Reviewerly/exHarmony huggingface dataset:
The Covid-19 Pandemic and the New Age of Higher Education: the Experience of Bulgaria

Education is a fundamental right and the COVID-19 pandemic has had a dramatic and disruptive impact on all involved in education. During that period measures constantly influence universities in all sorts of manners. This article aims to review and synthesize the findings of the international literature related to the impact of COVID-19 on higher education at global level and on that base to describe and analyze the measures implemented by the government and the higher education community in Bulgaria. It does not aim by any means to present in details the situation in different regions or countries of the world. In general, with the outbreak of the crisis academic teaching was catapulted into the virtual world and thus it entered into a new age. Therefore, the time frame of the research article is limited to the 2020-2021 period and its scope covers the key theme identified with the impact of COVID-19 on higher education institutions in the broad sense, including all areas of universities, and challenges faced by these institutions across the world to enter into the new age of education. A special focus of the research is the approach followed in Bulgaria, as in many other countries in the world, to accelerate and adopt online and distance learning models, to introduce necessary measures and to make the best use of available resources, tools and teaching strategies. The findings suggest that the emergency solutions adopted are to be considered as innovative, useful and successful although it will take time to find out how the quality of learning has been affected by the shift from face-to-face to hybrid and to online teaching. Obviously, there are some important lessons that have to be learned so that we can try to repair the damage caused by COVID-19’s interruptions to core academic activities – teaching and learning.
Internet-of-Things-based Multi-sensor Structure for Geriatric Healthcare

In the care of the elderly, patients with chronic diseases, Alzheimer's disease, Parkinson's disease, and mobility problems are often encountered, and medical staff will spend more time attending to specific conditions than in the normal case.In this paper, we mainly propose a Healthcare Internet of Things, which is an intelligent monitoring system to assist medical staff in caring for the elderly.We adopt cost-effective sensors for various applications that include the acquisition of location information, the detection of infusion and urine bag weights, and the detection of heartbeat; all patient information can also be transmitted to a cloud server via a wireless network.Thus, medical staff can examine the real-time condition of a patient through an application via a mobile device, and it is more flexible and efficient for medical treatment.
A user interface to communicate interpretable AI decisions to radiologists

Tools for computer-aided diagnosis based on deep learning have become increasingly important in the medical field. Such tools can be useful, but require effective communication of their decision-making process in order to safely and meaningfully guide clinical decisions. Inherently interpretable models provide an explanation for each decision that matches their internal decision-making process. We present a user interface that incorporates the Interpretable AI Algorithm for Breast Lesions (IAIA-BL) model, which interpretably predicts both mass margin and malignancy for breast lesions. The user interface displays the most relevant aspects of the model’s explanation including the predicted margin value, the AI confidence in the prediction, and the two most highly activated prototypes for each case. In addition, this user interface includes full-field and cropped images of the region of interest, as well as a questionnaire suitable for a reader study. Our preliminary results indicate that the model increases the readers’ confidence and accuracy in their decisions on margin and malignancy.
Low voltage user demand response management platform based on blockchain technology

In order to meet the power demand of low-voltage users and reduce the power cost, a low-voltage user demand response management platform based on blockchain technology is proposed. Based on the blockchain structure, the overall architecture of the demand response management platform is designed, including the response layer, the main chain layer and the decision-making layer. The platform hardware mainly includes the consensus mechanism module and the orderly power consumption management module of the low-voltage user demand response chain. The platform software part calculates the power supply cost of the power terminal and the power consumption cost of the low-voltage user, and constructs the objective function according to the cost calculation result. The reverse induction algorithm is used to calculate the optimal power consumption of low-voltage users, and the demand impact management problem of low-voltage users is transformed into a single objective solution problem. By solving the objective function, the demand response management of low-voltage users is completed. The experimental results show that the platform can play a good peak shaving effect, and can effectively improve the safety of the platform and the safety of low-voltage users.
Application research of landmarks in path planning algorithms of mobile robots

A way of incorporating landmark information matrix QR to affect the global path planning algorithm is proposed for the use of landmarks in robot localization and navigation. It is also investigated how this method affects the pace at which the original algorithm solves problems. The reward and punishment items in this technique include dangerous locations with lots of obstacles around them, landmarks with location information connected to the ground, and the two combined. Three representative common robot path planning algorithms are also imposed as influences. Through MATLAB simulation experiment, find the optimal path planning algorithm that is appropriate for applying reward and punishment by comparing the quality of the robot's driving path and the algorithm solution speed before and after the reward and punishment. According to the simulation results, the reward and punishment approach is practical and efficient. The heuristic search algorithm is better suited for reward and punishment, and both its path quality and speed of solution have greatly increased.
A BRIEF SURVEY ON VISUAL SPEECH RECOGNITION USING CNN

