Reviewerly/exHarmony huggingface dataset:
Trusted Data Interaction Technology Based on Alliance Chain

The current IP architecture is faced with scalability, security and dynamic problems, while the current DNS also has the problems of centralized management and server security. The defects of IP system make it unable to adapt to the current rapid development of business. In the current solution, the Content Centric Network (CCN) in the Information Centric Network (ICN) is considered to be able to better meet the needs of users. It takes the content as the center for data transmission, and uses the data itself to name, which completely changes the original network transmission system. Blockchain is a distributed platform in peer-to-peer network environment, in which multiple nodes participate and reach a consensus through a specific consistency algorithm. It has the characteristics of unforgeability, tamper proof and traceability, and carries trusted data and computing services. Alliance chain is a kind of blockchain, the characteristics of the alliance's blockchain, such as decentralization, non tampering, traceability, access mechanism, and information disclosure within the network, point out the direction for the realization of a new domain name system of co management and co governance.This paper gives full play to the trustworthiness and non tampering characteristics of blockchain technology, integrates the naming and addressing scheme based on alliance chain into Name Data Network(NDN) architecture, and proposes an open system of independent naming and marking and exchanging data content index in peer-to-peer network environment. This system uses OpenID to identify any data content object, and combines with cryptography technology to realize the trusted transmission of data in peer-to-peer network. Experiments show that the system designed in this paper has the functions of data index registration, update, cancellation and query, the efficiency is basically the same as the existing domain name system, and has stronger security.
Elimination Mechanism-based PSO Algorithm for Cloud Workflow Scheduling with Budget Constraints

As a new computing paradigm for hosting and executing large-scale scientific applications, cloud computing can provide users with scalable and elastic computational resources. Workflow scheduling is NP-complete and becomes even more challenging under the dynamic environment of the cloud since certain quality of service (QoS) needs to be met in most cases. In this paper, a meta-heuristic scheduling algorithm, named EMPSO (Elimination Mechanism-based PSO), is proposed to minimize the makespan while satisfying the budget constraint. Firstly, a new technique for choosing the global best solution is developed to avoid the premature convergence caused by the original totally greedy selection method, which improves the global search performance of our algorithm. Then, for accelerating the convergence speed towards the global optima, time-varying parameters are introduced to keep a good balance between global search and convergence by updating particles’ velocity adaptively during different optimizing stages. Finally, an elimination mechanism is embedded to further improve the overall performance by promoting the quality of inferior particles. Extensive experiments are conducted on well-known scientific workflows of different sizes and types. The experimental results demonstrate that EMPSO outperforms GA and PSO with respect to meeting the budget constraint and minimizing the makespan.
Segmentation and Automatic Identification of Vasculature in Coronary Angiograms

Coronary angiography is the "gold standard" for the diagnosis of coronary heart disease, of which vessel segmentation and identification technologies are paid much attention to. However, because of the characteristics of coronary angiograms, such as the complex and variable morphology of coronary artery structure and the noise caused by various factors, there are many difficulties in these studies. To conquer these problems, we design a preprocessing scheme including block-matching and 3D filtering, unsharp masking, contrast-limited adaptive histogram equalization, and multiscale image enhancement to improve the quality of the image and enhance the vascular structure. To achieve vessel segmentation, we use the C-V model to extract the vascular contour. Finally, we propose an improved adaptive tracking algorithm to realize automatic identification of the vascular skeleton. According to our experiments, the vascular structures can be successfully highlighted and the background is restrained by the preprocessing scheme, the continuous contour of the vessel is extracted accurately by the C-V model, and it is verified that the proposed tracking method has higher accuracy and stronger robustness compared with the existing adaptive tracking method.
A New Initialization Method for Neural Networks with Weight Sharing

A proper initialization of parameters in a neural network can facilitate its training. The Xavier initialization introduced by Glorot and Bengio which is later generalized to Kaiming initialization by He, Zhang, Ren and Sun are now widely used. However, from experiments we find that networks with heavy weight sharing are difficulty to train even with the Xavier or the Kaiming initialization. We also notice that a certain simple network can be decomposed in two ways, where one is difficult to train while the other is easy to train, when both are properly initialized by the Xavier or the Kaiming initialization. In this paper we will propose a new initialization method which will increase training speed and training stability of neural networks with heavy weight sharing. We will also propose a simple yet efficient method to adjust learning rates layer by layer which is indispensable to our initialization.
Vulnerabilities and Open Issues of Smart Contracts: A Systematic Mapping

