Reviewerly/exHarmony huggingface dataset:
The Fast $k-\text{NN}$ Algorithm Based on a Fixed Grid Method

<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k-\text{Nearest}$</tex> Neighbor <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(k-\text{NN})$</tex> is a well-known instance-based learning algorithm; widely used in pattern recognition. A <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k-\text{NN}$</tex> classifier can generate highly accurate predictions if provided with sufficient training instances. Thus, it plays a pivotal role in many fields. However, though its accuracy can improve with more data, the need for computational resources increases as well. In this paper, we propose a novel approach which pre-partitions instance space into smaller cells in order to reduce computational cost and greatly reduce the time complexity. Assume every instance is mapped to a point in the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$d-\text{dimensional}$</tex> Euclidean space and a training set <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$D$</tex> contains <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> training instances. Given a query instance, the brute force <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k-\text{NN}$</tex> algorithm has the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(nd)$</tex> time complexity for predicting the class of a query instance. This algorithm can improve the time complexity.
The Innovation of Library Service by Artificial Intelligence Robot

The introduction of Intelligent Robot Service is the direction of library development. The application of intelligent robot technology will greatly improve the library service efficiency and service level. At present, there are still some problems in library service of intelligent robot, such as the core technology needs to be mastered skillfully, the level of intelligent service is not enough, and the construction of Corpus needs to be improved. In the era of library intelligent service, intelligent robot should pay more attention to the application of multi-purpose intelligent robot system in library, the fusion of Intelligent Library and robot technology under the background of Internet of things, and the development trend of intelligent robot providing various care services for different readers. The introduction of intelligent robot technology will greatly improve the efficiency of books on and off shelves, and expand the space-time limit of reference service. In the process of building the intelligent library, the infrastructure and service of the traditional library are reconstructed by using the automatic book access robot and the Consulting robot system, it can be concluded that Book Auto-access Technology and IM consultation system are most suitable for library management system, and Book auto-access robot technology can solve the bottleneck problem of library intelligent robot technology, promoting the transformation and upgrading of libraries to smart libraries.
The Free Path Planner Based on Windmill Decomposition

In this article, a new obstacle-avoidance path planner is proposed based on the windmill decomposition to find the shortest path in the Euclidean plane with rectangle obstacles. Using the Dijkstra's shortest path algorithm in a connected graph, this study constructs a windmill decomposition of the free space (obstacle avoidance space), and uses their centroids to plan the shortest free path from the start point to the end point. The advantage of this research is that, compared with other traditional algorithms, the time complexity of this algorithm is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(n\text{log}\ n)$</tex> , and the free path for obstacle avoidance can be planned in a short time, where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> is the number of obstacles.
Can We Mitigate Backdoor Attack Using Adversarial Detection Methods?

Deep Neural Networks are well known to be vulnerable to adversarial attacks and backdoor attacks, where minor modifications on the input are able to mislead the models to give wrong results. Although defenses against adversarial attacks have been widely studied, investigation on mitigating backdoor attacks is still at an early stage. It is unknown whether there are any connections and common characteristics between the defenses against these two attacks. We conduct comprehensive studies on the connections between adversarial examples and backdoor examples of Deep Neural Networks to seek to answer the question: can we detect backdoor using adversarial detection methods. Our insights are based on the observation that both adversarial examples and backdoor examples have anomalies during the inference process, highly distinguishable from benign samples. As a result, we revise four existing adversarial defense methods for detecting backdoor examples. Extensive evaluations indicate that these approaches provide reliable protection against backdoor attacks, with a higher accuracy than detecting adversarial examples. These solutions also reveal the relations of adversarial examples, backdoor examples and normal samples in model sensitivity, activation space and feature space. This is able to enhance our understanding about the inherent features of these two attacks and the defense opportunities.
Quality- and Availability-Based Device Scheduling and Resource Allocation for Federated Edge Learning

