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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Jeong, Ujun Ding, Kaize Cheng, Lu Guo, Ruocheng Shu, Kai Liu, Huan Arizona State University School of Computing and Augmented Intelligence United States University of Illinois Chicago Department of Computer Science United States Bytedance Ai Lab London United Kingdom Illinois Institute of Technology Department of Computer Science United States

ISBN: (纸本)9781665480451

Nowadays, fake news easily propagates through online social networks and becomes a grand threat to individuals and society. Assessing the authenticity of news is challenging due to its elaborately fabricated contents, making it difficult to obtain large-scale annotations for fake news data. Due to such data scarcity issues, detecting fake news tends to fail and overfit in the supervised setting. Recently, graph neural networks (GNNs) have been adopted to leverage the richer relational information among both labeled and unlabeled instances. Despite their promising results, they are inherently focused on pairwise relations between news, which can limit the expressive power for capturing fake news that spreads in a group-level. For example, detecting fake news can be more effective when we better understand relations between news pieces shared among susceptible users. To address those issues, we propose to leverage a hypergraph to represent group-wise interaction among news, while focusing on important news relations with its dual-level attention mechanism. Experiments based on two benchmark datasets show that our approach yields remarkable performance and maintains the high performance even with a small subset of labeled news data. © 2022 IEEE.

关键词： Graph neural networks

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On the necessity of collaboration for online model selection with decentralized data 24

On the necessity of collaboration for online model selection...

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

作者： Junfan Li Zheshun Wu Zenglin Xu Irwin King School of Computer Science and Technology Harbin Institute of Technology Shenzhen Pengcheng Lab and Artificial Intelligence Innovation and Incubation (AI 3 ) Institute Fudan University and Shanghai Academy of AI for Science Department of Computer Science and Engineering The Chinese University of Hong Kong

ISBN: (纸本)9798331314385

We consider online model selection with decentralized data over M clients, and study the necessity of collaboration among clients. Previous work proposed various federated algorithms without demonstrating their necessity, while we answer the question from a novel perspective of computational constraints. We prove lower bounds on the regret, and propose a federated algorithm and analyze the upper bound. Our results show (i) collaboration is unnecessary in the absence of computational constraints on clients; (ii) collaboration is necessary if the computational cost on each client is limited to o(K), where K is the number of candidate hypothesis spaces. We clarify the unnecessary nature of collaboration in previous federated algorithms for distributed online multi-kernel learning, and improve the regret bounds at a smaller computational and communication cost. Our algorithm relies on three new techniques including an improved Bernstein's inequality for martingale, a federated online mirror descent framework, and decoupling model selection and prediction, which might be of independent interest.

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Camouflaged Object Segmentation with Transformer 6th

Camouflaged Object Segmentation with Transformer

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6th International Conference on Cognitive Systems and Signal Processing, ICCSIP 2021

作者： Wang, Haiwen Wang, Xinzhou Sun, Fuchun Song, Yixu State Key Lab on Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing China School of Electronic and Information Engineering Tongji University Shanghai China

ISBN: (纸本)9789811692468

The Vision Transformer (ViT) [6] directly applies a Transformer architecture to image classification and achieves an impressive result compared with convolutional networks. This paper presents a new ViT-base camouflaged object segmentation method, called COS Transformer, which aims to identify and segment objects concealed in a complex environment. The high intrinsic similarities between object and surrounding makes the task challenging than salient object detection. Most recent camouflaged object segmentation methods(e.g., EGNet [29], PraNet [10] and SINet [9]) adopt convolutional network with an encoder-decoder architecture and focused on increasing the receptive field, which is limited by the depth of the network. In camouflaged object segmentation (COS) task, the camouflage is mainly relied on contrast of the whole surrounding instead of the local information. We introduce transformer with global context awareness in this paper, for self-attention allowing COS Transformer to aggregate features globally even in the lowest layers. Specifically, the architecture is composed of a transformer-based encoder and a multi-layers feature aggregation refinement module. After training on the COD10K [9] dataset, COS Transformer attains excellent results compared to state-of-the-art convolutional networks, e.g. 11.7% improvement of E [8] on the COD10K contrasted to SINet. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Convolution

