Reviewerly/exHarmony huggingface dataset:
Predicting mental disorder from noisy questionnaires: an anomaly detection approach based on keyword extraction and machine learning techniques

The early warning of mental disorders is of great importance for the psychological well-being of college students. The accuracy of conventional scaling methods on questionnaires is generally low in predicting mental disorders, as the questionnaires contain much noise, and the processing on the questionnaires is rudimentary. To address this problem, we propose a novel anomaly detection framework on questionnaires, which represents each questionnaire as a document, and applies keyword extraction and machine learning techniques to detect abnormal questionnaires. We also propose a new keyword statistic for the calculation of option significance and three interpretable machine learning models for the calculation of question significance. Experiments demonstrate the effectiveness of our proposed methods.
Image Encryption using an Image Pattern based on Advanced Encryption Standard

Due to the global pandemic now more than ever, there is a need to send and receive reliable text or graphic information, whether for work, study, or any other activity. And this is where encryption plays an important role in improving data protection. On the other hand, the Advanced Encryption Algorithm (AES) is a symmetric block cypher that is worldwide implemented for the encryption of data generally. However, in the last years, due to its high degree of reliability and efficiency has been used in image encryption, but the AES's main drawback is that it uses a static key text, which compromises the security of AES, and the information may be exposed to different attacks. For that reason, this paper proposes to use an image as a key to increasing the AES security level whose results have been compared with the conventional AES using correlation coefficients and it has shown a significant improvement in terms of effectiveness.
An Improved Whale Optimization Algorithm Based on Nonlinear Function and Local Search

In order to improve the searching ability of whale optimization algorithm in continuous optimization function, an improved whale optimization algorithm based on nonlinear function and local search (NLWOA) is proposed. First, because the linear decreasing convergence function cannot balance the exploitation and exploration ability of WOA, this paper designs a nonlinear convergence function to make the algorithm have outstanding exploitation ability in the early stage and excellent exploration ability in the later stage. Second, the original whale optimization algorithm is too divergent in the random search stage. Thus, this paper introduces the historical optimization of whale population and individual. Finally, the proposed algorithm is tested in 23 benchmark functions and compared with other optimization algorithms. The experimental results show that NLWOA can better balance the exploitation and exploration capabilities. So NLWOA has better optimization capability.
Low Cost Analog Video Transmission Security of Unmanned Aerial Vehicle (UAV) based on Linear Feedback Shift Register (LFSR)

The security of video transmission in Unmanned Aerial Vehicle (UAV) become noteworthy since the UAV broadly used for military purposes to protect confidential information from unauthorized person. The confidentiality of information becomes an important thing because UAV hijacking occurs quite frequently. We develop an analog video transmission security for UAV with image encryption process by using Pseudo-Random Number Generator and assembling random image pieces with Linear Feedback Shift Register algorithm. The points to this security are the LFSR used as a seed that acts like a key to set the pattern of randomization from the images captured by the camera and processed by software on Raspberry Pi which is all mounted on UAV. The randomized image is sent using Analog Video Transmitter. The decryption is done on the ground station. The image result that received using a video receiver is decrypted using Raspberry Pi equipped with video grabber. In the experiment, we compared the similarity level between the original, scrambled, and decrypted images. It has 3.94 Percent similarity between original and scrambled images, 98.24 Percent similarity between original and decrypted images, and has 2.2 seconds latency.
Multi-Sensor Fusion Based Action Recognition in Ego-Centric Videos with Large Camera Motion

Real-time action recognition on smartphones and wearable cameras is a challenging task. The effect of camera motion on action recognition algorithms is not trivial. To our advantage, smartphones and wearable devices are often augmented with depth, audio, geo-location, and inertial data, which can be incorporated into action recognition framework. Our study aims to quantify the effect of ego-centric motion on standard action recognition algorithms. As a part of the study, we collected multi-sensor video dataset with seven actions from the same subjects captured in three different camera motion scenarios—minimal, pure rotational and both rotational and translational. We plan to create a multi-sensor fusion based action recognition framework to improve the recognition accuracy on smartphones and wearable cameras, which are equipped with inertial sensors. We present preliminary experiment results on our multi-sensor video dataset.
Optimum Installation Inclination of Solar Vacuum Tubular Collector in Lhasa

