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SSRN 2024年

作者： Kalaria, Rudri Kayes, A.S.M. Rahayu, Wenny Pardede, Eric Salehi, Ahmad S. La Trobe University VIC3086 Australia Data Engineering and Knowledge Management Group Australia

Securing Internet of Things (IoT) devices is paramount to mitigate unauthorised access and potential cyber threats, safeguarding the integrity of transmitted and processed data within interconnected devices. Identifying malicious IoT devices necessitates vigilant monitoring of network traffic, behaviour analysis, and implementing security measures, including Anomaly Detection Systems (ADSs), Intrusion Detection Systems (IDSs), and regular firmware updates. Traditional security approaches need to be revised for safeguarding IoT systems due to their inherent limitations in accommodating the resource-constrained nature of these *** introduce IoTPredictor, an advanced security approach designed to predict and detect malicious activities in IoT devices. Leveraging Hidden Markov Models (HMMs), IoTPredictor integrates an ADS to proactively detect and thwart attacks within the complex IoT-fog computing landscape. Our conceptual approach begins with categorising IoT devices into genuine, compromised, and counterfeit. We propose an HMM-based state transition model that captures potential transitions between states, such as normal, compromised, or counterfeit operations. We introduce an algorithm for estimating probabilities associated with next-state predictions to facilitate predictive ***, we present a formal approach for analysing communications between different states, enhancing the precision of the security framework. To validate the effectiveness of IoTPredictor, we conduct a series of experiments and provide a comprehensive evaluation. The results demonstrate the robustness and efficiency of our proposed security framework in predicting and preventing malicious activities, thereby contributing to the overall security enhancement of IoT devices within the complex IoT-fog computing network. © 2024, The Authors. All rights reserved.

关键词： Hidden Markov models

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Adaptive Context-Aware Access Control for Iot Environments Leveraging Fog Computing

SSRN

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SSRN 2023年

作者： Kalaria, Rudri Kayes, A.S.M. Rahayu, Wenny Pardede, Eric Salehi, Ahmad S. La Trobe University VIC3086 Australia Data Engineering and Knowledge Management Group United States

The increasing use of the Internet of Things (IoT) has driven the demand for enhanced and robust access control methods to protect resources from unauthorized access. A cloud-based access control approach brings significant challenges in terms of communication overhead, high latency, and complete reliance. In this paper, we propose a Fog-Based Adaptive Context-Aware Access Control (FB-ACAAC) framework for the IoT which addresses unauthorized resource access. The main purpose of FB-ACAAC is to prevent unauthorized access to IoT devices and provide adaptability to changing access behaviors and context by bringing decision-making and information about policies closer to the end nodes of the network. FB-ACAAC improves the availability of resources and reduces the amount of time for information to be processed. The eXtensible Access Control Markup Language (XACML) is widely used to manage access control decisions, however, traditional XACML-based methods do not take into account changing environments, different contexts, and changing access behaviors and are vulnerable to certain types of attacks. To address these issues, FB-ACAAC proposes an adaptive context-aware XACML scheme for heterogeneous distributed IoT environments using fog computing and is designed to be more context-aware and adaptable and also more secure in the face of unauthorized access. The effectiveness and efficiency of this new scheme is verified through experiments, and it has a low processing time overhead while providing extra features and improved security. © 2023, The Authors. All rights reserved.

关键词： Access control

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DC-Instruct: An Effective Framework for Generative Multi-intent Spoken Language Understanding

DC-Instruct: An Effective Framework for Generative Multi-int...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Xing, Bowen Liao, Lizi Huang, Minlie Tsang, Ivor W. Beijing Key Laboratory of Knowledge Engineering for Materials Science School of Computer and Communication Engineering University of Science and Technology Beijing China Singapore Management University Singapore Group Tsinghua University China CFAR and IHPC Agency for Science Technology and Research Singapore College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798891761643

In the realm of multi-intent spoken language understanding, recent advancements have leveraged the potential of prompt learning frameworks. However, critical gaps exist in these frameworks: the lack of explicit modeling of dual-task dependencies and the oversight of task-specific semantic differences among utterances. To address these shortcomings, we propose DC-Instruct, a novel generative framework based on Dual-task Inter-dependent Instructions (DII) and Supervised Contrastive Instructions (SCI). Specifically, DII guides large language models (LLMs) to generate labels for one task based on the other task's labels, thereby explicitly capturing dual-task inter-dependencies. Moreover, SCI leverages utterance semantics differences by guiding LLMs to determine whether a pair of utterances share the same or similar labels. This can improve LLMs on extracting and discriminating task-specific semantics, thus enhancing their SLU reasoning abilities. Extensive experiments on public benchmark datasets show that DC-Instruct markedly outperforms current generative models and state-of-the-art methods, demonstrating its effectiveness in enhancing dialogue language understanding and reasoning. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Neural networks for semantic gaze analysis in XR settings

arXiv

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arXiv 2021年

作者： Stubbemann, Lena Dürrschnabel, Dominik Refflinghaus, Robert Department of Quality and Process Management University of Kassel Germany Knowledge & Data Engineering Group University of Kassel Germany Interdisciplinary Research Center for Information System Design University of Kassel Germany

