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International Conference on Measurement

作者： Anupam Ghosh Madhurima Bhattacharjee Aditi Kathpalia Martin Brunovský Milan Paluš Department of Complex Systems Institute of Computer Science of the Czech Academy of Sciences Prague Czech Republic Clinical Research Programme National Institute of Mental Health Klecany Czech Republic

In this paper, we infer the direction of information transfer between interacting subsystems using a cross-dependence index derived from the phase dynamics of coupled subsystems. First, we apply this method to unidirectionally coupled Rössler oscillators. This index always has a higher value along the direction of coupling than that opposite to the coupling. Finally, we employ this method in analysis of EEG signals. The aim of this analysis is to identify the direction of information transfer in the brain in patients suffering from depression and quantify its changes during the treatment process.

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Compression Spectrum: Where Shannon meets Fourier

arXiv

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arXiv 2023年

作者： Kathpalia, Aditi Nagaraj, Nithin Department of Complex Systems Institute of Computer Science of the Czech Academy of Sciences Prague Czech Republic Consciousness Studies Programme National Institute of Advanced Studies Indian Institute of Science Campus Bengaluru560 012 India

Signal processing and Information theory are two disparate fields used for characterizing signals for various scientific and engineering applications. Spectral/Fourier analysis, a technique employed in signal processing, helps estimation of power at different frequency components present in the signal. Characterizing a time-series based on its average amount of information (Shannon entropy) is useful for estimating its complexity and compressibility (eg., for communication applications). Information theory doesn't deal with spectral content while signal processing doesn't directly consider the information content or compressibility of the signal. In this work, we attempt to bring the fields of signal processing and information theory together by using a lossless data compression algorithm to estimate the amount of information or 'compressibility' of time series at different scales. To this end, we employ the Effort-to-Compress (ETC) algorithm to obtain what we call as a Compression Spectrum. This new tool for signal analysis is demonstrated on synthetically generated periodic signals, a sinusoid, chaotic signals (weak and strong chaos) and uniform random noise. The Compression Spectrum is applied on heart interbeat intervals (RR) obtained from real-world normal young and elderly subjects. The compression spectrum of healthy young RR tachograms in the log-log scale shows behaviour similar to 1/f noise whereas the healthy old RR tachograms show a different behaviour. We envisage exciting possibilities and future applications of the Compression Spectrum. Copyright © 2023, The Authors. All rights reserved.

关键词： Spectrum analysis

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Deadline-constrained Multi-resource Task Mapping and Allocation for Edge-Cloud Systems

Deadline-constrained Multi-resource Task Mapping and Allocat...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Chuanchao Gao Aryaman Shaan Arvind Easwaran School of Computer Science and Engineering Energy Research Institute @ NTU Interdisciplinary Graduate Programme Nanyang Technological University Singapore

ISBN: (纸本)9781665435413

In an edge-cloud system, mobile devices can offload their computation intensive tasks to an edge or cloud server to guarantee the quality of service or satisfy task deadline requirements. However, it is challenging to determine where tasks should be offloaded and processed, and how much network and computation resources should be allocated to them, such that a system with limited resources can obtain a maximum profit while meeting the deadlines. A key challenge in this problem is that the network and computation resources could be allocated on different servers, since the server to which a task is offloaded (e.g., a server with an access point) may be different from the server on which the task is eventually processed. To address this challenge, we first formulate the task mapping and resource allocation problem as a non-convex Mixed-Integer Nonlinear Programming (MINLP) problem, known as NP-hard. We then propose a zero-slack based greedy algorithm (ZSG) and a linear discretization method (LDM) to solve this MINLP problem. Experiment results with various synthetic tasksets show that ZSG has an average of 2.98% worse performance than LDM with a minimum unit of 5 but has an average of 6.88% better performance than LDM with a minimum unit of 15.

关键词： Wireless communication Processor scheduling Quality of service Programming Mobile handsets Servers Resource management

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EEG Connectivity in Treatment of Major Depressive Disorder: Tackling the Conductivity Effects

EEG Connectivity in Treatment of Major Depressive Disorder: ...

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International Conference on Measurement

作者： Milan Paluš Aditi Kathpalia Martin Brunovský Department of Complex Systems Institute of Computer Science of the Czech Academy of Sciences Prague Czech Republic Clinical Research Programme National Institute of Mental Health Klecany Czech Republic

Brain connectivity, extracted from EEG is considered for quantitative characterization of changes in brain function due to a mental disease and its treatment. Weighted imaginary coherence is chosen as a connectivity measure reflecting true brain interactions without distortions due to scalp conductivity and a common reference electrode. This measure is used in analyses of EEG data from patients suffering from major depressive disorder and its potential in prediction of efficacy of antidepressant treatment is demonstrated.

