Reviewerly/exHarmony huggingface dataset:
Low-Computation IoT System Framework for Face Recognition Using Deep Learning Algorithm

With the growing concept of smart cities, IoT applications such as face-authenticated smart-home door-lock security systems have gained importance and become popular. The major challenge in implementing the algorithms on IoT devices is a lack of computational power. The major challenge in implementing the algorithms on IoT devices is a lack of computational power. The state-of-the-art methods run successfully in high-end computing units, are typically not used in Raspberry pi, considered as an IoT device. This paper aims to design an authorized door lock system using the framework Raspberry pi. Instead of Euclidean distance-based similarity measure between the test and the trained face images, here for face identification the pretrained FACENET model is used that reduces the false cases. In the current approach, Haar-Cascade is used to detect the faces and next we train the fully connected layers on the collected facial embeddings of FACENET. The proposed Haar-Cascade FACENET Fully Connected Face Authentication (HFFCFA) model eliminates the faults which occur in the Euclidean-based approach, thus reducing false positive and false negative cases. The network model works perfectly together with the Raspberry Pi pipeline for face recognition.
Research on Kinematics Analysis and Trajectory Planning of Loading Robot

In order to realize object loading and improve loading efficiency, a six-degree-of-freedom robot is taken as the research object, and its kinematics analysis and trajectory planning research are carried out. Establish a kinematic model for the loading robot, and perform forward and inverse kinematics analysis; use Monte Carlo method to solve the robot’s workspace, and obtain a point cloud image of the workspace at the end of the robot; perform a trajectory planning simulation on the loading robot to obtain The position, velocity and acceleration curve of each joint during the movement. The results show that the robot moves continuously and smoothly, the scope of the working space is appropriate, and the loading work can be completed.
The importance of 5G healthcare using blockchain technologies

With the revolution in network technology and the Internet of things (IoT), pervasive computing becomes tangible by its usage in abundant applications at the global level, including the healthcare industry. It enables billions of objects to communicate purposefully with high-speed data processing (data transfer, collection, management, and processing) particularly in the 5G-enabled healthcare industry/environment. The evolution in the healthcare industry has grown exponentially from Healthcare 1.0 to 4.0. The evolution originated with Healthcare 1.0, which was doctor centric, and was subsequently followed by Healthcare 2.0 where manual records were made digital, and Healthcare 3.0 in which the technology became patient centric. Next, Healthcare 4.0 was equipped with advanced digital technologies such as IoT, cloud computing, and fog computing for sharing or communicating the data between various stakeholders such as doctors, patients, laboratories, nurses, relatives/caretakers, etc. As communication became easy and in real time in Healthcare 4.0, it utilizes a centralized architecture suffering from major issues such as access control mechanisms, security, and privacy. Majorally, the challenges include a longer time in retrieval and storage of data from heterogeneous devices as well as threats of tampering of sensitive medical records of patients/hospitals. The issue related to the access control mechanism can be resolved by 5G healthcare, which introduces a high data speed, low latency, increased reliability, availability, and massive network capacity in the current healthcare framework. In addition, the concerns of privacy and security can be resolved by adopting Blockchain technology in 5G healthcare. This emerging technology supports both the privacy and security of sensitive data by offering a safe and trustworthy secure system to record and share these data. The data, once encrypted using this technology, cannot be modified later. Thus, Blockchain is an appropriate alternative to resolve tampering issues and achieving privacy, security, and trust in applications related to healthcare. Therefore, in this chapter, the basic architecture and main features of Blockchain and 5G are introduced with the major issues faced by the healthcare industry. In addition, the adoption and importance of Blockchain with 5G healthcare is discussed in detail along with its architecture and opportunities. Further, the opportunities, scope, issues, limitations, and research directions of 5G healthcare using Blockchain with a variety of use case scenarios and case studies/applications are explored that help researchers and developers understand its importance and applicability in the healthcare domain.
Quantum Gate-Based Quantum Private Comparison Protocol Using Four-Particle GHZ States

The previous quantum private comparison (QPC) protocols often utilize quantum technology to provide security and the classic computing to perform exclusive-OR operation. In this paper, a secure quantum gate-based quantum private protocol without classical computing is proposed, which uses four-particle GHZ states as quantum resource and quantum circuit to perform computing. Four-particle GHZ states are easy to be prepared with current technology. The proposed protocol doesn’t need swapping entanglement technology but single-particle measurement and Bell state measurement. Additionally, a semi-honest third party (TP) is involved to help the two participants accomplish the private comparison. Compared with the previous quantum private comparison protocols with classic computing, the proposed protocol is of simplicity and has a quantity of security. Finally, the analysis shows its correctness and it can withstand various attacks including outside attacks and participant ones.
ANURL: An Innovative Management Scheme for Web Uniform Resource Locators

