Reviewerly/exHarmony huggingface dataset:
Cell Counting with Channels Attention

The low-quality images and the occlusions impede the accuracy of cell counting. Many networks focus on enlarging the receptive fields or expanding the single network to a multi-resolution network to enhance the counting accuracy. However, few networks are interested in channel adjustment. In this paper, we propose a weighted channel module to emphasize the channels contributing more to the target. It aims to analyze the interrelation between channels and enhance the representation of feature maps. The module is designed as the residual connection and can be plugged into any network. Experiments on two public datasets show that the proposed module is helpful to boost the performance of the network.
Artificial Intelligence for Blockchain II

Blockchain technology and artificial intelligence (AI) are proving their worth individually in the field of the healthcare industry. Blockchain is an emerging technology and well known for its valid data and security. The blockchain data structures are well structured to retrieve data easily and it stores data in a decentralized way. It provides the facility to automate tasks using smart contracts and it logs all the transactions in the ledger. But the blockchain cannot assure the smart contract quality and cannot detect data behaving maliciously. Blockchain challenges can be overcome with the help of AI. AI provides intelligence through decision making throughalgorithms. AI trains machines with the help of data to predict outcomes using various machine-learning algorithms. Blockchain helps to provide great quality data for these algorithms. In this fast-growing world, there are data-hungry applications, especially the healthcare industry, that need data with high security and privacy. In this chapter we provide information on how the integration of blockchain and AI would help in data analysis, creating quality assured smart contracts, and facilitating personalized treatment plans in the healthcare industry.
Unsupervised Gaze: Exploration of Geometric Constraints for 3D Gaze Estimation

Eye gaze estimation can provide critical evidence for people attention, which has extensive applications on cognitive science and computer vision areas, such as human behavior analysis and fake user identification. Existing typical methods mostly place the eye-tracking sensors directly in front of the eyeballs, which is hard to be utilized in the wild. And recent learning-based methods require prior ground truth annotations of gaze vector for training. In this paper, we propose an unsupervised learning-based method for estimating the eye gaze in 3D space. Building on top of the existing unsupervised approach to regress shape parameters and initialize the depth, we propose to apply geometric spectral photometric consistency constraint and spatial consistency constraints across multiple views in video sequences to refine the initial depth values on the detected iris landmark. We demonstrate that our method is able to learn gaze vector in the wild scenes more robust without ground truth gaze annotations or 3D supervision, and show our system leads to a competitive performance compared with existing supervised methods.
Robust Object Tracking via Graph-based Transductive Learning with Subspace Representation

Although many tracking algorithms have been studied in recent years, target tracking is still a very basic research topic. In this study, we propose a novel robust object tracking via graph-based transductive learning with subspace representation (GTLSR). Firstly, probabilistic hypergraph ranking theory is developed to capture the local affinity information amount all vertices. Then, we transform the object tracking into the transductive learning problem based on Bayesian inference framework. Third, to deal with the occlusions of target, we use subspace representation to constrain the indication vector of template set. Finally, a dynamic updating strategy based on double threshold is proposed to solve the challenges of pose change and occlusion. The extensive experiments reflect that the proposed GTLSR outperforms other baseline methods in accuracy and robustness.
Bank card number recognition system based on deep learning

With the widespread application of mobile payment, identifying the bank card number can help us bind the bank card quickly. The angle of bank card shooting, the light and shade of light, and the background colour of cards from different companies will affect card number recognition accuracy. Therefore, this paper designs a bank card number recognition system, which mainly includes three parts: card number location, character recognition and GUI interface design. Firstly, the improved EAST algorithm is proposed to locate the card number, and then the neural network training and character recognition are carried out. In this process, the CRNN-BLSTM-CTC architecture of the mainstream OCR algorithm is mainly used. Finally, the GUI is used to demonstrate the results. The experimental results show that the system significantly improves the accuracy and efficiency of bank card number recognition.
Extraction and classification of Non-Functional Requirements from Text Files: A Supervised Learning Approach

Non-functional requirements play a critical role in choosing various alternative model and ultimate implementation criteria. It is extremely significant in the earlier stages of software development that requirement engineering produces successful technology and eliminates system failure. The recent work has shown that the automated extraction and classification of quality attributes from text files have been demonstrated by artificial intelligence approaches including machine learning and text mining. In the automated extraction and classification of non-functional specifications, we suggest a supervised categorization approach. To test our approach to obtain interesting outcomes, a very well-known dataset is used. In terms of security and performance, we obtained a specific range of 85% to 98% and obtained a best result together for security, performance and usability.
Histogram Publication over Numerical Values under Local Differential Privacy

