Reviewerly/exHarmony huggingface dataset:
Intelligent Student Fighting Recognition System in Schools

In this paper, we design an intelligent student fighting recognition system in real schools, which can prevent school violence and protect students' security. Recognizing these fighting actions in real scenarios is quite challenging mainly due to complicated and various fighting actions, lack of temporal information for leading frames, and hard examples that appear infrequently. To tackle the above-mentioned problems, we first set up a real student fighting dataset collected from schools, labeling each fighting action frame-by-frame. Then we propose an improved YOWO, a representative two-stream-based model. Specifically, we first propose a novel sample filling method using adaptive average pooling to help supplement the temporal information for the beginning frames in video clips, and thus to improve the speed to recognize fighting. Secondly, we decouple 3D convolutions to extract robust spatiotemporal features by separating spatial and temporal information to obtain better performance. Thirdly, we design a weighted loss to strengthen the recognition accuracy for hard examples. The proposed model outperforms YOWO on UCF101-24 and our real student fighting dataset with an improvement of 0.7% mAP and 3.4% mAP respectively while maintaining a fast speed of 40.8ms per frame on our dual 2080Ti machine.
Information and Records Management Department Students' Attitudes Towards Mobile Learning

Determining university students' approaches to mobile learning gains is important in terms of future education planning. Information and records management department students are expected to have the knowledge, skills, and techniques to enable the selection, analysis, evaluation, organisation, storage, access, and use of information that exists, especially in printed or electronic information carriers. It is considered that these students' approaches and attitudes towards mobile learning are an issue that should be considered and investigated in terms of their professional development. This study aims to determine the attitudes of Karatekin University, Department of Information and Records Management students towards mobile learning. The results of the study show that the knowledge professional candidates' attitudes towards mobile learning were positive. Based on results, it is suggested to use, in the future, mobile learning tools more in both theoretical education and practical education.
Few-shot sketch recognition for plotting system

Plotting system refers to a software system that can draw a series of vector symbols, typesetting, saving, reading and so on. The traditional plotting system uses key and mouse operation to draw. With the development of electronic equipment touch screen in recent years, the traditional menu selection is not convenient enough, but the way of using hand-painted sketch and recognized by the system is more natural and fast. Traditional image recognition methods need huge amount of data for training, but the vector symbol set in the plotting system is not a common symbol set, so it is difficult to obtain a large amount of data. Therefore, this paper proposes a &beta;-TCVAE-Attention hand-drawn plot recognition method which can obtain better recognition accuracy in the case of small samples. The model first uses the Image dataset to pre-train &beta;TCVAE, and then uses the small sample data of the plot sketch to input the &beta;-TCVAE encoder to obtain the sketch features. After the sketch features are enhanced with the attention module, they are input into the Softmax classifier based on cosine similarity. The neural network is fine-tuned according to the classification results of the classifier. Finally, when using network prediction, input the support set and query graph into the network for classification. In this paper, a 20-way-5- shot plotting data set is collected for experiment, and the experiment proves that the model has certain accuracy.
Norm-Correlation Based Filter Pruning to Accelerating Networks

recently, high-performance deep learning models have been developed in various fields. Model compression research is also underway to apply deep learning models to edge device such as mobile phone and embedded platforms. Representatively, pruning is a popular model compression method to accelerate the inference process of deep neural network. So we accept channel pruning based method to reduce memory consumption and model complexity. Our method considers norm importance of filters and the correlation between filters, and select filter that has small norm with large correlation. This approach reduces redundancy of filter and preserves only important filters. Norm-Correlation based pruning not only preserves uncorrelated filters that extract unique features with small importance, but also removes replaceable filters with large importance. Our method is applicable to Convolutional Neural Networks (CNNs) and on ILSCRC-2012, we reduce 30% parameters and 41.1% FLOPs of ResNet-34 within 1% top-5 accuracy loss.
Barberpole tempo illusions

“Barberpole” tempo illusions are a family of auditory illusions based on the synchronization of faded rhythmic streams playing at different rates, often manufacturing experiences of seemingly eternal acceleration or deceleration. The forefather of all such illusions, based on layers whose rates are powers of two apart (“octaves”), was studied by Jean-Claude Risset in the late seventies and is now known as Risset rhythm. This article provides a mathematical framework for barberpole tempo illusions, generalizing Risset rhythms for arbitrary numbers of subdivisions, non-integer proportions, arbitrary rate modulation, and increasingly accelerating tempi. Furthermore, this article describes a new illusion of eternal rallentando/accelerando based on the full harmonic spectrum of rates. This construction shows that Risset rhythms are related to barberpole variable-rate polyrhythms. A notable application of the study of divisional structures that barberpole illusions underpin is the construction of bistable auditory figures (accelerating or decelerating depending on the stream being focused).
Taking the standardized management of Wushu resource files as an example to improve the efficiency of asset library

