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$\mathsf{NCF}$NCF: A Neural Context Fusion Approach to Raw Mobility Annotation

IEEE Transactions on Mobile computing 2020年第1期21卷 226-238页

作者： Renjun Hu Jingbo Zhou Xinjiang Lu Hengshu Zhu Shuai Ma Hui Xiong SKLSDE Lab Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Business Intelligence Lab Baidu Research National Engineering Laboratory of Deep Learning Technology and Application Beijing China Talent Intelligence Center Baidu Inc. Beijing China Management Science and Information Systems Department Rutgers Business School Rutgers University Newark NJ USA

Understanding human mobility patterns at the point-of-interest (POI) scale plays an important role in enhancing business intelligence in mobile environments. While large efforts have been made in this direction, most studies simply utilize POI check-ins to mine the concerned mobility patterns, the effectiveness of which is usually hindered due to data sparsity. To obtain better POI-based human mobility for mining, in this paper, we strive to directly annotate the POIs associated with raw user-generated mobility records. We propose a neural context fusion approach which integrates various context factors in people's POI-visiting behaviors. Our approach evaluates the preference and transition factors via representation learning. Notably, we incorporate an attention mechanism to deal with the randomized transitions in raw mobility. The domain knowledge factors, i.e., distance, time and popularity, remain effective and our approach further includes them from a data-driven perspective. Factors are automatically fused with a feed-forward neural network. Furthermore, we exploit a multi-head architecture to enhance the model expressiveness. Using two real-life data sets, we conduct our experimental study and find that our approach consistently outperforms the state-of-the-art baselines by at least 32 percent in accuracy. Besides, we demonstrate the utility of the obtained POI-based human mobility with a POI recommendation example.

关键词： Annotations Trajectory Mobile computing Computer architecture Business intelligence Neural networks Semantics

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Development and Testing of Mobile Assisted Language Learning Application to Improve Oral Clinical Case Presentation of Student Nurses

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第15期17卷 171-188页

作者： Milton, Cynthia Subramaniam, Aruna Sridevi, S. Kumar, S. Ganesh Faculty of Allied Health Sciences Sri Ramachandra Institute of Higher Education Tamil Nadu Chennai India Faculty of Nursing Sri Ramachandra Institute of Higher Education Tamil Nadu Chennai India Sri Ramachandra Engineering and Technology Sri Ramachandra Institute of Higher Education Tamil Nadu Chennai India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai India

Background: Nurses’ oral case presentation is a core clinical communication skill with distinctive linguistic qualities that require specific language training. The present paper is a user feedback analysis of a start-up Mobile Assisted Language Learning application (MALL app) developed and implemented to provide English language training to improve Oral Case Presentation (OCP) of student nurses pursuing baccalaureate degrees from a nursing college in South India. Objective: The aim was to develop and test an Android based OCP-MALL application for user acceptability and effectiveness in improving OCP performance and decreasing the perceived language difficulty. Methodology: Sixty-two student nurses from a reputed nursing college in Chennai, South India, using Android mobile phones participated in the study. All the participants were given access and instructions on the use of OCP-MALL app. A comparison of pre and post-test of perceived level of language difficulty and OCP pre and post score along with users’ acceptance were collected. Results: The users’ acceptability showed that all participants rated the clarity in reading the text with highest mean score of 8.29 and std of 1.276, simplicity in navigation – moving to next page with next highest mean score of 8.19 and std of 1.526, clarity in text application layout with a mean score of 8.14 and std of 1.497 and user friendliness of the application with a mean score of 8.13 and std of 1.393. Conclusion: This startup OCP-MALL app user satisfaction feedback proves that Indian ELT trainers can create cost-effective and need-based m-learning applications. © 2023 by the authors of this article. Published under CC-BY.

关键词： Students

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Decoding Urban Industrial Complexity: Enhancing Knowledge-Driven Insights via IndustryScopeGPT

arXiv

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arXiv 2024年

作者： Wang, Siqi Liang, Chao Gao, Yunfan Liu, Yang Li, Jing Wang, Haofen Tongji University Shanghai China Department of Computing The Hong Kong Polytechnic University Hong Kong Hong Kong Guangdong Guodi Institute of Resources and Environment Guangzhou China Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China College of Design and Innovation Tongji University Shanghai China Department of Computing The Hong Kong Polytechnic University Hong Kong Research Centre on Data Science & Artificial Intelligence Hong Kong China

