Reviewerly/exHarmony huggingface dataset:
Autonomous Human-Following Drone for Monitoring a Pedestrian from Constant Distance and Direction

We propose an autonomous drone system that follows a pedestrian from a certain distance and direction for human video analysis without environmental constraints. The proposed system adopts human pose estimation via a monocular drone camera and calculates control parameters from the skeleton points. Specifically, our system uses 3D pose estimation to obtain information about human direction for following a moving subject from a certain direction. Experimental results in the real-world show that our system runs with reasonable response time and positional stability.
Power Usage Efficiency (PUE) Optimization with Counterpointing Machine Learning Techniques for Data Center Temperatures

The rapid increase in the IT infrastructure has led to demands in more Data Center Space &amp; Power to fulfil the Information and Communication Technology (ICT) services hosting requirements. Due to this, more electrical power is being consumed in Data Centers therefore Data Center power &amp; cooling management has become quite an important and challenging task. Direct impacting aspects affecting the power energy of data centers are power and commensurate cooling losses. It is difficult to optimise the Power Usage Efficiency (PUE) of the Data Center using conventional methods which essentially need knowledge of each Data Center facility and specific equipment and its working. Hence, a novel optimization approach is necessary to optimise the power and cooling in the data center. This research work is performed by varying the temperature in the data center through a machine learning-based linear regression optimization technique. From the research, the ideal temperature is identified with high accuracy based on the prediction technique evolved out of the available data. With the proposed model, the PUE of the data center can be easily analysed and predicted based on temperature changes maintained in the Data Center. As the temperature is raised from 19.73 oC to 21.17 oC, then the cooling load is decreased in the range 607 KW to 414 KW. From the result, maintaining the temperature at the optimum value significantly improves the Data Center PUE and same time saves power within the permissible limits.
Performance Evaluation of Convolution Neural Network Based Object Detection Model for Bangladeshi Traffic Vehicle Detection

Vehicle detection has numerous applications in modern day like smart toll plaza, parking, traffic management, etc. Many Convolution Neural Network (CNN) based object detection models, designed to train specific object detection and test them in real world. However, the challenge is preparing these models for vehicle detection according specific custom datasets and train them with minimal devices such as low GPU, memory, and test those models with embedded devices. Both accuracy and speed are matter for real-time vehicle detection and counting. In general, real-time object detection model based on CNN are 2 types—One stage method (YOLOv3, SSD) and two stage method (Faster-RCNN resnet50, resnet101). Both of the methods have complex network architecture which makes the real-time detection slow. But two stage method is slower than one stage method and in particular YOLO series is faster as it requires less GPU than other models. This paper has shown the performance evaluation of YOLOv3, YOLOv3-tiny, and YOLOv4-tiny in terms of precision, recall, F1-Score, mAP (Mean Average Precision), IoU (Intersection over Union), and Average FPS (Frame per second) for moving traffic vehicle detection and count them using the dataset named ‘Dhaka-AI (Dhaka traffic detection challenge dataset). Experiments show that YOLOv4-tiny performs better compared to other two models in terms of recall, F1-Score, AVG_FPS and mAP.
EyeLoop: An Open-Source System for High-Speed, Closed-Loop Eye-Tracking

Eye-trackers are widely used to study nervous system dynamics and neuropathology. Despite this broad utility, eye-tracking remains expensive, hardware-intensive, and proprietary, limiting its use to high-resource facilities. It also does not easily allow for real-time analysis and closed-loop design to link eye movements to neural activity. To address these issues, we developed an open-source eye-tracker – EyeLoop – that uses a highly efficient vectorized pupil detection method to provide uninterrupted tracking and fast online analysis with high accuracy on par with popular eye tracking modules, such as DeepLabCut. This Python-based software easily integrates custom functions using code modules, tracks a multitude of eyes, including in rodents, humans, and non-human primates, and operates at more than 1,000 frames per second on consumer-grade hardware. In this paper, we demonstrate EyeLoop’s utility in an open-loop experiment and in biomedical disease identification, two common applications of eye-tracking. With a remarkably low cost and minimum setup steps, EyeLoop makes high-speed eye-tracking widely accessible.
Development of a Mini Pole-Climbing Robot

This article introduces the design of a mini pole-climbing robot that can climb up and down a pole without using wheels as the locomotion mechanism. The system is implemented with a microcontroller, Arduino MEGA, while the robot’s climbing mechanism uses servo motors and a combination of grippers and gears. The robot imitates the locomotion of a worm. In the results, the pole-climbing robot can climb up a 3.2 cm diameter pole with a speed of 2.696 mm/s and climbing down with a speed of 2.75 mm/s. For the operation of the servo motor, the microcontroller outputs a voltage of 2.94 V.
The perception and satisfaction analysis of AI Pengtalk

