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International Conference on computer Supported Cooperative Work in Design

作者： Ye Liu Yan Pan Jian Yin School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Artificial Intelligence Department Lizhi Inc. Beijing China Big Data Department Lizhi Inc. Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China School of Artificial Intelligence Sun Yat-sen University Zhuhai China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Image hash coding learning based on deep neural network have a wide range of application scenarios and become a research hotspot in recent years. In the framework of most existing deep hashing methods, it is necessary to first design a loss function that can converge quickly, and then select an appropriate pre-training deep network and tune it. In order to balance global and local constraints, this paper combines the design of loss functions required by the processes of image feature learning and image hash coding, and adds the constraints of triple loss of hash centers and dissimilar pairs. The proposed novel loss function can give consideration to the characteristics of global hash centers, local dissimilarity of images and image classification labels in the training process. The pre-trained large models have been incorporated into the framework, which can extract image embeddings to assist in the generation of hash codes. The comparative experiments on several image datasets show that the DTLH method proposed in this paper can achieve better results than the traditional hashing methods and the deep hashing methods under different coding lengths.

关键词： Training Representation learning Image coding Codes Federated learning Computational modeling Image retrieval

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Deep Learning Classification of Intrahepatic Cholangiocarcinoma and Hepatocellular Carcinoma with Four-Phase Ct Images

SSRN

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SSRN 2024年

作者： Zhu, Wentao Wu, Yuanfeng Feng, Zhan Cui, Heng Rao, Fan Ni, Yangfan Chen, Geng Xia, Yong Zhejiang Lab Hangzhou China Department of Radiology College of Medicine The First Affiliated Hospital Zhejiang University China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China

Liver cancer is one of the leading causes of cancer-related death globally. The differentiation of hepatic tumors is critical for designing an accurate treatment plan and improving the five-year survival rate. Recently, multiphase CT images have become the first-line examination approach for clinical diagnosis. Existing methods have been proposed to distinguish hepatic tumors based on deep learning using multiphase CT. However, CNN-based models fail to explicitly establish temporal relationships among different phases, and RNN-based methods rely on expensive calculations due to the recurrent mechanism. In this paper, we proposed a transformer-based deep learning method for identifying intrahepatic cholangiocarcinoma and hepatocellular carcinoma using four-phase CT images as input. Specifically, a difference attention module was developed to improve the feature representation by using the phase difference as interphase temporal information. Moreover, a transformer-based structure was used for classification to explore the long-term context and capture the temporal relationship among phases using a multihead self-attention module. Experiments with clinical datasets are implemented to evaluate the performance of the proposed method and mainstream methods. In the experiments, the proposed method demonstrated superior performance in classifying ICC and HCC on the test set, with an accuracy of 83.72%, a sensitivity of 0.7511, a specificity of 0.9200 and an AUC of 0.90. The results indicate that the proposed method outperforms the traditional deep learning based methods. The architecture can be utilized in other researches using multiple images as input. © 2024, The Authors. All rights reserved.

关键词： Classification (of information)

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Aspect-oriented requirement analysis based on formal method

Aspect-oriented requirement analysis based on formal method

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2021 Asia-Pacific Conference on Image Processing, Electronics and computers, IPEC 2021

作者： Qu, Can Zhang, Xuhui Chen, Haihua Zhang, Lichen School of Information City College of Huizhou Huizhou China School of Electrical and Computer Engineering Nanfang College Guangzhou Guangzhou China School of Data Science Guangzhou Huashang College Guangzhou China School of Computers Guangdong University of Technology Guangzhou China

Aspect-Oriented Programming (AOP) can effectively solve the code-tangling and code-scattering which are caused by crosscutting attributes, However, much work is presented on the programming and the implementation phase, and few works have addressed the early model stages. In this paper, we do research on the requirements engineering phase of software by using AOP technology. First in the requirements analysis phase, we identified the functional requirements and non-functional requirements by the separation of concerns, then to realize the functional requirements with components and the non-functional requirements with aspects. Then we represent the components and aspects by combining with the formal language Aspect-Z which have characteristics of accurate description;As actions accompany with conflicts while two or more aspects effect on the same joint point synchronously, we propose a method to solve by defining aspects priority level. © Published under licence by IOP Publishing Ltd.

