Reviewerly/exHarmony huggingface dataset:
Research on Application of Intelligent Analysis in Monitoring of Classroom Teaching

Strengthening classroom teaching monitoring is the key to comprehensively improving teaching quality. Traditional methods such as academic administrator’s inspection, teaching supervision or expert’s evaluation of lectures, and students’ evaluation of teaching are mostly adopted in the conventional classroom teaching monitoring. With the development of science and technology, many schools have achieved whole process tracking and recording of classroom teaching, but the analysis and research on classroom teaching videos and image data are still very limited. This paper aims to explore and discuss how to apply intelligent analysis technology to monitor the whole process in the classroom, comprehensively and dynamically check the classroom teaching process, conduct records and analysis of teachers` teaching situation of teachers and students` learning situation, discover and analyze the main problems in the teaching process and the main problems that affect the teaching quality, and give teachers useful feedback on classroom teaching and course construction, so as to improve the quality of teaching, so that the supervision of classroom teaching and teaching management gradually become scientific, institutionalized and standardized.
Overdrive Fault Attacks on GPUs

Graphics processing units (GPUs) are widely used to accelerate applications including cryptographic operations. The reliability and security of GPUs have become a concern. Prior work reported power and timing side-channel attacks on GPUs. In this paper, we present our project about a new class of fault attacks targeting modern GPUs, the overdrive fault attacks. This attack exploits voltage-frequency scaling features present on most commercial GPUs to introduce random faults during kernel execution. We demonstrate an effective fault- based attack on a commercial GPU, recovering the AES keys in minutes and inducing misclassifications in a CNN kernel. Such software-controlled fault injections also pose serious threats to data integrity and service availability in the cloud.
Developing Suicide Risk Idea Identification for Teenager (SERIINA) Mobile Apps Prototype using Extended Rapid Application Development

Suicide death is the number 15 cause of death in the world. Suicide cases go undetected because the perpetrator shows no signs beforehand. Therefore, it is necessary to identify the risk of suicide in adolescents early, accessed quickly, maintains user privacy, and understand user needs. Using the Risk Factors of Suicidal Ideation (RFSI) questionnaire, it hoped to detect early suicide incidents in adolescents. This research proposed a Suicide Risk Idea Identification for Teenager (SERIINA), a mobile application developed using the Extended Rapid Application Development (ERAD) method. The ERAD method combines the concept of design sprint and RAD in the system development cycle. Based on the research results, SERIINA mobile apps development is completed in 19 days. The functional testing using Black-Box testing shows that the application works well and compatible with the five main scenarios. The results of interface design testing using heuristic evaluation indicate that the application has been well designed, with a usability value of 2.34 or about 72-85% according to design rules.
Polarity Detection Across the Globe Using Sentiment Analysis on COVID-19-Related Tweets

In the face of a crisis, how different cultures respond and react depends largely on external factors. Government's decision has a high impact on the behavior of the society. It has been largely observed that people across nations use social media to express their feeling. Even during the spread of coronavirus (COVID-19), the countries located as various longitudes expressed different emotions. The purpose of this study is to analyze different emotions of people from different countries. Text classification was used for estimating the sentiment polarity from extracted tweets. Various machine learning models have been trained to achieve accuracy on the sentiment dataset. It was observed that although people of different race and culture are spread across the globe over a long longitude area, the sentiments or emotions of the people are the same.
Impact of Substrate Temperatures On the Properties of V<sub>2</sub>O<sub>5</sub> Thin Films Deposited by Pulsed Laser Deposition

Abstract Vanadium pentoxide (V 2 O 5 ) thin films were fabricated by pulsed laser deposition (PLD) on fused silica substrate at temperatures (T s ) ranged from ambient temperature up to 300°C. UV-VIS-NIR spectral measurements, X-ray diffraction (XRD) X-ray photoelectron spectroscopy (XPS), and scanning electron microscopy (SEM) were made to understand the influence of substrate temperature on optical, structural, and compositional properties. The substrate temperature displayed a robust effect on the construction and visual characteristics. The photosensitive band gap of PLD V 2 O 5 films was powerfully dependent on the substrate temperature and was reduced from 2.36 eV to 2.08 eV with the growth of substrate temperature from ambient temperature to 300°C. However, the refractive index showed an increase from 2.28 to 2.69 for the same temperature range. V 2 O 5 films grown at T s = 300°C exhibited a crystalline nature as evidenced by XRD and SEM studies. The chemical composition of V 2 O 5 films has been studied by XPS and the data revealed pure V 2 O 5 compound was formed.
A Novel Cargo Ship Detection and Directional Discrimination Method for Remote Sensing Image Based on Lightweight Network