Abstract It is important to accurately design the installation inclination of solar collector for improving the utilization rate. This paper selected Lhasa area as a typical city which was rich in solar energy, setting a mathematical model for energy collection of vacuum tube collector to dynamically analyse the south towards, 0°∼90° angle of solar radiation on the surface of the collection on 8760 hours. As the results showed: the ability of the vacuum tube collector to collect solar reflection, scattering and direct radiation varied with the installation inclination a lot. When the inclination angle was 90, the maximum annual reflection radiation value was collected by the vacuum tube collector; when the inclination angle was 0, the maximum annual scattering radiation value was collected; when the inclination angle was local latitude, the maximum annual direct radiation value was collected; direct radiation in Lhasa was the main factor, so the maximum installation inclination of solar energy collected during the whole year and heating season was close to the local latitude.
Graph-Based Analysis of Drama Text—Case Study Based on Shakespeare’s ‘Measure for Measure’

In the present paper some analyses of a drama text are performed. The adequate bibliography was listed. Graph theoretical approach is utilised. The modelling is done via steps (exemplary): (a) the graph vertices represent heroes and the graph edges their mutual connections, (b) the graph vertices represent words and the graph edges show relationships between particular words. This approach gives a new inside into the drama texts. Deeper understanding the vocabulary and characters connections and their mutual links in versatile aspects allow us for new evaluation and comparisons of dramas.
Training Sparse Fuzzy Classifiers Using Metaheuristic Optimization

This paper proposes a novel classification model called sparse fuzzy classifier (SFC), which uses a sparse Takagi-Sugeno fuzzy system to classify data. This system is considered with Gaussian fuzzy sets in the antecedents and first-order polynomials in the consequents of fuzzy rules. The input fuzzy sets are determined using a metaheuristic optimization method (particle swarm optimization, genetic algorithm, simulated annealing, or pattern search). The output polynomials are obtained by a sparse regression represented by the elastic net. A quality criterion based on the confusion value is used to select the best model. The experiments showed that the proposed method could reduce the confusion and simplify fuzzy models by zeroing some of the polynomial coefficients.
Learning device in the “STEMpedia” mobile learning application

Abstract Access to the results of the audience and the use of innovative mobile learning with the aim of innovation, skills in technology, and media information are indispensable. The implementation of science, technology, engineering and mathematics (STEM) learning as innovative learning in elementary school must be accessible to the teacher in real life. STEMpedia’s mobile learning application was developed using Design-Based Research model Reeves, which consists of four stages: 1) identification and analysis of the problem; 2) Development of solutions based on information obtained; 3) Perform repeated processes to test and repair; 4) Reflections. The results of this research in the form of mobile learning application in STEM learning in elementary school that contains learning device and video of STEM learning implementation, this mobile learning application can be accessed on Android smartphone version. The product is accessed by the teacher as a medium to learn STEM learning so that the real picture of the condition in its implementation.
Self-Adaptive Social Spider Algorithm optimization for Solving Travelling Salesman Problem

In this paper, a new method is presented based on Social Spider Algorithm (SSA) for solving travelling salesman problem (TSP). Since the SSA is applied to continuous problem and TSP is discrete NP-hard problem, adaptive version of the SSA is introduced that called Adaptive SSA (DSSA). The DSSA algorithm was implemented on 36 instances of TSPLIB benchmarks (a library of sample instances for the TSP) that include symmetric and asymmetric traveling salesman problems. In order to implement the DSSA, MATLAB 2017 was used. After simulation, DSSA found the optimal solution for 24 instances out of 36 datasets. The simulation results showed that DSSA is superior to other algorithms for solving both the TSP and ATSP problems. We propose the Adaptive Social Spider Algorithm (DSSA) based on SSA for solving adaptive optimization problems such as TSP problem. The DSSA is main contribution of this paper. In this section, the changes of original SSA are described to create adaptive version of SSA.
Access security system in University Tun Hussein Onn Malaysia (Pagoh campus)

The study of access security system in University Tun Hussein Onn Malaysia (UTHM) in Pagoh branch is literally a research that enclosed to the safety research that involves in the entrance of the buildings to avoid any threats upcoming that can contribute to the loss of property and economic part. University is the place that involves in strong protection of security guided and commonly we all know that this system works 24 hours per day. Thus, we will be found out in this study on how the access security system is working on the campus and what are the implements that they want to obtain from working out this system. The objectives of this project are to determine the type of access security system in UTHM Pagoh campus and to study the user’s awareness about the access security system in UTHM Pagoh campus. This research study is completed by applying qualitative of research method to achieve the first objective and using quantitative method which is this project will use the questionnaire to complete and achieve the second objective. These data are then analyzed to exploit with the Statistical Package for Social Science (SPSS) version 19 and Microsoft Excel software. The data will be processed and presented in the form of chart and percentage by using this software. Last but not least, this project will find out the types of access security system available in the campus and to figure the user’s awareness of the access security in UTHM Pagoh campus.
Feasibility of Using Attention Mechanism in Abstractive Summarization

