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29th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2021

作者： Constantinides, Christodoulos Constantinides, Argyris Belk, Marios Fidas, Christos Pitsillides, Andreas University of Cyprus Department of Computer Science Cyprus University of Cyprus and Cognitive Ux Ltd Department of Computer Science Cyprus Cognitive Ux GmbH and University of Cyprus Department of Computer Science Germany University of Patras Department of Electrical and Computer Engineering Greece

ISBN: (纸本)9781450383677

Picture gesture authentication (PGA), utilized by millions of users worldwide, is a cued-recall graphical authentication system which requires users to select an image and subsequently draw gestures on that image to create their picture password. A crucial component for enhancing the security of PGA-like schemes is the accurate quantification of the user-chosen passwords through a picture password strength meter. Despite the huge adoption of PGA worldwide, there is rather limited knowledge on the implementation aspects of an accurate picture password strength meter that would assist users in creating secure picture passwords. In this paper, we present the implementation and evaluation of an assistive picture password strength meter system within PGA-like schemes, which is based on image analysis through computer vision techniques. Results of the evaluation study (n=34) revealed that different computer vision approaches perform different across various datasets used during training. These findings could drive the design of intelligent security mechanisms for quantifying the strength of the user-chosen passwords, and ultimately assist end-users towards making better picture password selections by providing feedback about the strength of their passwords, as well as assist service providers in terms of integration of assistive security mechanisms. © 2021 ACM.

关键词： Authentication

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Clustering Analysis of a Spatiotemporal Dataset with a Novel Kernel Density Estimator

Clustering Analysis of a Spatiotemporal Dataset with a Novel...

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2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Yu, Jen-Chien Yang, Chun-Chieh Gilbert, John Reuben Liu, Rou-Jun Oyang, Yen-Jen Yang, Meng-Han National Kaohsiung University of Science and Technology Department of Computer Science and Information Engineering Kaohsiung City807618 Taiwan National Taiwan University Master Program in Statistics Taipei106216 Taiwan National Taiwan University Department of Computer Science & Information Engineering Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taiwan

ISBN: (纸本)9798350303780

A vast number of spatiotemporal datasets collected from a wide range of sources has motivated scientists to develop effective approaches to identify interesting patterns hidden in these datasets. In this respect, kernel density estimators, which belong to a class of non-parametric estimators in statistics, have been widely exploited in recent years. With this background, we have developed a novel kernel density estimator aiming to provide accurate analysis results. According to the evaluation with a real spatiotemporal dataset, which collected emergency medical service records in a county in the United States, the proposed kernel density estimator can approximate the probability density function significantly more accurately than a conventional kernel density estimator. Furthermore, we have exploited the proposed kernel density estimator to identify interesting patterns hidden in the real spatiotemporal dataset. © 2023 IEEE.

关键词： Probability density function

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Indonesian-Sundanese Language Machine Translation using Bidirectional Long Short-term Memory Model

Indonesian-Sundanese Language Machine Translation using Bidi...

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2023 International Conference on computer science, Information Technology and Engineering, ICCoSITE 2023

作者： Heryadi, Yaya Wijanarko, Bambang Dwi Fitria Murad, Dina Tho, Cuk Hashimoto, Kiyota BINUS Graduate Program - Doctor of Computer Science Computer Science Department Bina Nusantara University Jakarta11480 Indonesia BINUS Online Learning Bina Nusantara University Computer Science Department Jakarta11480 Indonesia BINUS Online Learning Information Systems Department Bina Nusantara University Jakarta11480 Indonesia Prince of Songkla University Interdisciplinary Graduate School or Earth System Science and Andaman Natural Disaster Management Phuket83120 Thailand

ISBN: (纸本)9798350320954

Translating a language to another language has become instrumental when peoples interact with other people who speak a different language. However, the language translation is not an easy computation task when there is a language-resource gap. This paper presents empirical results on the performance of two models: the Long Short-term Memory and the Bidirectional Long Short-term Memory models as machine language translation models involving Bahasa Indonesia and the Sundanese language. The empiric results showed that the Bidirectional Long Short-term Memory model achieves higher performance as a language translator from the Sundanese language to Bahasa Indonesia and vice versa (0.95 and 0.95 average training accuracy respectively;and 0.90 and 0.89 average testing BLEU scores respectively) than the Long Short-term Memory model as a language translator from the Sundanese language to Bahasa Indonesia and vice versa (0.93 and 0.92 average training accuracy respectively;and 0.91 and 0.88 average testing BLEU scores). These results validate some previously reported studies that claim the Bidirectional Long Short-term Memory model potentially outperform the Long Short-term Memory model when it is used to process a sequence dataset. © 2023 IEEE.