关键词： Formal verification

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Double-Active-IRS Aided Wireless Communication: Deployment Optimization and Capacity Scaling

arXiv

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arXiv 2023年

作者： Kang, Zhenyu You, Changsheng Zhang, Rui The Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Shenzhen518055 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China

In this letter, we consider a double-active-intelligent reflecting surface (IRS) aided wireless communication system, where two active IRSs are properly deployed to assist the communication from a base station (BS) to multiple users located in a given zone via the double-reflection links. Under the assumption of fixed per-element amplification power for each active-IRS element, we formulate a rate maximization problem subject to practical constraints on the reflection design, elements allocation, and placement of active IRSs. To solve this non-convex problem, we first obtain the optimal active-IRS reflections and BS beamforming, based on which we then jointly optimize the active-IRS elements allocation and placement by using the alternating optimization (AO) method. Moreover, we show that given the fixed per-element amplification power, the received signal-to-noise ratio (SNR) at the user increases asymptotically with the square of the number of reflecting elements;while given the fixed number of reflecting elements, the SNR does not increase with the per-element amplification power when it is asymptotically large. Last, numerical results are presented to validate the effectiveness of the proposed AO-based algorithm and compare the rate performance of the considered double-active-IRS aided wireless system with various benchmark systems. © 2023, CC BY.

关键词： Amplification

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An advanced algorithm for discrimination prevention in data mining

An advanced algorithm for discrimination prevention in data ...

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2022 IEEE Conference on Telecommunications, Optics and computer science, TOCS 2022

作者： Wang, Shuoyu Ren, Jie Fang, Hao Pan, Jun Hu, Xin Zhao, Tingyun Tibet University College of Information Science and Technology Lhasa China Lyuliang University Department of Computer Science and Technology Lvliang China Liaoning University of Traditional Chinese Medicine College of Foreign Languages Shenyang China Beihang University School of Electronic and Information Engineering Beijing China Yangtze Normal University School of Mathematics and Statistics Chongqing China Changzhou College of Information Technology School of Software and Big Data Changzhou China

ISBN: (纸本)9781665470537

data mining has been effectively utilized in huge expanded application areas. Associations overall go with significant business choices in light of mining of data. Discrimination in navigation and preservation of protection of data are a couple of significant difficulties in data mining. Discrimination is of two sorts, Direct Discrimination and Indirect Discrimination. Discrimination prevention includes two significant angles disclosure of direct and additionally roundabout discrimination and data transformation as alteration of separating rules without influencing data quality. Existing strategies for discrimination prevention utilize one of the three methodologies, in particular, the pre-process, in-process or post-process approach. Tests for the exhibition assessment of all calculations by fluctuating these info boundaries, each in turn to review and break down impact of these boundaries. © 2022 IEEE.

关键词： data mining

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Weakly-Supervised Action Localization by Hierarchically-structured Latent Attention Modeling

Weakly-Supervised Action Localization by Hierarchically-stru...

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International Conference on computer Vision (ICCV)

作者： Guiqin Wang Peng Zhao Cong Zhao Shusen Yang Jie Cheng Luziwei Leng Jianxing Liao Qinghai Guo School of Computer Science and Technology Xi’an Jiao Tong University School of Mathematics and Statistics Xi’an Jiao Tong University National Engineering Laboratory for Big Data Analytics Xi’an Jiao Tong University ACS Lab Huawei Technologies

