COMPSAC 2023 Symposium on Data Sciences, Analytics & Technologies (DSAT)
Technical Program
*= in-person presentation in Torino
LogKT: Hybrid Log Anomaly Detection Method for Cloud Data Center*
Xuedong Ou and Jing Liu
DAC-PPYOLOE+: A Lightweight Real-time Detection Model for Early Apple Leaf Pests and Diseases under Complex Background
Bin Liu, Xiaoyu Bai, Xinyue Su, Chenxi Song, Zhuohan Yao, Xing Wei and Haixi Zhang
Context-aware, Composable Anomaly Detection in Large-scale Mobile Networks*
Nguyen Ngoc Nhu Trang and Hong-Linh Truong
Failure Prediction in 2D Document Information Extraction with Calibrated Confidence Scores*
Juhani Kivimäki, Aleksey Lebedev and Jukka Nurminen
A Hybrid Intrusion Detection System Based on Feature Selection and Voting Classifier
Rong Liu, Zemao Chen and Jiayi Liu
Adversarial Human Context Recognition: Evasion Attacks and Defenses
Abdulaziz Alajaji, Kavin Chandrasekaran, Luke Buquicchio, Walter Gerych, Emmanuel Agu and Elke Rundensteiner
Towards course of disease based epidemiological modelling: motivation and computational optimization*
Yu-Heng Wu and Torbjörn E. M. Nordling
Signal Processing Based Method for Real-Time Anomaly Detection in High-Performance Computing
Tanzima Islam, Arunavo Dey, Chase Phelps and Christopher Kelly
ContrastNER: Contrastive-based Prompt Tuning for Few-shot NER*
Amirhossein Layegh, Amir H. Payberah, Ahmet Soylu, Dumitru Roman and Mihhail Matskin
Application Recommendation based on Metagraphs: Combining Behavioral and Published Information
Jinyi Wang, Tong Li and Hongyu Gao
Human Activity Dataset of Top Frequent Elderly Emergencies for Monitoring Applications using Kinect
Raoudha Nouisser, Salma Kammoun Jarraya and Mohamed Hammami
Generating Host-Based Data from Network Traces for Intrusion Detection
Patrick Day, Stefano Iannucci and Ioana Banicescu
Discrepancy Scaling for Fast Unsupervised Anomaly Localization*
Juha Mylläri and Jukka K. Nurminen
Historical Redundant Process Data Recovery based on Genetic Algorithm
Ying-Feng Hsu
Anomaly Localization in Audio via Feature Pyramid Matching*
Jorma Valjakka, Juha Mylläri, Lalli Myllyaho, Juhani Kivimäki and Jukka K. Nurminen
Examining Feasibility and Efficacy of Traditional Stream Clustering Algorithms on Complex Human Activity Recognition Data*
Martin Woo, Farhana Zulkernine and Hanady M. Abdulsalam
GX-HUI: Global Explanations of AI Models based on High-Utility Itemsets*
Davide Napolitano and Luca Cagliero
Improved Deep Embedded K-Means Clustering with Implicit Orthogonal Space Transformation
Xinrui Liu, Wenzheng Liu and Yuxiang Li
An Analysis of Grading Patterns in Undergraduate University Courses
Gary Weiss, Luisa Rosa, Hyun Jeong and Daniel Leeds
Performance Evaluation of Transformer-based NLP Models on Fake News Detection Datasets*
Raveen Narendra Babu, Chung-Horng Lung and Marzia Zaman
PrefixCDD: Effective Online Concept Drift Detection over Event Streams using Prefix Trees
Jesus Huete, Abdulhakim Qahtan and Marwan Hassani
DA-Parser: A Pre-trained Domain-aware Parsing Framework for Heterogeneous Log Analysis*
Shimin Tao, Yilun Liu, Weibin Meng, Jingyu Wang, Yanqing Zhao, Chang Su, Weinan Tian, Min Zhang, Hao Yang and Xun Chen
NP-Free: A Real-Time Normalization-free and Parameter-tuning-free Representation Approach for Open-ended Time Series*
Ming-Chang Lee, Jia-Chun Lin and Volker Stolz
A Real-Time Text Analysis System
Chi Mai Nguyen, Phat Trien Thai, Duy Khang Lam and Van Tuan Nguyen
Spatio-Temporal Agnostic Deep Learning Modeling of Forest Fire Prediction Using Weather Data*
Mutakabbir Abdul Mutakabbir, Chung-Horng Lung, Samuel Ajila, Marzia Zaman, Kshirasagar Naik, Richard Purcell and Srinivas Sampalli
The Data Sciences, Analytics, and Technologies (DSAT) Symposium is an integral part of the overall IEEE COMPSAC conference. DSAT uniquely positions itself as a forum for both researchers and practitioners in Big Data as it relates to data sciences, data analytics, and associated technologies. DSAT invites authors to present recent findings, innovations, theories, experiences, and ideas. Technical contributions accepted by DSAT will likely cover theory, applications, pragmatics, systems and services enabled by the Web, underlying technologies, data science, e-science, and concomitant big data analytics. The goal of DSAT is to deepen the understanding of, fostering innovation in, and sharing of practical applications around Big Data.