The Prevalence of information and its magnitude mandates a short description of the core of a document, an article, or legal documents. Abstractive summarization helps to concur with this problem utilizing the evolutions in machine learning and deep neural network. Attention-mechanism has extensively applied in the challenging issue of abstraction a text, in shorter length yet informative. We noticed in [13] after removing the attention layer from their proposed model, the performance only experience soft drawback, even can be ignored. Thus, motivates us to survey the latest models using attention-mechanism and its achievements, and the second objective is to run an experiment to compare standard stacked 3- Long Short-Term Memory (LSTM) layers incorporated with attention layer only (without any other hand-crafted algorithm) to explore how efficient this technique can generate better summarization, then a stand-alone model. The standard proposed model incorporated with attention-mechanism suffered from drawback performance and scored less than a stand-alone model by at least 6 point scores on ROUGE-1&2.
Research on Entity Recognition and Alignment Methods in Knowledge Graph Construction of Multi-source Tourism Data

In recent years, the tourism field related websites are increasing day by day, the network has produced massive tourist generation data. Based on the semi-structured data of scenic spots, hotels and caterings on tourist websites and the travel notes published by tourists, this paper constructed the tourism knowledge graph. The extraction of entities from travel notes was faced with the problems of named entity recognition and entity alignment. In order to improve the accuracy of extracting entities from travel notes, in this paper, the named entity recognition model based on BiLSTM-CRF and the entity alignment model based on siamese network were proposed. F values can reach 90.8% and 93.0%, respectively.
Audio-Visual Salient Object Detection

This paper studies audio-visual salient object detection. The task of salient object detection is to detect and mark the objects that are most concerned by people in the visual scene. Traditionally, visual salient object detection uses only images or video frames to detect salient objects, without modeling human multi-modal perception which includes the interaction between vision and hearing. Therefore, in order to improve the visual salient object detection, we incorporate audio modality into the traditional visual salient object detection task by applying a two-stream audio-visual deep learning network. To this end, we also build an audio-visual salient object detection dataset called AVSOD based on the existing dataset. To verify the effectiveness of audio modality in salient object detection, we compare the experimental performance of the deep learning model with and without audio modality. The experimental results demonstrate that audio modality has a good supplementary effect on the task of visual salient object detection, and also verified the effectiveness of the proposed dataset.
Improved error-correcting from extracted handwritings in Chinese

Errors exist in extracted Chinese handwritings even importing language models because of casualness and diversity of handwriting input, which would also affect the accuracy of recognition. Chinese handwritings cannot be converted into encoded texts until extracted and recognized correctly. Extracted handwritings may contain wrong language types, symbols, words, and word pairs. The conventional approach is based on context to adaptively correct theses errors. However, each writing character extraction candidates are fully visualized in bounding boxes, the overlaps of which bring more cognitive burden. Furthermore, the operation gesture needs to be accurate to stroke-level in convention that reduces the efficiency of correction. Therefore, an improved approach of error-correcting is proposed that an adaptive visualization as correcting reference and gesture analysis are taken into consideration. Experiments using real-life Chinese handwritings are conducted and compared the proposed approach with others. Experimental results demonstrate that the proposed approach is effective and robust.
Semantic Discovery from Sensors and Image Data for Real-Time Spatio-Temporal Emergency Monitoring

Modern district heating (DH) systems are essential contributors to large-scale city heating infrastructures. They consist of sensors, nodes, and methods for monitoring the status of the DH network. Sensing, processing, and analyzing data to locate an actual problematic emergency is a complicated task. This article presents the Spatio Temporal Emergency Monitoring (STEM) method that processes the semantic from multivariable sensors and infrared image data to locate real-time heating water leak emergencies in the DH network system. The sensory data includes multi-parameter DH network sensor data, such as water temperature, sweat rate, energy delivered, etc. The multimedia data is the infrared image data from a camera mounted on an unmanned aerial vehicle (UAV) for monitoring the environment at the underground DH network locations. The DH network monitoring system's primary purpose is to automate the central heating network's emergency monitoring. The presented real-time emergency monitoring approach processes the correlations between semantic and context of incoming data sets to determine an emergency's actual risk of a water leak. The STEM approach processes the network's sensors' data and the corresponding multimedia image data into single observations to monitor real-time Spatio-temporal emergency events.
Explication of Computer Science Ontology with DSpace

