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System Report for CCL24-Eval Task 1: Construction of CFSP Model Based on Non-Finetuning Large Language Model 23

23rd Chinese National Conference on Computational Linguistics, CCL 2024

作者： Huang, Fugeng Guo, Zhongbin Li, Wenting Cheng, Haibo School of Software and Microelectronics Peking University Beijing102600 China School of Computer and Technology Beijing Institute of Technology Beijing100081 China School of Information Engineering Beijing Institute of Graphic Communication 102600 China National Engineering Research Center for Software Engineering Peking University 100871 China

ISBN: (纸本)9780000000002

Chinese Frame Semantic Parsing (CFSP) is an important task in the field of Chinese Natural Language Processing(NLP). Its goal is to extract the frame semantic structure from the sentence and realize the deep understanding of the events or situations involved in the sentence. This paper mainly studies the application of Large Language Model (LLM) for reasoning through Prompt Engineering without fine-tuning the model, and completes three subtasks of Chinese Framework Semantic Parsing tasks: frame identification, argument Identification and role identification. This paper proposes a Retrieval Augmented Generation (RAG) method for target words, and constructs more refined sample Few-Shot method. We achieved the second place on the B rankings in the open track in the "CCL2024-Eval The Second Chinese Frame Semantic Parsing" competition* © 2024 China National Conference on Computational Linguistics.

关键词： Semantics

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High-Accuracy UWB Positioning System for Mine Tunnels: Optimization of Positioning Algorithm for Quasi-One-Dimensional Environments 5

High-Accuracy UWB Positioning System for Mine Tunnels: Optim...

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5th International Conference on Frontiers technology of Information and computer, ICFTIC 2023

作者： Wang, Qifan Shuai, Jiaqi Guo, Yan Wu, Junmin University of Science and Technology of China School of Software Suzhou China University of Science and Technology of China School of Computer Science and Technology Suzhou Institute for Advanced Research Suzhou China

ISBN: (纸本)9798350309034

The Ultra-Wideband (UWB) technology boasts notable attributes, including robust signal penetration capabilities, exceptional resistance to interference, precision in positioning, and admirable signal stability. These attributes render UWB particularly well-suited for high-speed wireless access within indoor environments. Nevertheless, the intricate nature of indoor settings poses an ongoing challenge for achieving precise positioning. Traditional trilateration-based positioning methods excel in vast, unobstructed indoor spaces but yield suboptimal results in confined, elongated areas such as underground mine tunnels. This paper proposes a positioning algorithm founded on two-point ranging for this kind of quasi-one-dimensional indoor space whose length-to-width ratio is enormous. And we use the Median Absolute Deviation (MAD) method for outlier elimination to mitigate location inaccuracies. The presented algorithm addresses the challenges associated with intricate hardware deployment and substantial Non-Line-of-Sight (NLOS) errors prevalent in the conventional method. Systematic experiments were undertaken to methodically assess the performance of the positioning system. © 2023 IEEE.

关键词： indoor positioning Two Way Ranging (TWR) underground mine tunnel UWB positioning

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Radar Probing Optimization for Joint Beamforming and UAV Trajectory Design in UAV-Enabled Integrated Sensing and Communication

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IEEE Transactions on Communications 2025年第6期73卷 4469-4485页

作者： Liu, Yaxi Mao, Wencan He, Boxin Huangfu, Wei Huang, Tianyao Zhang, Haijun Long, Keping University of Science and Technology Beijing Beijing Advanced Innovation Center for Materials Genome Engineering Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services School of Computer and Communication Engineering Shunde Innovation School Beijing100083 China National Institute of Informatics Information Systems Architecture Science Research Division Tokyo101-8430 Japan University of Science and Technology Beijing Beijing Advanced Innovation Center for Materials Genome Engineering Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services School of Computer and Communication Engineering Beijing100083 China