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Interfacial modification using the cross-linkable tannic acid for highly-efficient perovskite solar cells with excellent stability

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Journal of Energy Chemistry 2024年第4期91卷 236-244页

作者： Xing Gao Lirong Rong Fei Wu Yen-Hung Lin Ye Zeng Junhong Tan Rongxing He Cheng Zhong Linna Zhu Chongqing Key Laboratory for Advanced Materials and Technologies of Clean Energy School of Materials&EnergySouthwest UniversityChongqing 400715China Hubei Key Lab on Organic and Polymeric Optoelectronic Materials Department of ChemistryWuhan UniversityWuhan 430072HubeiChina Key Laboratory of Luminescence Analysis and Molecular Sensing Ministry of EducationSchool of Chemistry and Chemical EngineeringSouthwest UniversityChongqing 400715China Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyClear Water BayKowloon 999077Hong KongChina State Key Laboratory of Advanced Displays and Optoelectronics Technologies The Hong Kong University of Science and TechnologyClear Water BayKowloon 999077Hong KongChina

Although the performance of perovskite solar cells(PSCs)has been dramatically increased in recent years,stability is still the main obstacle preventing the PSCs from being *** device instability can be caused by a variety of reasons,including ions diffusion,surface and grain boundary defects,*** this work,the cross-linkable tannic acid(TA)is introduced to modify perovskite film through post-treatment *** numerous organic functional groups(–OH and C=O)in TA can interact with the uncoordinated Pb^(2+)and I^(-)ions in perovskite,thus passivating defects and inhibiting ions *** addition,the formed TA network can absorb a small amount of the residual moisture inside the device to protect the perovskite ***,TA modification regulates the energy level of perovskite,and reduces interfacial charge ***,following TA treatment,the device efficiency is increased significantly from 21.31%to 23.11%,with a decreased hysteresis ***,the treated device shows excellent air,thermal,and operational *** light of this,the readily available,inexpensive TA has the potential to operate as a multipurpose interfacial modifier to increase device efficiency while also enhancing device stability.

关键词： Tannic acid Defect passivation lons diffusion Hydrophilic Stability Perovskite solar cells

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ChiseLLM: Unleashing the Power of Reasoning LLMs for Chisel Agile Hardware Development

arXiv

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arXiv 2025年

作者： Wang, Bowei Gao, Jiaran Feng, Yelai Chen, Renzhi Li, Shanshan Wang, Lei Computer Department National University of Defense Technology Changsha China School of Computer & Communication Engineering University of Science and Technology Beijing Beijing China Intelligent Microelectronics Center Qiyuan Lab Beijing China College of Computer Science and Technology National University of Defense Technology Changsha China Defense Innovation Institute Academy of Military Science Beijing China

The growing demand for Domain-Specific Architecture (DSA) has driven the development of Agile Hardware Development Methodology (AHDM). Hardware Construction Language (HCL) like Chisel offers high-level abstraction features, making it an ideal language for HCL-Based AHDM. While Large Language Models (LLMs) excel in code generation tasks, they still face challenges with Chisel generation, particularly regarding syntax correctness and design variability. Recent reasoning models have significantly enhanced code generation capabilities through test-time scaling techniques. However, we found that reasoning models without domain adaptation cannot bring substantial benefits to Chisel code generation tasks. This paper presents ChiseLLM, a solution comprising data processing and transformation, prompt-guided reasoning trace synthesis, and domain-adapted model training. We constructed high-quality datasets from public RTL code resources and guided the model to adopt structured thinking patterns through prompt enhancement methods. Experiments demonstrate that our ChiseLLM-7B and ChiseLLM-32B models improved syntax correctness by 18.85% and 26.32% respectively over base models, while increasing variability design ability by 47.58% compared to baseline reasoning models. Our datasets and models are publicly available, providing high-performance, cost-effective models for HCL-Based AHDM, and offering an effective baseline for future research. Github repository: https://***/observerw/ChiseLLM © 2025, CC BY.

