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International Joint Conference on Neural Networks (IJCNN)

作者： Lei Shen Shuai Yu Xiaoyu Shen GEB Beijing China School of Computer Science and Technology Donghua University Shanghai China Department of Informatics Eastern Institute of Technology Ningbo China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Cross-lingual transfer is important for developing high-quality chatbots in multiple languages to address the imbalanced distribution of language resources. A typical approach of cross-lingual transfer, which has been proved effective on classification tasks, is to leverage machine translation (MT) systems to utilize either the training corpus or models from high-resource languages. In this work, we investigate whether it is helpful to utilize MT for cross-lingual transfer in dialog generation tasks. We collect a benchmark dataset for the low-resource scenario, assuming access to limited Chinese dialog data in the movie domain and large amounts of English dialog from multiple domains. Experiments show that leveraging English dialog corpora can improve the naturalness, relevance, and cross-domain transferability in Chinese. However, directly using English corpora in their original form is better than translating them into Chinese. As the topics and wording habits in dialogs are strongly culture-dependent, translating them can reinforce the bias from high-resource languages. To avoid this issue and also reduce the embedding mismatch, we propose to use embedding freezing and post-alignment, which align words from different languages as translation would, but without introducing translation biases. Experiments show that embedding freezing and post-alignment can further improve generation performance. The results of the analysis together with the collected benchmark dataset are presented to draw attention to this area and support future research.

关键词： Training Metalearning Neural networks Benchmark testing Motion pictures Chatbots Machine translation

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Highly-Compact Wideband High-Gain Four-Element MIMO Antenna for 5G New Radio IoT

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IEEE Internet of Things Journal 2025年第12期12卷 22350-22365页

作者： Khan, Shahid Khan, Owais Shah, Syed Ahson Ali Nasir, Jamal Malik, Bilal Tariq Khan, Salahuddin Koziel, Slawomir Department of Electrical and Computer Engineering COMSATS University Islamabad Abbottabad-Campus Islamabad Pakistan Gwangju Institute of Science and Technology Graduate School of Energy Convergence Gwangju Korea Republic of King Saud University College of Engineering P.O. Box 800 Riyadh11421 Saudi Arabia Gdansk University of Technology Faculty of Electronics Telecommunications and Informatics Gdansk80-233 Poland Reykjavik University Engineering Optimization and Modeling Center Reykjavik101 Iceland

Wideband millimeter-wave (mm-wave) coverage is essential for the high-speed, low-latency communication required in next-generation 5G New Radio (NR) Internet of Things (IoT) systems. This study develops a T-shaped four-element, highly compact wideband multiple-input multiple-output (MIMO) antenna covering the mm-wave n260 (37.40 GHz) and n259 (42.43.5 GHz) bands for 5G NR IoT applications. The antenna is designed on a 0.76 mm-thick Rogers RO4350B substrate with overall dimensions of 28 28 mm2 (3.3 × 3.3 λ2). For design simplicity, a T-shaped patch backed by a full-ground plane is devised to serve as the radiator of a single-element antenna with the dimensions of (Ls × Ws) mm2, optimized through a systematic three-step design process for improved performance. Moreover, the design has evolved into a 4× 4 orthogonal MIMO configuration, achieving improved gain, polarization diversity, and higher data rates, with each element exhibiting wideband characteristics across 11.5 GHz (36.5 to 48 GHz) with high gains of 9.8 dBi and 6.6 dBi at n260 and n259 5G mm-wave bands, respectively. Additionally, arc-shaped complementary split-ring resonators (CSRR) are integrated into the ground plane to significantly enhance the gain while effectively reducing mutual coupling, remarkably achieving a maximum gain of 9.8 dBi. Furthermore, the MIMO antenna exhibits an envelope correlation coefficient of less than 0.10 between any two MIMO elements encountering the required condition of © 2014 IEEE.

关键词： Microwave antennas

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Concierge: Towards Accuracy-Driven Bandwidth Allocation for Video Analytics Applications in Edge Network

Concierge: Towards Accuracy-Driven Bandwidth Allocation for ...

