When it comes to digital business, Andrew McAfee knows a thing or two. A principal research scientist at MIT, prolific writer and management expert, McAfee is a leader in understanding and explaining how digital technologies are changing business, the economy, and society. At the recent SAP Leonardo Live event in Chicago that focused on digital transformation, McAfee urged his audience to throw out the business playbook they've been using for the past 30 years. "The right way to run a factory in the steam era became a really, really bad way to run it in the era of electrical power," he said. "Similarly, during a technology transition -- and afterwards -- the advice you used to follow becomes bad advice."

Declarative modeling uses symbolic expressions to represent models. With such expressions one can formalize high-level mathematical computations on models that would be difficult or impossible to perform directly on a lower-level simulation program, in a general-purpose programming language. Examples of such computations on models include model analysis, relatively general-purpose model-reduction maps, and the initial phases of model implementation, all of which should preserve or approximate the mathematical semantics of a complex biological model. Multiscale modeling benefits from both the expressive power of declarative modeling languages and the application of model reduction methods to link models across scale. Based on previous work, here we define declarative modeling of complex biological systems by defining the semantics of an increasingly powerful series of declarative modeling languages including reaction-like dynamics of parameterized and extended objects, we define semantics-preserving implementation and semantics-approximating model reduction transformations, and we outline a "meta-hierarchy" for organizing declarative models and the mathematical methods that can fruitfully manipulate them.

The terms "artificial intelligence" and "machine learning" immediately bring up thoughts from movies like "The Matrix" where machines become self-aware and want to end the world. While this may make for an exciting plot in Hollywood, it is not reality outside of the theater. In real life, however, machine learning--which gives computers the ability to see hidden patterns in existing data and progressively improve performance ("learn") without being explicitly programmed--serves as a practical tool to data analysts. The job is not to turn robots into people, but instead efficiently find recurring themes that would otherwise remain obscured inside of large amounts of data to provide end-users with actionable information. These technologies have played a pivotal role in reducing fraud, waste and abuse in organizations of all types and sizes, including departments of revenue that collect taxes.

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.