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SSRN 2022年

作者： Dalla Palma, Stefano van Asseldonk, Chiel Catolino, Gemma Di Nucci, Dario Palomba, Fabio Tamburri, Damian Andrew Tilburg University Jheronimus Academy of Data Science ’s-Hertogenbosch Netherlands University of Salerno Software Engineering Lab Fisciano Italy Eindhoven University of Technology Jheronimus Academy of Data Science ’s-Hertogenbosch Netherlands

Infrastructure-as-Code (IaC) helps keep up with the demand for fast, reliable, high-quality services by managing and provisioning infrastructures through machine-readable configuration files. While ensuring efficient and repeatable routines for system provisioning, those files might still be affected by code smells that negatively affect quality and code maintenance. While they are broadly researched for traditional source code development, none exists for the "Topology and Orchestration Specification for Cloud Applications" (TOSCA), the technology-agnostic industry standard for IaC. In this paper, we investigate a prominent traditional implementation code smell potentially applicable to TOSCA, that is, Large Class , or "Blob Blueprint" in IaC terms. We compare several popular clustering techniques with a detector applying the Interquartile Rule on a large dataset of manually validated observations related to Blob Blueprints . We provide insights on structural code metrics that corroborate previous findings and empirically show that metric-based detectors reach high performance in detecting Blob Blueprints . We deem our results put forward a new research path toward dealing with this problem, e.g., in the scope of fully automated service pipelines. © 2022, The Authors. All rights reserved.

关键词： Blueprints

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Domain Adaptation for Large-Vocabulary Object Detectors

arXiv

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arXiv 2024年

作者： Jiang, Kai Huang, Jiaxing Xie, Weiying Lei, Jie Li, Yunsong Shao, Ling Lu, Shijian State Key Laboratory of Integrated Services Networks Xidian University Xi’an710071 China S-lab School of Computer Science and Engineering Nanyang Technological University Singapore UCAS-Terminus AI Lab University of Chinese Academy of Sciences Beijing China School of Electrical and Data Engineering The University of Technology Sydney Australia

Large-vocabulary object detectors (LVDs) aim to detect objects of many categories, which learn super objectness features and can locate objects accurately while applied to various downstream data. However, LVDs often struggle in recognizing the located objects due to domain discrepancy in data distribution and object vocabulary. At the other end, recent vision-language foundation models such as CLIP demonstrate superior open-vocabulary recognition capability. This paper presents KGD, a Knowledge Graph Distillation technique that exploits the implicit knowledge graphs (KG) in CLIP for effectively adapting LVDs to various downstream domains. KGD consists of two consecutive stages: 1) KG extraction that employs CLIP to encode downstream domain data as nodes and their feature distances as edges, constructing KG that inherits the rich semantic relations in CLIP explicitly;and 2) KG encapsulation that transfers the extracted KG into LVDs to enable accurate cross-domain object classification. In addition, KGD can extract both visual and textual KG independently, providing complementary vision and language knowledge for object localization and object classification in detection tasks over various downstream domains. Experiments over multiple widely adopted detection benchmarks show that KGD outperforms the state-of-the-art consistently by large margins. Copyright © 2024, The Authors. All rights reserved.

关键词： Distillation

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MeG2: In-Memory Acceleration for Genome Graphs Analysis

MeG2: In-Memory Acceleration for Genome Graphs Analysis

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Design Automation Conference

作者： Yu Huang Long Zheng Haifeng Liu Zhuoran Zhou Dan Chen Pengcheng Yao Qinggang Wang Xiaofei Liao Hai Jin National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Laboratory Huazhong University of Science and Technology Wuhan China Zhejiang Lab Hangzhou China

