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Efficient Top/Bottom-k Fraction Estimation in Spatial databases Using Bounded Main Memory

Tsinghua science and Technology 2022年第2期27卷 223-234页

作者： Jinbao Wang Zhuojun Duan Xixian Han Donghua Yang School of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001China Computer Science Department James Madison UniversityHarrisonburgVA 22807USA

Spatial databases store objects with their locations and certain types of attached items.A variety of modern applications have been developed by leveraging the utilization of locations and items in spatial objects,such as searching points of interest,hot topics,or users’attitude in specified spatial *** many scenarios,the high and low-frequency items in a spatial region are worth noticing,considering they represent the majority’s interest or eccentric users’***,existing works have yet to identify such items in an interactive manner,despite the significance of the endeavor in decision-making *** study recognizes a novel type of analytical query,called top/bottom-k fraction query,to discover such items in spatial *** achieve fast query response,we propose a multilayered data summary that is spread out across the main memory and external memory.A memory-based estimation method for top/bottom-k fraction queries is *** maximize the use of the main memory space,we design a data summary tuning method to dynamically allocate memory space among different spatial *** proposed approach is evaluated with real-life datasets and synthetic datasets in terms of estimation *** results demonstrate the effectiveness of the proposed data summary and corresponding estimation and tuning algorithms.

关键词： exploratory analytic top-k items bottom-k items spatial database

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User Task Priority Based Resource Allocation with Multi Class Task Scheduling Strategy and Load Balancing in Cloud Environment

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SN computer science 2024年第8期5卷 970页

作者： G, Nida Kousar C, Gopala Krishnan Department of Computer Science and Engineering GITAM School of Technology Bengaluru 561203 India

An effective task scheduling method can accommodate user needs, boost resource usage, and boost cloud computing's overall efficiency. However, the unchanging task needs are generally the focus of grid computing's job scheduling, leading to low resource usage. Distributing the dynamic user tasks fairly among all cloud nodes is the goal of load balancing, a relatively new field of study. The primary difficulty with cloud computing is load balancing. By making better use of available resources, load balancing methods improve cloud performance. Load balancing primary goal is to lessen the burden on the environment by cutting down on energy use and carbon emissions. The most crucial characteristics that can both satisfy user needs and maximize resource utilization are used to determine the order of priorities. Existing systems often ignore user priority suggestions in favor of optimal scheduling to improve load balancing. Scheduling that takes into account user-guided priorities uses a data-driven strategy, which helps improve load balancing. Scheduling algorithms that take user priorities into account can optimize load distribution more effectively. The primary objective of this research is to provide a priority based randomized load balancing technique that assigns tasks to virtual machines in a random fashion based on criteria such as the number of users, the amount of time the task takes to run, the type of software being used, the cost of the software, and the amount of available resources. This method maximizes system performance by decreasing response time and resource consumption while increasing metrics like fault tolerance and scalability. This system for scheduling tasks not only accommodates user needs but also achieves excellent resource usage. This research proposes a User Task Priority based Resource Allocation with Multi Class Task Scheduling Strategy and Load Balancing (UPRA-MCTSS-LB) Model for enhancing the cloud service quality. The proposed method res

关键词： Cloud computing Load balancing Multi class tasks Quality of service Resource utilization Task scheduling User task priority

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Two Stage Video Classification Approach Using Convolution Neural Network 2

Two Stage Video Classification Approach Using Convolution Ne...

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2nd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Veena, H.N. Rajani Gowda, Naveen Chandra Ramani, D Roja SJB Institute of Technology Department of Computer Science and Engineering Bengaluru India School of Computer Science and Engineering REVA University Bengaluru India Newhorizon College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350327533

This research study focuses on video categorization, which is a crucial area of computer vision with uses in entertainment, education, and surveillance. Convolutional Neural Networks (CNNs) are used in a two-stage approach in the suggested methodology. The video is first broken down into frames, which are subsequently supplied into a CNN model. Another CNN model that predicts the video class uses the output from this model as input. A publicly accessible dataset is used to assess the efficacy of this approach, demonstrating its competitive accuracy performance in comparison to the most advanced techniques. The outcomes indicate that the technique can effectively classify videos and has the potential to be used in a variety of real-world scenarios. © 2024 IEEE.

