Reviewerly/exHarmony huggingface dataset:
Design of blockchain based smart agriculture framework to ensure safety and security

The country's entire population depends on agriculture, so it is absolutely essential to the economy. Its links and interactions with all of the country's relevant enterprises enable it to benefit from every part of the country. When gauging the social and economic well-being of a country, an agricultural base must be substantial. Agriculture is by far the main source of employment in most countries. It has many components, including supply chain management, crop insurance, and shipping of goods, which requires numerous unverified parties, such as farmers, retailers, customers, and wholesalers (assessor, surveyors). A case could be made that due to the presence of a middle man (i.e. human beings or human-operated agencies) crucial issues such as accessibility, expediency, security, and immutability have been raised. As a result, financial loss, crop contamination, and waste have occurred. Blockchain, the technology behind the digital currency bitcoin, is a decentralized shareholding ledger that collects data from multiple parties involved in the Bitcoin network transactions. For this reason, the Bitcoin network relies on the Blockchain to keep track of transaction history and other transaction-related information, such as the transaction's completion, the addresses of the sender(s) and the recipient(s), and information about whether the transaction was successful. Donors can be confident that the money will be given to only one organization. To keep information private, every piece of data is encrypted on the Blockchain. This article describes a blockchain-based agriculture infrastructure in which data regarding farmers and farming is safely and securely recorded.
Direct construction of an optical linear transform and its application on optical complex data generation

Optical computing technique has emerged as a promising platform for highly parallel data processing. In most optical computing architectures, optical linear transform is the basic composition, which is commonly designed by the established deep learning-based methods or general-purpose optimizers. There still lacks in-depth study to construct a solver targeted at optical linear transform applications. In this work, we propose a new algorithm that solves the transformation matrix of a linear optical system consisting of cascaded phase masks directly and show that its efficiency is significantly higher than those common solvers. As a direct application of this method, we can create target array of optical beams even with a single layer of phase mask in the experiment. The amplitude and phase of each beam in the array can be controlled independently without affecting each other. The optical system requires only one light source and one programmable phase mask. This setup can be readily incorporated into most current optical computing configurations. Our method may find broad applications in classic and quantum optical information processing.
Experiences on the School Work Performance of Fisheries High School Teachers

The purpose of this study was to find out the experiences of the teachers of fisheries high schools and to clarify their meanings. In-depth interviews were conducted with 6 fisheries teachers. As a result, teachers were experiencing difficulties in class matters, student guidance, administrative work, and job roles. Since the teacher
A Blockchain-Based Approach to Detect Counterfeit Drugs in Medical Supply Chain

The production and circulation of counterfeit drugs in the supply chain are an earnest and progressively vital issue. However, the existing supply chain management system fails to guarantee genuine medicines to the patient. To ensure authentic medicine and to mitigate supply chain issues, we propose a blockchain-based approach to ensure authentic drugs for the patients. The blockchain-based distributed system will empower all the stakeholders, including patients, to know and trace the authenticity of the medicine. To make the process faster, we have adopted the Hyperledger Fabric platform to develop peer-to-peer distributed applications for drugs supply chain. Besides, smart contracts make the supply chain management system automated, more robust, and transparent to detect counterfeit drugs so that patients can get original products produced by a legitimate manufacturer. The experimental analysis demonstrates that the proposed system runs smoothly on a Hyperleadger Fabric platform, and each transaction can handle efficiently with the distributed smart contracts.
Small facial image dataset augmentation using conditional GANs based on incomplete edge feature input

Image data collection and labelling is costly or difficult in many real applications. Generating diverse and controllable images using conditional generative adversarial networks (GANs) for data augmentation from a small dataset is promising but challenging as deep convolutional neural networks need a large training dataset to achieve reasonable performance in general. However, unlabeled and incomplete features ( e.g., unintegral edges, simplified lines, hand-drawn sketches, discontinuous geometry shapes, etc.) can be conveniently obtained through pre-processing the training images and can be used for image data augmentation. This paper proposes a conditional GAN framework for facial image augmentation using a very small training dataset and incomplete or modified edge features as conditional input for diversity. The proposed method defines a new domain or space for refining interim images to prevent overfitting caused by using a very small training dataset and enhance the tolerance of distortions caused by incomplete edge features, which effectively improves the quality of facial image augmentation with diversity. Experimental results have shown that the proposed method can generate high-quality images of good diversity when the GANs are trained using very sparse edges and a small number of training samples. Compared to the state-of-the-art edge-to-image translation methods that directly convert sparse edges to images, when using a small training dataset, the proposed conditional GAN framework can generate facial images with desirable diversity and acceptable distortions for dataset augmentation and significantly outperform the existing methods in terms of the quality of synthesised images, evaluated by Fréchet Inception Distance (FID) and Kernel Inception Distance (KID) scores.
Study on the law of aging in Library and Information Science