Genome graphs analysis has emerged as an effective means to enable mapping DNA fragments (known as reads) to the reference genome. It replaces the traditional linear reference with a graph-based representation to augment the genetic variations and diversity information, significantly improving the quality of genotyping. The in-depth characterization of genome graphs analysis uncovers that it is bottlenecked by the irregular seed index access and the intensive alignment operation, stressing both the memory system and computing *** on these observations, we propose MeG 2 , a lightweight, commodity DRAM-compliant, processing-in-memory architecture to accelerate genome graphs analysis. MeG 2 is specifically integrated with the capabilities of both near-memory processing and bitwise in-situ computation. Specifically, MeG 2 leverages the low access latency of near-memory processing with the index-centric offload mechanism to alleviate the irregular memory access in the seeding procedure, and harnesses the row-parallel capacity of in-situ computation with the distance-aware technique to exploit the intensive computational parallelism in the alignment process. Results show that MeG 2 outperforms the CPU-, GPU-, and ASIC-based genome graphs analysis solutions by 502× (30.2×), 272× (15.1× ), and 5.5× (8.3×) for short (long) reads, while reducing energy consumption by 1628× (85.6×), 1443× (77.1×), and 7.8× (11.7×), respectively. We also demonstrate that MeG 2 offers significant improvements over existing PIM-based genome sequence analysis accelerators.

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Robust Cross-Domain Speaker Verification with Multi-Level Domain Adapters

Robust Cross-Domain Speaker Verification with Multi-Level Do...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Wen Huang Bing Han Shuai Wang Zhengyang Chen Yanmin Qian AI Institute Department of Computer Science and Engineering Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence Shanghai Jiao Tong University Shanghai China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China

Speaker verification encounters significant challenges when confronted with diverse domain data, often resulting in performance degradation due to domain mismatch. To enhance performance in cross-domain scenarios, we introduce the Domain Adapter, an adaptable module designed for specific domains. This module learns and integrates domain-specific information with speaker-related data, mitigating domain-related variations and promoting convergence of utterance embeddings from the same speaker across diverse domains. It offers configurability across multiple levels and is adaptable to various backbone architectures. Our proposed module substantially enhances cross-domain performance with minimal parameter increments while effectively generalizing to previously unseen domains. In our experiments, we present results on the 3D-Speaker dataset, which provides acoustically-relevant attributes crucial for domain categorization and the subsequent learning of domain information. The top-performing system integrated with domain adapters achieved 10.8%, 14.8%, and 21.1% EER improvements over the baseline across three 3D-Speaker dataset trials.

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Stabilization of switched systems with unstable modes via mode-partition-dependent ADT

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Journal of the Franklin Institute 2023年第2期360卷 1308-1326页

作者： Jin, Cui-Li Wang, Qing-Guo Wang, Rui Wu, Di Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education and School of Control Science and Engineering Dalian University of Technology Dalian116024 China Institute of Artificial Intelligence and Future Networks Beijing Normal University at ZhuhaiGuangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College Zhuhai519087 China Key Laboratory of Advanced Technology for Aerospace Vehicles Liaoning Province and School of Aeronautics and Astronautics Dalian University of Technology Dalian116024 China

This paper concerns the stability of switched systems orchestrating between unstable modes. First, a mode partition is applied to select stabilizing switching from the switching between modes from different subsets. Second, a piecewise Lyapunov-like function is applied to decline at stabilizing switching times. Then, the stability criteria build on mode-partition-dependent average dwell time by collecting enough stabilizing switching behavior to stabilize the destabilizing switching behavior and unstable modes. Finally, the simulation verification shows the feasibility and effectiveness of the method. © 2022 The Franklin Institute

关键词： Stability criteria

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Towards Unified Prompt Tuning for Few-shot Text Classification

Towards Unified Prompt Tuning for Few-shot Text Classificati...