Industrial parks are critical to urban economic growth. Yet, their development often encounters challenges stemming from imbalances between industrial requirements and urban services, underscoring the need for strategic planning and operations. This paper introduces IndustryScopeKG, a pioneering large-scale multi-modal, multi-level industrial park knowledge graph, which integrates diverse urban data including street views, corporate, socio-economic, and geospatial information, capturing the complex relationships and semantics within industrial parks. Alongside this, we present the IndustryScopeGPT framework, which leverages Large Language Models (LLMs) with Monte Carlo Tree Search to enhance tool-augmented reasoning and decision-making in Industrial Park Planning and Operation (IPPO). Our work significantly improves site recommendation and functional planning, demonstrating the potential of combining LLMs with structured datasets to advance industrial park management. This approach sets a new benchmark for intelligent IPPO research and lays a robust foundation for advancing urban industrial development. The dataset and related code are available at https://***/Tongji-KGLLM/IndustryScope. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Network slicing for service-oriented networks under resource constraints

arXiv

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arXiv 2017年

作者： Zhang, Nan Liu, Ya-Feng Farmanbar, Hamid Chang, Tsung-Hui Hong, Mingyi Luo, Zhi-Quan School of Mathematical Sciences Peking University State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Huawei Canada Research Center Ottawa Canada Department of Electrical and Computer Engineering University of Minnesota United States Shenzhen Research Institute of Big Data Chinese University of Hong Kong Shenzhen China

To support multiple on-demand services over fixed communication networks, network operators must allow flexible customization and fast provision of their network resources. One effective approach to this end is network virtualization, whereby each service is mapped to a virtual subnetwork providing dedicated on-demand support to network users. In practice, each service consists of a prespecified sequence of functions, called a service function chain (SFC), while each service function in a SFC can only be provided by some given network nodes. Thus, to support a given service, we must select network function nodes according to the SFC and determine the routing strategy through the function nodes in a specified order. A crucial network slicing problem that needs to be addressed is how to optimally localize the service functions in a physical network as specified by the SFCs, subject to link and node capacity constraints. In this paper, we formulate the network slicing problem as a mixed binary linear program and establish its strong NP-hardness. Furthermore, we propose efficient penalty successive upper bound minimization (PSUM) and PSUM-R(ounding) algorithms, and two heuristic algorithms to solve the problem. Simulation results are shown to demonstrate the effectiveness of the proposed algorithms. Copyright © 2017, The Authors. All rights reserved.

关键词： Network function virtualization

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Sports-QA: A Large-Scale Video Question Answering Benchmark for Complex and Professional Sports

arXiv

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arXiv 2024年

作者： Li, Haopeng Deng, Andong Liu, Jun Rahmani, Hossein Guo, Yulan Schiele, Bernt Bennamoun, Mohammed Ke, Qiuhong School of Computing and Information Systems University of Melbourne Australia Center for Research in Computer Vision University of Central Florida United States Pillar Singapore University of Technology and Design Singapore School of Computing and Communications Lancaster University United Kingdom School of Electronics and Communication Engineering Sun Yat-sen University China Department of Computer Vision and Machine Learning Max Planck Institute for Informatics Saarland Informatics Campus Germany School of Physics Maths and Computing University of Western Australia Australia Department of Data Science & AI Monash University Australia

Reasoning over sports videos for question answering is an important task with numerous applications, such as player training and information retrieval. However, this task has not been explored due to the lack of relevant datasets and the challenging nature it presents. Most datasets for video question answering (VideoQA) focus mainly on general and coarse-grained understanding of daily-life videos, which is not applicable to sports scenarios requiring professional action understanding and fine-grained motion analysis. In this paper, we introduce the first dataset, named Sports-QA, specifically designed for the sports VideoQA task. The Sports-QA dataset includes various types of questions, such as descriptions, chronologies, causalities, and counterfactual conditions, covering multiple sports. Furthermore, to address the characteristics of the sports VideoQA task, we propose a new Auto-Focus Transformer (AFT) capable of automatically focusing on particular scales of temporal information for question answering. We conduct extensive experiments on Sports-QA, including baseline studies and the evaluation of different methods. The results demonstrate that our AFT achieves state-of-the-art performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Question answering

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A Review of the Effectiveness of Management Information System in Decision Making

A Review of the Effectiveness of Management Information Syst...

