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Software Services Engineering (SSE) IEEE International Conference on

作者： Jiangyi Fang Liyue Chen Di Chai Yayao Hong Xiuhuai Xie Longbiao Chen Leye Wang Key Lab of High Confidence Software Technologies Ministry of Education China School of Computer Science Peking University Beijing Hong Kong University of Science and Technology Hong Kong Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen School of Informatics Xiamen University Xiamen

ISBN: (数字)9798350368512

ISBN: (纸本)9798350368529

Spatiotemporal prediction (STP) service is one of the key infrastructure applications in smart cities. Currently, most of the existing STP services are constructed following the workflow of building deep learning (DL) applications while neglecting the importance of domain knowledge and region partition. However, the performance and interpretability of STP are highly related to them. As a result, there is an urgent requirement to develop a thorough and tailored workflow for STP services. To address this gap, we propose a novel workflow including two factors above as intermediate procedures. Based on the workflow, we design and implement an STP toolbox called UCTB (Urban computing Tool Box) assisting practitioners in the rapid construction of STP services, which can manage multiple spatiotemporal do-main knowledge, support various region partition algorithms, and possess state-of-the-art models simultaneously. The relevant code and supporting documents have been open-sourced at https://***/uctb/UCIB.

关键词： Deep learning Smart cities Buildings Predictive models Prediction algorithms Software Extensibility

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DRFM Jamming Mode Identification Leveraging Deep Neural Networks 10

DRFM Jamming Mode Identification Leveraging Deep Neural Netw...

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10th International Conference on Control, Automation and Information Sciences, ICCAIS 2021

作者： Gao, Jiannan Wang, Min Chen, Longbiao Hui, Bingwei Wang, Cheng Fan, Hongqi Xiamen University Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen China National University of Defense Technology National Key Laboratory of Science and Technology on Automatic Target Recognition Changsha China

ISBN: (纸本)9781665440295

In modern information warfare, radar must apply some anti-jamming technologies. Digital radio frequency memory (DRFM) and its technology can adapt to the complex threat signal environment and have an excellent jamming effect that other technical means in the past cannot match. Currently, traditional DRFM jamming identification is mainly based on the method of extracting and modeling radar signal features. It is unable to identify various types of jamming and lacks a large amount of radar data. In this work, we propose a three-phase framework to identify DRFM jamming. First, we design a hardware system based on PXI module to generate jamming signal data. Second, we perform range-doppler processing on the raw radar signal data to generate range-doppler map (RD-map) which can accurately represent jamming signals. Finally, we use deep learning to identify DRFM jamming and use pre-trained models to improve identification accuracy. We evaluate our proposed framework using real DRFM jamming data collected from PXI system. The valuation results confirm that our approach effectively identifies DRFM jamming, achieving an average accuracy of 84.4%. © 2021 IEEE.

关键词： Deep neural networks

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An Hybrid Modelling for Optimal Utilisation on Cloud Resources using Machine Learning Algorithm

An Hybrid Modelling for Optimal Utilisation on Cloud Resourc...

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2022 International Conference on Electronics and Renewable Systems, ICEARS 2022

作者： Atyam, Nageswara Rao Babu P., Ramesh Nayak, Nihar Ranjan Saikia, Utpal Ananda Kumar, K.S. Aravindharamanan, S. Cmr Institute of Technology Department of Electrical and Electronics Engineering Bengaluru India Wollega University Department of Computer Science College of Engineering Technology Nekemte Ethiopia Department of Mca Andhra Pradesh Chittoor India Silapathar College Department of Mathematics Silapathar Assam Dhemaji India School of Computing and Informatics College of Engineering and Technology Dilla University Snnpr Dilla Ethiopia Vellore Institute of Technology Tamil Nadu Chennai India

ISBN: (纸本)9781665484251

The great majority of data centres are now underutilised, which is a concern. It is estimated that they are only used at 15% of their maximum capacity the great majority of the time. As a result of significant increases in server demand, additional space in these data centres has been added. This study presents the development of a hybrid paradigm for the efficient utilisation of cloud resources. Cloud resource provisioning is made more efficient through the use of machine learning and particle swarm optimization techniques. In order to determine if the model performs as intended, CloudSim is used to simulate the model's performance, and the results are compared against a range of performance measurements, such as the make span, delay, and throughput. © 2022 IEEE.

