Reviewerly/exHarmony huggingface dataset:
Bus Epidemic Monitoring System Based on K210

Out of the normalization of the epidemic, urban bus epidemic management system is a safety monitoring system that focuses on the detection of large-scale public health safety in public transport, which is assembled in both vehicle and cloud. Compared with the previous mainstream stand-alone epidemic surveillance system, three kinds of detection including mask, face and temperature can be done in the vehicle before the face information is uploaded to the cloud to be processed and extracted for digital facial features, which can be reserved with the trip record and health identification of designated individuals, providing an effective deep search that can quickly screen the persons who have a risk of contact with the designated individuals and give feedback to the car. The cloud platform is linked with the command center, indicating that the vehicle terminal will give an alarm as soon as a risk person gets on board while the cloud will also send details to the command center. This system adopts the architecture of edge computing and cloud collaboration, innovatively proposing the edge cloud monitoring structure, which has high precision and speed under normal flow and meets the demand of massive detection in public transport during the epidemic. The vehicle terminal centralizes the computation on edge extended, allowing for faster response of web service even with numerous functions without compromising the epidemic surveillance. In addition, a large amount of redundant computing power saved by edge computing can assist the secondary treatment and judgment of recognition results, and a data visualization platform can be built for comprehensive management.
An Orthogonal Classification Layer with Kasami Sequences for Discriminative Feature Learning in Neural Networks

This paper proposes a novel Orthogonal Classification Layer (OCL) utilizing Kasami sequences for neural networks trained for classification problems. OCL consists of a fully connected layer with fixed orthogonal weights and zero biases for all of its output neurons. Attaching the OCL to the end of any neural network encourages the network to generate a unique latent representation (orthogonal code) at its last hidden layer for each data class. This is achieved by associating and fixing the weights for each of the output neurons to a unique code from a set of orthogonal sequences, such as Kasami. When networks use OCL the latent representations they learn for each data class at the end of the network converge to values equivalent to the fixed weights of the OCL. Auto-correlation and cross-correlation properties of orthogonal codes maximize the output of the correct class and increase its separation from the outputs of all other classes. Therefore, networks trained with OCL benefit from a wider classification decision margin than networks without OCL. Moreover, the feature sets extracted by the network for different data classes are well separated and more amenable to human interpretation. The implementation of OCL is simple and OCL can easily be integrated into any neural network architecture without a need for network architecture modifications or changes to the training scheme (for example optimization, normalization, or regularization) adopted in the original network architecture. The computational and memory cost required for the OCL is lower than conventional classification layers since the weights are fixed and no gradient is required. Though simple and practical the proposed OCL enhances learning of discriminative latent representations, generates explainable features, and provides high classification accuracy. We demonstrate this through a set of evaluation experiments comparing the performance of equivalent networks with and without OCL.
Optimization of Resource Allocation using Quantum Genetic Algorithm for Cloud Data

The term "optimization" refers to the process of identifying the best solution for a given problem. Optimization techniques such as TSP, categorization, scan, and ERP systems are used to solve a variety of real-world problems. Scheduling is an important domain of the optimization technique, and it is one of their primary application fields. The primary goal of this paper is to investigate and optimize cloud computing-based resource scheduling techniques. As a result, this paper proposes a quantum genetics-based resource allocation optimization strategy. The technique starts with quantum physics for encoding and population generation, then moves on to a genetic algorithm for searching the generated population. Following a search, the genetic algorithm returns resource sequences that match the search criteria, as well as a task list. The CPU consumption cycles are efficiently minimized by using the resulting sequences. The CloudSim simulation tool is also used to test the efficacy of the proposed algorithm for resource scheduling. Finally, the system's output is determined by the algorithm's time complexity, space complexity, and CPU usage cycles. A comparison analysis of historically available resource allocation techniques is also carried out and compared to similar criteria. When compared to conventional resource allocation techniques, experimental findings indicate that the proposed quantum genetics concept provides superior efficiency.
Study of fractal regulators

In this paper is proposed a study on no-integer order regulators which is related to the synthesis in which different values are selected around the cutoff frequency of the object to which their parameters are setting. After synthesis, they are applied in an automatic control system. To prove the efficiently it is made a comparative analysis of the quick response and stability margin.
A Hands-on Approach to Cloudifying Curriculum in Computing and Engineering Education

