INESC-ID

About: INESC-ID is a nonprofit organization based out in Lisbon, Portugal. It is known for research contribution in the topics: Computer science & Context (language use). The organization has 932 authors who have published 2618 publications receiving 37658 citations.

SOAR: A Synthesis Approach for Data Science API Refactoring

Abstract: With the growth of the open-source data science community, both the number of data science libraries and the number of versions for the same library are increasing rapidly. To match the evolving APIs from those libraries, open-source organizations often have to exert manual effort to refactor the APIs used in the code base. Moreover, due to the abundance of similar open-source libraries, data scientists working on a certain application may have an abundance of libraries to choose, maintain and migrate between. The manual refactoring between APIs is a tedious and error-prone task. Although recent research efforts were made on performing automatic API refactoring between different languages, previous work relies on statistical learning with collected pairwise training data for the API matching and migration. Using large statistical data for refactoring is not ideal because such training data will not be available for a new library or a new version of the same library. We introduce Synthesis for Open-Source API Refactoring (SOAR), a novel technique that requires no training data to achieve API migration and refactoring. SOAR relies only on the documentation that is readily available at the release of the library to learn API representations and mapping between libraries. Using program synthesis, SOAR automatically computes the correct configuration of arguments to the APIs and any glue code required to invoke those APIs. SOAR also uses the interpreter's error messages when running refactored code to generate logical constraints that can be used to prune the search space. Our empirical evaluation shows that SOAR can successfully refactor 80% of our benchmarks corresponding to deep learning models with up to 44 layers with an average run time of 97.23 seconds, and 90% of the data wrangling benchmarks with an average run time of 17.31 seconds.

ICA of linear and nonlinear mixtures based on mutual information

Abstract: In independent component analysis (ICA), both linear and nonlinear, one of the best objective functions is the mutual information (MI) of the estimated components. However, use of the MI demands the estimation of the probability densities of those components from a finite number of training samples. Several forms of smoothing have been used to estimate these densities from data, including series expansions and Gaussian kernels. This paper proposes a new way to estimate these densities, simultaneously with the ICA operation. The resulting system is a neural network with a specialized architecture, optimized by a single objective function - the output entropy. The paper includes experimental results, which also illustrate that it is possible to perform nonlinear blind source separation when the mixtures have smooth nonlinearities.

Fast Fading Characterization for Body Area Networks in Circular Metallic Indoor Environments

Abstract: With the increasing development of 5G and Body Area Network based systems being implemented in unusual environments, propagation inside metallic structures is a key aspect to characterize propagation effects inside ships and other similar environments, mostly composed of metallic walls. In this paper, indoor propagation inside circular metallic structures is addressed and fast fading statistical distributions parameters are obtained from simulation, being assessed with measurements at 2.45 GHz in a passenger ferry discotheque with an 8 m diameter circular shape. It is observed that, in this kind of environments, second order reflections are particularly relevant due to the walls' high reflective nature. Globally, it is concluded that the Rayleigh distribution can be used to characterize fast fading effects with no significant loss of accuracy compared to the Rice one, since a low value of the Rice parameter is observed, being below 3.1 dB, even under Line-of-Sight conditions. Moreover, it is observed that, from the fast fading viewpoint, the best transmitter position is at the circle center.

Accelerating the Computation of Induced Dipoles for Molecular Mechanics with Dataflow Engines

Abstract: In Molecular Mechanics simulations, the treatment of electrostatics is the most computational intensive task. Modern force fields, such as the AMOEBA, which include explicit polarization effects, are particularly computationally demanding. We propose a static dataflow architecture for accelerating polarizable force fields. Results, obtained with Maxeler's MaxCompiler, show a speed-up factor of about 14x on a Maxeler 1U MaxNode, when compared to a 12-core CPU node while using half of the dataflow engine capacity. Projections for a full chip implementation indicate that speed-up results of up to 29x per node can be reached. Moreover, our implementation on the Maxeler system shows improvements between 2.5x and 4x compared to NVIDIA Fermibased GPUs. The current work shows the potential of dataflow engines in accelerating this field of applications.

Decentralized Enforcement of Business Process Control Using Blockchain

Abstract: Lack of traceability and control is a problem nowadays identified by industries. There are many situations that prove the existence of this problem: lack of trust between actors, lack of information about defected products within business transactions, exception handling, actors performing workarounds and not conforming prescriptions, etc. To tackle this problem, we consider knowledge from (i) DEMO, an Enterprise Ontology that models business transactions and human interactions on organizations, and (ii) Blockchain, a technology that eliminates the need of intermediaries, provides trust among the actors and traceability over business transactions. Hyperledger Composer (HC) is a toolset example to develop Blockchain applications. This paper relates and integrates concepts between DEMO business transactions and HC, then applies the conceptualization to a context of business transactions supporting food supply and distribution. Moreover, an initial prototype implementation, supported on HC with two-clients using a user interface, shows traceability and control capabilities.