Virtual-reality (VR) and augmented-reality (AR) technology is increasingly combined with eye-tracking. This combination broadens both fields and opens up new areas of application, in which visual perception and related cognitive processes can be studied in interactive but still well controlled settings. However, performing a semantic gaze analysis of eye-tracking data from interactive three-dimensional scenes is a resource-intense task, which so far has been an obstacle to economic use. In this paper we present a novel approach which minimizes time and information necessary to annotate volumes of interest (VOIs) by using techniques from object recognition. To do so, we train convolutional neural networks (CNNs) on synthetic data sets derived from virtual models using image augmentation techniques. We evaluate our method in real and virtual environments, showing that the method can compete with state-of-the-art approaches, while not relying on additional markers or preexisting databases but instead offering cross-platform *** Codes 68T45 Copyright © 2021, The Authors. All rights reserved.

关键词： Semantics

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Simple and Deep Graph Convolutional Networks

arXiv

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arXiv 2020年

作者： Chen, Ming Wei, Zhewei Huang, Zengfeng Ding, Bolin Li, Yaliang School of Information Renmin University of China China Gaoling School of Articial Intelligence Renmin University of China China Beijing Key Lab of Big Data Management and Analysis Methods China MOE Key Lab of Data Engineering and Knowledge Engineering China School of Data Science Fudan University China Alibaba Group

Graph convolutional networks (GCNs) are a powerful deep learning approach for graph-structured data. Recently, GCNs and subsequent variants have shown superior performance in various application areas on real-world datasets. Despite their success, most of the current GCN models are shallow, due to the over-smoothing problem. In this paper, we study the problem of designing and analyzing deep graph convolutional networks. We propose the GCNII, an extension of the vanilla GCN model with two simple yet effective techniques: Initial residual and Identity mapping. We provide theoretical and empirical evidence that the two techniques effectively relieves the problem of over-smoothing. Our experiments show that the deep GCNII model outperforms the state-of-the-art methods on various semi- and full-supervised tasks. Code is available at https://***/chennnM/GCNII. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

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The Minority Matters: A Diversity-Promoting Collaborative Metric Learning Algorithm

arXiv

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arXiv 2022年

作者： Bao, Shilong Xu, Qianqian Yang, Zhiyong He, Yuan Cao, Xiaochun Huang, Qingming State Key Laboratory of Information Security Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China School of Computer Science and Tech. University of Chinese Academy of Sciences China Alibaba Group China School of Cyber Science and Technology Shenzhen Campus Sun Yat-sen University China Key Laboratory of Big Data Mining and Knowledge Management CAS China Peng Cheng Laboratory China

Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and Collaborative Filtering. Following the convention of RS, existing methods exploit unique user representation in their model design. This paper focuses on a challenging scenario where a user has multiple categories of interests. Under this setting, we argue that the unique user representation might induce preference bias, especially when the item category distribution is imbalanced. To address this issue, we propose a novel method called Diversity-Promoting Collaborative Metric Learning (DPCML), with the hope of considering the commonly ignored minority interest of the user. The key idea behind DPCML is to include a multiple set of representations for each user in the system. Based on this embedding paradigm, user preference toward an item is aggregated from different embeddings by taking the minimum item-user distance among the user embedding set. Furthermore, we observe that the diversity of the embeddings for the same user also plays an essential role in the model. To this end, we propose a diversity control regularization term to accommodate the multi-vector representation strategy better. Theoretically, we show that DPCML could generalize well to unseen test data by tackling the challenge of the annoying operation that comes from the minimum value. Experiments over a range of benchmark datasets speak to the efficacy of DPCML. © 2022, CC BY-NC-SA.

关键词： Embeddings

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AdAUC: End-to-end Adversarial AUC Optimization Against Long-tail Problems

arXiv

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arXiv 2022年

作者： Hou, Wenzheng Xu, Qianqian Yang, Zhiyong Bao, Shilong He, Yuan Huang, Qingming Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China State Key Laboratory of Information Security Institute of Information Engineering CAS Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Alibaba Group Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China Artificial Intelligence Research Center Peng Cheng Laboratory Shenzhen China

It is well-known that deep learning models are vulnerable to adversarial examples. Existing studies of adversarial training have made great progress against this challenge. As a typical trait, they often assume that the class distribution is overall balanced. However, long-tail datasets are ubiquitous in a wide spectrum of applications, where the amount of head class instances is larger than the tail classes. Under such a scenario, AUC is a much more reasonable metric than accuracy since it is insensitive toward class distribution. Motivated by this, we present an early trial to explore adversarial training methods to optimize AUC. The main challenge lies in that the positive and negative examples are tightly coupled in the objective function. As a direct result, one cannot generate adversarial examples without a full scan of the dataset. To address this issue, based on a concavity regularization scheme, we reformulate the AUC optimization problem as a saddle point problem, where the objective becomes an instance-wise function. This leads to an end-to-end training protocol. Furthermore, we provide a convergence guarantee of the proposed algorithm. Our analysis differs from the existing studies since the algorithm is asked to generate adversarial examples by calculating the gradient of a min-max problem. Finally, the extensive experimental results show the performance and robustness of our algorithm in three long-tail datasets. © 2022, CC BY.