Recently, cargo ship detection in remote sensing images based on deep learning is of great significance for cargo ship monitoring. However, the existing detection network is not only unable to realize autonomous operation on spaceborne platforms due to the limitation of computing and storage, but the detection result also lacks the directional information of the cargo ship. In order to address the above problems, we propose a novel cargo ship detection and directional discrimination method for remote sensing images based on a lightweight network. Specifically, we design an efficient and lightweight feature extraction network called the one-shot aggregation and depthwise separable network (OSADSNet), which is inspired by one-shot feature aggregation modules and depthwise separable convolutions. Additionally, we combine the RPN with the K-Mean++ algorithm to obtain the K-RPN, which can produce a more suitable region proposal for cargo ship detection. Furthermore, without introducing extra parameters, the directional discrimination of the cargo ship is transformed into a classification task, and the directional discrimination is completed when the detection task is completed. Experiments on a self-built remote sensing image cargo ship dataset indicate that our model can provide relatively accurate and fast detection for cargo ships (mAP of 91.96% and prediction time of 46 ms per image) and discriminate the directions (north, east, south, and west) of cargo ships, with fewer parameters (model size of 110 MB), which is more suitable for autonomous operation on spaceborne platforms. Therefore, the proposed method can meet the needs of cargo ship detection and directional discrimination in remote sensing images on spaceborne platforms.
Analysis of Harassment Complaints to Detect Witness Intervention by Machine Learning and Soft Computing Techniques

This research is aimed to analyze textual descriptions of harassment situations collected anonymously by the Hollaback! project. Hollaback! is an international movement created to end harassment in all of its forms. Its goal is to collect stories of harassment through the web and a free app all around the world to elevate victims’ individual voices to find a societal solution. Hollaback! pretends to analyze the impact of a bystander during a harassment in order to launch a public awareness-raising campaign to equip everyday people with tools to undo harassment. Thus, the analysis presented in this paper is a first step in Hollaback!’s purpose: the automatic detection of a witness intervention inferred from the victim’s own report. In a first step, natural language processing techniques were used to analyze the victim’s free-text descriptions. For this part, we used the whole dataset with all its countries and locations. In addition, classification models, based on machine learning and soft computing techniques, were developed in the second part of this study to classify the descriptions into those that have bystander presence and those that do not. For this machine learning part, we selected the city of Madrid as an example, in order to establish a criterion of the witness behavior procedure.
Multiple Optimizations-Based ESRFBN Super-Resolution Network Algorithm for MR Images

Magnetic resonance (MR) images can detect small pathological tissue with the size of 3–5 image pixels at an early stage, which is of great significance in the localization of pathological lesions and the diagnosis of disease. High-resolution MR images can provide clearer structural details and help doctors to analyze and diagnose the disease correctly. In this paper, MR super-resolution based on the multiple optimizations-based Enhanced Super Resolution Feed Back Network (ESRFBN) is proposed. The method realizes network optimization from the three perspectives of network structure, data characteristics and heterogeneous network integration. Firstly, a super-resolution network structure based on multi-slice input optimization is proposed to make full use of the structural similarity between samples. Secondly, aiming at the problem that the L1 or L2 loss function is based on a per-pixel comparison of differences, without considering human visual perception, the optimization method of multiple loss function cascade is proposed, which combines the L1 loss function to retain the color and brightness characteristics and the MS-SSIM loss function to retain the contrast characteristics of the high-frequency region better, so that the depth model has better characterization performance; thirdly, in view of the problem that large deep learning networks are difficult to balance model complexity and training difficulty, a heterogeneous network fusion method is proposed. For multiple independent deep super-resolution networks, the output of a single network is integrated through an additional fusion layer, which broadens the width of the network, and can effectively improve the mapping and characterization capabilities of high- and low-resolution features. The experimental results on two super-resolution scales and on MR images datasets of four human body parts show that the proposed large-sample space learning super-resolution method effectively improves the super-resolution performance.
An Analysis of the Cooperation between Social Organizations and Enterprises to Promote the Employment of the Disabled Take the Nuanxi Disabled Assistance Project in D City as an Example

With the development of the theory of multiple governance, the methods and methods of collaborative governance of multiple subjects have been widely used in the treatment of social problems and the subjects that solve the employment problem of the disabled are gradually diversifying. This study takes the Nuanxi Disabled Assistance Project in D City as an example, with the cooperation between social organizations and enterprises as an entry point of view. The study found that in the process of social enterprise cooperation to promote the employment of persons with disabilities, there are problems such as insufficient attention to details of cooperation, unstable employment of persons with disabilities, limited and single participation of social organizations, and unsatisfactory cooperation effects, and put forward perfect countermeasures and suggestions for these problems.
Rotated Box Is Back: An Accurate Box Proposal Network for Scene Text Detection