关键词： Machine learning

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Adapting Whisper for Code-Switching through Encoding Refining and Language-Aware Decoding

Adapting Whisper for Code-Switching through Encoding Refinin...

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

作者： Jiahui Zhao Hao Shi Chenrui Cui Tianrui Wang Hexin Liu Zhaoheng Ni Lingxuan Ye Longbiao Wang College of Intelligence and Computing Tianjin University China Graduate School of Informatics Kyoto University Kyoto Japan College of Computing and Data Science Nanyang Technological University Singapore Meta New York USA Key Laboratory of Speech Acoustics and Content Understanding Institute of Acoustics Beijing China Huiyan Technology (Tianjin) Co. Ltd China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Code-switching (CS) automatic speech recognition (ASR) faces challenges due to the language confusion resulting from accents, auditory similarity, and seamless language switches. Adaptation on the pre-trained multi-lingual model has shown promising performance for CS-ASR. In this paper, we adapt Whisper, which is a large-scale multilingual pre-trained speech recognition model, to CS from both encoder and decoder parts. First, we propose an encoder refiner to enhance the encoder’s capacity of intra-sentence swithching. Second, we propose using two sets of language-aware adapters with different language prompt embeddings to achieve language-specific decoding information in each decoder layer. Then, a fusion module is added to fuse the language-aware decoding. The experimental results using the SEAME dataset show that, compared with the baseline model, the proposed approach achieves a relative MER reduction of 4.1% and 7.2% on the dev_man and dev_sge test sets, respectively, surpassing state-of-the-art methods. Through experiments, we found that the proposed method significantly improves the performance on non-native language in CS speech, indicating that our approach enables Whisper to better distinguish between the two languages.

关键词： Adaptation models Fuses Refining Speech enhancement Signal processing Encoding Decoding Multilingual Long short term memory Faces

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Rough Fermatean fuzzy decision-based approach for modelling IDS classifiers in the federated learning of IoMT applications

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Neural computing and Applications 2023年第30期35卷 22531-22549页

作者： Albahri, O.S. Al-Samarraay, Mohammed S. AlSattar, H.A. Alamoodi, A.H. Zaidan, A.A. Albahri, A.S. Zaidan, B.B. Jasim, Ali Najm Computer Techniques Engineering Department Mazaya University College Thi-Qar Nassiriya12733 Iraq Faculty of Computing and Meta-Technology Universiti Pendidikan Sultan Idris Perak Tanjong Malim35900 Malaysia Department of Business Administration College of Administrative Science The University of Mashreq Baghdad10021 Iraq SP Jain School of Global Management Lidcombe SydneyNSW2141 Australia Iraqi Commission for Computers and Informatics Baghdad10022 Iraq Future Technology Research Center College of Future National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Foundation of Alshuhda Baghdad Iraq MEU Research Unit Middle East University Amman Jordan

Intrusion detection systems (IDSs) are commonly employed to mitigate network security threats in various fields, including federated learning applications within the Internet of Medical Things (IoMT). However, IDSs face challenges owing to the sheer volume of network traffic, high-dimensional datasets and the necessity for real-time detection. Although machine learning integration assists IDSs in overcoming these challenges, modelling difficulties persist due to varied evaluation criteria and levels of conflict and importance. Multi-criteria decision-making (MCDM) solutions have been utilised in IoMT and IDS, yet they fall short in capturing the subjective judgements of experts and rely on normalisation approaches, which can impact results. This study seeks to address these issues through the integration of robust MCDM methodologies, namely fuzzy-weighted zero-inconsistency (FWZIC) and fuzzy decision by opinion score method (FDOSM). Utilising rough Fermatean fuzzy sets (RFFSs), this integration produces precise solutions with reduced uncertainty. Our methodology involves adopting a decision matrix for IDS classifiers based on integrated evaluation criteria, followed by deriving new formulations and developments for RFFSs-based FDOSM and FWZIC for the modelling and weighting of criteria, respectively. Evaluations using datasets involving 125,973 records and 41 features across 17 evaluation criteria revealed that accuracy–security and training time–performance weights yielded the highest scores, whereas false negative rate–security and CPU time–performance criteria received the lowest weights. The random forest emerged as the optimal IDS classifier. Systematic modelling, sensitivity analysis and comparative studies confirmed the robustness of our results. © 2023, Crown.

