Portugal

Big Data, Data Science and Security are being integrated to solve many of the security and privacy challenges. For example, machine learning techniques are being applied to solve security problems such as malware analysis and insider threat detection. However, there is also a major concern that the machine learning techniques themselves could be attacked. Therefore, the machine learning techniques are being adapted to handle adversarial attacks. This area is known as adversarial machine learning. In addition, privacy of the individuals is also being violated through these machine learning techniques as it is now possible to gather and analyze vast amounts of data and therefore privacy enhanced data science techniques are being developed.To assess the developments on the integration of Big Data, Data Science and Security over the past decade and apply them to the Internet of Transportation, the presentation will focus on four aspects. First it will examine the developments on applying Data Science techniques for detecting cyber security problems such as insider threat detection as well as the advances in adversarial machine learning. Some developments on privacy aware and policy-based data management frameworks will also be discussed. Next it will discuss how cloud technologies may be used to securely and privately share the information for various Big Data applications such as the Internet of Things. Finally, it will describe ways in which Big Data, Data Science and Security could be incorporated into these applications.

IC3K 2021

Integrating Big Data, Data Science and Cyber Security with Applications in Cloud and Internet of Things

Current developments show that soon the wide availability of low cost, short-range and long-range radio technology, along with advances in wireless networking, will enable wireless adhoc sensor networks to become commonly deployed. In these networks, each node may be equipped with a variety of sensors, such as acoustic, seismic, infrared, motion, biomedical and chemical sensors with a higher level of information to enable inference associated with identification, embedded signal processing and networking. This conference intends to be the meeting point of researchers and practitioners to share experiences and ideas on innovative developments in any aspect of sensor networks, including Hardware of Sensor Networks, Wireless Communication Protocols, Sensor Networks Software and Architectures, Wireless Information Networks, Data Manipulation, Signal Processing, Localization and Object Tracking through Sensor Networks, Machine Learning in sensor networks leading to resource optimizations and many Applications.
CONFERENCE AREAS<br><br>
1 . Sensor Networks and Architectures<br>
2 . Intelligent Data Analysis and Processing for Sensor Networks<br>
3 . Sensor Networks Applications

SENSORNETS 2022

This conference intends to be the meeting point of researchers and practitioners to share experiences and ideas on innovative developments in any aspect of sensor networks, including Hardware of Sensor Networks, Wireless Communication Protocols, Sensor Networks Software and Architectures, Wireless Information Networks, Data Manipulation, Signal Processing, Localization and Object Tracking through Sensor Networks, Machine Learning in sensor networks leading to resource optimizations and many Applications.

keynote

The purpose of the IC3K is to bring together researchers, engineers and practitioners on the areas of Knowledge Discovery, Knowledge Engineering and Knowledge Management.

There is growing interest in large interdisciplinary data portals to support a wide range of research and research communities. In this talk, we will introduce emerging best practices in building large data and knowledge portals that aim to be long lived. We will provide examples in multiple disciplines of semantic knowledge portals that are collaboratively designed and maintained by interdisciplinary teams.

Designing Knowledge Enabled Systems for Interdisciplinary Settings

A data-driven computing paradigm has risen to rival the previously dominant knowledge-driven paradigm. As modern society becomes ever more digitalized, all kinds of human activities are increasingly mediated by a mix of data-driven and knowledge-driven software. The wondrous technical capabilities of software systems are evoking utopian dreams as well as dystopian nightmares. Users, regulators, and the general public are calling for responsible AI and transparency and accountability from software professionals.In software engineering, requirement analysis techniques have served to mediate between human needs and wants on one hand, and technical system design on the other. This talk will outline the role of social modeling in requirements engineering, as illustrated by the i* framework. In i*, actors depend on each other to achieve what they want. By applying i* modeling, one can explore the potential impacts of various system designs on stakeholders, in search of design options that would better meet the desires and aspirations of all concerned.The widespread adoption of data-driven computing in today’s highly digitalized world suggests that requirements analysis needs to engage with the full cognitive cycle – the cycle by which actors sense and interpret data, generate knowledge, execute actions, and in turn producing new data. Various software technologies, data-driven and knowledge-driven, enhance and complement specific aspects of the cognitive cycles of humans and organizations, often bringing about spectacular gains as well as vulnerabilities. Social modeling encompassing the full cognitive cycle can potentially help system designers and stakeholders jointly explore design options in order to derive the most benefits while avoiding negative consequences when developing today’s advanced software applications.

Leveraging Knowledge from Humans and from Data: Why Requirements Engineering (Still) Matters

The evaluation of algorithms has been debated in computer science for decades. Recent years have seen an increasing use of different kinds of evaluations based on empirical research methods and sample data. Various applied areas of computing struggle with different concerns of validity, presumably because an overarching theoretical framework has been missing. In this keynote, we introduce a framework for researching algorithms that highlights several validity concerns. We discuss, in turn, research strategies to address these concerns. Our framework can be useful for any type of applied research in computer science that builds on the evaluation of algorithms.

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