关键词： Algorithmic languages

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LBA: Matching Theory Based Latency-Sensitive Binary Offloading in IoT-Fog Networks

LBA: Matching Theory Based Latency-Sensitive Binary Offloadi...

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International Communication Systems and Networks and Workshops, COMSNETS

作者： Priyanka Soni Omkar Chand Deshlahre Anurag Satpathy Sourav Kanti Addya Department of Computer Science and Engineering Cloud and Smart System Services Lab National Institute of Technology Karnataka India Missouri University of Science and Technology Rolla MO USA

The Internet of Things (IoT) is growing more popular with applications like healthcare services, traffic monitoring, video streaming, smart homes, etc. These applications produce an enormous amount of data, so a realistic option in this instance is to offload computational tasks to their proximity fog nodes (FNs) instead of the remote cloud. However, a negligent offloading strategy may cause anomalous computational traffic load at the FNs, causing congestion that may adversely affect the latency. However, the latency of task flows from IoT devices comprises communications latency at BS and computational latency at FNs. Therefore, designing offloading algorithms to distribute the computational load at FN evenly and efficiently utilize the FN resources is crucial. To solve this problem, we proposed LBA in a fog network with a binary offloading strategy using the matching theory-based approach. We utilize the Analytic Hierarchy Process (AHP) to generate the preference list. Furthermore, the binary offloading technique follows the deferred acceptance algorithm (DAA) to produce a stable assignment, and the complete offloading problem is modeled as a one-to-many matching game. Comprehensive simulations ensure that LBA can accomplish a better-balanced assignment for homogeneous and heterogeneous input concerning all the baseline algorithms.

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Scale-Aware Super-Resolution Network With Dual Affinity Learning for Lesion Segmentation From Medical Images

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IEEE Transactions on Neural Networks and Learning Systems 2024年第6期36卷 10530-10543页

作者： Luyang Luo Yanwen Li Zhizhong Chai Huangjing Lin Pheng-Ann Heng Hao Chen Department of Computer Science and Engineering The Hong Kong University of Science and Technology Sai Kung Hong Kong Department of Biomedical Informatics Harvard University Cambridge MA USA Imsight AI Research Lab Shenzhen China Department of Computer Science and Engineering The Chinese University of Hong Kong Shatin Hong Kong Department of Computer Science and Engineering Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong China HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute Futian Shenzhen China

Convolutional neural networks (CNNs) have shown remarkable progress in medical image segmentation. However, the lesion segmentation remains a challenge to state-of-the-art CNN-based algorithms due to the variance in scales and shapes. On the one hand, tiny lesions are hard to delineate precisely from the medical images which are often of low resolutions. On the other hand, segmenting large-size lesions requires large receptive fields, which exacerbates the first challenge. In this article, we present a scale-aware super-resolution (SR) network to adaptively segment lesions of various sizes from low-resolution (LR) medical images. Our proposed network contains dual branches to simultaneously conduct lesion mask SR (LMSR) and lesion image SR (LISR). Meanwhile, we introduce scale-aware dilated convolution (SDC) blocks into the multitask decoders to adaptively adjust the receptive fields of the convolutional kernels according to the lesion sizes. To guide the segmentation branch to learn from richer high-resolution (HR) features, we propose a feature affinity (FA) module and a scale affinity (SA) module to enhance the multitask learning of the dual branches. On multiple challenging lesion segmentation datasets, our proposed network achieved consistent improvements compared with other state-of-the-art methods. Code will be available at: https://***/poiuohke/SASR_Net .

关键词： Lesions Image segmentation Feature extraction Medical diagnostic imaging Superresolution Diseases Decoding Computed tomography Technological innovation Skin

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A Lightweight Security Scheme for Failure Detection in Microservices IoT-Edge Networks 14th

A Lightweight Security Scheme for Failure Detection in Micro...

