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14th International Conference on Formal Ontology in Information Systems, FOIS 2024

作者： Pomarlan, Mihai de Giorgis, Stefano Ringe, Rachel Hedblom, Maria M. Tsiogkas, Nikolaos Applied Linguistics Department University of Bremen Bremen Germany Institute of Cognitive Sciences and Technologies National Research Council Catania Italy Digital Media Lab University of Bremen Bremen Germany Department of Computing Jönköping School of Engineering Jönköping Sweden Department of Computer Science KU Leuven Leuven Belgium

ISBN: (纸本)9781643685618

Situationally-aware artificial agents operating with competence in natural environments face several challenges: spatial awareness, object affordance detection, dynamic changes and unpredictability. A critical challenge is the agent’s ability to identify and monitor environmental elements pertinent to its objectives. Our research introduces a neurosymbolic modular architecture for reactive robotics. Our system combines a neural component performing object recognition over the environment and image processing techniques such as optical flow, with symbolic representation and reasoning. The reasoning system is grounded in the embodied cognition paradigm, via integrating image schematic knowledge in an ontological structure. The ontology is operatively used to create queries for the perception system, decide on actions, and infer entities’ capabilities derived from perceptual data. The combination of reasoning and image processing allows the agent to focus its perception for normal operation as well as discover new concepts for parts of objects involved in particular interactions. The discovered concepts allow the robot to autonomously acquire training data and adjust its subsymbolic perception to recognize the parts, as well as making planning for more complex tasks feasible by focusing search on those relevant object parts. We demonstrate our approach in a simulated world, in which an agent learns to recognize parts of objects involved in support relations. While the agent has no concept of handle initially, by observing examples of supported objects hanging from a hook it learns to recognize the parts involved in establishing support and becomes able to plan the establishment/destruction of the support relation. This underscores the agent’s capability to expand its knowledge through observation in a systematic way, and illustrates the potential of combining deep reasoning with reactive robotics in dynamic settings. © 2024 The Authors.

关键词： Optical flows

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DG-SMOTE: A Distance-Angle-Based Genetic Synthetic Minority Over-Sampling Technique for Unbalanced Data Learning

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IEEE Transactions on Evolutionary Computation 2024年

作者： Pei, Wenbin Cui, Yuyang Xue, Bing Zhang, Mengjie Zhang, Jiqing Hou, Yaqing Zou, Guangyu Qiang, Zhang Dalian University of Technology School of Computer Science and Technology Dalian116024 China Ministry of Education Key Laboratory of Social Computing and Cognitive Intelligence Dalian University of Technology Dalian116024 China Victoria University of Wellington School of Engineering and Computer Science Wellington6140 New Zealand Dalian Maritime University Information Science and Technology College Dalian116024 China

Many real-world applications often generate unbalanced data. Learning from such data may lead to biased classifiers that perform poorly on the class of interest. Oversampling methods have been shown to be effective in re-balancing unbalanced data to help classifiers avoid performance bias. However, many existing oversampling methods rely on a pre-designed linear model structure and the neighborhood information of an original instance. This may lead to the generation of noisy instances when the original data has noise. In this study, we develop a novel oversampling method in which genetic programming is introduced to automatically select good-quality instances and evolve a model structure that combines the selected instances to create a new instance. In the proposed oversampling method, an individual is used to represent a generated instance, which is evaluated by the fitness function designed based on the Euclidean distance and cosine theorem. In the experiments, we examine the effectiveness of the proposed oversampling method in assisting different types of classifiers to solve the issue of class imbalance, and compare it with popular sampling methods in unbalanced classification. The results have been analyzed comprehensively, indicating that the new method successfully addressed the class imbalance issue by generating a group of good-quality instances for the minority class and outperformed the compared sampling methods in almost all cases. © 1997-2012 IEEE.