Weakly-supervised action localization aims to recognize and localize action instancese in untrimmed videos with only video-level labels. Most existing models rely on multiple instance learning(MIL), where the predictions of unlabeled instances are supervised by classifying labeled bags. The MIL-based methods are relatively well studied with cogent performance achieved on classification but not on localization. Generally, they locate temporal regions by the video-level classification but overlook the temporal variations of feature semantics. To address this problem, we propose a novel attention-based hierarchically-structured latent model to learn the temporal variations of feature semantics. Specifically, our model entails two components, the first is an unsupervised change-points detection module that detects change-points by learning the latent representations of video features in a temporal hierarchy based on their rates of change, and the second is an attention-based classification model that selects the change-points of the foreground as the boundaries. To evaluate the effectiveness of our model, we conduct extensive experiments on two benchmark datasets, THUMOS-14 and ActivityNet-v1.3. The experiments show that our method outperforms current state-of-the-art methods, and even achieves comparable performance with fully-supervised methods.

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Cell2Sentence: teaching large language models the language of biology 24

Cell2Sentence: teaching large language models the language o...

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Proceedings of the 41st International Conference on Machine Learning

作者： Daniel Levine Syed Asad Rizvi Sacha Lévy Nazreen Pallikkavaliyaveetil David Zhang Xingyu Chen Sina Ghadermarzi Ruiming Wu Zihe Zheng Ivan Vrkic Anna Zhong Daphne Raskin Insu Han Antonio Henrique De Oliveira Fonseca Josue Ortega Caro Amin Karbasi Rahul M. Dhodapkar David Van Dijk Department of Computer Science Yale University New Haven CT School of Engineering Applied Science University of Pennsylvania Philadelphia PA School of Computer and Communication Sciences Swiss Federal Institute of Technology Lausanne Lausanne Switzerland Department of Computer Science Yale University New Haven CT and Department of Neuroscience Yale School of Medicine New Haven CT Department of Computer Science Yale University New Haven CT and Department of Neuroscience Yale School of Medicine New Haven CT and Wu Tsai Institute Yale University New Haven CT Google and Yale Institute for Foundations of Data Science New Haven CT and Department of Computer Science Yale University New Haven CT and Yale School of Engineering and Applied Science New Haven CT Roski Eye Institute University of Southern California Los Angeles CA and Department of Internal Medicine (Cardiology) Yale School of Medicine New Haven CT Department of Computer Science Yale University New Haven CT and Yale Institute for Foundations of Data Science New Haven CT and Wu Tsai Institute Yale University New Haven CT and Cardiovascular Research Center Yale School of Medicine New Haven CT and Interdepartmental Program in Computational Biology & Bioinformatics Yale University New Haven CT and Department of Internal Medicine (Cardiology) Yale School of Medicine New Haven CT

We introduce Cell2Sentence (C2S), a novel method to directly adapt large language models to a biological context, specifically single-cell transcriptomics. By transforming gene expression data into "cell sentences," C2S bridges the gap between natural language processing and biology. We demonstrate cell sentences enable the fine-tuning of language models for diverse tasks in biology, including cell generation, complex cell-type annotation, and direct data-driven text generation. Our experiments reveal that GPT-2, when fine-tuned with C2S, can generate biologically valid cells based on cell type inputs, and accurately predict cell types from cell sentences. This illustrates that language models, through C2S fine-tuning, can acquire a significant understanding of single-cell biology while maintaining robust text generation capabilities. C2S offers a flexible, accessible framework to integrate natural language processing with transcriptomics, utilizing existing models and libraries for a wide range of biological applications.

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Doubly Perturbed Task Free Continual Learning

arXiv

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arXiv 2023年

作者： Lee, Byung Hyun Oh, Min-Hwan Chun, Se Young Department of Electrical and Computer Engineering Seoul National University Korea Republic of Graduate School of Data Science Seoul National University Korea Republic of INMC & IPAI Seoul National University Korea Republic of

