Reviewerly/exHarmony huggingface dataset:
Access Control Requirements for Autonomous Robotic Fleets

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Access control enforces security policies for controlling critical resources. For V2X (Vehicle to Everything) autonomous military vehicle fleets, network middleware systems such as ROS (Robotic Operating System) expose system resources through networked publisher/subscriber and client/server paradigms. Without proper access control, these systems are vulnerable to attacks from compromised network nodes, which may perform data poisoning attacks, flood packets on a network, or attempt to gain lateral control of other resources. Access control for robotic middleware systems has been investigated in both ROS1 and ROS2. Still, these implementations do not have mechanisms for evaluating a policy's consistency and completeness or writing expressive policies for distributed fleets. We explore an RBAC (Role-Based Access Control) mechanism layered onto ROS environments that uses local permission caches with precomputed truth tables for fast policy evaluation. To demonstrate the features, we will compare policy outputs against SROS (Secure ROS) policies and test our approach against simulated malicious adversaries with penetration testing and fuzzing techniques.&lt;/div&gt;&lt;/div&gt;
Deep reinforcement learning for next-generation IoT networks

The growing reliance of industry 4.0/5.0 on emergent technologies has dramatically increased the scope of cyber threats and data privacy issues. Recently, federated learning (FL) based intrusion detection systems (IDS) promote the detection of large-scale cyber-attacks in resource-constrained and heterogeneous industrial systems without exposing data to privacy issues. However, the inherent characteristics of the latter have led to problems such as a trusted validation and consensus of the federation, unreliability, and privacy protection of model upload. To address these challenges, this paper proposes a novel privacy-preserving secure framework, named PPSS, based on the use of blockchain-enabled FL with improved privacy, verifiability, and transparency. The PPSS framework adopts the permissioned-blockchain system to secure multi-party computation as well as to incentivize cross-silo FL based on a lightweight and energy-efficient consensus protocol named Proof-of-Federated Deep-Learning (PoFDL). Specifically, we design two federated stages for global model aggregation. The first stage uses differentially private training of Stochastic Gradient Descent (DP-SGD) to enforce privacy protection of client updates, while the second stage uses PoFDL protocol to prove and add new model-containing blocks to the blockchain. We study the performance of the proposed PPSS framework using a new cyber security dataset (Edge-IIoT dataset) in terms of detection rate, precision, accuracy, computation, and energy cost. The results demonstrate that the PPSS framework system can detect industrial IIoT attacks with high classification performance under two distribution modes, namely, non-independent and identically distributed (Non-IID) and independent and identically distributed (IID).
Simulation research on safety evaluation of auto parts manufacturing enterprises based on structure entropy weight method and forward cloud algorithm model

In order to solve the influence of expert personal factors on the final evaluation results in the process of safety evaluation of Chinese parts manufacturing enterprises, the cloud model of safety evaluation of automobile parts manufacturing enterprises based on the structure entropy weight method is established. Using the structure entropy weight method to distribute the weight can effectively avoid the subjectivity of the expert rating. Then the cloud algorithm is used for comprehensive evaluation, which effectively considers the randomness of risks and the fuzziness of expert comments. The research results show that this method can accurately evaluate the auto parts manufacturing enterprises, and can find the defects in safety production and management, and the graphical evaluation results are more intuitive.
Sketch-Based Face Recognition Using Deep Neural Network for Criminal Investigation

Face recognition is vital for criminal investigations. When the suspect photo is not available, his/her sketch is used by the police to match with the mugshot database manually. This process is difficult, inaccurate and time consuming. Hence, this paper proposes automated sketch-based face recognition method for criminal investigation. The Histogram of Oriented Gradients (HOG) algorithm is used to detect face from a sketch because HOG output will be same for sketch as well as photo even in different light conditions. After face is detected, 128 face encodings are generated by deep neural network. The network is then tuned so that the encodings of two images of the same person are closer to each other and the encodings of the different persons are farther away. These encodings of an input face are compared with the encodings of all the face images in the database using cosine distance metric. Based on the minimum distance, the closest match is retrieved. The method is evaluated on CUHK database, and from the results, it is evident that the proposed method performs accurately.
Some Properties of the Members of Petersen Family

This article has restudied the Petersen family in Graph Theory. It discussed the process of establishing this family. This discussion leads to discovering some new properties of the Petersen family's members.
Overview of the CEAMMC PureData distribution and library

