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14th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2022

作者： Fu, Zufeng Wang, Haiying Li, He Liu, Jinjiang Nanyang Normal University School of Computer Science and Technology Nanyang China Nanyang Normal University Henan Engineering Research Center of Intelligent Processing for Big Data of Digital Image Nanyang China

ISBN: (数字)9781665461696

ISBN: (纸本)9781665461696

Dealing with uncertainty in optimization is increasingly recognized as a key necessity for tackling real-world problems. A (1,0)-super solution has better robust, and can protect against default risk of variable assignment. When flipped a value of any one of variables, the (1,0)-super solution still is a solution. In this work, we study the completeness of (1,0)-regular (k,s)-SAT. (1,0)-regular (k,s)-SAT is a problem to decide whether a regular (k,s)-CNF formula has a (1,0)-super solution. We give a reduction method that transform from (k,s)-SAT to (1,0)-regular (k+1,s)-SAT if an unsatisfiable (k,s)-CNF formula exists, and a structure method of forced-regular (k,s)-CNF formula. This implies that (1,0)-regular (k +1,s)-SAT problem has a sudden jump from triviality to NP-completeness for every k > 3. © 2022 IEEE.

关键词： Uncertainty Transforms Proposals Optimization Man-machine systems Cybernetics

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Watch Out for Your Agents! Investigating Backdoor Threats to LLM-Based Agents

arXiv

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arXiv 2024年

作者： Yang, Wenkai Bi, Xiaohan Lin, Yankai Chen, Sishuo Zhou, Jie Sun, Xu Gaoling School of Artificial Intelligence Renmin University of China Beijing China Center for Data Science Peking University China Pattern Recognition Center WeChat AI Tencent Inc. China National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China

Driven by the rapid development of Large Language Models (LLMs), LLM-based agents have been developed to handle various real-world applications, including finance, healthcare, and shopping, etc. It is crucial to ensure the reliability and security of LLM-based agents during applications. However, the safety issues of LLM-based agents are currently under-explored. In this work, we take the first step to investigate one of the typical safety threats, backdoor attack, to LLM-based agents. We first formulate a general framework of agent backdoor attacks, then we present a thorough analysis of different forms of agent backdoor attacks. Specifically, compared with traditional backdoor attacks on LLMs that are only able to manipulate the user inputs and model outputs, agent backdoor attacks exhibit more diverse and covert forms: (1) From the perspective of the final attacking outcomes, the agent backdoor attacker can not only choose to manipulate the final output distribution, but also introduce the malicious behavior in an intermediate reasoning step only, while keeping the final output correct. (2) Furthermore, the former category can be divided into two subcategories based on trigger locations, in which the backdoor trigger can either be hidden in the user query or appear in an intermediate observation returned by the external environment. We implement the above variations of agent backdoor attacks on two typical agent tasks including web shopping and tool utilization. Extensive experiments show that LLM-based agents suffer severely from backdoor attacks and such backdoor vulnerability cannot be easily mitigated by current textual backdoor defense algorithms. This indicates an urgent need for further research on the development of targeted defenses against backdoor attacks on LLM-based agents.3 Warning: This paper may contain biased content. © 2024, CC BY.

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RECALL: A Benchmark for LLMs Robustness against External Counterfactual Knowledge

arXiv

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arXiv 2023年

作者： Liu, Yi Huang, Lianzhe Li, Shicheng Chen, Sishuo Zhou, Hao Meng, Fandong Zhou, Jie Sun, Xu National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China Pattern Recognition Center WeChat AI Tencent Inc. China Center for Data Science Peking University China

LLMs and ai chatbots have improved people\'s efficiency in various ***, the necessary knowledge for answering the question may be beyond themodels\' knowledge boundaries. To mitigate this issue, many researchers try tointroduce external knowledge, such as knowledge graphs and Internet contents,into LLMs for up-to-date information. However, the external information fromthe Internet may include counterfactual information that will confuse the modeland lead to an incorrect response. Thus there is a pressing need for LLMs topossess the ability to distinguish reliable information from externalknowledge. Therefore, to evaluate the ability of LLMs to discern thereliability of external knowledge, we create a benchmark from existingknowledge bases. Our benchmark consists of two tasks, Question Answering andText Generation, and for each task, we provide models with a context containingcounterfactual information. Evaluation results show that existing LLMs aresusceptible to interference from unreliable external knowledge withcounterfactual information, and simple intervention methods make limitedcontributions to the alleviation of this issue. © 2023, CC BY-NC-SA.

