WORKSHOPS

Monday, 10th September 2018

PAP 2018: 2nd International Workshop on Personal Analytics and Privacy

Abstract:
The purpose of this workshop is to encourage principled research that will lead to the advancement of personal data analytics, personal services development, privacy, data protection and privacy risk assessment. The workshop will seek top-quality submissions addressing important issues related to personal analytics, personal data mining and privacy in the context where real individual data (spatio-temporal data, call details records, tweets, mobility data, social networking data, etc.) are used for developing a data-driven service, for realizing a social study aimed at understanding nowadays society, and for publication purposes. Papers can present research results in any of the themes of interest for the workshop as well as application experiences, tools and promising preliminary ideas. However, papers dealing with synergistic approaches that integrate privacy requirements and protection in personal data analytics are especially welcome.

Workshop web page

 

Machine Learning and Data Mining for Sports Analytics (MLSA)

Abstract:
Sports Analytics has been a steadily growing and rapidly evolving area over the last decade, both in US professional sports leagues and in European football leagues. The recent implementation of strict financial fair-play regulations in European football will definitely increase the importance of Sports Analytics in the coming years. In addition, there is the popularity of sports betting. The majority of techniques used in the field so far are statistical but there has been growing interest in the Machine Learning and Data Mining community in past years. We believe that the workshop offers a great opportunity to bring people from outside of the Machine Learning community into contact with typical ECML/PKDD contributors as well as to highlight what the community has done and can do in the field of Sports Analytics.

Workshop web page

 

New Frontiers in Mining Complex Patterns

Abstract:
Modern automatic systems are able to collect huge volumes of data often with a complex structure. The massive and complex data pose new challenges for current research in Knowledge Discovery and Data Mining. They require new methods for storing, managing and analyzing them by taking into account various complexity aspects: Complex structures (e.g. multi-relational, time series and sequences, networks, and trees) as input/output of the data mining process; Massive amounts of high dimensional data collections flooding as high-speed streams and requiring (near) real time processing and model adaptation to concept drifts; New application scenarios involving security issues, interaction with other entities and real-time response to events triggered by sensors. The purpose of the workshop is to bring together researchers and practitioners of data mining and machine learning interested in analysis of complex and massive data sources such as blogs, event or log data, medical data, spatio-temporal data, social networks, mobility data, sensor data and streams.

Organizers:
Annalisa Appice, University of Bari Aldo Moro, Bari, Italy
Michelangelo Ceci, University of Bari Aldo Moro, Bari, Italy
Corrado Loglisci, University of Bari Aldo Moro, Bari, Italy

Workshop web page

 

DARE: Data Analytics for Renewable Energy Integration

Abstract:
A lot of attention has been devoted to the development of technologies for generating renewable energy. However, of equal importance is the integration of this energy into existing distribution and transmission systems. The relevant challenges are numerous and include the prediction of supply and demand, fault detection, stability assessment and even social media analytics to determine attitudes towards and penetration of renewable energy. All of these rely heavily on the development and appropriate use of techniques and algorithms for handling large quantities of data. Data Analytics is the science that encompasses machine learning (including deep learning), and big data, focusing on cleaning, transforming, modeling and extracting actionable information from large, complex data sets. The focus of this workshop is on the use of various data analytics techniques in the different areas of renewable energy integration, and to provide researchers working in the intersection of these two areas with a forum to present and discuss their findings and ideas.

Workshop web page

 

Interactive Adaptive Learning

Abstract:
The workshop aims at discussing techniques and approaches for optimising the whole learning process, including the interaction with human supervisors, processing systems, and includes adaptive, active, semi-supervised, and transfer learning techniques, and combinations thereof in interactive and adaptive machine learning systems. Our objective is to bridge the communities researching and developing these techniques and systems in machine learning and data mining. Therefore, we welcome contributions that present a novel problem setting, propose a novel approach, or report experience with the practical deployment of such a system and raise unsolved questions to the research community.

Workshop web page

 

KnowME

 

Combined Workshops with Tutorials

Learning with Imbalanced Domains: Theory and Applications

Abstract:
Many real-world data-mining applications involve obtaining and evaluating predictive models using data sets with strongly imbalanced distributions of the target variable. Frequently, the least-common values are associated with events that are highly relevant for end users. This problem has been thoroughly studied in the last decade with a specific focus on classification tasks. However, the research community has started to address this problem within other contexts. It is now recognized that imbalanced domains are a broader and important problem posing relevant challenges for both supervised and unsupervised learning tasks, in an increasing number of real world applications. This workshop+tutorial proposal is focused on providing a significant contribution to the problem of learning with imbalanced domains, and to increasing the interest and the contributions to solving some of its challenges.

Workshop and tutorial web page

 

IoT Large Scale Machine Learning from Data Streams

Abstracts:
Nowadays, the volume of data is rapidly increasing in the form of data streams. Learning from this ever-growing amount of data requires flexible learning models that self-adapt over time. Traditional one shot memory based learning methods trained offline from a static historic data cannot cope with evolving data streams. This workshop and tutorial aim at presenting new research advances related to data streams processing and applications.