Topic areas to include, but not limited to: Applied data science, Anomaly detection, computational intelligence for Big Data analytics, Business analytics, Business informatics, Data information and knowledge, Data mining, Data warehousing, Exploring and visualizing data, Interactive visualization, Modeling and simulation, Privacy, and Security. Consider potential focus with the interconnected relationships of Big Data, Cloud, Fog, Edge Computing, IoT, mobile computing, and pervasive computing.
Some authors will be invited to submit expanded papers for the IEEE Transactions on Big Data journal COMPSAC special issue.
Paper Templates
IEEE Paper templates are available in MS Word 2003 and LaTex. All submissions must use US 8.5×11 letter page format.
Important Dates
Main Conference/Symposium
Main conference/symposium papers due: 15 January 2023Extended to 15 February 2023
Notification: 7 April 2023
Camera-ready and registration due: 7 May 2023 Updated: 18 May 2023
Journal then Conference Submissions
Due date: April 7, 2023
Notifications: April 30, 2023
Workshops, Fast Abstract, SRS Programs
EXTENDED: Workshop papers due: 21 April 2023
UPDATED: Notifications: 7 May 2023
UPDATED: Camera-ready and registration due: Updated: 18 May 2023
Submission Link
Please submit your paper on EasyChair
IEEE Conference Publishing Policies
All submissions must adhere to IEEE Conference Publishing Policies.
IEEE Cross Check
All submission will be screened for plagiarized material through the IEEE Cross Check portal.
DSAT Symposium Chairs
Kathy Grise, IEEE
Email: k.l.grise@ieee.org
Wejdan Althobaiti, Telecom
Email: walthobaiti@stc.com.sa
May Wang, Georgia Institute of Technology
Email: maywang@gatech.edu
Program Committee
Gary Grise, IBM
Mohammad Nikouei, Stevens Institute of Technology
Chetan Yadati, Next gen Analytics
Roberto Saracco, IEEE
Hiroyuki Sato, University of Tokyo
Jon Rokne, University of Calgary
Yu-Wen Chen, New York City College of Technology – City University of New York
Xin Liu, Miovision
Tong Li, Tsinghua University
Di Zhu, Stevens Institute of Technology
Yuanda Zhu, Georgia Institute of Technology
Theofanis Raptis, IIT-CNR
Jeewika Ranaweera
Christos Chrysoulas, Edinburgh Napier University
Hongji Yang, University of Leicester
Partha Ghosh, Sainapse
Ayoub Khan, University of Bisha
Gouri Deshpande, University of Calgary
Jinsong Wu, Universidad de Chile
Saim Ghafoor, Atlantic Technological University
Wenqi Shi, Georgia Institute of Technology
Wo Chang, NIST
Zhiyuan Yu, Shandong University
Soheil Sarmadi, Capgemini
Suresh Peddoju Kumar, Florida International University
Duygu Celik, Eastern Mediterranean University
