Increasing trust in and adoption of Artificial Intelligence (AI) are necessary ingredients for economic growth and the fuel for future innovations that can benefit society as a whole. In this complex context which stimulates and promotes the use and dissemination of AI technologies, also Italy has developed its AI national strategy as part of the Coordinated Plan launched by the European Commission in December 2018. Over the period until now, the Italian government has stressed the importance of discussing about the specific approach that the country should adopt to fully benefit from the advantages of AI, while mitigating the risks that are often associated with its use. As Prof. Anis H. Bajrektarevic pointed out in his report: "Artificial Intelligence is essentially a dual-use technology and its mighty implications, either positive or negative, will be increasingly hard to anticipate, frame, and restrain, let alone mitigate and regulate" (The answer to AI is intergovernmental Multilateralism, New Europe, Brussels, March 2020). Therefore, a national strategy is more than ever essential because AI can represent the starting point for a new edge filled with economic, social and cultural prosperity for Italy.

Recent advancements in quantum computing have driven the scientific community's quest to solve a certain class of complex problems for which quantum computers would be better suited than traditional supercomputers. To improve the efficiency with which quantum computers can solve these problems, scientists are investigating the use of artificial intelligence approaches. In a new study, scientists at the U.S. Department of Energy's (DOE) Argonne National Laboratory have developed a new algorithm based on reinforcement learning to find the optimal parameters for the Quantum Approximate Optimization Algorithm (QAOA), which allows a quantum computer to solve certain combinatorial problems such as those that arise in materials design, chemistry and wireless communications. "It's a bit like having a self-driving car in traffic; the algorithm can detect when it needs to make adjustments in the'dials' it uses to do the computation." "Combinatorial optimization problems are those for which the solution space gets exponentially larger as you expand the number of decision variables," said Argonne computer scientist Prasanna Balaprakash.

There is an interesting link between critical theory and certain genres of literature that may be of interest to the current debate on AI ethics. While critical theory generally points out certain deficiencies in the present to criticize it, futurology and literary genres such as Cyberpunk, extrapolate our present deficits in possible dystopian futures to criticize the status quo. Given the great advance of the AI industry in recent years, an increasing number of ethical matters have been raised and debated, usually in the form of ethical guidelines and unpublished manuscripts by governments, the private sector, and academic sources. However, recent meta-analyses in the field of AI ethics have raised important questions such as: what is being omitted from published ethical guidelines? Does AI governance occur inclusively and diversely? Is this form of "ethics", based on soft rules and principles, efficient? In this study, I would like to present aspects omitted or barely mentioned in the current debate on AI ethics and defend the point that applied ethics should not be based on creating only soft versions of real legislation, but rather on criticizing the status quo for everything of value that is disregarded.

This thesis develops a conceptual framework considering social data as representing the surface layer of a hierarchy of human social behaviours, needs and cognition which is employed to transform social data into representations that preserve social behaviours and their causalities. Based on this framework two platforms were built to capture insights from fast-paced and slow-paced social data. For fast-paced, a self-structuring and incremental learning technique was developed to automatically capture salient topics and corresponding dynamics over time. An event detection technique was developed to automatically monitor those identified topic pathways for significant fluctuations in social behaviours using multiple indicators such as volume and sentiment. This platform is demonstrated using two large datasets with over 1 million tweets. The separated topic pathways were representative of the key topics of each entity and coherent against topic coherence measures. Identified events were validated against contemporary events reported in news. Secondly for the slow-paced social data, a suite of new machine learning and natural language processing techniques were developed to automatically capture self-disclosed information of the individuals such as demographics, emotions and timeline of personal events. This platform was trialled on a large text corpus of over 4 million posts collected from online support groups. This was further extended to transform prostate cancer related online support group discussions into a multidimensional representation and investigated the self-disclosed quality of life of patients (and partners) against time, demographics and clinical factors. The capabilities of this extended platform have been demonstrated using a text corpus collected from 10 prostate cancer online support groups comprising of 609,960 prostate cancer discussions and 22,233 patients.

Yara rules are a ubiquitous tool among cybersecurity practitioners and analysts. Developing high-quality Yara rules to detect a malware family of interest can be labor- and time-intensive, even for expert users. Few tools exist and relatively little work has been done on how to automate the generation of Yara rules for specific families. In this paper, we leverage large n-grams ($n \geq 8$) combined with a new biclustering algorithm to construct simple Yara rules more effectively than currently available software. Our method, AutoYara, is fast, allowing for deployment on low-resource equipment for teams that deploy to remote networks. Our results demonstrate that AutoYara can help reduce analyst workload by producing rules with useful true-positive rates while maintaining low false-positive rates, sometimes matching or even outperforming human analysts. In addition, real-world testing by malware analysts indicates AutoYara could reduce analyst time spent constructing Yara rules by 44-86%, allowing them to spend their time on the more advanced malware that current tools can't handle. Code will be made available at https://github.com/NeuromorphicComputationResearchProgram .