Smart Contracts (SCs) are programs stored in a Blockchain to ensure agreements between two or more parties. The combination of factors like the newness of the field, with less than a decade of practical use, and a high and growing popularity in industry, leads to an increase in the possibility of severe security issues occurrence. Even though, systems based on Blockchain and SCs technologies inherit several benefits, like a tamper-proof decentralized ledger and anonymous transactions. This way, even with security issues, the trend is that its popularity will increase. Due the unchangeable essence of Blockchain, failures or errors in SCs become perpetual once published, being critical to mitigate them, since the involvement of huge economic assets are common. To handle this, the academic and industrial communities have been expanding their efforts in this field, with a growth in research publications similar to exponential. Aiming at reasoning about the current state-of-art in vulnerabilities and open issues over Blockchain SCs, we conducted a systematic literature mapping over 32 primary and 18 secondary selected articles. As contribution, this work discusses and relates the selected papers, identifying gaps that may lead to research topics for future work.
Research on unstructured terrain semantic recognition of outdoor mobile robot

Whether the mobile robot can accurately recognize the terrain features will affect the motion control of the mobile robot. Since the outdoor unstructured terrains have not obvious boundary information, it is necessary to segment the terrain boundary. The paper builds the outdoor unstructured terrain dataset, then chooses the real-time semantic segmentation network-EDAN et to train and test the effort of the unstructured terrain segmentation. The experimental results show that the EDA et can achieve a better balance between segmentation accuracy and segmentation speed for unstrnctured terrain recognition.
An Edge-Fencing Strategy for Optimizing SSSP Computations on Large-Scale Graphs

The Single-Source Shortest Path (SSSP) problem is to compute the shortest distances in a weighted graph from a source vertex to every other vertex. This paper focuses on parallel efficiency and scalability of SSSP computations on large-scale graphs. We propose an edge-fencing strategy to customize a SSSP algorithm's schedule for every SSSP computation, and devise a path-centric SSSP algorithm with this strategy. This strategy aims at reducing both the relaxed edges and relaxations repeated on each edge. It exploits a few fence values to select the relaxed edges and schedule edge relaxations according to lengths of the created paths. The path-centric algorithm works on a hierarchical graph model, and exploits the edge-fencing strategy to schedule edge relaxations in parallel settings. The hierarchical graph model quantifies the length distribution of shortest paths in large-scale graphs, provides appropriate fence values for every SSSP computation. The algorithm was evaluated on a wide range of synthetic graphs and real-world graphs. The experimental results suggest that our algorithm is efficient and scalable for graphs with skewed degree distributions, and its performance is relatively insensitive to the hierarchical graph model's accuracy.
Quantum-behaved PSO-based Lyapunov control of closed quantum systems

A Lyapunov-based control scheme is presented to drive closed quantum systems into any target eigenstate with as high population as possible by the quantum-behaved particle swarm optimization (PSO) algorithm. Based on a Lyapunov function with a Hermitian operator to be constructed, a control law with the unknown parameters contained in the Hermitian operator is designed. To achieve high-population state transfer to the target state, we first initialize those unknown parameters by choosing a path to the target state in its energy-level connectivity graph and setting their values along the path. Then, a set of optimal parameters is found by the quantum-behaved PSO algorithm. Finally, numerical simulation experiments are performed on a five-level quantum system and a four-qubit system to demonstrate the effectiveness of the control scheme in this paper.
An Approach to Safety Analysis of Train Control Systems with Coloured Petri Nets

In this paper, we propose a model-based approach to qualitative safety analysis. With this approach, paths that lead to hazardous events or accidents can be automatically identified. Moreover, further quantitative analysis is possible on the basis of the established system model. The approach is implemented by the formal modelling language of Coloured Petri Nets and illustrated by analysing the safety of the CTCS-3. The analysis result shows the feasibility of the approach.
Integration of Artificial Intelligence and Higher Education in the Internet Era

With the continuous development of science and technology and the wide application of AI technology, AI system based on computer platform is gradually introduced into the field of education. In order to strengthen the integration of AI and higher education, the integration process is more rapid and innovative, the application of AI system is very necessary. This paper summarizes the concept of AI, puts forward the prediction model of AI, and discusses the application of AI in teaching. At the same time, it gives an overview of higher education and puts forward the management decision of higher education. The results show that: the number of papers published is on the rise, with 657 papers published in 2020, which is consistent with the rapid growth trend of AI technology after 2018, which also urges more and more scholars to explore the integration of AI technology and education.
FOB-Net: A Semantic Segmentation Method Focusing on Boundary Information

