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Safety and Ethical Concerns of Large Language Models 22

Safety and Ethical Concerns of Large Language Models

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22nd Chinese National Conference on Computational Linguistics, CCL 2023

作者： Xi, Zhiheng Zheng, Rui Gui, Tao School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Intelligent Information Processing Shanghai China Institute of Modern Languages and Linguistics Fudan University Shanghai China

ISBN: (纸本)9781713889915

Recent months have witnessed significant progress in the field of large language models (LLMs). Represented by ChatGPT and GPT-4, LLMs perform well in various natural language processing tasks and have been applied to many downstream applications to facilitate people’s lives. However, there still exist safety and ethical concerns. Specifically, LLMs suffer from social bias, robustness problems, and poisoning issues, all of which may induce LLMs to spew harmful contents. We propose this tutorial as a gentle introduction to the safety and ethical issues of LLMs. © 2023 China National Conference on Computational Linguistics.

关键词： Computational linguistics

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Joint Extraction of Interaction Features and Attention Mechanism Fused with Entity-Relations 36

Joint Extraction of Interaction Features and Attention Mecha...

引用

36th International Conference on Software Engineering and Knowledge Engineering, SEKE 2024

作者： Su, Lisha Yan, Rong College of Computer Science Inner Mongolia University Inner Mongolia Key Laboratory of Multilingual Artificial Intelligence Technology National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot010021 China

ISBN: (纸本)1891706594

Joint extraction of entities and relations is an essential task in information extraction. Recently, tagging-based models have gained attention but with poor performance on overlapping triplets, which confronted the issues like cascading errors, information redundancy, and sensitivity to subject extraction. To solve the issue of overlapping triplets, we propose a model combined Gated Attention Unit (GAU) and Multi-head Attention Mechanism (MHA) in this paper. This model distinguishes features for entities and relations, and performs entity recognition for each head entity, different head entities are matched to the same or different tail entities. Then specific tail entity tagging is applied to determine all possible relations and corresponding tails. At the same time, in order to better solve the long-distance dependence and complex multiple relations between entities, GAU and MHA are used to enhance the semantic representation, effectively identify entities and relations, and enhance the learning ability and generalization ability of the model. Experiment results on the public datasets DuIE2.0 and CMeIE show that our model performs better than baselines. © 2024 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Semantics

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Revisiting Unsupervised Domain Adaptation Models: A Smoothness Perspective 16th

Revisiting Unsupervised Domain Adaptation Models: A Smoothne...

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16th Asian Conference on computer Vision, ACCV 2022

作者： Wang, Xiaodong Zhuo, Junbao Zhang, Mengru Wang, Shuhui Fang, Yuejian School of Software and Microelectronics Peking University Beijing China Key Laboratory of Intelligent Information Processing Indian Institute of Computer Technology CAS Beijing China MEGVII Technology Beijing China

ISBN: (纸本)9783031263507

Unsupervised Domain Adaptation (UDA) aims to leverage the labeled source data and unlabeled target data to generalize better in the target domain. UDA methods utilize better domain alignment or carefully-designed regularizations to increase the discriminability of target features. However, most methods focus on directly increasing the distance between cluster centers of target features, i.e., enlarging inter-class variance, which intuitively increases the discriminability of target features and is easy to implement. However, due to intra-class variance optimization being under-explored, there are still some samples of the same class are prone to be classified into several classes. To handle this problem, we aim to equip UDA methods with the high smoothness constraint. We first define the model’s smoothness as the predictions similarity within each class, and propose a simple yet effective technique LeCo (impLicit smoothness Constraint) to promote the smoothness. We construct the weak and strong "views" of each target sample and enforce the model predictions of these two views to be consistent. Besides, a new uncertainty measure named Instance Class Confusion conditions the consistency is proposed to guarantee the transferability. LeCo implicitly reduces the model sensitivity to perturbations for target samples and guarantees smaller intra-class variance. Extensive experiments show that the proposed technique improves various baseline approaches by a large margin, and helps yield comparable results to the state-of-the-arts on four public datasets. Our codes are publicly available at https://***/Wang-Xiaodong1899/LeCo_UDA. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Large dataset

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Linear-to-Circular Polarization Converter Based on Transmission Metamaterial 4

Linear-to-Circular Polarization Converter Based on Transmiss...

引用

4th International Applied Computational Electromagnetics Society Symposium in China, ACES-China 2021

作者： Li, Huixiang Wu, Xianliang Zhang, Liang Anhui University Key Laboratory of Intelligent Computing Signal Processing Hefei China Anhui Province Key Laboratory of Simulation and Design for Electronic Information Key Laboratory of Intelligent Computing Signal Processing Hefei Normal University Hefei China

ISBN: (纸本)9781733509619

A broadband transmissive linear-to-circular polarization conversion metamaterial composed of 5-layer '-shaped units was designed, and it was placed directly above the ordinary linear polarization antenna so that the antenna can radiate circular polarization waves. The 3dB axial ratio bandwidth is 11.5GHz-14.5GHz, and the relative axial ratio bandwidth reaches 23%, which proves that the polarization conversion surface has good polarization conversion characteristics. © 2021 Applied Computational Electromagnetics Society (ACES).