关键词： 3D CNN Autoencoder Convolution Neural Networks (CNN) Long-Term Recurrent Convolutional Network (LRCN) Recurrent Neural Network(RNN)

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Method For Traffic Violation Detection Using Deep Learning

Method For Traffic Violation Detection Using Deep Learning

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2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Tangamus, Darren Thai, Nathanael Alvaro Shan, Jung Sihotang, Erna Fransisca Angela Irwansyah, Edy Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University School of Computer Science Statistics Department Jakarta Indonesia

ISBN: (纸本)9798350307306

Urban areas commonly encounter traffic congestion. One of its primary causes is motorists violating traffic regulations, such as travelling at excessive speeds. Besides risking the safety of other road users, speeding exacerbates traffic capacity. Artificial Intelligence will detect instances where vehicles exceed the maximum speed limit on a thoroughfare to address this issue. The YOLOv8 algorithm, a commonly utilised Convolutional Neural Network Architecture in computer Vision for object detection and classification, is employed in this study to detect instances of speeding vehicles in live video footage. The research model can accurately identify and categorise vehicles in real-time CCTV footage, showcasing superior bounding box prediction performance. However, the model's low Recall rate and Mean Average Precision scores at both IoU thresholds warrant further exploration. Issues such as misidentification of objects remain prevalent in the model, which may stem from inaccurate classification of object categories, computational restrictions, and partiality in the data. Issues such as misidentification of objects remain prevalent in the model, which may stem from inaccurate classification of object categories, computational restrictions, and partiality in the data. Nonetheless, this study reveals multiple possibilities for enhancing the model, which includes employing new data sets and incorporating supplementary detections. © 2023 IEEE.

关键词： computer vision

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Joint Mobile Energy Replenishment and data Gathering in Wireless Sensor Networks via Federated Deep Reinforcement Learning

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6460-6473页

作者： Huang, Haojun Zhang, Junhao Wang, Bang Miao, Wang Min, Geyong Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan430074 China University of Exeter Department of Computer Science ExeterEX44QF United Kingdom

Recent years have witnessed the proliferation of wireless energy transfer for Wireless Sensor Networks (WSNs), which are mainly used for data gathering in real-world applications. A number of studies have investigated mobile vehicle scheduling to charge sensor nodes via wireless Mobile Chargers (MCs). Unfortunately, most of them cannot parallelly charge all nodes in an intelligent manner with the global network attributes. Furthermore, the time-variable charging ignores the optimal data gathering, resulting in poor Joint Energy Replenishment and data Gathering (JERDG). To fill this gap, this paper proposes a Federated Deep Reinforcement Learning (FDRL)-based JERDG (FERG) solution for WSNs. To this end, FERG firstly partitions the networks into a set of clusters to distribute the workload evenly among multiple MCs, and then designs an FDRL-based framework that incorporates various time-variant network attributes to determine the optimal schedule for charging and data gathering via multiple MCs and a base station (BS). The BS as the cloud server is responsible for global training of JERDG models, while multiple MCs will parallelly train local models to jointly charge energy-exhausted nodes and gather the data from all nodes in clusters. To reserve more personalized characteristics of each cluster, a density-based partial aggregation strategy is designed to train the global model. Furthermore, a reward-weighted update and selection solution is proposed to generate and exploit reference samples with high rewards. Simulation results obtained from various scenarios demonstrate that FERG significantly outperforms the state-of-the-art approaches in terms of network lifetime, energy efficiency and data collection latency. © 2002-2012 IEEE.