关键词： Genetic programming

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Gain Enhancement of Antenna-on-Chip at 94 GHz with an Integrated Artificial Magnetic Conductor for 6G System-on-Chip

Gain Enhancement of Antenna-on-Chip at 94 GHz with an Integr...

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IFIP International Conference on Very Large Scale Integration (VLSI-SoC)

作者： Yiyang Yu Atif Shamim Electrical and Computer Engineering Program King Abdullah University of Science and Technology Thuwal Saudi Arabia

Silicon-based complementary metal oxide semiconductor (CMOS) process has become one of the most popular processes to realize system-on-chip (SoC). However, as one of the essential components of wireless SoC, antennas are typically suffering from the poor radiation because of the highly conductive silicon substrate. Such antennas are known as antenna-on-chip (AoC). To enhance the radiation performance of AoC, artificial magnetic conductors (AMC) with double periodic strip structure layers has been proposed in this paper that can not only provide in-phase reflection but also isolate the antenna from the lossy silicon substrate. The proposed AMC shows a gain enhancement of 4.5 dB. The AMC-backed AoC is well-matched within 77-125 GHz and provides a boresight gain of 2 dBi at 94 GHz.

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Multi-Resolution Location-Based Training for Multi-Channel Continuous Speech Separation

Multi-Resolution Location-Based Training for Multi-Channel C...

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

作者： Hassan Taherian DeLiang Wang Department of Computer Science and Engineering The Ohio State University USA Center for Cognitive and Brain Sciences The Ohio State University USA

The performance of automatic speech recognition (ASR) systems severely degrades when multi-talker speech overlap occurs. In meeting environments, speech separation is typically performed to improve the robustness of ASR systems. Recently, location-based training (LBT) was proposed as a new training criterion for multi-channel talker-independent speaker separation. Assuming fixed array geometry, LBT outperforms widely-used permutation-invariant training in fully overlapped utterances and matched reverberant conditions. This paper extends LBT to conversational multi-channel speaker separation. We introduce multi-resolution LBT to estimate the complex spectrograms from low to high time and frequency resolutions. With multi-resolution LBT, convolutional kernels are assigned consistently based on speaker locations in physical space. Evaluation results show that multi-resolution LBT consistently outperforms other competitive methods on the recorded LibriCSS corpus.

关键词： Training Geometry Time-frequency analysis Convolution Robustness Frequency estimation Speech processing

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Purchase intention of Amazon sustainable brands: a hybrid approach of structural equation modeling and Rasch model

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SN Business and Economics 2023年第12期3卷 1-29页

作者： Santos, Luiz Diego Vidal Holanda, Francisco Sandro Rodrigues de Santana Oliveira, Catuxe Varjão Pedrotti, Alceu de Menezes, Pedro Vinícius Bertulino Villwock, Ana Paula Schervinski Graduate Program in Intellectual Property Science Universidade Federal de Sergipe-UFS Sergipe São Cristóvão Brazil Agronomy Engineering Department Universidade Federal de Sergipe-UFS Sergipe São Cristóvão Brazil Computer Science Department Instituto Federal de Sergipe-UFS Sergipe Lagarto Brazil

This article conducts a comprehensive examination of brand-based equity closely linked to environmental sustainability in the Amazon Rainforest. The research is primarily dedicated to identifying the factors that influence consumers’ willingness to purchase products from sustainable brands within six categories: cosmetics, tourism, clothing, accessories, and food. The central aim of this study is to investigate how brand equity constructs, rooted in environmental sustainability within the Amazon Rainforest, impact consumer loyalty and purchase intention. To accomplish this objective, a mixed-methodology approach was employed, combining structural equation modeling with item response theory. This study involved 535 potential consumers, and data from eight selected brands served as the basis for the analysis. Within the framework of Purchase Intention (PI), the following variables were regarded as formative constructs: Strategic Brand Positioning (SBP), Perceived Brand Quality (PBQ), Retail Innovation (RI), and Brand Authenticity (BA). The results obtained from the structural equation models underscore the statistical significance and substantial impact of all pathways. Notably, SBP emerged as the most significant contributor to the effect on PI, as hypothesized. Brand equity and environmental sustainability were found to indirectly affect behavioral PI. Consumers exhibited a high level of latent trait (θ), with items consistently reflecting elevated values, confirming their acceptance of products, except brand differentiation. Overall, the findings highlight the effectiveness of branding strategies rooted in environmental sustainability in enhancing the brand equity of small forest companies. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Amazon Rainforest Brands Entrepreneurship Microenterprises Sustainability performance Sustainable development