The audio-visual speech recognition method using lip movement extracted from side-face images to attempt to increase noise-robustness in mobile environments.Although most previous bimodal speech recognition methods use frontal face (lip) images, these methods are not easy for users since they need to hold a device with a camera in front of their face when talking.Our proposed method capturing lip movement using a small camera installed in a handset is more natural, easy and convenient.Visual features are extracted combined with audio features in the framework of CNN-based recognition.
Collection management collaborations in Sweden

This paper will describe the background and progress on two collection management projects—the digitization of Swedish print materials and a collaboration on shared print. The author has many years’ experience of working on both projects, and the paper gives a detailed picture of the ambitions, work and results. It concludes with some potential long-term effects of the projects that go beyond the initial scope, like the need for a shift from a local to a national and global perspective and for shared infrastructure.
Purchase on demand via interlibrary loan

A library cannot and should not purchase every book ever published. It should however nurture a collection development policy guided by the needs of the patrons and environment in which it operates. Interlibrary loan offers patrons an affordable, easy and quick way to meet the most pressing needs where hiatuses in the collection are concerned. However, international loan requests for books can be costly and have a high delivery runtime with only a short lending period. Purchasing these titles rather than requesting them can turn out to be more cost- and time effective. In addition, adding requested titles to the collection is a sure way of developing the collection based on actual needs. The University of Antwerp Library implemented a Purchase on Demand pilot in 2018. This paper presents our findings after four years of POD-ILL as an additional acquisition model and partial alternative to international ILL. We provide a short introduction on the concept of POD-ILL and take a closer look at the collection purchased through POD-ILL. As expected, most purchases were made for the faculties of Law and Social Sciences and the departments of History and Literature. As an alternative to international ILL, the main benefit is the extended loan due to an owned versus lent copy. Our yearly budget was more than sufficient to cover the cost, which has provided some leeway in the purchase strategy and parameters. We have recently opted for inclusion of study books that are in high demand.
LALS staff seminar 2023

Following in the very competent footsteps of the recent LALS PhD student seminar, this Friday’s seminar features LALS staff. Each staff member has two minutes and two slides to efficiently and expertly explain their current research project. Come along and hear about the breadth and depth of linguistics research in the School! Physical location MYLT 220
Design of Spy Robot with Wireless Night Vision Camera Using Android

The primary goal of this study is to create a robot that can monitor human activity by using wireless night vision cameras and Using an Android Bluetooth application, robots can be manually controlled. A night vision wireless camera built into the robot can send videos of people. This robot’s job is to go around and collect audio and video data from the environment it is in, then communicate that data to the user. In this project, a mobile device is used to control the robot. This robot can send data to a distant IoT cloud database after collecting it from a faraway location. By transmitting commands via the Bluetooth software on our Android phone, we can direct the robot’s movement. In the proposed system, we are going to construct a low-cost Microcontroller Based Android operated Robot that can move in any of the directions of Forward, Left, Right, Backward, and Stop. The robot will travel in the directions indicated by the mobile device, including forward, backward, left, and right. There are numerous uses for this system. The goal of our project is to create a robotic vehicle with a wireless camera capable of night vision for remote monitoring/spying applications. Even in dimly lit areas, the night vision camera enables the transmission of real-time night vision footage. The camera’s footage can be watched on a PC for reference.
Using Blockchain to Overcome the Issues in Land Registry Management: A Systematic Review

A Blockchain network contains a distributed ledger that is used to store a secure and permanent record of transactions among multiple parties. As the registries of land records are historically stored in the form of paper documentation where, there might arise the issue of losing, destroying, and modifying needed documents. This land registration process is complicated and time-consuming and required a lot of effort to transfer ownership of the land registry or change any information. For this purpose, various contributions have been made by different researchers to overcome the problem of the classical land registration process by using blockchain technology. In this survey, we shed light on some advantages (reduce time and effort, security, permanency, etc.) of blockchain to identify some early problems in the paper-based land registration process.
Performance Analysis of Mammogram Tumor Classification using Deep Belief Network