关键词： Deep reinforcement learning

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Enhancing Document-Level Relation Extraction through Entity-Pair-Level Interaction Modeling

Enhancing Document-Level Relation Extraction through Entity-...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Wanlong Zeng, Dingyi Zhou, Li Xiao, Yichen Zhang, Malu Chen, Wenyu School of Computer Science and Engineering University of Electronic Science and Technology of China China School of Data Science The Chinese University of Hong Kong Shenzhen China

ISBN: (纸本)9798350368741

Document-level relation extraction aims at extracting relational facts between two entities in a document. Existing approaches mainly focus on target entities, utilizing techniques such as graph neural networks to enhance their representations. However, they ignore the rich semantic correlations among entity pairs which provide wider and multifaceted information at a higher level. In this paper, we propose the Relation-based Entity-pair-level Inference (REI) model, which facilitates information interaction at the entity-pair level, enhancing logical reasoning among entities and capturing semantic correlations among entity pairs. Our REI model comprises two modules: Relation-based Information Aggregation (RIA) and Entity-pair-level Information Interaction (EII). The RIA module builds and integrates relation representations to filter out distractions from unrelated entity pairs, while the EII module models entity-pair-level information interaction through multi-head attentions. Extensive experiments on the DocRED, DWIE, CDR, and GDA datasets demonstrate the superiority of the proposed REI model, outperforming previous state-of-the-art approaches. Furthermore, we provide detailed experimental analyses based on the performance gains and illustrate the interpretability. © 2025 IEEE.

关键词： Information Extraction Linguistic Inference Natural Language Processing Relation Extraction

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Systematic Literature Review of Transformer Model Implementations in Detecting Depression 6

Systematic Literature Review of Transformer Model Implementa...

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6th International Conference on computer and Informatics Engineering, IC2IE 2023

作者： Nanggala, Kenjovan Pardamean, Bens Elwirehardja, Gregorius Natanael Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798350345162

This systematic literature review explores the application of transformer models in early detection of human depression, encompassing text, audio, and video data modalities. Transformer architectures, notably BERT for text, have proven adept at capturing crucial contextual and linguistic patterns associated with depression. For audio and video data, hybrid approaches that combine transformer models with other architectures are prevalent. Key features considered include eye gaze, head pose, facial muscle movements, and audio characteristics such as MFCC and Log-mel Spectrogram, along with text embeddings. Performance comparisons underscore the superiority of text-based data in consistently delivering the most promising results, followed by audio and video modalities when utilizing transformer models. The fusion of multiple modalities emerges as an effective strategy for enhancing predictive accuracy, with the amalgamation of audio, video, and text data yielding the most precise outcomes. However, it is noteworthy that unimodal approaches also exhibit potential, with text data exhibiting superior performance over audio and video data. Nevertheless, several challenges persist in this research domain, including imbalanced datasets, the limited availability of comprehensive and diverse samples, and the inherent complexities in interpreting visual cues. Addressing these challenges remains imperative for the continued advancement of depression detection using transformer-based models across various modalities. © 2023 IEEE.

关键词： deep learning major depressive disorder multimodal transformer unimodal

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Optimized featured swarm convolutional neural network (OFSCNN) model based dialect recognition system for Bagri Rajasthani language

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International Journal of Information Technology (Singapore) 2025年第1期17卷 121-133页

作者： Kukana, Poonam Sharma, Pooja Bhardwaj, Neeru Department of Computer Science and Engineering University School of Engineering & amp Technology Rayat-Bahra University Punjab Mohali India Department of Computer Science and Engineering Chandigarh University Punjab Mohali India