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Explainable artificial intelligence approaches for brain-computer interfaces: a review and design space

arXiv

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arXiv 2023年

作者： Rajpura, Param Cecotti, Hubert Meena, Yogesh Kumar Department of Cognitive and Brain Sciences Indian Institute of Technology Gandhinagar India Department of Computer Science California State University FresnoCA United States Department of Computer Science & Engineering Department of Cognitive and Brain Sciences Indian Institute of Technology Gujarat Gandhinagar India

Objective. This review paper provides an integrated perspective of Explainable Artificial Intelligence (XAI) techniques applied to Brain-computer Interfaces (BCIs). BCIs use predictive models to interpret brain signals for various high-stake applications. However, achieving explainability in these complex models is challenging as it compromises accuracy. Trust in these models can be established by incorporating reasoning or causal relationships from domain experts. The field of XAI has emerged to address the need for explainability across various stakeholders, but there is a lack of an integrated perspective in XAI for BCI (XAI4BCI) literature. It is necessary to differentiate key concepts like explainability, interpretability, and understanding, often used interchangeably in this context, and formulate a comprehensive framework. Approach. To understand the need of XAI for BCI, we pose six key research questions (RQs) for a systematic review and meta-analysis, encompassing its purposes, applications, usability, and technical feasibility. We employ the PRISMA methodology – preferred reporting items for systematic reviews and meta-analyses to review (n=1246) and analyze (n=84) studies published in 2015 and onwards for key insights. Main results. The results highlight that current research primarily focuses on interpretability for developers and researchers, aiming to justify outcomes and enhance model performance. We discuss the unique approaches, advantages, and limitations of XAI4BCI from the literature. We draw insights from philosophy, psychology, and social sciences. We propose a design space for XAI4BCI, considering the evolving need to visualize and investigate predictive model outcomes customised for various stakeholders in the BCI development and deployment lifecycle. Significance. This paper is the first to focus solely on reviewing XAI4BCI research articles. This systematic review and meta-analysis findings with the proposed design space prompt important di

关键词： Brain computer interface

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Stream Attention Based U-Net for L3DAS23 Challenge 48

Stream Attention Based U-Net for L3DAS23 Challenge

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Wang, Honglong Fu, Yanjie Li, Junjie Ge, Meng Wang, Longbiao Qian, Xinyuan Tianjin University Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin China The Chinese University of Hong Kong Shenzhen China Co. Ltd Tianjin China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China

ISBN: (纸本)9781728163277

Machine learning applications of 3D audio are gaining increasing interest in recent years. In this paper, we propose a stream attention based U-Net to remove background noise and reverberation based on ICASSP Signal Processing Grand Challenge 2023: L3DAS23 Challenge1 Audio-only track task1 3D Speech Enhancement. Results show that proposed method achieves superior performance than the official baseline model. © 2023 IEEE.

