Reviewerly/exHarmony huggingface dataset:
Similarity Analysis of Knowledge Graph-based Company Embedding for Stocks Portfolio

The stocks are affected by various factors, and studies have shown that companies in the same industrial chain or with the same attributes are more closely related, and their share prices are influenced by each other. In the financial field, this kind of stock prices correlation can provide good investment suggestion. The previous method is that investors analyze the company's industrial chain relationship, industry and trading information, and then define the degree of relevance between companies. However, this approach is subjective and lack of statistical evidence. In this paper, we propose to analyze the correlation between companies by knowledge graph technology, the main algorithm is based on graph embedding. In order to analyze the production relationship between companies, we construct a company graph established from the logic of industrial chain and supplement nodes attributes with companies' information. Then, we train and calculate the vector representation of all companies by graph embedding model. Finally, we define the cosine distance of the vector as the similarity of the companies. The embedding vector of companies obtained by the industrial chain graph has achieved good results in link prediction task, visualization and practical application of stock portfolio, and the graph with weight has better effect than that without weight., the back test results show that the stock portfolio has obtained considerable extra-return.
PyPacho: A Python library that implements parallel basic operations on GPUs

Python has become an extremely popular programming language that is widely used in many different domains including scientific computing. Numpy is a Python library that includes numerous functions for numerical linear algebra. Numpy uses code that has been parallelized to take advantage of microprocessors with several cores making Numpy very fast and efficient on current microprocessors. Graphical Processing Units (GPUs) are available on many computers. Originally designed to accelerate the performance of graphical applications, they have become very useful to improve the speed of general purpose applications. In particular, GPUs work very well on large arrays. In this paper we present PyPacho, a library that is, by design, similar to Numpy. This library has been written to accelerate the execution of code on GPUs by using the services provided by PyCuda and OpenCL. Our goal is to create a library that will allow Python code originally written using Numpy methods to execute efficiently on GPUs. Our main strategy is to take advantage of the use of parallelism in basic operations on matrices and vectors. Furthermore, we implemented three different methods to solve systems of linear equations: Jacobi, Gradient Descent and Conjugate Gradient. We compared the execution times of those methods on different platforms. Initial results are encouraging.
FLPhish: Reputation-based Phishing Byzantine Defense in Ensemble Federated Learning

The increasing demand for privacy protection facilitates growing interests in Federated Learning (FL). Nevertheless, most of existing FL schemes are susceptible to malicious participating clients compromised by Byzantine attacks, which remains a challenging issue. In this paper, we propose a novel Byzantine-robust FL scheme, coined FLPhish. Specifically, we first design a ensemble learning-based FL architecture, named Ensemble Federated Learning (Ensemble FL). Second, a phishing mechanism is crafted for the FL architecture to detect abnormal client behaviors. Third, a reputation mechanism is developed to further identify malicious participating clients compromised by Byzantine attackers. We evaluate the performance of FLPhish by considering various fractions of Byzantine clients and various imbalance degrees of the data distribution. Extensive experiments demonstrate the high effectiveness of the proposed FLPhish scheme in resisting Byzantine attacks in Ensemble FL.
Research Experiences for Teachers in Machine Learning

Machine learning and Artificial Intelligence (AI) are national priority areas for research, education and workforce development. This work in progress paper describes a Research Experiences for Teachers program in sensors and machine learning launched in the summer of 2020. Motivated by national AI workforce needs, we designed a program that engaged high school teachers from STEM fields in machine learning research. In 2020, the program focused on AI algorithms for solar energy systems. Because of the COVID-19 conditions, the research experience was virtual and ran with a smaller teacher group than originally planned. The program included development of training content, algorithm and software training, research in solar energy monitoring, development of research reports and lesson plans, research presentations, and assessment. The assessment of the program included surveys, interviews, presentation observations, and follow-up in high school content delivery.
A Generative Deep Learning Approach for Forensic Facial Reconstruction

Forensic facial reconstruction currently relies on subjective manual methods to reconstruct a recognizable face from a skull. Automated approaches using algorithms and statistical norms have been able to reliably construct faces in real-time, but are unable to generate areas of the face that do not correlate strongly to the bone beneath, such as the eyes, lips and ears. Recent developments in deep learning have shown that generative models can produce realistic images indistinguishable from genuine human faces. Applying these techniques, we propose a generative deep learning solution to perform facial reconstruction directly from the bone with limited data and no background expertise. The model is trained on 665 3D Computed Tomography (CT) head scans that have been cleaned of noise, rotated to correct orientation and then filtered by density to find bone and soft tissue to be used as input and label respectively. It is trained with a combination of adversarial and VGGFace2 perceptual loss. The model is then compared to two baseline deep generative models and achieves a mIoU of 0.9410 and facial detection score of 88.32%. Results show the model is able to consistently generate accurate jaw and muscle structures. Additionally, it generates realistic ears, eyes and noses - features that are traditionally difficult to generate automatically with traditional techniques. Our model provides the basis for a complete, end-to-end solution for forensic facial reconstruction, with no prior knowledge or training on reconstruction techniques.
Matching User in Online Learning using Artificial Intelligence for Recommendation of Competition