Unmanned aerial vehicle (UAV)-enabled massive multiple-input-multiple-output (MIMO) integrated sensing and communication (ISAC) is an emerging platform to perform communication and sensing efficiently and flexibly. However, the existing works barely consider the radar probing tasks and neglect the benefits of the dedicated sensing signal. In this paper, we focus on joint optimizations in radar probing tasks, and a novel indicator is introduced, namely radar probing error. Two optimizations in radar probing tasks are established: i) joint transmit beamforming design for large-scale regional radar probing and communication task;ii) joint transmit beamforming and UAV trajectory design for communication enhancement and radar probing task. For the former task, we adopt both communication and novel sensing precoders to further support the MIMO radar. A semidefinite relaxation is utilized to relax the original non-convex problem, which is proven to be tight. For the latter task, we adopt block coordinate descent to alternately optimize the precoders and UAV trajectory where the fractional programming approach and successive convex approximation are further adopted. Experiment results testify the validation of the proposed methods for radar probing tasks in UAV-enabled MIMO ISAC. Moreover, results show the fundamental trade-off between the dual functions and reveal the effectiveness of the introduced sensing precoder. © 1972-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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AgileShard: Turning the Sharded Blockchain into a Real-Time Transaction Processing System 44

AgileShard: Turning the Sharded Blockchain into a Real-Time ...

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44th IEEE Real-Time Systems Symposium, RTSS 2023

作者： Shi, Jianfeng Wu, Heng Liu, Wang Gao, Heran Zhang, Wenbo University of Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Nanjing China Software Engineering Technology Research and Development Center Institute of Software Chinese Academy of Sciences Beijing China Nanjing Institute of Software Technology Nanjing China Institute of Software Chinese Academy of Sciences State Key Laboratory of Computer Science Beijing China

ISBN: (纸本)9798350328578

Blockchain, as an emerging transaction processing system, suffers from low throughput and high latency. Sharded blockchains can significantly increase throughput by dividing nodes into groups (i.e., shards) to process disjoint transactions in parallel. However, the diverse latency requirements of transactions in current sharded blockchains are not well met. Three challenges prevent the sharded blockchain from becoming a real-time transaction processing system, namely, the static block size, the first-come-first-served transaction packing strategy, and the load imbalance. Therefore, this paper proposes 3 methods to help turn the sharded blockchain into a real-time transaction processing system. First, we propose an inter-shard dynamic block size negotiation method that enables shards to adaptively determine the globally optimal block size based on the deadlines of pending transactions. Then, we propose a DAG-based transaction packing method for reducing the number of deadline violations and improving parallelism. Finally, we propose a minimum-cost-flow-based shard reconfiguration method to address load imbalance. Under real datasets on Ethereum, experimental results show that AgileShard using the above three methods can effectively improve the transaction deadline satisfaction rate. © 2023 IEEE.

关键词： Blockchain

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Multi-Task Learning for Alzheimer's Disease Diagnosis and Mini-Mental State Examination Score Prediction

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Big Data Mining and Analytics 2024年第3期7卷 828-842页

作者： Jin Liu Xu Tian Hanhe Lin Hong-Dong Li Yi Pan Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of SoftwareXinjiang UniversityUrumqi 830091China School of Science and Engineering University of DundeeDundeeDD14HNUK Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Accurately diagnosing Alzheimer's disease is essential for improving elderly ***,accurate prediction of the mini-mental state examination score also can measure cognition impairment and track the progression of Alzheimer's ***,most of the existing methods perform Alzheimer's disease diagnosis and mini-mental state examination score prediction separately and ignore the relation between these two *** address this challenging problem,we propose a novel multi-task learning method,which uses feature interaction to explore the relationship between Alzheimer's disease diagnosis and minimental state examination score *** our proposed method,features from each task branch are firstly decoupled into candidate and non-candidate parts for ***,we propose feature sharing module to obtain shared features from candidate features and return shared features to task branches,which can promote the learning of each *** validate the effectiveness of our proposed method on multiple *** Alzheimer's disease neuroimaging initiative 1 dataset,the accuracy in diagnosis task and the root mean squared error in prediction task of our proposed method is 87.86%and 2.5,*** results show that our proposed method outperforms most state-of-the-art *** proposed method enables accurate Alzheimer's disease diagnosis and mini-mental state examination score ***,it can be used as a reference for the clinical diagnosis of Alzheimer's disease,and can also help doctors and patients track disease progression in a timely manner.

