Reviewerly/exHarmony huggingface dataset:
HCShare: Blockchain Technology in Healthcare System

On this generous growing world, the medical healthcare system is expediting by driving new modernizations in the healthcare system by providing contemporaneous varieties for an inmate’s management. The enhancement of the medical system is done by collecting enormous preceding data, accommodating those records, scrutinizing of the data, and interchanging them with the different medical system layers. In the headway to enhance the medical system with further preservation of the health records, the Blockchain Technology takes the part in the system. The prospective system likewise supports to transfer or interchange the data of the inmates between different healthcare system, which is in the guaranteed way. HCShare is a confidential, protected, reliable and translucent data interchanging scheme between the different healthcare organizations in a scattered decentralized mechanism. In which some of the cryptographic algorithms are used to ensure the protection of the inmate’s confidential records stored in the ledger and to enhance the data integrity of the records.
Introduction to Stochastic Optimisation

Modern stochastic heuristicHeuristic" \f “A” and meta-heuristicMeta-heuristic" \f “A” optimisation methods are efficient tools to deal with the “black-boxBlack-box" \f “A”” problems in which the objective and constraint functions cannot be expressed as explicit functions of decision variables. This chapter presents a brief introduction to available conventional and stochastic optimisation approaches in the literature and discusses their applicability in dealing with real-world problems in science and technology. The chapter also provides a taxonomy of meta-heuristics based on their sources of inspiration.
A Vehicle Classification Model Based on Multi-scale Feature Fusion

In the field of intelligent transportation, the classification of vehicle types plays a very important role. This paper presents a model of SPP-RepVGG to overcome the problems of long training time and low classification accuracy of existing vehicle types classification model. Firstly, the RepVGG is employed in extracting effective feature data of the vehicle image. Then, the Spatial Pyramid Pooling (SPP) is connected between the convolution layer and the fully connected layer of the model, and the multi-scale features of the image are extracted through three different sizes of pooling windows, thus feature extraction and fusion of the vehicle image are realized. Finally, the classification results are obtained using the fully connected layer. The Stochastic Gradient Descent (SGD) method with Gradient Centralization (GC) is used to accelerate the network training. Four classification models are applied to the BIT-Vehicle data sets for comparative experiments. The experimental results demonstrate that the training time of SPP-RepVGG model is shortened, and the accuracy on the test sets is 96.653%. Compared with the VGG-16, ResNet-18 and RepVGG-A0 models, the Top-1 accuracy of the proposed model is increased by 3.034%, 2.197% and 1.988% respectively, this illustrates that our proposed model can extract vehicle image features more effectively, which can provide help for intelligent traffic management.
Efficient Transmission based on Genetic Evolutionary Algorithm

Today's society has grappled with the age of big data. Widespread use of informatization technology has promoted the development of artificial intelligence and communication technologies, which can play an important role in communication networks. In this paper, an energy-saving mechanism based on genetic algorithm in wireless sensor network (WSN) is proposed. The basic working principle and main characteristics of genetic algorithm (GA) are summarized, and the theory, technology and existing problems of GA are discussed. Through the analysis of the transmission efficiency of GA, a new genetic evolutionary algorithm combined with the characteristics of ant colony algorithm (ACO) is proposed. Through the simulation of the transmission performance of genetic optimization algorithm, the comparison of transmission energy consumption between GA and evolutionary algorithm is analyzed, and the evolutionary algorithm with higher transmission performance is obtained. Results showed that the proposed hybrid genetic algorithm with ant colony optimization (GACO) delivers 78.70% and 73.51% lower number of transmission failures than GA and ACO respectively.
iWarded: A Versatile Generator to Benchmark Warded Datalog+/– Reasoning

Warded Datalog+/– is a powerful member of the Datalog+/– family, which extends the logic language Datalog with existential quantification and provides full support for recursion. Such expressive power, paired with a promising trade-off with the offered data complexity, was the catalyst for the recent rise of the language as a relevant candidate for knowledge graph traversal and ontological reasoning applications. Despite the growing research and industrial interest towards Warded Datalog+/–, we observe a substantial lack of specific tools able to generate non-trivial settings and benchmark scenarios, essential to evaluate, analyze and compare reasoning systems over such tasks. In this paper, we aim at filling this gap by introducing iWarded, a versatile generator of Warded Datalog+/– benchmarks. Our system is able to efficiently create very large, complex, and realistic reasoning settings while providing extensive control over the theoretical underpinnings of the language. iWarded was developed and employed in the context of the Vadalog system, a state-of-the-art Warded Datalog+/—based reasoner.
Creating a Collaborative Environment in Primary School Classes

