Reviewerly/exHarmony huggingface dataset:
Adaptive and automated deep recommender systems

Dr. Xiangyu Zhao is an assistant professor of the school of data science at City University of Hong Kong (CityU). Prior to CityU, he completed his PhD (2021) at MSU under the advisory of Dr. Jiliang Tang, MS (2017) at USTC and BEng (2014) at UESTC. His current research interests include data mining and machine learning, especially (1) Personalization, Recommender System, Online Advertising, Search Engine, and Information Retrieval; (2) Urban Computing, Smart City, and GeoAI; (3) Deep Reinforcement Learning, AutoML, and Multimodal ML; and (4) AI for Social Computing, Finance, Education, Ecosystem, and Healthcare. He has published more than 30 papers in top conferences (e.g., KDD, WWW, AAAI, SIGIR, ICDE, CIKM, ICDM, WSDM, RecSys, ICLR) and journals (e.g., TOIS, SIGKDD, SIGWeb, EPL, APS). His research received ICDM'21 Best-ranked Papers, Global Top 100 Chinese New Stars in AI, CCF-Tencent Open Fund, Criteo Research Award, Bytedance Research Award and MSU Dissertation Fellowship. He serves as top data science conference (senior) program committee members and session chairs (e.g., KDD, AAAI, IJCAI, ICML, ICLR, CIKM), and journal reviewers (e.g., TKDE, TKDD, TOIS, CSUR). He serves as the organizers of DRL4KDD@KDD'19, DRL4IR@SIGIR'20, 2nd DRL4KD@WWW'21, 2nd DRL4IR@SIGIR'21, and a lead tutor at WWW'21/22 and IJCAI'21. He also serves as the founding academic committee members of MLNLP, the largest AI community in China with 800,000 members/followers. The models and algorithms from his research have been launched in the online system of many companies.
Suspicious Activity Detecting Camera

It is difficult to detect unusual activities in modern society with the help of existing surveillance systems. Therefore, a new method is proposed to improve the surveillance system. It can be used in several public places like banks, railway stations etc. It is done with the ML models which can be created with the help of Teachable Machine. To create and train an ML model, there are several flexible options such as TensorFlow, Google Teachable, Edge Impulse, Lobe etc. In this paper it is done with the help of Teachable Machine. In Google Teachable, select the Pose Net option for tracking the various body movements and actions. After feeding the required datasets, click on ‘Train Model’. A JavaScript code is obtained. Save the JavaScript code as. html and open it in any browser with JavaScript enabled. Click on the start button and perform the suspicious activity in front of the camera. The ML model will try to recognize it and if found unusual, will issue an alert. You can also add functions for automatically calling or messaging the police Then check whether activities such as beating, gun firing, gun possession etc., are detected by the ML model during video processing. The model gives the likelihood of a particular activity to occur in the range of 0.00 to 1.00. With the help of various functions alert messages can be sent.
Explicit factorization of xl1m1l2m2l3m3−a and <i>a</i>-constacyclic codes over a finite field

Let Fq be a finite field of order q, t be a prime and m1,m2,m3 be positive integers. In this article, we find all irreducible divisors of xl1m1l2m2l3m3−a over Fq where a∈Fq* and qt−1=l1v1l2v2l3v3c such that l1,l2,l3 are distinct odd primes and c is a positive integer with gcd(l1l2l3,c)=1 and gcd(l1l2l3,q(q−1))=1. Moreover, we construct an a-constacyclic code by using these irreducible divisors.
A Method of Improving Oral English Teaching Based on PLS-SEM

To improve the quality of oral English teaching, we must first analyze the factors that affect teaching. Second, it examines the relationship between these factors in order to aid the teaching staff’s oral English instruction and improve the learners’ oral English pronunciation. As a result, determining how to analyze the relationship between various factors in the process of oral English teaching is a problem that is still being worked on. In response to this issue, this paper proposes a partial least squares-structural equation modeling (PLS-SEM) method for improving oral English teaching. The central idea of this method is to first analyze and summarize all relevant factors affecting oral English teaching as thoroughly as possible. Second, the influencing factors are quantified into specific numerical data, and the data are subjected to a series of preprocessing steps. Third, the PLS-SEM model is trained, and the preprocessed data are fed into the statistical analysis. Finally, the relationship between the factors is summarized based on the statistical analysis results. This paper evaluates the PLS-SEM model in terms of reliability and validity in order to validate the effectiveness of the method used. The PLS-SEM model developed in this paper for improving oral English teaching has high reliability, validity, and explanatory power. This method-based oral English teaching strategy can improve students’ oral English levels and has a high practical application value.
Across the islands: A multimodal discourse analysis of government homepages