This article uses the standardized management of Wushu resource files as an example to introduce part of the research situation to improve the efficiency of asset library use, verify the feasibility of Wushu resource files according to the rules to code and accurately name, and obtain a higher efficiency of asset library file naming and accuracy, carrying out beneficial explorations and application innovations to improve the efficiency of asset library use.
HoloBurner:

The HoloBurner system is a mixed reality instrument of chemistry experiments for learning flame color reaction. It can provide users with the sense and enjoyment of performing experiments in a laboratory. Flame color reaction is a phenomenon which can identify an element by its unique color, with which students can learn that substances are comprised with various different elements. The HoloBurner system provide users a real feeling of operating a burner, and realistic image of flame with an aerial imaging display. Students can experience flame color reaction using the HoloBurner safely in natural way same as with real instruments.
Quality Enhancement of Color-Embedded- Grayscale Image Using Convolutional Neural Network

The color-embedded-grayscale image aims to transform a color image into its grayscale version. It involves the Discrete Wavelet Transformation (DWT) and chrominance-luminance color space. This technique hides the color information contained in the chrominance component into the DWT transformed of luminance component. The color-embedded-grayscale image can be processed to yield a recovered color image. However, the quality of this recovered image is less acceptable compared to the original version. Thus, this paper improves this recovered image using deep learning approach, i.e. Convolutional Neural Network (CNN). As documented in the experiments, the proposed method is superior in comparisons to the other existing schemes.
Speech Manipulation Detection Method using Audio Watermarking

We propose a method for detecting tampering in recordings using a blind watermarking detection method and audio fingerprinting. In the proposed method, fingerprints extracted from audio recordings are embedded into audio recordings as watermarks for tamper detection. At the decoder, this is matched with the fingerprint extracted from the audio recording itself. To achieve blind watermark detection, a Wiener filter is used to estimate the clean speech, subtracted from the recorded audio, and the embedded watermark is extracted from the residual signal. The vector distance between the fingerprints obtained from the watermark and the fingerprints obtained from the recorded audio is calculated. If the distance is greater than a predetermined threshold, it is judged to have been tampered with, and if it is less, the authenticity of the recording can be proven. We used recordings degraded with reverberation and additive noise to evaluate the detection accuracy of substitution tampering, and the detection accuracy of 99.87% was shown.
A Three-Stream CNN-LSTM Network for Automatic Modulation Classification

Deep learning (DL) has been used more and more in the field of automatic modulation classification (AMC) in recent years, but there are still many areas for improvement. In this paper, we propose a three-stream CNN-LSTM model, which takes three features carefully extracted as inputs. Different from the existing AMC methods, we extract phase features, amplitude features, and frequency features as the input of the neural network. We propose a three-stream network combining CNN and LSTM. CNN can extract the spatial features of the signal, and LSTM can extract the deep features of the signal from the time domain. The three-stream network can interactively fuse the three features and then further extract the spatiotemporal features of the signal through CNN-LSTM. Using this structure, we finally achieve a recognition rate of 94.6%. At the same time, the network complexity and training time are also very considerable. The experimental results confirm the superiority of the network structure in the existing networks.
The Development of A Collection of Art-Themed Mobile Application (Videre) using Challenged Based Learning

Videre is a technology business that provides a collection of art-themed apps. Videre's main goal is to help visual artists complete the creative process they go through in making a work of art. Videre believes that technology, especially that available on smartphones, can help visual artists work more effectively and efficiently. The decision to develop applications on mobile devices is also based on Videre's vision, "Art made easy with technology in the palm of the hand." Mobile phones are also believed to have faster performance than PCs (Personal Computers) [1]. Thus, it is believed that choosing a mobile phone as a device with high mobility and sufficient performance can help artists wherever and whenever they need the collection of applications provided by Videre. Videre develops various technology-based services, including a grid calculation application (Mumu), a layout visualization application (Lilo), an artwork coloring process planning application (Pica). This application collection was developed using the challenge-based learning framework and the Apple Human Interface Guidelines (HIG).
Develop Graphical Passwords Authentication System Resistant To Shoulder Surfing Attacks

