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arXiv 2024年

作者： Poulis, Angelos Tsalapati, Eleni Koubarakis, Manolis Dept. of Computer Science Boston University United States Athens Technology Center Greece AI Team Dept. of Informatics and Telecommunications National and Kapodistrian University of Athens Greece Archimedes Athena RC Greece

Recent advancements in transformer-based language models have sparked research into their logical reasoning capabilities. Most of the benchmarks used to evaluate these models are simple: generated from short (fragments of) first-order logic sentences with only a few logical operators and quantifiers. We construct the natural language dataset, DELTAD, using the expressive description logic language ALCQ. DELTAD comprises 384K examples and increases in two dimensions: i) reasoning dept., and ii) linguistic complexity. In this way, we systematically investigate the logical reasoning capabilities of a supervised fine-tuned DeBERTa-based model and two large language models (GPT-3.5, GPT-4) with few-shot prompting. We show that the DeBERTa-based model fine-tuned on our dataset can master the entailment checking task. Moreover, the performance of GPTs can improve significantly even when a small number of samples is provided (9 shots). We open-source our code and datasets. © 2024, CC BY.

关键词： Benchmarking

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Dynamic Blockchain Reconfiguration: Balancing the Trilemma Trade-off Using Digital Twins

Dynamic Blockchain Reconfiguration: Balancing the Trilemma T...

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International Workshop/Symposium on Distributed Simulation and Real-Time Applications (DS-RT)

作者： Georgios Diamantopoulos Nikos Tziritas Rami Bahsoon Georgios Theodoropoulos School of Computer Science University of Birmingham Birmingham UK Dept. of Informatics and Telecommunications University of Thessaly Lamia Greece Dept. of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

The trilemma trade-off problem between decentralisation, scalability, and security states that in blockchain systems the above properties are negatively correlated. Infrastructure, node configuration, choice of Consensus Protocol, and complexity of the underlying application are cited among the factors that affect the balance of the trade-off. Given that Blockchains are complex, dynamic systems, a dynamic approach to their management and reconfiguration at runtime is deemed necessary to reflect the changes in the state of the infrastructure and application. This work proposes the use of Digital Twins as the means of optimising the trilemma trade-off of blockchain i.e., re-configuring system parameters such as to maximise scalability, decentralisation and security. Specifically, through a bi-directional feedback loop between the system and the digital twin, simulation, what-if analysis and machine learning techniques will be employed for the computation of an optimal configuration given the current system state. Furthermore, a dynamic update mechanism is proposed to allow for blockchain reconfiguration without violating the decentralisation of the system.

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Federated Digital Twin

Federated Digital Twin

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International Workshop/Symposium on Distributed Simulation and Real-Time Applications (DS-RT)

作者： Christian Vergara Rami Bahsoon Georgios Theodoropoulos Wendy Yanez Nikos Tziritas School of Computer Science University of Birmingham Birmingham UK Dept. of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Dept. of Informatics and Telecommunications University of Thessaly Lamia Greece

Digital Twin (DT) is a virtual replica of a physical system that is constantly receiving information from different data sources, enhancing its operations and processes through data analytics, predictions and simulations. The development of DTs relies on advancements in cutting-edge technologies namely IoT, Big data, Cloud computing and Artificial Intelligence; and although it was initially conceived in manufacturing, it is currently contributing to the digital transformation of several fields including aeronautics, healthcare, urban planning and agriculture. The existing body of research suggests that it will be expanded in the next few years with the implementation of sophisticated applications, therefore different proposals to achieve collective work between DTs have been investigated. Nevertheless, much research is needed to develop and validate appropriate mechanisms to ensure its successful deployment in complex real-world cases that require collaboration among individual systems. A Federated Digital Twin (FDT) has been identified as a promising solution for this approach, since it allows the interconnection among autonomous DTs in the virtual space, leveraging their advantages and enabling interaction, collaboration and shared learning. Additionally, since a FDT is envisaged as a network of cooperative DTs, cognitive principles can be applied to assist the overall operations through knowledge acquisition and reasoning, leading to an informed and intelligent decision making. This study aims to expand the FDT concept, develop mechanisms for coordination and synchronization based on well-defined FDT goals and connectionism theory. Furthermore, four architectural styles are provided to enable the integration of collaborative DTs within a federated environment, aiming to improve the operations in complex real-world systems.