关键词： Circular polarization

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Multi-Agent Collaborative Search with Adaptive Heuristics and Weight Vectors for Aerospace Multi-objective Optimal Control Problems 13

Multi-Agent Collaborative Search with Adaptive Heuristics an...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Cao, Li Wang, Maocai Parsonage, Ben Maddock, Christie Dai, Guangming China University of Geosciences School of Computer China China University of Geosciences Hubei Key Laboratory of Intelligent Geo-Information Processing Wuhan430074 China University of Strathclyde Department of Mechanical & Aerospace Engineering Glasgow United Kingdom

ISBN: (纸本)9798350308365

Optimal control optimization problems are relative complex and widespread in aerospace field. The evolutionary algorithms are useful methods in solving those problems and Multi Agent Collaborative Search Algorithm (MACS) is a effective method for them. It's a mix of evolution approach and gradient-based method. The evolution part includes individualistic and social heuristics with different characteristics. The latest version of MACS is Multi-Agent Collaborative Search optimal control algorithm (MACSoc) which has good performance on multi-objective optimal control problems. However, this algorithm has two important limitations: The two kinds of heuristics are performed evenly on each individuals which may weaken the algorithm efficiency;The Pareto Front in objective space may be uneven but the initial weight vectors of MACSoc is even. This may lead to diversity losing. We proposed a new algorithm MACSoc with adaptive heuristics and weight vectors (MACSoc-AH) which contains the following improvements: The two kinds of heuristics are executed on individuals based on the their characteristics;A weight vector adjusting process with a original trigger is added to improve diversity. The new algorithm is compared with MACSoc and shows competitive results. © 2024 IEEE.

关键词： Aerospace industry

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Efficient CDI-FDTD Method for Modelling Graphene-Based Structures

Efficient CDI-FDTD Method for Modelling Graphene-Based Struc...

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2023 International Applied Computational Electromagnetics Society Symposium, ACES-China 2023

作者： Liu, Jiale Xie, Guoda Huang, Zhixiang Education of Anhui Province Anhui Province Key Laboratory of Target Recognition and Feature Extraction Anhui Luan237000 China Ministry of Education The Key Laboratory of Intelligent Computing and Signal Processing Hefei China Anhui Province Anhui University Key Laboratory of Electromagnetic Environmental Sensing Hefei230601 China Anhui University Hefei China

ISBN: (纸本)9781733509657

This paper presents a novel approach for calculating graphene-based structures in the complying divergence implicit finite-difference time-domain (CDI-FDTD) method. To begin, the differential iteration formula of graphene structure in the CDI-FDTD method is derived utilizing the auxiliary-differential-equation (ADE) method. Next, the transmission characteristics of an infinite graphene sheet in an open space are investigated by conducting simulation analyses within the CDI-FDTD framework, considering various magnetostatic or electrostatic biases. Finally, the simulation results were compared with analytical solutions and the results obtained from traditional FDTD methods, confirming the unconditional stability and reliability of the proposed method. © 2023 Applied Computational Electromagnetics Society (ACES).

关键词： Graphene

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A Privacy Leak Detection Mechanism based on Service Binding 10

A Privacy Leak Detection Mechanism based on Service Binding

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10th International Symposium on System Security, Safety, and Reliability, ISSSR 2024

作者： Wang, Boyang He, Jinling Du, Yuanhan Tang, Ming Xu, Xiaolong Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing China State Grid Jiangsu Electric Power Company Limited Nanjing China Nanjing University of Posts and Telecommunications School of Computer Science Nanjing China

ISBN: (纸本)9798350362930

With the development of mobile technology, users gradually store financial information and personal privacy data on mobile terminals, which makes mobile security particularly important. In order to effectively improve the detection speed of application privacy leaks, this paper proposes a concept of service binding, binding a minimum privacy permission set for each service provided by the application. Use the service binding concept to make the static analysis phase track only the sensitive data flow paths between APIs that apply the service's privacy permissions. In order to make up for the lack of static analysis to predict the behavior of application privacy leakage, this paper proposes a static analysis that effectively combines machine learning algorithms, abstracts the path characteristics of the static data streams obtained from static analysis into feature vectors, and further uses machine learning algorithms for learning classification. The experimental demonstration and performance analysis results show that the privacy leak detection mechanism based on service binding has faster detection speed and higher accuracy than FlowDroid. © 2024 IEEE.

关键词： Static analysis

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Harry: A Scalable SIMD-based Multi-literal Pattern Matching Engine for Deep Packet Inspection 42

Harry: A Scalable SIMD-based Multi-literal Pattern Matching ...