The objective of this study was to examine users’ perspectives and levels of satisfaction with AI Pengtalk, an English-speaking application powered by artificial intelligence. To accomplish this objective, three groups (395 students, 60 parents, and 33 teachers) were surveyed regarding their attitudes toward AI Pengtalk and satisfaction with the application. Findings indicated that all groups considered AI Pengtalk a useful tool for enhancing their English-speaking ability. Positive results were obtained across all groups in terms of their perceived efficacy of the system for improving English ability. Participants reported that the application provided them opportunities to practice specific language topics. Additionally, participants stated that developing a free conversation capability was one of the conditions for maximizing the system’s potential as an English-speaking application. Participants also commented that the interface was user-friendly and that its instructions and explanations given to users were straightforward, indicating general satisfaction with AI Pengtalk. Despite these perceived benefits, all groups reported limited usage of the application. Several technological difficulties such as misrecognition of students’ utterances were also discovered. Future recommendations for this application’s usage were discussed from both pedagogical and technological standpoints.
Micromedia News Dissemination Impact Assessment Integrated with Personalized Recommendation Algorithm

The development of the Internet has completely changed the way of recommending and disseminating news content. Traditional media forms of news dissemination effectiveness evaluation methods are no longer fully suitable for the evolving needs of new media news dissemination. Therefore, it is necessary to innovate methods for evaluating the effects of new media news dissemination. This article mainly combines personalized recommendation algorithms to evaluate the effectiveness of news dissemination. Currently, popular personalized recommendation algorithms include content-based recommendation algorithms, collaborative filtering recommendation algorithms, knowledge-based recommendation algorithms, and associated recommendation algorithms. These recommendation algorithms are effective. This promotes the dissemination of news, which also recommends news content that is more relevant to user preferences for most users. In addition, the quality of news content is further evaluated through the news rating system, thereby effectively improving the quality of news content.
Design of Psychological Consultation System Based on Weighted Fuzzy Hybrid Algorithm

In order to overcome the problems of low security and high message delay in traditional counseling systems, this article designs and proposes a new counseling system based on the weighted hybrid algorithm. According to the principle of system design, the system architecture diagram and system role are designed. In the hardware part of the system, the user management module, online consultation module, and knowledge base module are designed to realize the psychological consultation function of the system. In the software part, the weighted hybrid algorithm is used to divide the psychological information state of the consultants effectively. Combined with the hardware and software of the system, the overall design of the system is completed. The experimental results show that, compared with the traditional consulting system, the designed system can effectively ensure the information security of consultants and reduce the message delay of the system, which has higher practical application value.
Moving Object Detection and Marking Based on Frame Difference and Train Algorithm for Teaching Video

Moving object detection is an important branch of video image processing technology. It is widely used in military, transportation, aviation and other fields. However, there are still gaps in the field of teaching. In this paper, moving object detection is applied to the field of teaching video. The train algorithm is designed to mark the unconnected areas. Then a moving object detection and marking system is realized to assist teachers in managing the classroom. Firstly, the system uses the frame difference method to detect the moving object. Secondly, the custom threshold is used for binarization. Then the median filter and open operation is used to denoise and morphological processing. After that, the obtained unconnected region is segmented and located by the processing of the train algorithm. Finally, the moving region greater than the threshold is marked according to the set pixels and threshold in the block. The system results show the effectiveness of the algorithm and the accuracy of moving object detection in teaching video.
Development and application of three-dimensional visualization system for substation construction control based on “BIM+ Smart site” technology

Since 2020, the CPC Central Committee and the State Council have made important plans to accelerate the construction of new infrastructure such as 5G network, big data center, artificial intelligence and industrial Internet. The State-owned Assets Supervision and Administration Commission (SASAC) issued the Notice on Accelerating the Digital Transformation of State-owned Enterprises, which points out the direction for the digital transformation of state-owned enterprises. Relying on the State Grid Tianjin Electric Power Company&#x0027;s Tencent No.1110kV substation as a pilot project, the project team deepened the three-dimensional design results, explored a new digital construction management mode, and developed a three-dimensional visualization system for construction control. This paper makes a detailed study on the construction ideas and system framework of the three-dimensional visualization system for power transmission and transformation engineering construction control, completes the system research and development, and applies it in the whole construction process of Tencent 110kV substation in Tianjin. Through the integration of BIM technology and data collected by the IOT sensing device, the automatic loading of professional data such as progress, quality and safety in the three-dimensional scene is realized, so as to realize full perception of operation status, full sharing of construction and management data and full coverage of process control. According to the demand of partial and sub-item settlement, with the help of the thinking mode of block chain technology, accurate settlement of project quantity and dynamic visual control of project cost are realized. And the digital transformation of power grid construction is accelerated.
Peculiarities of the higher education development in a blended learning environment

