Reviewerly/exHarmony huggingface dataset:
Blockchain applications in supply chain management: a systematic review of reviews

Purpose A fragmented research field exists on the applications of disruptive technologies like blockchain in supply chain management. Thus, the purpose of this study is to present a systematic review of literature reviews, summarising the applications of blockchain in various kinds and facets of the supply chain to date. Design/methodology/approach The literature search was conducted using Scopus and Web Of Science databases, and reporting was performed using PRISMA guidelines. In total, 43 review papers were identified, and 15 reviews applicable to the study were synthesised. Findings Blockchain technology is still in its infancy; however, it is gaining high utility in supply chain management. The technology is constantly expanding its application base, and it has enormous potential to cut out intermediaries and improve the efficiency of supply chains. Taking a look at the past and the present, the current study explores unexplored research avenues for the future. Research limitations/implications This study provides key insights to academia interested in exploring and advancing the topic. Practical implications This study will benefit practitioners and business managers exercising the potential of such a technology in various industrial contexts. Social implications The outcome of this study has the potential to bring many benefits to society at large. These benefits, if fully realised, could positively impact the society. Originality/value To the best of the authors’ knowledge, this is the first broad systematic review of reviews analysing the information about blockchain applications and implementation in the supply chain. Based on its constructive overview of the review studies published to date, this study contributes to the supply chain management literature by providing a table of reference for future researchers.
Methodological principles of a two-level classification of categories of social upbringing

Introduction . Pedagogy of social education is a new branch of pedagogy, therefore its categorical and terminological apparatus is characterized by unformedness and undevelopedness. The presence of a categorical and terminological apparatus ensures the fundamentality and solidity of science, which requires its development. A correct and scientifically substantiated categorical and terminological apparatus of social education will contribute to the further concretized and objectified search for scientific knowledge about social education. Purpose setting . The theoretical analysis of publications indicates that systematic studies on the terminological content of pedagogy are isolated, and are more related to the historical aspects of the development of pedagogy in the analysis of individual categories/concepts, and not with its current state. It is revealed that the research of pedagogical categories and concepts is actively conducted in new branches of pedagogical science. Systematic studies of the categorical and terminological apparatus of social education are not carried out either in Russia or abroad. The purpose of the article is to search and analyze the classification of categories of social education. Methodology and methods of the study . The study used theoretical research methods: analysis, synthesis, systematization, comparison, and classification method. Results . A two-level classification of the scientific categories of social education has been developed, which includes categories of the first and second order. Conclusion . Categories of the first order created by means of mechanisms for the formation of categories of classification and include a key category, synthesized, integrated and additional categories. Categories of the second order are formed according to the criterion of the basic assignment of categories. Classification of categories of the second order includes the base category, system and process categories.
Contradictory nature of information resources of modern education

Introduction . The article examines the problem of the ambiguous influence of modern information technologies on education. Purpose setting. The authors analyze not only the positive effects of computerization. They also show the problems faced by the subjects of the educational process under the influence of digitalization. Methodology and methods of the study . As the article highlights, uncontrolled information flows prevent the formation of a holistic worldview among students. The dominance of information technologies in education also leads to the transformation of subject-object relations in the process of cognition. The authors investigate the factors that create the basis for structural changes in society. Digital technologies contribute to the activation of the communicative capabilities of representatives of various social groups, but they can also have negative consequences. In the modern world, there is a violation of traditional public relations, which creates conditions for potential conflicts in a certain society. According to the authors, in the information society, similar trends are observed in the relationship between representatives of different generations. Social inequality is also largely exacerbated by the active introduction of the latest information technologies, in particular, in schools. The article proves how the latest information technologies provoke social tension due to the impossibility of providing equal access to all information resources to citizens for all. Results . The role of the teacher is changing significantly, because he often acts as a coordinator. The use of gadgets in the educational process provides an opportunity to get an education at a distance. However, the widespread use of distance technologies leads to a drop in the quality of educational services. This is due to the need to develop new techniques. In addition, the constant updating of information technologies does not allow the teacher to develop a sustainable methodology for teaching his subject. Conclusion . The authors prove that the active use of digital technologies in the information society implies a harmonious combination of traditional and modern information technologies. This fully applies to the modern domestic education system.
Learning data teaching strategies via knowledge tracing