In the current context of educational content renewal, there are increasing demands on the competence of future teachers. Reforms in secondary education also require changes in the vocational education of future teachers. The relevance of improving the professional competencies of future teachers is also conditioned by the integration of national education into the global educational space. In this article the essence of such definition as “collaboration”, some features of organization of collaborative learning environment in the integral pedagogical process of the university are considered. The efficiency of creation of collaborative educational environment for revealing potential possibilities and professional competences of future teachers is revealed. The main emphasis is made on the importance of the facilitator's position in the organization of students’ collaborative activities. The influence of collaborative environment on the success of future teachers is justified.In the age of rapid development of knowledge-intensive technologies and comprehensive expansion of the information space, there is an urgent need to develop the spiritual and moral qualities of the individual, because in the realities of modernity there is a separation and isolation of a person from society, a crisis of spiritual development, the problem of lack of humanity is raised acutely, there is an alienation of young people from national and cultural values. In this connection, the society faces urgent tasks of development and preservation of human capital on the basis of renewal of the content of education, rethinking of its new humanitarian paradigm.
Improving Factual Consistency of Dialogue Summarization with Fact-Augmentation Mechanism

With the vigorous development of dialogue system in natural language processing fields, dialogue summarization has attracted the attention of more scholars, which aims to extract brief introduction and hit points from dialogue information for readers. As the participation of multiple roles and the trans-formation of perspectives in dialogue, one of the most difficult problems, i.e. factual consistency, is raised in the generation of dialogue summaries. It means that the summaries are usually factual wrong although with high matching metric by many methods. Previous methods improve factual consistency between source and target by incorporating knowledge. However, the role of knowledge for dialogue summarization models lacks convincing evidence. In this paper, we propose a Fact-Augmentation (FA) Mechanism based Dialogue Summarization model, called FA-DS model for dialogue summarization. Our FA-DS integrates the fact graph extracted from the dialogue into the summaries generation process, and augment the gain of the factual information through the fact-aware score penalty item. Experiments on the large-scale dialogue dataset SAMSum demonstrate that our fact-augmentation mechanism can improve the quality and factual consistency of dialogue summarization.
Aspect-Dependent Heterogeneous Graph Convolutional Network for Aspect-Level Sentiment Analysis

Aspect-based sentiment analysis aims to identify the sentiment polarity of a certain aspect in the context sentence. It only use the syntax dependency tree to construct graph convolution in previous sentiment analysis methods. But one aspect of sentiment sometimes only be determined by a few words, and relying entirely on the syntax tree may distract the model's attention. Also, due to the limitations of the corpus, the model is only able to learn limited knowledge. To address the above limitations, we propose a sentiment classification model based on aspect-dependent heterogeneous graph convolutional network (named ADHGCN). The model prunes the dependency tree, and then it can reduce the influence of featherweight information on the results. In addition, the model fuses multiple feature relationships between words by constructing a heterogeneous graph, and applies graph convolutional network (GCN) to seek meaningful representations for each node. It makes the model further integrate a variety of information on the original basis, and then it is no longer purely dependent on one relation. At the same time, this paper will introduce a common-sense knowledge base to participate in the construction of heterogeneous graphs, so that the model can learn knowledge beyond the corpus, thereby improving the accuracy of sentiment classification. Through experiments, we have demonstrated that our network performs better than others on five public datasets.
Effect Analysis of Each Reach Zone in Ergonomics During Putting and Pulling Movement of Shelf on VR Experiment

In the imitation of human motion for humanoid robots, the variation of the motion should be satisfied, i.e., the efficiency of the paths and stability of the whole body. In ergonomics, reach zones have been defined to indicate safe and efficient positions of both hands for human movements. In a neutral zone of the reach zone, the humans can move using only their arms without other bodies. Outside of the neutral zone, humans need to use the torso and legs for the far target position of the hands, it is harmful to the chest and the legs. In this research, we believe that the robot can conduct the task outside the neutral zone instead of the human. In this paper, we measure the putting and pulling movement of shelf, which is necessary for a convenience store, to check the effect of each reach zone in a Virtual Reality environment. To verify the effect of each reach zone in the ergonomics, we change the distance between the shelf, which has a box to move, and the torso of the human as a percentage of the length of the human hand. In the evaluation of the posture of the torso and legs, it was found that the participants used their torso and knees more in the distance considered to be the maximum reach zone than in the distance considered to be the neutral zone.
Automatic Graph Generation for Document-Level Relation Extraction