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Computer science and Emerging Technologies (CSET), International Conference on

作者： Hazem Mohammad Zeidan Al-kaseasbeh Firas Rashed Wahsheh Fatma Susilawati Mohamad Julaily Aida Jusoh Mazen Mohammed Farea Archana K V MIS BI and Data Analysis Department Faculty of Business and Financial Science Irbid National University Irbid Jordan Department of Management Information Systems Faculty of Business Ajloun National University Ajloun Jordan Faculty of Informatics and Computing UniSZA Malaysia Faculty of Finance & Administrative Sciences Al-Madinah International University Malaysia Jain Deemed To Be University Bangalore India

The rapid advancement of technology has revolutionized the way organizations operate, resulting in the widespread adoption of Management Information systems (MIS) as a crucial tool in decision-making processes. This research paper presents a comprehensive review of the effectiveness of Management Information systems in facilitating decision-making within various organizational contexts. The paper begins with an exploration of the fundamental role of MIS in collecting, processing, and presenting relevant data to decision-makers. Emphasis is placed on understanding how MIS enables the integration of data from diverse sources and transforms it into actionable insights, empowering managers to make informed and timely decisions. Furthermore, this review delves into the different types of Management Information systems, including Executive Information systems (EIS), Decision Support systems (DSS), and Transaction Processing systems (TPS). By analyzing case studies and real-world examples, the paper highlights the specific applications and benefits of each system type in enhancing decision-making efficiency and accuracy. To assess the effectiveness of MIS in decision-making, the review examines key performance indicators, such as the speed and accuracy of decision-making processes, resource optimization, and overall organizational performance. The role of MIS in improving strategic planning, risk management, and competitive advantage is also thoroughly investigated. Additionally, the review explores potential challenges and limitations associated with the implementation and utilization of MIS in decision-making. Factors such as data security, system reliability, user training, and resistance to change are addressed, offering insights into strategies for mitigating these obstacles. The paper concludes with a synthesized analysis of the existing literature and identifies gaps for future research in the field of Management Information systems and decision-making effectiveness

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Generative AI Agents with Large Language Model for Satellite Networks via a Mixture of Experts Transmission

arXiv

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arXiv 2024年

作者： Zhang, Ruichen Du, Hongyang Liu, Yinqiu Niyato, Dusit Kang, Jiawen Xiong, Zehui Jamalipour, Abbas Kim, Dong In The College of Computing and Data Science Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The University of Sydney SydneyNSW2006 Australia The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

In response to the needs of 6G global communications, satellite communication networks have emerged as a key solution. However, the large-scale development of satellite communication networks is constrained by the complex system models, whose modeling is challenging for massive users. Moreover, transmission interference between satellites and users seriously affects communication performance. To solve these problems, this paper develops generative artificial intelligence (AI) agents for model formulation and then applies a mixture of experts (MoE) approach to design transmission strategies. Specifically, we leverage large language models (LLMs) to build an interactive modeling paradigm and utilize retrieval-augmented generation (RAG) to extract satellite expert knowledge that supports mathematical modeling. Afterward, by integrating the expertise of multiple specialized components, we propose an MoE-proximal policy optimization (PPO) approach to solve the formulated problem. Each expert can optimize the optimization variables at which it excels through specialized training through its own network and then aggregates them through the gating network to perform joint optimization. The simulation results validate the accuracy and effectiveness of employing a generative agent for problem formulation. Furthermore, the superiority of the proposed MoE-ppo approach over other benchmarks is confirmed in solving the formulated problem. The adaptability of MoE-PPO to various customized modeling problems has also been demonstrated. © 2024, CC BY.

关键词： Satellite communication systems

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Toward Mixture-of-Experts Enabled Trustworthy Semantic Communication for 6G Networks

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IEEE Network 2024年

作者： He, Jiayi Luo, Xiaofeng Kang, Jiawen Du, Hongyang Xiong, Zehui Chen, Ci Niyato, Dusit Shen, Xuemin Guangdong University of Technology School of Automation Guangzhou510006 China Nanyang Technological University College of Computing and Data Science Singapore639798 Singapore Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore487372 Singapore University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Semantic Communication (SemCom) plays a pivotal role in 6G networks, offering a viable solution for future efficient communication. Through nonlinear mapping of semantic representations, Deep Learning (DL)-based semantic communications further enhance communication efficiency. However, the vulnerability of DL models to security threats, such as adversarial attacks, poses significant challenges for practical applications of SemCom systems. These vulnerabilities allow attackers to tamper with messages and eavesdrop on private information, especially in wireless communication scenarios. Although existing defenses attempt to address specific threats, they often fail to simultaneously handle multiple heterogeneous attacks. To overcome this limitation, we propose a novel Mixture-of-Experts (MoE)-based SemCom system, comprising a gating network and multiple experts, each specializing in different security challenges. The gating network adaptively selects suitable experts to counter heterogeneous attacks based on user-defined security requirements. Multiple experts collaborate to accomplish semantic communication tasks while meeting the security requirements of users. A case study in vehicular networks demonstrates the efficacy of the MoE-based SemCom system. Simulation results show that the proposed MoE-based SemCom system effectively mitigates concurrent heterogeneous attacks, with minimal impact on downstream task accuracy. © 1986-2012 IEEE.

