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AdapTraj: A Multi-Source Domain Generalization Framework for Multi-Agent Trajectory Prediction

AdapTraj: A Multi-Source Domain Generalization Framework for...

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international conference on data Engineering

作者： Tangwen Qian Yile Chen Gao Cong Yongjun Xu Fei Wang Chinese Academy of Sciences Institute of Computing Technology Beijing China University of Chinese Academy of Sciences Beijing China School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Multi-agent trajectory prediction, as a critical task in modeling complex interactions of objects in dynamic systems, has attracted significant research attention in recent years. Despite the promising advances, existing studies all follow the assumption that data distribution observed during model learning matches that encountered in real-world deployments. However, this assumption often does not hold in practice, as inherent distribution shifts might exist in the mobility patterns for deploy-ment environments, thus leading to poor domain generalization and performance degradation. Consequently, it is appealing to leverage trajectories from multiple source domains to mitigate such discrepancies for multi-agent trajectory prediction task. However, the development of multi-source domain generalization in this task presents two notable issues: (1) negative transfer; (2) inadequate modeling for external factors. To address these issues, we propose a new causal formulation to explicitly model four types of features: domain-invariant and domain-specific features for both the focal agent and neighboring agents. Building upon the new formulation, we propose AdapTraj, a multi-source domain generalization framework specifically tailored for multi-agent trajectory prediction. AdapTraj serves as a plug-and-play module that is adaptable to a variety of models. Extensive experiments on four datasets with different domains demonstrate that AdapTraj consistently outperforms other baselines by a substantial margin.

关键词： Degradation Adaptation models Buildings Predictive models data engineering data models Trajectory

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Causal Effect Prediction with Flow-based Inference

Causal Effect Prediction with Flow-based Inference

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ieee international conference on data Mining (ICDM)

作者： Shaogang Ren Dingcheng Li Ping Li Cognitive Computing Lab Baidu Research 10900 NE 8th St. Bellevue WA USA

Causal effect inference has many applications in data analysis and predictions, e.g., user behavior modeling, medical treatment effect prediction, etc. We introduce a new method to perform causal effect inference using flow-based latent-variable models. Our method leverages the expressive power of flow-based models and tries to recover the complex relationship between observations and unobserved confounders. A methodology has been developed to perform causal effect inference along with theoretical analysis. Experimental studies are presented to verify the proposed approach. Empirical results show that the proposed method outperforms baselines on different datasets.

关键词： Analytical models data analysis Medical treatment Predictive models data models Behavioral sciences data mining

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LESaET: Low-Dimensional Embedding Method for Link Prediction Combining Self-Attention and Enhanced-TuckER

LESaET: Low-Dimensional Embedding Method for Link Prediction...

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ieee international conference on Systems, Man and Cybernetics

作者： Lichao Ding Jing Zhao Kai Lu Zenghao Hao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Knowledge graphs (KGs) provide a structured representation of the real world through entity-relation triples. However, current KGs are often incomplete, typically containing only a small fraction of all possible facts. This involves inferring missing content from existing information, a task known as link prediction. Existing methods in the field of link prediction struggle with controlling the dimensionality of embedding vectors or suffer from overly complex models. In order to tackle these challenges, we introduce a method in this paper, named Low-Dimensional Embedding Method for Link Prediction Combining Self-Attention and Enhanced-TuckER (LESaET). LESaET leverages both self-attention mechanisms and tensor factorization to learn expressive contextual-enhanced representations of KGs. Specifically, LESaET employs the multi-head self-attention mechanism of Transformer as an encoder to capture the mutual information between entities and relationships, and utilizes Enhanced-TuckER as a decoder, ultimately achieving expressive low-dimensional embeddings for link prediction tasks. LESaET demonstrates competitive performance compared to advanced methods on standard datasets.

关键词： Tensors Stacking Knowledge graphs Predictive models Transformers Vectors Decoding Standards Mutual information Periodic structures

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Enhancing CS Education with LAs Using AI-Empowered AIELA Program

Enhancing CS Education with LAs Using AI-Empowered AIELA Pro...