Relation extraction, one of important natural language processing tasks, aims to find out the semantic relations between entities in text. It has been raised to the document level recently, which is a complex task that requires a logical inference to extract relations from multiple entities in intra-and inter-sentence. Existing methods mainly utilize co-references or syntactic trees to establish static document-level graphs. Different from these heuristic models which may not always yield the optimal structures, we propose a novel model that can automatically construct task-specific graphs. We regard the process of establishing a graph as a sequence problem and utilize a gated recurrent unit (GRU) recurrent neural networks (RNNs) to construct a document graph. Afterwards, we utilize the graph convolutional networks (GCNs) to calculate the node representation. Particularly, our model exhibits comparable performance to state-of-the-art models on the large-scale human annotated document-level relation extraction dataset (DocRED).
Trends in the implementation of digital transformation in the educational system

The article examines the problems of the distribution of digital technologies, notes the wide use of information and communication (digital) technologies in education related to changing relations in the field of socio-economic development, the main directions of development of digital educational technologies that help to expand the capabilities of traditional educational models based on the creation of a single digital educational space determined. The need to create an infrastructure, a normative-methodological base, which will contribute to the implementation of priority projects on the digital transformation of education, is substantiated. The main trends of digital transformation in the development of educational processes are highlighted.
Email Validation &amp; Arbitration Framework and Platform based on Blockchain for Legal Matters

Electronic mail (email or e-mail) is a method of exchanging messages between people using electronic devices. Such email communication has become the default mode of messaging for business and personal uses. E-mail is a form of documentary evidence that, like all other types of documentary evidence, may be admitted as an evidence in the court. However, the reliability and authenticity of email as an evidence, like that of other types of evidence, will be scrutinized against tampering. In this paper, we propose a solution that makes possible to accept emails as reliable, trustworthy and authentic documents that can be accepted as evidence in the court of law. We design and detail a third-party email validation arbitration service that guarantees the trust and reliability of emails by offering a way to resolve conflicts.
Investigation of Factorized Optical Flows as Mid-Level Representations

In this paper, we introduce a new concept of incorporating factorized flow maps as mid-level representations, for bridging the perception and the control modules in modular learning based robotic frameworks. To investigate the advantages of factorized flow maps and examine their interplay with the other types of mid-level representations, we further develop a configurable framework, along with four different environments that contain both static and dynamic objects, for analyzing the impacts of factorized optical flow maps on the performance of deep reinforcement learning agents. Based on this framework, we report our experimental results on various scenarios, and offer a set of analyses to justify our hypothesis. Finally, we validate flow factorization in real world scenarios.
A Mathematical Framework and Theory Governing the Verification of Autonomous Systems

Autonomous systems are varied, complex, and designed to accomplish dynamic goals. Consequently, the problem of verifying autonomous systems is difficult to stage and solve when only the performance of a system’s components is known. We define a mathematical framework that relies on category theory to represent capabilities in a way that allows us to usefully combine them into a measure of system performance. We allow for black box capabilities with vaguely defined performance metrics, and define a capability graph which can connect capabilities in an arbitrary structure. Our proposed framework has the flexibility to accommodate behavior patterns and goals for a large variety of current and future systems across disciplines. We discuss the framework, the mechanisms which can be used to verify autonomous systems and how sheaf theory can be utilized to estimate system performance. Notably, we apply key theorems of the framework to identify capability gaps, as well as discuss additional applications of the framework.
Research on Named Entity Recognition in Inorganic Chemistry Based on Bert-BiLSTM-CRF

Aiming at the problems of fuzzy entity boundary and poor recognition effect in named entity recognition in the field of inorganic chemistry, this paper adopts the BERT-BiLSTM-CRF model. First, the BERT pre-training model is used for semantic extraction to generate word vectors representing contextual semantic information, and then the word vectors are input into the BiLSTM model for training to obtain contextual features, and finally, the CRF model is used to obtain a reasonable sequence with the maximum probability. The entity recognition effect of the model used has the highest F1 value of 0.9429 on the test corpus. The experimental results show that the BERT-BiLSTM-CRF model can effectively identify named entities in the field of inorganic chemistry, and the recognition effect is better than other traditional models.
The Admission of Electronic Evidence in Civil and Arbitral Proceedings