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Visual Writing Prompts: Character-Grounded Story Generation with Curated Image Sequences

arXiv

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arXiv 2023年

作者： Hong, Xudong Sayeed, Asad Mehra, Khushboo Demberg, Vera Schiele, Bernt Dept. of Computer Vision and Machine Learning MPI Informatics Germany Dept. of Language Science and Technology Saarland University Germany Dept. of Philosophy Linguistics Theory of Science University of Gothenburg Sweden

Current work on image-based story generation suffers from the fact that the existing image sequences collections do not have coherent plots behind them. We improve visual story generation by producing a new image-grounded dataset, Visual Writing Prompts (VWP). VWP contains almost 2K selected sequences of movie shots, each including 5-10 images. The image sequences are aligned with a total of 12K stories which were collected via crowdsourcing given the image sequences and a set of grounded characters from the corresponding image sequence. Our new image sequence collection and filtering process has allowed us to obtain stories that are more coherent and more diverse compared to previous work. We also propose a character-based story generation model driven by coherence as a strong baseline. Evaluations show that our generated stories are more coherent, visually grounded, and more diverse than stories generated with the current state-of-the-art model. This is a pre-MIT Press publication version. © 2023, CC BY.

关键词： Image enhancement

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Observing Generator Performance by Finite Element Analysis Considering Coil Numbers 5

Observing Generator Performance by Finite Element Analysis C...

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5th International Conference on technology and Policy in Energy and Electric Power, ICT-PEP 2023

作者： Habibi, Muhammad Afnan Ali Irfani, Mochamad Farhan Gumilar, Langlang Kusumawardana, Arya Aripriharta Baskoro, Angger Universitas Negeri Malang Study Program of Power Plant Engineering Technology Malang Indonesia Universitas Negeri Malang Department of Electrical Engineering and Informatics Malang Indonesia National Yang Ming Chiao Tung University Dept. Electrical Engineering and Computer Sciences Hsinchu Taiwan

ISBN: (纸本)9798350369717

This paper conducts a comprehensive analysis of electrical generator performance using Finite Element Analysis (FEA), with a specific emphasis on the role of coil numbers in influencing generator efficiency and functionality. In the context of modern energy systems, where efficient power generation is paramount, this research aims to elucidate the relationship between the number of coils within a generator and its overall performance, including power output and electromagnetic behavior. Through systematic FEA simulations that vary coil numbers while keeping other parameters constant, this study provides valuable insights into the trade-offs associated with increased coil numbers and enhanced efficiency. These findings have significant implications for optimizing generator designs across various applications, from renewable energy systems to industrial power generation, ultimately advancing our understanding of generator dynamics and contributing to more sustainable and efficient power generation technologies. © 2023 IEEE.

关键词： Finite element method

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Zero-Shot Sentiment Analysis Exploring BART Models

Zero-Shot Sentiment Analysis Exploring BART Models

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Big Data, Cloud Computing, Data Science & Engineering (BCD)

作者： Konstantinos Kyritsis Isidoros Perikos Michael Paraskevas Electrical and Computer Engineering Dept. Computer Technology Institute and Press "Diophantus" University of Peloponnese Patras Greece Computer Engineering and Informatics Department University of Patras Patras Greece

The BART model is an advanced adaptation of transformers introduced by Facebook. It has incorporated elements from both BERT and GPT transformers, enabling significant advancements in language understanding and general speech processing. Utilizing both encoder and decoder components, BART proves versatile for various tasks, including translation, text completion, automatic sentence generation, entity recognition, sentiment analysis, and more. In this study, we focus on the study of pretrained models, BART and a modified version called distilbart, in the context of Zero-Shot Text Classification. In the experimental study we dive into the Zero-Shot technique applied to various pretrained Transformers. Our analysis demonstrates that, depending on the Model we utilize, we can achieve F1 scores of up to 88%, showcasing the model's effectiveness in discerning classes for this Sentiment Analysis problem using the Zero-Shot Text Classification technique.

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The Classification of Timor Weaving Using Artificial Neural Network

The Classification of Timor Weaving Using Artificial Neural ...

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International Conference on informatics and Computing (ICIC)

作者： Mendarissan Aritonang Adi Setiawan Krismiyati Theophilus Wellem Dept of Computer Science Faculty of Information Technology Satya Wacana Christian University Salatiga Indonesia Dept. Mathematics and Data Science Faculty of Science and Mathematics Satya Wacana Christian University Salatiga Indonesia Dept. Informatics & Computer Engineering Education Faculty of Information Technology Satya Wacana Christian University Salatiga Indonesia Department of Informatics Satya Wacana Christian University Salatiga Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

The number of woven fabric on the island of Timor is very large and varied, making it difficult to distinguish between types and origins. Many woven fabric motifs appear similar but represent different types. Therefore, this classification was used to perform pattern recognition of woven fabric in the system. The process allowed the system’s ability to be tested by applying the pattern recognition algorithm to woven images. In this context, the feature extraction method used was gray level co-occurrence matrix (GLCM), while the method used for classification was artificial neural network (ANN). The results showed an accuracy of $87.5 \%$ in the system. Finally, GUI system was developed, enabling tests to be performed on woven images for the identification of Timor Weaving image patterns.

