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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： Devadutta Indoria Barani Dakshinamoorthy M. Karthik Manish Sharma S. Kaliappan G. Manikandan Department of Commerce Vikram Dev University Jeypore Odisha India Department of Human Resource Management &OB Sharda School of Business Studies Sharda university Greater Noida Uttar Pradesh India Department of Management Studies Shri Vishnu Engineering College for Women Bhimavaram Andhra Pradesh India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Division of Research and Development Lovely Professional University Phagwara Punjab India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Financial institutions have recognized the value of AI and the IoT in enhancing strategic human resource management. The paper presents a comprehensive integration framework that utilizes AI and IoT technologies. The focus is on key aspects of human resources operations, such as employee recruitment, retention, employee engagement, and performance evaluations. Productivity and efficiency can be maximized by 72% and 92%, respectively, through the application of AI algorithms and predictive analytics in IoT devices used in the collection of real-time data for HR operations. The proposed framework will be diverse in performance indicators, have appropriate strategies for change management, robust security measures, and pipelined data meant for long-term use. It consequently contributes to the design of a dynamic and personalized human resource management strategy based on insights derived from data.

关键词： Performance evaluation Technological innovation Organizations Prediction algorithms Real-time systems Internet of Things Security Artificial intelligence Predictive analytics Recruitment

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Behavioral-pattern exploration and development of an instructional tool for young children to learn AI

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computers and Education: Artificial Intelligence 2021年 2卷

作者： Hsu, Ting-Chia Abelson, Hal Lao, Natalie Tseng, Yu-Han Lin, Yi-Ting Department of Technology Application and Human Resource Development National Taiwan Normal University Taiwan Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology United States

This study aimed at developing an instructional tool for the artificial intelligence education of young students, and used learning analytics to identify the sequential learning behavioral patterns of students during the process of learning with the instructional tool. The instructional experiment took 9 weeks. The first stage of the course was 5 weeks spent on individual learning of MIT App Inventor and Personal Image Classifier. The second stage was 4 weeks spent on cooperative learning to make a robot car and play a computational thinking board game. In the second stage, the students worked in pairs to make the robot car. Finally, they played the computational thinking board game with the personal image classification application they developed in the first stage and the robot car they made in the second stage. The innovative studies found meaningful behavioral patterns when the young students learned the application of artificial intelligence with the instructional tool developed and proposed in the study. © 2021 The Author(s)

关键词： Students

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DASentimental: Detecting depression, anxiety and stress in texts via emotional recall, cognitive networks and machine learning

arXiv

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arXiv 2021年

作者： Fatima, Asra Ying, Li Hills, Thomas T. Stella, Massimo CogNosco Lab Department of Computer Science University of Exeter United Kingdom Max Planck Institute for Human Development Germany Department of Psychology University of Warwick United Kingdom

Most current affect scales and sentiment analysis on written text focus on quantifying valence (sentiment) – the most primary dimension of emotion. However, emotions are broader and more complex than valence. Distinguishing negative emotions of similar valence could be important in contexts such as mental health. This project proposes a semi-supervised machine learning model (DASentimental) to extract depression, anxiety and stress from written text. First, we trained the model to spot how sequences of recalled emotion words by N=200 individuals correlated with their responses to the Depression Anxiety Stress Scale (DASS-21). Within the framework of cognitive network science, we model every list of recalled emotions as a walk over a networked mental representation of semantic memory, with emotions connected according to free associations in people’s memory. Among several tested machine learning approaches, we find that a multilayer perceptron neural network trained on word sequences and semantic network distances can achieve state-of-art, cross-validated predictions for depression (R = 0.7), anxiety (R = 0.44) and stress (R = 0.52). Though limited by sample size, this first-of-its-kind approach enables quantitative explorations of key semantic dimensions behind DAS levels. We find that semantic distances between recalled emotions and the dyad "sad-happy" are crucial features for estimating depression levels but are less important for anxiety and stress. We also find that semantic distance of recalls from "fear" can boost the prediction of anxiety but it becomes redundant when the"sad-happy" dyad is considered. Adopting DASentimental as a semi-supervised learning tool to estimate DAS in text, we apply it to a dataset of 142 suicide notes. We conclude by discussing key directions for future research enabled by artificial intelligence detecting stress, anxiety and depression. © 2021, CC BY.

关键词： Sentiment analysis

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Decomposition of electrical and electronic waste management by using artificial intelligence

Decomposition of electrical and electronic waste management ...