Aim: The main aim of the research is to analyze theperformance analysis of mammogram tumor image classification using Deep Belief Network (DBN) over Decision Tree (DT) with improved accuracy. Materials and Methods: The research includes two groups namely Decision Tree (DT) as group 1 and Deep Belief Network (DBN) is considered as group 2 algorithms are used here to find the accuracy of mammograms. Each group consists of 25 samples with a total sample size of 50 to evaluate the accuracy. For statistical analysis the SPSS tool was used. The sample size was calculated using G power with pretest power at 80%. Result: The accuracy of DBN is significantly improved with percentage and there is a statistical significance observed as 0.015 (p < 0.05). The mean accuracy and standard deviation for Group 1 is 88.54% with 0.60 and for group 2 is 94.52% with 0.89. Conclusion: The NovelDeep Belief Network (DBN) algorithm is significantly accurate compared to the Decision Tree (DT) to analyse the performance analysis of mammogram tumor image classification.
Current Situation and Development Measurements on Health Industry in Panzhihua City—A Research on Data of the Fourth National Economic Census

Health care industry is an important part of the modern service industry, and it is a new point for enhancing economic growth.Moreover, it is of great significance for Panzhihua city in successfully transforming from the traditional resource-based city.However, the statistical investigation methods and index system in the industry are not perfect, which seriously restricts the investigation and research of the health care industry.This paper uses the data of the fourth economic census combined with the standard system established for the health industry released by the Panzhihua Bureau of Statistics to analyze the current situation and problems of the health industry in the city, and discusses the development measurements on the industry, so as to help and promote the development of the health industry in the city.
Hand Posture Recognition on Photographic Images using YOLO Algorithm in Comparison with Convolution Neural Network to Improve Accuracy

Aim: The main aim of the research work is to compare the performance of a novel YOLO algorithm in comparison with Convolution Neural Networks (CNN) to improve accuracy. Materials and Methods: In this research, Group 1 is considered as the YOLO algorithm and Group 2 is considered as CNN, recognizing the YOLO algorithm for public use when compared to CNN with higher significance value with 20 samples each group consists of 10 samples. The G power is 0.8, the alpha is 0.05, the beta is 0.2. Results: The accuracy of YOLO is significantly improved than CNN and there is a statistical significance observed as 0.0049 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathbf{p} &lt; \boldsymbol{0.05})$</tex> . The accuracy for Group 1 is 95.66%, for Group 2 is 94.76%. Conclusion: YOLO algorithm is significantly more accurate compared to the convolution neural network.
User-Centric Federated Matrix Factorization Based on Differential Privacy

Matrix factorization is a popular recommendation method used in many fields, but it often lacks sufficient privacy protection. To address privacy concerns in connected living, we propose a user-centric federated matrix factorization model based on differential privacy (FMF-DP). First, we design a federated matrix factorization framework where each user is a participant and only uploads their gradients after completing the training task locally. Second, we introduce the user-average-rating bias into the matrix factorization method to improve accuracy. Finally, we use DP to enhance privacy protection without compromising computational efficiency. We have implemented the prototype of an FMF-DP and tested it on several datasets, showing promising results. Compared with the baseline model, the root-mean-square error is reduced by 0.25 on average, and the computational efficiency is also improved.
Jetson Nano-Based Pedestrian Density Detection in Subway Stations

Computer Vision is one of the popular research in the field of Deep Learning. As a hotspot in the field of computer vision research in recent years, pedestrian detection has a wide application prospect and great economic value in urban transportation, social public safety, national defense security construction, and so on. In this paper, the Yolov4-Tiny algorithm is used as the basic algorithm to implement pedestrian detection in crowded subway stations, and the network structure, detection principle, and training process of the algorithm are studied in detail. The map value achieved by the model after training is 84.42%, and pedestrian images are selected and then tested with good results. In this paper, the trained model is tested on the embedded motherboard Jetson Nano, and the processing speed fps value is about 7, which can meet the requirements of real-time pedestrian detection.
Exploring the Application of SketchUp and Twinmotion in Building Design Planning

With the rapid development of industry 4.0 technology, the demand for real-time 3D rendering technology in all walks of life is unprecedented, especially in architectural design and planning, In view of the problems existing in traditional design planning, such as creativity is difficult to express immediately and intuitively, 2D drawings are complex and difficult to understand, workflow is chaotic, and it is difficult for all parties to coordinate and review, This paper analyzes the real-time seamless collaborative creation of SketchUp and twinmotion as a reference to solve the above problems, and uses three cases to analyze the media output from preliminary conceptual design to film and television photo level, in order to build a concise architectural visualization workflow in the spirit of open innovation.
Research on Helmet Detection Algorithm Based on Improved YOLOv5s