关键词： Training Accuracy Social networking (online) Neurons Artificial neural networks Feature extraction Weaving Fabrics Pattern recognition Web sites

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Carotid plaque stroke risk assessment using multiscale AM-FM analysis based on DoG filterbanks

Carotid plaque stroke risk assessment using multiscale AM-FM...

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International Conference on Digital Signal Processing (DSP)

作者： Kyriacos P. Constantinou Ioannis P. Constantinou Marios S. Pattichis Efthyvoulos Kyriacou Constantinos S. Pattichis Dept. of Computer Science University of Cyprus Nicosia Cyprus Istognosis Ltd Nicosia Cyprus Dept. of Electrical and Computer Engineering University of New Mexico Albuquerque NM USA Dept. of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus

The objective of this work was the investigation of multiscale Amplitude Modulation - Frequency Modulation (AM-FM) analysis based on Difference of Gaussians (DoG) filterbanks representations in order to predict the risk of stroke by analysing carotid plaques ultrasound images of individuals with asymptomatic carotid stenosis. We computed the instantaneous amplitude, instantaneous phase and the magnitude of instantaneous frequency to extract histogram features on each plaque region. The Support Vectors Machine classifier was implemented to classify asymptomatic versus symptomatic plaques. A dataset of 100 carotid plaque images (50 asymptomatic and 50 symptomatic) were tested, and showed that the AM-FM features based on DoG filterbanks and simple histograms performed better than the traditional AM-FM features. Best results were obtained when an eight scale filterbank with a combination of scales was used reaching the accuracy of 75%.

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Enhanced Driver Stress Prediction from Multiple Biosignals via CNN Encoder-Decoder Model

Enhanced Driver Stress Prediction from Multiple Biosignals v...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Vishal Singh Roha Maggie Ezzat Gaber Gendy Kaveti Pavan Thomas M. Deserno Nagarajan Ganapathy Mehmet R. Yuce Dept. of Electrical & Computer Systems Monash University Melbourne Australia Dept. of Biomedical Engineering Indian Institute of Technology Hyderabad Hyderabad India PLRI Lab for Medical Informatics Technical University of Braunschweig Brunswick Germany

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

In this work, we present PhysioFuseNet, a novel framework designed to enhance driver stress state classification. PhysioFuseNet integrates a CNN-based encoder-decoder model with multimodal biosignal fusion. Using a driving simulator, different multimodal signals were acquired, namely electrocardiography, electrodermal activity, photoplethysmography, and respiration rate from (N = 25) healthy subjects. The experiment is of 35 minutes duration and contains different stress states (baseline (5 minutes), while normal, cognitive, and emotional sessions for 10 minutes). Multimodal features are extracted and employed in an encoder-decoder network. Extracted encoder features are combined through intermediate fusion and fed to support vector machine (SVM) and random forest (RF) classifiers. Experimental results demonstrate the efficacy of our approach, outperforming previous methods by achieving accuracies of 0.95 and 0.94 for SVM and RF, respectively. Notably, the framework excels in classifying emotional and cognitive stress states. In summary, the proposed framework could be useful in stress assessment in real-time and clinical conditions.

关键词： Support vector machines Radio frequency Biological system modeling Anxiety disorders Predictive models Electrocardiography Feature extraction Photoplethysmography Real-time systems Engineering in medicine and biology

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CONMIQ Multicast: A Scalable Multicast Video Streaming in LTE Networks 4

CONMIQ Multicast: A Scalable Multicast Video Streaming in LT...

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4th International Conference on Vocational Education and Electrical Engineering, ICVEE 2021

作者： Cholilur Rochman, Muhammad Iqbal Anistyasari, Yeni National Taiwan University of Science and Technology Dept. of Computer Science and Information Engineering Taipei Taiwan Universitas Negeri Surabaya Dept. of Informatics Surabaya Indonesia

ISBN: (纸本)9781665405997

Multicast transmission is a recurrent problem in wireless networking as the system has to cater to multiple numbers of users at the same time. Furthermore, video payload adds more complexity to the problem, i.e., video needs to be delivered in a timely and orderly manner. Using H.264/SVC standard for scalable video, scaling could be used to compromise the quality and the size of the video, yet it poses a complex dependency problem. We propose a multicast scheme named CONMIQ (Constrained Non-linear Model of Incremental Quality) that provides suboptimal video quality to all users and still fulfill resource requirement. The solution will be applied to LTE-A(Long-Term Evolution-Advanced) network, which provides robust user channel quality assessment and a better OFDMA channel. To find the maximum video quality, we model the video quality of each video block in a GOP (Group of Pictures) as a second-degree polynomial function, then solve the maximization problem on that function, along with the considerations of resource constraints, video block dependency, and the varying channel condition of subscribed users. The experimental results verify that our suboptimal solution outperforms naive approaches while still performing comparably well than the much slower optimal solution. © 2021 IEEE.

关键词： Video streaming

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