Motivated by models for multiway comparison data, we consider the problem of estimating a coordinate-wise isotonic function on the domain $[0, 1]^d$ from noisy observations collected on a uniform lattice, but where the design points have been permuted along each dimension. While the univariate and bivariate versions of this problem have received significant attention, our focus is on the multivariate case $d \geq 3$. We study both the minimax risk of estimation (in empirical $L_2$ loss) and the fundamental limits of adaptation (quantified by the adaptivity index) to a family of piecewise constant functions. We provide a computationally efficient Mirsky partition estimator that is minimax optimal while also achieving the smallest adaptivity index possible for polynomial time procedures. Thus, from a worst-case perspective and in sharp contrast to the bivariate case, the latent permutations in the model do not introduce significant computational difficulties over and above vanilla isotonic regression. On the other hand, the fundamental limits of adaptation are significantly different with and without unknown permutations: Assuming a hardness conjecture from average-case complexity theory, a statistical-computational gap manifests in the former case. In a complementary direction, we show that natural modifications of existing estimators fail to satisfy at least one of the desiderata of optimal worst-case statistical performance, computational efficiency, and fast adaptation. Along the way to showing our results, we improve adaptation results in the special case $d = 2$ and establish some properties of estimators for vanilla isotonic regression, both of which may be of independent interest.

The increased computerization in recent years has resulted in the production of a variety of different software, however measures need to be taken to ensure that the produced software isn't defective. Many researchers have worked in this area and have developed different Machine Learning-based approaches that predict whether the software is defective or not. This issue can't be resolved simply by using different conventional classifiers because the dataset is highly imbalanced i.e the number of defective samples detected is extremely less as compared to the number of non-defective samples. Therefore, to address this issue, certain sophisticated methods are required. The different methods developed by the researchers can be broadly classified into Resampling based methods, Cost-sensitive learning-based methods, and Ensemble Learning. Among these methods. This report analyses the performance of the Online Sequential Extreme Learning Machine (OS-ELM) proposed by Liang et.al. against several classifiers such as Logistic Regression, Support Vector Machine, Random Forest, and Na\"ive Bayes after oversampling the data. OS-ELM trains faster than conventional deep neural networks and it always converges to the globally optimal solution. A comparison is performed on the original dataset as well as the over-sampled data set. The oversampling technique used is Cluster-based Over-Sampling with Noise Filtering. This technique is better than several state-of-the-art techniques for oversampling. The analysis is carried out on 3 projects KC1, PC4 and PC3 carried out by the NASA group. The metrics used for measurement are recall and balanced accuracy. The results are higher for OS-ELM as compared to other classifiers in both scenarios.

Artificial intelligence (AI) is swiftly moving to the mainstream and emerging as a powerful engine for many organizations, prompting them to jump on the AI bandwagon to accelerate growth, innovate, and disrupt the market. The Indian government and industry bodies are extensively focusing on building an AI ecosystem that could help the country to develop and implement cutting-edge solutions (See: New CII forum formed to help build an AI ecosystem). However, according to a recent study, Indian enterprises need to beef up their risk-management capabilities to leverage AI's potential and dodge threats that may emerge after scaling up AI deployments. The study titled, Can enterprise intelligence be created artificially?, commissioned by global consulting major EY and trade association body Nasscom, says that 60% of Indian executive leaders believe that AI will disrupt their businesses within three years. Yet, only 25% of enterprises have deployed AI solutions.

Gerrymandering is one of the most insidious methods out there of influencing our political process. By legally changing the way votes are collected and counted, the outcomes can be influenced -- even fixed in advance for years. The solution may be an AI system that draws voting districts with an impartial hand. Ordinarily, districts that correspond to electoral votes within a state are drawn essentially by hand, and partisan operatives on both sides of the aisle have used the process to create distorted shapes that exclude hostile voters and lock in their own. It's so effective that it's become commonplace -- so much so there's even a font made out of gerrymandered districts shaped like letters.

Nozomi Networks Inc., the leader in OT and IoT security, announced its products are now available on the Schneider Electric Exchange, making it faster and easier for Schneider Electric customers to integrate Nozomi Networks solutions into their existing EcoStruxure product architecture and other Schneider Electric energy and automation software, devices and services. The Schneider Electric Exchange connects the company's ecosystem of digital partners to accelerate and scale innovation – and provide customers with easier access to the tools they need to operationalize artificial intelligence (AI) and machine learning (ML) for real-world problems. As a valued Master Technology Partner, Nozomi Networks delivers real-world OT and IoT cybersecurity expertise that's in high demand and the industrial-strength monitoring and threat detection that Schneider Electric's customers require. Advanced options like multi-site monitoring in the cloud and new innovations in IoT anomaly detection are available from Nozomi Networks to meet the most advanced cybersecurity requirements. "Digital transformation is improving our customers' business performance in ways they never imagined. Connectivity now spans IT, OT, and IoT operations and is used to extract, contextualize and apply new levels of rich data," said Jay Abdallah, Vice President, Cybersecurity Services, Schneider Electric.