We generalize, by a progressive procedure, the notions of conjunction and disjunction of two conditional events to the case of $n$ conditional events. In our coherence-based approach, conjunctions and disjunctions are suitable conditional random quantities. We define the notion of negation, by verifying De Morgan's Laws. We also show that conjunction and disjunction satisfy the associative and commutative properties, and a monotonicity property. Then, we give some results on coherence of prevision assessments for some families of compounded conditionals; in particular we examine the Fr\'echet-Hoeffding bounds. Moreover, we study the reverse probabilistic inference from the conjunction $\mathcal{C}_{n+1}$ of $n+1$ conditional events to the family $\{\mathcal{C}_{n},E_{n+1}|H_{n+1}\}$. We consider the relation with the notion of quasi-conjunction and we examine in detail the coherence of the prevision assessments related with the conjunction of three conditional events. Based on conjunction, we also give a characterization of p-consistency and of p-entailment, with applications to several inference rules in probabilistic nonmonotonic reasoning. Finally, we examine some non p-valid inference rules; then, we illustrate by an example two methods which allow to suitably modify non p-valid inference rules in order to get inferences which are p-valid.

Rehabilitation robotics combined with video game technology provides a means of assisting in the rehabilitation of patients with neuromuscular disorders by performing various facilitation movements. The current work presents ReHabGame, a serious game using a fusion of implemented technologies that can be easily used by patients and therapists to assess and enhance sensorimotor performance and also increase the activities in the daily lives of patients. The game allows a player to control avatar movements through a Kinect Xbox, Myo armband and rudder foot pedal, and involves a series of reach-grasp-collect tasks whose difficulty levels are learnt by a fuzzy interface. The orientation, angular velocity, head and spine tilts and other data generated by the player are monitored and saved, whilst the task completion is calculated by solving an inverse kinematics algorithm which orientates the upper limb joints of the avatar. The different values in upper body quantities of movement provide fuzzy input from which crisp output is determined and used to generate an appropriate subsequent rehabilitation game level. The system can thus provide personalised, autonomously-learnt rehabilitation programmes for patients with neuromuscular disorders with superior predictions to guide the development of improved clinical protocols compared to traditional theraputic activities.

Cyber fraud costs organizations billions of dollars each year, and its financial impact continues to climb as criminals are getting smarter and their attacks more complex. While the increasing need for rapid and complex fraud risk detection is common in many sectors, it is perhaps most acute among financial institutions and online merchants. Competition is fierce in these highly digitized markets, and margins are razor-thin. Customers are extremely demanding, and constantly seek better, more user-friendly payment options and channels. Cross-channel fraud detection has been an area of focus for both business and security leaders for nearly a decade. It began in earnest following the FFIEC's publication of guidance in January of 2011.

The Blockchain market is forecast to grow in a 61.5% Compound Annual Growth Rate (CAGR) between 2016 and 2021, developing from $.2B to $2.3B in 2021. The largest segments are in the company and financial services and technologies, telecom and media. The biggest protocols comprise Bitcoin, Ethereum, and Ripple. Deloitte discovered that banks have allegedly stored between $8B to12B annually with blockchain technology to enhance operational efficiencies. The Artificial Intelligence (AI) market is predicted to rise from $8B in 2016 to $72B from 2021, reaching a 55.1 percent CAGR.

In ontology-based data access (OBDA), the classical database is enhanced with an ontology in the form of logical assertions generating new intensional knowledge. A powerful form of such logical assertions is the tuple-generating dependencies (TGDs), also called existential rules, where Horn rules are extended by allowing existential quantifiers to appear in the rule heads. In this paper we introduce a new language called loop restricted (LR) TGDs (existential rules), which are TGDs with certain restrictions on the loops embedded in the underlying rule set. We study the complexity of this new language. We show that the conjunctive query answering (CQA) under the LR TGDs is decid- able. In particular, we prove that this language satisfies the so-called bounded derivation-depth prop- erty (BDDP), which implies that the CQA is first-order rewritable, and its data complexity is in AC0 . We also prove that the combined complexity of the CQA is EXPTIME complete, while the language membership is PSPACE complete. Then we extend the LR TGDs language to the generalised loop restricted (GLR) TGDs language, and prove that this class of TGDs still remains to be first-order rewritable and properly contains most of other first-order rewritable TGDs classes discovered in the literature so far.

In this paper we introduce a new class of tuple-generating dependencies (TGDs) called triangularly-guarded TGDs, which are TGDs with certain restrictions on the atomic derivation track embedded in the underlying rule set. We show that conjunctive query answering under this new class of TGDs is decidable. We further show that this new class strictly contains some other decidable classes such as weak-acyclic, guarded, sticky and shy, which, to the best of our knowledge, provides a unified representation of all these aforementioned classes.

The financial services industry is currently on the brink of a massive technological disruption. Financial institutions are now beginning to actively explore new technologies, such as Artificial Intelligence (AI) and robotic process automation (RPA) to further automate routine AML and KYC processes and thereby improve operational efficiencies and resource utilization. The first wave of AI technology deployment is already happening in global banks: rule-based AIs (typically based on'if-then' rules) are enhancing productivity in internal processes. With the advent of AI applications for Know your Client (KYC) and Anti-Money Laundering (AML) purposes, financial institutions' adoption of technology in these labor-intensive and high-risk areas seems certain to rapidly accelerate. One of the most powerful ways AI can be applied in a client due diligence context is in using Natural Language Processing (NLP) to'read' vast amounts of information in any language.

We introduce a new benchmark, WinoBias, for coreference resolution focused on gender bias. Our corpus contains Winograd-schema style sentences with entities corresponding to people referred by their occupation (e.g. the nurse, the doctor, the carpenter). We demonstrate that a rule-based, a feature-rich, and a neural coreference system all link gendered pronouns to pro-stereotypical entities with higher accuracy than anti-stereotypical entities, by an average difference of 21.1 in F1 score. Finally, we demonstrate a data-augmentation approach that, in combination with existing word-embedding debiasing techniques, removes the bias demonstrated by these systems in WinoBias without significantly affecting their performance on existing coreference benchmark datasets. Our dataset and code are available at http://winobias.org.

We present QARMA, an efficient novel parallel algorithm for mining all Quantitative Association Rules in large multidimensional datasets where items are required to have at least a single common attribute to be specified in the rules single consequent item. Given a minimum support level and a set of threshold criteria of interestingness measures such as confidence, conviction etc. our algorithm guarantees the generation of all non-dominated Quantitative Association Rules that meet the minimum support and interestingness requirements. Such rules can be of great importance to marketing departments seeking to optimize targeted campaigns, or general market segmentation. They can also be of value in medical applications, financial as well as predictive maintenance domains. We provide computational results showing the scalability of our algorithm, and its capability to produce all rules to be found in large scale synthetic and real world datasets such as Movie Lens, within a few seconds or minutes of computational time on commodity hardware.

The US and China have imposed tariffs on each other's goods. But will a skirmish between the world's two biggest economies turn into a full-on trade war? Perhaps it has already started. Both sides have struck initial blows. The US has imposed tariffs on imports of steel and aluminium.