引用

42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Xu, Hao Chang, Harry Zhu, Wenjun Hong, Yang Langdale, Geoff Qiu, Kun Zhao, Jin Fudan University School of Computer Science China Shanghai Key Laboratory of Intelligent Information Processing Shanghai China Intel Asia-Pacific Research & Development Ltd. Shanghai China

ISBN: (纸本)9798350334142

Deep Packet Inspection (DPI) is a significant network security technique. It examines traffic workloads by searching for specific rules. Since every byte of packets needs to be examined by many literal rules, multi-literal matching becomes the performance bottleneck of DPI. FDR, the fastest multi-literal matching engine on CPUs, takes advantage of Single-Instruction-Multiple-Data (SIMD) to alleviate this bottleneck and achieves a performance boost over the widely-used Aho-Corasick (AC) algorithm. However, FDR does not deeply exploit the data-level parallelism of SIMD and its SIMD vector utilization is only 50%. Besides, limited by certain SIMD shift instructions, it cannot benefit from advanced SIMD instruction sets. To overcome these issues, we propose Harry, a scalable and SIMD-based multi-literal matching engine. Harry adopts a column-vector-based matching algorithm to improve the data-level parallelism and SIMD vector utilization. To support the algorithm, it takes two encoding methods to compress the mask table. Also, it utilizes shuffle instruction to implement shift. We implement Harry on commodity CPU and evaluate it with real network traffic and DPI rules. Our evaluation shows that Harry reaches a throughput of 30∼70Gbit/s, up to 52x that of AC and 2.09x of FDR. It has been successfully deployed in Hyperscan. © 2023 IEEE.

关键词： Network security

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Enhanced Breakdown Voltage of p-GaN HEMT with a Lateral Gradient in Barrier Layer Component

Enhanced Breakdown Voltage of p-GaN HEMT with a Lateral Grad...

引用

IEEE International Workshop on Radio-Frequency Integration Technology (RFIT)

作者： Wei Zhi Xingang Ren Hui Wang Shubin Zhao Gang Wang Shuping He Zhixiang Huang Xianliang Wu Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Hefei China Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China Anhui Province Key Laboratory of Simulation and Design for Electronic Information System Hefei Normal University Hefei China Key Laboratory of Intelligent Computing & Signal Processing Anhui University Hefei China

ISBN: (数字)9798331541095

ISBN: (纸本)9798331541101

In this paper, an optimized enhanced-mode p-GaN high electron mobility transistor (p-GaN HEMT) is proposed, in which the barrier layer is set into a composite structure with a lateral gradient of Aluminum component (LGC) from the drain edge under the gate towards both source and drain, respectively. By leveraging the polarization effects from different heterojunctions, a two-dimensional electron gas (2DEG) with gradually changing density is formed laterally in the channel layer, which more precisely modulates the electric field distribution in the gate-drain region, enhances the voltage resistance of it, and reduces leakage current, thereby enhancing the breakdown voltage of the device and improving the output performance. The breakdown voltage of the proposed optimized LGC-HEMT under simulation is 716.73V, which is 13.1 % higher than that of the conventional p-GaN HEMT (633.64V), while the specific on-resistance $(\mathrm{R}_{\mathrm{o}\mathrm{n},\mathrm{s}\mathrm{p}})$ is only 1.34 $m\Omega\cdot cm^{2}$ , and the Baliga's figure of merit (BFOM) changes from 216.42 to 382.68 $MW/cm^2$ , which is 76.82% higher, hinting at the promising potential of the proposed HEMT.

关键词： Resistance Radio frequency Performance evaluation Breakdown voltage HEMTs Logic gates Impact ionization Leakage currents MODFETs Electric fields

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Accurate and Steady Inertial Pose Estimation through Sequence Structure Learning and Modulation 38

Accurate and Steady Inertial Pose Estimation through Sequenc...

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38th Conference on Neural Information processing Systems, NeurIPS 2024

作者： Wu, Yinghao Wang, Chaoran Yin, Lu Guo, Shihui Qin, Yipeng School of Informatics Xiamen University China School of Computer Science & Informatics Cardiff University United Kingdom Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism China

Transformer models excel at capturing long-range dependencies in sequential data, but lack explicit mechanisms to leverage structural patterns inherent in fixed-length input sequences. In this paper, we propose a novel sequence structure learning and modulation approach that endows Transformers with the ability to model and utilize such fixed-sequence structural properties for improved performance on inertial pose estimation tasks. Specifically, our method introduces a Sequence Structure Module (SSM) that utilizes structural information of fixed-length inertial sensor readings to adjust the input features of transformers. Such structural information can either be acquired by learning or specified based on users' prior knowledge. To justify the prospect of our approach, we show that i) injecting spatial structural information of IMUs/joints learned from data improves accuracy, while ii) injecting temporal structural information based on smooth priors reduces jitter (i.e., improves steadiness), in a spatial-temporal transformer solution for inertial pose estimation. Extensive experiments across multiple benchmark datasets demonstrate the superiority of our approach against state-of-the-art methods and has the potential to advance the design of the transformer architecture for fixed-length sequences. © 2024 Neural information processing systems foundation. All rights reserved.

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