&#x0D; &#x0D; &#x0D; This text is a report about the CEAMMC PureData distribution - also known as “Pd-ceammc” - and the CEAMMC external library for the official Pure Data distribution by Miller Puckette - also known as “Pd Vanilla”. The CEAMMC Pure Data repository is available at https://github.com/uliss/pure-data. This distribution is a software package that comprises the original Pure Data distribution with several user interface enhancements and the CEAMMC object library plus yet other external libraries. The CEAMMC library itself is also available via the Deken package manager so it can be installed in Pd Vanilla. This paper focuses on our main concepts for this work, current features and future plan.&#x0D; &#x0D; &#x0D;
The combination of lyrical and dramatic characteristics in the development of the imaginative world in the Sonata – remembrance op.38, no.1 by N. Metner

The proposed article analyzes the combination of two kinds of musical organization: lyrical and dramatic, presented in the „Sonata-Reminiscenza”, op. 38, no 1 by N. Medtner. In the composer’s creations, in general, a synthesis of lyrics and drama is clearly traced, creating a special type of piano dramaturgy, where the lyrical features provoke an in-depth knowledge of the inner world of a person, his thoughts, images, feelings, while the dramatic element, seems „to remove” the shade of subjectivity in the content, leaving it in the sphere of the objective. The idea of reminiscence embedded in the analyzed sonata is the reason for this type of combination of the above-mentioned kinds of musical organization, which directly affect the content and form of the entire creation.
Eye Tracking in Dogs: Achievements and Challenges

In this article, we review eye-tracking studies with dogs (Canis familiaris) with a threefold goal; we highlight the achievements in the field of canine perception and cognition using eye tracking, then discuss the challenges that arise in the application of a technology that has been developed in human psychophysics, and finally propose new avenues in dog eye-tracking research.For the first goal, we present studies that investigated dogs' perception of humans, mainly faces, but also hands, gaze, emotions, communicative signals, goal-directed movements, and social interactions, as well as the perception of animations representing possible and impossible physical processes and animacy cues.We then discuss the present challenges of eye tracking with dogs, like doubtful picture-object equivalence, extensive training, small sample sizes, difficult calibration, and artificial stimuli and settings.We suggest possible improvements and solutions for these problems in order to achieve better stimulus and data quality.Finally, we propose the use of dynamic stimuli, pupillometry, arrival time analyses, mobile eye tracking, and combinations with behavioral and neuroimaging methods to further advance canine research and open up new scientific fields in this highly dynamic branch of comparative cognition.
Prostate Cancer Detection using Deep Learning

According to WHO (World Health Organization), the estimated cancer cases in the year 2020 is 1.4 Million. Prostate cancer is one of the reason for death in men. Prostate cancer is cancer occurred in prostate gland. Prostate cancer begins when cells in the prostate gland start to grow out of control. It is detected using Ultrasound Image or Magnetic Resonance Image (MRI). Now a day after consulting with doctor there are other secondary tools which can help to detect prostate cancer. The system used for detecting prostate cancer are developed using different technology like Artificial Intelligence, Machine Learning and Deep Learning. In this paper we have trying to develop a system which can detect prostate cancer using input like ultrasound image and Magnetic Resonance Image.
The Effective Operation of e-School for Student-Athletes

The purpose of this study is to explore the effective operation cases of e-School for student-athletes and to derive policy insights through this. To achieve this, two student-athletes who effectively utilize e-School in their academic studies and two supervising teachers responsible for each student’s athletic club were selected as research participants. The data collected through in-depth interviews were analyzed according to the coding process presented in the grounded theory. The results are as follows: First, e-School was effective in compensatory learning and academic achievement experience for student-athletes. Second, effective internal factors were e-School learning content sufficiently aligned with the respective school curriculum and the functions of mock exams and unlimited learning. Third, effective external factors were student athletes' motivation for learning and a friendly learning environment. Based on these results, the effectiveness of e-School as a learning tool, the need for providing learning experiences that reflect the characteristics of student-athletes, and the need for various reforms to create a friendly learning environment for the school athletic club were discussed.
Research on Digital Image Encryption Based on Chaotic Syste