In civil and commercial dispute resolution, not all obtained evidence shall be admitted by the courts or arbitral tribunal. The admissibility of evidence is essential to the involved parties to win the case but the evolution of new information technology has brought not only advantages but also challenges to the admission of electronic evidence in litigations, especially when the process of finding electronic evidence has gone far beyond the national boundaries. Following the analysis of admissibility and relevance of evidence in Chap. 4 which also applies to electronic evidence, this chapter only presents brief remarks on the admissibility of electronic evidence and focuses on the admissibility of illegally obtained electronic evidence in civil and arbitral proceedings.
A Scalable Analytical Framework for Complex Event Episode Mining With Various Domains Applications

With the ubiquity of sensor networks and smart devices that continuously collect data, we face the challenge of analyzing the growing stream of data in real time. In recent years, there has been a huge need to gain useful knowledge by incrementally analyzing event sequence data. Although episode pattern mining techniques have existed for years, people have recently become more aware of their practical value in solving real-life domain problems such as manufacturing records, stock markets, and weather forecasts. The effective and efficient application of episode pattern mining techniques to analyze complex event data is becoming increasingly important for solving real-life problems in wide domains. However, few studies have focused on developing a scalable framework based on episode pattern mining of complex event sequences for applications in various domains. In this work, we propose a novel framework named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SAAF (Scalable Analytical Application Framework)</i> based on complex event episode mining techniques, including batch episode mining, delta episode mining, incremental episode mining, and pattern merging, to consider both efficiency and accuracy. Moreover, to enhance scalability, we adopt the lambda architecture with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Apache Spark</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Apache Spark Streaming</i> as the system development framework. Finally, the experimental results on three real datasets of different domains and two benchmark datasets showed that the proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SAAF</i> framework exhibits excellent performance in terms of efficiency, accuracy, and scalability.
Image Deblurring Based on Aggregate Residual Adversary Networks

In order to solve the problems that the existed deblurring algorithms canonlyprocess a single blur type and need long running time, an image deblurring algorithm based on the aggregation residual generation confrontation network is proposed. Firstly, the generation confrontation network is used to generate the reconstructed image discriminant label, so that the final image is closer to the clear image. Secondly, the feature extraction module is combined with the aggregate residual network and the channel attention module to extract the useful feature information of the intermediate layer. Finally, the combination of WGAN’s Wasserstein-1 distance and perceptual loss is used as the loss function to train the model to ensure the consistency of the generated image and the clear image. In the PyTorch environment, the proposed algorithm is tested with the GOPRO dataset and the Kohler dataset, and compared with other algorithms, such as<italic> L</italic><sub>0</sub> norm prior, dark channel prior, specific deblurring, DeepDeblur and DeblurGAN. Experimental results show that when restoring motion blurred images and Gaussian blurred images, the proposed algorithm outperforms other algorithms in terms of evaluating indicator such as PSNR and the running time.
Kinematics-Based Vehicle Trajectory Optimization for Obstacle Avoidance and Goal Satisfaction

The electric bicycle is one of the most important means of transportation in China, and it has a huge number of electric bicycle users. However, there are a lot of deaths caused by electric bicycle traffic accidents every year. In this paper, we design a driver-assisted electric bicycle and motion planning based on the iLQR algorithm that can avoid obstacles. The study provides a solution for the future generation of electric bicycles that can respond to emergencies and reduce accidents to improve travel safety.
Autoencoder for Synthetic to Real Generalization: From Simple to More Complex Scenes

Learning on synthetic data and transferring the resulting properties to their real counterparts is an important challenge for reducing costs and increasing safety in machine learning. In this work, we focus on autoencoder architectures and aim at learning latent space representations that are invariant to inductive biases caused by the domain shift between simulated and real images showing the same scenario. We train on synthetic images only, present approaches to increase generalizability and improve the preservation of the semantics to real datasets of increasing visual complexity. We show that pre-trained feature extractors (e.g. VGG) can be sufficient for generalization on images of lower complexity, but additional improvements are required for visually more complex scenes. To this end, we demonstrate a new sampling technique, which matches semantically important parts of the image, while randomizing the other parts, leads to salient feature extraction and a neglection of unimportant parts. This helps the generalization to real data and we further show that our approach outperforms fine-tuned classification models.