The term cloud computing is used to describe a pool of configurable virtualized computing resources (databases, software, storage, network, servers, etc.) delivered as a service via the Internet. The demand for cloud computing professionals continues to increase, yet academic institutions make little effort to integrate cloud computing into the computing curriculum. Lack of cloud computing resources has been identified as a major hindrance to teaching and learning cloud computing in higher education. This workshop aims to explore resources that could help computing educators teach cloud computing to students. Through hands-on activities, participants will learn about the various resources, tools, and technologies that are freely available for teaching and learning cloud computing.
Deep Representation Learning for Vietnamese Speaker Recognition

Speaker recognition is the process of identifying an individual from their voices, and it has been widely applied in many real-world applications. Recently, deep learning has instigated a revolutionary high success rate in speaker recognition. The major advantage of deep learning over conventional methods for speaker recognition is attributed to its representation ability, and the ability to produce highly abstract embedding features from utterances. Recent researches had revealed that deep learning method in learning speaker features from raw data, is strongly depending on a speaker's language. However, only minimal researches had done on deep learning over Vietnamese speaker recognition to present. Nevertheless, this paper has proposed a deep transfer learning method which integrates both transfer learning and deep learning to build models for Vietnamese speaker recognition. Our experimental results indicated that the proposed method is able to build accurate models for Vietnamese speaker recognition.
Reusing Agent's Representations for Adaptation to Tuned-environment in Fighting Game

Reinforcement learning agents have been used for one-on-one fighting battlefield or quality assurance (QA) of commercial games. In spite of its performance beyond human-level, it is not easy to apply because actual commercial games are frequently updated. In this paper, we propose a method to adapt reinforcement learning agent to slightly tuned environment by reusing representations of neural network. Agent trained by the proposed method converges at 2.11 million steps, which shows at least 3 times faster than those of fine-tuning and training from scratch to reach the same competence. We also tested larger representation layers with smaller actor-critic ones. Although it fails to train agent, it demonstrates distinct characteristics. Finally, action distributions of fully trained agent for each environment are analyzed. Entire process of adapting to new environment presented in this paper gives insights of game balancing framework to game developers.
A Method for Slope Detection of Planetary Surface in The Autonomous Obstacle Avoidance of Planetary Rover

Planetary rover is the most efficient way to explore planets. The exploration area of planetary rover is a complex and unfamiliar environment. Planetary rover needs to perceive surrounding terrain and obstacle information clearly so as to ensure that the planetary rover can effectively and accurately perform autonomous obstacle avoidance and path planning in the unfamiliar environment. There is a problem that the slope is misjudged as an obstacle in autonomous obstacle avoidance, which leads to wrong planning decision. Meanwhile the passable area is treated as restricted area. Therefore, it is necessary to detect and estimate slope and identify the slope passing ability of planetary rover. A method for slope detection of planetary surface in the autonomous obstacle avoidance of planetary rover is presented in this paper. The down-sampling of original point cloud is processed by voxel mesh filtering. Meanwhile the real time 3D point cloud of large-scale planetary surface is clipped and segmented. The ground and obstacle are separated by local ground plane fitting estimation. Then slopes are calculated and classified, through which the slope above the threshold is reserved as obstacle and the slope below the threshold is set as passable area. The method presented in this paper is able to detect and classify the slope accurately in real time, which is shown in the simulation results.
A Secure Authentication and Data Sharing Scheme for Wireless Sensor Networks based on Blockchain

In this paper, a blockchain based scheme is proposed to provide registration, mutual authentication and data sharing in wireless sensor network. The proposed model consists of three types of nodes: coordinators, cluster heads and sensor nodes. A consortium blockchain is deployed on coordinator nodes. The smart contracts execute on coordinators to record the identities of legitimate nodes. Moreover, they authenticate nodes and facilitate in data sharing. When a sensor node communicate and accesses data of any other sensor node, both nodes mutually authenticate each other. The smart contract of data sharing is used to provide a secure communication and data exchange between sensor nodes. Moreover, the data of all the nodes is stored on the decentralized storage called interplanetary file system. The simulation results show the response time of IPFS and message size during authentication and registration.
Neurophysiology Based on Deep Neural Network under Artificial Prosthesis Vision

