Reviewerly/exHarmony huggingface dataset:
A Design of “Postsecondary Options” Android Application for Junior High school Student

Abstract The purpose of this study is to develop a postsecondary option-based android application that is used by junior secondary students in determining plans for further study. Planning further studies from junior to senior high school and beyond, certainly requires a consideration. All preparations both externally (school, facilities, environment) and internally (academic ability, understanding of learning), and all of that include in postsecondary options. Postsecondary options are awareness the opportunity to obtain a career and develop experts using various abilities such as grouping careers, the ability to assess themselves, and many more. Optimizing postsecondary options can be applied in technology. The research method used is research and development, which develops Android applications based on postsecondary options in helping student’s postsecondary option. In this application, it contains information about the requirements, curriculum, job opportunities in a number of options for further study of students after junior high school and beyond.
Research on the Construction of University Financial Management Performance Appraisal System Based on Data Mining Technology

In the management system of colleges and universities in China, financial management is a core component. The theoretical research and practical exploration of performance evaluation of financial departments in colleges and universities in China is still in its infancy. The step-by-step operation mode makes the financial managers in colleges and universities lack the awareness of strategic management and can't combine all kinds of related elements well. Based on the data mining technology, this paper introduces the analytic hierarchy process (AHP) into the performance appraisal of financial management in colleges and universities. Combined with some methods of data mining, we can analyze the diagnostic data from the whole or a specific project, and build a financial management performance appraisal model based on these data. It provides an effective basis for colleges and universities to improve financial management effectiveness and risk prevention ability.
Violence Detection Based on Three-Dimensional Convolutional Neural Network with Inception-ResNet

Violence detection based on deep learning is a research hotspot in intelligent video surveillance. The pre-trained Three-Dimensional convolutional network (C3D) has a weak ability to extract spatiotemporal features of video. It can only achieve an accuracy of 88.2% on the UCF-101 data set, which cannot meet the accuracy requirements for detecting violent behavior in videos. Thus, this paper proposes a network architecture based on the C3D and fusion of the Inception-Resnet-v2 network residual Inception module. Through adaptive learning of feature weights, the three-dimensional features of violent behavior videos can be fully explored and the ability to express features is enhanced. Secondly, in view of the small amount of data in the data set for violence detection (HockeyFights), which easily leads to the problems of overfitting and low generalization ability, the UCF101 data set is used for fine-tune, so that the shallow layer of the network can fully extract the spatiotemporal features; Finally, the use of quantization tools to quantify network parameters and adjusting the sliding window parameters during inference can effectively improves the inference efficiency and improves the real-time performance while ensuring high accuracy. Through experiments, the accuracy of the network designed in the paper on the UCF-101 dataset is improved by 6.1% compared to the C3D network, and by 3.1% compared with the R3D network, indicating that the improved network structure can extract more spatiotemporal features, and finally achieved an accuracy of 95.1% on the HockeyFights test set.
Suppression of Gaussian Noise using Spatio-Spectral Total Variation Technique

Gaussian noise is additive in nature. When it is introduced in image, it added to the original image and corrupts it, as a result noisy image is obtained. Introduction of noise leads to the loss of information which was possessed in original image. In this paper spatio-spectral total variation technique has been used to reduce the effect of Gaussian noise from two dimensional images. The experimental results shows the effectiveness of the aforementioned technique which suppresses the Gaussian noise effectively and provides good quality denoised image.
Detection of Distracted Driving Based on MultiGranularity and Middle-Level Features

A so-called MGMN (Multiple-Granularity- Middle Network) algorithm is proposed to improve the detection accuracy of distracted driving, based on multigranularity features and middle-level features. The algorithm is derived from the ResNet50 neural network and is the first time to use multi-granularity features and mid-level features of images in the field of distracted driving detection. The multigranularity feature extraction module consists of three branches: the global branch to learn the global features without local information, the second branch to divide the image level into two parts and later to learn the local features of the upper and lower parts, and the third branch to divide the image level into three parts, and later to learn the local features of the upper, middle and lower parts. By extracting the features of the middle layer of the image, the feature extraction of the algorithm is enriched. While the multi-granularity features are individually input to the cross-entropy loss function, the multi-granularity features of the image and the middle-level features are combined and input into the cross-entropy loss function. The proposed algorithm has an accuracy of 99.8% on the dataset "Districted-Driver- Detection" published by State Farm Company, which is 1.5% to 3% higher than existing algorithms, and an improved accuracy of 98.7% on the dataset "AUC-Districted-Driver-Detection".
Covert Cognizance: A Novel Predictive Modeling Paradigm

