Fuzzy quantification is a subtopic of fuzzy logic which deals with the modelling of the quantified expressions we can find in natural language. Fuzzy quantifiers have been successfully applied in several fields like fuzzy, control, fuzzy databases, information retrieval, natural language generation, etc. Their ability to model and evaluate linguistic expressions in a mathematical way, makes fuzzy quantifiers very powerful for data analytics and data mining applications. In this paper we will give a general overview of the main applications of fuzzy quantifiers in this field as well as some ideas to use them in new application contexts.

Thousands of the world's pre-eminent technology experts called for a global ban on the development of lethal autonomous weapons, warning they could become instruments of "violence and oppression". More than 2,400 individuals and 150 companies from 90 different countries vowed to play no part in the construction, trade, or use of autonomous weapons in a pledge signed on Wednesday at the 2018 International Joint Conference on Artificial Intelligence in Stockholm, Sweden. Elon Musk, CEO of SpaceX and Tesla, and representatives of Google's DeepMind subsidiary were among supporters of the pledge. "The decision to take a human life should never be delegated to a machine," a statement said. "Lethal autonomous weapons - selecting and engaging targets without human intervention - would be dangerously destabilising for every country and individual."

Goldman Sachs' statistical model for the World Cup sounded impressive: The investment bank mined data about the teams and individual players, used artificial intelligence to predict the factors that might affect game scores and simulated 1 million possible evolutions of the tournament. The model was updated as the games unfolded, and it was wrong again and again. It certainly didn't predict the final between France and Croatia. The failure to accurately predict the outcome of soccer games is a good opportunity to laugh at the hubris of elite bankers, who use similar complex models for investment decisions. Tom Pair, founder of the Upper Left Opportunities Fund, a hedge fund, tweeted recently: "Of course, past data don't always predict the future; Goldman Sachs never tells clients to make decisions solely on the basis of its models' findings. And in any case, the model only generated probabilities of winning a game and advancing, and no team was given more than an 18.5 per cent chance of winning the World Cup. The moral of the story is probably that buzz-generating technologies such as big data and AI don't necessarily make statistical forecasting more accurate."

Goldman Sachs' statistical model for the World Cup sounded impressive: The investment bank mined data about the teams and individual players, used artificial intelligence to predict the factors that might affect game scores and simulated 1 million possible evolutions of the tournament. The model was updated as the games unfolded, and it was wrong again and again. It certainly didn't predict the final between France and Croatia. The failure to accurately predict the outcome of soccer games is a good opportunity to laugh at the hubris of elite bankers, who use similar complex models for investment decisions. Tom Pair, founder of the Upper Left Opportunities Fund, a hedge fund, tweeted recently: "Of course, past data don't always predict the future; Goldman Sachs never tells clients to make decisions solely on the basis of its models' findings. And in any case, the model only generated probabilities of winning a game and advancing, and no team was given more than an 18.5 per cent chance of winning the World Cup. The moral of the story is probably that buzz-generating technologies such as big data and AI don't necessarily make statistical forecasting more accurate."

There are different approaches to predict the results of FIFA World Cup. One approach is to simulate every single match in a paired comparison in terms of team's capabilities and the winning odds. Zeileis, Leitner, and Hornik (2018) used the same technique, and they predicted that Brazil would win the FIFA World Cup 2018 with a probability of 16.6%, and it is followed by Germany (15.8%) and Spain (12.5%) [1]. Swiss Bank UBS also predicted the same three teams as the top 3 teams but in a different order. They predicted Germany (24.0%) as the champion, followed by Brazil (19.80%) and Spain (16.1%).