In this paper, we will introduce an original neural network FOB-Net for semantic segmentation tasks. This network's innovation is that it pays more attention to the boundary information and gets the supervised information directly. After our extensive understanding and research about the several used networks in the semantic segmentation application scenario. We analyzed the various accepted network structures. In the end, we revise the existing network to get the FOB-Net.
On the configuration of multi-objective evolutionary algorithms for PLA design optimization

Search-based algorithms have been successfully applied in the Product Line Architecture (PLA) optimization using the seminal approach called Multi-Objective Approach for Product-Line Architecture Design (MOA4PLA). This approach produces a set of alternative PLA designs intending to improve the different factors being optimized. Currently, the MOA4PLA uses the NSGA-II algorithm, a multi-objective evolutionary algorithm (MOEA) that can optimize several architectural properties simultaneously. Despite the promising results, studying the best values for the algorithm parameters is essential to obtain even better results. This is also crucial to ease the adoption of MOA4PLA by newcomers or non-expert companies willing to start using search-based software engineering to PLA design. Three crossover operators for the PLA design optimization were proposed recently. However, reference values for parameters have not been defined for PLA design optimization using crossover operators. In this context, the objective of this work is conducting an experimental study to discover which are the most effective crossover operators and the best values to configure the MOEA parameters, such as population size, number of generations, and mutation and crossover rates. A quantitative analysis based on quality indicators and statistical tests was performed using four PLA designs to determine the most suitable parameter values to the search-based algorithm. Empirical results pointed out the best combination of crossover operators and the most suitable values to configure MOA4PLA.
Quantitative Modalities, Possible Applications to Information Analysis

The paper formalizes quantitative (numerical) modalities and describes their possible applications. We work via standard Kripke-like semantics for modal logics and introduce quantitative modalities as possibility to have predefined amount of different states (worlds) where formulas may be valid. Approach is applied to various modal semantics with distinct requirements to the accessibility relations (as e.g. to be reflexive, transitive). We illustrate possible applications in internet search, trading, banking transfers and another interference in internet network or another communication in information channels. Basic attention (as to mathematical subject) is directed to basic algorithmic problems - satisfiability and decidability problems, we find resolving algorithms. That is done for modal logic with quantitative modalities and for temporal logics generated by these quantitative modal logics by consideration temporal operations as bi-modalities. We conclude the paper with pointing open problems and possible further directions in research.
Research on online signature verification based on anomaly detection

Abstract With the advancement of modern information technology, personal information security has become the focus of social attention. As a biological behavior feature, online signatures have been trained for a long time, have very personal characteristics, and are collected as a real-time sequence. In view of this characteristic, we can know that it is difficult for forgers to imitate the intrinsic and essential characteristics of the signer. In addition, a signature is a rapid writing process driven by the central nervous system and personal writing habits. Even the signer himself may write an unstable and similar forgery signature, so it is necessary to analyze the variability and stability of signatures. Therefore, this paper proposes a method of using the variance and characteristics of local features to find a stable segment of the user’s signature for authentication. In addition, we also adopt a two-stage authentication strategy that combines the above method with the traditional anomaly detection algorithm. An experiment was conducted on the open-access online signature database MCYT, and an EER of 2.63% was obtained. The experiment proved the effectiveness and robustness of the method.
Light and fast: multiple object tracking based on light-weight architecture

In recent years, there has been a great progress on object-tracking task. Most of them are based on Joint Detection and Embedding (JDE) benchmark, which accomplish detection and Re-ID task in a single module and thus could reduce the time cost and help to gain a higher processing FPS. However, large computation requirement of existing JDE-based method, which usually demand several expensive GPU devices, is still an obstacle to wide application for industry. In this paper, we propose a new lightweight structure named ShuffleXnet, and further build a simple module named Pyramid-ShuffleXnet (PSXnet) for Multiple-Object Tracking (MOT) task. The motivation of this work is to reduce the amount of calculation and make the network easier to be employed for online and real-time applications. Experimental results show that our method could achieve nearly 28% higher FPS than FairMOT with just 6.7% less by Multi-Object Tracking Accuracy (MOTA) score on MOT17 dataset.
Multilingual Text Inversion Detection using Shape Context