With the continuous development of the information age, the speed of information dissemination is accelerating, the channels of dissemination are increasing, and the scope of dissemination is increasing, which has greatly improved our lives and brought a lot of convenience. But at the same time, the information technology has also brought many problems. The security of information is undoubtedly the top priority we should consider.If the security of information dissemination and storage is not guaranteed, personal privacy and national security will be greatly violated. In this situation, the level of information encryption technology is also rising. This paper studies the principle and application of digital image encryption algorithm. In this paper, the chaotic system based on logistic map is used to encrypt the image. Firstly, the related knowledge of chaos and cryptography is briefly introduced, and the principle and steps of digital image encryption are described; Furthermore, the encryption process is based on Logistic map, generating a group of chaotic sequences, which are XOR processed with the original image pixels to obtain a new image. Finally, the new image’s original content cannot be recognized by the naked eye, that is, the encrypted image, and the original image can be decrypted. By changing the key parameters, the encryption characteristics are studied to test the feasibility and practical application of the encryption algorithm.
Research on Efficient Parallelization of Spectral Clustering Algorithm Based on Big Data

Cluster analysis is an important research direction in data mining. In the past decades, a large number of clustering algorithms have been developed, among which spectral clustering has been widely used because of its excellent performance in nonlinear separable data. In addition, with the continuous development of the Internet, more and more data are generated in the network, forming big data. Therefore, how to apply spectral clustering algorithm to big data and mine useful information has become a very important research topic. With the explosion of data scale, some industries have reached terabytes or even petabytes of data. The storage capacity and computing capacity of a single computer can no longer meet the requirements. At the same time, the spectral clustering algorithm will spend a lot of time in the process of processing massive data, which seriously limits the efficiency of the cluster analysis of mass data by the clustering algorithm in tomorrow. Therefore, improving the computational efficiency of the spectral clustering algorithm has become a research focus. In addition to using sampling method to optimize spectral clustering algorithm and reduce its computational complexity, with the emergence and wide application of various distributed parallel computing frameworks such as MapReduce and Spark, researchers have gradually focused on the distributed parallelization of spectral clustering algorithm. In the big data environment, the parallelization of spectral clustering algorithm is realized. It can not only obtain better clustering effect, but also improve the efficiency of spectral clustering algorithm for largescale cluster analysis. For the massive data of various industries, how to improve the large-scale clustering of spectral clustering algorithm. It is of great practical significance to analyze efficiency so as to improve work efficiency. According to the problem of long running time of spectral clustering algorithm in processing large-scale data sets, based on the idea of data parallelism, we study the efficient parallelization of spectral clustering algorithm based on Dask+CPUIGPU platform, which effectively improves the efficiency of spectral clustering algorithm in processing large-scale data sets.
Anomaly Detection in the Air Compressors of Railway Vehicles by Using Attention Mechanism

Condition-based diagnostic analysis of air compressors of railway vehicles is essential for passenger safety and maintenance cost reduction. When conventional long short-term memory (LSTM) autoencoders are used for detecting anomalies in time series data, the input data cannot be reconstructed appropriately when the sequence length is large. Recently, the attention mechanism was proposed to solve this problem encountered in LSTM autoencoders. In the current study, the anomaly detection performance of railway vehicle air compressors was improved using the attention mechanism. The data reconstruction performance of the LSTM autoencoder model was compared with that of the LSTM autoencoder model with the attention mechanism and the transformer model comprising only the attention mechanism by using the sensor data of an air compressor of the Airport Railroad Express train in Seoul, Korea. For artificially generated abnormal data, three anomaly scenarios were successfully detected using the transformer model, which exhibited the best data reconstruction performance among the three models. The results confirmed that transformer models with attention mechanisms can detect anomalies in the air compressors of railway vehicles in a timely manner.
Robots and <scp>AI</scp>: A New Economic Era, Lili YanIng and Gene M.Grossman (eds), Routledge, London, 2023. Pp. 436 + xvii, ISBN 978‐1‐03223066‐5

Asian-Pacific Economic LiteratureVolume 37, Issue 1 p. 138-140 Book Review Robots and AI: A New Economic Era, Lili Yan Ing and Gene M. Grossman (eds), Routledge, London, 2023. Pp. 436 + xvii, ISBN 978-1-03223066-5 Michael Coelli, Corresponding Author Michael Coelli [email protected] Associate Professor, Department of Economics, University of Melbourne, Melbourne, Victoria, AustraliaSearch for more papers by this author Michael Coelli, Corresponding Author Michael Coelli [email protected] Associate Professor, Department of Economics, University of Melbourne, Melbourne, Victoria, AustraliaSearch for more papers by this author First published: 11 April 2023 https://doi.org/10.1111/apel.12377Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume37, Issue1May 2023Pages 138-140 RelatedInformation
Choreographic Programming of Isolated Transactions