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IEEE International Conference on Edge Computing (EDGE)

作者： Yuyang Huang Faishal Zharfan Hendrawan Hendrawan Haryadi S. Gunawi Junchen Jiang Department of Computer Science University of Chicago Chicago USA School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (数字)9798350368499

ISBN: (纸本)9798350368505

When performing inference on sensor data, edge video analytics applications may not always need high-fidelity data, since important information may not appear all the time. Consequently, each edge AI application’s bandwidth demand is highly dynamic. Thus, a shared edge system should dynamically allocate more bandwidth to the applications in need to reach high accuracy at each moment. However, previous bandwidth allocators are ill-suited because they are agnostic to the timevarying impact of bandwidth on each application’s *** short paper explores a new accuracy-driven approach to bandwidth allocation, which periodically re-allocates bandwidth across edge AI applications based on the sensitivity of each application’s accuracy to its bandwidth share. To examine its practical benefit and technical challenges, we present a concrete accuracy-driven bandwidth allocator called ConciERGE, which exposes a simple yet efficient interface to estimate each application’s sensitivity to a small change in its bandwidth *** run CONCIERGE on state-of-the-art video-analytics applications with real video streams and show its early promise in greatly improving the inference accuracy of video analytics.

关键词： Accuracy Sensitivity Visual analytics Estimation Bandwidth Edge AI Channel allocation

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A Systematic Literature Review of XAI-based Approaches on Brain Disease Detection using Brain MRI Images

A Systematic Literature Review of XAI-based Approaches on Br...

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Advanced Research in Computing (ICARC), International Conference on

作者： Savin Madapatha Pumudu Fernando Department of Computer Science and Engineering University of Westminster London UK School of Computing Informatics Institute of Technology Colombo Sri Lanka

Brain Magnetic Resonance Imaging (MRI) analysis is a widely used medical procedure for the early diagnosis of various brain diseases. Accurate pathology identification during the brain MRI analysis procedure is crucial as misdiagnoses or missed findings can greatly affect a patient's treatment and long-term prediction. With the recent advancement of Artificial Intelligence (AI) in the medical field, researchers have approached various techniques to detect brain diseases using AI. Although AI models exhibit high accuracy, they suffer from a lack of transparency and interpretability, paving the way for the development of eXplainable Artificial Intelligence (XAI) methods in brain disease diagnosis. Image segmentation, machine learning, deep learning and XAI are important for assisting the diagnostic procedure. In this paper, a comprehensive overview of various existing techniques in brain disease detection using MRI is presented, starting with image segmentation techniques, followed by classification techniques, and finally, XAI techniques. In conclusion, the paper identifies a critical need for further research on XAI integration to advance brain disease detection.

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Machine Learning based Classification Algorithm for Classification of Dengue (Dengue Fever -DF, Dengue Harmonic Fever -DHF & Serve Dengue -SD) 1

Machine Learning based Classification Algorithm for Classifi...

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1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Kapoor, Rajeev Ahuja, Sachin Kadyan, Virender Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Informatics School of Computer Science University of Petroleum and Energy Studies Dehradun India

ISBN: (纸本)9781607685395

Dengue is becoming a burden for society worldwide and become challenge for the world. The main objective of this research paper is to classify dengue at an early stage. The author has adopted a methodology that is divided into five phases namely Data Collection, Transmission of Raw Data in Structural data format, Data Preprocessing, Model building with Train & Test Datasets and Prediction. Further, the proposed prediction model has been trained with the help of 250 data samples from primary sources namely clinical labs and 1000 data sample in the form of synthetic datasets adopted from 20 research papers. The proposed prediction Model has been tested and validated with the help of comparative study of different machine learning algorithms namely Decision Tree, Logistical Regression, Support Vector Machine (SVM), Gaussian Naïve Bayes etc. The proposed model has achieved 97.39% accuracy, 0.98 Precision, 0.97 Recall, 0.97 F1 Score, 0.02 MS and 0.14 RMSE. © The Electrochemical Society

关键词： Dengue Prediction Model Machine Learning Prediction Model Vector Borne Disease

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Adaptive Power Control Aware Depth Routing in Underwater Sensor Networks

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computers, Materials & Continua 2021年第10期69卷 1301-1322页