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COVID-19 Vaccination Data Visualization: Issues and Challenges

COVID-19 Vaccination Data Visualization: Issues and Challeng...

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Visualization, Informatics and Technology Conference (IVIT), International

作者： Izzatul Syahirah Ismail Siti Hajar Aishah Samsudin Muhammad Adam Sani Mohd Sofian Hamidah Jantan Computer Science Programme Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA (UiTM) Cawangan Terengganu Kuala Terengganu Malaysia Department of Computer Science Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA Cawangan Terengganu Kuala Terengganu Malaysia

ISBN: (纸本)9781665486491

Data Visualization plays an important role for patterns and trends analysis in trillion of data rows Big Data analysis, where the data can be represented in some graphical forms. Hence, the data could be more comprehensible in its visual summary in dashboards and storyboards. This study aims to discuss some issues and challenges in visualizing COVID-19 vaccination datasets. There are some possible issues in data visualization, as it is not easy and may be challenging to produce a good dashboard that are interesting and easy for viewers to understand. Therefore, this study focuses on some issues that may arise during performing a data visualization on the COVID-19 dataset. In this study, there are three dashboards have been studied, which are the COVID-19 tracker, its effectiveness, and its acceptance. The first two dataset are derived from Ministry of Health Malaysia bank data, whereas the third dataset is from a survey to support this analysis. The selected attributes are states, the number of people who have received the vaccine as adults, children, and teenagers, and the number of people who already received boosters, and reasons to not get a booster. The visualization issues found within the dashboard are mis-choice of colors, mis-choice of visual object type, lack of interactivity, and plotting too much data. As a result, this proposed alternative solutions for those issues such as color deliberately, pick a suitable visual object, create an interactive dashboard, and reduce the information overload in visualizing the data.

关键词： COVID-19 Visualization Pediatrics Image color analysis Data visualization Big Data Market research

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Envisioning Artificial Intelligence Possibilities in Tracking Eidolon Helvum Bats: A Review

SSRN

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SSRN 2023年

作者： Aworinde, Halleluyah O. Adebayo, Segun Ayokunle, Damilare I. Atobatele, Oluwatosin E. Computer Science Programme College of Computing and Communication Studies Bowen University Iwo Nigeria Mechatronics Engineering College of Agriculture Engineering & Science Bowen University Iwo Nigeria Biological Sciences College of Agriculture Engineering & Science Bowen University Iwo Nigeria

Artificial intelligence (AI) is fast playing important roles as it is useful in several ways to curate data on movement patterns, wildlife observations, monitor ecological population and as well, understand behaviours. Here, in this study, current trends of how artificial intelligence are used to understand several activities of the Eidolon helvum fruit bats. The study reviewed 122 publications that is related to bats and its environment, of which just 20 (16%) used machine learning to assess the several activities of bats ranging from specie classification to sound signal processing. We also identified the most popular machine-learning techniques, as well as probable explanations for why they are rarely used in ecology and workable remedies. Furthermore, the study identified other AI-based technology like Edge Computing and the Internet of Things (IoT) as new opportunities in ecological *** conclusion, the study anticipates that the joint use cutting edge technologies for ecological research and conservation and the need for innovative methods and AI-driven solutions to track wildlife demographics. © 2023, The Authors. All rights reserved.

关键词： Biodiversity

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Phase-based Causality Analysis of EEG in Treatment of Major Depressive Disorder

Phase-based Causality Analysis of EEG in Treatment of Major ...

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International Conference on Measurement

作者： Madhurima Bhattacharjee Aditi Kathpalia Martin Brunovský Milan Paluš Department of Complex Systems Institute of Computer Science of the Czech Academy of Sciences Prague Czech Republic Clinical Research Programme National Institute of Mental Health Klecany Czech Republic

This study analyzes causal connections between cross-hemispheric brain regions in individuals diagnosed with major depressive disorder (MDD) through Electroencephalography (EEG) data. A phase-based causality analysis technique is first validated on coupled Rössler systems and then employed on EEG recordings from MDD patients. Our results provide significant insights regarding causal influences in cross-hemispheric brain connectivity of MDD patients who exhibit an early response to medication after commencing treatment.

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Developing Critical Thinking Practices Interwoven with Generative AI Usage in an Introductory Programming Course

Developing Critical Thinking Practices Interwoven with Gener...

