Reviewerly/exHarmony huggingface dataset:
An Application-oblivious Memory Scheduling System for DNN Accelerators

Deep Neural Networks (DNNs) tend to go deeper and wider, which poses a significant challenge to the training of DNNs, due to the limited memory capacity of DNN accelerators. Existing solutions for memory-efficient DNN training are densely coupled with the application features of DNN workloads, e.g., layer structures or computational graphs of DNNs are necessary for these solutions. This would result in weak versatility for DNNs with sophisticated layer structures or complicated computation graphs. These schemes usually need to be re-implemented or re-adapted due to the new layer structures or the unusual operators in the computational graphs introduced by these DNNs. In this article, we review the memory pressure issues of DNN training from the perspective of runtime systems and model the memory access behaviors of DNN workloads. We identify the iterative, regularity , and extremalization properties of memory access patterns for DNN workloads. Based on these observations, we propose AppObMem, an application-oblivious memory scheduling system. AppObMem automatically traces the memory behaviors of DNN workloads and schedules the memory swapping to reduce the memory pressure of the device accelerators without the perception of high-level information of layer structures or computation graphs. Evaluations on a variety of DNN models show that, AppObMem obtains 40–60% memory savings with acceptable performance loss. AppObMem is also competitive with other open sourced SOTA schemes.
CHARACTERISTICS OF PHYSIOLOGICAL CHANGES IN ATHLETE TRAINING BASED ON THE DATA MINING ALGORITHM

ABSTRACT Objective: In the competition of athletic training, it is imperative to use various physiological and biochemical indicators to study the changes they can bear. Methods: In this paper, national tennis players’ physiological and biochemical indicators are taken as samples, and Artificial Neural Network (ANN) in data mining algorithm is used to classify and predict the sample data. Based on this, to solve the BP neural network's failure in easily falling into a local minimum, the ant colony optimization (ACO) algorithm was introduced to train the changes in the neural network. Finally, the improved BP neural network technology of the ant colony optimization algorithm is used in the model to analyze the physiological changes in tennis players. Results: The research results show that the model successfully predicted the physiological change in athletes and could provide coaches with a basis for decision-making. Conclusions: The physiological change in athletes is combined with the neural network algorithm to establish a connection between the two, which provides an effective and reliable method for detecting the physical function of sports transportation with unique guidance in athletes’ training and competition. Level of evidence II; Therapeutic studies - investigation of treatment results.
Segmentation of Partially Shadowed Rust Images

The use of digital image processing for the analysis of corroded painted surfaces of constructions is considered. Rust segmentation of painted surface images in different illumination conditions, especially shadows, is a challenging task. Shadows influence on RGB pixel values in images and lead to further oversegmentation. The parameterized Retinex is used to eliminate non-uniform illumination. The influence of color restoration of partially shadowed rust images on segmentation results is studied. The color restoration is fulfilled using ColorChecker and least-squares approximation by a linear function. Gaussian mixture models based rust segmentation was used. Finally the results of experiments are shown and analyzed.
Dissipative entanglement generation between two qubits parametrically driven and coupled to a resonator

An entangled-state generation protocol for a system of two qubits driven with an ac signal and coupled through a resonator is introduced. We explain the mechanism of entanglement generation in terms of an interplay between unitary Landau-Zener-St\"uckelberg (LZS) transitions, induced for appropriate frequencies and strong amplitudes of the applied ac signal and dissipative processes dominated by photon loss. In this way, we find that the steady state of the system can be tuned to be arbitrarily close to a Bell state, which is stable against photon loss. Effective two-qubit Hamiltonians that reproduce the resonance patterns associated with LZS transitions are derived.
Hardware And Software System For Control Of Complex Sensorimotor Response And Coordination Parameters During Physical Training

Modern progress in the use of electronic methods in measuring instruments, systems, and devices creates an opportunity to modernize and intensify the means of control in the field of physical culture. Today, the process of modernization of physical education is evolutionary and is based on the introduction of new achievements, positive experience in this field of knowledge, and aggregate judgments on an interdisciplinary basis. At the same time, this process involves significant changes due to current trends in technology development. Issues related to the study of the peculiarities of the functioning of the central nervous system and the effectiveness of various activities based on the establishment of the time of motor response to date have been studied in great detail. The state of complex sensorimotor reaction is currently the subject of research in various fields of knowledge: psychology, physiology, inclusive pedagogy, physical education, and sports. Given that in practical terms motor responses are an indirect objective indicator of the maturity of different parts of the central nervous system, issues of their control arise as a result of intensive development of various fields of knowledge at the intersection of sciences, namely psychophysiology, physiology of higher nervous activity, neuropsychology, etc.
Convolutional Neural Network Based Network Distance English Teaching Effect Evaluation Method

