In the second season of BBC mystery thriller The Capture, deepfakes threaten the future of democracy and UK national security. In a dystopia set in present day London, hackers use AI to insert these highly realistic false images and videos of people into live news broadcasts to destroy the careers of politicians. But my team's research has shown how difficult it is to create convincing deepfakes in reality. In fact, technology and creative professionals have started collaborating on solutions to help people spot bogus videos of politicians and celebrities. We stand a decent chance of staying one step ahead of fraudsters.

The Industrial Internet of Things, or IIoT, has recently gained popularity due to its ability to create communication networks between different components of an industry and bring about the new revolution--Industry 4.0. Powered by wireless 5G connectivity and artificial intelligence (AI), IIoT holds the ability to analyze critical problems and provide solutions that can improve the operational performance of industries ranging from manufacturing to health care. IoT is highly user-centric--it connects TVs, voice assistants, refrigerators, etc.--whereas IIoT deals with enhancing the health, safety, or efficiency of larger systems, bridging hardware with software, and carrying out data analysis to provide real-time insights. However, while IIoT does have many advantages, it also comes with its share of vulnerabilities such as security threats in the form of attacks trying to disturb the network or siphoning resources. As IIoT is getting more popular in industries, it is becoming crucial to develop an efficient system to handle such security concerns.

"Today's complex world requires sustainable design thinking to innovate sustainably. Traditional design thinking approaches are not sustainable because they are not holistic. They are cosmetical, theatrical and cozy".-- ESG and SDGs are two complementary frameworks. The Environmental, Social, and Governance (ESG) framework was originated in the 70s, while the Sustainable Development Goals (SDGs) framework was established by the United Nations General Assembly in 2015, and focused on 17 critical areas to address economic, social, and environmental challenges by 2030. By the way, "The most important SDG #18 is missing in SDGs, which is GOOD PEOPLE. Action on climate change requires good people. The world has more bad people than good people, which is probably the reason why so few engage in climate change".

Aspiring bakers are frequently called upon to adapt award-winning recipes based on differing kitchen setups. Someone might use an eggbeater instead of a stand mixer to make prize-winning chocolate chip cookies, for instance. Being able to reproduce a recipe in different situations and with varying setups is critical for both talented chefs and computational scientists, the latter of whom are faced with a similar problem of adapting and reproducing their own "recipes" when trying to validate and work with new AI models. These models have applications in scientific fields ranging from climate analysis to brain research. "When we talk about data, we have a practical understanding of the digital assets we deal with," said Eliu Huerta, scientist and lead for Translational AI at the U.S. Department of Energy's (DOE) Argonne National Laboratory.

Fox News captured drone footage of trash and clothing discarded along a common crossing point for illegal immigrants near Normandy, Texas. Thousands of migrants have streamed across the border near Normandy, Texas, in recent weeks, leaving behind discarded trash and clothing in their wake. Drone footage of a frequent crossing point along the Rio Grande shows piles of discarded items at the end of an eroded walking trail. Similar scenes can be observed at the border in Eagle Pass and the surrounding area. Trash and clothing discarded along the Rio Grande in Normandy, Texas.

Hypergraphs, encoding structured interactions among any number of system units, have recently proven a successful tool to describe many real-world biological and social networks. Here we propose a framework based on statistical inference to characterize the structural organization of hypergraphs. The method allows to infer missing hyperedges of any size in a principled way, and to jointly detect overlapping communities in presence of higher-order interactions. Furthermore, our model has an efficient numerical implementation, and it runs faster than dyadic algorithms on pairwise records projected from higher-order data. We apply our method to a variety of real-world systems, showing strong performance in hyperedge prediction tasks, detecting communities well aligned with the information carried by interactions, and robustness against addition of noisy hyperedges. Our approach illustrates the fundamental advantages of a hypergraph probabilistic model when modeling relational systems with higher-order interactions.

Following the outbreak of a global pandemic, online content is filled with hate speech. Donald Trump's ''Chinese Virus'' tweet shifted the blame for the spread of the Covid-19 virus to China and the Chinese people, which triggered a new round of anti-China hate both online and offline. This research intends to examine China-related hate speech on Twitter during the two years following the burst of the pandemic (2020 and 2021). Through Twitter's API, in total 2,172,333 tweets hashtagged #china posted during the time were collected. By employing multiple state-of-the-art pretrained language models for hate speech detection, we identify a wide range of hate of various types, resulting in an automatically labeled anti-China hate speech dataset. We identify a hateful rate in #china tweets of 2.5% in 2020 and 1.9% in 2021. This is well above the average rate of online hate speech on Twitter at 0.6% identified in Gao et al., 2017. We further analyzed the longitudinal development of #china tweets and those identified as hateful in 2020 and 2021 through visualizing the daily number and hate rate over the two years. Our keyword analysis of hate speech in #china tweets reveals the most frequently mentioned terms in the hateful #china tweets, which can be used for further social science studies.

Dialogue systems, commonly known as chatbots, have gained escalating popularity in recent times due to their wide-spread applications in carrying out chit-chat conversations with users and task-oriented dialogues to accomplish various user tasks. Existing chatbots are usually trained from pre-collected and manually-labeled data and/or written with handcrafted rules. Many also use manually-compiled knowledge bases (KBs). Their ability to understand natural language is still limited, and they tend to produce many errors resulting in poor user satisfaction. Typically, they need to be constantly improved by engineers with more labeled data and more manually compiled knowledge. This book introduces the new paradigm of lifelong learning dialogue systems to endow chatbots the ability to learn continually by themselves through their own self-initiated interactions with their users and working environments to improve themselves. As the systems chat more and more with users or learn more and more from external sources, they become more and more knowledgeable and better and better at conversing. The book presents the latest developments and techniques for building such continual learning dialogue systems that continuously learn new language expressions and lexical and factual knowledge during conversation from users and off conversation from external sources, acquire new training examples during conversation, and learn conversational skills. Apart from these general topics, existing works on continual learning of some specific aspects of dialogue systems are also surveyed. The book concludes with a discussion of open challenges for future research.

It is important to guarantee that machine learning algorithms deployed in the real world do not result in unfairness or unintended social consequences. Fair ML has largely focused on the protection of single attributes in the simpler setting where both attributes and target outcomes are binary. However, the practical application in many a real-world problem entails the simultaneous protection of multiple sensitive attributes, which are often not simply binary, but continuous or categorical. To address this more challenging task, we introduce FairCOCCO, a fairness measure built on cross-covariance operators on reproducing kernel Hilbert Spaces. This leads to two practical tools: first, the FairCOCCO Score, a normalized metric that can quantify fairness in settings with single or multiple sensitive attributes of arbitrary type; and second, a subsequent regularization term that can be incorporated into arbitrary learning objectives to obtain fair predictors. These contributions address crucial gaps in the algorithmic fairness literature, and we empirically demonstrate consistent improvements against state-of-the-art techniques in balancing predictive power and fairness on real-world datasets.

To design a drug given a biological molecule by using deep learning methods, there are many successful models published recently. People commonly used generative models to design new molecules given certain protein. LiGAN was regarded as the baseline of deep learning model which was developed on convolutional neural networks. Recently, GraphBP showed its ability to predict innovative "real" chemicals that the binding affinity outperformed with traditional molecular docking methods by using a flow-based generative model with a graph neural network and multilayer perception. However, all those methods regarded proteins as rigid bodies and only include a very small part of proteins related to binding. However, the dynamics of proteins are essential for drug binding. Based on GraphBP, we proposed to generate more solid work derived from protein data bank. The results will be evaluated by validity and binding affinity by using a computational chemistry algorithm.