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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Wang, Jianing Wang, Chengyu Luo, Fuli Tan, Chuanqi Qiu, Minghui Yang, Fei Shi, Qiuhui Huang, Songfang Gao, Ming School of Data Science and Engineering East China Normal University Shanghai China Alibaba Group Hangzhou China Zhejiang Lab Hangzhou China Ant Group Hangzhou China Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention School of Psychology and Cognitive Science East China Normal University Shanghai China

Prompt-based fine-tuning has boosted the performance of Pre-trained Language Models (PLMs) on few-shot text classification by employing task-specific prompts. Yet, PLMs are unfamiliar with prompt-style expressions during pre-training, which limits the few-shot learning performance on downstream tasks. It would be desirable if the models can acquire some prompting knowledge before adapting to specific NLP tasks. We present the Unified Prompt Tuning (UPT) framework, leading to better few-shot text classification for BERT-style models by explicitly capturing prompting semantics from non-target NLP datasets. In UPT, a novel paradigm Prompt-Options-Verbalizer is proposed for joint prompt learning across different NLP tasks, forcing PLMs to capture task-invariant prompting knowledge. We further design a self-supervised task named Knowledge-enhanced Selective Masked Language Modeling to improve the PLM's generalization abilities for accurate adaptation to previously unseen tasks. After multi-task learning across multiple tasks, the PLM can be better prompt-tuned towards any dissimilar target tasks in low-resourced settings. Experiments over a variety of NLP tasks show that UPT consistently outperforms state-of-the-arts for prompt-based fine-tuning. © 2022 Association for Computational Linguistics.

关键词： Modeling languages

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HCA-NET: Hierarchical Context Attention Network for Intervertebral Disc Semantic labeling

HCA-NET: Hierarchical Context Attention Network for Interver...

引用

IEEE International Symposium on Biomedical Imaging

作者： Afshin Bozorgpour Bobby Azad Reza Azad Yury Velichko Ulas Bagci Dorit Merhof Faculty of Informatics and Data Science University of Regensburg Germany Electrical Engineering and Computer Science Department South Dakota State University USA Faculty of Electrical Engineering and Information Technology RWTH Aachen University Germany Machine and Hybrid Intelligence Lab Northwestern University Chicago IL USA Fraunhofer Institute for Digital Medicine MEVIS Germany

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Accurate and automated segmentation of intervertebral discs (IVDs) in medical images is crucial for assessing spine-related disorders, such as osteoporosis, vertebral fractures, or IVD herniation. We present HCA-Net, a novel contextual attention network architecture for semantic labeling of IVDs, with a special focus on exploiting prior geometric information. Our approach excels at processing features across different scales and effectively consolidating them to capture the intricate spatial relationships within the spinal cord. To achieve this, HCA-Net models IVD labeling as a pose estimation problem, aiming to minimize the discrepancy between each predicted IVD location and its corresponding actual joint location. In addition, we introduce a skeletal loss term to reinforce the model’s geometric dependence on the spine. This loss function is designed to constrain the model’s predictions to a range that matches the general structure of the human vertebral skeleton. As a result, the network learns to reduce the occurrence of false predictions and adaptively improves the accuracy of IVD location estimation. Through extensive experimental evaluation on multi-center spine datasets, our approach consistently outperforms previous state-of-the-art methods on both MRI T1w and T2w modalities. The code-base is accessible to the public on GitHub.

关键词： Adaptation models Accuracy Spinal cord Semantics Pose estimation Predictive models Skeleton

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Prototype and Instance Contrastive Learning for Unsupervised Domain Adaptation in Speaker Verification

arXiv

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arXiv 2024年

作者： Huang, Wen Han, Bing Chen, Zhengyang Wang, Shuai Qian, Yanmin Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence AI Institute Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China

Speaker verification system trained on one domain usually suffers performance degradation when applied to another domain. To address this challenge, researchers commonly use feature distribution matching-based methods in unsupervised domain adaptation scenarios where some unlabeled target domain data is available. However, these methods often have limited performance improvement and lack generalization in various mismatch situations. In this paper, we propose Prototype and Instance Contrastive Learning (PICL), a novel method for unsupervised domain adaptation in speaker verification through dual-level contrastive learning. For prototype contrastive learning, we generate pseudo labels via clustering to create dynamically updated prototype representations, aligning instances with their corresponding class or cluster prototypes. For instance contrastive learning, we minimize the distance between different views or augmentations of the same instance, ensuring robust and invariant representations resilient to variations like noise. This dual-level approach provides both high-level and low-level supervision, leading to improved generalization and robustness of the speaker verification model. Unlike previous studies that only evaluated mismatches in one situation, we have conducted relevant explorations on various datasets and achieved state-of-the-art performance currently, which also proves the generalization of our method. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Group RandAugment: Video Augmentation for Action Recognition 5

