1. Workshop on Smart and Precise Agriculture

URL: https://creds.iitpkd.ac.in/wspa

Organizers:

• Sahely Bhadra, IIT Palakkad, India
• Satyajit Das, IIT Palakkad, India
• Mrinal Das, IIT Palakkad, India
• Deepak Jaiswal, Postdoctoral Fellow at UIUC

Contact: creds@iitpkd.ac.in, mrinal@iitpkd.ac.in
Summary of the Workshop: Objective of the workshop is to inform, encourage, and showcase the important and emerging area of smart agriculture which can have a significant impact in the economy and life of a huge population. More importantly, the workshop aims to draw the attention of researchers in the area where there are novel and hard problems in the intersection of machine learning, edge devices, sensor devices, internet technologies and agriculture.

Accepted Papers:

• Identification of harvesting year of barley seeds using near-infrared hyperspectral imaging combined with convolutional neural network.
  Tarandeep Singh, Neerja Mittal Garg and S.R.S. Iyengar
• Plant Leaf Disease Segmentation using Compressed UNet Architecture
  Mohit Agarwal, Suneet Kr. Gupta and K.K. Biswas

2. MLMEIN – Workshop on Machine Learning for MEasurement INformatics

URL: http://www.ar.sanken.osaka-u.ac.jp/MLMEIN/2021/

Organizers:

• Takashi Washio,The Institute of Scientific and Industrial Research, Osaka University

Contact: washio@ar.sanken.osaka-u.ac.jp
Summary of the Workshop: The needs of advanced measurement technologies are now rapidly growing in scientific analyses, industrial productions and social services in our IoT society. The recent progress of machine learning is expected to enhance the performances of these measurement. The required studies are not only the application of the state-of-the-art machine learning but also theoretical extensions of their basic principles to process the complex and noisy outcomes of the sensing devices and instruments. This workshop aims to establish a new research field named “Machine Learning for MEasurement INformatics (MLMEIN),” and calls for papers on such innovative work on the machine learning, pattern recognition, signal processing and statistical techniques. The topics of interest include, but are not limited to the techniques including measurement optimization, robust measurement, complex measurement, large scale measurement, online and real time measurements, active measurement, edge computing implementation and their applications in various measurement problems.

Accepted Papers:

• Hierarchical Topic Model for Tensor Data and Extraction of Weekly and Daily Patterns from Activity Monitor Records
  Shunichi Nomura, Michiko Watanabe and Yuko Oguma
• Convolutional Neural Network to Detect Deep Low-Frequency Tremors from Seismic Waveform Images
  Ryosuke Kaneko, Hiromichi Nagao, Shin-ichi Ito, Kazushige Obara and Hiroshi Tsuruoka
• Unsupervised Noise Reduction for Nanochannel Measurement using Noise2Noise Deep Learning
  Takayuki Takaai and Makusu Tsutsui
• Classification Bandits: Classification Using Expected Rewards as Imperfect Discriminators
  Koji Tabata, Atsuyoshi Nakamura and Tamiki Komatsuzaki

3. LDRC'21 - The 3th Workshop on Learning Representation

URL: https://sites.google.com/view/pakdd-workshop-ldrc21/home

Organizers:

• Lazhar Labiod, University of Paris Descartes, France
• Mohamed Nadif, University of Paris Descartes, France
• Allou Same, IFSTTAR, France

Contact: lazhar.labiod@parisdescartes.fr, mohamed.nadif@parisdescartes.fr, allou-badara.same@ifsttar.fr
Summary of the Workshop: This workshop aims at discovering the recent advanced on data representation for clustering under different approaches. Thereby, the LDRC workshop is an opportunity to:
(1) present the recent advances in data representation based clustering algorithms,
(2) outline potential applications that could inspire new data representation approaches for clustering,
(3) explore benchmark data to better evaluate and study data representation based clustering models.