关键词： 3D Speech enhancement L3DAS23 Challenge

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A Novel Dual-band Electrically Small Quasi-isotropic Antenna

A Novel Dual-band Electrically Small Quasi-isotropic Antenna

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European Conference on Antennas and Propagation, EuCAP

作者： Hanguang Liao Atif Shamim Electrical and Computer Engineering Program King Abdullah University of Science and Technology Thuwal Saudi Arabia

Quasi-isotropic antennas have gained attention due to the emergence of the Internet of Things (IoT) and Wireless Sensing Networks (WSNs), for their orientation-insensitive communication ability. For those applications, electrically small (ES) antennas are usually preferred, which can save space for the IoT or sensing nodes, while reducing the material cost. Several compact isotropic antennas have been reported recently. However, only very few of them have shown dual-band operation ability. A novel design method to design a dual-band quasi-isotropic ES antenna is presented in this conference proceeding. The utilization of a band stop filter (BSF) enables the conventional single-band quasi-isotropic split ring resonator (SRR) antenna to behave in a dual-band operation, while maintaining the quasi-isotropic radiation for both bands. The proposed antenna is designed, fabricated, and measured, which shows a dual-band operation (both bands in ka<1 region) while maintaining decent performance.

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Modeling Misinformation with Q-Learning, Nim, and Multi-Agents

Modeling Misinformation with Q-Learning, Nim, and Multi-Agen...

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2022 IEEE MIT Undergraduate Research Technology Conference, URTC 2022

作者： Huang, Elijah Mui, Phil University of California Irvine Donald Bren School of Information and Computer Sciences Fremont United States Aspiring Scholars Directed Research Program Department of Computer Science and Engineering Fremont United States

ISBN: (纸本)9781665473453

Misinformation or so called 'fake news' has become a pressing issue around the world. This research proposes modeling the spread of misinformation through Q-learning, the game of Nim, and multi-Agent simulations. Through analyzing theoretical properties of the model and studying factors in how misinformation affects it, we show how the model reflects the real-life impact of fake news. The scalability of the framework and its emulation of real-human behavior make it versatile and believable. Through further study of this model, our hope is to shed light on effective tactics for combating fake news in the real world. © 2022 IEEE.

关键词： Game theory

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Deep Learning with Backtracking Search Optimization Based Skin Lesion Diagnosis Model

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computers, Materials & Continua 2022年第1期70卷 1297-1313页

作者： C.S.S.Anupama L.Natrayan E.Laxmi Lydia Abdul Rahaman Wahab Sait Jose Escorcia-Gutierrez Margarita Gamarra Romany F.Mansour Department of Electronics&Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Mechanical Engineering Saveetha School of EngineeringSIMATSChennai602105India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Archives and Communication King Faisal University31982Kingdom of Saudi Arabia Electronic and Telecommunicacions Program Universidad Autonoma del CaribeBarranquilla08001Colombia Department of Computational Science and Electronic Universidad de la CostaCUCBarranquilla08001Colombia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

Nowadays,quality improvement and increased accessibility to patient data,at a reasonable cost,are highly challenging tasks in healthcare *** of Things(IoT)and Cloud Computing(CC)architectures are utilized in the development of smart healthcare *** entities can support real-time applications by exploiting massive volumes of data,produced by wearable sensor *** advent of evolutionary computation algorithms andDeep Learning(DL)models has gained significant attention in healthcare diagnosis,especially in decision making *** cancer is the deadliest disease which affects people across the *** skin lesion classification model has a highly important application due to its fine-grained variability in the presence of skin *** current research article presents a new skin lesion diagnosis model i.e.,Deep Learning with Evolutionary Algorithm based Image Segmentation(DL-EAIS)for IoT and cloud-based smart healthcare ***,the dermoscopic images are captured using IoT devices,which are then transmitted to cloud servers for further ***,Backtracking Search optimization Algorithm(BSA)with Entropy-Based Thresholding(EBT)i.e.,BSA-EBT technique is applied in image *** by,Shallow Convolutional Neural Network(SCNN)model is utilized as a feature *** addition,Deep-Kernel Extreme LearningMachine(D-KELM)model is employed as a classification model to determine the class labels of dermoscopic *** extensive set of simulations was conducted to validate the performance of the presented method using benchmark *** experimental outcome infers that the proposed model demonstrated optimal performance over the compared techniques under diverse measures.

关键词： Intelligent models skin lesion dermoscopic images smart healthcare internet of things

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