KDIR: International Conference on Knowledge Discovery and Information Retrieval

Present CFP : 2019
11th International Conference on Knowledge Discovery and Information Retrieval KDIR 

website: http://www.kdir.ic3k.org/ 

September 17 – 19, 2019 Vienna, Austria 

Proceedings will be submitted for indexation by: DBLP, Thomson Reuters, EI, SCOPUS, Semantic Scholar and Google Scholar. 


Regular Paper Submission: April 29, 2019 
Authors Notification (regular papers): June 28, 2019 
Final Regular Paper Submission and Registration: July 12, 2019 

Knowledge Discovery is an interdisciplinary area focusing upon methodologies for identifying valid, novel, potentially useful and meaningful patterns from data, often based on underlying large data sets. A major aspect of Knowledge Discovery is data mining, i.e. applying data analysis and discovery algorithms that produce a particular enumeration of patterns (or models) over the data. Knowledge Discovery also includes the evaluation of patterns and identification of which add to knowledge. This has proven to be a promising approach for enhancing the intelligence of software systems and services. The ongoing rapid growth of online data due to the Internet and the widespread use of large databases have created an important need for knowledge discovery methodologies. The challenge of extracting knowledge from data draws upon research in a large number of disciplines including statistics, databases, pattern recognition, machine learning, data visualization, optimization, and high-performance computing, to deliver advanced business intelligence and web discovery solutions. Information retrieval (IR) is concerned with gathering relevant information from unstructured and semantically fuzzy data in texts and other media, searching for information within documents and for metadata about documents, as well as searching relational databases and the Web. Automation of information retrieval enables the reduction of what has been called “information overload”. Information retrieval can be combined with knowledge discovery to create software tools that empower users of decision support systems to better understand and use the knowledge underlying large data sets. The primary focus of KDIR is to provide a major forum for the scientific and technical advancement of knowledge discovery and information retrieval. 

Conference Topics: 
Area 1: KDIR – International Conference on Knowledge Discovery and Information Retrieval 
– BioInformatics & Pattern Discovery 
– Foundations of Knowledge Discovery in Databases 
– Information Extraction 
– Interactive and Online Data Mining 
– Machine Learning 
– Mining Multimedia Data 
– Mining Text and Semi-structured Data 
– Pre-processing and Post-processing for Data Mining 
– Process Mining 
– Software Development 
– Structured Data Analysis and Statistical Methods 
– Business Intelligence Applications 
– User Profiling and Recommender Systems 
– Visual Data Mining and Data Visualization 
– Web Mining 
– Clustering and Classification Methods 
– Collaborative Filtering 
– Concept Mining 
– Context Discovery 
– Data Analytics 
– Data Mining in Electronic Commerce 
– Data Reduction and Quality Assessment 

Joaquim Filipe, Polytechnic Institute of Setúbal / INSTICC, Portugal 

Ana Fred, Instituto de Telecomunicações and Instituto Superior Técnico – Lisbon University, Portugal 


KDIR Secretariat 
Address: Avenida de São Francisco Xavier Lote 7L Cave 
Tel: +351 265 520 184 
Fax: +351 265 520 186 
Web: http://www.kdir.ic3k.org/ 
e-mail: kdir.secretariat@insticc.org


Present CFP : 2019

KDD 2019 Call for Research Papers 

Details in https://www.kdd.org/kdd2019/calls/view/kdd-2019-call-for-research-papers 
Website for submissions: https://easychair.org/conferences/?conf=kdd2019 . 

Key dates 
Submission: February 3, 2019 
Notification: Apr 28, 2019 
Camera-ready: May 17, 2019 
Short Promotional Video (Required): June 2, 2019 
Conference (Anchorage, Alaska): August 4 to August 8, 2019 

We invite submission of papers describing innovative research on all aspects of knowledge discovery and data mining, ranging from theoretical foundations to novel models and algorithms for data mining problems in science, business, medicine, and engineering. Visionary papers on new and emerging topics are also welcome, as are application-oriented papers that make innovative technical contributions to research. Authors are explicitly discouraged from submitting incremental results that do not provide major advances over existing approaches 

All deadlines are at 11:59PM Alofi Time. There will be absolutely no exception to these deadlines. 