作者： Ghufran Ahmed Saiful Islam Ihsan Ali Isra Adil Hayder Abdelmuttlib Ibrahim Abdalla Ahmed Muhammad Talha Sultan S.Alshamrani Ag Asri Ag Ibrahim Department of Computer Science FAST National University of Computer and Emerging Sciences(NUCES)KarachiPakistan Dr.A.Q.Khan Institute of Computer Sciences and Information Technology KahutaPakistan Faculty of Computer Science and Information Technology University of MalayaKuala Lumpur50603Malaysia Department of Computer Science COMSATS University IslamabadIslamabadPakistan Deanship of Scientific Research King Saud UniversityRiyadhSaudi Arabia Department of Information Technology College of Computer and Information TechnologyTaif UniversityTaif21944Saudi Arabia Faculty of Computing and Informatics Universiti Malaysia SabahKota KinabaluSabahMalaysia

Underwater acoustic sensor network(UASN)refers to a procedure that promotes a broad spectrum of aquatic *** can be practically applied in seismic checking,ocean mine identification,resource exploration,pollution checking,and disaster *** confronts many difficulties and issues,such as low bandwidth,node movements,propagation delay,3D arrangement,energy limitation,and high-cost production and arrangement costs caused by antagonistic underwater *** wireless sensor networks(UWSNs)are considered a major issue being encountered in energy management because of the limited battery power of their ***,the harsh underwater environment requires vendors to design and deploy energy-hungry devices to fulfil the communication requirements and maintain an acceptable quality of ***,increased transmission power levels result in higher channel interference,thereby increasing packet *** the facts mentioned above,this research presents a controlled transmission power-based sparsity-aware energy-efficient clustering in *** contributions of this technique is ***,it uses the adaptive power control mechanism to utilize the sensor nodes’battery and reduce channel interference ***,thresholds are defined to ensure successful ***,clustering can be implemented in dense areas to decrease the repetitive transmission that ultimately affects the energy consumption of nodes and interference ***,mobile sinks are deployed to gather information locally to achieve the previously mentioned *** suggested protocol is meticulously examined through extensive simulations and is validated through comparison with other advanced UWSN *** show that the suggested protocol outperforms other procedures in terms of network lifetime and packet delivery ratio.

关键词： UWSNs UASNs QoS

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Privacy-Preserving Information Fusion Technique for Device to Server-Enabled Communication in the Internet of Things:A Hybrid Approach

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computers, Materials & Continua 2024年第7期80卷 1305-1323页

作者： Amal Al-Rasheed Rahim Khan Tahani Alsaed Mahwish Kundi Mohamad Hanif Md.Saad Mahidur R.Sarker Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science AbdulWali Khan University MardanMardan23200Pakistan Faculty of Computing and Informatics University Malaysia SabahKota KinabaluSabah88400Malaysia Applied College Taibah UniversityMadinah41477Saudi Arabia Maynooth International Engineering College Maynooth UniversityCo KildareW23 X021Irland Department of Mechanical Engineering Faculty of Engineering and Built EnvironmentUniversiti Kebangsaan MalaysiaBangiSelangor43600Malaysia Institute of Visual Informatics Universiti Kebangsaan MalaysiaBangiSelangor43600Malaysia University of Design Innovation and Technology(UDIT)Av.Alfonso XIII97Madrid28016Spain

Due to the overwhelming characteristics of the Internet of Things(IoT)and its adoption in approximately every aspect of our lives,the concept of individual devices’privacy has gained prominent attention from both customers,i.e.,people,and industries as wearable devices collect sensitive information about patients(both admitted and outdoor)in smart healthcare *** addition to privacy,outliers or noise are among the crucial issues,which are directly correlated with IoT infrastructures,as most member devices are resource-limited and could generate or transmit false data that is required to be refined before processing,i.e.,***,the development of privacy-preserving information fusion techniques is highly encouraged,especially those designed for smart IoT-enabled *** this paper,we are going to present an effective hybrid approach that can refine raw data values captured by the respectivemember device before transmission while preserving its privacy through the utilization of the differential privacy technique in IoT *** window,i.e.,δi based dynamic programming methodology,is implemented at the device level to ensure precise and accurate detection of outliers or noisy data,and refine it prior to activation of the respective transmission ***,an appropriate privacy budget has been selected,which is enough to ensure the privacy of every individualmodule,i.e.,a wearable device such as a smartwatch attached to the patient’s *** contrast,the end module,i.e.,the server in this case,can extract important information with approximately the maximum level of ***,refined data has been processed by adding an appropriate nose through the Laplace mechanism to make it useless or meaningless for the adversary modules in the *** proposed hybrid approach is trusted from both the device’s privacy and the integrity of the transmitted information *** and analytical results ha