Abstract With the vigorous development of electronic technology and computer technology, as well as the continuous advancement of research in the fields of neurophysiology, bionics and medicine, the artificial visual prosthesis has brought hope to the blind to restore their vision. Artificial optical prosthesis research has confirmed that prosthetic vision can restore part of the visual function of patients with non-congenital blindness, but the mechanism of early prosthetic image processing still needs to be clarified through neurophysiological research. The purpose of this article is to study neurophysiology based on deep neural networks under simulated prosthetic vision. This article uses neurophysiological experiments and mathematical statistical methods to study the vision of simulated prostheses, and test and improve the image processing strategies used to simulate the visual design of prostheses. In this paper, based on the low-pixel image recognition of the simulating irregular phantom view point array, the deep neural network is used in the image processing strategy of prosthetic vision, and the effect of the image processing method on object image recognition is evaluated by the recognition rate. The experimental results show that the recognition rate of the two low-pixel segmentation and low-pixel background reduction methods proposed by the deep neural network under simulated prosthetic vision is about 70%, which can significantly increase the impact of object recognition, thereby improving the overall recognition ability of visual guidance.
The Multi-objective Optimization of the Convolutional Neural Network for the Problem of IoT System Attack Detection

This paper proposes a new approach, applying multi-objective optimization to improve the convolutional neural network structure for the IoT system attack detection problem. The goal of the paper is to develop a global optimization method that balances detection speed and accuracy when using CNN. The accuracy objective function, the speed objective function and the global objective function are constructed to evaluate each network topology. Based on the value of the global objective function to choose the best network structure according to the Pareto multi-objective optimization method. The proposed method in the paper is experimentally evaluated by the K-fold method and has given positive results. The most balanced CNN structure has an accuracy of 99.94% and a classification time of 253,86 s.
Research on Massive Data Mining Technology Based on Map Reduce

MapReduce is a programming model, which can run in heterogeneous environment. It is easy to program and does not need to care about the details of the underlying implementation. It is used for the parallel operation of large-scale datasets. MapReduce is applied to three algorithms of data mining: Puqi Bayesian classification algorithm, κ—modes clustering algorithm and Eclat frequent itemset mining algorithm. Experimental results show that MapReduce can effectively improve the efficiency of massive data mining on the premise of ensuring the accuracy of the algorithm.
Unsupervised Learning Based Detection Method for The Life Cycle of Virtual Cryptocurrency

Since the birth and open source of Bitcoin, there are more than 10,000 kinds of virtual cryptocurrencies in the market. Every day, virtual cryptocurrencies are born, but also virtual cryptocurrencies die out. In the life cycle of virtual cryptocurrencies, different periods of abnormal transactions will occur. However, there are still deficiencies in the definition and related studies of the life cycle of virtual cryptocurrencies in existing researches. Machine learning (ML) can dig out the hidden rules from a large amount of data. We use unsupervised learning in machine learning to detect the life cycle of virtual cryptocurrencies. In this work, we divide and define each stage of the life cycle of virtual cryptocurrencies in detail. Based on the popularity value system of virtual cryptocurrencies and the similarity comparison algorithm, we establish a virtual cryptocurrencies life cycle detection tool to detect the life cycle stage of virtual cryptocurrencies. Experimental results demonstrate the effectiveness of the proposed unsupervised learning based virtual cryptocurrency life cycle detection tool.
Attention guided complementary feature integration for latent image recovery from noisy/blurry pairs

Low-light imaging using a hand-held camera is a challenging task due to excessive noise in the scene. Most of the existing methods try to address this problem either by denoising an image captured using high ISO or by deblurring an image captured using long exposure time. However these methods use a single image to estimate the latent scene and hence fail to leverage the complimentary information available in the scene. In this paper, we propose a method to estimate the latent image using a pair of images captured using high ISO and high exposure time respectively, to leverage the complimentary information present in the two captures. We propose a novel deep learning based method to efficiently extract and integrate the information present in the two images. Contrary to other methods, we use separate filters to extract the complimentary information from the two images. We also progressively integrate the extracted features using a novel attention-guided mechanism. Further, we address the spatially varying nature and localization of motion blur in real life captures by using spatial attention layers. The proposed method achieves state-of-the-art performance against single as well as other noisy/blurry approaches to the problem. We also show that the network learns spatial attention maps with strong correlation to the blur in the scene, and thus the proposed method is more interpretable and easier to analyze.
AI-based Stereoview to Multiview Generation by Using Deformable Convolution

Three dimension (3D) movies are the main trend in the film industry. In the current stereoview, the audiences require wearing 3D glasses to perceive 3D visualization. The 3D movies with stereoview with only left and right views, which cannot be directly displayed in the naked-eyes 3D displays. To directly support naked-eyes 3D displays, which require multiple views, we propose a deep learning based stereo to multiview conversion system by using the deformable convolution to synthesize additional virtual views. For immersive 3D multimedia services, we hope we can improve the quality of user 3D experiences without wearing 3D glasses without the needs of depth estimation and depth image based rendering functions.
Blockchain Adoption Intention by a Third-Party Logistics Company: A Malaysian Case Study

