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SITB-OSED: An Odia Speech Emotion dataset

SITB-OSED: An Odia Speech Emotion Dataset

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Bubai Maji Monorama Swain Dept. of E&CE Silicon Institute of Technology Bhubaneswar India

This Odia is one of the old Eastern Indo-Aryan languages, spoken by 49.8 million people across India. This article delineates the first open-sourced large speech emotional database in Odia language, named SITB-OSED. A key hurdle for effective speech emotion recognition (SER) systems is the lack of large corpora annotated with different emotional labels. This SER database is presented along with the consideration of its reliability based on machine learning analysis. The database contains six basic emotions, recorded in a quiet environment with the corresponding emotional texts by professional speakers. Speech data is of great interest to all research groups working on speech analysis and automatic speech recognition. Our database currently contains 5 major Odia dialects of speech data with a total of 12,110 utterances. We also tested several SER baseline systems on the SITB-OSED dataset and show the impact of recording diversity on performance. The corpus can be useful for both academic research and practical application, and will be available on https://***/

关键词： computer science Emotion recognition Speech analysis databases Machine learning data engineering Recording

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Analysis Support by Estimating Speaker's State in Sensor-Based Active Listening

Analysis Support by Estimating Speaker's State in Sensor-Bas...

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Shinjiro Yoshida Hiromitsu Shimakawa Fumiko Harada Graduate School of Information Science and Engineering Ritsumeikan University Shiga Japan College of Information Science and Engineering Ritsumeikan University Shiga Japan Research organization of Science and Technology Ritsumeikan University Shiga Japan

This study examines whether the speaker's state during active listening can be estimated using sensors. Few studies work on active listening, where the elderly get refreshed through their talking. This study extracts important features from the data obtained from the accelerometer and tension sensors measuring the speaker's body movement during active listening utilizing tips to promote talking. To verify the effectiveness of the proposed method, an experiment is conducted. New findings on listening were obtained. The delay of the body movements is clarified for each kind of tips. The transition of the speaker's state was found to be characterized by the speaker's body movement which appears after the transition. The data obtained from the tension sensor turns out to be the most important explanatory variable.

关键词： Accelerometers Estimation Sensor phenomena and characterization Feature extraction data models Neck Delays

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IoT Methods for Determining PPE Mask Fitness

IoT Methods for Determining PPE Mask Fitness

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Ashish Manchanda Kevin Lee Alireza Hassani School of Information Technology Deakin University Geelong VIC Australia

The COVID-19 pandemic showed the importance of Personal Protective Equipment (PPE), which has been instrumental in providing efficient and effective protection against exposure to the virus for its users. The widespread use of face masks has highlighted the shortcomings of single-use PPE, in particular, the fit of a mask has a large impact on its effectiveness. The research presented in this paper focuses on automatically determining the fitness of masks on an individual with any face structure. The approach in this paper is to use IoT sensors attached to PPE masks in order to detect the tightness and fit of the mask. This approach was tested using three different types of commercially available masks and showed that it is feasible to detect their tightness and fit of them. This study concludes that IoT-based methods can be used effectively to improve the safety of PPE masks.

关键词： Personal protective equipment COVID-19 computer science Pandemics computer viruses Instruments data engineering

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Improving Project Control by Utilizing Predictive data Analytic Models

Improving Project Control by Utilizing Predictive Data Analy...

引用

8th ieee asia-pacific conference on computer science and data engineering (ieee csde)

作者： Jaafar, Kamal Aloran, Ahmad Watfa, Mohamad Univ Wollongong Dubai Fac Engn & Informat Sci Dubai U Arab Emirates Rd & Transport Author Dubai U Arab Emirates

ISBN: (纸本)9781665495523

Project progress is an apprehension for every project, as it indicates how the project is likely to meet the associated milestones. Utilizing collected data from archived projects can assist managers to envisage project progress. By leveraging the power of data analytics, this research attempts to highlight data trends based on data collected from 279 infrastructure projects in the UAE. Specifically, this research rigorously analyses the relationships between project budget, duration, and progress using K-means clustering techniques and hypothesis testing. We then provide predictive models using Autoregressive Integrated Moving Average - ARIMA and Multivariate regression models that allow managers to predict with a 99.15% accuracy the monthly progress of an infrastructure project over the next 3 months. This research paper provides project managers with a comprehensive framework that combines data analytics techniques with agility practices to predict short term project progress in order to take proactive measures on the different influencing factors.

关键词： Project progress Project Management Hypothesis Testing Predictive models Clustering

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Generating Robust Convolutional Networks by Injecting Partial Noise in the Training data

Generating Robust Convolutional Networks by Injecting Partia...

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Artem Pilzak Jean-Philippe Thivierge School of Psychogy University of Ottawa Ottawa Canada

Convolutional Neural Networks (CNN) have emerged as a highly efficient deep learning algorithm for processing, classifying, and analyzing images. Currently, there is a high demand for networks that are robust to noise in the training and testing data. However, the presence of noise in images has been an ongoing problem when it comes to deploying CNN for real-world applications. In this paper, we propose a solution to this problem by incorporating noisy and noise-free images in the training set of a CNN network. Using the Modified National Institute of Standards and Technology database (MNIST), we demonstrate that marked improvements to classification accuracy only require a small proportion of noisy images in the training set. This result generalized across test sets with different amounts of noise and types of noise. In sum, our novel training method increases the generalization of a CNN model when processing noisy data.

关键词： Training Deep learning databases Computational modeling Training data NIST data models

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Application of artificial intelligence in secure decentralized computation enabled by TOTEM

Application of artificial intelligence in secure decentraliz...

