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2022 International Conference on Business Analytics for Technology and Security, ICBATS 2022

作者： Kamil, Samar Siti Norul, Huda Sheikh Abdullah Firdaus, Ahmad Usman, Opeyemi Lateef Al-Mansour University College Department of Computer Science and Information Systems Baghdad Iraq Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia University Malaysia Pahang Faculty of Computing College of Computing and Applied Sciences Pahang Darul Pekan Malaysia Tai Solarin University of Education Department of Computer Science Ogun State Nigeria

ISBN: (纸本)9781665409209

Cybersecurity is important in the field of information technology. One most recent pressing issue is information security. When we think of cybersecurity, the first thing that comes to mind is cyber-attacks, which are on the rise, such as Ransomware. Various governments and businesses take a variety of measures to combat cybercrime. People are still concerned about ransomware, despite numerous cybersecurity precautions. In ransomware, the attacker encrypts the victim's files/data and demands payment to unlock the data. Cybersecurity is a collection of tools, regulations, security guards, security ideas, guidelines, risk management, activities, training, insurance, best practices, and technology used to secure the cyber environment, organization, and user assets. This paper analyses ransomware attacks, techniques for dealing with these attacks, and future challenges. © 2022 IEEE.

关键词： Cybersecurity

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A Two-stage Reconfiguration in Network Function Virtualization: Toward Service Function Chain Optimization

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IEEE Transactions on Network and Service Management 2025年

作者： Assis, Karcius D. R. Almeida, Raul C. Baghban, Hojjat Santos, Alex F. Boutaba, Raouf Electrical and Computer Engineering Department Salvador Brazil University of Waterloo David R. Cheriton School of Computer Science Canada Department of Electronics and Systems Recife Brazil Chang Gung University Department of Artificial Intelligence Taiwan Chang Gung University College of Intelligent Computing Taiwan Center of Science and Technology in Energy and Sustainability Brazil Postgraduate Program in Computer Science Bahia Brazil

Network Function Virtualization (NFV), as a promising paradigm, speeds up the service deployment by separating network functions from proprietary devices and deploying them on common servers in the form of software. Any service in NFV-enabled networks is achieved as a Service Function Chain (SFC) which consists of a series of ordered Virtual Network Functions (VNFs). However, migration of VNFs for more flexible services within a dynamic NFV-enabled network is a key challenge to be addressed. Current VNF migration studies mainly focus on single VNF migration decisions without considering the sharing and concurrent migration of VNF instances. In this paper, we assume that each deployed VNF is used by multiple SFCs and deal with the optimal placement for the contemporaneous migration of VNFs based on the actual network situation. We formalize the VNF migration and SFC reconfiguration problem as a mathematical model, which aims to minimize the VNF migration between nodes or the total number of core changes per node. The approach is a two-stage MILP based on optimal order to solve the reconfiguration. Extensive evaluation shows that the proposed approach can reduce the change in terms of location or number of cores per node in a 6-node and 14-node networks while ensuring network latency compared with the model without reconfiguration. © 2004-2012 IEEE.

关键词： Network function virtualization

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Neural Network-Based Safety Optimization Control for Constrained Discrete-Time Systems 49

Neural Network-Based Safety Optimization Control for Constra...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Wang, Xueli Zhao, Shangwei Yang, Ming Wu, Xiaoming Wang, Xin Xu, Shuo Hu, Xu Qilu University of Technology Key Laboratory of Computing Power Network and Information Security Shandong Computer Science Center Jinan China Shanghai Jiao Tong University Department of Automation Shanghai China Public Security Bureau of Jinan Jinan China

ISBN: (纸本)9798350331820

This paper proposes a constraint-aware safety control approach via adaptive dynamic programming (ADP) to address the control optimization issues for discrete-time systems subjected to state constraints. First, the constrained control framework is established via the primal-dual approach with the modified Lagrangian based on the relaxed barrier function. Herein, the Lagrangian multiplier is designed to achieve the trade-off between optimization performance and state constraints. In addition, the sub optimality of the dual method is built by proving that the dual gap can be arbitrarily small. And the value iteration algorithm is utilized to implement the constraint-aware ADP controller. Furthermore, the weight estimation error is proved to be bounded when the learning rate satisfies a given sufficient condition. Finally, numerical simulation proves the effectiveness and superiority of the proposed method. © 2023 IEEE.

