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arXiv 2022年

作者： Xu, Dongfang Yu, Xianghao Kwan Ng, Derrick Wing Schmeink, Anke Schober, Robert Germany Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong The School of Electrical Engineering and Telecommunications The University of New South Wales Australia Information Theory and Data Analytics RWTH Aachen University Germany

In this paper, we study the robust resource allocation design for secure communication in an integrated sensing and communication (ISAC) system. A multi-antenna dual-functional radar-communication (DFRC) base station (BS) serves multiple single-antenna legitimate users and senses for targets simultaneously, where already identified targets are treated as potential single-antenna eavesdroppers. The DFRC BS scans a sector with a sequence of dedicated beams, and the ISAC system takes a snapshot of the environment during the transmission of each beam. Based on the sensing information, the DFRC BS can acquire the channel state information (CSI) of the potential eavesdroppers. Different from existing works that focused on the resource allocation design for a single snapshot, in this paper, we propose a novel optimization framework that jointly optimizes the communication and sensing resources over a sequence of snapshots with adjustable durations. Besides, artificial noise (AN) is exploited by the BS for joint sensing and physical layer security provisioning. To this end, we jointly optimize the duration of each snapshot, the beamforming vector, and the covariance matrix of the AN for maximization of the system sum secrecy rate over a sequence of snapshots while guaranteeing a minimum required average achievable rate and a maximum information leakage constraint for each legitimate user. The resource allocation algorithm design is formulated as a non-convex optimization problem, where we account for the imperfect CSI of both the legitimate users and the potential eavesdroppers. To make the problem tractable, we derive a bound for the uncertainty region of the potential eavesdroppers' small-scale fading based on a safe approximation, which facilitates the development of a block coordinate descent-based iterative algorithm for obtaining an efficient suboptimal solution. Simulation results illustrate that the proposed scheme can significantly enhance the physical layer securi

关键词： Beamforming

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Artificial intelligence to improve clinical coding practice in Scandinavia: a crossover randomized controlled trial

arXiv

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arXiv 2024年

作者： Chomutare, Taridzo Svenning, Therese Olsen Tejedor Hernández, Miguel Ángel Ngo, Phuong Dinh Budrionis, Andrius Markljung, Kaisa Hind, Lill Irene Torsvik, Torbjørn Mikalsen, Karl Øyvind Babic, Aleksandar Dalianis, Hercules Health Data Analytics Norwegian Centre for E-health Research Tromsø Norway Department of Computer Science UiT The Arctic University of Norway Tromsø Norway Department of Mathematics and Statistics UiT The Arctic University of Norway Tromsø Norway Department of Physics and Technology UiT The Arctic University of Norway Tromsø Norway University Hospital of North Norway Tromsø Norway University Hospital of North Norway Tromsø Norway Department of Computer and Systems Sciences Stockholm University Kista Sweden Group Research and Development DNV Høvik Norway

Trial design Crossover randomized controlled trial. Methods An AI tool, Easy-ICD, was developed to assist clinical coders and was tested for improving both accuracy and time in a user study in Norway and Sweden. Participants were randomly assigned to two groups, and crossed over between coding complex (longer) texts versus simple (shorter) texts, while using our tool versus not using our tool. Results Based on Mann-Whitney U test, the median coding time difference for complex clinical text sequences was 123 seconds (P Copyright © 2024, The Authors. All rights reserved.

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DP2-Pub: Differentially Private High-Dimensional data Publication with Invariant Post Randomization

arXiv

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arXiv 2022年

作者： Jiang, Honglu Yu, Haotian Cheng, Xiuzhen Pei, Jian Pless, Robert Yu, Jiguo The Department of Computer Science and Software Engineering Miami University OxfordOH45056 United States The Department of Computer Science The George Washington University WashingtonDC20052 United States The Department of Data Analytics The George Washington University WashingtonDC20052 United States The School of Computer Science and Technology Shandong University Qingdao266510 China The Departments of Computer Science Biostatistics and Bioinformatics Electrical and Computer Engineering Duke University DurhamNC27708 United States Big Data Institute Qilu University of Technology Jinan250353 China Shandong Fundamental Research Center for Computer Science Qilu University of Technology Shandong Jinan250353 China

A large amount of high-dimensional and heterogeneous data appear in practical applications, which are often published to third parties for data analysis, recommendations, targeted advertising, and reliable predictions. However, publishing these data may disclose personal sensitive information, resulting in an increasing concern on privacy violations. Privacy-preserving data publishing has received considerable attention in recent years. Unfortunately, the differentially private publication of high dimensional data remains a challenging problem. In this paper, we propose a differentially private high-dimensional data publication mechanism (DP2-Pub) that runs in two phases: a Markov-blanket-based attribute clustering phase and an invariant post randomization (PRAM) phase. Specifically, splitting attributes into several low-dimensional clusters with high intra-cluster cohesion and low inter-cluster coupling helps obtain a reasonable allocation of privacy budget, while a double-perturbation mechanism satisfying local differential privacy facilitates an invariant PRAM to ensure no loss of statistical information and thus significantly preserves data utility. We also extend our DP2-Pub mechanism to the scenario with a semi-honest server which satisfies local differential privacy. We conduct extensive experiments on four real-world datasets and the experimental results demonstrate that our mechanism can significantly improve the data utility of the published data while satisfying differential privacy. Copyright © 2022, The Authors. All rights reserved.