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International Conference on Image Information Processing

作者： Rahul Suryawanshi K Chatrapathy Ameer Al-Khaykan W. Deva Priya Ch. Venkata Krishna Reddy K.V.S. Prasad Department of Marketing Balaji Institute of Management and Human Resource Development (BIMHRD) Sri Balaji University(SBUP) Pune Maharashtra India Department of Information Technology School of Computing REVA University Bangalore (North) Karnataka India Department of Electronic and Communications Engineering University of Kufa Najaf Iraq Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Science (SIMATS) Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Hyderabad Telangana India Department of Basic Sciences and Humanities GMR Institute of Technology Vizianagaram Andhra Pradesh India

ISBN: (数字)9798350371406

ISBN: (纸本)9798350371413

The electrical and electronic waste or e-waste is the collection of used electrical and electronic components that are assigned for reuse or disposal through proper way without any hazardous consequences. The improper decomposition of the electrical and electronic waste leads to various constraints that leads to disturbances in the ecosystem. The highly toxic substances such as mercury and lead that are released from the electronic components may tend to penetrate in the ground which leads to degrade the soil fertility. This electrical and electronic components contain numerous nonrenewable resources such as silver, cobalt, copper and aluminum alloys and tend to destroy if they are not properly maintained and decomposed. This decomposition of electrical and electronic components are done with artificial intelligence technique to obtain with higher precision. This is accompanied through image processing with optimization techniques.

关键词： Silver Image processing Green products Ecosystems Electronic components Information processing Electronic waste

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On sampling-based training criteria for neural language modeling

arXiv

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arXiv 2021年

作者： Gao, Yingbo Thulke, David Gerstenberger, Alexander Tran, Khoa Viet Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department Rwth Aachen University Aachen52074 Germany AppTek GmbH Aachen52062 Germany

As the vocabulary size of modern word-based language models becomes ever larger, many sampling-based training criteria are proposed and investigated. The essence of these sampling methods is that the softmax-related traversal over the entire vocabulary can be simplified, giving speedups compared to the baseline. A problem we notice about the current landscape of such sampling methods is the lack of a systematic comparison and some myths about preferring one over another. In this work, we consider Monte Carlo sampling, importance sampling, a novel method we call compensated partial summation, and noise contrastive estimation. Linking back to the three traditional criteria, namely mean squared error, binary cross-entropy, and crossentropy, we derive the theoretical solutions to the training problems. Contrary to some common belief, we show that all these sampling methods can perform equally well, as long as we correct for the intended class posterior probabilities. Experimental results in language modeling and automatic speech recognition on Switchboard and LibriSpeech support our claim, with all sampling-based methods showing similar perplexities and word error rates while giving the expected speedups. Copyright © 2021, The Authors. All rights reserved.

关键词： Importance sampling

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Self-Normalized Importance Sampling for Neural language Modeling

arXiv

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arXiv 2021年

作者： Yang, Zijian Gao, Yingbo Gerstenberger, Alexander Jiang, Jintao Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University Aachen52074 Germany AppTek GmbH Aachen52062 Germany

To mitigate the problem of having to traverse over the full vocabulary in the softmax normalization of a neural language model, sampling-based training criteria are proposed and investigated in the context of large vocabulary word-based neural language models. These training criteria typically enjoy the benefit of faster training and testing, at a cost of slightly degraded performance in terms of perplexity and almost no visible drop in word error rate. While noise contrastive estimation is one of the most popular choices, recently we show that other sampling-based criteria can also perform well, as long as an extra correction step is done, where the intended class posterior probability is recovered from the raw model outputs. In this work, we propose self-normalized importance sampling. Compared to our previous work, the criteria considered in this work are self-normalized and there is no need to further conduct a correction step. Through self-normalized language model training as well as lattice rescoring experiments, we show that our proposed self-normalized importance sampling is competitive in both research-oriented and production-oriented automatic speech recognition tasks. Copyright © 2021, The Authors. All rights reserved.

关键词： Importance sampling

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BISECT: Learning to split and rephrase sentences with bitexts

arXiv

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arXiv 2021年

作者： Kim, Joongwon Maddela, Mounica Kriz, Reno Xu, Wei Callison-Burch, Chris Department of Computer and Information Science University of Pennsylvania School of Interactive Computing Georgia Institute of Technology Human Language Technology Center of Excellence Johns Hopkins University

An important task in NLP applications such as sentence simplification is the ability to take a long, complex sentence and split it into shorter sentences, rephrasing as necessary. We introduce a novel dataset and a new model for this 'split and rephrase' task. Our BISECT training data consists of 1 million long English sentences paired with shorter, meaning-equivalent English sentences. We obtain these by extracting 1-2 sentence alignments in bilingual parallel corpora and then using machine translation to convert both sides of the corpus into the same language. BISECT contains higher quality training examples than previous Split and Rephrase corpora, with sentence splits that require more significant modifications. We categorize examples in our corpus, and use these categories in a novel model that allows us to target specific regions of the input sentence to be split and edited. Moreover, we show that models trained on BISECT can perform a wider variety of split operations and improve upon previous state-of-the-art approaches in automatic and human evaluations. © 2021, CC BY.

