Artificial Intelligence (AI) in Cyber Security Market 2020 Industry Research Report Artificial intelligence (AI) is a result of software that tries to create a decision mechanism similar to human brain's decision mechanism. Artificial intelligence is playing a crucial role in cyber security by identifying threats and protecting organizations' data from lethal cyber-attacks. It speeds up the process of noticing attacks and enables organizations to adopt predictive measures in combating cyber-crimes. Market Overview: The Global Artificial Intelligence (AI) in Cyber Security market 2020 research provides a basic overview of the industry including definitions, classifications, applications and industry chain structure. The Global Artificial Intelligence (AI) in Cyber Security market analysis is provided for the international markets including Development trends, competitive landscape analysis, and key regions development status.

The proliferation and application of machine learning based Intrusion Detection Systems (IDS) have allowed for more flexibility and efficiency in the automated detection of cyber attacks in Industrial Control Systems (ICS). However, the introduction of such IDSs has also created an additional attack vector; the learning models may also be subject to cyber attacks, otherwise referred to as Adversarial Machine Learning (AML). Such attacks may have severe consequences in ICS systems, as adversaries could potentially bypass the IDS. This could lead to delayed attack detection which may result in infrastructure damages, financial loss, and even loss of life. This paper explores how adversarial learning can be used to target supervised models by generating adversarial samples using the Jacobian-based Saliency Map attack and exploring classification behaviours. The analysis also includes the exploration of how such samples can support the robustness of supervised models using adversarial training. An authentic power system dataset was used to support the experiments presented herein. Overall, the classification performance of two widely used classifiers, Random Forest and J48, decreased by 16 and 20 percentage points when adversarial samples were present. Their performances improved following adversarial training, demonstrating their robustness towards such attacks.

Many real-world applications need path planning algorithms to solve tasks in different areas, such as social applications, autonomous cars, and tracking activities. And most importantly motion planning. Although the use of path planning is sufficient in most motion planning scenarios, they represent potential bottlenecks in large environments with dynamic changes. To tackle this problem, the number of possible routes could be reduced to make it easier for path planning algorithms to find the shortest path with less efforts. An traditional algorithm for path planning is the A*, it uses an heuristic to work faster than other solutions. In this work, we propose a CNN encoder capable of eliminating useless routes for motion planning problems, then we combine the proposed neural network output with A*. To measure the efficiency of our solution, we propose a database with different scenarios of motion planning problems. The evaluated metric is the number of the iterations to find the shortest path. The A* was compared with the CNN Encoder (proposal) with A*. In all evaluated scenarios, our solution reduced the number of iterations by more than 60\%.

Recent developments in image classification and natural language processing, coupled with the rapid growth in social media usage, have enabled fundamental advances in detecting breaking events around the world in real-time. Emergency response is one such area that stands to gain from these advances. By processing billions of texts and images a minute, events can be automatically detected to enable emergency response workers to better assess rapidly evolving situations and deploy resources accordingly. To date, most event detection techniques in this area have focused on image-only or text-only approaches, limiting detection performance and impacting the quality of information delivered to crisis response teams. In this paper, we present a new multimodal fusion method that leverages both images and texts as input. In particular, we introduce a cross-attention module that can filter uninformative and misleading components from weak modalities on a sample by sample basis. In addition, we employ a multimodal graph-based approach to stochastically transition between embeddings of different multimodal pairs during training to better regularize the learning process as well as dealing with limited training data by constructing new matched pairs from different samples. We show that our method outperforms the unimodal approaches and strong multimodal baselines by a large margin on three crisis-related tasks.

As AI enters the workplace, we need to reflect upon the criteria by which human work is evaluated and human subjectivity depicted. Control panels are the obvious place to run operations centrally. The control rooms of Star Trek's fantastical Enterprise (and the hub of the actual Project Cybersyn under Chile's radical president Salvador Allende) in the 1960s and 70s were however operated by humans with relatively primitive technologies. Today, much of the work of the people we imagined in these rooms--the bouffanted women in silver A-line dresses and men in blue boiler suits pushing buttons to operate the manoeuvres of galactical imperialism--is done by computers. But what will happen when the proverbial windows looking out to the galaxies only display a cadre of robots and the control panels' blinking lights are the only reflective glimmer?