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Frontiers in Education (FIE) conference

作者： Vijayalakshmi Ramasamy Eli Kulpinski Thomas Beaupre Aaron Antreassian Yunhwan Jeong Peter J. Clarke Anthony Aiello Charles Ray College of Engineering and Computing Computer Science Georgia Southern University Statesboro USA Computer Science University of Wisconsin-Parkside Kenosha USA School of Computing and Information Sciences Florida International University Florida USA

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This innovative practice full paper delves into the transformative role of Learning Assistants (LAs) in Computer Science education, focusing on enhancing student engagement and improving learning outcomes. The LA model, which aligns with Vygotsky's Social Constructivist Learning Theory, fosters an environment of student-centered learning and social interaction. In a pilot study conducted in Spring 2024 at a public university, the LA program is implemented in two computer science courses. A quasi-experimental design has been used to evaluate the impact of LA-facilitated team activities on student learning outcomes. The study compares a control group receiving traditional instruction with an experimental group participating in LA-facilitated team activities. The experimental group engaged in weekly team-based activities, guided by LAs and faculty, to reinforce class concepts and promote collaboration among team members. Student engagement and learning have been evaluated using feedback from students and LAs collected through Discussion Boards (DBs). Preliminary findings suggest that LA-facilitated in-class activities promote active learning and enhance problem-solving skills. LAs provide valuable support and guidance to students, particularly those struggling to understand complex concepts. The study tested a working model of AIELA, an innovative AI-powered chatbot that assists human LAs in supporting students through knowledge-reinforcing questions and multimodal data analysis, powered by OpenAI API's gpt-4-turbo model. This research is a step towards embracing the challenges of modern CS education, inspiring further innovation in this critical field. The findings will benefit educators seeking innovative strategies to enrich student engagement and learning in engineering and computing disciplines.

关键词： Technological innovation Analytical models Computational modeling Education Active learning Collaboration Prototypes data models Problem-solving Springs

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Recognition of Colon Neoplasm using Contrast-Aware Capsule Network

Recognition of Colon Neoplasm using Contrast-Aware Capsule N...

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international conference on Adaptive Science & Technology (ICAST)

作者： Mavis Serwaa Yeboah Patrick Kwabena Mensah Adebayo Felix Adekoya Mighty Abra Ayidzoe Department of Computer Science and Informatics University of Energy and Natural Resources Sunyani Ghana Department of Computer Science Sunyani Technical University Sunyani Ghana Faculty of Computing Engineering and Mathematical Sciences Catholic University of Ghana Sunyani Ghana

ISBN: (数字)9798350385403

ISBN: (纸本)9798350385410

Colon neoplasms are the second leading cause of mortality worldwide, and despite advances in endoscopy, even experienced endoscopists miss 22-28% of cases. Intelligent systems can bridge this gap, but current approaches face significant challenges. For instance, Convolutional Neural Networks (CNNs) excel in image recognition but require large datasets, which are often scarce in healthcare. On the other hand, Capsule Networks, suitable for smaller datasets, struggle with complex images. To address these limitations, we propose a novel contrast-aware Capsule Network for colon neoplasm recognition. By incorporating a novel contrast-aware feature transformation algorithm, our proposed model enhances extracted features and focuses on processing relevant features, achieving remarkable recognition accuracies of 96.70%, 93.68%, and 99.99% on PolyGen, ETIS-Larib, and CVC-ClinicDB datasets, respectively. Our approach outperforms state-of-the-art Capsule Network algorithms and offers a promising solution to support endoscopists in accurate colon neoplasm detection, reducing the burden of image analysis and improving patient outcomes.

关键词： Uncertainty Accuracy Neural networks Estimation Training data Mortality Medical services Feature extraction Colon Neoplasms

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AI Applications in Satellite Image Processing: Enhancing Earth Observation and Environmental Monitoring

AI Applications in Satellite Image Processing: Enhancing Ear...

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ieee international conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Olakunle L. Ojo Yinka Ajiboye Adeniran S. Afolalu Ademola T. Morebise Ifeoluwa M. Omoyajowo Oladipo E. Abe Olugbenga Olumodimu David S. Adeyeye Olugbenga M. Agboola Oyelami Olawunmi Department of Mathematical and Physical Sciences Afe Babalola University Ado Ekiti Nigeria Department of Mathematical & Physical Sciences Afe Babalola University Ado-Ekiti Ekiti State Nigeria Department of Mechanical and Mechatronics Engineering Afe Babalola University Ado-Ekiti Nigeria Department of Mechanical Engineering Science University of Johannesburg South Africa Department of Mathematical and Physical Sciences Afe Babalola University Ado-Ekiti Ekiti State Nigeria Students Affair Unit Afe Babalola University Ado Ekiti Nigeria Department of Physics Federal University Oye Ekiti Institute of Cosmology and Gravitation University of Portsmouth UK Department of Geological Sciences Achievers University Owo Ondo State Nigeria Department of Tourism and Events Management Afe Babalola University Ado Ekiti Nigeria Geography Department University of Lagos Lagos Nigeria

