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Automatic Assessment of Speech Impediment for South African Early Literacy Readers 5th

Automatic Assessment of Speech Impediment for South African ...

引用

5th Southern African Conference for Artificial Intelligence Research

作者： Badenhorst, Jaco CSIR Next Generat Enterprises & Inst Cluster Voice Comp Res Grp Pretoria South Africa

ISBN: (纸本)9783031782541;9783031782558

Many children in South Africa cannot read for comprehension. This finding came from a recent international reading literacy study in which South Africa was placed last out of all 50 countries who participated [12]. During the study, it was pointed out that 75% of the learners participating came from disadvantaged backgrounds, a contributing factor to the overwhelming majority of Grade 4 learners that had already been deprived of proper school instruction. The development of automatic oral assessment applications could potentially alleviate this tragedy. A core component required by such foreseen software solutions is automatic pronunciation error detection (APED). Such APED capable of evaluating the speech produced by school learners in turn requires the development of local language child speech recognition (CSR) to succeed. CSR system development in the South African context may be attempted by refining adult pre-trained models. To this end, the paper provides a detailed breakdown of child data sets as well as reading assessment categories used to build and evaluate APED. While it is possible to select APED thresholds that do not penalise sentences read correctly, the more subtle reading disfluencies remained challenging to identify.

关键词： Child speech recognition Automatic pronunciation error detection computer assisted language learning Oral assessment

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Evaluating Immersion in Digital Video Using EEG and Subjective Measures: A Pilot Study 1

引用

8th International Conference on computer-Human Interaction Research and Applications-CHIRA-Annual

作者： Doumanis, Ioannis Economou, Daphne Tsioutas, Kostantinos Univ Cent Lancashire Preston Lancs England Univ Westminster London England Athens Univ Econ & Business Athens Greece

ISBN: (数字)9783031826337

ISBN: (纸本)9783031826320;9783031826337

Immersion in digital video refers to the degree to which digital content engages and absorbs viewers. Today, viewers primarily consume digital video online. Despite ample bandwidth availability, network disruptions (e.g., congestion and outages) can degrade quality and interrupt the viewing experience, thereby breaking immersion. In a pilot study, we evaluated viewer immersion while watching digital video content on two streaming services (IPTV and HLS) delivered via conventional IP and our POINT network under regular and exceptional network conditions (congestion and link failures). We used Electroencephalography (EEG) and user interviews to gather data. EEG data indicate that videos streamed through POINT create a more immersive experience in terms of presence, sensory engagement, realism, and detail compared to conventional IP, although the impact varies by service. Interview data corroborate the EEG measures but indicate that POINT's impact was independent of the specific service used (IPTV or HLS). These findings suggest that POINT enhances viewer immersion over traditional IP networks, particularly under challenging network conditions, but the impact may be service-specific. We plan to conduct a follow-up study with a larger user group and incorporate additional sensors (emotion analysis and eye-tracking) to more comprehensively measure viewers' emotional and cognitive states under each experimental condition.

关键词： Immersion Digital video Electroencephalography (EEG) Pilot Study POINT IP

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Accelerating Traditional Object Detection Methods on Sophisticated Embedded Systems 4th

Accelerating Traditional Object Detection Methods on Sophist...

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4th International Conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Sehairi, Kamal Bouhafs, Abdelkader Boucherit, Ibtissam Mama Bouwmans, Thierry Chouireb, Fatima Ecole Normale Super Laghouat Dept Phys Lab Appl Sci & Didact Laghouat Algeria Univ Amar Telidji Laghouat Dept Elect Telecommun Signals & Syst Lab Laghouat Algeria Univ La Rochelle Dept Comp Sci Lab Math Image & Applicat La Rochelle France

ISBN: (纸本)9783031821523;9783031821530

Identifying and locating objects in images and videos, including elements like traffic signs, vehicles, buildings, and people, constitutes a fundamental and demanding task in computer vision, known as object detection. Due to the higher computing complexity of this technique and the large amount of data carried by the video signal, it is nearly impossible for ordinary general-purpose processors GPPs or CPUs to run these techniques in real-time, especially for embedded systems applications. Therefore, special hardware that can acquire, control, or execute in parallel is required. These specialized hardware systems include Digital Signal Processors DSPs, Field Programmable Gate Arrays FPGAs, Visual Processing Units VPUs, Tensor Processing Units TPUs, Neural Processing Units NPUs or Graphics Processing Units GPUs. This work presents the benefits of accelerating traditional object detection methods on a high-end embedded system, the Jetson Nano Developer Kit. This single computer board is equipped with the Tegra K1 System on Chip SoC, which is composed of a quad-core ARM A15 and 192 cores of Kepler-embedded GPU. Computing acceleration was ensured via the use of the CUDA OpenCV library for both the Histogram of Oriented Gradients HOG and the Haar Cascade Classifier. For VGA resolution, results reveal that the GPU implementation on this embedded system is 1.4x faster than the CPU for the HOG method and 2x for the Haar Cascade Classifier method.

