Reviewerly/exHarmony huggingface dataset:
The Effect of Virtual Reality Technology on College Students' Interest in Sports Learning

The purpose of this study is to explore the influence of virtual reality technology on college students' interest in sports learning. This paper applies virtual reality technology to table tennis teaching practice, and constructs a new teaching mode with teachers as the leading role and students as the main body. In this study, 19 non-sports major students were selected as subjects, and a single-subject experiment was conducted to evaluate the indicators of interest in sports situations in the pre-experiment and post-experiment stages in a four-week intervention experiment from June to July 2019. The results show that the use of virtual reality games in table tennis learning can improve students' interest in sports learning, learning attitude is more positive.
Benchmarking The Imbalanced Behavior of Deep Learning Based Optical Flow Estimators

Optical Flow (OF) estimation is an important task which could be effectively used for a variety of Computer Vision (CV) applications. While a range of techniques have been already proposed, however accurately estimating the OF is still a very challenging task. The most recent approaches for OF estimation exploit advanced Deep Learning techniques which have resulted in a shift in the paradigm. These techniques have shown substantial improvements particularly in the case of large displacements, brightness change, and non-rigid motion. However, these approaches are data-driven and hence they can be biased towards the specific training data, which could in turn lead to considerable inaccuracy of the estimated OF. In this paper, we address this problem and provide a novel benchmark that can be used to identify and to measure the bias magnitude of the OF estimation. We have performed several experiments based on public datasets (Monkaa and Sintel) as well as on data designed on purpose 1. The results have shown that OF estimation based on some of the state-of-the-art deep learning techniques strongly depend on factors such as motion orientation within the data. Indeed, we have observed substantial degree of bias toward certain directions of motion. The framework can help researchers and practitioners in order to develop more effective data augmentation techniques and training schedules for deep learning based optical flow estimators.
Research on Classification and Recognition of Object Image Based on Convolutional Neural Network

Abstract Image recognition and classification using a convolution neural network is an important application of image processing. How too reasonably to design the convolutional layer of the convolutional neural network, the number of hidden layers and optimize the parameters of the convolutional neural network to ensure high accuracy and efficiency in image recognition and classification are an extremely important part. The core of this thesis is the model design and parameter optimization of deep convolutional neural networks. This paper mainly designs, implements, optimizes and adjusts the model structure of the convolutional network of Tensor Flow framework platform. We redesigned the convolutional neural network model to a depth of 19 layers and used two data sets for training, testing and parameter optimization. Experimental results show that the convolution neural network models presented in this paper is superior to other neural network models in accuracy and efficiency of image recognition and classification, and has a good guiding role in solving practical engineering problems.
Symmetric cryptosystem to secure images utilizing chaotic deterministic phase mask in Fresnel transform domain employing singular value decomposition

This research work proposes a lens less secure symmetric image encryption system using a novel chaotic deterministic phase mask (CDPM) in the light of Fresnel transform (FrT) deploying singular value decomposition (SVD). Here, two CDPM's are used instead of random phase masks (RPM) in Fresnel transform based double random phase encoding (DRPE) scheme with the addition of SVD. The variation of CDPM and SVD amplifies the security of the Fresnel transform based DRPE in comparison to the traditional one. The key space of the proposed cryptosystem is large constituting the complex phase masks (CDPM), the order of SVD, the distance between the significant planes and the wavelength of the Fresnel transform. The system is simple, fast and tough to break. The encrypted image can be sent over an unsecured channel since the security lies in the secrecy of the keys. The enhanced framework resists the possible attacks on traditional DRPE and therefore reinforces DRPE security. The utilization of a novel complex mask that gives advantages of both chaotic map and deterministic phase mask improves the quality of the cryptosystem. The performance analysis and the result outcomes exhibit the efficacy of the designed cryptosystem.
Construction of Experiential Learning Space Model Based on Virtual Reality Technology

Abstract In order to better help learners to improve their learning experience and learning efficiency, this paper starts from the interaction, immersion and imagination of virtual reality technology, and sorts out the evolutionary process of formal learning space, informal learning space and virtual learning space. It also proposes the conception of experiential learning space with the virtuality and reality combination. Aiming at the application of virtual reality technology in the knowledge presentation, simulation training and experience environment of experiential learning space, an experiential learning space model based on the virtuality and reality combination is constructed to enrich the learning environment and information presentation of learners, enhance learners’ situational experience and improve their learning efficiency.
Analysis of Determination of Strategy Promotion using Apriori Algorithm

