Image classification is the most accurate way to anticipate a classifier’s accuracy. Categorization approaches include logistic regression, K-nearest neighbors, Naive Bayes, decision trees, and support vector machine...
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This paper discusses the significance of research focused on progressive dementia due to the pressing needs of an aging population. Technological solutions have been explored as useful tools to diagnose, manage and tr...
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The performance of neural network-based speech enhancement systems is primarily influenced by the model architecture, whereas training times and computational resource utilization are primarily affected by training pa...
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Humans engage daily in procedural activities such as cooking a recipe or fixing a bike, which can be described as goal-oriented sequences of key-steps following certain ordering constraints. Task graphs mined from vid...
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Humans engage daily in procedural activities such as cooking a recipe or fixing a bike, which can be described as goal-oriented sequences of key-steps following certain ordering constraints. Task graphs mined from videos or textual descriptions have recently gained popularity as a human-readable, holistic representation of procedural activities encoding a partial ordering over key-steps, and have shown promise in supporting downstream video understanding tasks. While previous works generally relied on hand-crafted procedures to extract task graphs from videos, this paper introduces an approach based on gradient-based maximum likelihood optimization of edge weights, which can be used to directly estimate an adjacency matrix and can also be naturally plugged into more complex neural network architectures. We validate the ability of the proposed approach to generate accurate task graphs on the CaptainCook4D and EgoPER datasets. Moreover, we extend our validation analysis to the EgoProceL dataset, which we manually annotate with task graphs as an additional contribution. The three datasets together constitute a new benchmark for task graph learning, where our approach obtains improvements of +14.5%, +10.2% and +13.6% in F1 score, respectively, over previous approaches. Thanks to the differentiability of the proposed framework, we also introduce a feature-based approach for predicting task graphs from key-step textual or video embeddings, which exhibits emerging video understanding abilities. Beyond that, task graphs learned with our approach obtain top performance in the Ego-Exo4D procedure understanding benchmark including 5 different downstream tasks, with gains of up to +4.61%, +0.10%, +5.02%, +8.62%, and +15.16% in finding Previous Keysteps, Optional Keysteps, Procedural Mistakes, Missing Keysteps, and Future Keysteps, respectively. We finally show significant enhancements to the challenging task of online mistake detection in procedural egocentric videos, achieving
We provide an equational basis for McCarthy algebras, the variety generated by the three-element algebra defining the logic of McCarthy (the non commutative version of Kleene three-valued logics), solving a problem le...
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The frequency of malicious activities by cyber attackers is on the rise, posing a significant challenge to counter cyber attacks in the Mobile Edge Computing (MEC) environment. The vulnerability of wireless networks, ...
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As cryptocurrencies have grown in prevalence and value, associated malware threats have rapidly emerged, exploiting vulnerabilities in wallets, markets, and decentralization mechanisms. This paper provides a comparati...
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Rapid increase in the large quantity of industrial data,Industry 4.0/5.0 poses several challenging issues such as heterogeneous data generation,data sensing and collection,real-time data processing,and high request ar...
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Rapid increase in the large quantity of industrial data,Industry 4.0/5.0 poses several challenging issues such as heterogeneous data generation,data sensing and collection,real-time data processing,and high request arrival *** classical intrusion detection system(IDS)is not a practical solution to the Industry 4.0 environment owing to the resource limitations and *** resolve these issues,this paper designs a new Chaotic Cuckoo Search Optimiza-tion Algorithm(CCSOA)with optimal wavelet kernel extreme learning machine(OWKELM)named CCSOA-OWKELM technique for IDS on the Industry 4.0 *** CCSOA-OWKELM technique focuses on the design of feature selection with classification approach to achieve minimum computation complex-ity and maximum detection *** CCSOA-OWKELM technique involves the design of CCSOA based feature selection technique,which incorpo-rates the concepts of chaotic maps with ***,the OWKELM technique is applied for the intrusion detection and classification *** addition,the OWKELM technique is derived by the hyperparameter tuning of the WKELM technique by the use of sunflower optimization(SFO)*** utilization of CCSOA for feature subset selection and SFO algorithm based hyperparameter tuning leads to better *** order to guarantee the supreme performance of the CCSOA-OWKELM technique,a wide range of experiments take place on two benchmark datasets and the experimental outcomes demonstrate the promis-ing performance of the CCSOA-OWKELM technique over the recent state of art techniques.
Accurate body composition assessment is essential for evaluating health and diagnosing conditions like sar copenia and cardiovascular disease. Approaches for accurately measuring body composition, such as Dual Energy ...
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The scope of this paper was to find out how the students in computerscience perceive different teaching styles and how the teaching style impacts the learning desire and interest in the course. To find out, we design...
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