关键词： Artificial intelligence

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Multivariate Time-Series Deep Learning for Joint Prediction of Temperature and Relative Humidity in a Closed Space 8

Multivariate Time-Series Deep Learning for Joint Prediction ...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Gunawan, Fergianto E. Budiman, Arief S. Pardamean, Bens Juana, Endang Romeli, Sugiarto Cenggoro, Tjeng W. Purwandari, Kartika Hidayat, Alam A. Redi, Anak A.N.P. Asrol, Muhammad Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta11440 Indonesia PT Impack Pratama Industri Jakarta14350 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mathematics Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

An accurate predictive model of temperature and humidity plays a vital role in many industrial processes that utilize a closed space such as in agriculture and building management. With the exceptional performance of deep learning on time-series data, developing a predictive temperature and humidity model with deep learning is propitious. In this study, we demonstrated that deep learning models with multivariate time-series data produce remarkable performance for temperature and relative humidity prediction in a closed space. In detail, all deep learning models that we developed in this study achieve almost perfect performance with an R value over 0.99. © 2023 The Authors. Published by Elsevier B.V.

关键词： closed space deep learning humity prediction indoor temperature prediction

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RGraph: Asynchronous graph processing based on asymmetry of remote direct memory access

RGraph: Asynchronous graph processing based on asymmetry of ...

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作者： Chen, Hanhua Yuan, Jie Jin, Hai Wang, Yonghui Wu, Sijie Jiang, Zhihao National Engineering Research Center for Big Data Technology and System Cluster and Grid Computing Lab Services Computing Technology and System Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

The scale of real-world graphs is constantly growing. To deal with large-scale graphs, distributed graph processing has attracted much research efforts. Existing distributed graph processing systems are commonly built on traditional TCP/IP communication stack, which leads to network bottleneck because of low bandwidth and heavy kernel stack operations. Meanwhile, in real power-law graphs, the average number of mirror vertices after graph partitioning is very large, resulting in significant communication overhead among nodes. The emerging high-performance Remote Direct Memory Access (RDMA) network has the features of low latency, high bandwidth, and low CPU overhead, which brings new opportunities for distributed graph processing systems. Existing RDMA-assisted graph processing systems focus on synchronous execution, which imposes barriers between consecutive iterations. Synchronous execution transfers bulk data among nodes and thus only needs a small number of network transfers. However, synchronous execution is usually less efficient than asynchronous execution because of bulk synchronization. Asynchronous execution accelerates graph processing by eliminating barriers, which in turn requires to transfer a large amount of small size data. In this paper, we propose RGraph, an RDMA-assisted asynchronous distributed graph processing system. RGraph distributes edges into two parts to isolate master and mirror vertices. RGraph exploits the asymmetry of RDMA to accelerate the one-to-many communication between master and mirror vertices. We implement RGraph on top of PowerGraph and conduct comprehensive experiments with large-scale real graphs to evaluate its performance. Results show that compared to existing designs, RGraph reduces the execution time by up to 81%. © 2021 John Wiley & Sons Ltd.

关键词： Mirrors

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Fusion of Natural Language and Knowledge Graph for Multi-hop Reasoning 19th

Fusion of Natural Language and Knowledge Graph for Multi-hop...

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19th International Conference on Web Information Systems and Applications, WISA 2022

作者： Lu, Xun Zhao, Feng Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9783031203084

Multi-hop reasoning has been widely studied for its important application values in the domain of intelligent search and question answering. Real-world applications are often dominated by natural language input, and it is difficult to directly model the relation semantics in natural language to fit the multi-hop reasoning model. In addition, the extremely large scale and complex structure of knowledge graphs increase the challenge. We propose a natural language and knowledge graph fusion model (NLKGF) for multi-hop reasoning. NLKGF embeds knowledge graph by fusing natural language semantics during the graph propagation process and adds a relation attention mechanism to enable entity representations to perceive the contribution of different relations. A relation path encoder is designed to encode the relation path by an improved recurrent neural network, the reasoning entity is obtained by calculating the correlation score with the natural language. We tested the performance of the NLKGF model on two datasets requiring multi-hop reasoning. The experimental results show that NLKGF beats advanced benchmark models in multi-hop reasoning tasks, which proves superiority of our model. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Knowledge graph

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Hyperbolic Neural Network Based Preselection for Expensive Multi-Objective Optimization

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Li, Bingdong Yang, Yanting Hong, Wenjing Yang, Peng Zhou, aimin Shanghai Institute of AI for Education the School of Computer Science and Technology and the Shanghai Frontiers Science Center of Molecule Intelligent Syntheses East China Normal University Shanghai China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science and Engineering Guangdong Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen China

A series of surrogate-assisted evolutionary algorithms (SAEAs) have been proposed for expensive multi-objective optimization problems (EMOPs), building cheap surrogate models to replace the expensive real function evaluations. However, the search efficiency of these SAEAs is not yet satisfactory. More efforts are needed to further exploit useful information from the real function evaluations in order to better guide the search process. Facing this challenge, this paper proposes a Hyperbolic Neural Network (HNN) based preselection operator to accelerate the optimization process based on limited evaluated solutions. First, the preselection task is modeled as a multi-label classification problem where solutions are classified into different layers (ordinal categories) through -relaxed objective aggregation. Second, in order to resemble the hierarchical structure of candidate solutions, a hyperbolic neural network is applied to tackle the multi-label classification problem. The reason for using HNN is that hyperbolic spaces more closely resemble hierarchical structures than Euclidean spaces. Moreover, to alleviate the data deficiency issue, a data augmentation strategy is employed for training the HNN. In order to evaluate its performance, the proposed HNN-based preselection operator is embedded into two surrogate-assisted evolutionary algorithms. Experimental results on two benchmark test suites and three real-world problems with up to 11 objectives and 150 decision variables involving seven state-of-the-art algorithms demonstrate the effectiveness of the proposed method. IEEE

关键词： Multiobjective optimization

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A Trajectory Prediction Model for Commercial Flight based on Representative Trajectory 5

A Trajectory Prediction Model for Commercial Flight based on...