Aiming at the low accuracy of English teaching quality evaluation, a user perception evaluation modeling method based on convolutional neural network was proposed. Make full use of product usage data to establish quantitative mapping between teaching quality assessment and teaching quality to support product design improvement. On this basis, a convolutional neural network structure suitable for user perception evaluation model is established. Then, the optimal hyperparameters of the model were determined by k-fold cross validation analysis, and the overfitting problem of the model was improved. Finally, an example of user perception modeling for smart phones is taken to verify the effectiveness of the method. It is used for user perception assessment and prediction, which can help English teaching methods timely and accurately assess performance and provide decision support for English teaching improvement. SKYPE is currently the world's most popular Internet remote video, audio, text, and real-time interactive platform; the real-time audio and video communication is very clear; the remote education will expand to the family, especially to promote the remote universality and universal English education that can play a big role. In this paper, the basic function and performance of the platform are presented, and distance education in English presents a practical approach to the application aspects.
Primitive normal values of rational functions over finite fields

In this paper, we consider rational functions [Formula: see text] with some minor restrictions over the finite field [Formula: see text] where [Formula: see text] for some prime [Formula: see text] and positive integer [Formula: see text]. We establish a sufficient condition for the existence of a pair [Formula: see text] of primitive normal elements in [Formula: see text] over [Formula: see text] Moreover, for [Formula: see text] and rational functions [Formula: see text] with quadratic numerators and denominators, we explicitly find that there are at most [Formula: see text] finite fields [Formula: see text] in which such a pair [Formula: see text] of primitive normal elements may not exist.
Neural-network-based Gesture Detection for Capacitive Sensing

This work considers the algorithms, which make it possible to control various devices with movements of our hands. Signals from a small number of capacitive sensors are used to detect the simplest gestures. The classical feedforward neural network supplemented by some properties of the spiking neural networks allows identifying the type of a gesture – the direction of a hand movement above the plane of sensors – based on sensor signals change. The results of numerical experiments are presented, which confirm the efficiency of the proposed gesture detection algorithm for controlling various devices.
Statistically Empirical Integrated Approach for Knowledge Refined Text Classification

Automated text mining is an especially important task in modern data analysis, both from theoretical and experimental points of view. This particular problem has a major interest in the digital age that is related to “Artificial Intelligence, Machine learning and Information Retrieval”. Feature selection and classification of high dimensionality of text data are challenging tasks. In this paper, we adopted an optimal method for dealing with high dimensionality of data. Later, we chose an appropriate strategy (learning algorithm) for an effcient model training. Our empirical evaluation and experimental analysis show that the proposed method performs better compared with other variable selection-based dimension reduction and further text categorisation methods. We exploited several systematic and careful experimentation scenarios in this work to discover what architecture works best for this BBC news dataset. We used 3 hidden layers, each layer with 128 neurons. We observed this architecture optimal as per our specific problem experimentation. Moreover, our proposed method can be useful for improving efficiency and speed-up the calculations on certain datasets.
A spatio-temporal reference trajectory planner approach to collision-free continuum deformation coordination

This paper proposes a spatio-temporal reference trajectory planner approach to safely plan continuum deformation coordination of a multi-agent system (MAS) in an obstacle-laden environment. We formulate a desired n-D continuum deformation as a leader–follower problem guided by n+1 leaders and followed by the remaining follower agents either through direct communication with leaders or communication with neighboring agents. To plan a safe continuum deformation coordination, we first provide sufficient conditions for inter-agent and obstacle collision avoidance. Then, we spatially optimize the continuum deformation coordination using the A* search to determine leaders’ desired waypoints in an obstacle-laden environment. Leaders’ desired waypoints are connected through C1 continuous paths that are assigned by the spline curve fitting method. The desired trajectories of the leaders are then determined by planning a piece-wise constant reference input such that travel time is minimized and safety of the continuum deformation coordination is ensured.
Fully convolutional Deep Stacked Denoising Sparse Auto encoder network for partial face reconstruction