Scene text detection is a challenging task because it must be able to handle text in various fonts and from various perspective views. This makes it difficult to use rectangular bounding boxes to detect text locations accurately. To detect multi-oriented text, rotated bounding box-based methods have been explored as an alternative. However, they are not as accurate for scene text detection as rectangular bounding box-based methods. In this paper, we propose a novel region-proposal network to suggest rotated bounding boxes and an iterative region refinement network for final scene text detection. The proposed region-proposal network predicts rotated box candidates from pixels and anchors, which increases recall by creating more candidates around the text. The proposed refinement network improves the accuracy of scene text detection by correcting the differences in the locations between the ground truth and the prediction. In addition, we reduce the backpropagation time by using a new pooling method called rotated box crop and resize pooling. The proposed method achieved state-of-the-art performance on ICDAR 2017, that is, an f-score of 75.0% and competitive results with f-scores of 86.9% and 92.4% on ICDAR 2015 and ICDAR 2013, respectively. Furthermore, our approach achieves a significant increase in performance over previous methods based on rotated bounding boxes.
Sentiment Analysis of Hotel Reviews Based on Deep Leaning

With the development of e-commerce, a large number of hotel reviews have been produced. According to the short text features of hotel reviews, sentiment classification method based on emotion dictionary needs a lot of emotional database resources, while machine learning method needs complex artificial design features and feature extraction process. In this paper, long short term memory is proposed. The text classification algorithm is used to analyze the sentiment tendency. Firstly, word2vec and word segmentation technology are used to process the short comment text into the LSTM network, and dropout algorithm is added to prevent over fitting to get the final classification model, Using the unique characteristics of short-term memory of LSTM network, it has achieved a good effect on sentiment classification of hotel reviews, with an accuracy rate of more than 95%.
Comparison of Visual Feature Extraction Methods of Sperms in Semen Microscopic Videos

The morphology, number, density and sperm motility of male sperm are important indicators to measure male reproductive health. In microscopic videos, visual feature plays a fundamental role in sperm recognition and classification. For this purpose, we compare the effectiveness of different visual features of sperms in recognition and classification with four different classifiers. In the experiment, we use a sperm microscopic video data set to evaluate the visual feature, including two types of 1374 sperms, and obtain the evaluation of different visual features.
Research of Immersive Geospatial Data Visualization based on Embodied Cognition

Geospatial data is a kind of big data with large quantities and complex construction. The conventional statistical analysis method and data visualization cannot meet the requirements of information mining and analysis, while the immersive geospatial data visualization supports the immersive data analysis and can better guide users to make insightful analysis and decision. This study takes embodied cognition theory as the foundation and combines the principles of immersive analysis and geospatial visualization to proposes some strategies of immersive geospatial visualization design. Moreover, a case study is used to introduce the application of embodied cognition and immersive analytics.
Learning Adaptive Target-and-Surrounding Soft Mask for Correlation Filter Based Visual Tracking

Visual tracking is a very critical issue in computer vision and video processing. For Discriminative Correlation Filter (DCF)-based tracking methods, it is very essential and meaningful to adaptively incorporate reliable target and surrounding information from video frames. However, most existing DCF-based trackers solely rely on pre-defined and fixed constraints such as a binary mask or quadratic function-based regularization to improve the discrimination. Unfortunately, such attempts fail to adjust the constraints according to the change of tracking circumstance in the video sequence, and thus lead to the lack of reliability of learned filters. To mitigate these problems, we present a novel DCF-based tracking method that introduces an adaptive target-and-surrounding soft mask (ATSM) into the learning formula. The adaptive soft mask that is represented by float numbers contains the detail information for both target region and its surrounding information: first, for the background area, it introduces meaningful background information and suppressing uninformative one; second, for the target area inside the bounding box, it helps to focus on the reliable area and repress the rapidly changing area; third, the target-and-surrounding soft mask is adaptively adjusted based on the variations of the target and its surrounding during the tracking process. By jointly modeling the filter and the adaptive soft mask, our ATSM tracker achieves an efficient integration of meaningful information of both foreground and background and performs favorably against state-of-the-art algorithms on seven well-known benchmarks.
A New Self-Adaptive Hybrid Markov Topic Model Poi Recommendation in Social Networks