Can predictive models develop cognizance or awareness of how they have been used? Can models detect if they are being manipulated or executed in nonauthorized manners? Can a software track information propagation through its subroutines to improve execution efficiency? Can this be achieved in a covert manner, i.e., avoiding the use of additional variables, additional lines of code, and conventional logging files, and instead rely directly on the physics being simulated to develop the required cognizance? Achieving these goals under the looming threat of insiders is considered an open challenging problem. This paper introduces a new modeling paradigm to covertly develop cognizance that is of critical value when predictive software is used in both adversarial and nonadversarial settings. Given the wide range of applications possible with this new modeling paradigm, the paper will focus on introducing the mathematical theory and limit the initial demonstration to a physics-based model of a nuclear reactor. This model describes a representative industrial control system of a nuclear reactor model containing two coupled subsystems: a heat-producing core and a steam generator. The goal is to demonstrate how each subsystem physics model can remain cognizant of the state of the subsystem. The proposed methodology will provide communication solutions for future reactor technologies to enable advanced reactor control and remote reactor operations.
Tunicate Swarm-Based Black Hole Entropic Fuzzy Clustering for Data Clustering using COVID Data

The corona-virus (COVID-19) pandemic outburst from China has infected number of peoples and causes many deaths. In addition, the count of infections and deaths rates augment quickly. The adaption of data mining for performing recognition of infectious patterns is utilized for analyzing he spread patterns of COVID-19 infection. The grouping of clinical information is major method for determining the concealed instances from large clinical dataset. The clustering assist to group the information obtained from different gatherings. This paper devises a novel methodology, namely Tunicate swarm algorithm-based Black-hole entropic fuzzy clustering (TSAbased BHEFC) for clustering the COVID data. The clustering is performed using a Black Hole Entropic Fuzzy Clustering (BHEFC) technique. The weighted coefficients equivalent to cluster centers is optimally found using Tunicate swarm algorithm (TSA). Here, the log transformation is adapted for transforming the data in order to make it suitable for further processing. In addition, significant features are selected using Pearson Correlation coefficient. Subsequently, the chosen features are fed to clustering phase, where the clustering of COVID patients are performed with proposed TSA-based BHEFC. The proposed TSA-based BHEFC algorithm outperformed with maximal accuracy of 95.061%, maximal jaccard coefficient of 90.852% and maximal dice coefficient of 90.420%.
S-NEAR-DGD: A Flexible Distributed Stochastic Gradient Method for Inexact Communication

We present and analyze a stochastic distributed method (S-NEAR-DGD) that can tolerate inexact computation and inaccurate information exchange to alleviate the problems of costly gradient evaluations and bandwidth-limited communication in large-scale systems. Our method is based on a class of flexible, distributed first-order algorithms that allow for the tradeoff of computation and communication to best accommodate the application setting. We assume that the information exchanged between nodes is subject to random distortion and that only stochastic approximations of the true gradients are available. Our theoretical results prove that the proposed algorithm converges linearly in expectation to a neighborhood of the optimal solution for strongly convex objective functions with Lipschitz gradients. We characterize the dependence of this neighborhood on algorithm and network parameters, the quality of the communication channel and the precision of the stochastic gradient approximations used. Finally, we provide numerical results to evaluate the empirical performance of our method.
Entropy-Based Modeling for Estimating Adversarial Bit-flip Attack Impact on Binarized Neural Network

Over past years, the high demand to efficiently process deep learning (DL) models has driven the market of the chip design companies. However, the new Deep Chip architectures, a common term to refer to DL hardware accelerator, have slightly paid attention to the security requirements in quantized neural networks (QNNs), while the black/white -box adversarial attacks can jeopardize the integrity of the inference accelerator. Therefore in this paper, a comprehensive study of the resiliency of QNN topologies to black-box attacks is examined. Herein, different attack scenarios are performed on an FPGA-processor co-design, and the collected results are extensively analyzed to give an estimation of the impact's degree of different types of attacks on the QNN topology. To be specific, we evaluated the sensitivity of the QNN accelerator to a range number of bit-flip attacks (BFAs) that might occur in the operational lifetime of the device. The BFAs are injected at uniformly distributed times either across the entire QNN or per individual layer during the image classification. The acquired results are utilized to build the entropy-based model that can be leveraged to construct resilient QNN architectures to bit-flip attacks.
NAG: Neural Feature Aggregation Framework for Credit Card Fraud Detection