关键词： Internet of things information fusion differential privacy dynamic programming Laplace function

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A Fair and In-Depth Evaluation of Existing End-to-End Entity Linking Systems

arXiv

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arXiv 2023年

作者： Bast, Hannah Hertel, Matthias Prange, Natalie University of Freiburg Department of Computer Science Germany Karlsruhe Institute of Technology Institute for Automation and Applied Informatics Germany

Existing evaluations of entity linking systems often say little about how the system is going to perform for a particular application. There are two fundamental reasons for this. One is that many evaluations only use aggregate measures (like precision, recall, and F1 score), without a detailed error analysis or a closer look at the results. The other is that all of the widely used benchmarks have strong biases and artifacts, in particular: a strong focus on named entities, an unclear or missing specification of what else counts as an entity mention, poor handling of ambiguities, and an over- or underrepresentation of certain kinds of entities. We provide a more meaningful and fair in-depth evaluation of a variety of existing end-to-end entity linkers. We characterize their strengths and weaknesses and also report on reproducibility aspects. The detailed results of our evaluation can be inspected under https://***/emnlp2023. Our evaluation is based on several widely used benchmarks, which exhibit the problems mentioned above to various degrees, as well as on two new benchmarks, which address the problems mentioned above. The new benchmarks can be found under https://***/ad-freiburg/fair-entitylinking-benchmarks. Copyright © 2023, The Authors. All rights reserved.

关键词： Petroleum reservoir evaluation

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TEACHING: A Computing Toolkit for Building Efficient Autonomous appliCations Leveraging Humanistic INtelliGence 3

TEACHING: A Computing Toolkit for Building Efficient Autonom...

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3rd Workshop on Flexible Resource and Application Management on the Edge, FRAME 2023, held in conjunction with the HPDC 2023 and the FCRC 2023 Conferences

作者： Bacciu, Davide Tserpes, Konstantinos Coppola, Massimo MacHer, Georg Gallicchio, Claudio Veledar, Omar Anaxagorou, Anna Maria Dazzi, Patrizio University of Pisa Department of Computer Science Pisa Italy Harokopio University Athens Department of Informatics and Telematics Athens Greece National Research Council of Italy Institute of Information Science and Technologies Pisa Italy Tu Graz Department of Informatics and Telematics Graz Austria Avl Graz Austria Information Technology for Market Leadership Athens Greece

ISBN: (纸本)9798400701641

TEACHING proposes a distributed, trustworthy AI integrating continuous human feedback, supporting CPSoS application design and deployment. TEACHING envisions an intelligent environment, empowering humans through cybernetic assistance. It advances autonomous safety-critical systems, improving safety, reliability and acceptability through human-centred design and formal validation crossing paradigms. TEACHING brings humans and AI together, enabling participatory development, optimisation and oversight. © 2023 ACM.

关键词： Cyber Physical System

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Improving bot detection of fake followers on Twitter via a hybrid B-HC optimisation algorithm

Improving bot detection of fake followers on Twitter via a h...

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International Conference on Electronics, Communication and Computing Technologies (ICECCT)

作者： Haider Alzeyadi Fecir Duran Department of Computer Science Informatics Institute Gazi University Ankara Turkey Department of Computer Engineering Engineering Faculty of Technology Gazi University Ankara Turkey

Sophisticated cyber threats are seen on Online Social Networks (OSNs) social media accounts automated to imitate human behaviours has an impactful effect on distorting public thoughts and opinions. OSNs are weaponized to diffuse deception, misinformation, and malicious activities, that forms a serious threat to society. The deceptive nature of imitating human behaviour has become a challenging and crucial task to detect automated accounts (socialbots). This research, however, proposes a hybrid metaheuristic optimisation algorithm for socialbot detection. Specifically, a hybrid B-Hill Climbing (B-HC) optimisation algorithm works in tandem with a k-NN nearest neighbour classifier to accurately select a relevant feature subset. It is applied to be tested for fake followers account on Twitter data. Experimental results showed that the proposed method is better than the traditional and the latest feature selection techniques as well as the rule-set methods. The B-HC alongside with k-NN method achieved promising results using only relevant feature subset.

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