Abstract- Security is a critical component of most computer systems especially those used over the internet, so it is important to have a secure authentication technique to keep the system safe. One of the most common authentication techniques is the text password because it is easy to use and learn for users and does not require any additional or expensive hardware. However, traditional passwords have drawbacks from a usability point of view. To address these drawbacks, alternative techniques are proposed called "Graphical Password" This paper proposes a hybrid graphical password technique based on the Pass-Image scheme, which overcomes security and usability problems that faced graphical password schemes.
Research on electronic evidence collection in the investigation of duty crime based on big data

In the context of the era of big data, with the wide use of electronic network and smart phones and other high-tech equipment, virtual space has been rapid development. Because its internal contains a large amount of data information, so in the development of practice has been gradually applied to various fields. Especially for the electronic evidence collection of duty crime investigation, it can not only solve the more secret and advanced crime mode, but also efficiently integrate the investigation resources. Therefore, on the basis of understanding the process of electronic evidence forensics, based on BP neural network model and its algorithm, this paper studies the application advantages of electronic evidence forensics in duty crime investigation.
Fast MILP Modelings for Sboxes

Converting the process of finding a differential characteristic into an automatic solution to the MILP problem can greatly improve the efficiency of cryptographic algorithm evaluation and analysis. S-box is a key component of confusion in block ciphers. When the number of inequalities for modeling S-box difference characteristic is significantly reduced, the solution rate tends to be higher. This article proposes two new algorithms(aggregation algorithm and filter algorithm) for fast modeling of S-boxes. Both algorithms have low time complexity and are easy to implement. The combination of the two algorithms only takes about 10 minutes to describe the 8-bit S-box. In actual engineering applications, the two algorithms can greatly improve the efficiency of users.
A method for automatic detection of teacher's gaze area based on smart classroom system

The quality of teaching is closely related to the ability of the teacher, which is still mostly assessed in the traditional way. With the development of artificial intelligence, smart classroom systems are beginning to appear, which can quantify and analyze the teaching data in the classroom to produce corresponding results for teaching management or teaching evaluation. Therefore, this paper proposes an automatic method for detecting the teacher's position and gaze area based on the smart classroom system. The method mainly uses adaptive area face detection technology, face feature point matching, head pose acquisition and teacher gaze point calculation algorithms to automatically obtain the teacher's position and gaze area distribution in the classroom. Through experiments and public classroom tests, it was found that the maximum mean error in teacher position obtained by this method was 11.72cm and that the teacher's area of gaze was largely consistent with the actual change. This method can also be used to assess teaching ability and quality, and the data obtained can also be used as a learning resource for novice teachers or average students, so that they can continuously improve their teaching ability.
A Dataset for Table-Meeting Analysis Using Spherical Camera

A gaze estimation is very important for multi-person discussion such as a table meeting. In this paper, we use a spherical camera to observe multi-person sitting around the table and want to estimate their gaze point. In comparison with the conventional method which uses a camera for each person, we can observe all people simultaneously by using a single spherical camera. Based on this idea, we built a dataset using a spherical camera for four person table-meeting in three different cases, i.e., two people look at a different target, two people look at the same target and three people look at the same target. As the future work, we will use this dataset to train artificial neural network to estimate gaze point and evaluate their accuracy.
The prospect of cryptocurrencies becoming money

The FinTech revolution has the potential to improve the existing bank-based monetary system. A case in point is the payment service providers (PSPs), which gave indirect access to basic bank services to millions of unbanked households and firms in emerging economies. Yet contrary to the initial hopes and declarations, cryptocurrencies, which rank among the flagships of the FinTech revolution, will not replace, at least in the near future, the existing bank-based monetary system since the trust in the blockchain technology alone cannot substitute for trust in reputation-based public institutions which stabilize the value of money and provides its legal protection. The current system cannot be easily replaced by an alternative one as it is the product of a long evolution that continually adjusted its properties to the needs of the economy. Stablecoins at their today's state of development are yet to become eligible candidates for displacing the bank-based money. In point of fact, they owe their stability to the existing international currencies ensured by the currency board mechanism. Moreover, their wide acceptance, as originally envisaged for Facebook's Libra, might entail serious risks to monetary and financial stability. The proposal to issue the Central Bank Digital Currencies (CBDCs) is a response to this threat.
A Web Service Composition Method for Edge Computing QoS

