This book constitutes the refereed proceedings of the 11th Annual Conference on Theory and Applications of Models of Computation, TAMC 2014, held in Chennai, India, in April 2014. The 27 revised full papers presented ...
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ISBN:
(数字)9783319060897
ISBN:
(纸本)9783319060880
This book constitutes the refereed proceedings of the 11th Annual Conference on Theory and Applications of Models of Computation, TAMC 2014, held in Chennai, India, in April 2014. The 27 revised full papers presented were carefully reviewed and selected from 112 submissions. The papers explore the algorithmic foundations, computational methods and computing devices to meet today's and tomorrow's challenges of complexity, scalability and sustainability, with wide-ranging impacts on everything from the design of biological systems to the understanding of economic markets and social networks.
Question generation is an important task in natural language processing that involves generating questions from a given text. This paper proposes a novel approach for dynamic question generation using a context-aware ...
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Question generation is an important task in natural language processing that involves generating questions from a given text. This paper proposes a novel approach for dynamic question generation using a context-aware auto-encoded graph neural model. Our approach involves constructing a graph representation of the input text, where each node in the graph corresponds to a word or phrase in the text, and the edges represent the relationships between them. We then use an auto-encoder model to learn a compressed representation of the graph that captures the most important information in the input text. Finally, we use the compressed graph representation to generate questions by dynamically selecting nodes and edges based on their relevance to the context of the input text. We evaluate our approach on four benchmark datasets (SQuAD, Natural Questions, TriviaQA, and QuAC) and demonstrate that it outperforms existing state-of-the-art methods for dynamic question generation. In the experimentation, to evaluate the result four performance metrics are used i.e. BLEU, ROUGE, F1-Score, and Accuracy. The result of the proposed approach yields an accuracy of 92% on the SQuAD dataset, 89% with QuAC, and 84% with TriviaQA. while on the natural questions dataset, the model gives 79% accuracy. Our results suggest that the use of graph neural networks and auto-encoder models can significantly improve the accuracy and effectiveness of question generation in NLP. Further research in this area can lead to even more sophisticated models that can generate questions that are even more contextually relevant and natural-sounding.
Advancement in IT and communication technology provides the opportunity for social media users to communicate their ideas and thoughts across the globe within no time as well big data promulgated in a result of the co...
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Advancement in IT and communication technology provides the opportunity for social media users to communicate their ideas and thoughts across the globe within no time as well big data promulgated in a result of the communication process itself has immense challenges. Recently, the provision of freedom of speech has witnessed immense promulgation of offensive and hate speech content on the internet aimed the basic human rights violation. The detection of abusive content on social media for rich resource language has become a hot area for researchers in the recent past. However, low-resource languages are underprivileged due to the non-availability of large corpus and its complexity to understand. The proposed methodology mainly has two parts. One is to detect abusive content and the other is to have a demographical analysis of the Indigenously developed dataset. The process starts with the development of a unique unlabeled Urdu dataset of 0.2 M from Twitter through a web scrapper tool named snscraper. The dataset is collected against the 36 districts of Punjab from Pakistan and from the duration 2018- Apr 2022. The dataset is labeled into three target classes Neutral, Offensive, and Hate Speech. After data cleaning, the feature extraction process is achieved with the help of traditional techniques such as Bow and tf-idf with the combination of word and char n-gram and word embedding word2Vec. The dataset is trained on both machine learning algorithms SVM and Logistic regression and deep learning techniques Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNN). The best F score achieved through LSTM on this dataset is 64 and accuracy is 93 through CNN algorithms. A Choropleth map is used for visualization of the dataset distributed among 36 districts of Punjab and a time series plot for time analysis covers five years duration from 2018-Apr to 22.
There is no doubt that the popularity of smart devices and the development of deep learning models bring individuals too much convenience. However, some rancorous attackers can also implement unexpected privacy infere...
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There is no doubt that the popularity of smart devices and the development of deep learning models bring individuals too much convenience. However, some rancorous attackers can also implement unexpected privacy inferences on sensed data from smart devices via advanced deep-learning tools. Nonetheless, up to now, no work has investigated the possibility of riskier overheard, referring to inferring an integral event about humans by analyzing polyphonic audios. To this end, we propose an Audio-based integral evenT infERence (alTER) model and two upgraded models (alTER-p and alTER-pp) to achieve the integral event inference. Specifically, alTER applies a link-like multi-label inference scheme to consider the short-term co-occurrence dependency among multiple labels for the event inference. Moreover, alTER-p uses a newly designed attention mechanism, which fully exploits audio information and the importance of all data points, to mitigate information loss in audio data feature learning for the event inference performance improvement. Furthermore, alTER-pp takes into account the long-term co-occurrence dependency among labels to infer an event with more diverse elements, where another devised attention mechanism is utilized to conduct a graph-like multi-label inference. Finally, extensive real-data experiments demonstrate that our models are effective in integral event inference and also outperform the state-of-the-art models.
Recently, emotion analysis and classification of tweets have become a crucial area of research. The Arabic language had experienced difficulties with emotion classification on Twitter(X), needing preprocessing more th...
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Recently, emotion analysis and classification of tweets have become a crucial area of research. The Arabic language had experienced difficulties with emotion classification on Twitter(X), needing preprocessing more than other languages. Emotion detection is a major challenge in Natural Language Processing (NLP), which allows machines to ascertain the emotions expressed in the text. The task includes recognizing and identifying human feelings such as fear, anger, sadness, and joy. The discovered sentiments and feelings expressed in tweets have gained much recognition in recent years. The Arab region has played a substantial role in international politics and the global economy needs to scrutinize the emotions and sentiments in the Arabic language. Lexicon-based and machine-learning techniques are two common models that address the problems of emotion classification. This study introduces a Chimp Optimization algorithm with a Deep Learning-Driven Arabic Fine-grained Emotion Recognition (COADL-AFER) technique. The presented COADL-AFER technique mainly aims to detect several emotions in Arabic tweets. In addition to its academic significance, the COADL-AFER technique has practical applications in various fields, including enhancing applications of E-learning, aiding psychologists in recognising terrorist performance, improving product quality, and enhancing customer service. The COADL-AFER technique applies the long short-term memory (LSTM) model for emotion detection. Finally, the hyperparameter selection of the LSTM method can be accomplished by COA. The experimental validation of the COADL-AFER system, a crucial step in our research, is verified utilizing the Arabic tweets dataset. The simulation results stated the betterment of the COADL-AFER technique, further reinforcing the reliability of our research.
After further review and discussions among the authors, we want to do further experiment to improve the existing premature results. Specifically, authors want to add more complex analysis to support the results. Given...
After further review and discussions among the authors, we want to do further experiment to improve the existing premature results. Specifically, authors want to add more complex analysis to support the results. Given the importance of maintaining the highest standards of academic integrity, we believe that withdrawal is the most appropriate course of action. all authors are fully aware of this decision and have agreed to the withdrawal. We have had several rounds of correspondence regarding this matter, and all authors have been included in these communications to ensure transparency and prevent any potential disputes in the future.
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