Topics of interest include, but are not limited to: 

Big Data: Large-scale systems for text and graph analysis, machine learning, optimization, parallel and distributed data mining (cloud, map-reduce), novel algorithmic and statistical techniques for big data. 
Data Science: Methods for analyzing scientific and business data, social networks, time series; mining sequences, streams, text, web, graphs, rules, patterns, logs data, spatio-temporal data, biological data; recommender systems, computational advertising, multimedia, finance, bioinformatics. 
Foundations: Models and algorithms, asymptotic analysis; model selection, dimensionality reduction, relational/structured learning, matrix and tensor methods, probabilistic and statistical methods; deep learning; manifold learning, classification, clustering, regression, semi-supervised and unsupervised learning; personalization, security and privacy, visualization. 

Submission Directions 
KDD is a dual track conference hosting both a Research track and an Applied Data Science track. Due to the large number of submissions, papers submitted to the Research track will not be considered for publication in the Applied Data Science track and vice versa. Authors are encouraged to read the track descriptions carefully and to choose an appropriate track for their submissions. Submissions are limited to a total of nine (9) pages, including all content and references, and must be in PDF format and formatted according to the new Standard ACM Conference Proceedings Template. For LaTeX users: unzip acmart.zip, make, and use sample-sigconf.tex as a template; 

Additional information about formatting and style files is available online at: https://www.acm.org/publications/proceedings-template. 

Papers that do not meet the formatting requirements will be rejected without review. In addition, authors can provide an optional two (2) page supplement at the end of their submitted paper (it needs to be in the same PDF file and start at page 10) focused on reproducibility. This supplement can only be used to include (i) information necessary for reproducing the experimental results, insights, or conclusions reported in the paper (e.g., various algorithmic and model parameters and configurations, hyper-parameter search spaces, details related to dataset filtering and train/test splits, software versions, detailed hardware configuration, etc.), and (ii) any pseudo-code, or proofs that due to space limitations, could not be included in the main nine-page manuscript, but that help in reproducibility (see reproducibility policy below for more details). 

The Research track follows a double-blind review process. Submitted papers must not include author names and affiliations and they must be written in a way so that they do not break the double-blind reviewing process. Papers that violate the double-blind review requirements will be desk rejected. 

Because of the double-blind review process, non-anonymous papers that have been issued as technical reports or similar, in particular in arXiv, either prior to KDD submission or during the review process, cannot be submitted to the research track. 

Website for submissions: https://easychair.org/conferences/?conf=kdd2019 . 

Important Policies 
Submitted papers will be assessed based on their novelty, technical quality, potential impact, insightfulness, depth, clarity, and reproducibility. Authors are strongly encouraged to make their code and data publicly available whenever possible. In addition, authors are strongly encouraged to also report, whenever possible, results for their methods on publicly available datasets. Algorithms and resources used in a paper should be described as completely as possible to allow reproducibility. This includes experimental methodology, empirical evaluations, and results. The authors are encouraged to take advantage of the optional two-page supplement to provide the appropriate information. The reproducibility factor will play an important role in the assessment of each submission. 

Important Note: To encourage reproducibility of the results presented in KDD, only papers that include this two-page supplement with the necessary reproducibility-related information will be considered for the best paper awards. 

Every person named as the author of a paper must have contributed substantially both to the work described in the paper and to the writing of the paper. Every listed author must take responsibility for the entire content of a paper. Persons who do not meet these requirements may be acknowledged, but should not be listed as authors. Post-submission changes to the author list are not allowed. 

Dual Submissions 
No dual submissions are allowed. Submitted papers must describe work that is substantively different from work that has already been published, or accepted for publication, or submitted in parallel to other conferences or journals. 

Conflicts of Interest 
During the submission process, enter the email domains of all institutions with which you have an institutional conflict of interest. You have an institutional conflict of interest if you are currently employed or have been employed at this institution in the past three years, or you have extensively collaborated with this institution within the past three years. Authors are also required to identify all PC/SPC members with whom they have a conflict of interest, e.g., advisor, student, colleague, or coauthor in the last five years. 

Additional information about ACM’s Conflict of Interest policy, which KDD follows, can be found at https://www.acm.org/publications/policies/conflict-of-interest. 

Retraction Policy 
KDD follows ACM’s policies, which are described at https://www.acm.org/publications/policies/retraction-policy . 