关键词： Object detection Machine Learning Haar Cascade HOG Local feature descriptors GPU implementation OpenCV CUDA SoC Tegra X1 Jetson Nano

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A Systematic Mapping Study of LLM Applications in Mobile Device Research 7th

A Systematic Mapping Study of LLM Applications in Mobile Dev...

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8th Asia Pacific Web and Web-Age information Management Joint International Conference on Web and Big Data

作者： Chen, Chong Wang, Bo Lin, Youfang Beijing Jiaotong Univ Sch Comp Sci & Technol Beijing Peoples R China

ISBN: (纸本)9789819600540;9789819600557

The extensive utilization of Large Language Models (LLMs) has significantly influenced academic research in mobile device-related fields, encompassing application testing, malware detection, voice control, and software development enhancement. Concurrently, the increasing demand for user access to LLMs on mobile devices for tasks like question answering has introduced new research directions, such as developing native LLMs by reducing parameter sizes. We aim to study the relationship between the evolution of LLMs and mobile device-related research, exploring their integration into traditional tasks and their adaptation to mobile platforms, from early transformer-based models to modern architectures like GPT-4. We have reviewed 55 recent papers, including 50 novel approaches, 1 benchmark, and 4 empirical studies, covering various aspects of LLM applications in mobile devices.

关键词： Systematic Mapping Large Language Models Mobile Device Mobile Testing

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Artificial Intelligence-Based on Automatic Detection of Diabetic Eye Diseases : A Systematic Review 4th

Artificial Intelligence-Based on Automatic Detection of Diab...

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4th International Conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Ba, Mandicou Mbaye, Amadou Diop, Idy Bah, Alassane Cheikh Anta Diop Univ UCAD Med Imaging & Bioinformat Lab LIMBI Polytech Higher Sch ESP Dakar Senegal

ISBN: (纸本)9783031821554;9783031821561

The diagnosis of eye diseases, especially those related to diabetes, has long posed enormous challenges for ophthalmologists in developing countries. In Africa, the main difficulty stems from the limited number of technologies and/or equipment available. Nowadays, with the advancement of technology and the proliferation of artificial intelligence models, the detection and analysis of eye diseases are becoming increasingly easier. It is clear that existing prediction systems can diagnose eye disorders such as glaucoma, cataracts, diabetic retinopathy, etc., but sometimes with very low accuracy. Manual diagnosis of fundus images by ophthalmologists also constitutes a slow, expensive, tedious task and may even be prone to errors. However, despite this, it is worth noting that some doctors still continue to practice this method. This paper highlights the crucial role of artificial intelligence systems, particularly those based on machine learning or deep learning, in the early detection of diabetes-related eye disorders in Africa. In a continent where the prevalence of diabetes is increasing, but resources are limited, these technologies offer significant potential to improve access to ocular healthcare and reduce the workload of healthcare professionals. This also underscores the importance of promoting research in the field of artificial intelligence in ophthalmology, especially in the African context.

关键词： Artificial Intelligence Machine Learning Deep Learning Diabetic Retinopathy Glaucoma Cataracts

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Revisiting the Nexus Between Handwriting and Personality: Graphology 8th

Revisiting the Nexus Between Handwriting and Personality: Gr...

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2024 World Congress in computer science, computer Engineering and Applied Computing-CSCE

作者： Xu, Yan Tang, Yufang Suen, Ching Y. Concordia Univ Montreal PQ H3G 1M8 Canada Shandong Normal Univ Jinan 250014 Shandong Peoples R China

ISBN: (纸本)9783031856273;9783031856280

Handwriting analysis offers a valuable tool for predicting personality traits, as individuals typically find it challenging to mask their personality in their handwriting. Despite awareness of handwriting analysis techniques, consistently altering one's writing to obscure personality traits is difficult. Continuous writing naturally reveals aspects of the writer's personality through pen movements and strokes. Furthermore, compared to other biometric features such as fingerprints, irises, or facial recognition, handwriting is easier to obtain and involves fewer privacy concerns, aside from personal signatures and private diaries. Our study leverages the integration of psychological questionnaires and the expertise of graphologists to enhance handwriting analysis. By employing deep learning methods from the field of computer vision, we aim to extract more complex features from handwriting samples, thereby improving prediction accuracy compared to traditional machine learning approaches. This innovative approach capitalizes on the strengths of deep learning to advance the field of personality prediction through handwriting analysis.

关键词： Handwriting analysis Human personality Deep learning Brain cognition Subconscious Psychology

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A Voxel-Representation-Based Data Generator of Adversarial Objects for Robotic Manipulators 8th

A Voxel-Representation-Based Data Generator of Adversarial O...

引用

2024 World Congress in computer science, computer Engineering and Applied Computing-CSCE

作者： Akshay Katz, Garrett E. Mohan, Chilukuri K. Syracuse Univ Syracuse NY 13244 USA

ISBN: (纸本)9783031856273;9783031856280

In order to develop robust grasping AI algorithms, it is important to generate objects that become progressively more adversarial and complex with each iteration, as in curriculum learning, in a manner agnostic to the particular visual processing method used by the robot. We present a data generator of voxel-based 3D adversarial object geometries for training grasping algorithms, which operates independently of any particular visual encoding/decoding pipeline. Our novel approach uses genetic algorithms and can generate arbitrarily many samples, bounded only by the user-specified voxel resolution. The success of our approach is demonstrated in terms of the number of adversarial objects generated, how difficult they are to grasp, and how similar they are to more easily graspable objects.

