A Database Interface for Complex Objects

We describe a formal design for a logical query language using psi-terms as data structures to interact effectively and efficiently with a relational database. The structure of psi-terms provides an adequate representation for so-called complex objects. They generalize conventional terms used in logic programming: they are typed attributed structures, ordered thanks to a subtype ordering. Unification of psi-terms is an effective means for integrating multiple inheritance and partial information into a deduction process. We define a compact database representation for psi-terms, representing part of the subtyping relation in the database as well. We describe a retrieval algorithm based on an abstract interpretation of the psi-term unification process and prove its formal correctness. This algorithm is efficient in that it incrementally retrieves only additional facts that are actually needed by a query, and never retrieves the same fact twice

Probabilistic abductive logic programming using Dirichlet priors

Probabilistic programming is an area of research that aims to develop general inference algorithms for probabilistic models expressed as probabilistic programs whose execution corresponds to inferring the parameters of those models. In this paper, we introduce a probabilistic programming language (PPL) based on abductive logic programming for performing inference in probabilistic models involving categorical distributions with Dirichlet priors. We encode these models as abductive logic programs enriched with probabilistic definitions and queries, and show how to execute and compile them to boolean formulas. Using the latter, we perform generalized inference using one of two proposed Markov Chain Monte Carlo (MCMC) sampling algorithms: an adaptation of uncollapsed Gibbs sampling from related work and a novel collapsed Gibbs sampling (CGS). We show that CGS converges faster than the uncollapsed version on a latent Dirichlet allocation (LDA) task using synthetic data. On similar data, we compare our PPL with LDA-specific algorithms and other PPLs. We find that all methods, except one, perform similarly and that the more expressive the PPL, the slower it is. We illustrate applications of our PPL on real data in two variants of LDA models (Seed and Cluster LDA), and in the repeated insertion model (RIM). In the latter, our PPL yields similar conclusions to inference with EM for Mallows models

CATS: linearizability and partition tolerance in scalable and self-organizing key-value stores

Distributed key-value stores provide scalable, fault-tolerant, and self-organizing storage services, but fall short of guaranteeing linearizable consistency in partially synchronous, lossy, partitionable, and dynamic networks, when data is distributed and replicated automatically by the principle of consistent hashing. This paper introduces consistent quorums as a solution for achieving atomic consistency. We present the design and implementation of CATS, a distributed key-value store which uses consistent quorums to guarantee linearizability and partition tolerance in such adverse and dynamic network conditions. CATS is scalable, elastic, and self-organizing; key properties for modern cloud storage middleware. Our system shows that consistency can be achieved with practical performance and modest throughput overhead (5%) for read-intensive workloads

Introduction to the GiNaC Framework for Symbolic Computation within the C++ Programming Language

The traditional split-up into a low level language and a high level language in the design of computer algebra systems may become obsolete with the advent of more versatile computer languages. We describe GiNaC, a special-purpose system that deliberately denies the need for such a distinction. It is entirely written in C++ and the user can interact with it directly in that language. It was designed to provide efficient handling of multivariate polynomials, algebras and special functions that are needed for loop calculations in theoretical quantum field theory. It also bears some potential to become a more general purpose symbolic package

Elemantra: An End-to-End Automated Framework Empowered with AI and IoT for Tackling Human-Elephant Conflict in Elephant-Range Countries

The cohabitation of elephants and humans has evolved into a human-elephant conflict (HEC) due to the increasing loss of historical elephant habitations. Since HEC is a substantial threat to both species, advanced sensing methods are utilized to develop HEC prevention frameworks which still lack unification. Here, we propose an end-to-end automated framework for HEC prevention consisting of three main modules: a distributed deep learning-assisted elephant detection module using infrared and seismic sensing, an on-site repelling system with time-varying acoustic and light deterrents and a mesh network for device communication. The framework is equipped with novel decision-making pipelines and algorithms such that it has the potential to operate with no human intervention. The preliminary results from each module confirm that the proposed framework is effective in performing their individual tasks towards collaboratively achieving the prevention of HEC. The codes are publicly available at https://github.com/nuwansribandara/elemantra.Comment: Submitted to IEEE Sensors Letters, 4 pages, 5 figure

WiSHFUL : enabling coordination solutions for managing heterogeneous wireless networks

The paradigm shift toward the Internet of Things results in an increasing number of wireless applications being deployed. Since many of these applications contend for the same physical medium (i.e., the unlicensed ISM bands), there is a clear need for beyond-state-of-the-art solutions that coordinate medium access across heterogeneous wireless networks. Such solutions demand fine-grained control of each device and technology, which currently requires a substantial amount of effort given that the control APIs are different on each hardware platform, technology, and operating system. In this article an open architecture is proposed that overcomes this hurdle by providing unified programming interfaces (UPIs) for monitoring and controlling heterogeneous devices and wireless networks. The UPIs enable creation and testing of advanced coordination solutions while minimizing the complexity and implementation overhead. The availability of such interfaces is also crucial for the realization of emerging software-defined networking approaches for heterogeneous wireless networks. To illustrate the use of UPIs, a showcase is presented that simultaneously changes the MAC behavior of multiple wireless technologies in order to mitigate cross-technology interference taking advantage of the enhanced monitoring and control functionality. An open source implementation of the UPIs is available for wireless researchers and developers. It currently supports multiple widely used technologies (IEEE 802.11, IEEE 802.15.4, LTE), operating systems (Linux, Windows, Contiki), and radio platforms (Atheros, Broadcom, CC2520, Xylink Zynq,), as well as advanced reconfigurable radio systems (IRIS, GNURadio, WMP, TAISC)