关键词： Computational linguistics

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EFFICIENT PATTERN-BASED ANOMALY DETECTION IN A NETWORK OF MULTIVARIATE DEVICES

arXiv

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arXiv 2023年

作者： Feremans, Len Cule, Boris Goethals, Bart Department of Computer Science University of Antwerp Belgium Department of Cognitive Science and Artificial Intelligence Tilburg University Netherlands

Many organisations manage service quality and monitor a large set devices and servers where each entity is associated with telemetry or physical sensor data series. Recently, various methods have been proposed to detect behavioural anomalies, however existing approaches focus on multivariate time series and ignore communication between entities. Moreover, we aim to support end-users in not only in locating entities and sensors causing an anomaly at a certain period, but also explain this decision. We propose a scalable approach to detect anomalies using a two-step approach. First, we recover relations between entities in the network, since relations are often dynamic in nature and caused by an unknown underlying process. Next, we report anomalies based on an embedding of sequential patterns. Pattern mining is efficient and supports interpretation, i.e. patterns represent frequent occurring behaviour in time series. We extend pattern mining to filter sequential patterns based on frequency, temporal constraints and minimum description length. We collect and release two public datasets for international broadcasting and X from an Internet company. BAD achieves an overall F1-Score of 0.78 on 9 benchmark datasets, significantly outperforming the best baseline by 3%. Additionally, BAD is also an order-of-magnitude faster than state-of-the-art anomaly detection methods. © 2023, CC BY.

关键词： Anomaly detection

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Neural concept binder 24

Neural concept binder

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Wolfgang Stammer Antonia Wüst David Steinmann Kristian Kersting Computer Science Department TU Darmstadt and Hessian Center for AI (hessian.AI) Computer Science Department TU Darmstadt Computer Science Department TU Darmstadt and Hessian Center for AI (hessian.AI) and German Research Center for AI (DFKI) and Centre for Cognitive Science TU Darmstadt

ISBN: (纸本)9798331314385

The challenge in object-based visual reasoning lies in generating concept representations that are both descriptive and distinct. Achieving this in an unsupervised manner requires human users to understand the model's learned concepts and, if necessary, revise incorrect ones. To address this challenge, we introduce the Neural Concept Binder (NCB), a novel framework for deriving both discrete and continuous concept representations, which we refer to as "concept-slot encodings". NCB employs two types of binding: "soft binding", which leverages the recent SysBinder mechanism to obtain object-factor encodings, and subsequent "hard binding", achieved through hierarchical clustering and retrieval-based inference. This enables obtaining expressive, discrete representations from unlabeled images. Moreover, the structured nature of NCB's concept representations allows for intuitive inspection and the straightforward integration of external knowledge, such as human input or insights from other AI models like GPT-4. Additionally, we demonstrate that incorporating the hard binding mechanism preserves model performance while enabling seamless integration into both neural and symbolic modules for complex reasoning tasks. We validate the effectiveness of NCB through evaluations on our newly introduced CLEVR-Sudoku dataset. Code and data at: https://***/NeuralConceptBinder/.

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Multiple Realizability and the Rise of Deep Learning

arXiv

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arXiv 2024年

作者： McGrath, Sam Whitman Russin, Jacob Department of Philosophy Department of Cognitive and Psychological Sciences Brown University United States Department of Computer Science Department of Cognitive and Psychological Sciences Brown University United States

The multiple realizability thesis holds that psychological states may be implemented in a diversity of physical systems. The deep learning revolution seems to be bringing this possibility to life, offering the most plausible examples of man-made realizations of sophisticated cognitive functions to date. This paper explores the implications of deep learning models for the multiple realizability thesis. Among other things, it challenges the widely held view that multiple realizability entails that the study of the mind can and must be pursued independently of the study of its implementation in the brain or in artificial analogues. Although its central contribution is philosophical, the paper has substantial methodological upshots for contemporary cognitive science, suggesting that deep neural networks may play a crucial role in formulating and evaluating hypotheses about cognition, even if they are interpreted as implementation-level models. In the age of deep learning, multiple realizability possesses a renewed significance. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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A Comparative Analysis of User Experience and Educational Outcomes in Math Memory Games

A Comparative Analysis of User Experience and Educational Ou...