关键词： Adversarial machine learning

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Training Operator in VR: A Scalable Solution for the Creation of VR Training Scenes 5th

Training Operator in VR: A Scalable Solution for the Creatio...

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5th International Conference on Innovative Intelligent Industrial Production and Logistics

作者： Melzani, Geoffrey Quach, Tony Soderlund, Henrik Li, Dan Mugur, Puranjay Johansson, Bjorn Chalmers Univ Technol Chalmersplatsen 4 S-41296 Gothenburg Sweden Volvo Car Corp Gunnar Engellaus Vag 8 S-41878 Gothenburg Sweden

ISBN: (纸本)9783031807749;9783031807756

In the automotive industry, just like in other complex product-based industries, fierce competition is ongoing, and companies must cope with the challenges. In such industries, operator training is a cornerstone, as many of the most important product manufacturing processes rely on human-performed assembly. Thus, with the factory environments that are getting increasingly complex, and efficiency-dedicated, the importance of training is enhanced. With the generalization of Virtual Reality (VR) technologies, VR operator training emerges more rapidly. However, the many advantages of VR training, such as safety, cost, and flexibility, have not yet been fully realized in the industry as large-scale implementations still have not been reached. Creating VR training scenes is still time-consuming, and therefore expensive, hindering large-scale implementation and adaptation. This paper first recapitulates the data needed to populate such VR training scenes, then it exemplifies the automatic generation of VR training scenes through an industry use case, enabling large-scale automated implementation. Finally, it highlights the challenges related to data availability and handling and that companies can encounter on the way to the large-scale implementation of VR training.

关键词： Assembly Operator 5.0 Training Virtual reality Automated Scalable PLM IPS

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An Empirical Method for Processing I/O Traces to Analyze the Performance of DL Applications 12th

An Empirical Method for Processing I/O Traces to Analyze the...

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12th Conference on Cloud Computing, Big Data and Emerging Topics

作者： Parraga, Edixon Leon, Betzabeth Mendez, Sandra Rexachs, Dolores Suppi, Remo Luque, Emilio Univ Autonoma Barcelona Comp Architecture & Operating Syst Dept Campus UAB Bellaterra 08193 Spain Barcelona Supercomputing Ctr BSC Dept Comp Sci Barcelona 08034 Spain

ISBN: (纸本)9783031708060;9783031708077

The exponential growth of data handled by Deep Learning (DL) applications has led to an unprecedented demand for computational resources, necessitating their execution on High Performance Computing (HPC) systems. However, understanding and optimizing Input/Output (I/O) of the DL applications can be challenging due to the complexity and scale of DL workloads and the heterogeneous nature of I/O operations. This paper addresses this issue by proposing an I/O traces processing method that simplifies the generation of reports on global I/O patterns and performance to aid in I/O performance analysis. Our approach focuses on understanding the temporal and spatial distributions of I/O operations and related with the behavior at I/O system level. The proposed method enables us to synthesize and extract key information from the reports generated by tools such as Darshan tool and the seff command. These reports offer a detailed view of I/O performance, providing a set of metrics that deepen our understanding of the I/O behavior of DL applications.

关键词： DL I/O Analysis HPC I/O behavior patterns

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Real-Time Bearing Health Monitoring Using Diffusion-Based Spectral Analysis: A Self-adaptive Approach to Predictive Maintenance 5th

Real-Time Bearing Health Monitoring Using Diffusion-Based Sp...

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5th International Conference on Innovative Intelligent Industrial Production and Logistics

作者： Prevost, Guillaume Boutet, Jerome Cabanillas, Esteban Ioana, Cornel Univ Grenoble Alpes CEA Leti F-38000 Grenoble France Univ Grenoble Alpes Gipsa Lab Grenoble France

ISBN: (纸本)9783031807749;9783031807756

This paper introduces a novel approach to real-time health monitoring of rolling bearings using a diffusion-based model. We propose a method that leverages Denoising Diffusion Implicit Models (DDIM), exploiting their intrinsic denoising capability, to process frequency spectra of vibration signals and construct a health index without prior system information. Our approach employs a continuous-time DDIM to model the complex distribution of frequencies in bearing vibration signals, enabling the detection of subtle spectral changes indicative of incipient faults. The method operates in real-time, adapting to specific bearing characteristics and learning normality over time. The health index (HI) is constructed using the reconstruction error between input features and their denoised versions and standardized using Welford's algorithm for online mean and variance estimation. We validate our method on the XJTU-SY bearing dataset. Results demonstrate the efficacy of our diffusion-based indicator, particularly in complex degradation scenarios. The method's ability to capture complex distributions and early degradation signs without prior system information presents a promising tool for predictive maintenance.

关键词： Predictive maintenance Health index Diffusion models Real-time monitoring Health stage division Prognostic health management

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