Teaching plays a fundamental role in human learning. Typically, a human teaching strategy involves assessing a student’s knowledge progress for tailoring the teaching materials to enhance the learning progress. A human teacher can achieve this by tracing a student’s knowledge over essential learning concepts in a task. Albeit, such a teaching strategy is not well exploited yet in machine learning as current machine teaching methods tend to directly assess the progress of individual training samples without paying attention to the underlying learning concepts in a learning task. In this paper, we propose a novel method, called Knowledge Augmented Data Teaching (KADT), which can optimize a data teaching strategy for a student model by tracing its knowledge progress over multiple learning concepts in a learning task. Specifically, the KADT method incorporates a knowledge tracing model to dynamically capture the knowledge progress of a student model in terms of latent learning concepts. We further develop an attention-pooling mechanism to distill knowledge representations of a student model with respect to class labels, which enables to develop a data teaching strategy on critical training samples. We have evaluated the performance of the KADT method on four different machine learning tasks, including knowledge tracing, sentiment analysis, movie recommendation, and image classification. The KADT method consistently outperforms the state-of-the-art methods on all these tasks.
CAreFL: Enhancing smart healthcare with Contribution‐Aware Federated Learning

Abstract Artificial intelligence (AI) is a promising technology to transform the healthcare industry. Due to the highly sensitive nature of patient data, federated learning (FL) is often leveraged to build models for smart healthcare applications. Existing deployed FL frameworks cannot address the key issues of varying data quality and heterogeneous data distributions across multiple institutions in this sector. In this paper, we report our experience developing and deploying the Contribution‐Aware Federated Learning (CAreFL) framework for smart healthcare. It provides an efficient and accurate approach to fairly evaluate FL participants’ contributions to model performance without exposing their private data, and improves the FL model training protocol by allowing the best performing intermediate sub‐models to be distributed to participants for FL training. Since its deployment by Yidu Cloud Technology Inc. in March 2021, CAreFL has served eight well‐established medical institutions in China to build healthcare decision support models. It can perform contribution evaluations close to three times faster than the best existing approach and has improved the average accuracy of the resulting models by more than 2% compared to the previous system (which is significant in industrial settings). To the best of our knowledge, it is the first CAreFL successfully deployed in the healthcare industry.
Predicting Cloud Performance Using Real-time VM-level Metrics

The vast range of cloud service offerings can easily overwhelm users and cause them to select ones that are unsuitable for their needs. As such, the literature has a number of proposals to predict application performance based on a history of executing a certain application or benchmark. However, this requires significant cost to pre-run the application on different service levels before identifying the most suitable one. We propose a machine learning model that enables a cloud user to select the optimal cloud service based on real-time execution without the need to do an exhaustive search. We develop and test this model using a popular benchmark suite on Microsoft Azure, a leading cloud provider. The key insight of this work is that fluctuations in rather than the absolute amount of utilization levels of CPU and memory can be strongly indicative of how well an application is executing.
Neural Networks for Learning Control Laws

In this video, we provide an overview of developments in deep reinforcement learning, along with leading algorithms and impressive applications.
Overview of Deep Reinforcement Learning Methods

This video gives an overview of methods for deep reinforcement learning, including deep Q-learning, actor-critic methods, deep policy networks, and policy gradient optimization algorithms.
Research of Standardized Design and Reusable Management of User Story in Agile Testing

The research is based on the background of high efficiency in test requirement acquisition for user stories in agile testing.As an example of the user story of "payment online" for an e-commerce system, this study is devoted to exploring a feasible and standardized design method of a user story for reusable management process, which it has positive advantages of reusability in the Springs of agile testing for various categories of complex testing projects.Furthermore, further work of standardized user stories designing for non-functional requirements, development of a system for reusable process management of user stories, and bugs detection for relationship of user stories are put forward in the future.
Consensus Mechanism and Financial Applications of Blockchain Principles Adoption in Agricultural Value Chain Financing

Blockchain is a new technology based on the interdisciplinary development of cryptography, statistics, economics, and computer science.Its application is a research hotspot under the background of the development of Internet technology.This paper studies the working principle, algorithm and consensus mechanism represented by workload proof of emerging technology blockchain.In addition, this paper expounds the advantages of blockchain technology in promoting and improving agricultural value chain financing.Finally, this paper confirms the positive impact of blockchain and other technologies on rural credit through data.
Secure Forward Aggregation for Vertical Federated Neural Networks