Abstract Promotion is an activity or marketing idea commonly carried out by business actors to attract the attention of consumers either to introduce an item or to increase consumers so they want to buy the item. Apriori is an algorithm that can perform several itemset required by knowledge to determine the rules in making information related to determining candidates who will emerge as a result based on the support and trust determined by the analyst. The results of the analysis using the Apriori Algorithm in this study are that the 2 itemet rules that have been made can be used as a strategy for the promotion of goods based on sales transaction data. Because what is produced is high, the highest level of trust is 100%, and the lowest is 84%. Goods that have the highest trust value can be used as a promotional strategy.
Power Attack on VHDL Implementation of Continuously Running Block Ciphers

The Correlation Power Analysis (CPA) is used to compromise the security of crypto systems by measuring and analyzing physical leakage that is the power consumption. Unlike classical cryptanalysis techniques, it requires very less computations to extract the secret information of the cipher systems. But, one of the main hindrances in mounting the CPA attack is the segregation of single power trace of multiple encryptions performed continuously (without pause) among number of traces corresponding to individual encryptions. To overcome this limitation, a new technique is proposed in this paper to split the power traces of AES and DES algorithms running continuously on FPGA. This (energy of samples based algorithm) finds the start of the encryption and computes number of samples in each clock of FPGA. It exploits the repetition of specific patterns available in the traces to determine the encryption length in terms of number of samples. With this information, it splits a power trace of several consecutive encryptions among number of traces corresponding to each encryption. The important thing is that it does not require information about clock frequency of FPGA board and sampling rate of the Oscilloscope. This algorithm was applied on the traces of 25, 50, 100, 125 and 250 samples per clock (when processing was performed at 1, 2 & 4 MHz clock frequencies). In all the cases, the key of AES and DES were retrieved by mounting the CPA attack on splitted traces resulting from the splitting technique.
Distributed Electronic Health Records Semantic Interoperability Based on a Fuzzy Ontology Architecture

Realthcare is one of the main domains where sharing information is an essential requirement. Medical information systems store all the clinical data in many different kinds of formats. Subsequently, there is an urgent requirement to address the semantic interoperability problem. This paper proposes a fuzzy-ontology framework that could integrate most existing EHR different data models. In the proposed framework, each input source is represented into an ontology representation. Those data sources may be relational databases, XML documents, Excel spreadsheets, CSV files, or EHRs standards. Second, all those output ontologies are merged and combined in only one ontology. DL-Query Protégé plug-in is used for providing specific queries. All the results are explained with the help of screenshots. We expect our framework to be a step towards solving the specific problem without losses of data and semantics.
High‐speed, two‐dimensional digital image correlation algorithm using heterogeneous (CPU‐GPU) framework

Abstract Two‐dimensional digital image correlation (2D‐DIC) is an experimental technique used to measure in‐plane displacement of a test specimen. Real‐time measurement of full‐field displacement data is challenging due to enormous computational load of the algorithm. In order to improve the computational speed, the focus of recent research works has been on the approach of parallelization across subsets within image pairs using graphics processing unit (GPU). But alternate GPU‐based parallelization approaches to improve the performance of this algorithm as per the order of data processing have not been explored. To address this research gap, our method utilizes parallelism within a subset as well as across subsets for each computation step in an iteration cycle. A heterogeneous (CPU‐GPU) framework in combination with a pyramid‐based initial values estimation for subsets (in parallel) is proposed in this work. The precompute steps of the proposed framework are implemented using CPU, whereas the main iterative steps are realized using GPU. It is demonstrated that the overall computational speed of the proposed heterogeneous framework improves by compared to a sequential CPU‐based implementation for a pair of gray‐scale images with a resolution of pixels. As an important milestone, feasibility to measure deformations in real time ( 1 s) is manifested in this study.
iWEP: An Intelligent WLAN Early Warning Platform Using Edge Computing

In the last decades, Wireless Local Area Network (WLAN) has been emerging as one of the most prevailing networking architectures. It is expected that current WLAN technologies will further evolve to obtain much higher performance, more energy efficiency and more robustness. However, the WLAN is still prone to a variety of attacks regardless of the existence of data protection and security association mechanisms. They include but not limited to dictionary attacks against the pre-shared secret key of Wi-Fi Protected Access (WPA)/WPA2, the key reinstallation attack (KRACK) against the handshake procedure of WPA2, etc. Although a brand new WPA3 has been recently standardized by Wi-Fi Alliance to address new security threats, it needs a long time to upgrade currently used access points. Hence, there is a significant gap between security and deployment cost. To fill this gap, we design and implement an intellignet WLAN Early warning Platform (iWEP) to provide an early warning service for clients. Specifically, iWEP adopts intelligence algorithms, e.g., machine learning, to provide the capability of defeating existing popular attacks, including Wired Equivalent Privacy (WEP) secret cracking, WPA/WPA2 dictionary attack, Denial-of-Service and KRACK, by handling behaviour features that are extracted from the compromising procedures in real experimental environments. Moreover, iWEP uses edge computing technology to make a good tradeoff between system performance and WLAN security. Finally, we implement a prototype system of iWEP, and the real results demonstrate its effectiveness.
Performance Evaluation of Fuzzy Association Rule Mining Algorithms