关键词： Integration

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Abusing iOS Permissions: A Security Perspective

Abusing iOS Permissions: A Security Perspective

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Fares Alanazy Ramzi Saifan Mousa Al-Akhras Samer Atawneh Adeeb Alhomoud College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia School of Engineering The University of Jordan Amman Jordan King Abdullah II School of Information Technology The University of Jordan Amman Jordan College of Science and Theoretical Studies Saudi Electronic University Riyadh audi Arabia

ISBN: (数字)9798350374131

ISBN: (纸本)9798350374148

Smartphone users have become accustomed to installing Apps of different categories such as gaming, business, educational, lifestyle, and many more Apps. However, even with the growing awareness of developing more secure Apps, there is no evidence that the available Apps users are completely safe. This paper explores the correlation between iOS permissions that can be abused and the safety of user information. More specifically, we study the permissions of the most popular iOS educational Apps. The study findings revealed that there is a positive correlation between abusing iOS permissions and the safety of user information. About 98 % of the Apps have abused permissions and consequently, they are classified as suspicious. In addition, the results of the study showed that the users used to download Apps and grant them inordinate permissions. Thus, placing their personal data at risk of exposure to unscrupulous third parties.

关键词： Correlation Software Safety Security Business

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Machine Learning for Plastic Waste Detection: State-of-the-art, Challenges, and Solutions 3

Machine Learning for Plastic Waste Detection: State-of-the-a...

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3rd International Conference on Communications, Information, Electronic and Energy Systems, CIEES 2022

作者： Tamin, Owen Moung, Ervin Gubin Ahmad Dargham, Jamal Yahya, Farashazillah Omatu, Sigeru Angeline, Lorita Universiti Malaysia Sabah Faculty of Computing And Informatics Kota Kinabalu Malaysia Universiti Malaysia Sabah Faculty of Engineering Kota Kinabalu Malaysia Education Research Centre Higashi-Hiroshima739-0046 Japan Taylor's University School of Computer Science Faculty of Innovation And Technology Selangor Malaysia

ISBN: (数字)9781665491495

ISBN: (纸本)9781665491495

Machine learning has advanced rapidly in recent technologies of artificial intelligence. The abundant and cheap computation in machine learning has created more knowledge and reliability of this method. Therefore, more researchers have relied on machine learning to solve plastic waste pollution, which contributes to global greenhouse gas emissions. Datasets are fundamental requirements to build the foundation of the plastic waste detection system. This paper provides an overview of several state-of-the-art deep learning models, focusing on the dataset used for waste classification and identification. The performance of each deep learning model in classification accuracy is also discussed. The challenges of implementing deep learning in plastic waste detection are identified;(i) Data deficiency for training, (ii) Quality of the existing plastic waste dataset, and (iii) Color spaces. Lastly, the proposed solution to each challenge is presented. © 2022 IEEE.

关键词： Object detection

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Optimum Supply Voltage for High Gain Amplifier in Telemetry Circuitry for Ultra-Low Power Implantable Cardiac Pacemaker

Optimum Supply Voltage for High Gain Amplifier in Telemetry ...