关键词： multi-task learning Alzheimer's disease diagnosis mini-mental state examination score prediction

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Makespan and Security-Aware Workflow Scheduling for Cloud Service Cost Minimization Using Firefly Optimizer 22nd

Makespan and Security-Aware Workflow Scheduling for Cloud ...

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22nd International Conference on Algorithms and architectures for Parallel Processing, ICA3PP 2022

作者： Zhou, Chengliang Wang, Tian Li, Liying Sun, Jin Zhou, Junlong School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China National Trusted Embedded Software Engineering Technology Research Center East China Normal University Shanghai200062 China

ISBN: (纸本)9783031226762

As efficient commercial information technology, cloud computing has attracted more and more users to submit their requests to the cloud platform and pay for them based on the amount and quality of services. One of the most important concerns of cloud service providers is to minimize the monetary cost for completing users’ requests while meeting constraints on the quality of services such as makespan and security. To solve this concern, this paper proposes a makespan and security-aware workflow scheduling method based on an improved firefly optimizer that consists of a new solution initialization scheme, a firefly position updating scheme, a firefly-to-solution mapping operator, and two task-to-virtual machine assignment schemes. We carry out extensive simulation experiments on real-world workflows to validate our proposed workflow scheduling method. The results reveal that the monetary cost reduction achieved by our method can be up to 54.0% compared to a baseline and two state-of-the-art approaches. © 2023, Springer Nature Switzerland AG.

关键词： Cloud computing

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The Parables of the Mustard Seed and the Yeast: Extremely Low-Budget, High-Performance Nighttime Semantic Segmentation 39

The Parables of the Mustard Seed and the Yeast: Extremely Lo...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Shiqin Xu, Xin Chen, Haoyang Jiang, Kui Wang, Zheng School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)157735897X

Nighttime Semantic Segmentation (NSS) is essential to many cutting-edge vision applications. However, existing technologies overly rely on massive labeled data, whose annotation is time-consuming and laborious. In this paper, we pioneer a new task focusing on exploring the potential of training strategy and framework design with limited annotation to achieve high-performance NSS. Insufficient information at very low labeling budgets can easily lead to under-optimization or overfitting of the model. Our solution comprises two main components: i) a novel region-based active sampling strategy called Contextual-Aware Region Query (CARQ), which identifies highly informative target nighttime regions for labeling;and ii) an innovative Fragmentation Synergy Active Domain Adaptation framework (FS-ADA), which progressively broadcasts the limited annotation to the unlabeled regions, achieving high performance with a minimal annotation budget. Extensive experiments demonstrate that our method outperforms state-of-the-art UDA-NSS & ADA-SS methods across four day-to-nighttime benchmarks, and generalizes well to foggy, rainy, & snowy scenes. In particular only with 1% target nighttime data annotation, our method is on par with the mainstream fully-supervised methods on the BDD100K-Night val dataset. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Budget control

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A Time Series Analysis-Based Forest Disturbance Time Backtracking Method in Northeastern China, the Changbai Mountain 7

A Time Series Analysis-Based Forest Disturbance Time Backtra...

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7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Yan, Yuxiao Zhang, Changsheng Fang, Lei Zhang, Bin Dong, Xingjun Guo, Ying College of Software Northeastern University Shenyang110819 China Environment Research Institute Shandong University Qingdao266237 China College of Computer Science and Engineering Ningxia Institute of Science and Technology Shizuishan753000 China

ISBN: (纸本)9798350350890

Forest disturbance is a crucial factor in evaluating carbon sink changes in ecological studies. Current research on forest disturbances mainly focuses on forest disturbance detection, forest disturbance classification, and other analyses and statistics of forest changes methods. These studies are mostly based on time-series images or forest remote sensing features to achieve dynamic monitoring of forest changes. However, it is often impossible to construct disturbance information with a lack of images from early remote sensing. This paper addresses this limitation by proposes a method of forest disturbance time backtracking which can get historical information on forest disturbance over a wider temporal distribution. Using this method, we ultimately achieved forest disturbance detection on early remote sensing images from the 1960s. This method is based on machine learning and deep learning algorithms to construct two forest disturbance datasets to achieve this goal. The first dataset, for categorizing forest disturbances, is the first of its kind in Northeast China. This data can be widely used in similar studies. The second dataset is the forest disturbance recovery year dataset. We estimate the timing of early forest disturbance by classifying this dataset. The experimental results showed that the first dataset has an accuracy of over 90%, and the backtracking historical disturbance results are consistent with the actual historical information. This method is significant in its ability to fill in gaps in the forest disturbance classification present in Northeast China and enhance the confidence and time span of the forest disturbance detection method. © 2024 IEEE.

