Reviewerly/exHarmony huggingface dataset:
DETECTING OF FRAUD CLICK ON ADVERTISEMENT

Nowadays, advertising plays a major role in boosting a business. And every company has to pay some amount for it. As we know, mobile technology is handy and almost available in everybody's hands. As business owners, many business personals want to boost their business on mobile devices to see the advertisement. But there is a challenge in it. Mobile promotions are abused with bogus snaps that might be a significant test for the publicizing organization. But reported advancement frameworks use different strategies to recognize click deception; they don't safeguard the customer from possible interest among merchants and commercial frameworks. What's extra, advancement frameworks can't evaluate the customer's activity for click theft area whenever they're engaged to the promoting web content after tapping the advertisement, i.e., in a website, there will be focuses or some specific section after we click on the genuine section it'll be locked in to the publicizing website. We propose Click Fraud Crowdsourcing one all the more freely support-based for the most part structure recommended, that works identified with the two sponsors and advancement organizes to safeguard the two social events from any conceivable snap offensive exhibits. The framework paybacks by seeing of each ad
An Improved Differential Evolution Biogeography-Based Optimization Algorithm

In order to further improve the performance of biogeography-Based optimization algorithm (BBO), an improved differential evolution biogeography optimization algorithm is proposed. By combining the search ability of differential evolution algorithm with the utilization of biogeography optimization algorithm, the elite retention mechanism is adopted to retain the individuals with high fitness, and the inertia weight strategy is introduced to adjust the proportion of mutation operation in the hybrid migration operation to improve the global search ability of the algorithm, and then the small probability perturbation is added to prevent the algorithm from changing with the iteration It is found that the optimal solution is local. Finally, six test functions are used for experiments, and the results show that the improved algorithm is better than other algorithms in optimization results, convergence speed and stability.
Using Attractive–Repulsive Binary Local Gradient Contours for Sample-Consensus Background Modeling

Background subtraction being one of the most crucial steps in numerous real-world video-based applications has been studied extensively to date. Several researchers have provided solutions to this problem by exploiting the patterns and information hidden in a set of initial raw videoframes with the help of sophisticated machine learning techniques. While others have proposed to uncover the hidden patterns in raw data by employing a variety of texture patterns, which now have become an inseparable part of the background subtraction problem. Hence, the paper proposes to employ a novel feature combination of Attractive–repulsive local binary gradient contours with RGB channels and luminance information into a consensus-based modeling technique. The results obtained on sample videos from the standard Change Detection dataset [] support for the superiority of the proposed methodology.
Data Loss Prevention from a Malicious Insider

This study targeted cloud service providers (CSPs) and presented a depiction of their security awareness to mitigate a malicious insider threat. CSPs must ensure the privacy of customers’ data. Cloud service customers (CSCs) place their data on the Cloud, relieving them from data security and management; however, they lose control over the data. Several organizations are concerned about the privacy of data on the cloud due to sophisticated threats like malicious insider. To reveal whether CSPs are better protecting CSCs data, a survey was conducted in New Zealand and Australia to assess the data loss prevention from a malicious insider in the Cloud. An inductive quantitative approach was used in this research, and data was collected from 79 service providers based in New Zealand and Australia. This study provides insights about insider threats risks and security recommendations are provided to CSPs’ to develop awareness and how CSPs should prevent data loss.
A Multi-criteria Evaluation of Evolutionary Algorithms Against Segment Based Shilling Attacks

Collaborative Filtering (CF) has shown notable results within the field of information retrieval providing users with the relevant recommendations. Evolutionary approaches namely, Genetic Algorithm based Recommender System (GARS) and Memetic Algorithm based Recommender System (MARS) are CF techniques that provide significantly improved recommendation accuracy as compared to traditional CF techniques. However, these approaches are vulnerable to profile injection attacks such as Segment based Shilling attacks which can generate bias in the recommendations generated. Such attacks are mounted for fun and profit. In this work, we examine the impact of bias generated by the Bi-clustering based Memetic Algorithm Recommender System’s response to the target user, by inserting fake user profiles in the database on evolutionary approaches and traditional CF techniques. Prediction Shift and Hit Ratio are the two evaluation metrics that have been used for investigation. A publicly available dataset, namely Movielens of different sizes have been used for conducting experiments. Results depict the increasing prediction shift and hit ratio with increasing attack size.
Skeleton-based Action Recognition with Two-Branch Graph Convolutional Networks

