Reviewerly/exHarmony huggingface dataset:
Graph Eigenvalue based Structural Method towards Phonetic Boundary Detection

Phoneme boundary analysis is an important prob-lem in the domain of speech processing to identify the bound-aries between different phonetic units. It has significance in the annotation of data sets and in recognition systems as well. This problem has been addressed by various frameworks such as Hidden Markov Modeling, Artificial Neural Networks, and Deep Learning. Even though they are effective, the task is challenging for zero or low resource languages because of the less availability of data sets. Moreover, the methods that do not rely on training processes are desirable because they can produce the required results with less amount of data. In this paper, a graph-based segmentation framework for phoneme boundary detection that uses structural properties and graph eigenvalues is proposed. The method identifies the boundary points in a single scan by using the temporal and spectral variations represented as graph eigenvalues. The method is proven to be effective on a sample data set of Indian accented English words. The proposed method with detailed analysis is presented in the paper.
Contour recognition method of Hall-effect-based whisker sensor

Abstract This paper introduces a novel Hall-effect-based whisker sensor and proposes a contour recognition algorithm for distinguishing and recognizing objects. The mechanical arm drives the sensor to move according to the contour recognition algorithm in the experiments, and the object contour is distinguished and recognized by processing the detected spatial magnetic field data. The results of identifying the measured object with harder carbon fiber and softer ABS whiskers were compared and analyzed, and a new design was proposed based on the advantages of soft and hard materials. The newly designed whisker with glass fiber and stainless steel rod nested reduced the contour recognition error to 4.1%. The experimental results showed that the sensor could distinguish the contour of the straight line and circular objects, and the glass fiber and stainless steel rod nested sensor had a better recognition effect.
From Reality to Virtuality: Re-discussing Cities with the Concept of Metaverse

The increase in the transaction volume of cryptocurrencies, the widespread use of blockchain technology, and the increase in the number of augmented reality devices (virtual, augmented, and mixed reality) have led to the formation of a suitable environment for Metaverse. Metaverse is a virtual or augmented reality created beyond the physical world via an internet connection with augmented reality devices, game consoles, computers, tablets, or mobile phones. This universe may be beyond the real world, or it is created by the virtualization of real-world objects to a certain extent. While Metaverse has the opportunity to be applied comfortably in all areas of daily life, it can also be designed as an urban policy design tool. In the study, the conceptual framework of the metaverse will be presented, the metaverse investments of global companies will be determined, the concept of metaverse will be examined in the context of virtual reality relationship, the reflections of metaverse applications on the city will be examined, and finally, the effects and role of the metaverse in urban policy production will be discussed and conclusions and evaluations will be given.
Lithological mapping for complex geological formations with mixed classifiers using Landsat 8 data

Lithological studies and geological unit mappings are generally applicable to many fields of natural resource management. Relatively suitable aquifers have been formed in complex formations in northwest Shahrood due to the presence of carbonate rocks as well as erosion and tectonic forces in the region. This study aims to identify and separate the calcareous formations that can form karst aquifers in the study area. As a result of erosion and tectonic forces, the rocks of the region exhibit spectral fluctuations, making it difficult for mapping geological formations using multispectral images. Therefore, Landsat 8 satellite-based images were processed by adopting the minimum noise fraction (MNF), independent component analysis (ICA), and band ratio (BR). The indices of calcareous formations and shale formations were created by the BR through the spectral behavior of pure pixels. Moreover, the support vector machine (SVM) and maximum likelihood (ML) were employed for classification. The SVM classifier proved more capable of classification than the ML classifier, and the transform ICA outperformed the MNF in the separation of formations. The lithological maps were extracted using the SVM with an overall accuracy (OA) of 68.04%. Furthermore, a method decision tree (DT) was employed to improve the classification accuracy. The DT classifier was then utilized to reclassify lithological maps that were classified by SVM and ML through morphological characteristics and indices of formations The DT classifier improved the lithological map accuracy by 10%. The boundaries of calcareous formations were extracted from non-calcareous formations with an accuracy of 93%, and the regional constructions were separated with an accuracy of ∼80 % . Finally, the lithological map was developed with a kappa of 0.734 and an OA of 78.59%.
Performance Comparison of Variants Based on Swarm Intelligence Algorithm of Mathematical and Structural Optimization