4. Second Pacific Asia Workshop on Game Intelligence & Informatics (GII)

URL: https://web.northeastern.edu/guii/gii2021/

Organizers:

• Koyel Mukherjee, Games24x7, India
• Sabbir Ahmad, Northeastern University
• Magy Seif El-Nasr, University of California at Santa Cruz, USA
• Tridib Mukherjee, Games24x7, India

Contact: koyel.mukherjee@games24x7.com, ahmad.sab@northeastern.edu
Summary of the Workshop: The Game Intelligence and Informatics (GII) workshop aims to engage the larger data mining and AI community with discussions around the state of the art in machine learning and AI innovations for digital game data science, game user research, and game design. We seek to highlight the unique challenges that lie in this domain and its niche subsets, and show how knowledge discovery and intelligent data analytics can help advance this domain. This workshop not only targets AI advances in gameplay, but also, more importantly, how AI and ML help provide predictive and prescriptive insights about game users that can thereby enable a personalized and adaptive player journey.

5. The First Workshop & Shared Task on Scope Detection of the Peer Review Articles

URL: https://sdpra-2021.github.io/website/

Organizers:

• Saichethan Miriyala Reddy, Indian Institute of Information Technology, Bhagalpur
• Naveen Saini, University of Toulouse

Contact: sdpra2021@gmail.com, miriyala.cse.1725@iiitbh.ac.in
Summary of the Workshop: For years, peer review has been the formal part of scientific communication that validates a scientific research article’s quality. A particular research article goes through discrete filtering steps to get published in a reputed journal or conference. The first step in the peer review process is the editor’s initial screening(s). The editor’s job, who is also an expert in the particular field, decides whether an article should be rejected without further review or forwarded to expert reviewers for meticulous evaluation. Acceptance of paper depends heavily on the reviewers. It’s becoming more common for people to share their reviews on social media, especially when reviewers reject their work on spurious grounds. To demystify and improve such an obscure process, we propose, the 1st Workshop & Shared task on Scope Detection of the Peer Review Articles, to address these gaps. We seek to reach the broader NLP and AI/ML community to pool the distributed efforts to improve peer review of the scholarly documents and build downstream applications. SDPRA 2021 will comprise a research track and a Shared Task.

Accepted Papers:

• Overview and Insights from Scope Detection of the Peer Review Articles Shared Tasks 2021
  Saichethan Miriyala Reddy and Naveen Saini
• Scholarly Text Classification with Sentence BERT and Entity Embeddings
  Guangyuan Piao
• Domain Identification of Scientific Articles using Transfer Learning and Ensembles
  Adeep Hande, Karthik Puranik, Ruba Priyadharshini and Bharathi Raja Chakravarthi
• Identifying Topics of Scientific Articles with BERT-based Approaches and Topic Modeling
  Anna Glazkova
• Using Transformer based Ensemble Learning to classify Scientific Articles
  Sohom Ghosh and Ankush Chopra

6. Data Assessment and Readiness for Artificial Intelligence

URL: http://data-readiness-2021.mybluemix.net/

Organizers:

• Bortik Bandyopadhyay, Apple Inc.
• Sambaran Bandyopadhyay, IBM Research AI
• Srikanta Bedathur, Indian Institute of Technology, Delhi
• Nitin Gupta, IBM Research AI
• Sameep Mehta, IBM Research AI
• Shashank Mujumdar, IBM Research AI
• Srinivasan Parthasarathy, The Ohio State University
• Hima Patel, IBM Research AI

Contact: sambband@in.ibm.com
Summary of the Workshop: The goal of this workshop will be to get researchers working in the fields of data acquisition, data labeling, data quality, data preparation and AutoML areas to understand how the data issues, their detection and remediation will help towards building better models. This workshop invites researchers from academia and industry to submit novel propositions for systematically identifying and mitigating data issues for making it AI ready. Methods of data assessment can change depending on the modality of the data. This workshop will invite submissions for data readiness for different modalities: structured (or tabular) data, unstructured (such as text) data, graph structured (relational, network) data, time series data, etc. We would like to explore state-of-the-art deep learning and AI concepts such as deep reinforcement learning, graph neural networks, self-supervised learning, capsule networks and adversarial learning to address the problems of data assessment and readiness.