Face recognition is one of the most successful applications of image analysis. Since 1960s, automatic face recognition research has been carried out, but the problem is still unresolved. Therefore, in this manuscript, a novel Partial face reconstruction (PFR) algorithm called Self- motivated feature mapping (SMFM) combining a Fully Convolutional Network (FCN) and Deep Stacked Denoising Sparse Autoencoders (DS-DSA) algorithm is proposed to overcome the challenges. The proposed approach focuses on the generation of feature maps from the Fully Convolutional Network and it is used Deep Stacked Denoising Sparse Autoencoders to perform the partial face reconstruction. The spatial maps are generated by extracting the features from Fully Convolutional Network and it is supplied as the input for partial reconstruction and re-identification to the Deep Stacked Denoising Sparse Autoencoders network. The main aim of the proposed work is "to enhance the accuracy during facial reconstruction". The proposed approach is implemented in MATLAB platform. The performance of the proposed approach attains 23.45% and 20.41% accuracy,25.93`% and 19.43% sensitivity, 22.21% and 24.41% precision and20.21% and 23.41% Specificity greater than the existing approaches, like Partial Face Reconstruction using generative adversarial networks (GANs), Partial Face Reconstruction using Deep Recurrent neural network (DRNN).
Cloud Video System Availability Assessment Using Markov and Semi-Markov Models

Comparative analysis of Markovian and Semi-Markovian modelling for cloud video system availability is performed considering its architecture and dependability characteristics of various components. Initial data for modelling is based on review of research and operation data of cloud systems. The results of modelling provide decision making related to choice of technique of modelling and availability assessment. Besides, they contributes to ensuring the high availability of cloud systems and can be used by developers in order to address the issues into DevOps engineering area.
Factors Influencing the Adoption of Advanced Cryptographic Techniques for Data Protection of Patient Medical Records

Healthcare organizations that maintain and process Electronic Medical Records are at risk of cyber-attacks, which can lead to breaches of confidentiality, financial harm, and possible interference with medical care. State-of-the-art methods in cryptography have the potential to offer improved security of medical records; nonetheless, healthcare providers may be reluctant to adopt and implement them. The objectives of this study were to assess current data management and security procedures; to identify attitudes, knowledge, perceived norms, and self-efficacy regarding the adoption of advanced cryptographic techniques; and to offer guidelines that could help policy-makers and data security professionals work together to ensure that patient data are both secure and accessible.We conducted 12 in-depth semi-structured interviews with managers and individuals in key cybersecurity positions within Israeli healthcare organizations. The interviews assessed perceptions of the feasibility and benefits of adopting advanced cryptographic techniques for enhancing data security. Qualitative data analysis was performed using thematic network mapping.Key data security personnel did not perceive advanced cybersecurity technologies to be a high priority for funding or adoption within their organizations. We identified three major barriers to the adoption of advanced cryptographic technologies for information security: barriers associated with regulators; barriers associated with healthcare providers; and barriers associated with the vendors that develop cybersecurity systems.We suggest guidelines that may enhance patient data security within the healthcare system and reduce the risk of future data breaches by facilitating cross-sectoral collaboration within the healthcare ecosystem.
Multicategory Image Recognition Based on Image Semantic Features and Transformer

In the field of computer vision research, generative adversarial networks (GAN) are used for general object recognition. In recent years, however, GAN have learned only from image data without using label information. In recent years, however, unsupervised learning, which learns GAN only from image data without using label information, where GAN are learned from image data alone without using label information, has been introduced. In this paper, we describe research on unsupervised learning of GAN since the introduction of transformer, reviewing trends in computer vision/artificial intelligence-related research since the introduction of transformer from a visual neuroscience perspective.
IODTDLCNN: Implementation of Object Detection and Tracking by using Deep Learning based Convolutional Neural Network

Video object detection plays the major role in variety applications including security, remote sensing and hyperspectral. Deep learning-based algorithms have made significant advances in video object recognition in recent years. The conventional machine learning applications are resulted in poor accuracy. In this article, a unified deep learning based convolutional neural network (DLCNN) is developed for composite multi object recognition in videos. To enhance composite object recognition, DLCNN analyses a composite item as a collection of background and adds part information into feature information. Correct component information may help forecast the shape and size of a feature data, which helps solve challenges caused by different forms and sizes of various objects. Finally, the DLCNN draws a bounding box to detected object by using the background features. Further, the simulation results shows that the performance of proposed method is improved as compared to the state of art approaches.
Analysis on the Sharing Method of English Mobile Education Resources Based on Wireless Broadband Connection