Now, there is much competition for students but sometimes students fail and do not have confidence because students don&#x0027;t have a guide or recommendation to join the competition. The problem is that sometimes students were wrong to choose a competition because they don&#x0027;t have the ability in this or just join the competition not to win the competition. The purpose of research solves the problem that the researchers built a system to recommend for a student to choose a competition based on their activity in online learning in school or university as profile matching in online learning. The system made recommendations based a database online learning that database is processed by artificial intelligence. The system gives a recommendation based on profile matching students in online learning. In this research, after the system gives a recommendation, 10 students enrolled the online learning given feedback about the accuracy of the recommendation. The result of testing in the system has an average of accuracy that is 70&#x0025;.
TVAnet: a spatial and feature-based attention model for self-driving car

End-to-end methods facilitate the development of self-driving models by employing a single network that learns the human driving style from examples. However, these models face problems of distributional shift problem, causal confusion, and high variance. To address these problems we propose two techniques. First, we propose the priority sampling algorithm, which biases the training sampling towards unknown observations for the model. Priority sampling employs a trade-off strategy that incentivizes the training algorithm to explore the whole dataset. Our results show uniform training on the dataset, as well as improved performance. As a second approach, we propose a model based on the theory of visual attention, called TVAnet, by which selecting relevant visual information to build an optimal environment representation. TVAnet employs two visual information selection mechanisms: spatial and feature-based attention. Spatial attention selects regions with visual encoding similar to contextual encoding, while feature-based attention selects features disentangled with useful information for routine driving. Furthermore, we encourage the model to recognize new sources of visual information by adding a bottom-up input. Results in the CoRL-2017 dataset show that our spatial attention mechanism recognizes regions relevant to the driving task. TVAnet builds disentangled features with low mutual dependence. Furthermore, our model is interpretable, facilitating the intelligent vehicle behavior. Finally, we report performance improvements over traditional end-to-end models.
Clustering Ornamental Plants Turnover Data using K-Means Algorithm

Ornamental plants are a commodity with high production in Indonesia, with a 17.61 million stalk increase recorded in 2018. (9.55%). Ornamental plants have capability enterprise possibilities in Indonesia as properly. The increase and decrease in ornamental plant turnover can be attributed to a variety of factors such as beauty awareness, the development of the tourism industry, ornamental plant trends, and the construction of housing and hotel complexes. A few of the factors mentioned can have an indirect impact on the sustainability of the ornamental plant business. To resolve these concerns, the grouping method was used with K-Means Clustering to determine the equation of ornamental plant turnover data based on plant commodities and monthly turnover values. Clustering with K-Means Algorithm is used in this study to group turnover data based on crop commodities and turnover value. The WEKA application's grouping results utilizing the K-Means Clustering Algorithm resulted in two clusters with values of 11% (8 data) and 89% (66 data) from a total of 74 data, where the two cluster values appeared after three time iterations.
A Method of Federated Learning Based on Blockchain

Currently many enterprises face issues regarding insufficient data collection samples and data recording dimensions, thus it's hard to make efficient predictions. Since it is limited by the requirement of protecting privacy and trade secrets, data can't be effectively shared among enterprises. Federated learning is an effective method to solve this problem, but there are some performance bottlenecks, information security issues and data trust issues still existed, which need to be improved in combination with other advanced technologies to meet the practical requirements. This paper combines the blockchain technology with federated learning technology, and uses decentralized blockchain system to replace the traditional centralized federated learning architecture. We adopt training method of updating models to achieve machine learning. In this way, we can avoid transmission of intermediate computing data and achieve mechanism of node access, model evaluation, motivation and audit with combination of block chain. In terms of the algorithm, the horizontal federated learning adopts the integrated learning algorithm, and the vertical federated learning adopts the deep learning algorithm. It will be described in detail below.
Recommender System based on Deep Neural Network and Long Short Term Memory