Graph convolution is a popular technique for action recognition based on human skeleton data. Due to the fact that human skeleton data can be treated as a graph in three dimensions, Graph Convolutional Networks (GCN) represent the input data as a graph structure to perform the recognition task, and thus numerous approaches based on GCN to recognize actions have achieved great results. In spite of the input data being structured as a four-dimensional tensor in GCN, it still can not fully exploit the contained rich action-related information. Therefore, we proposed a new model that the three-dimensional skeleton data is put into both the Convolutional Neural Network (CNN) and the Graph Convolutional Neural Network branches to perform feature extraction in the spatiotemporal dimension separately, then the output information is fused for prediction. Given the richness of time-domain information characteristics, feature extraction is enhanced by increasing the model's depth. After the pooling layer and the fully connected layer, we concatenate the outputs at the ends of the graph data stream and the convolution data stream to obtain the network's final output. Finally, the prediction results can be obtained via the SoftMax layer. On the Kinetic 400 dataset, our suggested model outperforms Benchmark STGCN in terms of accuracy. The experiment results indicate that the proposed novel model successfully increases the generalization ability and classification performance for action recognition.
Human Action Recognition Using Skeleton Data from Two-Stage Pose Estimation Model

This paper presents a method of human action recognition based on the key points of skeleton, aiming to guide the robot to follow a leader in complex environments. We propose a two-stage human pose estimation model which combines the Single Shot Detector (SSD) algorithm based on ResNet with Convolutional Pose Machines (CPMs) to obtain the key points positions of the human skeleton in 2D images. Based on the position information, we construct structure vectors. Feature models consisting of eight angle features and four modulus ratio features are then extracted as the representation of actions. Finally, multi-classification SVM is used to classify the feature models for action recognition. The experimental results demonstrate the validity of the two-stage human pose estimation model to accomplish the task of human action recognition. Our method achieves 97% recognition accuracy on the self-collected dataset composed of six command actions.
Accounting digital resource sharing model of small and medium sized enterprises under “cloud +” mode

Under the ”cloud +” mode, in order to improve the security and efficiency of accounting digital resource sharing of small and medium-sized enterprises in Shaanxi Province, this paper proposes and constructs the accounting digital resource sharing model of small and medium-sized enterprises. Firstly, this paper analyzes the current situation of digital resource transformation of small and medium-sized enterprises in Shaanxi Province, and formulates evaluation indicators through questionnaire survey. Based on the determined evaluation index, the weight of evaluation index is calculated. According to the weight calculation results, the digital resource sharing model of small and medium-sized enterprises is constructed in the ”cloud +” mode. The experimental results show that the model has high sharing security and efficiency.
Hybrid Quantum Differential Evolution

The increasing interest, in the last two decades, of evolutionary computation community in the combination of quantum computing and evolutionary computing, has led to the definition of a novel class of quantum-inspired evolutionary algorithms which exploit the concepts of quantum bits and quantum gates with the aim of improving the efficiency of current optimization methods. In this paper, a new method which hybridizes differential evolution with quantum computing is proposed. The method consists of two phases. In the first phase, it performs a new quantum-inspired evolutionary method until it does not get stuck into a local optimum, then, in the second phase, it runs differential evolution by using as initial population that obtained at the end of the first phase. Experiments on classical benchmark functions show that the hybridization outperforms standard methods by sensibly improving the fitness value and speeding up the convergence process.
Research on Real-Time Reentry Trajectory Reconstruction Base on Multiple Model

In order to solve the problem that it is difficult to establish an accurate dynamic model of spacecraft geocentric under predictive guidance, an interactive multi model real-time reentry trajectory reconstruction method is proposed in this paper. Multiple reentry guidance rates are designed, using multiple parallel filters to filter each guidance rate model, and the calculated model probability is used to realize data fusion finally. Through the reentry data test of the new generation spacecraft mission, this method can correct the reentry dynamics model, solve the problem of real-time trajectory reconstruction when the model is not accurate, and provide support for the real-time reentry trajectory reconstruction of the subsequent Chang'e-5 mission.
Research on Technology of Pipeline Detection Robot Based on Spiral Propulsion