It is common to have aberrations in manually scanned textual documents. Document inversion is among the most frequent but a harder anomaly to detect efficiently. Moreover, an algorithm that may detect text inversion in one language may not work for another language. Deep Learning can be a language-agnostic solution, but they are not the most efficient. In this paper, we present an inversion detection algorithm based on shape context, which is a mathematical descriptor that uses log-polar histograms to encode relative shape information. Furthermore, to localize text blocks inside images, an efficient text bounding box algorithm has been proposed. The end-to-end algorithmic pipeline can localize text and detect inversion in multi-lingual text documents. The experiments demonstrate the method to have around 17.5x speed improvement vis-a-vis a standard Deep Learning model, with near 100% accuracy on the test dataset.
Learning Disentangled Representation for Fine-Grained Visual Categorization

Fine-grained visual categorization (FGVC) that aims to recognize objects from subcategories with very subtle differences remains a challenging task due to the large intra-class and small inter-class variation caused by, e.g., deformation, occlusion, illumination, background clutter, etc. A great deal of recent work tackles this problem by forcing the network to focus on partial discriminable features using attention mechanisms or part-based methods. However, these methods neglect the point that the network may learn to discriminate objects from identity-unrelated features, for instance, when backgrounds are discriminable in training samples, degrading the network’s generalization ability. In this paper, for the first time, we use disentangled representation learning to disentangle the fine-grained visual feature into two parts: the identity-related feature and the identity-unrelated feature. Only the identity-related feature is used for the final classification. Since identity-unrelated information is neglected in classification, intra-class variation is reduced while inter-class variation is amplified through the disentanglement, improving the classification performance as a result. Experimental results on three standard fine-grained visual categorization datasets, i.e., CUB-200-2011 (CUB), Stanford Cars (CAR) and FGVC-Aircraft (AIR), demonstrate the effectiveness of our method and show that we achieve state-of-the-art performance on the benchmarks.
ALGEBRAIC SEMANTICS FOR RELATIVE TRUTH, AWARENESS, AND POSSIBILITY

Abstract This paper puts forth a class of algebraic structures, relativized Boolean algebras (RBAs), that provide semantics for propositional logic in which truth/validity is only defined relative to a local domain. In particular, the join of an event and its complement need not be the top element. Nonetheless, behavior is locally governed by the laws of propositional logic. By further endowing these structures with operators—akin to the theory of modal Algebras—RBAs serve as models of modal logics in which truth is relative. In particular, modal RBAs provide semantics for various well-known awareness logics and an alternative view of possibility semantics.
Automatic Picture-Matching of Crested Newts

This article highlights a group of image processing, deep learning, and pattern matching techniques that can be used together in order to automatically identify different specimens of newts from a single species (Triturus cristatus Laurenti 1768). First, each image of newt will be: augmented, segmented, straightened. Then, patterns of images will be detected and compared between each other, allowing the differentiation of newts living in selected areas.
Binary Auto-Encoders Hashing with Manifold Similarity-preserving for Image Retrieval

By minimizing reconstruction loss, binary auto-encoders algorithm makes the hash codes keep the important information of the original input. However, the algorithm only considers the reconstruction loss and doesn't consider the local geometric structure information between the original data, which is bad for learning high quality hash functions. Therefore, in this paper, we propose a new image retrieval algorithm based on binary auto-encoders hashing with manifold similarity-preserving (MSP-BAH). First, the supervised Laplacian eigenmaps algorithm for the generation of the referenced hash codes is used to generate referenced hash codes, which keep the local geometric structure information of the original input data. Then by using the hash codes as a reference, the manifold similarity-preserving loss is constructed between the hash codes generated by the encoder and the referenced hash codes to guide the learning process of the model, so that the MSP-BAH model can provide strong characterization ability while keeping the local geometric structure information unchanged as much as possible. We perform some experiments on three benchmark datasets, i.e., CIFAR10, MNIST, and NUS-WIDE. The results show that the MSP-BAH method has better performance comparing with several existing image retrieval methods.
Development of a data model for ventilator operation and maintenance

The purpose of this study was to construct a data model that can contain data related to ventilator operation, maintenance, and inspection required by clinical engineers by utilizing a reference information model (RIM) that integrates multiple domains.