The sharing of English educational resources has always been a research hotspot in the English industry. The traditional English educational resource sharing system has low resource sharing efficiency and no real-time contribution capability. Therefore, this paper proposes a method to analyze the characteristics of online educational resources. By comparing the main features of the IEEE802 wireless standard series, the IEEE802.16 protocol stack model is constructed by using the IEEE802.16 protocol, the business layer design and CS layer of the English education resource system are realized through the MAC layer and the CPS layer, and the mobile English education resource sharing system is analyzed. Live voting service: in order to improve the sharing speed of mobile English education resources, an NRTPS uplink bandwidth allocation mechanism is proposed, and the design of a mobile English education resource sharing system based on wireless broadband connection is realized. The experimental results show that the resource sharing efficiency of the system is high, and it plays an important role in the development of English education.
The Illicit Ecosystem of Hacking: A Longitudinal Network Analysis of Website Defacement Groups

Over the past four decades, research on hackers has widely propagated within the social sciences. Although this area of scholarship yields rich insight into the interpersonal dynamics of hackers, research on the unique ecosystems they create and inhabit is scant in comparison. The current study aims to offer a more complete assessment of hackers’ ecosystems by incorporating the group affiliations which link hackers to one another. Using 12 months of archived website defacement data containing individuals’ self-reported group affiliations alongside their hacking activities, the study reconstructs the social network of hacker groups over time. Findings reveal the illicit ecosystem to be loosely connected, yet densely clustered around a few central groups. The ecosystem also maintained its network features across the observation period with no sign of structural degradation. These findings corroborate extant research on the social environments of hackers, offer an innovative look into the illicit ecosystem of website defacers, and serve as a steppingstone to extend investigations of criminal behavior to the group-level.
Image Reconstruction Based on Progressive Multistage Distillation Convolution Neural Network

To address the problem that some current algorithms suffer from the loss of some important features due to rough feature distillation and the loss of key information in some channels due to compressed channel attention in the network, we propose a progressive multistage distillation network that gradually refines the features in stages to obtain the maximum amount of key feature information in them. In addition, to maximize the network performance, we propose a weight-sharing information lossless attention block to enhance the channel characteristics through a weight-sharing auxiliary path and, at the same time, use convolution layers to model the interchannel dependencies without compression, effectively avoiding the previous problem of information loss in channel attention. Extensive experiments on several benchmark data sets show that the algorithm in this paper achieves a good balance between network performance, the number of parameters, and computational complexity and achieves highly competitive performance in both objective metrics and subjective vision, which indicates the advantages of this paper's algorithm for image reconstruction. It can be seen that this gradual feature distillation from coarse to fine is effective in improving network performance. Our code is available at the following link: https://github.com/Cai631/PMDN.
HLifeRL: A hierarchical lifelong reinforcement learning framework

Deep reinforcement learning research in a single-task environment has made remarkable achievements. However, it is often plagued by catastrophic forgetting, prohibitively low sample efficiency and lack of scalability problems when facing multi-task environment. To solve these issues, a Hierarchical Lifelong Reinforcement Learning framework (HLifeRL) is proposed to enhance the ability of agents to deal with a sequence of tasks in the way of skill discovery (we treat option as low-level skill in this paper) and hierarchical policy. HLifeRL can automatically extract task-related knowledge without any human intervention or priori knowledge. Moreover, with the help of a scalable library and the master policy, we can flexibly combine various skills to complete multiple tasks in the form of call-and-return. The experimental results show that HLifeRL can accelerate the speed of single-task training and deliver remarkable stability along with scalability in a lifelong setting environment.
Method of Binary Polyadic Sequences Structural Coding

Describes an approach to constructing a method for encoding binary polyadic numbers. The method is based on the recurrent finding of the weight coefficients of the elements of structural numbers in a binary polyadic space and the identification of zero strings of the processed sequences. Thus, it becomes possible to reduce the total number of bits for the presentation of the initial data more significant, in comparison with standardized approaches to coding. A feature of the proposed method is the focus on reducing new types of redundancy. These types of redundancy include, in particular, in addition to statistical, also structural and combinatorial. Separately, it should be said that due to the simplicity and triviality of the method under consideration, its implementation in the existing technological basis does not require special conditions regarding the computing power of the hardware platform.
Definition System of Human Body Position in Virtual Reality

System for determining the position of the human body in the virtual world has been designed, implemented and researched. The system consists of two independent subsystems: subsystem for collecting, processing and transmitting data on the position of the human body and subsystem for visualizing collected data to the virtual world. In general, the system measures the position of the human body, processes the data, generates the set of commands for the virtual world and sends these commands to the virtual reality helmet for processing. Schemes of the system and algorithm of system's functioning are offered. The system using quaternions and Euler angles was investigated. Also was shown that the system works more efficiently when using quaternions.