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5th International Conference on computer Information science and Application Technology, CISAT 2022

作者： Zhao, Weifan Tong, Qiang Cao, Jianzhi Chen, Yuli Liu, Xiulei Laboratory of Data Science and Information Studies Computer School Beijing Info. Science & Technology University Beijing China Beijing Advanced Innovation Center for Materials Genome Engineering Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9781510660076

In commercial flight trajectory prediction, the models based on deep learning have high versatility and accurate forecast. However, these prediction models have the problem of poor effect when using small data sets. This paper proposes a commercial flight trajectory prediction model based on representative trajectory. This model consists of two parts: Representative trajectory generation and Trajectory prediction. Firstly, our model clusters the commercial flight trajectories on the same route, then generate representative trajectories to represent the route pattern. Secondly, our model uses the Savitzky-Golay filter to eliminate the noise characteristics in these representative trajectories, then combines those with a deviation correction algorithm to predict flight trajectories. By comparing with several other prediction models, the experiment results support that our model performs better in a small data set of the commercial flight trajectory. © 2022 SPIE.

关键词： Clustering algorithms

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Autonomous Navigation of Agricultural Robots Utilizing Path Points and Deep Reinforcement Learning

Autonomous Navigation of Agricultural Robots Utilizing Path ...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Jipeng Kang Zhibin Zhang Yajie Liang Xu Chen Key Laboratory of Wireless Networks and Mobile Computing School of Computer Science Inner Mongolia University Hohhot China Engineering Research Center of Ecological Big Data School of Computer Science Inner Mongolia University Hohhot China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

When using deep reinforcement learning algorithms for reactive navigation, it is common to encounter situations where the agent gets stuck in a local optimum. In autonomous exploration without any prior knowledge, the exploration of environments is often suboptimal. Moreover, the agricultural robots performing tasks in farm fields face even more great challenges. To address this, we propose an Agricultural Robot Autonomous Exploration System (ARAES) that uses deep reinforcement learning algorithms to drive the robot to explore unknown farm environments. The whole system consists of a Global Path Planning (GPP) module and a Local Path Planning (LPP) module. The GPP module utilizes path points acquired along the heading direction in the environment and selects waypoints based on the collected path points to guide the robot towards the global goal, effectively alleviating local optima issues in reactive navigation. The LPP module learns local navigation motion policies in a simulated environment. It incorporates the learned policies into the motion planning stack, enabling the robot to move towards global goals based on the path points and way points. The advantages of the ARAES system are its autonomy and real-time performance, allowing the robot to operate without needing to know detailed prior information about the environment and enabling real-time adjustments of navigation policies according to current environments. Meanwhile, ARAES can construct maps in real-time as the robot moves in the environment. Experiments demonstrate that ARAES is advantageous because it does not rely on complex static and dynamic environment maps or prior information and delivers superior performance compared to other methods.

关键词： Agricultural robots Simultaneous localization and mapping Navigation Production Deep reinforcement learning Real-time systems Path planning

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Aggregating Structural Features for Computational Analysis of Mutations in BRCT Domains of BRCA1 Protein

Aggregating Structural Features for Computational Analysis o...

引用

International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Alam Ahmad Hidayat Rudi Nirwantono Mahmud Isnan Joko Pebrianto Trinugroho Bens Pardamean Mathematics Department School of Computer Science Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Biotechnology Department Faculty of Engineering Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department Bioinformatics and Data Science Research Center BINUS Graduate Program Jakarta Indonesia

Understanding the mechanistic interpretability of mutation effects in a protein can help predict the clinical implications of the genetic variants. Hence, computational variant effect predictions that involve protein structural features of the protein mutations might be suitable in this case. In this work, we focus on BRCT domains of BRCA1 gene that is widely studied in breast cancer studies. We retrieved 88 selected missense variants found in BRCT domains annotated in both ClinVar and gnomAD databases. To computationally characterize the pathogenic property of the mutations we used two types of features extracted from protein structures: a change in free Gibbs energy and a set of features derived from molecular dynamics simulations of each mutant. Using a dimensional reduction and Gaussian mixture model (GMM)-based clustering we demonstrate that the variants are segregated into two regions that may correspond to their pathogenic status. This method can be a potential computational pipeline for providing the preliminary mechanistic interpretation of mutation effects in terms of their thermodynamic and structural features.

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