To provide relevant recommendations for clients, a recommendation system is essential in online commerce, streaming services, and news article websites. Existing methods in recommendation systems are limited by the cold start problem. The Deep Neural Network (DNN) &#x2013; Long Short-Term Memory (LSTM) technique is developed in this study to improve the efficiency of recommendation systems. The DNN method is used to predict new user ratings based on prior user ratings, while the LSTM method is used to recommend a relevant movie to the user. The user-item similarity was calculated and used in the LSTM algorithm to offer the relevant recommendation. The LSTM approach has the advantage of storing relevant information over time and making appropriate recommendations. The proposed DNN-LSTM (Deep Neural Network-Long Short-Term Memory) technique in the recommendation system is evaluated using the MovieLens 100k and 1M datasets. In the MovieLens 100k dataset, the proposed DNN-LSTM approach has an RMSE of 0.431, while the existing HCBCF (Hellinger Coefficient Based Collaborative Filtering) method has an RMSE of 0.871.
Real-time Falling Detection System for Elderly using CNN

Fall in the elderly often occurs and can cause fatal injuries. In the worst scenario, the elderly did not get a quick and proper help while they live alone after falling. Thus, many researchers have proposed methodologies to monitor the activity of the elderly. The monitoring system can detect when falling occurs and alert the people responsible to help them as soon as possible. The monitoring system was built based on few types of sensors such as wearable devices and video-based monitoring systems. This study was aimed to build a video-based monitoring system for falling detection for the elderly by using a convolutional neural network (CNN). Our CNN model can predict the falling occurrence with an accuracy of 96.67% and with an F1 Score of 92.30%. In conclusion, the CNN model can be utilized to detect the falling occurrence of the elderly in real-time with a very good accuracy. Soon, this system can help the elderly getting quick help and proper treatment after falling.
Tievs: Classified Advertising Enhanced Using Machine Learning Techniques

The scarce use of tangible periodicals led to a consistently soaring popularity of online classified advertising. Nevertheless, existing platforms retain complications. Most recommendation systems are built with conventional technologies that are less scalable, less accurate, and having high latency processes. Moreover, customers find it tiring when clarifying a reliable, precise price value for items they are trying to sell through the classified advertising system. Additionally, strict validation techniques to identify and prevent fraudulent content or images from being published in the advertising portals have been neglected. Therefore, authors have inaugurated a superior classified advertising system, Tievs, as a solution, by appraising said predicaments. It wields a flexible, process-simplifying, concurrency-induced recommendation breakthrough implemented from Universal Sentence Encoding incurred Natural Language Processing and Deep Learning routines. Furthermore, an innovative price prediction system having a supervised regression-based ensemble model forged ensuing a comparative study, having excellent accuracy in proactively predicting item prices as to cater hassles faced by customers, was satisfied. Light Gradient Boosting classifier-driven fake description analysis and a Convolution Neural Network powered figure deception recognition system were introduced, which gained prodigious precision with moral clarity in fraud detection and prevention. Hence, the proposed solution's objective of surpassing former classified advertising systems in delivering customers' necessities, using the most lucrative, time-saving, human-centric, and error-preventive approaches, was accomplished. It was affirmative by the positively responded questionnaire regulated among prospective users by the authors.
Leftovers Food Recognition using Deep Neural Network and Regression Approach for Objective Visual Analysis Estimation

Understanding the nutritional intake is essential for basic life since every human being must have insight into what food they have eaten. A nutritionist can help in guiding what the body should consume, where each patient may have different diet and treatment patterns. One indicator used by dietitians or nutritionists is by estimating the leftovers consumed by the patient. They measure it by visually named Comstock method, which is divided into scales. This method's drawback is subjective from one another dietitians or nutritionists so that an objective assessment with a machine learning-based approach is acquired. This paper proposes a novel stage of defining food recognition and measuring its leftovers using visual analysis. The food image recognition method used CNN to estimate food waste using pixel-based AFLE and regression approach to fit into six scales. The best result of food image recognition was 92.5% using dropout 0.3 with image augmentation and ReLu activation function, while the accuracy result of visual estimation application compared to experts was 85%. It is proved that the combined proposed algorithm is robust for the application of recognizing and estimating leftovers.
Research and Design of Automatic Scoring Algorithm for English Composition Based on Machine Learning