Vertical federated learning (VFL) is attracting much attention because it enables cross-silo data cooperation in a privacy-preserving manner. While most research works in VFL focus on linear and tree models, deep models (e.g., neural networks) are not well studied in VFL. In this work, we focus on SplitNN, a well-known neural network framework in VFL, and identify a trade-off between data security and model performance in SplitNN. Briefly, SplitNN trains the model by exchanging gradients and transformed data. On the one hand, SplitNN suffers from the loss of model performance since multiply parties jointly train the model using transformed data instead of raw data, and a large amount of low-level feature information is discarded. On the other hand, a naive solution of increasing the model performance through aggregating at lower layers in SplitNN (i.e., the data is less transformed and more low-level feature is preserved) makes raw data vulnerable to inference attacks. To mitigate the above trade-off, we propose a new neural network protocol in VFL called Security Forward Aggregation (SFA). It changes the way of aggregating the transformed data and adopts removable masks to protect the raw data. Experiment results show that networks with SFA achieve both data security and high model performance.
LiteHandNet: A Lightweight Hand Pose Estimation Network via Structural Feature Enhancement

This paper presents a real-time lightweight network, LiteHandNet, for 2D hand pose estimation from monocular color images. In recent years, keypoint heatmap representation is dominant in pose estimation due to its high accuracy. Nevertheless, keypoint heatmaps require high-resolution representation to extract accurate spatial features, which commonly means high computational costs, e.g., high delay and tremendous model parameters. Therefore, the existing heatmap-based methods are not suitable for the scenes with computation-limited resources and high real-time requirements. We find that high-resolution representation can obtain more clear structural features of a hand, e.g., contours and key regions, which can provide high-quality spatial features to the keypoint heatmap, thus improving the robustness and accuracy of a model. To fully extract the structural features without introducing unnecessary computational costs, we propose a lightweight module, which consists of two parts: a multi-scale feature block (MSFB) and a spatial channel attention block (SCAB). MSFB can extract structural features from hands using multi-scale information, while SCAB can further screen out high-quality structural features and suppress low-quality features. Comprehensive experimental results verify that our model is state-of-the-art in terms of the tradeoff between accuracy, speed, and parameters.
COMIM-GAN: Improved Text-to-Image Generation via Condition Optimization and Mutual Information Maximization

Language-based image generation is a challenging task. Current studies normally employ conditional generative adversarial network (cGAN) as the model framework and have achieved significant progress. Nonetheless, a close examination of their methods reveals two fundamental issues. First, the discrete linguistic conditions make the training of cGAN extremely difficult and impair the generalization performance of cGAN. Second, the conditional discriminator cannot extract semantically consistent features based on linguistic conditions, which is not conducive to conditional discrimination. To address these issues, we propose a condition optimization and mutual information maximization GAN (COMIM-GAN). To be specific, we design (1) a text condition construction module, which can construct a compact linguistic condition space, and (2) a mutual information loss between images and linguistic conditions to motivate the discriminator to extract more features associated with the linguistic conditions. Extensive experiments on CUB-200 and MS-COCO datasets demonstrate that our method is superior to the existing methods.
Feature Enhancement and Reconstruction for Small Object Detection

Due to the small size and noise interference, small object detection is still a challenging task. The previous work can not effectively reduce noise interference and extract representative features of the small object. Although the upsampling network can alleviate the loss of features by enlarging feature maps, it can not enhance semantics and will introduce more noises. To solve the above problems, we propose CAU (Content-Aware Upsampling) to enhance feature representation and semantics of the small object. Moreover, we propose CSA (Content-Shuffle Attention) to reconstruct robust features and reduce noise interference using feature shuffling and attention. Extensive experiments verify that our proposed method can improve small object detection by 2.2% on the traffic sign dataset TT-100K and 0.8% on the object detection dataset MS COCO compared with the baseline model.
A Fuzzy Decision Support Method of Product Purchase Based on Text Reviews