关键词： Bayesian networks

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Leveraging Probe data and Machine Learning to Derive and Interpret Macroscopic Fundamental Diagrams Across U.S. Cities

Leveraging Probe Data and Machine Learning to Derive and Int...

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2023 IEEE International Conference on Big data, Bigdata 2023

作者： Jin, Ling Xu, Xiaodan Wang, Yuhan Sadabadi, Kaveh Farokhi Lazar, Alina Don, Duleep Rathgamage Needell, Zachary Spurlock, C Anna Amirgholy, Mahyar Asudegi, Mona Energy Technology Area Berkeley National Laboratory BerkeleyCA94720 United States University of Maryland Center for Advanced Transportation Technology College ParkMD20742 United States Youngstown State University Department of Computer Science and Information YoungstownOH44555 United States Kennesaw State University School of Data Science and Analytics MariettaGA30060 United States Energy Analysis and Environmental Impacts Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Kennesaw State University Civil and Environmental Engineering Department MariettaGA30060 United States Office of Transportation Policy Studies Federal Highway Administration DC WashingtonDC20590 United States

ISBN: (纸本)9798350324457

Macroscopic fundamental diagram (MFD) captures an orderly relationship among traffic flow, density, and speed at the network level. Understanding network-wide traffic through MFDs can optimally allocate demand to existing networks, improving performance by maximizing network production and avoiding congestion. However, due to historical data limitations, empirically derived MFD models are sparse in the literature, especially for the U.S. cities. Leveraging a large-scale and granular census-tract-level flow and density derived from vehicle probe data, this research is the first to develop a machine learning approach to both derive MFD models and interpret their underlying difference among urban networks across the entire United States. Among the four machine learning methods tested here XGBoost is found to deliver the best performance to predict the network traffic flow for given vehicular density and location attributes. Interaction Shapley Additive explanation (SHAP) values are used to interpret the factors, such as land use, transportation infrastructure, and network topology, that influence the flow-density relationships among locations. The analysis framework developed in this work can generate datadriven MFDs and a deeper understanding of their shape dependence on network, infrastructure, and land use characteristics, which can be used by transportation authorities to derive and optimize location-specific MFDs facilitating more informed management and planning decisions at the network level. © 2023 IEEE.

关键词： Interaction Shapley values machine learning models macroscopic fundamental diagram TreeExplainer United States vehicle probe data

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Estimating Smart Grid Stability with Hybrid RNN+LSTM Deep Learning Approach

Estimating Smart Grid Stability with Hybrid RNN+LSTM Deep Le...

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International Smart Grid Workshop and Certificate Program (ISGWCP)

作者： Saadin Oyucu Şeref Sağıroğlu Ahmet Aksöz Emre Biçer Department of Computer Engineering Faculty of Engineering Adıyaman Türkiye Gazi University Artificial Intelligence and Big Data Analytics Security R&D Center Ankara Türkiye Mobilers Team Sivas Cumhuriyet University Sivas Türkiye Faculty of Engineering and Natural Sciences Battery Research Laboratory Sivas University of Science and Technology Sivas Türkiye

ISBN: (数字)9798350361612

ISBN: (纸本)9798350361629

Smart grids are faced with a range of challenges, such as the development of communication infrastructure, cybersecurity threats, data privacy, and the protection of user information, due to their complex structure. Another key challenge faced by smart grids is the stability issues arising from variable energy sources and consumption patterns. In these complex grid systems where energy demand and supply need to be balanced instantly, stability predictions play a significant role in foreseeing potential disruptions and optimizing energy flow. Therefore, within the scope of this study, a hybrid structure utilizing Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks is employed for stability classification to predict grid stability. This hybrid model combines the ability of RNN to recognize relationships between consecutive data points with LSTM’s capability to preserve long-term dependencies. The results obtained indicate that the model exhibited stable performance with accuracy rates of 98.06% and 98.02% at 50 and 100 epochs, respectively. The findings of this study contribute valuable insights to research on the management and stability of smart grids, enabling energy systems to be operated more reliably and efficiently.