Local differential privacy has been considered the standard measurement for privacy preservation in distributed data collection. Corresponding mechanisms have been designed for multiple types of tasks, like the frequency estimation for categorical values and the mean value estimation for numerical values. However, the histogram publication of numerical values, containing abundant and crucial clues for the whole dataset, has not been thoroughly considered under this measurement. To simply encode data into different intervals upon each query will soon exhaust the bandwidth and the privacy budgets, which is infeasible for real scenarios. Therefore, this paper proposes a highly efficient framework for differentially private histogram publication of numerical values in a distributed environment. The proposed algorithms can efficiently adopt the correlations among multiple queries and achieve an optimal resource consumption. We also conduct extensive experiments on real-world data traces, and the results validate the improvement of proposed algorithms.
MODEL ARGUMEN PARAGRAF ARGUMENTATIF DALAM ARTIKEL JURNAL TERINDEKS SINTA RUMPUN PENDIDIKAN EKSAKTA: PERSPEKTIF DOUGLAS WALTON

Argument as a type of rhetoric of language is absolutely necessary in constructing, maintaining, and concluding ideas. With the existence of a solid argument construction in the body of scientific articles, the validity and reliability of facts, data, and inferences can be arguably justified. In this regard, this study aims to describe the argumentative paragraph model of the argumentative paragraphs in Sinta indexed journal articles for the exact education family. In collecting data, the observation method is used. The listening method is applied through reading and note-taking techniques. In data analysis, researchers made use of content analysis methods. Through data analysis, it was found that in the exact science education Sinta indexed journal there are three argument models, namely (1) verbal classification arguments, (2) rules arguments, and (3) witness arguments. The verbal classification argument model found includes (1) fact / perception arguments and (2) consequence arguments. The rule argument model found consists of (1) public opinion arguments, (2) personal opinion arguments, and (4) causal relationship arguments. Meanwhile, the findings of the witness argument model are (1) expert opinion argument, (2) analogy argument, and (3) fact-hypothesis argument. Based on these findings, it can be concluded that Douglas Walton’s argument models have not been fully accommodated in Sinta indexed journal articles for the exact education family. The authors of the articles are advised to continue to improve their publications by utilizing Walton's argument models in the hope that the resulting articles can be justified argumentatively-scientifically.
A Survey On Fraud Detection Techniques in E-Commerce

Electronic commerce or e-commerce is a business model that lets companies and persons over the internet buy and sell anything. Recently, in the age of the Internet and forwarding to E-commerce, lots of data are stored and transferred from one location to another. Data that transferred can be exposed to danger by fraudsters. There is a massive increase in fraud which is leading to the loss of many billions of dollars worldwide every year. There are various modern ways of detecting fraud that is regularly proposed and applied to several business fields. The main task of Fraud detection is to observe the actions of tons of users to detect unwanted behavior. To detect these various kinds, data mining methods & machine learning to have been proposed and implemented to lessen down the attacks. A long time ago, many methods are utilized for fraud detection system such as Support Vector Machine (SVM), K-nearest Neighbor (KNN), neural networks (NN), Fuzzy Logic, Decision Trees, and many more. All these techniques have yielded decent results but still needing to improve the accuracy even further, by developing the techniques themselves or by using a hybrid learning approach for detecting frauds. In this paper, a review to describe the latest studies on fraud detection in e-commerce between (2018-2020), and a general analysis of the results- achieved and upcoming challenges for further researches. This will be useful for giving us complete visualization about how can we present the most suitable, most accurate methods for fraud detection in e-commerce transactions.
Simulation Experiment Research on Six Degrees of Freedom Test Bench Based on Fuzzy PID Control Strategy

The quality of the control system depends on various factors such as the characteristics of the controlled object, the control scheme, the form and size of interference, etc. In a control system where the characteristics of the object and the hardware and software have been basically determined, the control quality of the system depends on the control algorithm. The control algorithm will make the motion process control have better speed, accuracy and stability. The study of control law is an important part of the control system design of the entire six-degree-of-freedom test bench. The characteristics of the controlled object and the existence of interference in this control system require the designed control law to have the characteristics of strong robustness, certain intelligence, and easy implementation, so as to achieve stable and precise control of the system and achieve the control indicators required by the system. The control strategy of this six-degree-of-freedom test bench adopts Fuzzy PID control, which combines fuzzy theory with the mature traditional PID control theory and uses fuzzy theory to tune the three control parameters of PID to form a parameter self-tuning Fuzzy PID control Device. The Fuzzy PID control strategy is simulated by MATLAB simulation software.
Energy Internet Security Risk Evaluation Index System