In the field of data mining, interesting rules are generated based on manifold criteria for the classification of itemsets. Association rule mining is used to generate rules based on support, confidence, lift, leverage, etc. by following three techniques - Boolean, Quantitative and Fuzzy association rule mining. Boolean and Quantitative association rule mining techniques work with crisp value and quantitative numeric value respectively for which many interesting rules are being lacked. On the other hand, Fuzzy association rule mining uses linguistic values and membership functions to determine the rules and so it provides more realistic values from the other two association rule mining techniques. Instead of using crisp and quantitative values for rule mining, Fuzzy logic system with Evolutionary algorithm framework can resolve this lacking. The Fuzzy logic system, adapted by Evolutionary Genetic Algorithm which is a global search heuristic technique that optimizes rule generation. In this paper, the performance of Fuzzy association rule mining algorithms is compared. For performance analysis, classical Apriori algorithm, FuzzyApriori algorithm, and Evolutionary GeneticFuzzyAprioriDC algorithm are being used.
WITHDRAWN: RST invariant blind image watermarking schemes based on discrete Tchebichef transform and singular value decomposition

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VLSI Architecture for Fine Grained Pipelined Feature Extraction using Histogram of Oriented Gradient

The most popular field to support the massive growth of the intelligent based system in the recent year is object detection. The fundamental of the object detection technology is feature extraction. It becomes the main challenge due to the variability of application and the computation requirement. One of the challenging issue is to implement this computing system into hardware level architecture to get lower power, high speed, and lower cost computing system. According to some study, Histogram of Oriented Gradient (HOG) is the current most robust feature extraction. This paper proposes a novel hardware architecture for HOG based feature extraction. The architecture brings performance improvement and it opens various benefit in the utilization of VLSI system instead of GPU. This architecture implements tasks for HOG feature extraction including gradient extractor using edge filter algorithms, polar data representation using vectoring mode CORDIC, and histogram normalization using Barni's approximation method. The simulation shows very low (fraction of frame rate) output latency for various image size, therefore the system could support real-time processing, for those imaging rate. The processor successfully implemented for Stratix IV FPGA EP4SGX230 with logic utilization about 10%. With 100 MHz clock rate, it potentially performs up to 1.5 times faster than the GPU fast HOG implementation.
An intelligent algorithm with interactive learning mechanism for high‐dimensional optimization problem based on improved animal migration optimization

Summary Recently, a new intelligent algorithm called animal migration optimization (AMO) is proposed. The AMO algorithm, which is inspired by the behavior of animal migration, exhibits good performance on a series of benchmark functions whose dimensionality is less than or equal to 30. However, its performance deteriorates evidently when the dimensionality is more than 30. To overcome this drawback, we proposed an improved AMO (IAMO) with interactive learning mechanism in this article. First, an interactive learning mechanism is designed to improve the intelligence of AMO. During the the search process, individuals learn from each other by the exchange of information, and the search mode is adjusted dynamically. Second, a refined search method is adopted to enhance the search ability of AMO. Individuals will search around the current solutions in order to improve the quality of solutions. Third, a birth‐and‐death strategy is introduced to avoid local optimum. In addition, the property convergence of IAMO is analyzed theoretically. The proposed algorithm is validated on several standard benchmark functions whose dimensionality is 50, 100, and 150, respectively. The empirical results demonstrate that the performance of our proposed algorithm is promising.
Clustering of the water characteristics of the Cirata reservoir using the k-means clustering method

Pollution prevention must be carried out in the upstream, middle and downstream parts. One of the Citarum watersheds located in the middle part is the Cirata reservoir. Pollution in the Cirata reservoir does not only originate from the reservoir environment, but also from rivers that flow into the Cirata reservoir. Cirata Reservoir besides being a Hydroelectric Power Plant unit, this reservoir is also used by the community for fish farming in the cage aquaculture technique. This study aims to know the water characteristics Cirata reservoir. Through data on the content of water entering the Cirata reservoir, the characteristics of water at each point can be identified. The research data was obtained by obtaining clean water quality testing data from several estuary points of the river flow towards the Cirata reservoir and 4 centers of the Cirata reservoir. Data acquisition is done by taking primary data as well as secondary data related to water quality. Data processing techniques are carried out using Data Mining K-Means Clustering. This study obtained three groups of clusters, namely clusters. The first cluster is Organic Compounds Cluster which is dominated by the presence of organic compounds such as compounds that contain nitrogen. The second is Dominant Minerals Cluster that tends to be dominated by the presence of content of rock and soil minerals. Lastly is Middle Area Cluster which is characterized by the presence of mud rock sediments and dissolved organic matter into the river flow.
k-partial permutations and the center of the wreath product $${\mathcal {S}}_k\wr {\mathcal {S}}_n$$ algebra