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International Conference on computer Engineering, Network and Intelligent Multimedia (CENIM)

作者： Nicholas Martin Raffel Nathanael Doni Jeriyanto Eric Savero Hermawan Janshen Fung Francisco Maruli Panggabean Computer Science Department Game Application & Technology Program School of Computer Science Bina Nusantara University Jakart Indonesia Master of Computer Science Binus Graduate Program Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350368802

ISBN: (纸本)9798350368819

This research explores multiple varieties of effective and child-friendly learning approaches for mathematics and memorization among young children in today's increasingly digital age considering their specific developmental needs. This type of approach includes in-person teaching, engaging online videos, interactive textbooks, and innovative educational games. This research employs a detailed comparative analysis, considering both ‘User Experience’ and measurable learning outcome factors, and spans a range of age groups from preschoolers to elementary school children. Through highlighting the strengths and weaknesses of each method, this research aims to inspire the adoption of efficient, child-centric, and novel teaching techniques by educators and institutions, thereby advancing the quality of education in Indonesia. The results show that using serious game in education presents high usefulness score alongside positive user experience, suggesting its viability for educational purposes.

关键词： Measurement Video games Education Games Educational technology Information age User experience Mathematics Usability Systematic literature review

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A Pilot Study on Clinician-AI Collaboration in Diagnosing Depression from Speech

arXiv

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arXiv 2024年

作者： Feng, Kexin Chaspari, Theodora Department of Computer Science and Engineering Texas A&M University College StationTX United States Institute of Cognitive Science Department of Computer Science University of Colorado Boulder BoulderCO United States

This study investigates clinicians’ perceptions and attitudes toward an assistive artificial intelligence (AI) system that employs a speech-based explainable ML algorithm for detecting depression. The AI system detects depression from vowel-based spectrotemporal variations of speech and generates explanations through explainable AI (XAI) methods. It further provides decisions and explanations at various temporal granularities, including utterance groups, individual utterances, and within each utterance. A small-scale user study was conducted to evaluate users’ perceived usability of the system, trust in the system, and perceptions of design factors associated with several elements of the system. Quantitative and qualitative analysis of the collected data indicates both positive and negative aspects that influence clinicians’ perception toward the AI. Results from quantitative analysis indicate that providing more AI explanations enhances user trust but also increases system complexity. Qualitative analysis indicates the potential of integrating such systems into the current diagnostic and screening workflow, but also highlights existing limitations including clinicians’ reduced familiarity with AI/ML systems and the need for user-friendly and intuitive visualizations of speech information. © 2024, CC BY.

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Crafting chirality in three dimensions via a novel fabrication technique for bound states in the continuum metasurfaces

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Light(science & Applications) 2024年第3期13卷 390-392页

作者： Zaid Haddadin Anna My Nguyen Lisa V.Poulikakos Department of Electrical&Computer Engineering University of California San DiegoLa JollaCAUSA Department of Mechanical&Aerospace Engineering University of California San DiegoLa JollaCAUSA Program of Materials Science&Engineering University of California San DiegoLa JollaCAUSA

An additional deposition step was added to a multi-step electron beam lithographic fabrication process to unlock the height dimension as an accessible parameter for resonators comprising unit cells of quasi-bound stat... 详细信息

关键词： dimensions dimension intrinsic

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3D Transfer Learning for Lung Nodule Malignancy Binary Classification

3D Transfer Learning for Lung Nodule Malignancy Binary Class...

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computer sciences, Engineering, and Technology Innovation (ICoCSETI), International Conference on

作者： Nur Adhianti Heryanto Mahmud Isnan Matthew Martianus Henry Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331508616

ISBN: (纸本)9798331508623

Many studies have focused on classifying lung nodules in CT scans, primarily utilizing 2D approaches. However, CT scans are inherently 3D representations, which presents challenges for traditional convolutional neural networks (CNNs) designed for 2D images. In this work, a novel combination of 3D transfer learning and batch oversampling was adopted to classify benign and malignant lung nodules using the Lung Nodule Analysis - Intelligent Systems in Medical Imaging (LUNA22-ISMI) dataset. The preprocessing pipeline consisted of excluding indeterminate labeled nodules, clamping patches to the lung window, extracting nodule regions based on diameter, and applying z-score normalization. Batch oversampling with augmentation was implemented for the training set. Multiple models pre-trained on ImageNet with weights adapted for 3D were evaluated using a five-fold cross-validation setup. Notably, EfficientNetB1 achieved the highest overall and balanced metrics on the test sets, surpassing 90% in sensitivity, specificity, precision, and accuracy, highlighting its potential for binary classification of lung nodules. https://***/nheryanto/luna22

关键词： Training Solid modeling Adaptation models Three-dimensional displays Lungs Computed tomography Transfer learning Windows Cancer Biomedical imaging

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