Electronic commerce platforms provide decision support for their users due to information overload.How to rank products through online reviews is an important research topic.And, hesitancy and fuzziness exist in text reviews.This paper applies fuzzy logic and methods to solve decision support problems.Given this, the proposed method introduces probabilistic intuitionistic hesitant fuzzy numbers (PIHFNs) to characterize text reviews.A PIHFN can reflect multiple positive, neutral and negative information in text reviews.In addition, this paper proposes a distance formula based on PIHFNs, which is used in the proposed method to provide decision support.The proposed method generates suggested products through the technique for order preference by similarity to an ideal solution.The performance of the method in terms of accuracy is measured by the total relative difference metric.The comparisons and experiments are further conducted to illustrate the advantages of the proposed method.Converting the identified positive, neutral, and negative sentiment orientations into PIHFNs is a new idea for processing online reviews.The proposed method can be applied to tackle the decision support problem accurately.
IOWA-Based Dynamic Trusted Assessment Model for System Operational State Behavior

In the era of “digital intelligence”, the security of critical information systems is vitally important. We propose a trusted assessment model to guarantee the trusted of the system operation process, which is a three-dimensional trustworthiness assessment model. The model comprehensively considered entity trustworthiness, access control compliance trustworthiness, and the trusted of atomic behavior. Subsequently, the Induced Ordered Weighted Averaging (IOWA) is introduced to improve the accuracy and flexibility of the weight setting of the different metric values.
Discriminative information learning with Feature Erasing for Video-Based Person Re-Identification

Person re-identification can be a promising non-intrusive way for individual identification in real scenario application, due to the widespread surveillance camera. It has awoke increasing research interests. Comparing with image-based method, video-based person re-identification (ReID) has shown many advantages, due to its abundant movement and temporal information. However, in the real scenario, due to long capture distance, low resolution of the video, similar clothing, etc, different individuals can appear quite similar. This leads to very small inter-class distance as well as false matching, which is now the most challenging issue in person re-identification. To combat with this, a dual-branch network, containing a feature erasing branch and a global branch, is proposed in this paper. Since global feature extraction branch usually pay attention to the obvious but similar regions among different pedestrians, which will ignore the subtle but critical discriminative information among different pedestrian, especial for those with similar appearance, and therefore decline the performance of the re-id network. The feature erasing branch is proposed as a promising complement. It can drive the network to learn more comprehensive and subtle information through-randomly erasing features in different regions of the frame sequences. While the global branch are introduced to acquire global feature as well as supervise the training of the erasing branch. We perform comparative experiments on three representative datasets, MARS, DukeMTMC-VideoReID, and iLIDS-VID datasets, and the experiment results show that our method has good performance.
Blockchain Scalability Technologies

As the underlying implementation technology of the current main-stream digital currency, blockchain can establish a trusted distributed system without relying on third-party trusted institutions or a privacy-protect system. The decentralized characteristics of blockchain have broad application scenarios, such as the Internet of Things, financial technology and other fields. However, the scalability of the current blockchain is seriously insufficient, such as limited throughput in performance, small storage capacity, and difficulty in scaling functions. This paper introduces the definition and technical classification of scalability, analyzes the current problems of scalability and briefly introduces the current main-stream expandable technologies such as sharding, on-chain and off-chain storage, off-chain payment channel and cross-chain technology from two aspects of performance and function, as well as the principles and ideas of these technologies. Finally, the research progress of current blockchain extension technology is summarized, and the problems faced by the current extension scheme are pointed out, which provides a direction for future research work.
Face Mask Detection Using Multi-Task Cascaded Convolutional Neural Networks

Detecting faces is a prevalent and substantial technology in current ages. It became interesting with the use of diverse masks and facial variations. The proposed method concentrates on detecting the facial regions in the digital images from real world which contains noisy, occluded faces and finally classification of images. Multi-task cascaded convolutional neural network (MTCNN)—a hybrid model with deep learning and machine learning to facial region detection is proposed. MTCNN has been applied on face detection dataset with mask and without mask images to perform real-time face detection and to build a face mask detector with OpenCV, convolutional neural networks, TensorFlow and Keras. The proposed system can be used as an application in the recent COVID-19 pandemic situations for detecting a person wears mask or not in controlling the spread of COVID-19.
The First International Workshop on Cryptographic Security and Information Hiding Technology for IoT System (CSIHTIS 2022): Preface

This Special Collection aims at seeking original articles with novel perspectives and solutions to address the cryptographic security and information hiding technology for Cloud or Fog-based IoT system. We expect this Special Collection can provide scientists, researchers, and industrial practitioners with a chance to publish original manuscripts that demonstrate and explore current advances in all aspects of security, privacy, trust and covert communication issue for Cloud or Fog computing/architecture IoT system.