For each accepted paper, at least one author must attend the conference and present the paper. Authors of all accepted papers must prepare a final version for publication, a poster, and a three-minute short video presentation (details will be in the acceptance notification). 

Accepted papers will be published in the conference proceedings by ACM and also appear in the ACM Digital Library. The rights retained by authors who transfer copyright to ACM can be found here. 

AUTHORS TAKE NOTE: The official publication date is the date the proceedings are made available in the ACM Digital Library. This date for KDD 2019 is on or after July 11, 2019. The official publication date affects the deadline for any patent filings related to published work. 

Contact Information 
Email: research2019@kdd.org 

George Karypis and Evimaria Terzi 

Research Track PC co-Chairs of KDD-2019 

ICMLA 2019 : 18th IEEE International Conference on Machine Learning and Applications

Present CFP : 2019
ICMLA 2019 aims to bring together researchers and practitioners to present their latest achievements and innovations in the area of machine learning (ML). 

The conference provides a leading international forum for the dissemination of original research in machine learning, with emphasis on applications as well as novel algorithms and systems. 

Following the success of previous ICMLA conferences, the conference aims to attract researchers and application developers from a wide range of ML related areas, and the recent emergence of Big Data processing brings an urgent need for machine learning to address these new challenges. The conference will cover both machine learning theoretical research and its applications. 

Contributions describing machine learning techniques applied to real-world problems and interdisciplinary research involving machine learning, in fields like medicine, biology, industry, manufacturing, security, education, virtual environments, games, are especially encouraged. 

Contributions describing applications of machine learning (ML) techniques to real-world problems, interdisciplinary research involving machine learning, experimental and/or theoretical studies yielding new insights into the design of ML systems, and papers describing development of new analytical frameworks that advance practical machine learning methods are especially encouraged. 

The technical program will consist of, but is not limited to, the following topics of interest: 

statistical learning 
neural network learning 
learning through fuzzy logic 
learning through evolution (evolutionary algorithms) 
reinforcement learning 
multi-strategy learning 
cooperative learning 
planning and learning 
multi-agent learning 
online and incremental learning 
scalability of learning algorithms 
inductive learning 
inductive logic programming 
Bayesian networks 
support vector machines 
case-based reasoning 
machine learning for bioinformatics and computational biology 
multi-lingual knowledge acquisition and representation 
grammatical inference 
knowledge acquisition and learning 
knowledge discovery in databases 
knowledge intensive learning 
knowledge representation and reasoning 
machine learning and information retrieval 
machine learning for web navigation and mining 
learning through mobile data mining 
text and multimedia mining through machine learning 
distributed and parallel learning algorithms and applications 
feature extraction and classification 
theories and models for plausible reasoning 
computational learning theory 
cognitive modeling 
hybrid learning algorithms 

Applications of machine learning in: 

medicine, health, bioinformatics and systems biology 
industrial and engineering applications 
security applications 
smart cities 
game playing and problem solving 
intelligent virtual environments 
economics, business and forecasting applications, etc. 

The conference will include a number of interesting keynote plenary talks, which will be announced on the conference web site as arrangements are finalized. Previous invited speakers included numerous fellows of IEEE, AMIA, AAAS, AAAI, etc. 

Paper Submission 

High quality papers in all Machine Learning areas are solicited. Papers that present new directions in ML will receive careful reviews. Authors are expected to ensure that their final manuscripts are original and are not appearing in other publications. Paper should be limited to 8 pages and submitted in IEEE format (double column). Papers will be reviewed by the Program Committee on the basis of technical quality, originality, significance and clarity. All submissions will be handled electronically. Accepted papers will be published in the conference proceedings, as a hardcopy. Authors of the accepted papers need to present their papers at the conference. A selected number of accepted papers will be invited for possible inclusion, in an expanded and revised form, in some journal special issues. 

ICMLA’19 Best Paper Award and ICMLA’19 Best Poster Award will be conferred at the conference to the authors of the best research paper and best poster presentation, respectively, based on the reviewers and Programme Committee recommendations. 

VISAPP 2020 : 15th International Conference on Computer Vision Theory and Applications

Present CFP : 2020
15th International Conference on Computer Vision Theory and Applications VISAPP 

website: http://www.visapp.visigrapp.org/ 

February 27 – 29, 2020 Valletta, Malta 

Proceedings will be submitted for indexation by: DBLP, Thomson Reuters, EI, SCOPUS, Semantic Scholar, Google Scholar, Microsoft Academic and DBLP. 