This article aims to identify the peculiarities of the higher education pedagogy transformation through the blended learning model. The methodology was based on a statistical, correlational analysis by using The Teaching and Learning International Survey database containing the survey results of 7,823 educators in France and the Netherlands. The study revealed an average level of teacher preparation for blended learning (50.5%) and preparation for information and communication technology (ICT) use in student learning (48.4%) components inclusion. A significant level of teachers’ professional development of ICT skills was revealed (44% take courses). An average level of teachers’ unpreparedness both for blended learning (28.9% not ready, 36.6% not prepared enough) and ICT use in practice was established (30.3% almost not ready, 34.25 not prepared enough). A positive correlation was established between the teachers’ preparation for the blended learning environment and the passage of professional ICT skills development courses.
Domain-Adversarial Based Model with Phonological Knowledge for Cross-Lingual Speech Recognition

Phonological-based features (articulatory features, AFs) describe the movements of the vocal organ which are shared across languages. This paper investigates a domain-adversarial neural network (DANN) to extract reliable AFs, and different multi-stream techniques are used for cross-lingual speech recognition. First, a novel universal phonological attributes definition is proposed for Mandarin, English, German and French. Then a DANN-based AFs detector is trained using source languages (English, German and French). When doing the cross-lingual speech recognition, the AFs detectors are used to transfer the phonological knowledge from source languages (English, German and French) to the target language (Mandarin). Two multi-stream approaches are introduced to fuse the acoustic features and cross-lingual AFs. In addition, the monolingual AFs system (i.e., the AFs are directly extracted from the target language) is also investigated. Experiments show that the performance of the AFs detector can be improved by using convolutional neural networks (CNN) with a domain-adversarial learning method. The multi-head attention (MHA) based multi-stream can reach the best performance compared to the baseline, cross-lingual adaptation approach, and other approaches. More specifically, the MHA-mode with cross-lingual AFs yields significant improvements over monolingual AFs with the restriction of training data size and, which can be easily extended to other low-resource languages.
Authenticated Encryption Based on Chaotic Neural Networks and Duplex Construction

In this paper, we propose, implement and analyze an Authenticated Encryption with Associated Data Scheme (AEADS) based on the Modified Duplex Construction (MDC) that contains a chaotic compression function (CCF) based on our chaotic neural network revised (CNNR). Unlike the standard duplex construction (SDC), in the MDC there are two phases: the initialization phase and the duplexing phase, each contain a CNNR formed by a neural network with single layer, and followed by a set of non-linear functions. The MDC is implemented with two variants of width, i.e., 512 and 1024 bits. We tested our proposed scheme against the different cryptanalytic attacks. In fact, we evaluated the key and the message sensitivity, the collision resistance analysis and the diffusion effect. Additionally, we tested our proposed AEADS using the different statistical tests such as NIST, Histogram, chi-square, entropy, and correlation analysis. The experimental results obtained on the security performance of the proposed AEADS system are notable and the proposed system can then be used to protect data and authenticate their sources.
Path planning with multiple constraints and path following based on model predictive control for robotic fish

This paper discusses the path planning and path following control problems of robotic fish. In order to avoid obstacles when robotic fish swim in a complex environment, a path planning method based on beetle swarm optimization (BSO) algorithm is developed. This method considers the influence of the robotic fish's volume and motion constraints on the path planning task, which can eliminate the collision risk and meet the constraint of the minimum turning radius when the robotic fish obtains the planned path. In constructing the path following controller, a multilayer perception based model predictive control (MPC) is adopted to design the optimal control method, and the objective function of the optimal control is dynamically adjusted according to the path curvature. The simulation results show that this proposed method can effectively overcome the complexity of robotic fish kinematics modelling and adapt well to the reference paths of different curvatures given by the path planner.
A Novel Approach to Arabic Chabot, Utilizing Google Colab and the Internet of Things: A Case Study at a Computer Center

A Chabot is a software program for humans to interact with natural-language computers. It has numerous applications in business, service, education, and healthcare, among others. Arabic Chabot’s, on the other hand, fight to generate and display Arabic characters correctly because of linguistic problems. In this paper, we propose a new method for the development of effective Arabic Chabot’s, which is improved by the use of the Internet of things (IOT). An experiment was performed utilizing Google Colab and the Python Chatterbot library to build and deploy an Arabic Chabot for a computer center based on IOT.
Advertising Popularity Feature Collaborative Recommendation Algorithm Based on Attention-LSTM Model