To achieve an efficient federated edge learning (FEEL) system, the scheme of device scheduling and resource allocation should jointly perceive the device availability, wireless channel quality, and local gradient quality. The existing literature on FEEL rarely considers these three aspects simultaneously, so the schemes they proposed still have room for improving the efficiency of FEEL, which motivates our work. In this letter, by mathematically modeling the device availability, wireless channel quality, and gradient quality, and deriving the convergence bound for model training in the FEEL system, we formulate a joint device scheduling and resource allocation problem, aiming to improve the FEEL efficiency. The formulated problem is a challenging non-convex problem. By exploring its structural properties and utilizing the KKT conditions, we obtain an optimal solution in closed-form. The analytical results enable us to gain some important insights into how the device availability, wireless channel quality, and gradient quality affect device scheduling and resource allocation in the FEEL system.
Adversarial Attacks Against Acoustic Monitoring of Industrial Machines

The recent rise of adversarial machine learning exposed the serious vulnerabilities existing in current frameworks depending on the smooth operation of such automated solutions. This article focuses on the critical field of monitoring the health of industrial machines based on the respective acoustic emissions. After building an audio-based monitoring solution using log-Mel spectrograms and convolutional neural networks, we systematically evaluate the applicability of four types of adversarial attacks: 1) fast gradient sign; 2) projected gradient descent; 3) Jacobian saliency map; and 4) Carlini and Wagner <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{\infty }$ </tex-math></inline-formula> . Seeing the problem from the attacker perspective, we designed two different attack types, aiming at inducing either false positives or false negatives. We define three figures of merit specifically designed to assess the performance of each attack type from diverse points of view. The experimental setup relies on a publicly available data set including acoustic emissions representing four industrial machines, i.e., fan, pump, slider rail, and valve.
Efficient Top-k Graph Similarity Search With GED Constraints

It is essential to identify similarity between graphs for various tasks in data mining, machine learning and pattern recognition.Graph edit distance (GED) is the most popular graph similarity measure thanks to its flexibility and versatility.In this paper, we study the problem of top-k graph similarity search, which finds k graphs most similar to a given query graph under the GED measure.We propose incremental GED computation algorithms that compute desired GED lower and upper bounds.Based on the algorithms, we develop novel search frameworks to address the top-k search problem.Our frameworks are also designed to use a state-of-the art indexing technique to speed up top-k search.By conducting extensive experiments on real datasets, we show that the proposed frameworks significantly improve the performance of top-k graph similarity search.
A Functional Circuit for Executive Control

Online date: November 16, 2020 This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)
An evolutionary approach to data valuation

Data valuation in machine learning comprises computational methods for the estimation of the importance of individual training instances. It has been used to remove noise, uncover biases, and improve the accuracy of trained models. Current data valuation techniques do not scale up for large datasets and do not work for regression tasks, where the objective is to predict a numerical outcome rather than a small number of nominal class labels. In this work, an evolutionary approach for qualitative and quantitative data valuation, is presented. The proposed approach is tested on regression and classification benchmarks, and on several bioinformatics and health informatics datasets. In addition, models trained with most valuable subsets of data are validated on independently acquired tests, demonstrating the generalizability as well as the practical utility of the proposed approach.
Optimal Multi-agent Collision-Free Path Planning with Temporal Logic Constraints

In this paper, we present a novel optimal multi-agent collision-free path planning algorithm (OMPATL) for the problem of multi-robot path planning with temporal logic constraints. Given a high-level mission specification in the form of linear temporal logic (LTL), OMPATL can automatically synthesize a set of trajectories for the multi-agent system (MAS), which achieves the collision avoidance among agents and minimizes the cost of the maximum traveling time between successive satisfactions. To synthesize the optimal trajectories, the motion of all agents, together with the mission area, is modeled as a weighted team transition system (TS) with a set of atomic propositions (APs). If the collision avoidance were not required, the optimal trajectories would be easily synthesized by an algorithm such as shortest paths. However, once the multi-agent systems are required to be safety-critical and collision-free, these trajectories may become infeasible. In OMPATL, a strategy is proposed for adding a set of transitions including “wait” and “go-back” to each individual TS based on the optimal run which does not consider collision avoidance. Then, the states and transitions with collisions can be removed such that the optimal collision-free trajectories are founded by the synthesis algorithm. The synthesized run balances collision avoidance, mission fulfillment, and optimality. The optimal run is synthesized offline and online adjustments are not needed.
Neural Network and Collaborative Computing for Image-based Train Location Data Acquisition