Under the premise of edge computing and QoS optimization, an improved whale optimization algorithm is proposed for edge service combination. The algorithm introduces Iterative mapping to initialize the population, which improves the diversity of the population. Secondly, in order to improve the convergence speed and optimization accuracy, nonlinear convergence factors and inertia weights are added. Through the analysis of the results of service composition simulation experiments under QoS constraints, it is concluded that the efficiency and stability of the algorithm are improved and the solution quality is improved in the optimization of service composition of different scales.
Edpuzzle and Canvas as distance learning tools during the lockdown

The following study was carried out during the teaching-learning process in the Tec21 educational model of Tecnológico de Monterrey (Mexico), an educational model based on challenges, during confinement forced by a pandemic in 2020. Specifically, it shows how Edpuzzle and Canvas applications are tools that improve motivation and understanding of topics in the online teaching process during the COVID-19 pandemic and how these resources can successfully change traditional education to a distance learning system with the help of online classes. In this study, the perceptions of university students about the use of the Edpuzzle application together with the Canvas learning management system were explored to identify if students consider that it favors the educational process and to know if it motivates the learning of basic sciences in the first years semesters of a university degree during confinement. Qualitative data were collected from new university students (n=163). The results show that Edpuzzle and Canvas contributed as a motivational element and as a cognitive resource, positively impacting student performance.
Hierarchical Triplet Attention Pooling for Graph Classification

Recent years, graph neural network have been introduced to handle the structural data in non-Euclidean space. Graph neural network learns the representation of the whole network through the representation of nodes. The pooling layer plays an important role in gradually reducing the network to a sufficient small coarse graph. This work will propose an end-to-end pooling method, which builds pseudo-edge weights to form a subset of important features. It updates the coarse-grained node embedding by focusing on the common information at different locations, to improve the attention to the valuable information. Moreover, we propose a transfer operator that integrates into the convolutional layer, which not only integrates the intrinsic features of the node but also learns rich community properties. Experimental results show that our proposed pooling method can be combined with multiple convolutional layers to achieve optimal results in graph classification tasks. In addition, our proposed model method is generally superior to most baseline graph classification methods.
Matrix decomposition model of E-commerce based on collaborative filtering algorithm

Currently, there is a distinct contradiction between the massive information and the lack of personalized information. In this case, the recommendation system has been widely used as an important technology to discover the potential interests of users and recommend the items of interest to the target users. Considering that the probability matrix decomposition model can show its prediction mechanism more clearly, in this paper, the probabilistic matrix factorization model is used in the matrix factorization method. Based on the probabilistic matrix factorization, a recommendation method framework considering both extreme rating behavior similarity and rating matrix information fusion is proposed. The framework integrates the local neighbor relationship of users into the global rating optimization process of matrix factorization, and thus improves the prediction accuracy and robustness of sparse data. Simulation results show that the proposed method reduces the MAE by 0.68%, 1.12%, 2.85% and 1.19% compared with the suboptimal method. It is proved that the proposed method can effectively implement long tail project recommendation and ensure high recommendation accuracy.
Estimation method of calorie intake by deep learning using depth images obtained through a single camera smartphone

In this study, we propose a method of estimating calorie intake considering leftover food using a single camera smartphone. To obtain depth images of the meals, we used the augmented reality core depth application programming interface, which creates depth images using a single camera smartphone. From photos and depth images of the meals, we estimated the type, quantity, and container of the meal both before and after consumption using deep learning. Using these estimated values, we calculated the volume of calorie intake. This method of estimating calorie intake can be applied to home healthcare and health management.
Use of Blockchain Mechanisms in PLC Control and Safety Critical Processes

Abstract In a few past years, a lot of cyber-attacks on industrial systems were accomplished. The main point of vulnerability of industrial control systems (ICS) is their connection to the Internet. Standard ICS rely on local solutions; however, with the revolution in the shape of Industry 4.0 concept, there are only a few industrial sectors with no connection to the global network. Some researchers have revealed critical vulnerability of the control systems. In this paper, we briefly summarize the current situation, and introduce our solution to the check of changes in PLC via other nodes in industrial network. The way how to do it is possible through using a checksum of actual code, and comparing with the checksums stored in other nodes.