Nearly 30 local governments are planning to or are interested in introducing an artificial intelligence system designed to assess the seriousness of school bullying cases in hopes of better responding to them, a source close to the matter said Thursday. Otsu Municipal Government, which came under fire for the way it handled a high-profile bullying case in 2011, has teamed up with information technology services provider Hitachi Systems Ltd., to develop the AI system, which predicts how a case of bullying has the potential to become serious based on an analysis of past cases. School bullying has long been a concern in Japan, with education ministry data showing that elementary, junior and high schools as well as special-needs schools nationwide reported 612,496 cases in the year through March, up 68,563 from a year earlier. When a new case of bullying is reported, information on the incident, such as time, place and perpetrator, is fed into the system, which then searches its database to come up with an estimate of how serious the case is, expressed as a percentage. In all, about 50 pieces of data are used for analysis.

Hungarian public prosecutors have followed a recommendation from the European Anti-Fraud Office (OLAF) and opened proceedings against individuals accused of illegally overcharging for the renovation of children's playgrounds using EU money. Prosecutors are calling for prison sentences for the fraudsters, who illegally pocketed more than €1.7 million in European and Hungarian funding. OLAF Director General Ville Itälä said: "I welcome the decision by the Hungarian authorities to bring proceedings against the fraudsters investigated by OLAF, in line with our initial recommendations. This was a clear case of fraud against EU and Hungarian taxpayer money, and it is good to see that the Hungarian prosecutors agree with this assessment. This case is a prime example of how OLAF and national judicial authorities work together to take on the fraudsters to ensure that every euro of European funding is spent as and where it should be. This kind of investigation is at the heart of what OLAF does and I am delighted that our collaboration with the Hungarian authorities in this case has led to such a positive outcome."

In a new international cross disciplinary study, researchers have used artificial intelligence to analyze large amounts of historical photos from World War II. Among other things, the study shows that artificial intelligence can recognize the identity of photographers based on the content of photos taken by them. Artificial Intelligence (AI) is now able to identify photographers based on the content of images they've taken. This is the conclusion of a new study at AU Engineering, Aarhus University, where, in collaboration with Tampere University and the Finnish Environment Institute, researchers have used state-of-the-art artificial intelligence to trawl through photographs taken by 23 well-known Finnish photographers during the Second World War. The photographs used in the study are part of the publicly available Finnish Wartime Photograph Archive containing around 160,000 photographs from Finnish Winter, Continuation, and Lapland Wars captured in 1939-1945.

Later in the chapters, the research methodologies and the market tools that were used for the market analysis is mentioned.

In early 2018, officials at University College London were shocked to learn that meetings organized by "race scientists" and neo-Nazis, called the London Conference on Intelligence, had been held at the college the previous four years. The existence of the conference was surprising, but the choice of location was not. UCL was an epicenter of the early 20th-century eugenics movement--a precursor to Nazi "racial hygiene" programs--due to its ties to Francis Galton, the father of eugenics, and his intellectual descendants and fellow eugenicists Karl Pearson and Ronald Fisher. In response to protests over the conference, UCL announced this June that it had stripped Galton's and Pearson's names from its buildings and classrooms. After similar outcries about eugenics, the Committee of Presidents of Statistical Societies renamed its annual Fisher Lecture, and the Society for the Study of Evolution did the same for its Fisher Prize. In science, these are the equivalents of toppling a Confederate statue and hurling it into the sea. Unlike tearing down monuments to white supremacy in the American South, purging statistics of the ghosts of its eugenicist past is not a straightforward proposition. In this version, it's as if Stonewall Jackson developed quantum physics. What we now understand as statistics comes largely from the work of Galton, Pearson, and Fisher, whose names appear in bread-and-butter terms like "Pearson correlation coefficient" and "Fisher information." In particular, the beleaguered concept of "statistical significance," for decades the measure of whether empirical research is publication-worthy, can be traced directly to the trio. Ideally, statisticians would like to divorce these tools from the lives and times of the people who created them. It would be convenient if statistics existed outside of history, but that's not the case.

The entire world is going cashless and banking activities and services seem to have moved online. Most customers perform their banking activities by adopting online services. Conversational AI is playing a significant role in attending to bank-related tasks and enabling the smooth functioning of the banks despite Covid constraints. Chatbot acts as a friendly banking assistant and enhances the overall financial services regime. Conversational AI in the financial sector usually deals with confidential user data including credit/ debit cards, bank accounts, sensitive personally identified information, and social security numbers.

In October 2020 the US House Antitrust Subcommittee, chaired by Congressman David Cicilline, published its report on competition in digital markets. It conducted a full review of the market from top to bottom, focusing on the dominance of the giants in the industry: Facebook, Amazon, Apple and Google. The report zeroes in on their business practices, and how these could potentially amount to monopolies. They found that each platform had become, in one way or another, in direct and singular control of channels of mass distribution. They are no longer disruptive and innovative start-ups, but now resemble business monoliths akin to the oil barons and railroad tycoons of the past, controlling their respective industries, absorbing or removing competitors with ease.

Knowledge graph reasoning is the fundamental component to support machine learning applications such as information extraction, information retrieval and recommendation. Since knowledge graph can be viewed as the discrete symbolic representations of knowledge, reasoning on knowledge graphs can naturally leverage the symbolic techniques. However, symbolic reasoning is intolerant of the ambiguous and noisy data. On the contrary, the recent advances of deep learning promote neural reasoning on knowledge graphs, which is robust to the ambiguous and noisy data, but lacks interpretability compared to symbolic reasoning. Considering the advantages and disadvantages of both methodologies, recent efforts have been made on combining the two reasoning methods. In this survey, we take a thorough look at the development of the symbolic reasoning, neural reasoning and the neural-symbolic reasoning on knowledge graphs. We survey two specific reasoning tasks, knowledge graph completion and question answering on knowledge graphs, and explain them in a unified reasoning framework. We also briefly discuss the future directions for knowledge graph reasoning.

Statistical methods for the analysis and forecasting of time series data have a long history (Tsay, 2000). The well-accepted Box-Jenkins analysis and forecasting methods have been applied in a wide range of applications, from finance to medicine, and the classic book that laid out the theory is now in its fourth edition with over 55,000 citations (Box et al., 2015). In this paper, we focus on the specialized area of time series regression where the goal is to predict one time series with the help of covariates that include elements which also have a time series nature. Some authors refer to this as dynamic regression (Hyndman and Athanasopoulos, 2018), others use the term regARIMA (Gómez and Maravall, 1994; Maravall et al., 2016). Pankratz (2012) provides an excellent overview.

Perturbations targeting the graph structure have proven to be extremely effective in reducing the performance of Graph Neural Networks (GNNs), and traditional defenses such as adversarial training do not seem to be able to improve robustness. This work is motivated by the observation that adversarially injected edges effectively can be viewed as additional samples to a node's neighborhood aggregation function, which results in distorted aggregations accumulating over the layers. Conventional GNN aggregation functions, such as a sum or mean, can be distorted arbitrarily by a single outlier. We propose a robust aggregation function motivated by the field of robust statistics. Our approach exhibits the largest possible breakdown point of 0.5, which means that the bias of the aggregation is bounded as long as the fraction of adversarial edges of a node is less than 50\%. Our novel aggregation function, Soft Medoid, is a fully differentiable generalization of the Medoid and therefore lends itself well for end-to-end deep learning. Equipping a GNN with our aggregation improves the robustness with respect to structure perturbations on Cora ML by a factor of 3 (and 5.5 on Citeseer) and by a factor of 8 for low-degree nodes.