Abstract In this paper, three different variants of the canonical particle swarm optimization (PSO) algorithm are compared in terms of their optimal solutions to engineering optimization problems. The PSO often face to premature convergence due to its weak capability in local exploitation, which leads to a low optimization precision or even failure. Moreover, the computational burden is also one of the obstacles for the PSO when solving complex problems. To overcome these drawbacks, the original PSO algorithm is integrated with the knowledge sharing and auxiliary mechanisms of comprehensive learning (CL) and Gaussian local search (GLS) strategies to form the so-called GLS-PSO, CLPSO, and GLS-CLPSO algorithms. To demonstrate the accuracy and robustness of the three variants, their performance is tested on a benchmark spatial truss structure with continuous variables. The obtained optimization results are then compared with those by other PSO variants in the literature.
Defense and Attack Techniques Against File-Based TOCTOU Vulnerabilities: A Systematic Review

File-based <i>Time-of-Check to Time-of-Use</i> (TOCTOU) race conditions are a well-known type of security vulnerability. A wide variety of techniques have been proposed to detect, mitigate, avoid, and exploit these vulnerabilities over the past 35 years. However, despite these research efforts, TOCTOU vulnerabilities remain unsolved due to their non-deterministic nature and the particularities of the different filesystems involved in running vulnerable programs, especially in Unix-like operating system environments. In this paper, we present a systematic literature review on defense and attack techniques related to the file-based TOCTOU vulnerability. We apply a reproducible methodology to search, filter, and analyze the most relevant research proposals to define a global and understandable vision of existing solutions. The results of this analysis are finally used to discuss future research directions that can be explored to move towards a universal solution to this type of vulnerability.
New Investigation Perspectives in Psychology and Behavioral Sciences: Informational Activity and Psychological/ Behavioral Expression of the Cognitive Centers of Consciousness

The aim of Skeena Publishers is to propagate the scientific work by publishing a completely reliable information on recent innovations and developments in the field of Medicine, Pharmacy, Engineering, Science and Technology. For the better usage, effect, and visibility of the research work, all the published articles are freely available online across the world. We aim to focus on Scientific, Technical, Systemic, Logical, and Perspective articles publishing opportunities for the Researchers, Professors, Scientists and Students for their progress.
IT Governance and Enterprise Security Policy in the 6G Era

With the complexity of human–machine interactions, the concerns of various users in this interaction have also increased. In the 6G generation, users are witnessing the introduction of advanced technologies that will easily control all their life options in the near future. With using Internet of Things (IoT) devices, the activities of users will be recorded and analyzed with the help of advanced artificial intelligence technologies. Blockchain technology will affect all economic interactions and social communication. For example, smart contracts can easily influence people's daily lives with the help of Blockchain technology. On the other hand, an attack by hackers can affect the medical data of a hospital and make it difficult to diagnose the disease by replacing incorrect data. These issues can easily affect the privacy and security of human society. So, there is a greater need for a security policy by organizations to address the security risks of 6G generation. In this chapter, we explore IT governance and enterprise security policy in the 6G era.
An Optimal Path Planning Algorithms for a Mobile Robot

Abstract- The goal of navigating a mobile robot is to find the optimal path to direct its movement, so path planning is the best solution to find the optimal path. Therefore, the two most important problems of path planning must be solved; the first is that the path must avoid collision with obstacles, and second it must reduce the length of the path to a minimum. This paper will discuss finding the shortest path with the optimum cost function by using the Chaotic Particle Swarm Optimization (CPSO), and A*, compare the results between them and the proposed hybrid algorithm that combines A* and Chaotic Particle Swarm Optimization (ACPSO) algorithms to enhance A* algorithm to find the optimal path and velocities of the wheeled mobile robot. These algorithms are simulated by MATLAB in a fixed obstacles environment to show the effectiveness of the proposed algorithm in terms of minimum number of an evaluation function and the shortest path length as well as to obtain the optimal or near optimal wheel velocities. Index Terms—Mobile robot, Path planning, Chaotic Particle Swarm Optimization (CPSO), A* algorithm, Fixed obstacles.
Research on the Relationship between Employee Mental Health and Enterprise Work Performance

Knowledge, capabilities, and quality can be used as three temperatures in employee value-added characteristics in corporate employee performance. They are closely coupled, and knowledge is the basis of ability and quality. The ability is to form and develop in the process of mastering knowledge. The quality is a potential ability. The quality itself is not the ability. Quality is a relatively stable quality and accomplishment formed by internalizing knowledge and skills acquired from the outside into people's body and mind through individual cognition and social practice on the basis of innate physiology and under the influence of acquired environment and psychology. Quality determines whether the employee's knowledge and ability can function correctly and effectively, and is the director of knowledge capabilities. According to the analysis and learning from scholars of literature employees' job performance evaluation, we constructed an employee performance evaluation index system of evaluation on the basis of value-based employees. In this system, there are three levels of evaluation indicators. The level indicators are as follows: knowledge of value-added indicators, the ability to add value, and quality index value. These indicators are consistent with the analysis of employee training objectives and psychological quality. In this study, for each level of each indicator we have specific labeling instructions, which is more convenient for the relevant calculations.
Indirect measurement for optimal quantum communication enhanced by binary non-standard coherent states