The dialects of a language hold a significant place in speech processing (SP) applications. The objective of dialect identification is to categorize speech sample data into a specific dialect of a speaker's spoken language. A dialect recognition system must effectively distinguish between different dialects of a standard language, as they tend to possess many similarities. The dialect of a language is not a distinct characteristic, as it can be impacted by the utterer's birthplace. Researchers in the domain of automatic speech recognition (ASR) face difficulties in identifying the speech patterns unique to each dialect or language. The proposed work recognizes the dialects of the Bagri Rajasthani language from undefined expressions of speech. Rajasthani language is one of the eldest and most famous languages in the Indo-Aryan languages. It comprises the different dialects and for recognizing the dialects, it used dissimilar phases of acoustic and spectral characteristics of the speech signal (SS). The spectral and acoustic features of SSs are measured to design the system. As there is no specific speech dataset for Bagri dialects, the database is built, to verify the Bagri dialects of the Rajasthani language. To improve the accuracy rate, and error rate in recognizing the Bagri dialects, the acoustic and spectral characteristics of speech expressions are joined. To verify several Bagri dialects of the Rajasthani language, different simulations for classification and investigation are carried out i.e., OFSCNN model, GA-NN, etc. The outcomes are important and the accuracy of 96.95% for the OFSCNN model, 80.63% for GA-NN, and 93.45% for the SVM method is an achievement. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： CNN (convolutional neural network Dialects MFCC (Mel frequency co-efficient cepstral) OFSCNN (optimized featured swarm convolutional neural network) PSO (particle swarm optimization) Speech Dialect Recognition

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TOWARDS ROBUST OUT-OF-DISTRIBUTION GENERALIZATION BOUNDS VIA SHARPNESS 12

TOWARDS ROBUST OUT-OF-DISTRIBUTION GENERALIZATION BOUNDS VIA...

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12th International Conference on Learning Representations, ICLR 2024

作者： Zou, Yingtian Kawaguchi, Kenji Liu, Yingnan Liu, Jiashuo Lee, Mong-Li Hsu, Wynne School of Computing National University of Singapore Institute of Data Science National University of Singapore Singapore Department of Computer Science & Technology Tsinghua University China

Generalizing to out-of-distribution (OOD) data or unseen domain, termed OOD generalization, still lacks appropriate theoretical guarantees. Canonical OOD bounds focus on different distance measurements between source and target domains but fail to consider the optimization property of the learned model. As empirically shown in recent work, the sharpness of learned minima influences OOD generalization. To bridge this gap between optimization and OOD generalization, we study the effect of sharpness on how a model tolerates data change in domain shift which is usually captured by "robustness" in generalization. In this paper, we give a rigorous connection between sharpness and robustness, which gives better OOD guarantees for robust algorithms. It also provides a theoretical backing for "flat minima leads to better OOD generalization". Overall, we propose a sharpness-based OOD generalization bound by taking robustness into consideration, resulting in a tighter bound than non-robust guarantees. Our findings are supported by the experiments on a ridge regression model, as well as the experiments on deep learning classification tasks. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Regression analysis

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Modelling High-Order Social Relations for Item Recommendation (Extended Abstract) 39

Modelling High-Order Social Relations for Item Recommendatio...

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39th IEEE International Conference on data Engineering, ICDE 2023

作者： Liu, Yang Chen, Liang He, Xiangnan Peng, Jiaying Zheng, Zibin Tang, Jie Sun Yat-sen University School of Computer Science and Engineering Guangzhou China University of Science and Technology of China School of Data Science School of Information Science and Technology Hefei China Tsinghua University Department of Computer Science and Technology Beijing China

ISBN: (纸本)9798350322279

Personalized recommendation is becoming increasingly important in online information systems in the current era of information explosion. In real-world scenarios, when a user considers which items to consume, the decision choice may be affected by her friends. For example, she may ask her friends for suggestions or be attracted by products purchased by one friend. As such, to provide satisfactory recommendation service, it is important to account for the evidence in social relations when they are available to use. Several prior efforts have been made to leverage social relations to build the recommender system and verified their utility. However, most existing methods, such as the well-known TrustSVD, leverage only first-order social relations, i.e., the direct neighbors that are connected to the target user. The high-order social relations, e.g., the friends of friends, which are very informative to reveal user preference, have been largely ignored. © 2023 IEEE.

关键词： Online systems

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