The developments in the Internet, social media platforms, and cloud technologies have necessitated the need for sharing online contents of various kinds. Despite a slew of popular shorteners for uniform resource locators (URL) that are available such as Bitly and TinyURL, we delineate the need for a more comprehensive URL management scheme. The current paper reports the development of an innovative and comprehensive management scheme for Web uniform resource locators (URL) named ANURL. The paper presents a detailed account of its features and its several use cases, which makes it stand out over the conventional ones. A brief description of the ANURL architecture and its working is provided. Finally, some preliminary data on the tool evaluation and user testing is presented to substantiate its growing user base with geography and time.
Development of a Three-Dimensional (3D) Virtual Reality Apprenticeship Program (VRx) for Training of Medication Safety Practices

<strong>Background:</strong> Medication errors is a top concern for patient safety globally. Healthcare staff need to be competent in skillsets in medication safety practices. COVID-19 has negatively impacted programs with on-the-job training. Virtual reality (VR) provides an attractive way to conduct such training. We developed a virtual apprenticeship program (called VRx) for the training of medication safety practices. <strong>Methods:</strong> VRx was developed in a three-dimensional virtual environment whereby trainees had to complete a series of key competency tasks in prescription processing, pharmaceutical calculations, and preparing, picking, packing and dispensing medications. Pilot testing involved two phases – an alpha-test (Ph1) with 30 pharmacy staff/trainees and a roll-out (Ph2) to 43 new hires at our institution. Participants rated their awareness of VR and perceptions of their training experience through an online self-administered questionnaire. Descriptive statistics and Chi-squared/Fisher’s Exact tests were used for analysis. <strong>Results:</strong> Over half of participants were slightly aware of VR (Ph1 = 63.3%, Ph2 = 67.4%). One-quarter was aware of and had used VR (Ph1 = 26.7%, Ph2 = 25.6%). Majority agreed that VRx was easy to use (Ph1 = 83.3%, Ph2 = 72.1%), interactive and fun (Ph1 = 90.0%, Ph2 = 79.1%), and helped them develop prescription processing skills (Ph1 = 90.0%, Ph2 = 86.0%). More participants who were slightly aware of VR indicated that VRx was easy to use (94.7% versus 33.3% not aware of VR, p = 0.038, Ph1). Over half felt that the time spent on VRx training was acceptable, and that VRx should be part of their training (Ph1 = 80.0%, Ph2 = 67.4% each). More participants who were aware of and had used VR indicated that VRx should be part of their training (100% versus 55.2% slightly aware, p = 0.007, Ph2). <strong>Conclusion:</strong> Overall, pharmacy staff/trainees found VRx to provide a realistic, engaging and interactive experience to acquire proficiencies in medication safety practices. VRx can complement the training and assessment needs of pharmacy staff/trainees at our institution.
Research on Detection and Recognition of Traffic Signs Based on Convolutional Neural Networks

The road traffic sign detection and identification (TSDR) system is a subsystem of the advanced driver assistance system. It has received extensive attention from domestic and foreign researchers. The TSDR system is mainly composed of two parts: the detection of traffic signs and the identification and output of traffic signs. This paper focuses on the research of TSDR system and proposes a method based on faster R-CNN combined with part of the low-level feature map. The lightweight model MobileNet is used to finish the traffic sign detection. Experiments show that this method has a certain effect on the detection of small-scale traffic signs, and the detection speed is fast. The LeNet5 and Inception V3 network model are used for traffic sign recognition. The model is optimized by adjusting parameters such as the learning rate and batch size. It shows that for LeNet5, when the learning rate = 0.001, the recognition rate can reach 92.5%. For the Inception V3 model, when the learning rate is 0.005, it has a higher recognition rate than the LeNet5 model by 4.9 percentage points.
One-Shot HDR Imaging via Stereo PFA Cameras

High Dynamic Range (HDR) imaging techniques aim to increase the range of luminance values captured from a scene. The literature counts many approaches to get HDR images out of low-range camera sensors, however most of them rely on multiple acquisitions producing ghosting effects when moving objects are present. In this paper we propose a novel HDR reconstruction method exploiting stereo Polarimetric Filter Array (PFA) cameras to simultaneously capture the scene with different polarized filters, producing intensity attenuations that can be related to the light polarization state. An additional linear polarizer is mounted in front of one of the two cameras, raising the degree of polarization of rays captured by the sensor. This leads to a larger attenuation range between channels regardless the scene lighting condition. By merging the data acquired by the two cameras, we can compute the actual light attenuation observed by a pixel at each channel and derive an equivalent exposure time, producing a HDR picture from a single polarimetric shot. The proposed technique results comparable to classic HDR approaches using multiple exposures, with the advantage of being a one-shot method.
Prototype Development of an Interpretative Game with Location-Based AR for Ecomuseum