It is well known that the Helstrom bound can be improved by generalizing the form of a coherent state. Thus, designing a quantum measurement achieving the improved Helstrom bound is important for novel quantum communication. In this paper, we analytically show that the improved Helstrom bound can be achieved by a projective measurement composed of orthogonal non-standard Schrödinger cat states. Moreover, we numerically show that the improved Helstrom bound can be nearly achieved by an indirect measurement based on the Jaynes–Cummings model. As the Jaynes–Cummings model describes an interaction between a light and a two-level atom, we emphasize that the indirect measurement considered in this paper has potential to be experimentally implemented.
A Mining-Rewarding Mechanism for Peer-to-Peer Energy Trading Blockchain

Direct transactions between prosumers without a central authority is allowed by the peer-to-peer energy trading market. Blockchain technology is considered as the optimal system to support P2P transactions and provide the internal payment platform. Miners as a significant role are motivated by the financial incentive to maintain the blockchain operation. This paper proposes a consortium blockchain system under Proof-of-Stake (PoS) consensus protocol to maintain the security and transparency of the energy transactions and a mining-rewarding mechanism to lower trading cost. The structure of the proposed blockchain as well as its implementation process are discussed in this paper. The results of a case study prove the effectiveness and feasibility of the proposed blockchain based method.
Attention-Aware Recognition of Activities of Daily Living Based on Eye Gaze Tracking

Eye movements are essential in comprehending the environment. Users’ attention may be directed via eye gazing, which can enhance Human–Computer Interaction (HCI). Gaze estimation can make HCI more natural in a non-intrusive way. For example, tiredness detection, biometric identification, illness diagnosis, activity recognition, an estimate of alertness level and gaze-contingent display are all possible uses of eye-tracking. It's been available for decades, but it's not been widely used in consumer applications. The high cost of eye-tracking technology and the lack of consumer-level applications are the main reasons behind this. When it comes to Activities of Daily Living (ADL), assistive robots may restore essential levels of independence for the elderly and others who are disabled. Although people can communicate their wishes in numerous ways, linguistic patterns and gaze-based implicit communication of intentions remain underdeveloped, such as bodily expressions or actions. In this study, based on the eye view, a new, implicit, nonverbal communication paradigm for HCI is introduced. Conventional gaze detection systems rely on infrared lights and cameras with high-resolution sensors to achieve outstanding performance and durability. But these systems need to be modified. Thus they are confined to laboratory research and challenging to apply in the real world. Here, we recommend that the gaze be followed using a webcam. Using a webcam to obtain 2D coordinates, we provide a practical visual monitoring framework based on models. This work on the platform is intended to ease HCI and thus increase useful technology and consumers’ privacy in their daily lives. The test results of this experiment indicated that implicit human gaze patterns on visualized objects could efficiently be used for contact with people's intentions. Studies have also shown that it is simple to comprehend and use contact with the gaze. It is further suggested that the subject-dependent eye parameters are calculated using a specific reference system. Finally, an implicit communication system for monitoring and interpreting purposes has been developed. After understanding the user's implicit intention to support the elderly with the action of the eye-gaze, the device can distinguish the activities/requirements required from the home environment. Finally, to direct caregivers to deliver the correct service, the implied purpose may then be used.
Design of Pedagogy Course Information Sharing System Based on Wireless Sensor Network

In order to improve the information sharing effect of pedagogy courses, it is necessary to improve the system information transmission network algorithm. This paper improves the wireless sensor network algorithm and analyzes and verifies the performance analysis model of the Aloha protocol in multihop underwater acoustic sensor networks (UASNs). Based on the improved performance analysis model of the Aloha protocol on the String topology network, the expected throughput of the String topology network and the average end-to-end delay can be obtained, and the transmission effect of pedagogy information in the sensor network can be improved. In addition, this paper constructs a pedagogy course information sharing system based on wireless sensor network to verify the information sharing effect of the model constructed in this paper. The experimental research results show that the system model proposed in this paper has certain effects.
Research on Wushu Sports Feedback System Based on Real-Time Target Intelligent Tracking Video Processing