关键词： Deep learning

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The minority matters: a diversity-promoting collaborative metric learning algorithm 22

The minority matters: a diversity-promoting collaborative me...

引用

Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Shilong Bao Qianqian Xu Zhiyong Yang Yuan He Xiaochun Cao Qingming Huang State Key Laboratory of Information Security Institute of Information Engineering CAS and School of Cyber Security University of Chinese Academy of Sciences Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS School of Computer Science and Tech. University of Chinese Academy of Sciences Alibaba Group School of Cyber Science and Technology Shenzhen Campus Sun Yat-sen University Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and School of Computer Science and Tech. University of Chinese Academy of Sciences and Key Laboratory of Big Data Mining and Knowledge Management CAS and Peng Cheng Laboratory

ISBN: (纸本)9781713871088

Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and Collaborative Filtering. Following the convention of RS, existing methods exploit unique user representation in their model design. This paper focuses on a challenging scenario where a user has multiple categories of interests. Under this setting, we argue that the unique user representation might induce preference bias, especially when the item category distribution is imbalanced. To address this issue, we propose a novel method called Diversity-Promoting Collaborative Metric Learning (DPCML), with the hope of considering the commonly ignored minority interest of the user. The key idea behind DPCML is to include a multiple set of representations for each user in the system. Based on this embedding paradigm, user preference toward an item is aggregated from different embeddings by taking the minimum item-user distance among the user embedding set. Furthermore, we observe that the diversity of the embeddings for the same user also plays an essential role in the model. To this end, we propose a Diversity Control Regularization Scheme (DCRS) to accommodate the multi-vector representation strategy better. Theoretically, we show that DPCML could generalize well to unseen test data by tackling the challenge of the annoying operation that comes from the minimum value. Experiments over a range of benchmark datasets speak to the efficacy of DPCML.

关键词：

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Advances in Machine Learning for the Behavioral Sciences

arXiv

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arXiv 2019年

作者： Kliegr, Tomáš Bahník, Štěpán Fürnkranz, Johannes Department of Information and Knowledge Engineering Faculty of Informatics and Statistics University of Economics Prague Czech Republic Department of Management Faculty of Business Administration University of Economics Prague Czech Republic Computational Data Analytics Group Department of Computer Science Johannes Kepler Universität Linz Altenbergerstraße 66B Linz4040 Austria

The areas of machine learning and knowledge discovery in databases have considerably matured in recent years. In this article, we briefly review recent developments as well as classical algorithms that stood the test of time. Our goal is to provide a general introduction into different tasks such as learning from tabular data, behavioral data, or textual data, with a particular focus on actual and potential applications in behavioral sciences. The supplemental appendix to the article also provides practical guidance for using the methods by pointing the reader to proven software implementations. The focus is on R, but we also cover some libraries in other programming languages as well as systems with easy-to-use graphical interfaces. Copyright © 2019, The Authors. All rights reserved.

关键词： data mining

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Ontology Driven Semantic Profiling and Retrieval in Medical Information Systems

SSRN

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SSRN 2018年

作者： Bhatt, Mehul Rahayu, Wenny Soni, Sury Prakash Wouters, Carlo Cognitive Systems Lab Universität Bremen Bremen28359 Germany Data Engineering and Knowledge Management Group Department of Computer Science and Computer Engineering La Trobe University Australia

We propose the application of a novel sub-ontology extractionmethodology for achieving interoperability and improving the semantic validity of information retrieval in the medical information systems (MIS) domain. The system offers advanced profiling of a user’s field of specialization by exploiting the concept of sub-ontology extraction, i.e., each sub-ontology may subsequently represent a particular user profile. Semantic profiling of a user’s field of specialization or interest is necessary functionality in any medical domain information retrieval system;this is because the (structural and semantic) extent of information sources is massive and individual users are only likely to be interested in specific parts of the overall knowledge documents on the basis of their area of specialization. The prototypical system, OntoMOVE, has been specifically designed for application in themedical information systems domain. OntoMOVE utilizes semantic web standards like RDF(S) and OWL in addition to medical domain standards and vocabularies encompassed by the UMLS knowledge sources. © 2018, The Authors. All rights reserved.

关键词： Medical information systems

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