Abstract As the construction of the Energy Internet has launched into a novel method, the dependence on energy and information networks has been greatly increased. The interaction of energy and information flow has become recurring, and the deep coupling of energy and information flow has been realized. As a result, security risk factors have increased substantially. Given that, this article from the perspective of energy and information flow, combined with the “Source-Grid-Load-Storage” coordination scenario of the energy Internet and the actual development needs of the information network, this article proposes a risk assessment for many security risks that threatens the stable construction of the energy Internet Index system and corresponding index analysis.
Analisis Mitigasi Bencana terhadap Kondisi Sosial Budaya di Gorontalo

The purpose of this study was to analyze the socio-cultural conditions related to disaster mitigation in Gorontalo. The method used in this research is a survey with data collection techniques. The data analysis technique used is SWOT (strengths, weaknesses, opportunities, and threats) by looking at the socio-cultural conditions of the people of Gorontalo related to disaster mitigation in Gorontalo. The results of the study show that most information on disaster mitigation is known from school as well as from various media sources. Community, students, and teachers often experience disaster mitigation such as earthquakes, floods, and landslides, so it is necessary to have an evacuation place that is ready to be occupied. Lack of public awareness to join organizations on disaster mitigation means that most people do not know about preparedness in facing disasters that will occur. Disaster mitigation practice activities taught in schools are a solution to teach students to understand more about how to save themselves from disasters and not easily panic and fear.
Efficient 3D Homing Path Planning for AUV Docking

This study explores a 3D homing path planning method for AUV docking. Using the improved RRT* algorithm, the obstacle-free 3D path for AUV homing to docking station is generated. By using the 3D Dubins curve to improve the local planner and cost evaluation function of the RRT* algorithm, we propose a method to obtain the homing path satisfying the terminal constraints and kinematic constraints with a smaller calculation cost. In addition, greedy method is used to improve the efficiency of the algorithm. Path pruning is used to remove unnecessary way points to obtain a more concise path. Two numerical examples are presented to illustrate the effectiveness of the proposed method.
Optimization of Photovoltaic Array Orientation and Performance Evaluation of Solar Tracking Systems

In recent years, solar energy has received tremendous attention in accordance with the global efforts in moving towards clean energy generation. A solar tracking system shifts the position of solar panel following the Sun's movement, allowing more energy to be harvested. This paper aims to evaluate the performance of solar tracking system in Malaysia, including the east-west single-axis tracking system, north-south single-axis tracking system, vertical axis tracking system, and dual-axis tracking system. The periodical tilt angle adjustment of fixed-tilt photovoltaic system is also investigated to optimize its performance. A mathematical model is formulated to compute the amount of solar radiation collected in a photovoltaic system and the optimization model is solved using genetic algorithm to determine the optimal tilt and azimuth angles for maximum solar radiation yield. Based on the results, PV system with dual-axis solar tracker has 22.14 % more solar radiation collected compared to the fixed-tilt system, while monthly tilt angle adjustment improves the yield by 2.15 %.
Lossy compression of Earth system model data based on a hierarchical tensor with Adaptive-HGFDR (v1.0)

Abstract. Lossy compression has been applied to the data compression of large-scale Earth system model data (ESMD) due to its advantages of a high compression ratio. However, few lossy compression methods consider both global and local multidimensional coupling correlations, which could lead to information loss in data approximation of lossy compression. Here, an adaptive lossy compression method, adaptive hierarchical geospatial field data representation (Adaptive-HGFDR), is developed based on the foundation of a stream compression method for geospatial data called blocked hierarchical geospatial field data representation (Blocked-HGFDR). In addition, the original Blocked-HGFDR method is also improved from the following perspectives. Firstly, the original data are divided into a series of data blocks of a more balanced size to reduce the effect of the dimensional unbalance of ESMD. Following this, based on the mathematical relationship between the compression parameter and compression error in Blocked-HGFDR, the control mechanism is developed to determine the optimal compression parameter for the given compression error. By assigning each data block an independent compression parameter, Adaptive-HGFDR can capture the local variation of multidimensional coupling correlations to improve the approximation accuracy. Experiments are carried out based on the Community Earth System Model (CESM) data. The results show that our method has higher compression ratio and more uniform error distributions compared with ZFP and Blocked-HGFDR. For the compression results among 22 climate variables, Adaptive-HGFDR can achieve good compression performances for most flux variables with significant spatiotemporal heterogeneity and fast changing rate. This study provides a new potential method for the lossy compression of the large-scale Earth system model data.
Characterization of different crowd behaviors using novel deep learning framework