Task Free online Continual Learning (TF-CL) is a challenging problem where the model incrementally learns tasks without explicit task information. Although training with entire data from the past, present as well as future is considered as the gold standard, naive approaches in TF-CL with the current samples may be conflicted with learning with samples in the future, leading to catastrophic forgetting and poor plasticity. Thus, a proactive consideration of an unseen future sample in TF-CL becomes imperative. Motivated by this intuition, we propose a novel TF-CL framework considering future samples and show that injecting adversarial perturbations on both input data and decision-making is effective. Then, we propose a novel method named Doubly Perturbed Continual Learning (DPCL) to efficiently implement these input and decision-making perturbations. Specifically, for input perturbation, we propose an approximate perturbation method that injects noise into the input data as well as the feature vector and then interpolates the two perturbed samples. For decision-making process perturbation, we devise multiple stochastic classifiers. We also investigate a memory management scheme and learning rate scheduling reflecting our proposed double perturbations. We demonstrate that our proposed method outperforms the state-of-the-art baseline methods by large margins on various TF-CL benchmarks. © 2023, CC BY-NC-SA.

关键词： Decision making

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Establishment of Chinese Speech Emotion database of Broadcasting

Establishment of Chinese Speech Emotion Database of Broadcas...

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2021 International Conference on Culture-Oriented science and Technology, ICCST 2021

作者： Tu, Zhongwen Liu, Bin Zhao, Wei Cao, Baoyu Communication University of China Broadcasting and Anchoring School Beijing China Communication University of China School of Information and Communication Engineering Beijing China Communication University of China School of Data Science and Intelligent Media Beijing China Communication University of China School of Computer and Cyber Science Beijing China

ISBN: (纸本)9781665442541

In order to provide an effective and available database for speech emotion recognition and research on broadcast speech, an speech emotion database based on broadcast vocalization method is established, which contains seven discrete emotions, namely, happy, very happy, sad, very sad, angry, very angry and neutral. The database is recorded by four graduate students (two male and two female) from Communication University of China, who majoring in broadcasting and anchoring, at the professional recording room in a performance way. To prove the effectiveness of the database, an evaluation group of 10 people, both professional and nonprofessional, was organized to conduct a subjective evaluation experiment on the randomly selected samples from the database. The experiment results show that this database is an effective and reliable Chinese speech emotion database of broadcasting. © 2021 IEEE.

关键词： database systems

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Tackling the Incomplete Annotation Issue in Universal Lesion Detection Task By Exploratory Training

arXiv

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arXiv 2023年

作者： Bai, Xiaoyu Ma, Benteng Li, Changyang Xia, Yong National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Poly-technical University Xi’an710072 China Sydney Polytechnic Institute NSW2000 Australia

Universal lesion detection (ULD) has great value for daily clinical practice as it aims to detect various types of lesions in multiple organs on medical images. Deep learning methods have shown promising results, but demanding substantial volumes of annotated data for effective training. Nevertheless, annotating medical images is costly and requires specialized knowledge. Additionally, the diverse forms and contrasts of objects in medical images make fully annotation even more challenging, resulting in incomplete annotations. Directly training ULD detectors on such datasets can yield suboptimal results. Pseudo-label-based methods examine the data in the training set and mine unlabelled objects for retraining, which have shown to be effective to tackle this issue. Presently, top-performing methods rely on a dynamic label-mining mechanism, operating at the mini-batch level. However, the model’s performance varies at different iterations, leading to inconsistencies in the quality of the mined labels during training. This inconsistency limits their potential for performance enhancement. Inspired by the observation that deep models learn concepts with increasing complexity, we introduce an innovative exploratory training strategy to assess the reliability of mined lesions over time. Specifically, we introduce a teacher-student detection model as the basis, where the teacher model’s predictions are combined with incomplete annotations to train the student model. On top of that, we design a prediction bank to record all high-confidence predictions. Each sample is trained several times, allowing us to get a sequence of records for each sample. If a prediction consistently appears in the record sequence, it is likely to be a true object, otherwise it may just a noise. This serves as a crucial criterion for selecting reliable mined lesions for subsequent retraining. Our experimental results substantiate that the proposed framework surpasses state-of-the-art methods on two med

关键词： Forecasting

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