How to improve the performance and safety of the train has long been a question of great interest. The object of this study is to achieve the recognition of train speed and mileage that are provided by the train Driver Machine Interface (DMI) and reduce the time consumed by model training. For recognition part, a Convolutional Neural Network (CNN) and a Connectionist Temporal Classification (CTC) layer have been combined together to achieve an end-to-end recognition. For the part of time-consuming reduction, a collaborative computing was being utilized to offload the training tasks to each edge device. In addition, the process of offloading was modeled with Stochastic Petri Net (SPN). Overall, the recognition accuracy was significantly improved, compared to the traditional Optical Character Recognition (OCR) and CNNs, what is more, the time consumed on model training was also brought down as expect.
Optimal Attack Against Coverage Path Planning in Multi-robot System

Optimal path planning is a universal problem, but in the field of multi-robot system (MRS), most of the cases are NP. How to get the optimal solution of the problem is important and well worth studying. In this paper, we consider the confrontational scenario of MRS which performs a full coverage path planning (CPP) to execute missions. To prevent competitive MRS from completing missions, we develop an optimal attack path planning method to attack competitive MRS. We design an efficient breadth-first search (BFS) algorithm, through sacrificing part of the space complexity, realized in the obstacle environment for multi-robot system. The environment considered in this paper is represented by node connected graph and decomposed by univariate method. Finally, a series of simulation experiments are carried out, and the results are compared with another typical greedy algorithm. The comparison between the optimal and the worst case is obtained, which shows the superiority of the proposed algorithm.
Entry traceability technology based on requirement knowledge graph

With the outbreak of information technology, software development companies provide personalized and comprehensive software systems for users' needs, providing many conveniences to their lives. As the basis of software development, requirements run through the whole process of software or system development and determine the quality of software projects. Requirements traceability is applicable to a variety of development scenarios and optimizes the development process. Establishing a complete requirements traceability can effectively speed up the development process and reduce development problems, which has important practical significance. Therefore, in order to improve the efficiency and accuracy of requirements traceability and facilitate the display of traceability results, an entry traceability algorithm based on requirement knowledge graph is proposed. The model uses BERT+BiLSTM+CRF to extract entities to form triples, and uses the pointer generation network to supplement the entries lacking titles to form the knowledge graph. Combining the theory in complex networks, the importance of entities is sorted, and the similarity between entities in the document knowledge graph is calculated by restarting random walk algorithm. Extract subgraphs related to the target entry according to the characteristics of the nodes and a breadth-first algorithm. Finally, the graph kernel method in the graph matching algorithm is used to calculate the structural similarity and entity similarity between the related entry and the target entry, and determine the relationship. Through the experimental analysis of real requirements documents, the results show that the method can quickly and accurately trace the requirements of the set of input entries.
KubeRM: a distributed rule-based security management system in cloud native environment

Recently, Microservice orchestration has attracted a great deal of attention in IT community. As a breaking technique to redefine our cloud, Microservice orchestration service, such as Kubernetes [1], As development teams have rapidly embraced the technique, the importance of securing the orchestration service has become the focal point. Hence, security practitioners are facing great challenges from the new eco-system. In this paper, we propose a distributed security management system, KubeRM, for cloud native environment. One of the greatest challenges we are facing is how to effectively and efficiently synchronize security detecting abilities across all machines in our large Kubernetes cluster. Hence, instead of implementing a centralized management system, our proposal focuses on designing a distributed security management system, which achieves seconds level security enforcement across all machines. In further, we also demonstrate our prototype system, KubeRM, which can effectively enforce not only the exploits detection on vulnerable K8s servers but also detect abnormal network traffic against the orchestrator. It leverages a rules-based detection strategies to profile all orchestrator behaviors.
Efficient and interpretable monitoring of high-dimensional categorical processes