This article examines the homepages of two island governments: American Hawaii and Chinese Hainan. Under Pauwels' (2012 Pauwels, L. 2012. “A Multimodal Framework for Analyzing Websites as Cultural Expressions.” Journal of Computer-Mediated Communication 17 (3): 247–265.[Crossref], [Web of Science ®] , [Google Scholar]) multimodal framework for analysing websites as cultural expressions, we investigate how the homepages construct identities and multimodally portray the islands as tourist destinations. Through attractive imagery, interpersonal linguistic choices, and interactive links to social media, Hawaii’s homepage depicts the state’s identity as a modern, welcoming indigenous community. Hainan’s website has a Chinese version and an English version. With a newspaper type of official and formal presentation, the Chinese homepage promotes the provincial government’s identity as a traditional administration and information hub by extensively using impersonal semiotic resources with limited interactivity. In contrast, the English homepage creates an identity as a thoughtful caretaker and attentive administrator who provides practical resources to meet the needs of foreign guests. In terms of semiotic use and visual design, Hainan’s English homepage seems to position itself between two extremes: Hainan’s Chinese homepage and the Hawaii homepage. The homepage differences can be explained from the standpoint of communication purposes and cultural values. Although all three belong to a pragmatic discourse type, these homepages situate themselves at different points on a continuum. This paper highlights the importance of researching the relationship between cultural specificity and webpage design.
Role of Explainable Artificial Intelligence (XAI) in Prediction of Non-Communicable Diseases (NCDs)

Explainable artificial intelligence (XAI) concentrated on methods and models that simplify the comprehending and analysis of the ML models operation. Using XAI, systems deliver the essential facts to defend outcomes, mostly when unpredicted conclusions are made. It also certifies that there is an auditable and demonstrable way to guard algorithmic judgments including the factors of unbiased and being principled, which lead to building trust. Swift upsurge of non-communicable diseases (NCDs) turns out to be one of the severe health matters and one of the leading origins of death globally. In this chapter, the authors discussed XAI in healthcare, its benefits, and the deep Shapley additive explanations (DeepSHAP)-based deep neural network (DeepNN) framework provided with a feature selection method for prediction and explanation of non-communicable diseases followed by case study discussion about detection and progression of Alzheimer's disease (AD) with the help of XAI-based predictive models.
Sensor and Attitude Analysis of Track and Field Training Action Recognition Based on Artificial Intelligence

In order to provide effective information support to athletics training and to increase the effectiveness of athletics training, in-depth analysis of motion recognition sensors and attitudes based on artificial intelligence was conducted. First of all, the basic conditions of type analysis, cognitive technology, and the current situation were studied, and the basic theory related to it was studied, and on this basis, a human position analysis and recognition system based on artificial intelligence movement training sensors was developed. We studied the technology in depth. Experiments have shown that the approach data collected by the system’s inertia node is transmitted wirelessly to the computer-side software to restore the trend and identify each trend and parameter with high accuracy. During the 30-minute test, the static error was within 1° and the dynamic error was within 5°, which is acceptable and adheres well to dynamic conditions. The system can overcome the limitations of traditional wired or optical methods and be widely used in sports training, human-computer interaction monitoring, rehabilitation medicine, games, film, and television production.
Analysis on the Application of Dependent Information System Optimization Algorithm in Music Education in Colleges and Universities

Although the influence of pop music is huge and the emergence of pop music talents is also increasing, its education has great problems. Moreover, both theoretical research and professional discipline construction are still very backward. In order to improve the efficiency of music teaching in colleges and universities, this paper applies the information system optimization algorithm to the intelligent analysis of music education in colleges and universities, selects the appropriate method for music information processing, and builds the music education system in colleges and universities on this basis. At the same time, this paper compiles a music system questionnaire for the two main groups (teachers and students) who use the music system to understand the needs of teachers and students for the music system. Then, this paper analyzes the feasibility of the system, constructs an intelligent music education system in colleges and universities, and verifies the effect of the system in combination with experiments. In addition, this paper evaluates the music information processing and educational effect of the system in combination with the actual situation and counts the test results. The test results verify the reliability of the system in this paper.
An Efficient Analysis of E-Commerce Site using Big Data Assisted Modified Learning Strategy based on Novel Customer Recommendations

Now-a-days, the tendency of shopping and purchasing products from vendors are totally changed with respect to the development of online shopping services. The logic is briefly called as E-Commerce, in which this port allows the user to register the identity and start purchasing the products based on their needs. This system attracts many corporate and commercial organizations to change their business strategy and start selling their products via online. This paper is intended to design a new approach for supporting both customer and seller ends in clear manner with respect to the adaptation of latest technologies such as Deep Learning, BigData and Mining Schemes. This paper provides a novel analysis to assess the customer recommendations and provide the required details regarding that to the further customer end as well as the seller end to upgrade their purchasing and selling points. The proposed learning principle is called as BigData Assisted Modified Learning Strategy (BMLS), in which it is derived from the mining scheme called Apriori and the Deep Learning approach. This paper survey several literatures to identify the existing problems in this domain and provide a proper resolution to the recommendation system in fine manner. This paper provides a brief discussion on designing ideas of the proposed approach using learning scheme as well as the detailed survey of the literature in further sections. The discussion section of this paper provides a proper summary of the proposed scheme with implementation ideas in proper way with clear specifications. The logic of BigData is applied to handle the huge volume of data in simple way as well as the specific technology provides congestion free data management over the server end.
Histopathological cancer detection: a comparison study of different convolutional neural networks