Regular Paper Submission: October 4, 2019 
Authors Notification (regular papers): December 3, 2019 
Final Regular Paper Submission and Registration: December 17, 2019 

Position Paper Submission: November 15, 2019 
Authors Notification (position papers): December 20, 2019 
Final Regular Paper Submission and Registration: January 8, 2020 

The International Conference on Computer Vision Theory and Applications aims at becoming a major point of contact between researchers, engineers and practitioners on the area of computer vision application systems. Five simultaneous tracks will be held, covering all different aspects related to computer vision: Image Formation and Preprocessing; Image and Video Analysis and Understanding; Motion, Tracking and Stereo Vision; and Applications and Services. We welcome papers describing original work in any of the areas listed below. Papers describing advanced prototypes, systems, tools and techniques as well as general survey papers indicating future directions are also encouraged. Paper acceptance will be based on quality, relevance to the conference themes and originality. The conference program will include both oral and poster presentations. Special sessions, dedicated to case-studies and commercial presentations, as well as technical tutorials, dedicated to technical/scientific topics, are also envisaged. 

Conference Topics: 
Area 1: Image and Video Formation, Preprocessing and Analysis 
– Image Formation, Acquisition Devices and Sensors 
– Device Calibration, Characterization and Modeling 
– Image Enhancement and Restoration 
– Image and Video Coding and Compression 
– Multimodal and Multi-Sensor Models of Image Formation 
– Image Registration 
– Segmentation and Grouping 
– Early and Biologically-Inspired Vision 
– Color and Texture Analyses 
– Shape Representation and Matching 
– Features Extraction 
– Visual Attention and Image Saliency 

Area 2: Image and Video Understanding 
– Cognitive Models for Interpretation, Integration and Control 
– Computational Photography 
– Deep Learning for Visual Understanding 
– Near Duplicate Image Retrieval 
– Machine Learning Technologies for Vision 
– Face and Expression Recognition 
– Content-Based Indexing, Search, and Retrieval 
– Object and Face Recognition 
– Object Detection and Localization 
– Categorization and Scene Understanding 
– Event and Human Activity Recognition 

Area 3: Motion, Tracking and Stereo Vision 
– Image-Based Modeling and 3D Reconstruction 
– Stereo Vision and Structure from Motion 
– Simultaneous Localization and Mapping 
– Optical Flow and Motion Analyses 
– Tracking and Visual Navigation 
– Video Surveillance and Event Detection 
– Video Stabilization 

Area 4: Mobile and Egocentric Vision for Humans and Robots 
– Understanding from Wearable and Mobile Cameras 
– Gaze Traking and Estimation 
– Mobile and Egocentric Object Detection and Recognition 
– Mobile and Egocentric Action Recognition 
– Mobile and Egocentric Summarization 
– Mobile and Egocentric Behavioural Analysis 
– Egocentric Vision for Learning to Act 
– Anticipation and Forecasting from Egocentric Vision 
– Mobile and Egocentric Localization 
– Egocentric Vision for Interaction Understanding 
– Mobile Vision 
– First Person Vision 
– Vision for Robotics 
– Active Vision 

Area 5: Applications and Services 
– Entertainment Imaging Applications 
– Media Watermarking and Security 
– Multimedia Forensics 
– Assistive Computer Vision 
– Mobile Imaging 
– Imaging and Vision for Cultural Heritage 
– Camera Networks and Vision 
– Document Imaging in Business 
– Medical Image Applications 
– Pervasive Smart Cameras 
– Human and Computer Interaction 
– Digital Photography 

Jose Braz, Escola Superior de Tecnologia de Setúbal, Portugal 
Jose Braz, Escola Superior de Tecnologia de Setúbal, Portugal 

Giovanni Maria Farinella, Università di Catania, Italy 
Petia Radeva, Computer Vision Center, Universitat de Barcelona, Spain 


VISAPP Secretariat 
Address: Avenida de S. Francisco Xavier, Lote 7 Cv. C 
Tel: +351 265 520 185 
Fax: +351 265 520 186 
Web: http://www.visapp.visigrapp.org/ 
e-mail: visapp.secretariat@insticc.org