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Dhanya Therese Jose Chunming Rong Antorweep Chakravorty Department of Computer Science & Electrical Engineering University of Stavanger Stavanger Norway

TOTEM is a framework that allows users to execute their own code on access restricted datasets with controlled computation. It provides data security by restricting the exchange of data across the network, instead, the data will be executed within the data owner's environment itself. This ensures the data owners have full control over their data. The framework has been patented under US Patent No: US11,121,874 B2 in 2021. In this study, we are focusing on two areas, (i) how to improve the core features of the TOTEM architecture to an advanced level by introducing AI and (ii) the application of the TOTEM architecture in various sectors. Blockchain-enabled federated learning is getting more attention these days and its unique advantages while used within the TOTEM architecture are also discussed in this article.

关键词： Patents Codes Machine learning algorithms Federated learning data security Estimation Training data

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Gender Bias in Pre-Trained Deep Learning Models

Gender Bias in Pre-Trained Deep Learning Models

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Ahsan Ul Islam ABM Rezbaul Islam Department of Computer Science SAM Houston State University Huntsville Texas USA

People's opinions and actions in everyday life are increasingly influenced by artificial intelligence. However, over representation in the design of these technologies has the potential to quietly undo decades of progress in gender and ethnicity. Gender bias has become a pervasive concern in numerous domains, including the field of artificial intelligence specifically within pre-trained deep learning models. The ability to learn from various and vast amounts of data, hold immense potential for shaping our society and decision-making processes. However, they are not immune to the biases present in the data they are trained on and can unintentionally perpetuate and amplify societal biases, including those related to *** bias in pre-trained deep learning models has become a critical concern recently. As these models have gained prominence in various applications, it has been observed that they often perpetuate and reinforce existing societal biases related to gender. This paper presents a systematic and empirically grounded examination of gender bias within a diverse array of pre-trained deep-learning models. By meticulously analyzing the performance of these models about gender-related predictions, valuable insights are unearthed regarding the presence and extent of bias. Furthermore, this work introduces a unique and innovative remedy in the form of a singular CNN model that demonstrates the potential to address and mitigategender bias effectively.

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Digital Image Processing Using Noise Removal Technique: A Non-Linear Approach

Digital Image Processing Using Noise Removal Technique: A No...

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Rishal Ravikesh Chand Mohammed Farik Neeraj Anand Sharma Department of Computer Science and Mathmatics School of Science and Technology The University of Fiji

An undesirable by-product of a captured image is Image Noise - random distribution of brightness and color misinformation. Denoising is one of the common components which is present in digital image processing techniques, Image processing has become an integral part of today's era amongst rapidly growing technologies. The process of manipulating images by using various algorithms exploring techniques to pre-process an image, improving its pictorial information for human interpretation. This research paper explores different types of image noise that can be present in a digital image and degradation by photo electronic noise, impulse (salt & pepper) noise and gaussian noise, thus reviews on linear approaches to denoise the image using 2D convolution filters and median non-linear filters.

关键词： Degradation Maximum likelihood detection Image color analysis Convolution Digital images Noise reduction Nonlinear filters

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On the use of XAI for CNN Model Interpretation: A Remote Sensing Case Study

On the use of XAI for CNN Model Interpretation: A Remote Sen...

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Loghman Moradi Bahareh Kalantar Erfan Hasanpour Zaryabi Alfian Abdul Halin Naonori Ueda College of Engineering University of Tehran Tehran Iran Disaster Resilience Science Team RIKEN AIP Tokyo Japan Department of Multimedia Universiti Putra Malaysia Serdang Malaysia

In this paper, we investigate the use of Explainable Artificial Intelligence (XAI) methods for the interpretation of two Convolutional Neural Network (CNN) classifiers in the field of remote sensing (RS). Specifically, the SegNet and Unet architectures for RS building information extraction and segmentation are evaluated using a comprehensive array of primary- and layer-attributions XAI methods. The attribution methods are quantitatively evaluated using the sensitivity metric. Based on the visualization of the different XAI methods, Deconvolution and GradCAM results in many of the study areas show reliability. Moreover, these methods are able to accurately interpret both Unet's and SegNet's decisions and managed to analyze and reveal the internal mechanisms in both models (confirmed by the low sensitivity scores). Overall, no single method stood out as the best one.

关键词： computer science Sensitivity Deconvolution Computational modeling Information retrieval data engineering data models

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Object Detection in Aerial Images with Attention-based Regression Loss

Object Detection in Aerial Images with Attention-based Regre...

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14th Annual Summit and conference of the asia-pacific-Signal-and-Information-Processing-Association (APSIPA ASC)

作者： Doloriel, Chandler Timm C. Cajote, Rhandley D. Univ Philippines Diliman Philippines

ISBN: (数字)9786165904773

ISBN: (纸本)9786165904773

Object detection is a computer vision technique used to identify objects that are usually present in natural scenes. However, the methods used for this case are not easily transferable to detect objects in aerial images. Objects in aerial images are mostly arbitrary-oriented, small, and in complex backgrounds compared to upright and well-focused objects in natural scenes. To effectively detect objects in aerial images, we propose a new regression loss function based on the attention mechanism through attention weights. Using the relative position of the attention weights to the bounding box, the foreground is given more attention, which highlights the target object and effectively suppresses the noise and background. Preliminary experiments are conducted on an attention-based object detector using the DOTA dataset to test the capability of attention mechanism in extracting the contextual information of objects, especially in complex environments.

关键词： computer vision Object detection Information processing Detectors Feature extraction Object recognition data mining

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