关键词： Iterative methods

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LanCOPE: Language-Guided Category-Level Object Pose Estimation from a Single RGB Image

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IEEE Robotics and Automation Letters 2025年第7期10卷 7555-7562页

作者： Yang, Hui Sun, Wei Liu, Jian Zheng, Jin Dai, Zhenqi Mian, Ajmal Hunan University National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Changsha 410082 China Central South University School of Architecture and Art Changsha 410082 China The University of Western Australia Department of Computer Science and Software Engineering 6009 WA Australia

Monocular RGB-based category-level object pose estimation is more practical and cost-effective for robotics. However, existing methods do not fully exploit the rich semantic and contextual information in multimodal data (e.g. language) that provides additional object attributes to guide the model in extracting category features more reliably. We propose a language-guided category-level object pose estimation method (LanCOPE), taking a single RGB image as input. Our method uses DINOv2 to recover depth from a single RGB image and converts it into point cloud to perceive the object's geometry. We then introduce language descriptions for the RGB image, estimated point cloud and overall scene to better guide the point cloud encoder and image encoder in learning category features. We develop a cross-modal differential perception feature fusion network to fuse multimodal features. This network employs a differential perception module to eliminate redundant information across different modalities, highlighting signifcant semantic differences and similarities. Furthermore, it uses a cross-attention mechanism to fuse the semantic information of the language and vision features, improving the overall perception. Finally, we design a denoising network based on the skip fusion transformer to recover the object pose accurately. Extensive experiments on REAL275 and Wild6D datasets show that LanCOPE achieves state-of-the-art performance. Our code is available at LanCOPE. © 2016 IEEE.

关键词： category-level object pose estimation languag RGB image

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Towards Semantic Web of Things: Reference Architecture and Gap Analysis

Towards Semantic Web of Things: Reference Architecture and G...

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IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social computing (CPSCom) Green computing and Communications (GreenCom)

作者： Xiang Su Ekaterina Gilman Xiaoli Liu Department of Computer Science Norwegian University of Science and Technology Gjøvik Norway Center for Ubiquitous Computing University of Oulu Oulu Finland Department of Computer Science University of Helsinki Helsinki Finland

Internet of Things (IoT) connects objects and allows data exchange between smart things and the Internet. Web of Things (WoT) further integrates smart things into the Web architecture by leveraging existing standardized Web technologies. In this paper, we envision Semantic Web of Things (SWoT), aiming to exchange semantically rich and machine understandable information with smart things. This article discusses the landscape of IoT and WoT through the past and its future prospects, presents a SWoT reference architecture, and conducts a gap analysis. Aiming to highlight the deficiencies of existing solutions, we carry out a gap analysis with respect to (i) extension of WoT, (ii) semantic knowledge base, (iii) semantic reasoning, (iv) semantic interoperability, and (v) semantic service composition. Based on the results of our analysis, we conclude this article with a list of recommendations in order to fill the gaps. The main contributions pertain to a SWoT reference architecture with key SWoT building blocks and an analysis of technologies to support SWoT.

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Frequency-noise-insensitive universal control of Kerr-cat qubits

arXiv

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arXiv 2025年

作者： Seifert, Lennart Maximilian Hann, Connor T. Noh, Kyungjoo Department of Computer Science University of Chicago ChicagoIL60637 United States AWS Center for Quantum Computing PasadenaCA91125 United States

We theoretically study the influence of frequency uncertainties on the operation of a Kerr-cat qubit. As the mean photon number increases, Kerr-cat qubits provide an increasing level of protection against phase errors induced by unknown frequency shifts during idling and X rotations. However, realizing rotations about the other principal axes (e.g., Y and Z axes) while preserving robustness is nontrivial. To address this challenge, we propose a universal set of gate schemes which circumvents the tradeoff between protection and controllability in Kerr-cat qubits and retains robustness to unknown frequency shifts to at least first order. Assuming an effective Kerr oscillator model, we theoretically and numerically analyze the robustness of elementary gates on Kerr-cat qubits, with special focus on gates along nontrivial rotation axes. An appealing application of this qubit design would include tunable superconducting platforms, where the induced protection against frequency noise would allow for a more flexible choice of operating point and thus the potential mitigation of the impact of spurious two-level systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Qubits

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OneBit: towards extremely low-bit large language models 24

OneBit: towards extremely low-bit large language models

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Yuzhuang Xu Xu Han Zonghan Yang Shuo Wang Qingfu Zhu Zhiyuan Liu Weidong Liu Wanxiang Che Research Center for Social Computing and Information Retrieval Harbin Institute of Technology Harbin China Department of Computer Science & Technology Tsinghua University Beijing China

ISBN: (纸本)9798331314385

Model quantification uses low bit-width values to represent the weight matrices of existing models to be quantized, which is a promising approach to reduce both storage and computational overheads of deploying highly anticipated LLMs. However, current quantization methods suffer severe performance degradation when the bit-width is extremely reduced, and thus focus on utilizing 4-bit or 8-bit values to quantize models. This paper boldly quantizes the weight matrices of LLMs to 1-bit, paving the way for the extremely low bit-width deployment of LLMs. For this target, we introduce a 1-bit model compressing framework named OneBit, including a novel 1-bit parameter representation method to better quantize LLMs as well as an effective parameter initialization method based on matrix decomposition to improve the convergence speed of the quantization framework. Sufficient experimental results indicate that OneBit achieves good performance (at least 81% of the non-quantized performance on LLaMA models) with robust training processes when only using 1-bit weight matrices. Code and checkpoints are available at https://***/xuyuzhuang11/OneBit

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Neural Machine Translation for Kashmiri to English and Hindi using Pre-trained Embeddings 20

Neural Machine Translation for Kashmiri to English and Hindi...