The transcriptomics of cancer tumors are characterized with tens of thousands of gene expression features. Patient prognosis or tumor stage can be assessed by machine learning techniques like supervised classification tasks given a gene expression profile. Feature selection is a useful approach to select the key genes which helps to classify tumors. In this work we propose a feature selection method based on Multiple Kernel Learning that results in a reduced subset of genes and a custom kernel that improves the classification performance when used in support vector classification. During the feature selection process this method performs a novel latent regularisation by relaxing the supervised target problem by introducing unsupervised structure obtained from the latent space learned by a non linear dimensionality reduction model. An improvement of the generalization capacity is obtained and assessed by the tumor classification performance on new unseen test samples when the classifier is trained with the features selected by the proposed method in comparison with other supervised feature selection approaches.

In reinforcement learning (RL), dealing with non-stationarity is a challenging issue. However, some domains such as traffic optimization are inherently non-stationary. Causes for and effects of this are manifold. In particular, when dealing with traffic signal controls, addressing non-stationarity is key since traffic conditions change over time and as a function of traffic control decisions taken in other parts of a network. In this paper we analyze the effects that different sources of non-stationarity have in a network of traffic signals, in which each signal is modeled as a learning agent. More precisely, we study both the effects of changing the \textit{context} in which an agent learns (e.g., a change in flow rates experienced by it), as well as the effects of reducing agent observability of the true environment state. Partial observability may cause distinct states (in which distinct actions are optimal) to be seen as the same by the traffic signal agents. This, in turn, may lead to sub-optimal performance. We show that the lack of suitable sensors to provide a representative observation of the real state seems to affect the performance more drastically than the changes to the underlying traffic patterns.

With the spiraling pandemic of the Coronavirus Disease 2019 (COVID-19), it has becoming inherently important to disseminate accurate and timely information about the disease. Due to the ubiquity of Internet connectivity and smart devices, social sensing is emerging as a dynamic sensing paradigm to collect real-time observations from online users. In this vision paper we propose CovidSens, the concept of social-sensing-based risk alerting systems to notify the general public about the COVID-19 spread. The CovidSens concept is motivated by two recent observations: 1) people have been actively sharing their state of health and experience of the COVID-19 via online social media, and 2) official warning channels and news agencies are relatively slower than people reporting their observations and experiences about COVID-19 on social media. We anticipate an unprecedented opportunity to leverage the posts generated by the social media users to build a real-time analytic system for gathering and circulating vital information of the COVID-19 propagation. Specifically, the vision of CovidSens attempts to answer the questions of: how to track the spread of the COVID-19? How to distill reliable information about the disease with the coexistence of prevailing rumors and misinformation in the social media? How to inform the general public about the latest state of the spread timely and effectively and alert them to remain prepared? In this vision paper, we discuss the roles of CovidSens and identify the potential challenges in implementing reliable social-sensing-based risk alerting systems. We envision that approaches originating from multiple disciplines (e.g. estimation theory, machine learning, constrained optimization) can be effective in addressing the challenges. Finally, we outline a few research directions for future work in CovidSens.

In most real-world applications, it is seldom the case that a given observable evolves independently of its environment. In social networks, users' behavior results from the people they interact with, news in their feed, or trending topics. In natural language, the meaning of phrases emerges from the combination of words. In general medicine, a diagnosis is established on the basis of the interaction of symptoms. Here, we propose a new model, the Interactive Mixed Membership Stochastic Block Model (IMMSBM), which investigates the role of interactions between entities (hashtags, words, memes, etc.) and quantifies their importance within the aforementioned corpora. We find that interactions play an important role in those corpora. In inference tasks, taking them into account leads to average relative changes with respect to non-interactive models of up to 150\% in the probability of an outcome. Furthermore, their role greatly improves the predictive power of the model. Our findings suggest that neglecting interactions when modeling real-world phenomena might lead to incorrect conclusions being drawn.

HRI2020 has already kicked off with workshops and the Industry Talks Session on April 3, however the first release of videos has only just gone online with the welcome from General Chairs Tony Belpaeme, ID Lab, University of Ghent and James Young, University of Manitoba. There is also a welcome from the Program Chairs Hatice Gunes from University of Cambridge and Laurel Riek from University of San Diego, requesting that we all engage with the participants papers and videos. The theme of this year's conference is "Real World Human-Robot Interaction," reflecting on recent trends in our community toward creating and deploying systems that can facilitate real-world, long-term interaction. This theme also reflects a new theme area we have introduced at HRI this year, "Reproducibility for Human Robot Interaction," which is key to realizing this vision and helping further our scientific endeavors. This trend was also reflected across our other four theme areas, including "Human-Robot Interaction User Studies," "Technical Advances in Human-Robot Interaction," "Human-Robot Interaction Design," and "Theory and Methods in Human-Robot Interaction."