Blockchain is a distributed ledger technology that continues to make headlines as a potential silver bullet for companies wanting to harness its benefits like transparency, integrity, and the ability to track and trace. Pioneered by the financial industry, other sectors are beginning to follow through with adoption, including the logistics and transportation sector. However, current studies on adoption by this specific sector remains underrepresented in the literature. This research uses qualitative data from in-depth interviews of a single third-party logistics (3PL) company to elicit feedback on their perceived benefits of blockchain adoption and the factors that might drive or inhibit adoption. The results show that factors identified support previous literature as well as providing rich qualitative data explaining the barriers that inhibit adoption. The results contribute to both literature and practice, providing insights to other companies within the 3PL industry.
Analysis of predictive capabilities of metacognitive strategies in the expression of professional thinking of military personnel

The article examines professional thinking in relation to the conditions of military activity. The goal is to identify a model of cadets’ metacognitive competence, contributing to the successful formation of professional thinking in the conditions of military activity. The construction of two regression models is carried out by means of multiple stepwise regression analysis with inclusion. In the first model, the variable response was the over-situational style of thinking of servicemen, determined by the questionnaire «Determination of the dominant level of problematicity in solving service-professional problem situations.» In the second model of the variable, the response was the type of professional thinking determined on the basis of the case method. Cadets’ metacognitive strategies were used as variable predictors. It was possible to establish that both regression models are statistically significant, the level of error probability is less than 0.01% and explain more than 40% of the variability of the variable responses. All metacognitive strategies (six variable predictors) in the first model have a statistically significant relationship with variable responses (p≤0.05). The predictive influence on the dominance of a certain type of professional thinking among cadets has the character of structural interaction. Metacognitive strategies such as information acquisition, concentration, time management and the level of reflection are facilitators for the formation of a supra-situational level of cadets’ professional thinking. Whereas metacognitive knowledge and metacognitive activity act as inhibitors.
Quantization noise in low bit quantization and iterative adaptation to quantization noise in quantizable neural networks

Abstract Quantization is one of the most popular and widely used methods of speeding up a neural network. At the moment, the standard is 8-bit uniform quantization. Nevertheless, the use of uniform low-bit quantization (4- and 6-bit quantization) has significant advantages in speed and resource requirements for inference. We present our quantization algorithm that offers advantages when using uniform low-bit quantization. It is faster than quantization-aware training from scratch and more accurate than methods aimed only at selecting thresholds and reducing noise from quantization. We also investigated quantization noise in neural networks for low-bit quantization and concluded that quantization noise is not always a good metric for quantization quality.
The importance of the online storage of patient data in Indian COVID-19 scenario: A narrative review

Generation Z has seen drastic changes in the medical sector with the health-care industry constantly updating itself with newer tools to provide quality treatment. The COVID-19 pandemic has greatly tested the services in every aspect of life and has led to an unprecedented increase in the online storage of patient data. Electronic health records (EHRs) are real-time records that make health information of any patient available securely to authorized users. EHRs in the Indian scenario are still in their budding stages, described as "islands of excellence in an ocean of inadequacy." The central institutes and corporate hospitals have implemented it, but the state medical colleges and peripheral health centers have miles to go. These patient data records become an essential tool in physician's decision-making, expertise, and management. One can review the data which is just a click away even after the patients have been discharged, especially in the follow-up period. The current scenario is such that health-care workers and nonhealth-care workers alike share the data of the patients in respect to their records, radiographs, and laboratory data using social media such as WhatsApp, Facebook, and Telegram. In the absence of adequate regulations, the reliability of the EHRs is questionable and doubt creates a preference for the traditional medical services among the health-care workers. To conclude, a clear and precise guideline that can enlighten the patient and health-care workers is the need of the hour. The concerns of online storage of data need to be alleviated and privacy regulations need to be addressed.
FORMATION OF PROFESSIONAL AND PEDAGOGICAL COMPETENCIES OF FUTURE TEACHERS IN THE TEACHING PROCESS (ON THE EXAMPLE OF THE UNIVERSITY OF BREMEN)

The article is devoted to the current problem of formation of professional and pedagogical competencies of future teachers of higher education in the learning process on the example of the University of Bremen. The problems and features of the main competencies that are formed in applicants for higher education in the learning process are considered. The content and structure of competencies of future teachers on the example of the University of Bremen are revealed. The basic competencies of students at the stages of university and practical training are analyzed