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20th OITS International Conference on Information Technology, OCIT 2022

作者： Sheshadri, Shailashree K Gupta, Deepa Costa-Jussa, Marta R. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India Universitat Politecnica de Catalunya - Upc Talp Research Center Barcelona Spain

ISBN: (纸本)9781665493482

Neural Machine Translation (NMT) is one of the advanced approaches of Machine Translation (MT) that has recently gained popularity. A significant amount of parallel corpus is required to achieve a sound translation system, but most languages have a deficit worldwide. Many SoTA NMT systems are available for low-resource langauges that are developed using transfer learning, knowledge transfer, and zero-shot learning mechanisms. Most Indic languages fall into low-resource and zero-resource due to the non-availability of rich parallel and monolingual corpora. Though many Indian border languages have social and economic significance, they lack resources and automated machine translation systems. Kashmiri, one such Indian border language, belongs to the zero-resource category with limited corpora and no significant translation system. This paper uses pre-trained word embeddings to create the first NMT system specifically for Kashmiri-English and Kashmiri-Hindi translation. mBPE pre-trained word embeddings for Kashmiri language are used to develop the NMT system. A pre-trained word embedding model shows +2.58 BLEU improvisation in comparison to Vanilla NMT. © 2022 IEEE.

关键词： Embeddings

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SETTP: Style Extraction and Tunable Inference via Dual-Level Transferable Prompt Learning 27

SETTP: Style Extraction and Tunable Inference via Dual-Level...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Jin, Chunzhen Huang, Yongfeng Wang, Yaqi Cao, Peng Zaïane, Osmar Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang China Amii University of Alberta EdmontonAB Canada

ISBN: (纸本)9781643685489

Text style transfer, an important research direction in natural language processing, aims to adapt the text to various preferences but often faces challenges with limited resources. In this work, we introduce a novel method termed Style Extraction and Tunable Inference via Dual-level Transferable Prompt Learning (SETTP) for effective style transfer in low-resource scenarios. First, SETTP learns source style-level prompts containing fundamental style characteristics from high-resource style transfer. During training, the source style-level prompts are transferred through an attention module to derive a target style-level prompt for beneficial knowledge provision in low-resource style transfer. Additionally, we propose instance-level prompts obtained by clustering the target resources based on the semantic content to reduce semantic bias. We also propose an automated evaluation approach of style similarity based on alignment with human evaluations using ChatGPT-4. Our experiments across three resourceful styles show that SETTP requires only 1/20th of the data volume to achieve performance comparable to state-of-the-art methods. In tasks involving scarce data like writing style and role style, SETTP outperforms previous methods by 16.24%. © 2024 The Authors.

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Decoding six basic emotions from brain functional connectivity patterns

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science China(Life sciences) 2023年第4期66卷 835-847页

作者： Chunyu Liu Yingying Wang Xiaoyue Sun Yizhou Wang Fang Fang School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health Peking UniversityBeijing 100871China Department of Psychology and Behavioral Sciences Zhejiang UniversityHangzhou 310028China IDG/McGovern Institute for Brain Research Peking UniversityBeijing 100871China Peking-Tsinghua Center for Life Sciences Peking UniversityBeijing 100871China Center on Frontiers of Computing Studies School of Computer SciencePeking UniversityBeijing 100871China

Although distinctive neural and physiological states are suggested to underlie the six basic emotions,basic emotions are often indistinguishable from functional magnetic resonance imaging(f MRI)voxelwise activation(VA)***,we hypothesize that functional connectivity(FC)patterns across brain regions may contain emotion-representation information beyond VA *** collected whole-brain f MRI data while human participants viewed pictures of faces expressing one of the six basic emotions(i.e.,anger,disgust,fear,happiness,sadness,and surprise)or showing neutral *** obtained FC patterns for each emotion across brain regions over the whole brain and applied multivariate pattern decoding to decode emotions in the FC pattern representation *** results showed that the whole-brain FC patterns successfully classified not only the six basic emotions from neutral expressions but also each basic emotion from other *** emotion-representation network for each basic emotion that spanned beyond the classical brain regions for emotion processing was ***,we demonstrated that within the same brain regions,FC-based decoding consistently performed better than VA-based *** together,our findings revealed that FC patterns contained emotional information and advocated for paying further attention to the contribution of FCs to emotion processing.

关键词： decoding basic emotions functional connectivity voxelwise activation multivariate pattern analysis

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