关键词： Deep learning data privacy Recurrent neural networks Power system stability Stability analysis data models Smart grids

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A Methodological Approach for Approximate Memoization of Applications in Constrained Computational Resources

SSRN

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SSRN 2022年

作者： Castañé, Gabriel Gonzalez Tolosana-Calasanz, Rafael Mateos, Alejandro Calderón Insight Centre for Data Analytics School of Computer Science and Information Technology University College Cork Cork Ireland Departamento de Informática e Ingeniería de Sistemas Universidad de Zaragoza Zaragoza Spain Computer Science and Engineering Department Universidad Carlos III de Madrid Leganés Spain

The growing amount of data demands significant computational capacity on applications and services. These applications can become ineffectual when computational resources are lower than expected, as it is the case of edge computing devices applied to different domains. Approximate computing is a paradigm that can be used to alleviate this problem, however, existing solutions consist of a reduction of the precision of bits for approximation, mostly applied at the level of hardware. As a consequence its applicability cannot flexibly adapt to a broader selection of problems and relies on the hardware to make it possible. In this paper, we introduce approximate memoization as a software optimisation technique consisting of a combination of approximate computing and memoization that improves the performance of computationally expensive (dense) and repeated operations. We formulate it in terms of a methodology to guide software developers in the appropriate steps to conduct it for different applications. Our approach reduces both memory footprint and the execution time of applications. Our experiments are conducted in two application scenarios: an energy simulation kernel and a DNN-based application for video analysis, demonstrating a speed up of 35% with a 12% error and an overhead of around 100 ns. © 2022, The Authors. All rights reserved.

关键词： Edge computing

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Cooperative Multi-source data Trading

Cooperative Multi-source Data Trading

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IEEE Conference on Global Communications (GLOBECOM)

作者： Jin Cheng Ningning Ding John C. S. Lui Jianwei Huang School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society The Chinese University of Hong Kong Shenzhen Data Science and Analytics Thrust Information Hub Hong Kong University of Science and Technology Guangzhou Department of Computer Science and Engineering The Chinese University of Hong Kong School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network and CSIJRI Joint Research Centre on Smart Energy Storage The Chinese University of Hong Kong Shenzhen

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

In the era of big data, data trading significantly enhances data-driven technologies by facilitating data sharing. Despite the clear advantages often experienced by data users when incorporating multiple sources, the topic of multi-source data trading remains largely unexplored. This paper designs a novel data trading framework, which enables multi-source data trading through multi-source cooperation. The proposed framework aims to improve data usage efficiency and increase seller revenue. In particular, we model data sellers’ cooperative decisions through the Nash bargaining framework and systematically outline the interactions between sellers and buyers as a two-stage Stackelberg game. A key contribution of this work is the consideration of coupling among diverse data products, which is essential but often overlooked in prior studies. We properly classify data’s utility into endogenous and relational categories to disentangle the coupling. Despite the inherent non-convex nature of the optimization problem, we methodically derive the closed-form optimal solutions by decomposing the problem into several subproblems. Interestingly, we reveal that, under our proposed framework, sellers’ revenue initially remains steady with the increase of product coupling level, but begins to rise once the level exceeds a certain threshold due to the substitute effect. Finally, experimental results show that our proposed framework can improve the seller’s profit by up to 46.32% compared to traditional data trading methods in the current data market.

关键词： Couplings Games Big data data models Global communication Optimization

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Modeling Path Importance for Effective Alzheimer’s Disease Drug Repurposing

arXiv

引用

arXiv 2023年

作者： Xiang, Shunian Lawrence, Patrick J. Peng, Bo Chiang, ChienWei Kim, Dokyoon Shen, Li Ning, Xia Biomedical Informatics Department The Ohio State University ColumbusOH43210 United States Computer Science and Engineering Department The Ohio State University ColumbusOH43210 United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania PhiladelphiaPA19104 United States Translational Data Analytics Institute The Ohio State University ColumbusOH43210 United States

Recently, drug repurposing has emerged as an effective and resource-efficient paradigm for AD drug discovery. Among various methods for drug repurposing, network-based methods have shown promising results as they are capable of leveraging complex networks that integrate multiple interaction types, such as protein-protein interactions, to more effectively identify candidate drugs. However, existing approaches typically assume paths of the same length in the network have equal importance in identifying the therapeutic effect of drugs. Other domains have found that same length paths do not necessarily have the same importance. Thus, relying on this assumption may be deleterious to drug repurposing attempts. In this work, we propose MPI (Modeling Path Importance), a novel network-based method for AD drug repurposing. MPI is unique in that it prioritizes important paths via learned node embeddings, which can effectively capture a network’s rich structural information. Thus, leveraging learned embeddings allows MPI to effectively differentiate the importance among paths. We evaluate MPI against a commonly used baseline method that identifies anti-AD drug candidates primarily based on the shortest paths between drugs and AD in the network. We observe that among the top-50 ranked drugs, MPI prioritizes 20.0% more drugs with anti-AD evidence compared to the baseline. Finally, Cox proportional-hazard models produced from insurance claims data aid us in identifying the use of etodolac, nicotine, and BBB-crossing ACE-INHs as having a reduced risk of AD, suggesting such drugs may be viable candidates for repurposing and should be explored further in future studies. © 2023, CC BY.