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On architectures and training for raw waveform feature extraction in ASR

arXiv

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arXiv 2021年

作者： Vieting, Peter Lüscher, Christoph Michel, Wilfried Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University Aachen52074 Germany AppTek GmbH Aachen52062 Germany

With the success of neural network based modeling in automatic speech recognition (ASR), many studies investigated acoustic modeling and learning of feature extractors directly based on the raw waveform. Recently, one line of research has focused on unsupervised pre-training of feature extractors on audio-only data to improve downstream ASR performance. In this work, we investigate the usefulness of one of these front-end frameworks, namely wav2vec, in a setting without additional untranscribed data for hybrid ASR systems. We compare this framework both to the manually defined standard Gammatone feature set, as well as to features extracted as part of the acoustic model of an ASR system trained supervised. We study the benefits of using the pre-trained feature extractor and explore how to additionally exploit an existing acoustic model trained with different features. Finally, we systematically examine combinations of the described features in order to further advance the performance. Copyright © 2021, The Authors. All rights reserved.

关键词： Feature extraction

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Automatic Speaker Verification System Substantiating Children’s Dialects in School Settings

Automatic Speaker Verification System Substantiating Childre...

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International Conference on Parallel Distributed and Grid Computing (PDGC)

作者： Virender Kadyan Puneet Bawa Richa Choudhary Bhushan Dua Speech and Language Research Centre School of Computer Science University of Petroleum and Energy Studies (UPES) Dehradun Uttarakhand India University Centre for Research and Development Centre of Excellence for Speech and Multimodal Laboratory Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science and Engineering Chandigarh University Mohali Punjab India

ISBN: (纸本)9781665454025

The usage of Automatic Speaker Verification (ASV) in educational contexts has grown as a result of measurements of the speech signal in the claimed identity's native tongue. Even though resource-rich languages have received a lot of attention, low-resource languages, like Punjabi, still require work to be done on the development of ASV systems. In this study, an effort has been put forth to create an ASV system based on an i-vector for children while authenticating the student's dialects in Punjab region. Additionally, it has been determined how changing the Universal Background Model (UBM) size and i-vector dimensionality for the proposed system would affect the performance measure i.e. Equal Error Rate (EER). Finally, the EER values for the Malwa and Majha regions were lower than expected, at 8.47% and 9.26%, respectively. However, the output shows a large EER of 14.25% in Doaba region in comparison to that of Malwa and Majha regions.

关键词： Training Tongue Error analysis System performance Size measurement Grid computing

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Medical Spoken Named Entity Recognition

arXiv

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arXiv 2024年

作者： Le-Duc, Khai Thulke, David Tran, Hung-Phong Vo-Dang, Long Nguyen, Khai-Nguyen Hy, Truong-Son Schlüter, Ralf Lehrstuhl Informatik 6 - Machine Learning and Human Language Technology Group Computer Science Department RWTH Aachen University Aachen52074 Germany AppTek GmbH Aachen52062 Germany University of Toronto Canada University Health Network Canada FPT Software AI Center Viet Nam Indiana State University United States Hanoi University of Science and Technology Viet Nam University of Cincinnati United States College of William and Mary United States

Spoken Named Entity Recognition (NER) aims to extracting named entities from speech and categorizing them into types like person, location, organization, etc. In this work, we present VietMed-NER - the first spoken NER dataset in the medical domain. To our best knowledge, our real-world dataset is the largest spoken NER dataset in the world in terms of the number of entity types, featuring 18 distinct types. Secondly, we present baseline results using various state-of-the-art pre-trained models: encoder-only and sequence-to-sequence. We found that pre-trained multilingual models XLM-R outperformed all monolingual models on both reference text and ASR output. Also in general, encoders perform better than sequence-to-sequence models for the NER task. By simply translating, the transcript is applicable not just to Vietnamese but to other languages as well. All code, data and models are made publicly available here. © 2024, CC BY.

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