Crowd behavior understanding is recognized as a complex problem due to unpredictable behavior of humans and complex interactions of individuals in groups. For crowd managers, it is crucial to understand the crowd dynamics to manage the crowd efficiently and effectively. Current practice of crowd management is based on manual analysis of the scene. Such manual analysis of the scene is a tedious job and usually prone to errors due to limited human capabilities. Therefore, the task of automatizing crowd analysis has received tremendous attention from the research community during the recent years. In this paper, we propose a deep model framework that automatically characterizes different crowd behaviors based on motion and appearance. We first extract dense trajectories from the input video segment and then generate trajectory image by projecting trajectories on to image plane. Trajectory image effectively captures relative motion in the scene. We use stack of trajectory images to train deep convolutional network that learns compact and powerful representation of motion in the scene. We evaluate our approach on UCF, CUHK, and Crowd-11 benchmark datasets. From the experiment results, we demonstrate, both in quantitative and qualitative ways, that the proposed framework outperforms other existing methods by a great margin.
Model for Analyzing Psychological Parameters Recommending Student Learning Behavior using Machine Learning

One of the main objectives of Educational Data Mining (EDM) is to improve the education system to increase student retention, help students to score high and attain holistic development.The purpose of the study is to analyze the psychological parameters of students to predict their intellectual performance and generate recommendations to be utilized by institutes and students to improve academic performance.This study performs matrix factorization using single value decomposition (SVD) to predict missing parameters related to the psychological behaviour of students with root mean square equals 0.059 and uses the userbased collaborative filtering technique to predict their grade with RMSEA as 0.055.It makes use of decision tree (ID3) algorithm for generating decision rules that produces results with an accuracy of 76% and provides suggestions on how to improve learning by changing the psychological behaviour of students.The results showed that three parameters of personality (namely conscientiousness, openness and need for cognition), six of motivation construct (intrinsic motivation, optimistic, goal orientation, concentration, locus of control and self-efficacy), five of self-regulatory learning strategies construct (rehearsal, elaboration, metacognition, peer learning, time/study management) highly impacted academic performance in positive way.Students belonging to upper and middle socioeconomic status avail more from learning facilities.Also, learning the in-depth knowledge of the topic enhance student intellectual performance.It is noted that social integration and academic integration help students to learn the subject matter in friendly environment and reduces depression.The key findings highlight the parameters positively impacting students' intellectual performance.This help in improving students' intellectual performance which further addresses student retention, progress and employability.
Fraud Attack Detection in Remote Verification Systems for Non-enrolled Users

Over recent years, remote authentication systems have experienced increased interest from industry, especially for services such as banking and digital commerce. Processes based on images of faces (selfies) may be used extensively since most mobile devices and computers now have cameras. In most situations, authentication is mainly based on comparing the input data of the user (selfie) with information previously registered from the same individual (database). This process requires a pre-step of enrolling all users of the system which would limit its use. Remote authentication systems based on the verification of users against information provided by an official identity document (i.e. ID card, passport, or driver’s license amongst others) have also been explored. Some countries provide a national ID card with all of an individual’s personal information embedded onto a chip. Those cards can be read directly using a Near Field Communication (NFC)-enabled mobile device. The information (face photo) is then matched with the user selfie to validate identity. Unfortunately, such ID cards are not yet widely implemented. In most cases, a simple ID card without a chip is the only information available. In such cases, the key challenge of the authentication system is to ensure that the ID card (face photo) has not been altered. This work explores methods to determine whether an image of an ID card provided remotely by the user is real or has been deliberately manipulated (faked). There are several ways of manipulating ID cards; however, only two were considered in this work: (i) where the face image is manipulated manually (by cutting and sticking a different face onto the document) and (ii) where the face photo is digitally altered. Several algorithms such as Binarized Statistical Image Feature, Uniform Local Binary Pattern, Holistic Edge Detection, and a CNN were used to detect if the ID card had been manipulated. A private database of Chilean ID card images (1,525) taken from 316 individuals was used for all experiments. In order to train the algorithms, a database of ‘fake’ examples (manually and digitally altered) was created. This was a time-consuming task as a large number of images were needed to be created in order to achieve a robust algorithm performance. The best results were achieved by a shallow convolutional neural network that classified ID cards into two classes, fake and real. By applying transfer learning, the algorithm was able to detect fake images (ID cards) using only the initial layers, becoming robust towards low-resolution degradation.