Ontology is the taxonomy representation of the Knowledge which enhances the capability of the representation of the data and makes data in machine readable format. It is the need of Semantic Web Technologies. Now a dayâ€™s many of the organizations want to be the part of it. Authors provide a survey of Ontology making, integration with the DSpace and its uses. The paper contributes as follows: 1) an overview of semantic technology and ontology 2) Ontology development and uses 3) Investigation of the concepts and relationships for Computer Science Domain Ontology 4) Integration and deployment of Computer Science Ontology with the DSpace. 5) Searching and filtering the results after querying the Ontology.
Optimized 3D path planner for steerable catheters with deductive reasoning

Keyhole neurosurgery is challenging, due to the complex anatomy of the brain and the inherent risk of damaging vital structures while reaching the surgical target. This paper presents a path planner for safe and effective neurosurgical interventions. The strengths of the proposed framework lay in the integration of multiple risk structures combined into a deductive method for fast and intuitive user interaction, and a modular architecture. The tool is intended to support neurosurgeons at quickly determining the most appropriate surgical trajectory through the brain matter with minimized risk; the user interface guides the user through the decision making process and helps save planning time of neurosurgical interventions. Risk structures and trajectories can be visualized in an intuitive way, thanks to a 3D brain surgery simulator developed with Unity. A qualitative evaluation with clinical experts shows the practical relevance, while a quantitative performance and functionality analysis proves the robustness and effectiveness of the system with respect to literature.
Indexing Subject Ontogeny

This paper discusses about origin and evolution of subjects—the subject ontogeny, and the changes in it that gets reflected in indexing language. We argue subjects originate from ideas that are products of intellectual efforts. The function of indexing language is to make access to knowledge of subjects easier through scientific mechanisms (schemes) and methodologies that give users much freedom and convenience in their search for information. We undertake a formal discussion on the theory of subject ontogeny linked to intellectual history of idea generation and how changes in its ontogeny are captured and retained through indexing languages.
Adaptive filter based on NLMS algorithm applied to beam position measurement in heavy ion synchrotron

In the running process of heavy ion accelerator, the revolution frequency is always changing during the acceleration. It is difficult to measure the beam position, because there are many working equipment when the accelerator is accelerating, and the electromagnetic environment is complicated, which is always doped with various noises. As a result, an adaptive filter based on normalized least mean square algorithm (NLMS algorithm) has been put forward in this paper to reduce the noises. The noises can be filtered by adaptively adjusting the weight of the filter output at different times according to the features of beam signals. Simulation results indicate the NLMS algorithm has better noise attenuation effect, which can be applied to actual beam position measurement.
The sentiment analysis model with multi-head self-attention and Tree-LSTM

In the natural language processing task.We need to extract information from the tree topology. Sentence structure can be achieved by the dependency tree or constituency tree structure to represent.The LSTM can handle sequential information (equivalent to a sequential list), but not tree-structured data.Multi-headed self-attention is used in this model. The main purpose of this model is to reduce the computation and improve the parallel efficiency without damaging the effect of the model.Eliminates the CNN and RNN respectively corresponding to the large amount of calculation, parameter and unable to the disadvantage of parallel computing,keep parallel computing and long distance information.The model combines multi-headed self-attention and tree-LSTM, and uses maxout neurons in the output position.The accuracy of the model on SST was 89%.
Radius kNN Classifier Using Aggregation of Fuzzy Equivalences

The paper presents a modified classification method based on the k-nearest neighbor algorithm. In the modified kNN algorithm some aggregations of fuzzy equivalences are used instead of metrics and the selection of the nearest neighbors is limited by their closeness from a tested object. This procedure is intended to improve suitability of the kNN algorithm, when a significant part of the closest neighbors is not close enough to the tested object. Additionally, some theoretical results concerning fuzzy equivalences and their aggregations are included in the paper.
Creative and Collaborative Practices in Virtual Immersive Environments

A decade ago, the Immersed in Learning project was developed to evaluate the use of 3D virtual worlds as a teaching and learning tool in Digital Media at the University of Wolverhampton, UK. A question that the research set out to explore was what were the benefits of integrating 3D immersive learning with face-to-face learning for students who were already comfortable inhabiting the digital realm. The purchase and development of Kriti Island in Second Life saw the online virtual space rapidly assume a sense of real presence and became a focus for collaboration, nationally, and internationally. Although Kriti Island came to the end of its cycle in 2014, the ongoing research focuses on virtual and mixed-reality environments as platforms for creative practice. The chapter will analyse the development of creative practices in virtual environments and the implications for creativity and new forms of collaboration through pedagogy and arts practice. In itself, Second Life may stand the test of time as a continued interface to examine issues of the real and the virtual and may contribute to further theoretical and philosophical discussions of new technologies and artistic practice.