In order to better improve the martial arts learning effect, this paper puts forward the design method of martial arts motion feedback system based on target intelligent tracking video processing, optimizes the hardware configuration of the system, adds a composite tracker to the system, so as to effectively track the motion changes of different human targets, and further combines the real-time target intelligent tracking video processing technology and builds a moving target tracking model; then, based on the calculation of motion characteristic parameters, CRF method is used for motion behavior recognition and feedback. Finally, simulation studies reveal that a martial arts motion feedback system based on real-time target intelligent tracking video processing can track moving targets more accurately and has the potential for practical implementation.
Research on Ship Detection in the SAR Image Algorithm Based on Improved SSD

With the rapid development of deep learning, target detection technology has been widely used in various fields. At this stage, ship detection method in the SAR image based on the SSD algorithm, the accuracy was more improved than traditional methods, but the SSD algorithm takes a long time, and it was difficult to detect and locate the ship target in real time in the missile-borne SAR image. In addition, the SSD model has a large amount of calculation, and has a deviation in the detection of small targets. A ship detection method in the SAR image is proposed in this paper. The method improves the shallow network structure of the SSD algorithm, increases the network of different modules for feature fusion, reduces the loss of information in the training process, improves the feature extraction ability of small ships in the SAR image, and increases the receptive field of shallow features. Experiments was carried on SSDD and SAR-Ship-Dataset datasets. Compared with SSD algorithm, It was increased about 6% and 7% of mAP, which reduced the calculation amount of convolution, and the model size was reduced by about 7M and 10M.
Collaborative Interactive Workspace Environment Using Augmented Reality

The advent of new augmented reality applications in workplaces and education centers will greatly enhance the productivity exponentially. As the world moves toward online workspace because of the COVID-19 pandemic situation, it is vital that we increase the productivity at home without compromising on the collaboration aspect. We focus on a Collaborative Interactive Workspace Environment (CIWE) as an almost ideal alternative to current office workspace setting. In CIWE, multiple users can influence a shared virtual workspace environment through an Optical Transparent Head-Mounted Device. CIWE utilizing augmented reality allows better approaches for cooperation and perception and helps to build client engagement and commitment. Furthermore, we also present a hybrid workspace solution which uses both the PC and a OTHD in sync to extend the display capabilities of the PC from conventional monitors to the digitally augmented workspace, thus reducing the need of large tables and also cutting the cost by giving the at most conveyances.
Intelligent Transport Surveillance Memory Enhanced Method for Detection of Abnormal Behavior in Video

The purpose is to build a better intelligent transport platform and improve the performance of surveillance video abnormal behavior detection systems under rapid progress of science and technology, to process large-scale traffic surveillance video data. Autoencoder (AE) can detect abnormal behavior by using reconstruction error information. However, it cannot decode some abnormal codes well, so an AE based on memory needs improvement. The objective of this research is to propose a model where abnormal surveillance video can be handled. Therefore, a self-coding method based on memory enhancement is proposed. The steps are as follows: different abnormal behavior detection system algorithms are analyzed at first. The characteristics of three different methods, namely, the original autoencoder (AE), recurrent neural network, and convolutional neural network, are compared. Then, a memory module is proposed to enhance the automatic encoder to reduce the reconstruction error of normal samples and increase the reconstruction error of abnormal samples. The effect image is obtained by Laplace transform and convolution for the image with low definition, and the image with noise is processed by guided filtering. Finally, different methods are used for experimental comparison. Experiments show that, on the dataset Avenue, the frame-level result of the method proposed is about 2% higher than that of the optimal ConvLSTM in the comparison method; on the Ped1 and Ped2 datasets, it is also about 3% higher than ConvLSTM. The comparison of different methods shows that the effect of the method proposed is the best. The self-coding traffic surveillance video abnormal behavior detection system based on memory enhancement is designed with a modular structure and it uses the self-coding method based on memory enhancement. The effectiveness of the proposed method in the real scene is verified by comparing the performance of different methods in the same data set (Xia and Li, 2021).
Jumping Particle Swarm Optimization

Classical Particle Swarm Optimization (PSO) has limitations of slow convergence rate and getting trapped in a local optimum solution, when the data dimensions are high. It is therefore important to propose an algorithm that has an ability to overcome the limitations of classical PSO. Keeping in view the above mentioned limitations, this paper proposes a variant of classical PSO that has an ability to overcome the problem of slow convergence and skipping the local optimum solution. The proposed algorithm is based on a jumping strategy which triggers the particles to jump whenever they are found stuck in a local optimum solution. The proposed jumping strategy in PSO not only enables the algorithm to skip the local optima but also enables it to converge at a faster rate. The effectiveness of the proposed jumping strategy is demonstrated by performing experiments on a benchmark dataset that contains both the unimodal and the multimodal test functions.