Accepted Papers:

• 1st International Workshop on Data Assessment and Readiness for AI
  Bortik Bandyopadhyay, Sambaran Bandyopadhyay, Srikanta Bedathur, Nitin Gupta, Sameep Mehta, Shashank Mujumdar, Srinivasan Parthasarathy and Hima Patel
• Cooperative Monitoring of Malicious Activity in Stock Exchanges
  Bhavya Kalra, Sai Krishna Munnangi, Kushal Majmundar, Naresh Manwani and Praveen Paruchuri
• Data-Debugging through Interactive Visual Explanations .
  Shazia Afzal, Arunima Chaudhary, Nitin Gupta, Hima Patel, Carolina Spina and Dakuo Wang
• Data Augmentation for Fairness in Personal Knowledge Base Population
  Lingraj S Vannur, Balaji Ganesan, Lokesh Nagalapatti, Hima Patel and Thippeswamy MN

7. MICMED - Workshop on Machine Intelligence Coinciding with Data Mining Applications in Biology and Medicine

URL: https://sites.google.com/kau.edu.sa/micmed-pakdd2021/

Organizers:

• Turki Turki, King Abdulaziz University, Jeddah, Saudi Arabia
• Y-h. Taguchi, Chuo University, Tokyo, Japan

Contact: tturki@kau.edu.sa; tag@granular.com
Summary of the Workshop: Data mining applications in biology and medicine rely on various machine intelligence methodologies for the analysis and interpretation of results. Examples of applications include detecting COVID-19 from chest X-ray images using deep learning, ranking safety of COVID-19 drugs of patients with cancer and chronic diseases using machine learning, identifying important genes in the underlying mechanism of COVID-19 and other neurological diseases such as Alzheimer’s disease using tensor decomposition-based unsupervised feature extraction, finding drugs for infectious disease such as COVID-19 (and Ebola) using unsupervised learning, and identifying cell types of single-cell data using unsupervised learning. The success of such applications depends on the performance, which is attributed to the used machine intelligence methodology.

8. Artificial Intelligence for Enterprise Process Transformation (AI4EPT)

URL: https://ai4ept-pakdd2021.mybluemix.net/

Organizers:

• Monika Gupta, Research Scientist, IBM Research
• Kushal Mukherjee, Research Scientist, IBM Research
• Shrihari Vasudevan, Principal Data Scientist, Ericsson
• Prerna Agarwal, Staff Research Engineer, IBM Research
• Rakesh R Pimplikar, Senior Research Engineer, IBM Research
• Sampath Dechu, Research Manager, IBM Research

Contact: mongup20@in.ibm.com, kushmukh@in.ibm.com
Summary of the Workshop: Enterprises continue to move towards automating business processes as they seek to be more cost-effective, reliable, and scalable. Technology-infused services enable them to reduce direct and indirect costs while also reducing human errors and improving productivity. In recent times, Business Process Automation has become an effective way to achieve these goals. The latest trends in automating business processes center around process digitization, process standardization, data integrity checks, workflow automation including robotic process automation, and generating actionable reports, views & interfaces to empower the end-user. The next wave of technology advancements will transform business processes by leveraging recent advances in AI, including Analytics/Machine Learning (ML), Optimization methods, and Automation. The workshop will focus on the theme of advanced AI (including analytics, automation, and optimization) for the transformation of core enterprise processes as well as for other processes, such as Information Technology (IT) processes and software development processes. Both research (theoretical or applied) and real-world application papers are welcome.

Accepted Papers:

• ROC Bot: Towards Designing Virtual Command Centre for Energy Management
  Amit Sangroya, Mrinal Rawat, Afaque Mohammed and Rishi Tiwari
• Digitize–PID: Automatic Digitization of Piping and Instrumentation Diagrams
  Shubham Singh Paliwal, Arushi Jain, Monika Sharma and Lovekesh Vig