Workshop and tutorial web page

 

 

Friday, 14th September 2018

Artificial Intelligence in Security

Abstract:
Artificial intelligence, and in particular machine learning, have an important role to play in improving the resilience of society to both digital and physical threats, and in safeguarding the privacy of individuals, as the world becomes increasingly interconnected. The IWAISe workshop is a forum where academics, researchers, and industry practitioners can meet, present and discuss the latest advances in AI and machine learning for the security domain. IWAISe solicits novel research, case studies and survey papers as well as system demonstrations in the following domains: Security Analytics; Forensics, Images, Text & Networks; Security in Finance; Physical Security; Digital Security; Software Engineering for Security; Robotics; and Privacy and Identity.

Workshop web page

 

Data Science for Human Capital Management

Abstract:
Human Capital Management (HCM) refers to the set of practices and systems that facilitate talent acquisition and management. It encompasses the areas of talent and labor market analytics, job advertising and distribution, professional social networks, candidate sourcing, tracking, onboarding, benefits administration and compliance. Robust job creation, a skilled population and engaged employees are important socioeconomic elements for the economic success and social welfare of communities. For stable labor markets, it is important to match employers with the right candidates, provide opportunities for reskilling of the labor force, and ensure that the (post-hire) workforce is engaged and productive. This workshop will focus on application of data science techniques to problems in the HCM space, an area which traditionally has not received much attention from the AI/Machine Learning/Data Science communities.

Workshop web page

 

Advanced Analytics and Learning on Temporal Data

Abstract:
Temporal data are frequently encountered in a wide range of domains such as bio-informatics, medicine, finance and engineering, among many others. They are naturally present in applications covering language, motion and vision analysis, or more emerging ones as energy efficient building, smart cities, dynamic social media or sensor networks. Contrary to static data, temporal data are of complex nature, they are generally noisy, of high dimensionality, they may be non stationary (i.e. first order statistics vary with time) and irregular (involving several time granularities), they may have several invariant domain-dependent factors as time delay, translation, scale or tendency effects. The aim of this workshop is to bring together researchers and experts in machine learning, data mining, pattern analysis and statistics to share their challenging issues and advance researches on temporal data analysis. Analysis and learning from temporal data cover a wide scope of tasks including learning metrics, learning representations, unsupervised feature extraction, clustering and classification.

Workshop web page

 

SoGood 2018, The Third Workshop on Data Science for Social Good

Abstract:
The aim of the workshop is to present applications of Data Science to Social Good, or else that take into account social aspects of Data Science methods and techniques. All application domains are considered. Another goal is to consider possibilities for setting up an European structure to coordinate and propose actions in this area.

Workshop web page

 

Urban Reasoning from Complex Challenges in Cities

 

Green Data Mining, International Workshop on Energy Efficient Data Mining and Knowledge Discovery

Abstract:
This workshop aims to bring together people from many different fields in machine learning and data mining that have a common interest in energy efficiency, energy aware computing, hardware, and embedded systems. These fields include, but are not limited to: deep learning, big data, Internet of Things (IoT), large-scale computing, stream mining, and distributed machine learning. The goal is to provide a venue for researchers to present their work, exchange ideas, and discuss challenges related to machine learning, hardware, and energy efficiency. We accept original work, already completed, or in progress. Position papers are also considered.

Workshop web page

 

The 3rd Workshop on MIning DAta for financial applicationS

Abstract:
Like the famous King Midas, popularly remembered in Greek mythology for his ability to turn everything he touched with his hand into gold, we believe that the wealth of data generated by modern technologies, with widespread presence of computers, users and media connected by Internet, is a goldmine for tackling a variety of problems in the financial domain. The MIDAS workshop is aimed at discussing challenges, potentialities, and applications of leveraging data-mining tasks to tackle problems in the financial domain. The workshop provides a premier forum for sharing findings, knowledge, insights, experience and lessons learned from mining data generated in various application domains.

Workshop web page

 

Decentralized Machine Learning on the Edge

Abstract:
Many of today’s parallel machine learning algorithms were developed for tightly coupled systems like computing clusters or clouds. However, the volumes of data generated from machine-to-machine interaction, by mobile phones or autonomous vehicles, surpass the amount of data that can be realistically centralized. Thus, traditional cloud computing approaches are rendered infeasible. To scale parallel machine learning to such volumes of data, computation needs to be pushed towards the edge, that is, towards the data generating devices. By learning models directly on the data sources – which often have computational power of their own, for example, mobile phones, smart sensors, and tablets – network communication can be reduced by orders of magnitude. Moreover, it enables training a central model without centralizing privacy-sensitive data. This workshop aims to foster discussion, discovery, and dissemination of novel ideas and approaches for decentralized machine learning.

Workshop web page

 

Combined Workshops with Tutorials

Nemesis'18: Recent advances in adversarial machine learning

Abstract:
Adversarial attacks of Machine Learning systems have become an undisputable threat. Attackers can compromise the training of Machine Learning models by injecting malicious data into the training set (so-called poisoning attacks), or by crafting adversarial samples that exploit the blind spots of Machine Learning models at test time (so-called evasion attacks). Adversarial attacks have been demonstrated in a number of different application domains, including malware detection, spam filtering, visual recognition, speech-to-text conversion, and natural language understanding. Devising comprehensive defences against poisoning and evasion attacks by adaptive adversaries remains an open challenge. The Nemesis’18 tutorial and workshop aims to bring together researchers and practitioners to discuss recent advances in the rapidly evolving field of Adversarial Machine Learning and exchange ideas for comprehensive defenses of real-world Machine Learning applications.

Workshop and Tutorial web page