The state-of-the-art feature-engineering method for fraud classification of electronic payments uses manually engineered feature aggregates, i.e. descriptive statistics of the transaction history. However, this approach has limitations, primarily that of being dependent on expensive human expert knowledge. There have been attempts to replace manual aggregation through automatic feature extraction approaches. They, however, do not consider the specific structure of the manual aggregates. In this paper, we define the novel Neural Aggregate Generator (NAG), a neural network based feature extraction module that learns feature aggregates end-to-end on the fraud classification task. In contrast to other automatic feature extraction approaches, the network architecture of the NAG closely mimics the structure of feature aggregates. Furthermore, the NAG extends learnable aggregates over traditional ones through soft feature value matching, and relative weighting of the importance of different feature constraints. We compare the performance of the NAG to the state-of-the-art approaches on a real-world dataset with millions of transactions. More precisely, we show that features generated with the NAG lead to improved results over manual aggregates for fraud classification, thus demonstrating its viability to replace them. Moreover, we compare the NAG to other automatic approaches such as the LSTM or a generic CNN. Here we also observe improved results, with the NAG requiring far less parameters. Unlike the LSTM or the CNN, our approach also provides further interpretability through the inspection of its parameters.
Intrusion Detection of Vehicle Based on Generative Adversarial Networks

Abstract In the process of applying deep learning to intrusion detection, in order to ensure the recognition accuracy of the model, a large number of data sets need to be classified manually, and then the model training is carried out after labeling. In practice, the efficiency of manual label designation for enough data sets is extremely low. This paper aims at the experimental data set encountered in the intrusion detection of intelligent network vehicles The problem of classification difficulty is proposed, and a method of vehicle intrusion detection based on Generative Adversarial Networks is proposed. Firstly, the vehicle driving data is collected, the collected data are put into the Generative Adversarial Networks for data classification, and the data set after training classification is used for model training. The experimental results show that the data classification can effectively improve work efficiency and reduce the resource overhead, which is practical in the application field.
System Quality of Human Physiological Signal Based Medical Auxiliary Diagnosis

In order to improve the quality of mobile medical assistant diagnosis, this paper proposes the quality research of medical assistant diagnosis system based on human physiological signal. Firstly, the human physiological signal is preprocessed by wavelet transform, and the signal is decomposed and reconstructed. Then, the feature extraction method based on windowing is used to extract the peak valley feature of the hyperbolic pulse of the denoised signals. Iterative self-organizing data analysis method is used to classify the extracted signal features. In addition, by changing the parameters between and within clusters, updating the cluster center and cluster category, and splitting or merging them, the quality of medical auxiliary diagnosis is improved. In the experimental part, 80 patients in Department of cardiovascular medicine and Department of cardiovascular surgery of a hospital were selected as the experimental objects, and 80 physiological signals were collected. Compared with the maximum likelihood method and semantic knowledge base method, the experimental results show that this method improves the quality of traditional medical auxiliary diagnosis. The diagnostic accuracy rate is higher than 98.5%, and the diagnostic sensitivity is higher than 96.5%.
On the Skolem problem and some related questions for parametric families of linear recurrence sequences

Abstract We show that in a parametric family of linear recurrence sequences $a_1(\alpha ) f_1(\alpha )^n + \cdots + a_k(\alpha ) f_k(\alpha )^n$ with the coefficients $a_i$ and characteristic roots $f_i$ , $i=1, \ldots ,k$ , given by rational functions over some number field, for all but a set of elements $\alpha $ of bounded height in the algebraic closure of ${\mathbb Q}$ , the Skolem problem is solvable, and the existence of a zero in such a sequence can be effectively decided. We also discuss several related questions.
Speckle Suppression in Echocardiogram based on Bilateral Filtering and Piecewise Linear Transformation

The speckle noise in the echocardiogram reduces the image quality and brings difficulties to the clinical diagnosis and subsequent image processing. In order to effectively remove noise, this paper proposes a method for suppressing echocardiographic speckle noise based on bilateral filtering and piecewise linear transformation. Firstly, bilateral filtering is applied to filter the echocardiogram, then a sine-type function is applied to enhance the brightness of the image, and finally the piecewise linear transformation is applied to adjust the contrast of the image. Experimental results show that this method can effectively suppress speckle noise in echocardiograms, highlight the various chamber areas in the echocardiogram, improve contrast, and provide a good basis for clinical diagnosis and subsequent image feature extraction and recognition.
Exploring the Impact of Multimodal Adaptive Learning with Tangible Interaction on Learning Motivation