Aiming at the space curved pipeline with an inner diameter of less than Φ15 cm, in order to realize its internal detection work, this paper studies a compact, functionally integrated micro pipeline inspection robot and the corresponding pipeline inspection and scanning strategy. First, the robot adopts the driving principle based on spiral propulsion, and uses only one power source to realize the robot's forward, backward, and stop motions in the pipeline, and adapt to the bending and spatial posture of the pipeline. Ensure the robot's traveling ability and traveling efficiency in the pipeline. Second, the robot is equipped with a camera and a variety of measuring sensors to realize the detection of the inside of the pipeline. With the help of the robot's spiral propulsion action, the laser ranging sensor can scan the inside of the pipeline at 360°. And based on the robot's helix angle and coordinate conversion algorithm, the Cartesian coordinate system point cloud data set of the pipeline inner wall is obtained, and the point cloud data is processed based on the Poisson surface reconstruction algorithm to realize the visual surface reverse modeling of the pipeline inner wall, thereby Realize the foreign body inspection inside the pipeline. Third, this paper proposes a pipeline roundness detection algorithm based on point cloud data, which realizes the pipeline roundness detection. Fourth, this paper proposes a method for determining the location of foreign objects based on point cloud data. Finally, this paper designs a verification experiment to verify the robot's traveling mode and detection ability.
Umitation: Retargeting UI Behavior Examples for Website Design

Interface designers often refer to UI behavior examples found in the wild (e.g., commercial websites) for reference or design inspiration. While past research has looked at retargeting interface and webpage design, limited work has explored the challenges in retargeting interactive visual behaviors. We introduce Umitation, a system that helps designers extract, edit, and adapt example front-end UI behaviors to target websites. Umitation can also help designers specify the desired behaviors and reconcile their intended interaction details with their existing UI. In a qualitative evaluation, we found evidence that Umitation helps participants extract and retarget dynamic front-end UI behavior examples quickly and expressively.
Improved Correlation Filter Visual Tracker By Using Scale Estimation Network

Object tracking is to accurately track target information in continuous video sequences, and the bounding box of target is also indispensable as an important metric for evaluating tracker algorithms. In recent years, correlation filter trackers have been successfully achieved powerful robustness. To estimate object scale, most correlation filter trackers use a simple multi-scale search that has limited the development of correlation filters in precision tracking. We propose a scale estimation network to solve the problem, which uses the experience of bounding box estimation in object detection. Through extensive offline learning, high-level knowledge is incorporated into target estimation. Our scale estimation network is trained to optimize object scale. We further introduce the fusion method between correlation filter and scale estimation network coordinated operation. Our improved tracker evaluated on three benchmarks: OTB2015, VOT2018, and VOT2019. The evaluation experiments on these benchmarks demonstrate that our tracker has better performance.
FGYM: Toolkit for Benchmarking FPGA based Reinforcement Learning Algorithms

FPGA-based heterogeneous computing platforms are promising candidates to enable fast training of Reinforcement Learning (RL) agents. Typically, an RL agent for an environment is trained via interactions with a software that simulates the environment. While several toolkits exist to quickly deploy RL training on CPU or GPU, there lacks a similar toolkit for FPGAs. To ease the deployment process of RL using FPGAs, we demonstrate FGYM (FPGA-GYM) - a toolkit that generates an end-to-end interface between the simulation environments running on the CPU and agents running on the FPGA. FGYM supports a variety of environments and automatically generates the memory interface using PCIe. FGYM supports multiple levels of parallelism including vectorized agent-environment interactions and memory port aggregation. It also provides profiling results for users to identify the execution bottlenecks.
Ensemble classifier using Fuzzy C5.0 decision Tree for Information Retrieval

The exposure of millions of individual remarks or thoughts has increased greatly as the global web boom has spread and the number of pages, review boxes, blogs, websites and other platforms in social networks has risen. There are many subjects on-line views or thoughts such as books, films, consumer goods, motorcycles, politics, etc. This results in a widespread use of opinion interpretation and domain adaptation. It is impossible for modern cross-domain opinion mining to predict the exact result of the occurrence in a cross-domain. Features such as text length and stylistic features should be taken into account. The sentences can be shown in different domains differently of costs and computations for the respective domain are very high. The uncontrolled models are considered for predictions that produce low precision and cannot be adapted to changing environments. Therefore, the ensemble classification is developed to handle data complexity and achieve optimum results using the Fuzzy C5.0 decision tree.
Analyzing Diversity and Equity in Virtual Classrooms