The histopathological method is important for cancer diagnosis. The standard histopathological test today is extremely time-consuming, expensive, and labor-intensitive. However, as image digitalization technology became ubiquitous today, automatic analysis using computer-aided diagnosis tools provides a possible alternative. Using deep learning, especially for convolutional neural networks, it is possible to obtain results that have comparable performance to pathologists with a higher diagnosis speed. In this paper, seven different CNN architectures were trained on the Histopathological Cancer Detection dataset. The dataset contains 20000 images extracted from histopathological scans of lymph node sections. The results showed that there is a correlation between the performance and the architecture design’s appropriateness on a specific dataset. DenseNet achieved the best result on this dataset with an accuracy of 0.9182 without data augmentation, and 0.9268 with data augmentation. One possible reason for the superior performance of DenseNet is that DenseNet mimics the diagnosis process of pathologists by integrating image features with multiple different scales. The experiment results suggest that DenseNet can serve as an effective tool to speed up the diagnosis and ease the workload of pathologists.
Budget performance evaluation of weapon R and D projects based on cloud matter-element model

In view of the actual needs and problems of weapon R&D project budget performance evaluation, a performance evaluation index system covering the whole process of weapon R&D projects is designed from three aspects: project decision-making, implementation process and project output. In order to avoid information distortion and loss in the evaluation process, the cloud model is combined with traditional matter-element-extension model. A cloud matterelement model is constructed to comprehensively evaluate the budget performance of weapon R&D projects, and the effectiveness and feasibility of the evaluation model are verified by an example.
PEDAGOGICAL INTERACTION OF TEACHER AND STUDENTS AS MEANS OF CREATIVE PERSONALITY FORMATION LEARNING PROCESSES

Pedagogical relationship between the teacher and pupils is a method of forming a creative personality in the process of studying.&#x0D; This article is talked about the peculiarities of forming the creative personality in the conditions of realizing the pedagogical relationship between the teacher and pupils. They analyze the influence of psychological development and demands of teenagers on the organized basis of creative relationships. The author stresses the contrasts between the theory of pedagogical relationship and real school life. The author tells about the necessary conditions for realizing the personality of a child and his self-determination in the process of studying.
A systematic review of paper-based versus computer-based testing in engineering and computing education

In the past two decades, there has been a consider-able increase in use of computer-based exams. Currently there is a lack of comprehensive understanding about equivalence between paper-based and computer-based exams in engineering and computing education. This study utilised a systematic literature review (using PRISMA) to synthesise the literature regarding paper and computer-based testing in engineering and computing education between 2010-2020 (inclusive). The research database that was used was Scopus. Publications were first screened by title, abstract and keywords. This was followed by screening the full text of each publication. Twenty-four papers were included in the final analysis. Outcomes demonstrated no clear result regarding equivalency between computer-based and paper-based exams. Similarly, many studies reported that students preferred computer-based exams, but this was not ubiquitous. Educators should consider these factors when considering transitioning from paper to computer-based testing.
ADD-based Spectral Analysis of Probing Security

In this paper, we introduce a novel exact verification methodology for non-interference properties of cryptographic circuits. The methodology exploits the Algebraic Decision Diagram representation of the Walsh spectrum to overcome the potential slow down associated with its exact verification against non-interference constraints. Benchmarked against a standard set of use cases, the methodology speeds-up 1.88x the median verification time over the existing state-of-the art tools for exact verification.
The <i>non-expert tax</i>

All major Cloud providers offer Data Stream Analytics as managed services, allowing non-expert users to easily extract knowledge from data streams. These services lift the burden of job deployment and maintenance off users by provisioning resources automatically. However, this automation often comes at a cost, as auto-scaling policies may choose to over-provision to meet performance goals. We term this cost the Non-Expert Tax: the relative error between the ideal cost of deployment if an expert user would carefully configure resource allocation and the actual cost incurred by executing the same job with auto-scaling enabled. We conduct an empirical evaluation study of auto-scaling in two popular Cloud-based data stream analytics services and we find that they aggressively scale out, allocating resources quickly to meet high demand, but are conservative when scaling down, thus, charging users for underutilized resources. We quantify the Non-Expert Tax and show it can be as high as 544% for short-term jobs and up to 332% per month for periodic workloads.
A Surface Target Recognition Algorithm Based on Coordinate Attention and Double-Layer Cascade