High-Dimensional (HD) processes have become prevalent in many data-intensive scientific domains and engineering applications. The monitoring of HD categorical data, where each variable of interest is evaluated by attribute levels or nominal values, however, has seldom been studied. As the joint distribution of HD categorical variables can be fully characterized by a high-way contingency table or a high-order tensor, we propose a Probabilistic Tensor Decomposition (PTD) which factorizes a huge tensor into a few latent classes (rank-one tensors) to dramatically reduce the number of model parameters. Moreover, to enable high interpretability of this latent-class-type PTD model, a novel polarization regularization is devised, which makes each latent class focus on only a few vital combinations of attribute levels of categorical variables. An Expectation-Maximization algorithm is designed for parameter estimation from a historical normal dataset in Phase I, and an exponentially weighted moving average control chart is built in Phase II to monitor the proportions of latent classes that act as surrogates for each original categorical vector. Extensive simulations and a real case study validate the superior inference and monitoring performance of our proposed efficient and interpretable method.
Construction and research of system capability training system based on cloud computing

With the development of cloud computing and big data related technologies, it becomes more and more important that the system ability of application-oriented talents under the background of new technologies. Based on the cloud platform and the idea of community cooperation, build a special teaching service cloud platform, which can solve complex engineering problems, realize the system design scheme and implement the application system. Combined with the talent thought under OBE mode, an applied computer professional practice system is gradually formed. Through the practice of the environment provided by the cloud platform, the training effect is enhanced and the improvement of system capability is highlighted.
Research and practice of JAVA virtual machine exception handling mechanism testing technology

In this paper, JAVA virtual machine is discussed through the description of the relevant content, the common classification of exception handling mechanism, and the design of the test system of exception handling mechanism. The key points of design include application of SOOT tool, selection of test algorithm, introduction of JIMPLE technology, abnormal code injection, reasonable selection of different virtual machines and so on. Through the establishment of reliable experiments, the practical value of the test system is evaluated. The purpose is to improve the timeliness of abnormal problem discovery and control the negative impact within a reasonable range.
A simulation model based on DCGAN to generate 2D animation avatars

The rising of machine learning has enabled computers to do difficult tasks that once and only be don e by human beings. However, many applications haven’t taught the machine about the field of art. To make machine more artistic, this paper designed a model based on GAN that enables the computer to generate 2D Japanese animation avatar. In the paper, GAN is found to be an effective method: the generator and discriminator work together to generate normal but real pictures. The experimental result is that in the 300 generated animations, only 3 images are distorted or unreal, indicating that the success rate is 99% and the proposed model has achieved excellent performance. Furthermore, it can be found that most of the generated pictures are beautiful, meaning that the machine is able to draw not only real, but also beautiful pictures.
A number theoretic transform accelerator with two parallel simplified butterfly units

Polynomial multiplication is the most compute-intensive in the Lattice-based post-quantum cryptography (PQC). This operation can be sped up using the number theoretic transform (NTT). A lot of research is currently being done on how to speed up the NTT algorithm. In light of these considerations, this study presents an NTT accelerator with a greatly simplified butterfly unit (BFU) architecture, a completely pipelined modular multiplication (MM) unit, and two parallel computing units. FPGA implementation is realized to evaluate this work. Through the experimental results, processing speed can achieve 2× improvements with controllable resource consumption.
Research on resource optimization scheme based on cloud computing environment

In the cloud computing environment, consider how to achieve efficient task scheduling, improve user satisfaction, and reduce task response time and time span. Consider energy consumption saving factors and users This paper also considers how to reduce the energy consumption in the scheduling process and improve the time and service quality of users. The specific research objectives of this paper are as follows. (1) To design an improved resource scheduling algorithm for efficient resource scheduling process in cloud environment for the purpose of reducing the response time and time span of tasks. (2) Propose an energy-efficient resource scheduling algorithm to reduce energy consumption in the scheduling process of cloud computing. An energy-efficient resource scheduling algorithm is proposed to reduce the energy consumption in the scheduling process and achieve load balancing. First, we surveyed the literature related to the resource scheduling problem in the cloud environment and summarized and analyzed the application scenarios and performance advantages and disadvantages of existing scheduling strategies; based on the research results, we divided into two subtopics: research on efficient resource scheduling algorithms under different scenarios and research on energy-efficient resource scheduling algorithms in the cloud environment. The research focus under different sub-topics is clarified; mathematical modeling is performed and scheduling algorithms are designed according to the research problems under the sub-topics; then the advantages and disadvantages of the algorithms are analyzed theoretically; finally, a simulation environment is constructed, and the performance of the proposed algorithms is verified through simulation and compared with existing cloud scheduling algorithms in the same application scenarios.