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.

A Wireless Sensor MAC Protocol for Bursty Data Traffic

Abstract: This paper proposes MH-MAC, a new MAC protocol for wireless sensor networks capable of handling applications that generate infrequent huge peaks of traffic. Existing protocols are not adapted to this kind of applications. Asynchronous protocols are energy efficient for the long inactive periods, but fail to cope with the bandwidth and latency requirements of the traffic peaks when more than two nodes are sending data to a common sink. Synchronous protocols that support contention free slots provide good throughput for handling the load peaks, but consume unnecessary energy maintaining clocks synchronized for very long idle periods. MH-MAC is a multimode hybrid protocol that can be configured by the application to run in asynchronous mode or in synchronous mode, with or without contention, providing the best possible trade-off. MH-MAC is a single-hop MAC, which supports multi-hop applications through a cross-layering API. The paper includes simulation results with the energy consumption, latency and throughput for the operation modes of MH-MAC, showing the asynchronous-synchronous trade-offs and the state transition overhead.

Load allocation in DMS with a fuzzy state estimator

Abstract: This paper describes a load allocation model to be used in a distribution management system (DMS) environment. A process of rough allocation is initiated, based on information on actual measurements and on data about installed capacity and power and energy consumption at LV substations. This process generates a fuzzy load allocation, which is then corrected by a fuzzy state estimator procedure in order to generate a crisp power flow compatible set of load allocations, coherent with available real time measurements recorded in the SCADA.

An exact algorithm for the maximal sharing of partial terms in multiple constant multiplications

Abstract: In this paper, we propose an exact algorithm that maximizes the sharing of partial terms in multiple constant multiplication (MCM) operations. We model this problem as a Boolean network that covers all possible partial terms which may be used to generate the set of coefficients in the MCM instance. The PIs to this network are shifted versions of the MCM input. An AND gate represents an adder or a subtracter, i.e., an AND gate generates a new partial term. All partial terms that have the same numerical value are ORed together. There is a single output which is an /spl and/ over all the coefficients in the MCM. We cast this problem into a 0-1 integer linear programming (ILP) problem by requiring that the output is asserted while minimizing the total number of AND gates that evaluate to one. A SAT-based solver is used to obtain the exact solution. We argue that for many real problems the size of the problem is within the capabilities of current SAT solvers. We present results using binary, CSD and MSD representations. Two main conclusions can be drawn from the results. One is that, in many cases, existing heuristics perform well, computing the best solution, or one close to it. The other is that the flexibility of the MSD representation does not have a significant impact in the solution obtained.

Supervised Topical Key Phrase Extraction of News Stories using Crowdsourcing, Light Filtering and Co-reference Normalization

Abstract: Fast and effective automated indexing is critical for search and personalized services. Key phrases that consist of one or more words and represent the main concepts of the document are often used for the purpose of indexing. In this paper, we investigate the use of additional semantic features and pre-processing steps to improve automatic key phrase extraction. These features include the use of signal words and freebase categories. Some of these features lead to significant improvements in the accuracy of the results. We also experimented with 2 forms of document pre-processing that we call light filtering and co-reference normalization. Light filtering removes sentences from the document, which are judged peripheral to its main content. Co-reference normalization unifies several written forms of the same named entity into a unique form. We also needed a “Gold Standard” ― a set of labeled documents for training and evaluation. While the subjective nature of key phrase selection precludes a true “Gold Standard”, we used Amazon's Mechanical Turk service to obtain a useful approximation. Our data indicates that the biggest improvements in performance were due to shallow semantic features, news categories, and rhetorical signals (nDCG 78.47% vs. 68.93%). The inclusion of deeper semantic features such as Freebase sub-categories was not beneficial by itself, but in combination with pre-processing, did cause slight improvements in the nDCG scores.