As a branch of target recognition, surface target recognition plays an irreplaceable role in both military and civilian applications. However, the large target size variation, low image resolution, and high real-time requirements pose challenges to existing algorithms. To address the issues, we take YOLOv5 as a backbone and adopt coordinate attention and a double-layer cascade structure to enhance both the recognition performance and speed. Specifically, coordinate attention is introduced to guide the corresponding network to focus on discriminative features by capturing channel and location information. Meanwhile, the double-layer cascade structure is designed for finely extracting and aggregating semantic features and spatial features at different scales. We test the model on the COCO dataset, the VOC dataset, and self-built surface target dataset. Experimental results show that proposed coordinate attention module and multiscale module improve the recognition effect of multiscale surface targets and meet the requirement of real time.
A Highly Integrated Ambient Light Robust Eye-Tracking Sensor for Retinal Projection AR Glasses Based on Laser Feedback Interferometry

Robust and highly integrated eye-tracking is a key technology to improve resolution of near-eye-display technologies for augmented reality (AR) glasses such as focus-free retinal projection as it enables display enhancements like foveated rendering. Furthermore, eye-tracking sensors enables novel ways to interact with user interfaces of AR glasses, improving thus the user experience compared to other wearables. In this work, we present a novel approach to track the user's eye by scanned laser feedback interferometry sensing. The main advantages over modern video-oculography (VOG) systems are the seamless integration of the eye-tracking sensor and the excellent robustness to ambient light with significantly lower power consumption. We further present an algorithm to track the bright pupil signal captured by our sensor with a significantly lower computational effort compared to VOG systems. We evaluate a prototype to prove the high robustness against ambient light and achieve a gaze accuracy of 1.62\,$^\circ$, which is comparable to other state-of-the-art scanned laser eye-tracking sensors. The outstanding robustness and high integrability of the proposed sensor will pave the way for everyday eye-tracking in consumer AR glasses.
Improved Exploration in Reinforcement Learning Environments with Low-Discrepancy Action Selection

Reinforcement learning (RL) is a subdomain of machine learning concerned with achieving optimal behavior by interacting with an unknown and potentially stochastic environment. The exploration strategy for choosing actions is an important component for enabling the decision agent to discover how to obtain high rewards. If constructed well, it may reduce the learning time of the decision agent. Exploration in discrete problems has been well studied, but there are fewer strategies applicable to continuous dynamics. In this paper, we propose a Low-Discrepancy Action Selection (LDAS) process, a novel exploration strategy for environments with continuous states and actions. This algorithm focuses on prioritizing unknown regions of the state-action space with the intention of finding ideal actions faster than pseudo-random action selection. Results of experimentation with three benchmark environments elucidate the situations in which LDAS is superior and introduce a metric for quantifying the quality of exploration.
Deep Learning Based Intelligent Screening Mechanism

Facial recognition is widely used for identification of people as one of the biometric authentications. Biometric authentication consists of two types physiological and behavioral features. In physiological biometrics, faces, iris, and fingerprints are used for identifying the person. In behavioral biometrics, their characteristic features namely voice, DNA and hand writing is used. While using facial recognition, an individual can be identified using the previously trained model using deep learning based on the Haar cascade algorithm. Biometric authentication has been generally used for surveillance purposes. However, due to the COVID 19 pandemic, people of each nation are in need to wear face masks for their safety. Our project uses deep learning and open cv to recognize the person and to identify whether he wears a face mask or not by using transfer learning techniques and convolution neural network. One large dataset of people with mask and people without a mask was used as a training model. Our project was able to achieve an accuracy of 96.8% during the training and testing phase.
Advancing Community Resilience through Community Radio: The Case of EK-FM in Western Kenya

Community radio can serve as a crucial channel to disseminate information in rural locations. This is the case of the Ekialo Kiona Community Youth Radio (EK-FM) on Mfangano Island, Kenya. EK-FM has been a platform for communicating health information during the outbreak of COVID-19. Now, over one year into the pandemic, our MDP student team alongside EK-FM staff are assessing community needs and EK-FM&rsquo;s opportunities and limitations in meeting them. Motivated by the current negative impacts of COVID-19 on less privileged communities, we are developing organizational, financial, social, and strategic routes to enhance the radio&rsquo;s ability to uphold its community resilience goals. By engaging Mfangano&rsquo;s communities to understand the context surrounding EK-FM, we facilitate the discovery of community aspirations for the radio to connect Mfangano residents regardless of their location, whether about local Suba culture, health, or ecological issues. We also explore possible social entrepreneurial pathways and partnerships toward EK-FM&rsquo;s lasting financial viability.