To improve the visiting effect of ecomuseum, this research proposes to utilize augmented reality (AR) technology and to study how to integrate appropriate interaction design can help get better experience in such museums. Augmented reality is a technology attaching computer generated images and information on the real world. AR users can see the original real world, and also can see supporting information images or virtual objects, that help provide related information and interaction of the real world and objects. With a perspective of interaction design, this research considers the form of an interpretative game with location-based AR to combine the benefits of field interpretation and game tasks. We took Beitou area in Taipei city as the sample of ecomuseum, to develop the contents of the game. Prototypes with location-based Web AR were made and tested. Field user tests were conducted to understand the issues and satisfaction of interaction design. Qualitative data were collected and analyzed. Issues about learnability, satisfaction, storytelling, user interface, and technology are discussed. Then a more suitable design guideline could be concluded to improve the formal model of interpretative game for ecomuseum.
Binary Fish Migration Optimization for Feature Selection Using Time-Varying Transfer Function

Dou, Zhi-Chao Zhuang, Zhongjie Kong, Ling-Ping Pan, Jeng-Shyang Chu, Shu-ChuanFish migration optimization (FMO) is an intelligent optimization algorithm based on fish migration behavior. It has been proven superior performance in terms of convergence, accuracy, and speed. Binary fish migration optimization (BFMO) is the binary version of FMO, which is designed to solve generator set assembly problem. In this paper, time-varying binary fish migration optimization (T $$_V$$ -BFMO) is proposed to balance the exploration and exploitation of the BFMO. Then, experiments are made on various $$\varphi _{\max }$$ and $$\varphi _{\min }$$ . The exact value of $$\varphi _{\max }$$ and $$\varphi _{\min }$$ is selected by comparing the performance of various $$\varphi $$ values on 23 standard benchmark functions. In the end, T $$_V$$ -BFMO successfully implements feature selection on UCI datasets.
Blockchain Technology with Supply Chain Management: Components, Opportunities and Possible Challenges

Supply chains have been challenged by giving transparency and confidence between contributors as well as investors for guaranteeing efficiency. Conversely, a few challenges are tough to solve as supply chain records may be exchanged by their contributors. The advent of blockchain is an unbreakable distributed ledger technology and has been acknowledged as an encouraging resolution to these encounters. The paper introduced the blockchain and examined the various components of the newly developed blockchain to uncover various supply chain challenges. The paper emphasises the acquisition of supply chain challenges and their management. In addition, this paper describes the classification of blockchain technology and elements of consensus algorithms. Furthermore, the paper outlines the characteristics of the blockchain structure for supply chain management, sums up the possible solutions for the blockchain for its effective implementation in future supply chain mechanisms and discusses a few challenges along with opportunities left in future research.
Translation Certification for Smart Contracts

Compiler correctness is an old problem, but with the emergence of smart contracts on blockchains that problem presents itself in a new light. Smart contracts are self-contained pieces of software that control (valuable) assets in an adversarial environment; once committed to the blockchain, these smart contracts cannot be modified. Smart contracts are typically developed in a high-level contract language and compiled to low-level virtual machine code before being committed to the blockchain. For a smart contract user to trust a given piece of low-level code on the blockchain, they must convince themselves that (a) they are in possession of the matching source code and (b) that the compiler has correctly translated the source code to the given low-level code. Classic approaches to compiler correctness tackle the second point. We argue that translation certification also squarely addresses the first. We describe the proof architecture of a novel translation certification framework, implemented in Coq, for a functional smart contract language. We demonstrate that we can model the compilation pipeline as a sequence of translation relations that facilitate a modular verification methodology and are robust in the face of an evolving compiler implementation.
Analysis on the Frame Model of Poor Student Identification and Funding Management Based on Financial Information Sharing System

In our country’s major colleges and universities, the special group of poor students exists widely. How to effectively solve their economic difficulties and make them complete their studies smoothly, so as to ensure the stability of colleges and even the whole society, has become a country, society and society. The focus of the school’s attention. This article first studies the financial information sharing system, and uses computer technology to design the system; then it designs the design of the overall function of the management system for the evaluation of poor students and the design of the public system database. Finally, the basic functional modules of the system are designed and implemented, and simple functions and performance tests are performed on the system. This study on the identification and funding management system of poor students in this article can not only provide experience to the staff related to the management of poor students, but also provide help for the identification and funding of poor students in China in the future.
Accelerating Federated Learning via Parallel Servers: A Theoretically Guaranteed Approach