Implementing distributed systems is hard; choreographic programming aims to make it easier.In this paper, we present the design of a new choreographic programming language that supports isolated transactions among overlapping sets of processes.The first idea is to track for every variable which processes are permitted to use it.The second idea is to use model checking to prove isolation.
Research on wearable multi-health parameter monitoring system based on Internet of Things

Strengthening the daily monitoring of health sign parameters can effectively avoid the occurrence of diseases and accidents. Some current daily monitoring devices are large, inconvenient to operate, and single-functional, which do not meet people's needs for portable daily wearable multi-health parameter monitoring. In response to the above situation, this paper researches and designs a wearable multi-health parameter monitoring system based on Internet of Things technology in combination with telemedicine monitoring technology. The hardware module of this system contains seven parts, including MCU microprocessor, acceleration sensor module, temperature collection module, heart rate and blood oxygen collection module, respiration detection module, Bluetooth module and network communication data transmission module. This system can realize the monitoring of several health parameters such as body temperature, step count, travel distance, heart rate, blood oxygen and respiration. The overall structure adopts miniaturized design, which is easy to be embedded into more portable products. The system is designed with a supporting cell phone APP, through which users can view the measurement results of health parameters and real-time monitoring of dynamic information, which greatly realizes the management of their own health records. In addition to self-monitoring, it can also be transmitted to doctors or medical institutions through the network, which can realize remote monitoring or diagnosis. The experimental results show that the monitoring system is fully functional, small in size and has many measurement parameters. The accuracy of body temperature is 0.1 degree, the error of pedometry is 5%, the respiratory range is ± 346pa, the error of heart rate can reach ±2%+1 times/min, and the error of blood oxygen can reach ±2% in the range of 70-100, which can conveniently meet the needs of people's home daily multi-health parameters monitoring and telemedicine.
Enhancing Smart Home Security with Face Recognition using Deep Learning

Abstract: Recently, security has been a growing concern for human life, and the cost is a significant concern. The research recommends implementing a real-time recognition system to rapidly handle photographs to reduce monitoring expenses and ensure safety in homes and offices by identifying people. The main goal of this study was to create an intelligent face recognition approach using deep learning for intelligent homes. To illustrate the study's effectiveness, the suggested model is contrasted with other cutting-edge techniques. The research introduces a tree-based deep model for facial recognition in the Cloud, which requires fewer computing resources but maintains accuracy. The input volume of the model is partitioned into multiple volumes, and trees are created for each volume according to their height and number of branches. A residual function, constructed from a convolutional layer and two non-linear functions, represents each branch. The proposed model is assessed on various publicly accessible databases and compared with the industry's best deep models for facial recognition.
Empowering Privacy-Preserving Machine Learning: A Comprehensive Survey on Federated Learning

As the need for machine learning models continues to grow, concerns about data privacy and security become increasingly important. Federated learning, a decentralized machine learning approach, has emerged as a promising solution that allows multiple parties to collaborate and build models without sharing sensitive data. In this comprehensive survey, we explore the principles, techniques, and applications of federated learning, with a focus on its privacy-preserving aspects
The Backbone of Computing: An Exploration of Data Structures

Data structures are the foundation of computing, providing efficient ways to store and manipulate data. They are essential for designing and implementing algorithms that can handle large amounts of information quickly and accurately. In this paper, we explore the world of data structures, examining their basic principles, properties, and uses. We start by introducing the most common data structures, such as arrays, linked lists, stacks, queues, trees, and graphs, and discuss their advantages and limitations.
On the Background Values of the Geochemical Spectra of Microelements of the Solid Phase of Snow During their Comparative Analysis in the Cities of Tobolsk And Tyumen

Local features of the geochemical spectra of trace elements in the snow dust of Tobolsk and Tyumen were revealed using a hierarchical cluster analysis of the normalized values of their content. Geochemical spectra of snow sampling points were studied in residential and industrial areas, as well as in significantly remote areas (conditional background). Four clusters were identified in Tobolsk and five in Tyumen, for each of them characteristic geochemical spectra and pollution sources were detected.