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Annual IEEE Northeast Workshop on Circuits and Systems (NEWCAS)

作者： Manisha G Amit Krishna Dwivedi Uma Maheshwari V Prasun Chakrabarti Martin Margala School of Computing and Informatics University of Louisiana at Lafayette USA School of Engineering University of Warwick United Kingdom Department of CSE Chaitanya Bharati Institute of Technology Hyderabad Telangana India ITM SLS Baroda University Vadodara Gujarat India

This work investigates the deep subthreshold device size for the CMOS logic gate inverter circuit at the 16-nm technology node using a predictive technology model (PTM). The device's channel length (L) is established in order to acquire the optimal threshold voltage at 150 mV of the supply voltage. Using the same source voltage, the aspect ratio of an inverter logic circuit is determined, and the analysis of symmetrical transient responses is carried out. When the aspect ratio equals 3.52, the inverter logic gate is found to be symmetrical. The propagation delay is also extracted, and the achieved results demonstrate that a higher aspect ratio can facilitate high-performance circuit design. For the 16-nm technology node, the optimal threshold voltage is achieved at L=66 nm. In this work, the minimum energy point (MEP)/ Optimum Supply Voltage (OSV) is also attained at a supply voltage of 150 mV. MEP/OSV ensures improved performance and minimal power dissipation in the circuit. Based on the results achieved in this research work, a high-gain amplifier circuit in telemetry circuitry at MEP is designed and discussed. The functionality of the high-gain amplifier is verified at the optimum supply voltage.

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Speech Dereverberation Based on Scale-Aware Mean Square Error Loss 28th

Speech Dereverberation Based on Scale-Aware Mean Square Err...

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28th International Conference on Neural Information Processing, ICONIP 2021

作者： Qiang, Luya Shi, Hao Ge, Meng Yin, Haoran Li, Nan Wang, Longbiao Li, Sheng Dang, Jianwu Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China Graduate School of Informatics Kyoto University Sakyo-ku Kyoto Japan Kyoto Japan Japan Advanced Institute of Science and Technology Ishikawa Japan

ISBN: (纸本)9783030923068

Recently, deep learning-based speech dereverberation appro-aches have achieved remarkable performance by directly mapping the input spectrogram to a target spectrogram or time-frequency mask. However, these approaches are usually optimized under distance-related objective functions—the mean square error (MSE). The traditional MSE training criterion results in a strong inherent uniform variance statistical assumption on the target speech and noise during training, which cannot be satisfied in real-world scenarios. To alleviate such an assumption mismatch problem, we propose a speech dereverberation solution called Scale-aware Speech Dereverberation (SaSD) based on scaled-MSE. Specifically, we modify the MSE with different scales for each frequency band and progressively reduce the gap between the low- and high-frequency ranges to make the error follow the assumption of MSE assumption. Experiments demonstrated that SaSD achieved 1.0 SRMR and 0.8 PESQ improvements over the mapping baseline system. © 2021, Springer Nature Switzerland AG.

关键词： Mean square error

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A Systematic Overview on Simulation of Connected Vehicles Infrastructure in Smart Cities

A Systematic Overview on Simulation of Connected Vehicles In...

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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Manik Gupta Imed Ben Dhaou Kamal Deep Garg Bhisham Sharma Chitkara University School of Engineering and Technology Chitkara University Himachal Pradesh India Department of Computer Science Hekma School of Engineering Computing and Informatics Dar Al-Hekma University Jeddah Saudi Arabia Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

The development and use of connected vehicles are becoming increasingly important areas of study in the wireless networking and transportation research communities. These networks can provide various services such as collision warning. Unlike other types of devices, such as hand-held devices, the vehicles’ nodes are not limited by their energy consumption and storage capacity. Therefore, the development of connected networks can be carried out with minimal resources. It is necessary to run a simulation in order to determine whether they are feasible. The availability of multiple devices and protocols has led to the creation of new utilization scenarios. Due to the complexity of the task, many simulation tools were developed during the past few years. This paper systematically reviews the current state of these tools and discusses their capabilities to evaluate the various scenarios related to the development of connected vehicles network.

关键词： Wireless communication Technological innovation Connected vehicles Systematics Smart cities Vehicular ad hoc networks Transportation

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