关键词： Forest ecology

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Can end-user feedback in social media be trusted for software evolution: Exploring and analyzing fake reviews

Can end-user feedback in social media be trusted for softwar...

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作者： Khan, Javed Ali Ullah, Tahir Khan, Arif Ali Yasin, Affan Akbar, Muhammad Azeem Aurangzeb, Khursheed Department of Computer Science Faculty of Physics Engineering and Computer Science University of Hertfordshire Hatfield United Kingdom School of Computer & Technology Beijing Institute of Technology Beijing China M3S Empirical Software Engineering Research Unit University of Oulu Oulu Finland School of Software Northwestern Polytechnical University Shaanxi Xian China Software Engineering Department Lappeenranta-Lahti University of Technology Lappeenranta Finland Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

End-user feedback in social media platforms, particularly in the app stores, is increasing exponentially with each passing day. software researchers and vendors started to mine end-user feedback by proposing text analytics methods and tools to extract useful information for software evolution and maintenance. In addition, research shows that positive feedback and high-star app ratings attract more users and increase downloads. However, it emerged in the fake review market, where software vendors started incorporating fake reviews against their corresponding applications to improve overall software ratings. For this purpose, we conducted an exploratory study to understand how end-users register and write fake reviews in the Google Play Store. We curated a research data set containing 68,000 end-user comments from the Google Play Store and a fake review generator, that is, the Testimonial generator (TG). Its purpose is to understand fake reviews on these platforms and identify the common patterns potential end-users and professionals use to report fake reviews by critically analyzing the end-user feedback. We conducted a detailed survey at the University of Science and technology Bannu, Pakistan, to identify the intelligence and accuracy of crowd-users in manually identifying fake reviews. In addition, we developed a ground truth to be compared with the results obtained from the automated machine and deep learning (M&DL) classifier experiment. In the survey, 512 end-users participated and recorded their responses in identifying fake reviews. Finally, various M&DL classifiers are employed to classify and identify end-user reviews into real and fake to automate the process. Unlike humans, the M&DL classifiers performed well in automatically classifying reviews into real and fake by obtaining much higher accuracy, precision, recall, and f-measures. The accuracy of manually identifying fake reviews by the crowd-users is 44.4%. In contrast, the M&DL classifiers obtained an

关键词： Application programs

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Sequential Recommendation via Temporal Data Augmentation and Fourier Convolution 7

Sequential Recommendation via Temporal Data Augmentation and...

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7th International Conference on Algorithms, Computing and Artificial Intelligence, ACAI 2024

作者： Luo, Junming Zhang, Tinghua Jin, Ke Yu, Weihao Huang, Jin School of Computer Science South China Normal University Guangzhou China China Electronics Product Reliability and Environmental Testing Research Institute Software and Systems Research Department Guangzhou China Research Institute of China Telecom Corporation Ltd Network technology research department Guangzhou China

ISBN: (纸本)9798331529314

Sequence recommendation aims to predict user preferences by modeling users' dynamic historical behaviors. In recent years, advancements in sequence deep learning models such as Transformer have significantly enhanced sequence rec-ommendation. However, most existing sequence recommendation approaches primarily focus on the sequential information of user behaviors while neglecting the critical temporal information. Additionally, noise in interaction records has consistently im-peded the performance of sequence recommendations. To address these two shortcomings, we propose a model named TAFC4Rec. First, to better exploit the relationship between temporal infor-mation and user interest changes, we improve three classical stochastic data augmentation methods by incorporating time interval calculations. Furthermore, following data augmentation, to enhance the encoder's noise filtering capability for better handling augmented data, we design a convolutional structure based on the Fourier transform to denoise the data and capture sequence features. Finally, we conducted experiments on four real-world datasets, and the experimental results demonstrate the effectiveness of our model. © 2024 IEEE.

关键词： Spatio-temporal data

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