Prof. David Camacho
Title: How AI can help us to counter against Fake News, misinformation, and other information disorders
David Camacho is a full professor at Universidad Politécnica de Madrid (UPM), and he is the head of the Applied Intelligence and Data Analysis (AIDA: https://aida.etsisi.uam.es) research group at UPM. He holds a Ph.D. in Computer Science from Universidad Carlos III de Madrid in 2001. He has published more than 300 journals, books, and conference papers. His research interests include: Machine Learning (Clustering/Deep Learning), Computational Intelligence (Evolutionary Computation, Swarm Intelligence), and Social Network Analysis, Fake News and Disinformation Analysis. He has participated/led more than 40 research projects (National and European: H2020, DG Justice, ISFP, and Erasmus+), related to the design and application of artificial intelligence methods for data mining and optimization for problems emerging in industrial scenarios (coal mining, steel), aeronautics, aerospace engineering, cybercrime/cyber intelligence, social networks applications, or video games among others. He is an Associate Editor of several journals, including Information Fusion, Ambient Intelligence & Humanized Computing, Expert Systems, and Cognitive Computation among others. Contact at: [email protected]
Google Scholar: https://scholar.google.com/citations?hl=es&user=fpf6EDAAAAAJ#
Information disorders (which is a term that includes all the different methods used to pollute information streams such as fake news, hoaxes, hyperpartisan content or rumors) can have a deep impact on the population’s opinion about several critical topics, such as economy, politics or health, among others. Anti-vax movements represent a threat for public health, the QAnon conspiracy or the Pizzagate events in Washington have also been boosted by the spread of false information. During the recent CoVID-19 crisis, the WHO created the term “infodemic” to refer to the high amount of fake news created regarding the pandemic. These and other events are the reasons why the interest in understanding and countering information disorders has grown so fast in recent years. This talk will provide both a small revision on the main (current and future) information disorders and a short introduction to some Artificial Intelligence and Machine Learning techniques, such as, Deep Learning, Transformers, or Natural Language Processing which are being currently used to counter against this toxic information. It will be briefly presented how these AI-ML techniques are currently employed to characterise, identify and prevent the misinformation spreading, specially through the online social networks.
Dr. Muhammad Sajjad
Title: Efficient Deep Learning Methods, IoT Applications, Current Trends/Challenges and Future Directions
Muhammad Sajjad received the Master’s degree from the Department of Computer Science, College of Signals, National University of Sciences and Technology, Rawalpindi, Pakistan in 2012, and the Ph.D. degree in digital contents from Sejong University, Seoul, South Korea in 2015. He is currently working as an ERCIM Research Fellow at NTNU, Norway. He is an Associate Professor with the Department of Computer Science, Islamia College University Peshawar, Pakistan. He is also the Head of the Digital Image Processing Laboratory with Islamia College University Peshawar, where many students are involved in different research projects under his supervision, such as Big data analytics, medical image analysis, multi-modal data mining, summarization, image/video prioritization and ranking, fog computing, the Internet of Things, autonomous navigation, and video analytics. His primary research interests include computer vision, image understanding, pattern recognition, robotic vision, and multimedia applications, with a current emphasis on economical hardware and deep learning, video scene understanding, activity analysis, fog computing, the Internet of Things, and real-time tracking. He has published more than 65 papers in peer-reviewed international journals and conferences. He is serving as a professional reviewer for various well-reputed journals and conferences. Currently, he is the associate editor at IEEE Access and acting as a guest editor at IEEE Transactions on Intelligent Transportation Systems.
Latest innovation in technologies and applications based on multi-disciplinary approaches such as wireless multimedia surveillance networks, embedded systems, medical diagnostics, Big data analytics, and sensing technologies exponentiate the expansion of the Internet of Things (IoT). An enormous number of sensors deployed in IoT infrastructures including smart cities, smart homes, smart transportation, smart healthcare, and smart precision agriculture produce both heterogeneous and homogenous data in large amounts. Lack of intelligent mechanisms for storage, indexing, retrieval, and management makes these tasks comparatively time-consuming for analysts to browse and gather actionable intelligence. An intelligent mechanism can guarantee to produce actionable intelligence for correct and timely decisions, increase productivity, improve revenue, and enhance life standard. However, deriving concealed information and gathering actionable intelligence from IoT data is a complicated task that cannot be accomplished by conventional Machine Learning (ML) paradigms. Deep learning (DL) plays an important role in the intelligence growing of IoT devices, pattern recognition, and computer vision. Researchers around the world are motivated by the performance of DL used in various problems such as smart surveillance systems, information retrieval, smart transportation, fault detection, and speech recognition, etc. Due to the limited resources of IoT devices, various lightweight DL models are investigated to simultaneously improve both the intelligence and efficiency while exploring their ability for data analysis over the IoT platform. Besides this, the hardware need not to be homogeneous in nature which raises compatibility issues for efficient processing and fusion of information. Hardware/software heterogeneity, data collection, data preprocessing, data generation of IoT in massive scale, and resource constrained nature of IoT devices are the current egoistic trends for the researchers to be investigated. In this talk, I will discuss the aforementioned aspects and will envision merging deep learning with IoT to explore new horizons of the applications such as health monitoring, disease analysis, indoor localization, intelligent control, home robotics, traffic prediction, traffic monitoring, autonomous driving, and manufacture inspection, which are the futuristic goals of the researchers today.