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14th International Conference on Sensing technology, ICST 2022

作者： Sodhro, Ali Hassan Lakhan, Abdullah Pirbhulal, Sandeep Groenli, Tor Morten Abie, Habtamu Department of Computer Science Kristianstad University Kristianstad291 88 Sweden Department of Technology Mobile Technology Lab Kristiania University College Oslo Norway Norwegian Computing Center Oslo0373 Norway

ISBN: (纸本)9783030988852

Nowadays, microservices-based applications such as E-Business, E-Healthcare, 3D-Gaming, and Augmented Reality have latterly drawn attention in the research area. The microservices enabled applications are different from traditional monolithic applications with high demand of security and fault detection, therefore, a lightweight secure and failure detection enabled schemes widely required for the new applications. This paper proposes a new lightweight microservices mobile cloud (Mob-Cloud) framework that replaces the heavyweight virtual machine (VM) based on mobile cloud computing (MCC). The study devises MFHE (Modified Fully Homomorphism Encryption) and WATFA (Workload Assignment Transient Fault Aware) schemes to deal with security and failures. Simulation results show that the proposals are practical for the considered problem. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cryptography

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A 120GHz Receiver with $1/f$ Noise Mitigation Technique for Near-Field IoT

A 120GHz Receiver with $1/f$ Noise Mitigation Technique for ...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Ademola A. Mustapha Mihai Sanduleanu Department of Electrical Engineering and Computer Science Khalifa University of Science and Technology UAE Systems-on-Chip Lab Khalifa University of Science and Technology UAE

This paper presents a 120 GHz receiver for Near-Field IoT sensors, without the conventional LNA and mixers building blocks. A new technique for the reduction of the low-frequency 1/f noise is also presented. The technique is used to improve the sensitivity of the 120GHz receiver with a data rate of 100kb/s, by lowering the 1/f noise corner frequency. The technique involves the complementary switching of two MOSFET transistors in order to effectively act as a single MOSFET transistor. It also involves the isolation of the drain terminals of these identical MOSFET transistors in order to eliminate any electric field from the complementary transistors which may affect the de-trapping process. The complementary switching in the literature has only been applied to low-frequency circuits with the highest operating frequency being 120 kHz. The proposed technique leads to a 9dB reduction in the drain current noise at frequencies up to 100 kHz. Realized in a 65nm CMOS LPE technology from GlobalFoundries™, the receiver has a measured sensitivity of −46dBm, power consumption of $\mathbf{52}\ \boldsymbol{\mu} \mathbf{W}$ , and energy efficiency of 5.2pJ/bit. The occupied on-chip area is 0.56mm 2 .

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An Approximation for Routing Planning, Mobile Charging, and Energy Sharing for Sensing Devices

An Approximation for Routing Planning, Mobile Charging, and ...

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IEEE International Conference on Web Services (ICWS)

作者： Zifeng Liu Dejun Kong Yucen Gao Haipeng Dai Xiaofeng Gao Tian He Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence Shanghai Jiao Tong University Shanghai China Department of Computer Science and Technology Nanjing University Nanjing China Department of Computer Science and Technology University of Minnesota Minnesota American

Wireless Charging Vehicles (WCVs) have been widely explored as a means of enabling continuous operation of sensors that are powered by batteries. However, the energy consumption of WCVs can be inefficient, leading to insufficient energy supply for sensors that are located in challenging-to-access areas. Consequently, there is a need to design an effective charging and energy sharing scheme for sensors to improve the quality of service in this setup. This paper focuses on the Joint optimization of Mobile charging and Energy sharing of sensors (JOIN-ME) problem, which is known to be NP-hard. To address this challenge, we first transform JOIN-ME into a submodular maximization problem with general constraints. Subsequently, we propose the Routing planning, Mobile charging, and Energy sharing for Sensing devices (RMES) algorithm, which has an approximation ratio of 1/8(1-1/e). Finally, we conduct experiments to showcase the superior performance of RMES compared to existing baselines, under varying scales and constraints. Our work on the design of an efficient charging and energy sharing scheme for sensors can significantly improve the reliability and longevity of wireless sensor networks, enabling the deployment of these networks in critical applications such as environmental monitoring, crowd sensing, and security surveillance.

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