Group RandAugment: Video Augmentation for Action Recognition

引用

5th International Conference on data science and Information Technology, DSIT 2022

作者： An, Fengmin Zhang, Bingbing Wang, Zhenwei Dong, Wei Zhang, Jianxin School of Computer Science and Engineering Dalian Minzu University Dalian China Institute of Machine Intelligence and Bio-computing Dalian Minzu University Dalian China SEAC Key Lab of Big Data Applied Technology Dalian Minzu University Dalian China School of Information and Communication Engineering Dalian University of Technology Dalian China

ISBN: (数字)9781665498685

ISBN: (纸本)9781665498685

data augmentation, as a critical strategy in deep learning, well improves the sample diversity for network training, leading to the obvious improvement of model generalization ability. Besides, automatic data augmentation, while sparking in image tasks, has attracted too little attention in video recognition task. Therefore, this work explores a novel group random augmentation (GRA) that automatically augments video data for recognition. GRA first collects 21 augmentation transformations to enrich the sample diversity, and then divides these transformations into four groups (i.e., pixel-transform, rigidtransform, erasing-transform, environment-transform) to reduce the excessive regularization caused by similar augmentation. To reduce the computational cost of finding the optimal setting, GRA adopts the combination form to select the optimal situation. For frames in the same video clip, GRA uses synchronous GRA. Additionally, the proposed GRA can be integrated into any existing video frameworks. To prove the effectiveness of GRA, we conduct experiments on two commonly used video action recognition benchmarks (Something-Something V1 & V2) and three typical frameworks (TSM, GSM, and TEA), whose results demonstrate GRA can improve performance in all cases without adding additional computational cost. © 2022 IEEE.

关键词： Deep learning

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An RTDNN-Based Hybrid Precoding and Combining Joint Optimization Algorithm for MUPC-MIMO Systems

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2025年

作者： Liu, Fulai Liu, Yubiao Yang, Xianghuan Duan, Zhuoyao Shi, Baozhu Ji, Li Du, Ruiyan Northeastern University at Qinhuangdao Lab of Key Technology of Millimeter-wave Large-scale MIMO System Hebei Key Laboratory of Marine Perception Network and Data Processing Qinhuangdao066004 China Hebei University College of Mathematics and Information Science Baoding071002 China Northeastern University School of Computer Science and Engineering Shenyang110819 China Qinhuangdao Vocational and Technical College Qinhuangdao066100 China School of Computer Science and Engineering Northeastern University Shenyang110004 China

To achieve a satisfactory balance between energy efficiency (EE) and spectral efficiency (SE) for hybrid precoding and combining (HPC), this paper presents a two-stage joint optimization HPC algorithm based on residual deep neural network (DNN) for multi-user partially connected (MUPC) structure, namely RTDNN algorithm. In the proposed algorithm, a two-stage framework is designed to optimize analog and digital coders at the transmitter and receiver simultaneously, which can effectively preserve coupling relationship of coders between the transmitter and receiver. Specially, the RTDNN architecture is mainly divided into two stage networks, which includes analog stage and digital stage networks. The analog stage network is developed to obtain the optimal analog precoder and combiner simultaneously. On this basis, the digital stage network is constructed to solve the digital precoder and combiner concurrently. To further improve the prediction accuracy of RTDNN-based HPC, residual modules are adopted into the developed two stage networks. Moreover, the proposed RTDNN is trained with unsupervised learning. Simulation results show that the EE of the proposed algorithm is at least enhanced by 0.6 bits/Hz/J with satisfactory SE than other methods. © 2017 IEEE.

关键词： Deep neural networks

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