关键词： Vehicular ad hoc networks

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Stable recovery of entangled weights: Towards robust identification of deep neural networks from minimal samples

arXiv

引用

arXiv 2021年

作者： Fiedler, Christian Fornasier, Massimo Klock, Timo Rauchensteiner, Michael Intelligent Control Systems Group Max Planck Institute for Intelligent Systems Heisenbergstr. 3 Stuttgart70569 Germany Institute for Data Science in Mechanical Engineering RWTH-Aachen University Dennewartstraße 27 Aachen50868 Germany Department of Mathematics Bolzmannstraße 3 85748 Garching Germany Department of Numerical Analysis and Scientific Computing Simula Research Laboratory Oslo Norway Department of Mathematics University of San Diego California San Diego United States

In this paper we approach the problem of unique and stable identifiability of generic deep artificial neural networks with pyramidal shape and smooth activation functions from a finite number of input-output samples. More specifically we introduce the so-called entangled weights, which compose weights of successive layers intertwined with suitable diagonal and invertible matrices depending on the activation functions and their shifts. We prove that entangled weights are completely and stably approximated by an efficient and robust algorithm as soon as O(D2 × m) nonadaptive input-output samples of the network are collected, where D is the input dimension and m is the number of neurons of the network. Moreover, we empirically observe that the approach applies to networks with up to O(D × mL) neurons, where mL is the number of output neurons at layer L. Provided knowledge of layer assignments of entangled weights and of remaining scaling and shift parameters, which may be further heuristically obtained by least squares, the entangled weights identify the network completely and uniquely. To highlight the relevance of the theoretical result of stable recovery of entangled weights, we present numerical experiments, which demonstrate that multilayered networks with generic weights can be robustly identified and therefore uniformly approximated by the presented algorithmic pipeline. In contrast backpropagation cannot generalize stably very well in this setting, being always limited by relatively large uniform error. In terms of practical impact, our study shows that we can relate input-output information uniquely and stably to network parameters, providing a form of explainability. Moreover, our method paves the way for compression of overparametrized networks and for the training of minimal complexity *** Codes 65D15, 68T07, 90C26 Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

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Image Based Feature Separation Using RBM Tech with ADBN Tech for Accurate Fruit Classification

Image Based Feature Separation Using RBM Tech with ADBN Tech...

引用

computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： V Revathi Balasubramanian Prabhu Kavin Arunadevi Thirumalraj E. Gangadevi Balamurugan Balusamy Shilpa Gite Department of Applied Sciences - Physics New Horizon College of Engineering Ring Road Bellandur Post Bengaluru India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chengalpattu Tamil Nadu India Department of Computer Science Engineering K.Ramakrishnan College of Technology Trichy Tamil Nadu Department of Computer Science Loyola College Chennai Delhi-National Capital Region (NCR) Shiv Nadar University AIML department Symbiosis Institute of Technology Pune Symbiosis International (Deemed University) Pune India

One of the fastest-growing subfields of computer vision and picture categorization is fruit identification. The fruit industry could start using a standardised categorization system so that shoppers can easily distinguish between different types and quality levels of fruit. From a health perspective, fruit quality is essential. The task of classifying fruits is crucial in many commercial contexts. A supermarket cashier may use a fruit categorization system to better recognise the varieties of fruit and their associated costs. It might also be used to determine whether or not a given kind of fruit is suitable for a person’s diet. The rising use of intelligent imaging equipment may be directly attributed to the proliferation and development of AI software. Deep learning methods, such as CNN, are now being used by researchers for picture categorization. CNN’s superior performance may be achieved without using any manually-crafted characteristics, in contrast to the older methods. For automated categorization, it employs a plethora of filters that strip relevant visual information. Classifying fruits accurately is a challenge in the horticulture sector and calls for the knowledge of an expert. For accurately and quantitatively analysing fruits, the currently mentioned categorization techniques fall short. Therefore, it is beneficial to review the latest suggestions for categorizing fruits. To solve this problem, we need a computerised system that can automatically sort fruits into their respective categories. Deep learning models, such as the Restricted Boltzmann Machine (RBM) and the Adam based Deep Belief Network (DBN), are used in this assignment to categorise fruits according to the attributes that have been determined to be optimum and generated. The proposed approach has been shown to have effective accuracy and quantitative analysis outcomes, and this is just the beginning of this realisation. In addition, the suggested scheme’s relatively strong computational mo

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