Embodied learning defines a contemporary pedagogical theory focusing on ensuring an interactive learning experience through full-body movement. Within this pedagogy, several studies in Human-Computer Interaction have been conducted, incorporating gestures, and physical interaction in different learning fields. This paper presents the design of a multimodal and adaptive space for embodied learning. The main aim is to give students the possibility to use gestures, body movement, and tangible interaction while interacting with adaptive learning content projected on the wall and the floor. Thus, this study aims to explore how tangible interaction, as a form of implementing embodied learning, can impact the motivation of students to learn compared to tablet-based learning. Eighteen primary school students aged nine and ten years old participated in the study. The average percentages of answers on the Questionnaire on Current Motivation (QCM) pointed out a higher motivation among students learning via tangible objects. Results revealed a positive score for the Interest of learning abstract concepts using a tangible approach with a mean score of 4.78, compared to 3.77 while learning via a tablet. Furthermore, Success and Challenge measures, with a mean score of 4.67 and 4.56 indicate that physical interaction via tangible objects leads to significantly higher motivation outcomes. These findings suggest that learning might benefit more from a multimodal and tangible physical interaction approach than the traditional tablet-based learning process.
SMaRT: A Framework for Social Media Based Recommender for Tourism

Social media has become a prominent source for information in multiple business domains as it provides a true and real-time reflection of societal inclinations. The tourism domain possesses multiple societal activities in the form of travel blogs, reviews, forums, feedbacks, etc. This information can be leveraged in multiple contexts like creation of travel recommender systems, tourism marketing, targeted advertisements for tourists, etc. Personalized travel recommender systems have become an essential part of the travel and tourism domain and also a key research area as it is still maturing. Conventional travel recommender systems based on collaborative filtering techniques prove to be computationally intensive and lack scalability against the social media big data. Considering the exponential growth and high dimensions of social media data, it is very much required to have efficient mechanisms in place for information retrieval. The adoption of an appropriate clustering algorithm and dimensionality reduction technique is considered to be the solution to deal with such a scenario. This paper proposes a loosely coupled, technology agnostic framework that can be adopted to build a tourism recommender system by taking social media big data as an input data source.
Predicting Quality of Delivery Metrics for Adaptive Video Codec Sessions

Predicting video quality will continue to be an active area of research given the dominance of video traffic for years to come. Network service practitioners that are poised to handle the strain on the existing limited bandwidth constraints are better placed to be SLA-compliant. The dynamic and time-varying nature of cloud-hosted services require improved techniques to realize accurate models of the systems. To address this challenge: (1) we propose Codec-aware Network Adaptation Agent (cNAA), an online light-weight data learning engine that achieves accurate and correct predictions of quality of delivery (QoD) metrics, namely jitter for video services. cNAA achieves this prediction accuracy by leveraging the available network information in the face of congestion and adaptive codecs; (2) we highlight the short-comings of some baseline machine learning techniques that fail to capture network dynamics and demonstrate their failure in comparison with cNAA; and finally, (3) we demonstrate the efficacy of cNAA under varying network and codec conditions and provide evidence showing that machine learning approaches that incorporate network dynamics are better placed to realize accurate and correct predictions.
A data interpolation method for missing irradiance data of photovoltaic power station

As the installed capacity of photovoltaic systems increases year by year in the world, PV energy has become an important energy source. For the output power of photovoltaic power station, irradiance is the most important factor. In order to solve the problem of lack of irradiance data in some power plants, the data are classified according to weather by decision tree classification method. Under different weather categories, according to current time, power and ambient temperature, different neural network parameters are used to fit irradiance sequence and interpolate the irradiance value. The results show that the average absolute error and root-mean-square error of irradiance data interpolation are 9.12% and 11.57% respectively, which are better than ANN, ARMA, SVM and Weighted KNN.
Detecting Deepfake Videos via Frame Serialization Learning

Deepfake, a video forgery technique based on Generative Adversarial Network (GAN), has been proved to be a serious threat to the public security. The images and videos generated with it can even fool human eyes. In this paper, we propose a deep learning-based method to hunt Deepfake videos. A 3D-ResNext based model is developed to effectively learn the leverageable difference between fake videos and benign ones from multiple serialized frames. Furthermore, to address the information loss in compressed videos, the data enhancement technique is introduced in data preprocessing to collect sufficient training samples from public datasets, e.g., FaceForensics++, DeepFakeDetection and DFDC. The experiments demonstrated that our method has good performance and generalization ability in the task of Deepfake videos detection.