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IEEE Education Engineering (EDUCON)

作者： Arne Styve Outi T. Virkki Usman Naeem Department of ICT and Natural Sciences Norwegian University of Science and Technology (NTNU) Ålesund Norway Information Technology Degree Programme Haaga-Helia University of Applied Sciences Helsinki Finland School of Electronic Engineering and Computer Science Queen Mary University of London London UK

ISBN: (数字)9798350394023

ISBN: (纸本)9798350394030

Software development has evolved significantly. In the past, developers were required to have comprehensive understanding of programming languages, algorithms, and computer architecture. However, with the emergence of the Internet, software libraries, frameworks, and forums became widely available, which utilize reusable software components that can reduce development time and costs. The advent of Generative Artificial Intelligence (AI) tools, such as ChatGPT, GitHub Copilot, and Amazon CodeWhisperer, has further enhanced the developer's toolkit, as these tools can be used for a wide variety of tasks such as code generation, documentation, commenting and reviewing. As programming is often slow and requires trial and error, novice programmers can be tempted to apply the first solution found on the Internet or proposed by an AI tool without much critical reflection or notion of responsibility. Hence, the advances of AI have raised both excitement and concerns among Information Technology (IT)/computer science (CS) students and educators. Yet, AI tools are here to stay, and students must learn to use them responsibly. The aim of this paper is to investigate how to design learning activities that introduce Generative AI tools (GitHub Copilot and ChatGPT) for programming while promoting critical thinking practices among students in an introductory programming course in the first semester. Students' opinions and customs were surveyed before and after the AI-based programming assignment. The results indicate that students' awareness of the possibilities and limitations of AI, as well as practices of critical thinking in programming increased. This is encouraging as critical thinking is an integral part of best programming practices.

关键词： Software libraries Generative AI Software algorithms Chatbots Software Reflection Internet

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Spin-UP: Spin Light for Natural Light Uncalibrated Photometric Stereo

arXiv

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arXiv 2024年

作者： Li, Zongrui Lu, Zhan Yan, Haojie Shi, Boxin Pan, Gang Zheng, Qian Jiang, Xudong Lab Interdisciplinary Graduate Programme Nanyang Technological University Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore College of Computer Science and Technology Zhejiang University China The State Key Lab of Brain-Machine Intelligence Zhejiang University China National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China National Engineering Research Center of Visual Technology School of Computer Science Peking University China

Natural Light Uncalibrated Photometric Stereo (NaUPS) relieves the strict environment and light assumptions in classical Uncalibrated Photometric Stereo (UPS) methods. However, due to the intrinsic ill-posedness and high-dimensional ambiguities, addressing NaUPS is still an open question. Existing works impose strong assumptions on the environment lights and objects' material, restricting the effectiveness in more general scenarios. Alternatively, some methods leverage supervised learning with intricate models while lacking interpretability, resulting in a biased estimation. In this work, we propose Spin Light Uncalibrated Photometric Stereo (Spin-UP), an unsupervised method to tackle NaUPS in various environment lights and objects. The proposed method uses a novel setup that captures the object's images on a rotatable platform, which mitigates NaUPS's ill-posedness by reducing unknowns and provides reliable priors to alleviate NaUPS's ambiguities. Leveraging neural inverse rendering and the proposed training strategies, Spin-UP recovers surface normals, environment light, and isotropic reflectance under complex natural light with low computational cost. Experiments have shown that Spin-UP outperforms other supervised/unsupervised NaUPS methods and achieves state-of-the-art performance on synthetic and real-world datasets. Codes and data are available at https://***/LMozart/CVPR2024-SpinUP. Copyright © 2024, The Authors. All rights reserved.

关键词： Photometry

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Language and Knowledge Representation A Stratified Approach

arXiv

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arXiv 2025年

作者： Bagchi, Mayukh Doctoral Programme in Information Engineering and Computer Science Department of Information Engineering and Computer Science University of Trento Italy

Semantic heterogeneity is conventionally understood as the existence of variance in the representation of the same target reality when computationally modelled by independent parties. It can have serious implications in critical application scenarios like that of Knowledge Graph-based multilingual data integration. In view of the above, the thesis argues that the current understanding of the problem of semantic heterogeneity as the ‘existence of variance’, while being crucially necessary, is not sufficient and under-characterized. There can be no variance without a prior notion of a unifying reference taken as the basis for computing the variance itself. To that end, the thesis proposes the problem of representation heterogeneity to emphasize the fact that heterogeneity is an intrinsic property of any representation, wherein, different observers encode different representations of the same target reality in a stratified manner using different concepts, language and knowledge (as well as data). The thesis then advances a top-down solution approach to the above stratified problem of representation heterogeneity in terms of several solution components, namely: (i) a representation formalism stratified into concept level, language level, knowledge level and data level to accommodate representation heterogeneity, (ii) a top-down language representation using Universal Knowledge Core (UKC), UKC namespaces and domain languages to tackle the conceptual and language level heterogeneity, (iii) a top-down knowledge representation using the notions of language teleontology and knowledge teleontology to tackle the knowledge level heterogeneity, (iv) the usage and further development of the existing LiveKnowledge catalog for enforcing iterative reuse and sharing of language and knowledge representations, and, (v) the kTelos methodology integrating the solution components above to iteratively generate the language and knowledge representations absolving representation heterogeneit

关键词： Knowledge graph

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