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

Satellite image processing is a cornerstone of modern Earth observation, providing critical data for meteorology, resource management, and urban studies. The integration of artificial intelligence (AI) has significantly enhanced image analysis through machine learning, deep learning, and data fusion techniques, improving efficiency and accuracy. This study examines AI-driven transformations in satellite image processing, focusing on applications in disaster monitoring, urban growth assessment, hyperspectral vegetation analysis, and air quality evaluation. We also explore AI algorithms such as supervised and unsupervised learning models, convolutional neural networks (CNNs), and data fusion techniques, which facilitate complex image interpretation. Additionally, challenges in AI integration with satellite systems are discussed, along with future research directions to advance Earth observation and environmental monitoring. Overall, this review highlights AI’s indispensable role in satellite imagery for global decision-making.

关键词： Earth Deep learning Ethics Machine learning algorithms Accuracy data integration Vegetation mapping Satellite images Environmental monitoring Unsupervised learning

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Improving Traffic Surveillance: Deep Learning Approach for Road Anomaly Detection in Videos

Improving Traffic Surveillance: Deep Learning Approach for R...

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computing and Machine Intelligence (ICMI), international conference on

作者： Sarfaraz Natha Muhammad Arif Syed Shahryar Jamil Fareed Ahmed Jokhio Muslim Jameel Syed Department of Software Engineering Sir Syed University of Engineering and Technology Karachi Pakistan Instituto Politécnico Nacional (IPN) Mexico MAJU Karachi Pakistan Department of Computer Systems Engineering Quaid-e-Awam University of Engineering and Technology Nawabshah Pakistan Department of Enterprise and Technology Atlantic Technological University Galway Ireland

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

In recent years, Closed-circuit Television (CCTV) cameras have been playing a vital role in the surveillance of both public and private areas. The primary objective of surveillance is to monitor human behavior and road conditions. In the real-world situation, detecting, and recognizing abnormal activities poses significant challenges due to the densely crowded environment and the complex nature of transportation systems. These factors make it difficult to automatically identify various anomalies that occur while traveling, leading to emergencies, and endangering human life and property. This study introduces an automatic detection framework for recognizing road anomalies such as accidents, fighting, car fires, and armed snatching (gunpoint) in road surveillance videos. After reviewing the literature, the review directs that convolutional neural networks (CNNs) are a specialized deep learning approach well suited for image and video analysis. The proposed methodology combines the pretrained CNN models with data Augmentation (DA) techniques to fine-tune hyperparameters such as learning rate and momentum that enhance the model learning accuracy and performance for recognizing road anomalies. Furthermore, it introduced a rolling prediction algorithm to solve the flickering problem during testing and created a new road anomaly dataset (RAD) as a benchmark consisting of road surveillance videos and images. Our proposed model combined with the InceptionV3 pre-trained model achieved a best accuracy is $98.81\%$ for detection and classification as compared to other deep learning models.

关键词： Deep learning Accuracy Roads Surveillance Computational modeling Prediction algorithms data augmentation

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DLP Based Processing-in-Memory Architecture Leveraging Content Addressable Memory

DLP Based Processing-in-Memory Architecture Leveraging Conte...

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conference on Information and Communication Technology (CICT)

作者： C Sri Vardhan B N H Nandan P Tej Kumar N Teja Krishna Rangababu Peesapati Vijayakumar Devarakonda Department of Electronics and Communication Engineering Indian Institute of Information Technology Design and Manufacturing Kurnool Kurnool India