To accurately predict the click-through rate (CTR) and use it for ad recommendation, we propose a deep attention AD popularity prediction model (DAFCT) based on label recommendation technology and collaborative filtering method, which integrates content features and temporal information. First, we construct an Attention-LSTM model to capture the popularity trends and exploit the temporal information based on users’ feedback; finally, we use the concatenate method to fuse temporal information and content features and design a Deep Attention Popularity Prediction (DAVPP) algorithm to solve DAFCT. We experimentally adjust the weighted composite similarity metric parameters of Query pages and verify the scalability of the algorithm. Experimental results on the KDDCUP2012 dataset show that this model collaborative filtering and recommendation algorithm has better scalability and better recommendation quality. Compared with the Attention-LSTM model and the NFM model, the F1 score of DAFCT is improved by 9.80 and 3.07 percentage points, respectively.
A 139 fps pixel-level pipelined binocular stereo vision accelerator with region-optimized semi-global matching

Binocular stereo matching is an essential topic in computer vision. However, the high complexity and massive computation restrict its applicability in real-time. To cache large amounts of data, the primary method [1] involved an external DRAM and reached 133MHz, but power consumption is as high as 2.313W on the Virtex-5 FPGA platform. In addition, a fully parallel and pipelined architecture based on dependency relaxation [2] attained a processing speed of 81 fps performed on Virtex-7, whose error rate is 10.5% based on the KITTI 2015 dataset with 64 disparity range. Another two-cycle time-sharing pipelined architecture [3] reaches 200MHz with a 128 disparity range on VCU-118. A compact hardware architecture is still challenging research [4 $\sim$ 6].
Ontology-Driven Cultural Heritage Conservation: A Case of The Analects of Confucius

Confucianism, recognized as the belief system of Chinese, is one of the most important intangible cultural heritages of China. The main ideas of its founder, Confucius, are written in The Analects of Confucius. However, its scattered chapters and the obscurity of ancient Chinese have prevented many people from understanding it. In order to overcome this difficulty, it needs some modern ways to reveal the vague connotation of Confucianism. This paper aims to describe how to construct the Lunyu ontology in which all concepts are abstract within the core scope, i.e., morality of Confucianism. The key task of this project lies in identifying essential characteristics, a notion that is compliant with the ISO principles on Terminology (ISO 1087 and 704), according to which a concept is defined as a combination of essential characteristics. This paper proposed an approach in the practice of identifying essential characteristics of abstract concepts from different meanings of its Chinese terms in The Analects of Confucius. With this work, Lunyu ontology established a semantic, formal, and explicit representation system for concepts of Confucianism, and the new proposed approach provides a useful reference for other researchers.
What Were People Searching For? A Query Log Analysis of An Academic Search Engine

Academic search engines have served the research community for years, yet there is little work done on understanding the taxonomy of query semantics. In this work, we present our findings of analyzing the query log of an academic search engine in the past four years. We study the distribution of query intents to understand the information requested by users. We classify query strings by topics using shallow and latent features captured using a customized word embedding model. To this end, we create a dataset that has scientific keywords and titles labeled with fields of study. This dataset is later used to train a classifier that discriminates query logs by topics. Our work will help to train better learning-based ranking functions that improve user experiences for an academic search engine. In addition, we anonymize our 14,759,852 query logs and make them available to the research community for further exploration.
Cost-Effective Watermarking Scheme for Authentication of Digital Fundus Images in Healthcare Data Management

This paper presents a cost-effective watermarking scheme for the authentication of healthcare data management. The digital fundus images are one particular class of medical images and it is widely used for screening mass population, identifying early symptoms of various diseases in healthcare. The mass volume of such data and its management requires an effective authentication scheme, while it is exchanged on an open network. The proposed scheme uses a watermarking technique to authenticate the digital fundus images. The watermark is generated concerning the portions of the original image using Singular value decomposition (SVD) and the remaining portions are used for embedding. The embedding process uses interleaving concepts across the red and blue planes of the original images to make the number of embedding as constant. The constant number of embedding is fixed for the original size of the given image to make the scheme as computationally cost-effective. The experiment showed the maximum capacity of the proposed scheme is 329960 bits for an image of size 565x584x3. It modifies 43% of the total number of embedded pixels against jittering attacks at an average. Comparative analysis showed that the proposed scheme uses only 1/3 of the original image size for embedding by retaining good imperceptibility of 54 dB. The net performance of the proposed scheme is found to be constant and it makes a scheme as cost-effective.