Purpose Aging research has traditionally been an important research topic in the field of Library and Information Science(LIS). The study of aging enables us to grasp the extent of development and the status of aging in LIS. The purpose of this paper is to explore the current law of aging in LIS and to research the impact of interdisciplinary citations on the aging of the discipline. Design/methodology/approach By using citation analysis methods and fitting them using the Barnett aging model, the aging law of LIS is explored with the help of aging indicators such as citing half-life and Price Index. For interdisciplinary study, the authors explore the pattern of interdisciplinary citations distribution by distinguishing LIS and non-LIS citations by journal name. Findings The results show that LIS is currently aging slowly and has reached a relatively mature stage. It has a high reliance on archival literature. The interdisciplinary citations distribution is broadly consistent with the overall citation distribution, and interdisciplinary citations can increase the age of applicability of the literature. Originality/value Based on LIS journal citation data, the paper validates the rationality of Barnett model applied in the field of literature aging research using nonlinear regression analysis, which can effectively reflect the aging law of literature and enable scholars to predict its development trend more accurately. In addition, according to the current trend of interdisciplinary citation, this paper explores the impact of interdisciplinary citations on the aging of the literature and provides a new idea for future aging research.
YouTube based religious hate speech and extremism detection dataset with machine learning baselines

On YouTube, billions of videos are watched online and millions of short messages are posted each day. YouTube along with other social networking sites are used by individuals and extremist groups for spreading hatred among users. In this paper, we consider religion as the most targeted domain for spreading hate speech among people of different religions. We present a methodology for the detection of religion-based hate videos on YouTube. Messages posted on YouTube videos generally express the opinions of users’ related to that video. We provide a novel dataset for religious hate speech detection on Youtube comments. The proposed methodology applies data mining techniques on extracted comments from religious videos in order to filter religion-oriented messages and detect those videos which are used for spreading hate. The supervised learning algorithms: Support Vector Machine (SVM), Logistic Regression (LR), and k-Nearest Neighbor (k-NN) are used for baseline results.
A Design of Serverless Computing Service for Edge Clouds

The method of Serverless Computing at the Edge is drawing attention because of the efficiency of using resources. This paper presents a design of Serverless Computing for Edge Clouds, based on an open-source project. One of the methods of Serverless at the Edge is that centralized architecture with a Serverless Platform in the control plane cluster. However, in a centralized architecture, if the resources or the status of a particular Edge is not good, the control plane cluster must detect anomalies and redeploy the Functions considering the environment of other Edges. It will take time to select and deploy to the appropriate Edge. The proposed architecture introduces cross-monitoring which imports monitoring metrics directly from a nearby Edge. Grouping Edge sites as a Zone and cross-monitoring each other in the Zone. So that, Serverless computing-based Functions can operate quickly when adjacent Edges are in poor condition. As a result, the neighboring Edges of the Zone can back up with each other.
Sparse and Low-Rank Matrix Quantile Estimation With Application to Quadratic Regression

This paper studies matrix quantile regression where the covariate is a matrix and the response is a scalar.Statistical estimation of matrix regression is an active field of research.However, quantile regression with matrix covariates is rarely studied.We propose an estimation procedure based on convex regularizations in the high-dimensional setting.In order to reduce the dimensionality, the coefficient matrix is assumed to be low-rank and/or sparse.Thus we impose two regularizers to encourage the different low-dimensional structures.The asymptotic properties and implementation based on incremental proximal gradient algorithm are developed.We then apply the proposed estimator to quadratic quantile regression.The advantages of the proposed method in its applications to quadratic regression are also illustrated by simulations and real data analysis.
A Novel Convolutional Neural Network Architecture of Deep Joint Source-Channel Coding for Wireless Image Transmission

We propose a convolutional neural network (CNN) architecture with lower reconstruction distortion and stronger noise adaptability for deep joint source-channel coding for wireless image transmission. Instead of inserting complicated modules to the network, we design the inception modules and change the data flow via skip connection. And we expand the branches of the network using mask and attention mechanism. Experimental results demonstrate that the proposed network achieves lower distortion in additive white Gaussian noise (AWGN) channel. And noise adaptability of the proposed network is better in channel of varying signal-to-noise ratio (SNR).
Philosophical and Psychological Approach to Self-Development of Scientific and Pedagogical Workers