关键词： Embeddings

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Hybrid genetic-discretized algorithm to handle data uncertainty in diagnosing stenosis of coronary arteries

Hybrid genetic-discretized algorithm to handle data uncertai...

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作者： Alizadehsani, Roohallah Roshanzamir, Mohamad Abdar, Moloud Beykikhoshk, Adham Khosravi, Abbas Nahavandi, Saeid Plawiak, Pawel Tan, Ru San Acharya, U Rajendra Deakin University GeelongVIC Australia Department of Engineering Fasa Branch Islamic Azad University Fars Fasa Iran Centre for Pattern Recognition and Data Analytics Deakin University GeelongVIC Australia Department of Information and Communications Technology Faculty of Computer Science and Telecommunications Cracow University of Technology Warszawska 24 st. F-3 31-155 Krakow Poland Institute of Theoretical and Applied Informatics Polish Academy of Sciences Baltycka 5 Gliwice44-100 Poland National Heart Centre Singapore Singapore Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore Department of Bioinformatics and Medical Engineering Asia University Taiwan

Coronary artery disease (CAD) is the leading cause of morbidity and death worldwide. Invasive coronary angiography is the most accurate technique for diagnosing CAD, but is invasive and costly. Hence, analytical methods such as machine learning and data mining techniques are becoming increasingly more popular. Although physicians need to know which arteries are stenotic, most of the researchers focus only on CAD detection and few studies have investigated stenosis of the right coronary artery (RCA), left circumflex (LCX) artery and left anterior descending (LAD) artery separately. Meanwhile, most of the datasets in this field are noisy (data uncertainty). However, to the best of our knowledge, there is no study conducted to address this important problem. This study uses the extension of the Z-Alizadeh Sani dataset, containing 303 records with 54 features. A new feature selection algorithm is proposed in this work. Meanwhile, by discretization of data, we also handle the uncertainty in CAD prediction. To the best of our knowledge, this is the first study attempted to handle uncertainty in CAD prediction. Finally, the genetic algorithm (GA) is used to determine the hyper-parameters of the support vector machine (SVM) kernels. We have achieved high accuracy for the stenosis diagnosis of each main coronary artery. The results of this study can aid the clinicians to validate their manual stenosis diagnosis of RCA, LCX and LAD coronary arteries. © 2020 John Wiley & Sons, Ltd.

关键词： Feature Selection

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Findings of the Sentiment Analysis of Dravidian Languages in Code-Mixed Text 13

Findings of the Sentiment Analysis of Dravidian Languages in...

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Working Notes of FIRE - 13th Forum for Information Retrieval Evaluation, FIRE-WN 2021

作者： Chakravarthi, Bharathi Raja Priyadharshini, Ruba Thavareesan, Sajeetha Chinnappa, Dhivya Thenmozhi, Durairaj Sherly, Elizabeth McCrae, John P. Hande, Adeep Ponnusamy, Rahul Banerjee, Shubhanker Vasantharajan, Charangan Insight SFI Research Centre for Data Analytics Data Science Institute National University of Ireland Galway Ireland ULTRA Arts and Science College Tamil Nadu Madurai India Eastern University Sri Lanka Thomson Reuters United States Sri Sivasubramaniya Nadar College of Engineering Tamil Nadu India Indian Institute of Information Technology and Management Kerala India Indian Institute of Information Technology Tiruchirappalli India ADAPT Centre Data Science Institute National University of Ireland Galway Ireland Department of Computer Science and Engineering University of Moratuwa Sri Lanka

We present the results of the Dravidian-CodeMix shared task1 held at FIRE 2021, a track on sentiment analysis for Dravidian Languages in Code-Mixed Text. We describe the task, its organization, and the submitted systems. This shared task is the continuation of last year’s Dravidian-CodeMix shared task2 held at FIRE 2020. This year’s tasks included code-mixing at the intra-token and inter-token levels. Additionally, apart from Tamil and Malayalam, Kannada was also introduced. We received 22 systems for Tamil-English, 15 systems for Malayalam-English, and 15 for Kannada-English. The top system for Tamil-English, Malayalam-English and Kannada-English scored weighted average F1-score of 0.711, 0.804, and 0.630, respectively. In summary, the quality and quantity of the submission show that there is great interest in Dravidian languages in code-mixed setting and state of the art in this domain still needs more improvement. © 2021 Copyright for this paper by its authors.

关键词： Sentiment analysis

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