Around the country, school systems are trying to navigate through the ever-changing impact COVID-19 has caused. School districts scramble to equip teachers and students with technology as the world transitions to a mostly virtual setting. This document gives an overview of challenges educators encountered shifting from traditional face-to-face learning to virtual learning. This chapter also analyzes how differentiation and the use of multiple communication platforms help to address equity and diversity in the virtual setting.
The Effect of Distance Learning Methods, Learning Motivation and Parental Support in Students’ Learning Achievement

The purpose of the conducted research is to find out and determine the meaning of distance learning and all the supporting factors of parental support of students’ learning achievement by using the distance learning methods. The research was done by using a qualitative method to get described in brief in order to gain the needed information. A given questionnaire is one of the research instrument for this research that has been given to 30 SMP and SMA students of Jakarta and Bekasi. The technique that has been used for the sample is the random sampling technique. The result is explained and presented by the researcher.
Research on the Active Learning and Query Strategy Improvement Method Based on the Optimal Selection

research-article Share on Research on the Active Learning and Query Strategy Improvement Method Based on the Optimal Selection Author: Yicheng Zhou School of Computer Science and Engineering,Northeastern University, China School of Computer Science and Engineering,Northeastern University, ChinaView Profile Authors Info & Claims ICIEI '21: Proceedings of the 6th International Conference on Information and Education InnovationsApril 2021 Pages 80–84https://doi.org/10.1145/3470716.3470730Published:14 October 2021Publication History 0citation19DownloadsMetricsTotal Citations0Total Downloads19Last 12 Months12Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
The Construction of University Teachers’ Scientific Research Performance Evaluation System under Artificial Intelligence

Artificial intelligence is a branch of computer science, which refers to the simulation of human thinking and consciousness process. Artificial intelligence is a new technology and science. Colleges and universities have been trying to cooperate with it to improve performance management. In order to solve the problems encountered in the construction of University Teachers’ scientific research performance evaluation system, this study will start from the current situation of the construction of University Teachers’ scientific research performance evaluation system, combined with the relevant theories of artificial intelligence, analyze its advantages such as reducing the workload of managers, improving the fairness of evaluation work, as well as the disadvantages such as easy disclosure of teachers’ information and extra financial expenditure of universities. This study attempts to apply artificial intelligence to the construction of University Teachers’ scientific research performance evaluation system, and according to the characteristics of artificial intelligence, finally build a set of scientific and effective university teachers’ scientific research performance evaluation system.
Object Detection Using Color Dissimilarity Based Segmentation Method

Image segmentation is a very important process in object detection. The segmentation process becomes a critical determinant of the success of object detection. Various methods have been developed for this image segmentation process, but there is no general solution that can be applied. Associated with object detection, the image segmentation method based on color dissimilarity turns out to be able to give good results. This segmentation method divides the image based on the dissimilarity of the values of R, G and B on the adjacent image pixels. Color is considered different if the difference in the values of R, G and B of this pixel pair produces a Delta E value whose value exceeds the threshold that is able to distinguish eyes. If this comparison shows the color dissimilarity detected, the pixel is changed to white, and the resulting image is then converted into a black-and-white image. From the resulting segmentation space, the bounding box is then made. Based on this bounding box, then the object can be detected properly. The results of object detection shown by this method are very good. The weakness of this model is not being able to detect objects that overlap one another.
IoT Analytics-inspired Real-time Monitoring for Early Prediction of COVID-19 Symptoms

Abstract In this pandemic, providing a quality environment is considered one of the essential objectives of smart wellbeing observation. Therefore, the prediction of irregular events has become a fundamental requirement of assistive or clinical consideration. By concentrating on this need, a dew computation-inspired irregular physical event determination solution is presented to determine the symptoms of COVID-19 by analyzing the physical activities of the individuals at their initial stage. The benefit of the proposed solution is enhanced by forwarding the predicted outcomes and their occurrence within a speculated time on a private cloud database to decide the health seriousness. Furthermore, a dynamic decisive-module is introduced to notify medical specialists about the current wellbeing status of the individuals under monitoring. The real-time prediction efficiency of the proposed solution is determined by implementing and calculating the outcomes on both the dew and cloud platforms. The calculated outcomes exhibit the improved viability of the dew platform over the cloud platform by increasing the prediction speed of 46.27% for 40 and 45.54% for 30 frames per second. Moreover, the event prediction performance is justified over the state-of-the-art monitoring arrangements by achieving accuracy (92.88%), specificity (90.87%), sensitivity (88.26%) and F1-measure (89.53%) with the least decision-making delay.