High utility sequential pattern mining (HUSPM) aims to mine all patterns that yield a high utility (profit) in a sequence dataset. HUSPM is useful for several applications such as market basket analysis, marketing, an...
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In this paper, we present an Integrated Sensing and Communications (ISAC) system enabled by in-band Full Duplex (FD) radios, where a massive Multiple-Input Multiple-Output (MIMO) base station equipped with hybrid Anal...
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ISBN:
(纸本)9781665459761
In this paper, we present an Integrated Sensing and Communications (ISAC) system enabled by in-band Full Duplex (FD) radios, where a massive Multiple-Input Multiple-Output (MIMO) base station equipped with hybrid Analog and Digital (A/D) beamformers is communicating with multiple DownLink (DL) users, while simultaneously estimating, via the same signaling waveforms, the Direction of Arrival (DoA) as well as the range of radar target, which are randomly distributed within its coverage area. Capitalizing on a recent reduced-complexity FD hybrid A/D beamforming architecture, we devise a joint radar target tracking and DL data transmission protocol. An optimization framework for the joint design of the massive A/D beamformers and the self-interference cancellation unit, with the dual objective of maximizing the radar tracking accuracy and DL communication performance, is presented. Our simulation results at millimeter wave frequencies using 5G NR wideband waveforms, showcase the accuracy of the radar target tracking performance of the proposed system, which simultaneously offers increased sum rate compared with benchmark schemes.
In this work, we present PhysioFuseNet, a novel framework designed to enhance driver stress state classification. PhysioFuseNet integrates a CNN-based encoder-decoder model with multimodal biosignal fusion. Using a dr...
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ISBN:
(数字)9798350371499
ISBN:
(纸本)9798350371505
In this work, we present PhysioFuseNet, a novel framework designed to enhance driver stress state classification. PhysioFuseNet integrates a CNN-based encoder-decoder model with multimodal biosignal fusion. Using a driving simulator, different multimodal signals were acquired, namely electrocardiography, electrodermal activity, photoplethysmography, and respiration rate from (N = 25) healthy subjects. The experiment is of 35 minutes duration and contains different stress states (baseline (5 minutes), while normal, cognitive, and emotional sessions for 10 minutes). Multimodal features are extracted and employed in an encoder-decoder network. Extracted encoder features are combined through intermediate fusion and fed to support vector machine (SVM) and random forest (RF) classifiers. Experimental results demonstrate the efficacy of our approach, outperforming previous methods by achieving accuracies of 0.95 and 0.94 for SVM and RF, respectively. Notably, the framework excels in classifying emotional and cognitive stress states. In summary, the proposed framework could be useful in stress assessment in real-time and clinical conditions.
High-dimensional data requires a lengthy computation time and is more difficult to model, analyze and visualize. Feature selection algorithm is needed in order to obtain the best features and eliminate irrelevant ones...
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Detection of sarcasm sentences in the Natural Language Processing (NLP) field has a fairly high level of difficulty because it ignores elements of facial expressions and intonation of speech style. SentiStrength works...
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Detection of sarcasm sentences in the Natural Language Processing (NLP) field has a fairly high level of difficulty because it ignores elements of facial expressions and intonation of speech style. SentiStrength works by way of unsupervised learning so it is easier to use to classify sentiments more quickly. This study uses SentiStrengthID which has been developed for Indonesian texts. The tweets used in this study are tweets in Indonesian. SentiStrength will classify sentences into 3 classes, namely positive, negative, and neutral sentiments to detect the spread of sarcasm sentences within each sentiment group. The results of this study indicate the SentiStrength accuracy value of 54.52% in detecting sarcasm sentences in Indonesian-language tweets. SentiStrength managed to divide the detected sarcasm sentences into as many as 13 sentences on positive sentiment, 24 sentences on negative sentiment, and 70 sentences on neutral sentiment.
Transformer-based models have recently made significant achievements in the application of end-to-end (E2E) automatic speech recognition (ASR). It is possible to deploy the E2E ASR system on smart devices with the hel...
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This article explores the importance of IT risk management, especially at XYZ Vocational School. The agency is equipped to manage business risks, emphasizing the need to improve decision-making and risk mitigation by ...
This article explores the importance of IT risk management, especially at XYZ Vocational School. The agency is equipped to manage business risks, emphasizing the need to improve decision-making and risk mitigation by identifying potential hazards and evaluating internal and external threats, including hacking risks. The research investigates IT risk management at XYZ Vocational School using a qualitative and quantitative method with a focus on the APO12 COBIT 5 domain: Align, plan, and organize. It assesses subdomains such as data collection, risk analysis, profile maintenance, risk articulation, portfolio management, and response strategies. Conclusively, the research reveals institutional IT capabilities at level 1 (Performed Process) in the APO12 subdomain. Recommendations include developing COBIT-compliant SOPs for processes at this level, highlighting the need for further improvements in IT governance. This study provides valuable insights into IT risk management practices, offering recommendations for improving IT governance in educational settings.
Wheeled Indonesian Soccer Robot Contest (Wheeled KRSBI) is a national competition focused on the development of wheeled soccer robots. EEPIS Robot Soccer On Wheeled (ERSOW) is one of KRSBI's wheeled soccer robots....
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YouTube is a widely-used platform in Indonesia, with 93.8% of its users. As such, it presents a valuable opportunity for marketing tourist destinations, particularly in Riau province, which aims to become Indonesia...
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Acquisition of underwater images presents chal-lenges stemming from inherent distortions and the degradation of color and contrast within the aquatic environment. These challenges, characterized by image quality issue...
Acquisition of underwater images presents chal-lenges stemming from inherent distortions and the degradation of color and contrast within the aquatic environment. These challenges, characterized by image quality issues, pose significant obstacles to the training of supervised deep learning models on extensive and diverse datasets, potentially constraining the overall performance of the model. High-quality underwater images play an important role in the development of Autonomous Underwater Vehicles(AUVs}. In this work, we present MobileVitV2 Block Fusion Augmentor and Global-Local Feature Fusion Block with Attent-UNet, which are offered as blocks of generator. Through extensive experiments on different datasets, our proposed method achieves better visual quality, and the method can obtain better Quality images in poorer underwater environments.
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