Abstract We generalize the concept of partial permutations of Ivanov and Kerov and introduce k -partial permutations. This allows us to show that the structure coefficients of the center of the wreath product $${\mathcal {S}}_k\wr {\mathcal {S}}_n$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi>S</mml:mi><mml:mi>k</mml:mi></mml:msub><mml:mo>≀</mml:mo><mml:msub><mml:mi>S</mml:mi><mml:mi>n</mml:mi></mml:msub></mml:mrow></mml:math> algebra are polynomials in n with nonnegative integer coefficients. We use a universal algebra $${\mathcal {I}}_\infty ^k$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msubsup><mml:mi>I</mml:mi><mml:mi>∞</mml:mi><mml:mi>k</mml:mi></mml:msubsup></mml:math> , which projects on the center $$Z({\mathbb {C}}[{\mathcal {S}}_k\wr {\mathcal {S}}_n])$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>Z</mml:mi><mml:mo>(</mml:mo><mml:mi>C</mml:mi><mml:mrow><mml:mo>[</mml:mo><mml:msub><mml:mi>S</mml:mi><mml:mi>k</mml:mi></mml:msub><mml:mo>≀</mml:mo><mml:msub><mml:mi>S</mml:mi><mml:mi>n</mml:mi></mml:msub><mml:mo>]</mml:mo></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:math> for each n . We show that $${\mathcal {I}}_\infty ^k$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msubsup><mml:mi>I</mml:mi><mml:mi>∞</mml:mi><mml:mi>k</mml:mi></mml:msubsup></mml:math> is isomorphic to the algebra of shifted symmetric functions on many alphabets.
An efficient colossal closed itemset mining algorithm for a dataset with high dimensionality

The greater interest of research in the field of bioinformatics and the ample amount of available data across the different domains paved the way for the generation of the dataset with high dimensionality. The number of features in the dataset with high dimensionality are very high and number of rows are less. The significance of the Frequent Colossal Closed Itemsets (FCCI) is high for diverse applications and also for the field of bioinformatics. FCCI are very prominent in the process of the decision making. Amount of information extraction from the dataset with high dimensionality is huge and this extraction is a non-trivial task. The pruning of all the inadmissible features and rows is not performed by the state-of-the-art algorithms. The proposed work articulates the pruning of all the inadmissible features and rows, an efficient pruning strategy to snip the row enumeration mining search space and closure method for checking the closeness of the rowset. An efficient row enumeration algorithm enclosing the rowset closure checking method and pruning strategy is designed to efficiently mine the complete set of FCCI. The experimental results demonstrate the effectiveness of pruning all the inadmissible features and rows.
2-DOF Camera Stabilization Platform for Robotic Fish Based on Active Disturbance Rejection Control

This paper proposes a novel 2-DOF stabilization platform for vision application of robotic fish to enhance the stability of image and reject the periodic disturbance from yaw and roll channels caused by fish's swimming. The problem formulation and system framework of camera stabilization are first discussed. In order to achieve better control effect, forward and inverse kinematics of the 2-DOF gimbal are derived, which combine the feedback of IMU and target states to calculate controllers' ideal input. Meanwhile, linear active disturbance rejection control (ADRC) without tracking differentiator is adopted in our system, on account of its superior performance to compensate uncertainties and disturbance. Finally, experimental results demonstrate that the error angle of ADRC is obviously smaller than PD and feedback-feedforward control. Furthermore, compared with 1-DOF stabilization platform, the 2-DOF one exhibits the overwhelming advantage about the enhancement of image stability.
Effective-target Representation via LSTM with Attention for Aspect-level Sentiment Analysis

Target-dependent sentiment analysis aims to classify the sentiment polarities of a given target in its comment. Previous work has recognized the importance of the target in aspect-level sentiment analysis and focused on modeling relationships between targets with context. However, these studies always used averaging the hidden output of the target context as target representation. In some cases, this approach may not be appropriate, because different target words usually do not contribute equally. In this work, an attention mechanism to compute the importance of target words based on each left context word and right context word is proposed. As a result, effective- target representation can be used to capture the words most contributing sentiment in context. In particular, two gates to control the effects of left and right context information on the target polarities' tendency are added as a novel concept. To demonstrate the effectiveness of the proposed scheme, an experiment on restaurant datasets was performed.