered deliberately, and its potential uniqueness has yet to be proven. Starting with a cursory examination of landmarks in forensic voice analysis that exemplify how the voiceprint fallacy came about and why people think they can recognize people’s voices, we then provide a thorough inspection of over 100 excerpts from TV series. Through this analysis, we seek to characterize the narrative and aesthetic processes that fashion our perception of scientific evidence when it comes to identifying somebody based on voice analysis. These processes converge to exaggerate the reliability of forensic voice analysis. We complement our examination with plausibility ratings of a subset of excerpts. We claim that these biased representations have led to a situation where, even today, one of the main challenges faced by forensic voice specialists is to convince trial jurors, judges, lawyers, and police officers that forensic voice comparison can by no means give the sort of straightforward answers that fingerprints or DNA permit.
More than malware: unmasking the hidden risk of cybersecurity regulations

Abstract Cybersecurity investments are made within a complex and ever-evolving environment, where regulatory changes represent a significant risk factor. While cybersecurity regulations aim to minimize cyber risks and enhance protection, the uncertainty arising from frequent changes or new regulations can significantly impact organizational response strategies. This paper explores the determinants and implications of regulatory risks associated with cybersecurity, aiming to provide a deeper understanding of how these risks influence strategic decision-making. The study delves into the suggestion of preventive and mitigative controls that enable businesses to adapt to and mitigate potential disruptions caused by regulatory changes, thereby preserving their established cybersecurity practices. Another key contribution of this study is the introduction of a stochastic econometric model that illustrates how regulatory risks and uncertainties can affect investment behaviors, often prompting a “wait-and-see” stance. This model synthesizes the complex relationship among investment choices, regulatory changes, and cybersecurity risks, providing insights into the dynamic nature of cybersecurity investment strategies. The research findings offer valuable guidance for risk management and strategic planning in cybersecurity investments. By comprehensively understanding the drivers and impacts of regulatory risks, businesses and policymakers can develop more effective risk evaluation and management approaches. This is essential for sustaining a strong cybersecurity posture while navigating the changing regulatory environment.
Randomized Block Kaczmarz Methods for Inner Inverses of a Matrix

In this paper, two randomized block Kaczmarz methods to compute inner inverses of any rectangular matrix A are presented. These are iterative methods without matrix multiplications and their convergence is proved. The numerical results show that the proposed methods are more efficient than iterative methods involving matrix multiplications for the high-dimensional matrix.
Research on Path Planning Method of Solid Backfilling and Pushing Mechanism Based on Adaptive Genetic Particle Swarm Optimization

This paper investigates the path planning problem of the coal mine solid-filling and pushing mechanism and proposes a hybrid improved adaptive genetic particle swarm algorithm (AGAPSO). To enhance the efficiency and accuracy of path planning, the algorithm combines a particle swarm optimization algorithm (PSO) and a genetic algorithm (GA), introducing the sharing mechanism and local search capability of the particle swarm optimization algorithm. The path planning of the pushing mechanism for the solid-filling scenario is optimized by dynamically adjusting the algorithm parameters to accommodate different search environments. Subsequently, the proposed algorithm’s effectiveness in the filling equipment path planning problem is experimentally verified using a simulation model of the established filling equipment path planning scenario. The experimental findings indicate that the improved hybrid algorithm converges three times faster than the original algorithm. Furthermore, it demonstrates approximately 92% and 94% better stability and average performance, respectively, than the original algorithm. Additionally, AGAPSO achieves a 27.59% and 19.16% improvement in path length and material usage optimization compared to the GA and GAPSO algorithms, showcasing superior efficiency and adaptability. Therefore, the AGAPSO method offers significant advantages in the path planning of the coal mine solid-filling and pushing mechanism, which is crucial for enhancing the filling effect and efficiency.
Granite Exposure Mapping Through Sentinel‐2 Visible and Short Wave Infrared Bands

Abstract Nonmetallic minerals like granite and limestone have calcite and biotitic ingredients as their major part which exhibit wonderful absorption features in the visible and short wave range of the electromagnetic spectrum. This research puts emphasis on delineating granite and limestone deposits of the Mardan district through the latest multispectral Landsat‐9 and Sentinel‐2 sensors of which the latter provided 94% mapping accuracy in delineating granites (biotitic bearing minerals) and limestone (calcite‐bearing minerals). The Image processing techniques of minimum noise fraction, which is double cascaded principal components analysis and pixel purity index algorithms proved helpful to bring significant improvements in classification results and in the reduction of noise and data size. The outcomes of the research study show that supervised machine learning algorithms are impactful to map such minerals provided that the data must be well organized and limited in size. The results achieved were verified through validation steps like, (a) Independent reference data of high‐resolution Google Earth maps and (b) Ground survey reports. Arc GIS 10.2 and Envi 5.3 software suite were used to create (a) ground truth points at random for accuracy assessment (b) portraying study area maps (c) Image Processing and Preprocessing tools and (d) implementation of machine learning algorithms. Access to the data and software suite is being provided for open research work.
Unmanned Aerial Vehicle Inspection Routing and Scheduling for Engineering Management

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

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

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

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

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

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

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

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

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

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

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.