Data Mining - Concepts and Techniques.

In the last years, Artificial Intelligence (AI) has achieved a notable momentum that may deliver the best of expectations over many application sectors across the field. For this to occur, the entire community stands in front of the barrier of explainability, an inherent problem of AI techniques brought by sub-symbolism (e.g. ensembles or Deep Neural Networks) that were not present in the last hype of AI. Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is acknowledged as a crucial feature for the practical deployment of AI models. This overview examines the existing literature in the field of XAI, including a prospect toward what is yet to be reached. We summarize previous efforts to define explainability in Machine Learning, establishing a novel definition that covers prior conceptual propositions with a major focus on the audience for which explainability is sought. We then propose and discuss about a taxonomy of recent contributions related to the explainability of different Machine Learning models, including those aimed at Deep Learning methods for which a second taxonomy is built. This literature analysis serves as the background for a series of challenges faced by XAI, such as the crossroads between data fusion and explainability. Our prospects lead toward the concept of Responsible Artificial Intelligence, namely, a methodology for the large-scale implementation of AI methods in real organizations with fairness, model explainability and accountability at its core. Our ultimate goal is to provide newcomers to XAI with a reference material in order to stimulate future research advances, but also to encourage experts and professionals from other disciplines to embrace the benefits of AI in their activity sectors, without any prior bias for its lack of interpretability.

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI.

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI

In this paper, we define and explore proofs of retrievability (PORs). A POR scheme enables an archive or back-up service (prover) to produce a concise proof that a user (verifier) can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety.A POR may be viewed as a kind of cryptographic proof of knowledge (POK), but one specially designed to handle a large file (or bitstring) F. We explore POR protocols here in which the communication costs, number of memory accesses for the prover, and storage requirements of the user (verifier) are small parameters essentially independent of the length of F. In addition to proposing new, practical POR constructions, we explore implementation considerations and optimizations that bear on previously explored, related schemes.In a POR, unlike a POK, neither the prover nor the verifier need actually have knowledge of F. PORs give rise to a new and unusual security definition whose formulation is another contribution of our work.We view PORs as an important tool for semi-trusted online archives. Existing cryptographic techniques help users ensure the privacy and integrity of files they retrieve. It is also natural, however, for users to want to verify that archives do not delete or modify files prior to retrieval. The goal of a POR is to accomplish these checks without users having to download the files themselves. A POR can also provide quality-of-service guarantees, i.e., show that a file is retrievable within a certain time bound.

PORs: Proofs of Retrievability for Large Files

http://www.parkjonghyuk.net/lecture/2015-1st-lecture/networksecurity/Paper1.pdf

Security, privacy and trust in Internet of Things

A novel scheme of anonymous authentication on trust in Pervasive Social Networking

Graph Attention Network With Spatial-Temporal Clustering for Traffic Flow Forecasting in Intelligent Transportation System

Big data fusion in Internet of Things

Along with the exhaustion of fossil fuels and the environmental pollution problem, renewable energy will surely become the mainstream of the future energy sector in the world. The uncertainty of large-scale intermittent energy output brings a great challenge to the safe operation of power grids. In this paper, taking into account the volatility and randomness of wind power and solar energy, we present a multi-energy coupling model with the core of hydrogen energy based on energy hub. To maximize operational profits of multi-energy system, an optimization problem is formulated to achieve coordinated operation using matrix coupling methodology. The matrix coupling mode can represent the input-output relationship of energy carriers. The CPLEX software is used to obtain the optimal solution in the dispatching period. In contrast with the traditional operation mode without the utilization of hydrogen, simulation results indicate that the proposed model achieves the higher penetration of renewable energy and more profits.

Optimal operation of wind-solar-hydrogen storage system based on energy hub

At the same time as the Internet provides a lot of social value, it is bogged down by unwanted traffic, which is malicious, harmful or unexpected for its receiver. This paper proposes an unwanted traffic control solution through hybrid trust management. It can control unwanted traffic from its source to destinations according to trust evaluation at a Global Trust Operator and traffic and behavior analysis at hosts. Thus, it can support unwanted traffic control in both a distributed and centralized manner and in both a defensive and offensive way. Simulation based evaluation shows that the solution is effective with regard to botnet intrusion, malicious attack of ISP and DDoS intrusion via reflectors.

Unwanted Traffic Control via Hybrid Trust Management

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