The construction environment is complex and dangerous, and it is difficult to achieve all-round, whole-process and real-time helmet detection. In order to ensure the safety of personnel, this paper proposes a helmet detection algorithm based on improved YOLO v5s. First, replace the backbone feature extraction network of YOLOv5s with the end-side neural network architecture GhostNet, which greatly reduces the amount of network parameters. Introduce the lightweight module attention mechanism ECA-Net in the C3 module to improve the feature extraction ability, and finally use the CIOU as the loss function to improve the positioning accuracy. The average accuracy (mAP) of the improved model on the SHWD dataset reaches 93.86%, and the processing speed (FPS) reaches 49. Compared with the original YOLOv5s, the amount of parameters is reduced by 13.33% without reducing the mAP, and the size of the model is reduced. 26.6%, processing speed increased by 22.5%. The experimental results show that it can effectively reduce the amount and size of model parameters and meet the real-time detection requirements of embedded devices.
Research on Natural Language Processing Technology in Construction Project Management

This paper analyses the research and application of Natural Language Processing (NLP) technology in the management of construction projects, aiming at the problems such as the expansion of the scale of construction projects and the increasing number of document information, the difficulty and complexity of their management, the inefficiency and complexity of traditional manual collation and induction, the inability of technicians to fully, timely and efficiently consult, modify and improve document information, resulting in the degradation of project performance. Convert unstructured documents in the field of architecture into structured information to solve common problems in architecture.
Routing and Navigation Solutions for Emergency Vehicles in Urban Emergency Management

Urban population and expansion continue to rise, as have emergencies, and emergency response times are becoming longer in the city. Response times are critical in urban emergency management. Traditional navigation systems do not consider the preferential road usage rights and requirements crucial for emergency teams such as ambulances, fire departments, and police. In traditional navigation systems, features such as safety lanes and road width are not incorporated as routing criteria. Past emergency navigation studies do not contain many variables and this causes delays in emergency response processes. As a result, applications that process real-time traffic information and provide guidance according to different vehicle types are needed for emergency management.. This study aims to develop an application capable of routing for different vehicles according to criteria such as working hours, holidays, weather conditions, and seasonal effects. The application is developed with a novel approach in this context using machine learning methods and produces alternate routes with GPS navigation based on the real-time traffic situation. Moreover, the software also provides routing solutions for trucks, freight distribution, and automobiles and recommends unique routes for emergency vehicles based on factors including safety lanes, road width, corner turning angles, and right of way. The expected outcomes upon implementing this application include easy and fast access to the emergency locations, reduced response times, and improvement in urban emergency management services.
Educational Data Processing Centers; Determinants of Security Innovations at Higher Education Institutions

Higher education institutions are facing unprecedented security challenges due to the increasing recognition of the importance of integrating technology into the core business of teaching and learning.This study sought to investigate the determinants of security innovation for data processing centers of higher educational institutions.Studies related to security innovation of higher educational institutions are limited considering the vast amount of data processing centers deployed for academic operations.In all, 300 respondents were selected from higher education institutions to participate in a survey to test the proposed model by using multinomial logistic regression.The study found out that four predictor variables; relative advantage, complexity, board approval, and IT competencies were significant and hence the model was suitable for studying security innovation at the organizational level.
Multi-Strategy Boosted Spider Monkey Optimization Algorithm for Feature Selection

To solve the problem of slow convergence and easy to get into the local optimum of the spider monkey optimization algorithm, this paper presents a new algorithm based on multi-strategy (ISMO). First, the initial population is generated by a refracted opposition-based learning strategy to enhance diversity and ergodicity. Second, this paper introduces a non-linear adaptive dynamic weight factor to improve convergence efficiency. Then, using the crisscross strategy, using the horizontal crossover to enhance the global search and vertical crossover to keep the diversity of the population to avoid being trapped in the local optimum. At last, we adopt a Gauss-Cauchy mutation strategy to improve the stability of the algorithm by mutation of the optimal individuals. Therefore, the application of ISMO is validated by ten benchmark functions and feature selection. It is proved that the proposed method can resolve the problem of feature selection.