While tracking criminal activity on dark web marketplaces, a threat intelligence team Insikt Group of the security research firm Recorded Future discovered a hacker selling classified military documents for a meager amount of $150-200 on the Deep Web and Dark Web forum. According to the research team, the hacker got a hold on the documents after they intruded by exploiting an FTP vulnerability in Netgear routers that's been known for two years. Once the hacker got an access to the router, the intruder was easily able to invade into a captain's personal computer and steal a cache of sensitive documents. "While such course books are not classified materials on their own," Recorded Future said, "in unfriendly hands, they could provide an adversary the ability to assess technical capabilities and weaknesses in one of the most technologically advanced aircrafts." The documents include contained sensitive materials, like "the M1 Abrams maintenance manual, a tank platoon training course, a crew survival course, and documentation on improvised explosive device (IED) mitigation tactics."

In this work, we consider the problem of combining link, content and temporal analysis for community detection and prediction in evolving networks. Such temporal and content-rich networks occur in many real-life settings, such as bibliographic networks and question answering forums. Most of the work in the literature (that uses both content and structure) deals with static snapshots of networks, and they do not reflect the dynamic changes occurring over multiple snapshots. Incorporating dynamic changes in the communities into the analysis can also provide useful insights about the changes in the network such as the migration of authors across communities. In this work, we propose Chimera, a shared factorization model that can simultaneously account for graph links, content, and temporal analysis. This approach works by extracting the latent semantic structure of the network in multidimensional form, but in a way that takes into account the temporal continuity of these embeddings. Such an approach simplifies temporal analysis of the underlying network by using the embedding as a surrogate. A consequence of this simplification is that it is also possible to use this temporal sequence of embeddings to predict future communities. We present experimental results illustrating the effectiveness of the approach.

Score following is the process of tracking a musical performance (audio) with respect to a known symbolic representation (a score). We start this paper by formulating score following as a multimodal Markov Decision Process, the mathematical foundation for sequential decision making. Given this formal definition, we address the score following task with state-of-the-art deep reinforcement learning (RL) algorithms such as synchronous advantage actor critic (A2C). In particular, we design multimodal RL agents that simultaneously learn to listen to music, read the scores from images of sheet music, and follow the audio along in the sheet, in an end-to-end fashion. All this behavior is learned entirely from scratch, based on a weak and potentially delayed reward signal that indicates to the agent how close it is to the correct position in the score. Besides discussing the theoretical advantages of this learning paradigm, we show in experiments that it is in fact superior compared to previously proposed methods for score following in raw sheet music images.

This paper addresses the semantics of weighted argumentation graphs that are bipolar, i.e. contain both attacks and supports for arguments. We build on previous work by Amgoud, Ben-Naim et. al. We study the various characteristics of acceptability semantics that have been introduced in these works. We provide a simplified and mathematically elegant formulation of these characteristics. The formulation is modular because it cleanly separates aggregation of attacking and supporting arguments (for a given argument a) from the computation of their influence on a's initial weight. We discuss various semantics for bipolar argumentation graphs in the light of these characteristics. Based on the modular framework, we prove general convergence and divergence theorems. We show that all semantics converge for all acyclic graphs and that no sum-based semantics can converge for all graphs. In particular, we show divergence of Euler-based semantics for certain cyclic graphs. We also provide the first semantics for bipolar weighted graphs that converges for all graphs.

The detection of software vulnerabilities (or vulnerabilities for short) is an important problem that has yet to be tackled, as manifested by many vulnerabilities reported on a daily basis. This calls for machine learning methods to automate vulnerability detection. Deep learning is attractive for this purpose because it does not require human experts to manually define features. Despite the tremendous success of deep learning in other domains, its applicability to vulnerability detection is not systematically understood. In order to fill this void, we propose the first systematic framework for using deep learning to detect vulnerabilities. The framework, dubbed Syntax-based, Semantics-based, and Vector Representations (SySeVR), focuses on obtaining program representations that can accommodate syntax and semantic information pertinent to vulnerabilities. Our experiments with 4 software products demonstrate the usefulness of the framework: we detect 15 vulnerabilities that are not reported in the National Vulnerability Database. Among these 15 vulnerabilities, 7 are unknown and have been reported to the vendors, and the other 8 have been "silently" patched by the vendors when releasing newer versions of the products.