With the development of artificial intelligence and big data, the concept of “Internet plus education” has gradually become popular, including automatic scoring system based on machine learning. Countries all over the world vigorously promote the deep integration of information technology and discipline teaching in various fields. English is a medium of communication in the current era of education information development trend. English composition automatic scoring mode is gradually accepted by the majority of educators and applied in the actual classroom teaching. However, the research of English composition automatic grading in teaching space is not perfect. Most systems have used traditional algorithms. Therefore, this paper constructs the automatic scoring algorithm and sentence elegance feature scoring algorithm of English composition based on machine learning, explores the influence of the algorithm on English writing teaching, and proves the correctness of the design idea and algorithm of this paper through a lot of experiments.
Factory Production Machine Damage Detection System Using Case-Based Reasoning Method

Computers are essential in industrial processes because they play a part in the life cycle of company-produced product systems. Damage to production equipment happens frequently as a result of a lack of detailed periodic maintenance, making it difficult for operator and technician staff to maintain production machines. Because they are still utilizing the manual approach, repair times are long and costly accurate. Case-Based Reasoning (CBR), a problem-solving technique based on prior experience and applied in the present, is one discipline of computer science that is commonly employed by humans to help and facilitate work. CBR is used to find solutions by exploiting or analyzing previously collected case data. Case representation, case indexing, case retrieval, case adaptation, and case maintenance are the five goals of CBR in knowledge formation. The process of discovering and measuring the case with the greatest closeness is known as case retrieval. The goal of this research is to create a way to automatically detect system failures in machines, so that if a malfunction happens with a CBR-based system, it will be easier to detect early, repair faster, and be more accurate. The accuracy of the system utilized is 90%, according to the results of testing the tools manufactured, and it is effective for managing production machine repairs. While the test error is twenty times with the highest result of 33.33 % and the lowest is 0% according to the level of accuracy of the sensor on the object.
On the Applicability of Multi-Characteristics for the Continuous Authentication in IIoT Scenarios

By exploiting characteristics from channel state information (CSI) profiles and the behavioral habits of users during their routine work processes in IIoT systems, this paper proposes a passive multi-characteristics user authentication framework for continuous user authentication. We first characterize user physical layer identities using a well-known eXtreme Gradient Boosting (XGBoost) machine learning algorithm, and then depict user behavioral characteristics by formulating the authentication decision process as a Hidden Markov Model (HMM) to further confirm user identities. Extensive experiments are performed to show the authentication performance of the proposed user authentication framework in terms of ROC curves and accuracy in various IIoT scenarios. We also investigate the performance of the proposed framework for resisting impersonation attacks in the IIoT scenario.
Vehicle and pedestrian target detection in auto driving scene

Abstract To improve the target detection accuracy and speed of autonomous driving in various weather environments and small target traffic senarios,an improved YOLOV4 target detection model based on CSPDarknet45_G backbone network is proposed in this paper.By adding a new DBG module which consists of DArknetConv2D + BN + GELU activation function,this model is enhanced in generalization ability and accuracy. We also improved Res unit residual module to enhance shallow features fusing with deep feathers and reduced the number of neurons in the CSP module to simplify the module structure.The K-Means++ clustering algorithm is introduced to obtain the size of the prior box used for target detection to satisfy the data set in this paper. In the captured target vehicle image data set, the model detection result shows that the improved YOLOV4 model achieve an average detection accuracy of 90.45%, a recall of 94.37%, and an FPS of 50 frames per second when the IOU is taken as 0.5, which meet the real-time and accuracy of the detection task in this paper.
Tamper Detection using Self-Generating Watermarks based on Local Binary Patterns

Image watermarking is a technique used for versatile applications. This paper proposes a blind spatial domain fragile watermarking technique that aims at detecting tamper and distortion caused due to intentional or unintentional attacks. The image is first divided 4 equal sections and 2 pairs are formed. These pairs are together used for tamper localization. Each section is then divided into $4 \times 4$ blocks. For every sub-block, the average of 4 inner pixels is calculated. Using this average value, local binary pattern (LBP) is generated for the sub-block using the 12 boundary pixels. A 4-bit watermark is self-generated from the LBP and embedded into the sub-block. The same watermark is also embedded into corresponding section pair to ensure better tamper detection. The method is tested against a variety of attacks like copy-paste, copy-move, blurring, cropping, etc. and shows efficiency in detecting tampered regions.
Enter At Your Own Risk: The Impacts of Joining a Hateful Subreddit

The Internet has become one of the extreme right’s most valuable assets in disseminating information to a broader audience. We are interested in whether hateful alt-right online communities are simply rendezvous of individuals who already subscribe to hateful ideology, or if the existence of these online communities has the capacity to radicalize previously neutral individuals. Our preliminary findings show that joining a hateful subreddit does show evidence of increased hate word usage. These findings have broad implications on the role of the Internet in not only supporting, but growing the alt-right movement.