The study?s main purpose is to highlight the philosophical and psychological aspects of the self- development of scientific and pedagogical workers in the context of the development of the education sys- tem. The article uses various methods to conduct research, namely general scientific methods: formal- logical, systemic, structural-functional, concrete-historical. The pedagogical synergetics is considered as part of the research. Pedagogical synergetics, which explains the development of a complex system, edu- cation, is a new philosophy of the educational process. Pedagogical synergetics makes it possible to ap- proach the development of problems of the development of pedagogical systems and the pedagogical pro- cess in a new way, considering them primarily from the standpoint of openness, co-creation and orienta- tion towards self-development. As a result, philosophical, psychological and pedagogical approaches to self-improvement are characterized.
Simplifying Dynamic Public Key Certificate Graph for Certification Path Building in Distributed Public Key Infrastructure

Blockchain technology brings the decentralization of a single point of failure to a Public Key Infrastructure (PKI). We presented Meta-PKI that establishes the trust relationships among certificate authorities (CAs) for a decentralized PKI. In a decentralized PKI where trust relationships among CAs change dynamically, it is challenging to discover the certification path between two CAs because a public key certificate graph can be complex and huge. Here we propose certification path building on Meta-PKI by partition-based approach. The proposed method simplifies the public key certificate graph by constructing partitions based on the trustworthiness of certificates. It is experimentally shown that the simplified graph reduces the exponential growth of the processing time for certificate path building.
A dual evaluation multi‐scale template matching algorithm based on wavelet transform

It is difficult to achieve the requirements simultaneously of real-time operation and accuracy when the template matching method encounters the problems of rotation and scale invariance. This letter proposes a dual-evaluation multiscale template-matching algorithm based on wavelet transform. First, the image grids generated from a strengthened edge image based on the wavelet transform are defined to reduce the region for detecting feature points. Then, an evaluation strategy based on gradient direction entropy is proposed to evaluate the local pixel for detecting local candidate points that contain rich information. Another evaluation strategy, based on the dominant gradient direction and the uniform LBP model, has been proposed to extract rotation-invariant feature pixels. To conduct fast and robust matching, the proposed strategies extract fewer feature points with rich local information. The experimental results demonstrate that the proposed method is robust to rotation and scale invariance and achieves real-time accuracy.
Low-loss high-impedance circuit for quantum transduction between optical and microwave photons

Abstract Quantum transducers between microwave and optical photons are essential for long-distance quantum networks based on superconducting qubits. An optically active self-assembled quantum dot molecule (QDM) is an attractive platform for the implementation of a quantum transducer because an exciton in a QDM can be efficiently coupled to both optical and microwave fields at the single-photon level. Recently, the transduction between microwave and optical photons has been demonstrated with a QDM integrated with a superconducting resonator. In this paper, we present a design of a QD-high impedance resonator device with a low microwave loss and an expected large single-microwave photon coupling strength of 100s of MHz. We integrate self-assembled QDs onto a high-impedance superconducting resonator using a transfer printing technique and demonstrate a low-microwave loss rate of 1.8 MHz and gate tunability of the QDs. The corresponding microwave photon decay time of 88 ns is longer than the time necessary for the optical-microwave transduction process as well as the transmon-resonator swap operation time. This feature will facilitate efficient quantum transduction between an optical and microwave qubit.
MDD-Eval: Self-Training on Augmented Data for Multi-Domain Dialogue Evaluation

Chatbots are designed to carry out human-like conversations across different domains, such as general chit-chat, knowledge exchange, and persona-grounded conversations. To measure the quality of such conversational agents, a dialogue evaluator is expected to conduct assessment across domains as well. However, most of the state-of-the-art automatic dialogue evaluation metrics (ADMs) are not designed for multi-domain evaluation. We are motivated to design a general and robust framework, MDD-Eval, to address the problem. Specifically, we first train a teacher evaluator with human-annotated data to acquire a rating skill to tell good dialogue responses from bad ones in a particular domain and then, adopt a self-training strategy to train a new evaluator with teacher-annotated multi-domain data, that helps the new evaluator to generalize across multiple domains. MDD-Eval is extensively assessed on six dialogue evaluation benchmarks. Empirical results show that the MDD-Eval framework achieves a strong performance with an absolute improvement of 7% over the state-of-the-art ADMs in terms of mean Spearman correlation scores across all the evaluation benchmarks.
Distributed Redundant Integration of Data Center Battery Storage with the Grid for Regulation Services