Point-of-Interest recommendation is an efficient way to explore interesting unknown locations in social media mining of social networks. In order to solve the problem of sparse data and inaccuracy of single user model, we propose a User-City-Sequence Probabilistic Generation Model (UCSPGM) integrating a collective individual self-adaptive Markov model and the topic model. The collective individual self-adaptive Markov model consists of three parts such as the collective Markov model, the individual self-adaptive Markov model and the self-adaptive rank method. The former determines the topic sequence for all users in system and mines the behavioral patterns of users in a large environment. The later mines behavioral patterns for each user in a small environment. The last determines a self-adaptive-rank for each user in niche. We conduct a large amount of experiments to verify the effectiveness and efficiency of our method.
Explainability in Irony Detection

Irony detection is a text analysis problem aiming to detect ironic content. The methods in the literature are mostly for English text. In this paper, we focus on irony detection in Turkish and we analyze the explainability of neural models using Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME). The analysis is conducted on a set of annotated sample sentences.
Imbalanced Machine Learning Based Techniques for Breast Cancer Detection

Breast cancer is the world's second-leading cause of cancer death among women. It is the most frequent type of invasive cancer in women and the second biggest cause of mortality in the United States. Breast cancer is a disease in which the breast cells develop out of control. Many studies have been conducted in this area utilizing a machine learning technique, however some of them did not explore how to cope with imbalanced classes. The breast cancer dataset is an unbalanced dataset in which the total frequencies of benign class is larger than the total frequencies of malignant class. The problem with an unbalanced dataset is that it provides a high level of accuracy just by predicting the majority class, but fails to capture the minority class, which is usually the point of creating the model in the first place. Imbalanced classification approaches were employed in this study to predict breast cancer. The accuracy of prediction for multiple machine learning algorithms was determined utilizing imbalanced methodologies such as synthetic minority oversampling technique, random oversampling, and random under sampling technique respectively.
Provably Safe Model-Based Meta Reinforcement Learning: An Abstraction-Based Approach

While conventional reinforcement learning focuses on designing agents that can perform one task, meta-learning aims, instead, to solve the problem of designing agents that can generalize to different tasks (e.g., environments, obstacles, and goals) that were not considered during the design or the training of these agents. In this paper, we consider the problem of training a provably safe Neural Network (NN) controller for uncertain nonlinear dynamical systems that can generalize to new tasks that were not present in the training data while preserving strong safety guarantees. Our approach is to learn a set of NNs during the training phase. When the task becomes available at runtime, our framework will carefully select a subset of NNs and compose them to form the final NN controller. Critical to our approach is the ability to compute a finite-state abstraction of the nonlinear dynamical system. This abstract model captures the behavior of the closed-loop system under all possible NN weights, and is used to train the NNs and compose them when the task becomes available. We provide theoretical guarantees on the correctness of the resulting NN controller and show the efficacy of our approach via simulations.
Notice of Violation of IEEE Publication Principles: Sketch Based Image Retrieval System Using ExHoG

Over the past several years, one of the most vibrant study topics in the field of image processing has been Content-Based Visual Information Retrieval (CBVIR) or Content-Based Image Retrieval (CBIR). The majority of today's picture search engines, such as Google Picture Search and Yahoo Images, rely on image annotations in the form of text. In all of these technologies, photos are often manually labeled with keywords before being retrieved using text-based search approaches. As a result, the performance of these systems is inadequate. The goal of this research project is to design and construct a CBIR system based on free hand sketching. We sought to explain a feasible technique of designing an image retrieval system using a job specific descriptor, which can manage both the informational gap between a free hand drawing and a coloured picture, allowing for an efficient searching mechanism, with the assistance of current approaches. Following a series of pre-processing stages, the descriptor is used to compare the converted full colour picture to the input picture. Many feature extraction approaches are used in practise, such as Histogram of Gradients (HG) and Histogram of Oriented Gradients (HOG), among others. ExHoG (Extended Histogram of Gradients) is being utilized as a new feature for SBIR by investigating the limits of Histogram of Gradients (HG) and Histogram of Oriented Gradients (HOG).
Design of transmission line safety early warning system based on big data variable analysis

In order to improve the accuracy and efficiency of transmission line safety early warning, a transmission line safety early warning system based on big data variable analysis is proposed. Firstly, the overall architecture of the system is designed under the B / S architecture. Secondly, in the hardware part of the system, the security data real-time monitoring module, data transmission module and security warning module are designed to meet the functional requirements of the system. Finally, in the system software design part, the big data variable analysis method is used to calculate the hidden danger of transmission line safety, so as to improve the effectiveness of transmission safety early warning. The experimental results show that, compared with the traditional security early warning system, the early warning accuracy and efficiency of the designed system are significantly improved, which can ensure the safe operation of the transmission line.