With the growth of participating clients, the centralized parameter server (PS) will seriously limit the scale and efficiency of Federated Learning (FL). A straightforward approach to scale up the FL system is to construct a Parallel FL (PFL) system with multiple parallel PSes. However, it is unclear whether PFL can really accelerate FL or reduce the training time of FL. Even if the answer is yes, it is non-trivial to design a highly efficient parameter average algorithm for a PFL system. In this paper, we propose a completely parallelizable FL algorithm called P-FedAvg under the PFL architecture. P-FedAvg extends the well-known FedAvg algorithm by allowing multiple PSes to cooperate and train a learning model together. In P-FedAvg, each PS is only responsible for a fraction of total clients, but PSes can mix model parameters in a dedicatedly designed way so that the FL model can well converge. Different from heuristic-based algorithms, P-FedAvg is with theoretical guarantees. To be rigorous, we theoretically analyze the convergence rate of P-FedAvg in terms of the number of conducted iterations, the communication cost of each global iteration and the optimal weights for each PS to mix parameters with its neighbors. Based on theoretical analysis, we conduct a case study on five typical overlay topolgoies formed by PSes to further examine the communication efficiency under different topologies, and investigate how the overlay topology affects the convergence rate, communication cost and robustness of a PFL system. Lastly, we perform extensive experiments with real datasets to verify our analysis and demonstrate that P-FedAvg can significantly speed up FL than traditional FedAvg and other competitive baselines. We believe that our work can help to lay a theoretical foundation for building more efficient PFL systems.
Photographic Composition Guide for Photo Acquisition on Augmented Reality Glasses

Capturing meaningful moments by using cameras is a major application of Augmented Reality Glasses (AR Glasses). Taking photos with head-mounted cameras of AR Glasses bring a new photo acquisition experience to users because there are no viewfinders conventional cameras have. Users may experience difficulties to figure out the region the head-mounted camera will capture on AR Glasses. To address this issue, we propose a photographic composition guide for AR Glasses. The proposed method analyzes video streams from the camera and automatically determine the image region that has a high aesthetic quality score. Photos taken from the recommended position result in a better photographic composition. Our method achieved 4.03 in Mean-Opinion-Score (MOS) test, demonstrating that our method corresponds to human's expectation on aesthetic quality of photos.
Computer Training Devices (Simulators): Principles of Operation and Use in Pedagogical Education

Interest in the development and application of computer training devices - simulators (virtual simulators) in various areas of professional training of specialists involved in human-machine interfaces is due to the specifics of professional activities in modern society that include many risks, redundancy of information, unpredictable professional situations, complexity of professional tasks, the necessity to cope with stress, uncertainty, etc. The article examines the principles of operation and practical application of computer simulators (CS) - training simulators, which are imitation (reproduction) of real objects in a virtual environment, with the help of which various skills are acquired. Digital technologies, which are rapidly changing the face of the modern industrial and social environment, are mostly used in various training simulators and process simulators in industrial companies and educational institutions. One of the important factors determining the rapid spread of computer simulators is an extensive application of multimedia technologies, which have made training complexes more accessible. Another important prerequisite for the massive dissemination of these systems is the rethinking of the pedagogical possibilities that simulators provide in comparison with traditional forms of vocational training. By now there have arisen conditions for a more effective and widespread use of simulators in vocational training, due to which training based on the application of simulators increasingly precedes real practice in professional spheres. In pedagogical education the development and use of simulators began much later than in many other branches of industry and science. The article discusses the successful experience of creating and using an original Russian-French digital simulator of pedagogical activity at the Naberezhnye Chelny State Pedagogical University.
Extracting Surrogate Decision Trees from Black-Box Models to Explain the Temporal Importance of Clinical Features in Predicting Kidney Graft Survival

Prognostic modelling using machine learning techniques has been used to predict the risk of kidney graft failure after transplantation. Despite the clinically suitable prediction performance of the models, their decision logic cannot be interpreted by physicians, hindering clinical adoption. eXplainable Artificial Intelligence (XAI) is an emerging research discipline to investigate methods for explaining machine learning models which are regarded as ‘black-box’ models. In this paper, we present a novel XAI approach to study the influence of time on information gain of donor and recipient factors in kidney graft survival prediction. We trained the most accurate models regardless of their transparency level on subsequent non-overlapping temporal cohorts and extracted faithful decision trees from the models as global surrogate explanations. Comparative exploration of the decision trees reveals insightful information about how the information gain of the input features changes over time.
Market Uncertainty and Correlation Between Bitcoin and Ether

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