Data centers are one of the fastest growing loads in the electric grid. Since all data centers use energy storage as backup and fail-over to onsite generation, the growth in data center load is accompanied by a growth in storage capacity. This paper studies how these storages can be integrated with the grid to provide frequency services. We consider the distributed redundant architecture in data centers, where the computing loads are served even when single component fails. We provide an optimization framework for maximizing the economic benefit from using the storage subject to these redundancy constraints. We show the problem can be solved efficiently to allocate resources from multiple batteries inside a data center.
Computer Simulation of Three-Dimensional Gas Filtration Process in Porous Media

The paper deals with development of multidimensional model of gas filtration in porous media and numerical algorithm for solving the initial-boundary value problem. The model consists of nonlinear differential equation in partial derivatives with the appropriate initial and boundary conditions. The model makes it possible to evaluate impact of main factors affecting the filtration process such as permeability, porosity, thickness of layers, gas recovery coefficient, viscosity etc. That gives us opportunity to solve practical problems of forecasting the process of gas production as well as making decisions on exploitation of gas deposits.
Wearable Fall Detection for Elderly with Simple Belt Design Using Android Application

Falling is a human movement that can cause injury, either minor injury or serious injury can even cause someone’s death. Age is one of the causes of a person’s decline in movement. When elderly people experience falls, they experience not only physical but also psychological disorders such as loss of self-esteem and feelings of fear of walking to avoid the danger of falling. if emergency treatment arrives too late, a fall injury can result in disability, paralysis, and even death. The objectives are to develop tools and applications that can detect the fall of a person. The Method uses wearable fall detection with a simple belt design that implements a threshold-based detection algorithm that uses accelerometer and gyroscope sensors and can also be monitored through an application installed on an Android smartphone. The Results of the data that has been tested can be obtained with an accuracy value of 85%, a sensitivity value of 77%, and a specificity value of 100%. This device is very promising and being developed at the Sukabumi nursing home. The injuries suffered by falling victims will be slightly minimized because when someone falls it will be detected by the surrounding family so that the victim will be evacuated immediately.
Efficient Industrial Solution for Robotic Task Sequencing Problem With Mutual Collision Avoidance &amp; Cycle Time Optimization

In the automotive industry, several robots are required to simultaneously carry out welding sequences on the same vehicle. Coordinating and assigning welding points between robots is a manual and difficult phase that needs to be optimized using automatic tools. The cycle time of the cell strongly depends on different robotic factors such as the task allocation among the robots, the configuration solutions and obstacle avoidance. Moreover, a key aspect, often neglected in the state of the art, is to define a strategy to solve the robotic task sequencing with an effective robot-robot collision avoidance integration. In this paper, we present an efficient iterative algorithm that generates a high-quality solution for Multi-Robotic Task Sequencing Problem. This latter manages not only the mentioned robotic factors but also aspects related to accessibility constraints and mutual collision avoidance. In addition, a home-developed planner ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RoboTSPlanner</i> ) handling 6 axis has been validated in a real case scenario. In order to ensure the completeness of the proposed methodology, we perform an optimization in the task, configuration and coordination space in a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">synergistic</i> way. Comparing to the existing approaches, both simulation and real experiments reveal positive results in terms of cycle time and show the ability of this method to be interfaced with both industrial simulation software and ROS-I tools.
Application of virtual reality technology in the treatment of anxiety disorder

Virtual reality technology is a comprehensive technology based on computer technology. It comprehensively uses computer three-dimensional graphics technology, simulation technology, sensing technology and display technology to generate a realistic three-dimensional visual, auditory, tactile and olfactory world. Its goal is to form a virtual environment with a high sense of immersion. With the help of computer programming, virtual reality technology can make virtual scenes and stimuli needed for the treatment of anxiety disorders, which is immersive, interactive and imaginative. It can break through the limitations of traditional anxiety disorder therapy, and make the treatment process have good operability. This paper reviews the application, equipment requirements and mechanism of virtual reality technology in the treatment of anxiety disorderss. Compared with traditional therapy, it has unique advantages, but there are also some limitations. Its future development should consider technological innovation and standardization of treatment schemes.