ISBN: (数字)9798331531539

ISBN: (纸本)9798331531546

Processing-in-Memory (PiM) is one of the rapidly growing paradigms in this world of computer architecture, wherein the data is processed and stored simultaneously within the memory. This architecture will enable us to perform big data computing that require complex data transitions between memory and ALU with reduced latency and improved throughput. This scheme utilizes the concept of data Level Parallelism (DLP), to perform multiple operations with less number of clock cycle using a single instruction. In this paper, ALU operations such as addition, multiplication and logical operations are proposed using PiM with a single instruction by means of fixed memory locations. All the operations are implemented by the concept of Single Instruction Multiple data (SIMD) techniques. The addition is implemented using the Parallel Adder (PA), while multiplication operations are implemented using the karatsuba multiplier. In this work, parallel addition can be implemented with a frequency of 1.42 GHz within the memory, while parallel multiplications can be implemented at a maximum frequency of 2.31 GHz for $2 \times 2$ bit multiplication by incorporating the karatsuba technique. These implementations consume maximum resources upto 121268 cells of $\text{NAND}2 \times 1$ units when inferred in the form of logic cells. Post-layout implementation resulted in an area of area of $83.25 \ \mu m^{2}$ for inferring a 1-bit CAM cell.

关键词： Single instruction multiple data Memory architecture Memory management Computer architecture Machine learning Throughput Information and communication technology Logic Cryptography Clocks

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Cross-course Process Mining of Student Clickstream data - Aggregation and Group Comparison

Cross-course Process Mining of Student Clickstream Data - Ag...

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ieee conference on Commerce and Enterprise computing, CEC

作者： Tobias Hildebrandt Lars Mehnen Department of Computer Science University of Applied Sciences Technikum Wien Vienna Austria

ISBN: (数字)9798331529093

ISBN: (纸本)9798331529109

This paper introduces novel methods for preparing and analyzing student interaction data extracted from course management systems like Moodle to facilitate process mining, like the creation of graphs that show the process flow. Such graphs can get very complex as Moodle courses can contain hundreds of different activities, which makes it difficult to compare the paths of different student cohorts. Moreover, existing research often confines its focus to individual courses, overlooking potential patterns that may transcend course boundaries. Our research addresses these challenges by implementing an automated dataflow that directly queries data from the Moodle database via SQL, offering the flexibility of filtering on individual courses if needed. In addition to analyzing individual Moodle activities, we explore patterns at an aggregated course section level. Furthermore, we present a method for standardizing section labels across courses, facilitating cross-course analysis to uncover broader usage patterns. Our findings reveal, among other insights, that higherperforming students demonstrate a propensity to engage more frequently with available activities and exhibit more dynamic movement between objects. While these patterns are discernible when analyzing individual course activity-events, they become more pronounced when aggregated to the section level and analyzed across multiple courses.

关键词： Process mining Correlation Navigation Filtering Event detection databases Informatics Business

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Unsupervised Person Re-identification based on YOLOV5 with Mask Region Convolution Neural Network

Unsupervised Person Re-identification based on YOLOV5 with M...

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data Science and Information System (ICDSIS), ieee international conference on

作者： Jianbo Liang Mideth Abisado College of Computing and Information Technologies National University Manila Philippines School of Big Data Engineering Kaili University Kaili China

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

The unsupervised person re-identification is particularly relevant across various fields, including the continued growth of autonomous cars, communication between humans and computer units and human action research. However, the existing research are complex to identify the person accurately and efficiently. This research proposed an You Only Look Once Version 5 (YOLOV5) with mask Region-based Convolution Neural Network (RCNN) for unsupervised person re-identification effectively from the Pascol Voc dataset. It is preprocessed by data augmentation to tackle the overfitting which is preformed based on random rotation, flipping and shearing transformation. Then, these features are extracted through Local Ternary Pattern (LTP). The extracted features are provided into YOLOV5-mask RCNN for the recognition and detection of unsupervised person. The YOLOV5 adopts one neural network to predict bounding box and class possibilities straightly from the whole image in one forward pass. It shortens the detection and enhances the efficiency. The mask RCNN provide accurate pixel-level segmentation for every instance in image. The performance of YOLOV5-mask RCNN is calculated by metrics like Mean Average Precision (mAP), Inference Speed (FPS), precision, accuracy, recall and f1-score. The YOLOV5mask RCNN achieves 85.61% mAP, 38.5 Hz FPS, 95.37% precision, 95.68% accuracy, 94.51% recall and 94.29% f1-score when compared to Channel Attention (CA)-MobileNetv2YOLOv 4 and YOLOv2 based pedestrian detection (YOLOV2PD).

关键词： YOLO Image segmentation Accuracy Pedestrians Convolution Neural networks Feature extraction

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