Energy-Efficient Actor Execution for SDF Application on Heterogeneous Architectures

International audienceHeterogeneous systems promise to improve performance and endurance of power constrained systems, by utilizing computing elements of different power and performance characteristics. Such systems provide the possibility to trade number and types of core with Dynamic Voltage and Frequency Scaling (DVFS) levels and core utilization rate to achieve optimal energy efficiency. Therefore by making smart decisions on application scheduling and mapping we can exploit and maximize the benefits of using heterogeneous processors. At the same time, the application level of parallelism can conveniently be exposed by dataflow Models of Computation (MoCs). In this paper we show an energy efficient execution approach for heterogeneous architecture. We demonstrate the approach on a real-life streaming application modelled with Parameterized and Interfaced Synchronous Dataflow (PiSDF). The presented solution shows how to integrate our approach in the workflow of a dataflow application prototyping tool. The obtained results demonstrate that, by using an optimal scheduling and mapping, more than 30% of energy reduction can be achieved on a single actor level. © 2018 IEEE

Population-based case-control study of breast cancer in Albania

In Albania, breast cancer is an important cause of death among women, with increasing incidence from 65 cases in 1970, to 400 cases in 2007. This is the first study concerning breast cancer risk factors in Albania. We used a population-based case-control study of 948 women with breast cancer compared with 1019 controls recruited from other hospitals through random selection. Early age at menarche was found to be a significantly strong risk factor during the pre- and postmenopausal groups with OR 10.04 and 12.1, respectively. In addition, nulliparity is associated with higher risk while abortion did not indicate any influence in the multivariate model. The findings from this study have shown that reproductive and menstrual variables are significant predictors of breast cancer risk in Albanian women, as seen in studies of other western countries

The NeuroSent System at ESWC-2018 Challenge on Semantic Sentiment Analysis

Multi-domain sentiment analysis consists in estimating the polarity of a given text by exploiting domain-specific information. One of the main issues common to the approaches discussed in the literature is their poor capabilities of being applied on domains which are different from those used for building the opinion model. In this paper, we will present an approach exploiting the linguistic overlap between domains to build sentiment models supporting polarity inference for documents belonging to every domain. Word embeddings together with a deep learning architecture have been implemented for enabling the building of multi-domain sentiment model. The proposed technique is validated by following the Dranziera protocol in order to ease the repeatability of the experiments and the comparison of the results. The outcomes demonstrate the effectiveness of the proposed approach and also set a plausible starting point for future work

From Conditional Random Field (CRF) to Rhetorical Structure Theory (RST): incorporating context information in sentiment analysis

This paper investigates a method based on Conditional Random Fields (CRFs) to incorporate sentence structure (syntax and semantics) and context information to identify sentiments of sentences. It also demonstrates the usefulness of the Rhetorical Structure Theory (RST) taking into consideration the discourse role of text segments. Thus, this paper’s aim is to reconsider the effectiveness of CRF and RST methods in incorporating the contextual information into Sentiment Analysis systems. Both methods are evaluated on two, different in size and genre of information sources, the Movie Review Dataset and the Finegrained Sentiment Dataset (FSD). Finally, we discuss the lessons learned from these experimental settings w.r.t. addressing the following key research questions such as whether there is an appropriate type of social media repository to incorporate contextual information, whether extending the pool of the selected features could improve context incorporation into SA systems and which is the best performing feature combination to achieve such improved performance

The IRMUDOSA System at ESWC-2017 Challenge on Semantic Sentiment Analysis

Multi-Domain opinion mining consists in estimating the polarity of a document by exploiting domain-specific information. One of the main issue of the approaches discussed in literature is their poor capability of being applied on domains that have not been used for building the opinion model. In this paper, we present an approach exploiting the linguistic overlap between domains for building models enabling the estimation of polarities for documents belonging to any other domain. The system implementing such an approach has been presented at the third edition of the Semantic Sentiment Analysis Challenge co-located with ESWC 2017. Fuzzy representation of features polarity supports the modeling of information uncertainty learned from training set and integrated with knowledge extracted from two well-known resources used in the opinion mining field, namely Sentic.Net and the General Inquirer. The proposed technique has been validated on a multi-domain dataset and the results demonstrated the effectiveness of the proposed approach by setting a plausible starting point for future work

The CLAUSY System at ESWC-2018 Challenge on Semantic Sentiment Analysis

With different social media and commercial platforms, users express their opinion about products in a textual form. Automatically extracting the polarity(i.e. whether the opinion is positive or negative) of a user can be useful for both actors: the online platform incorporating the feedback to improve their product as well as the client who might get recommendations according to his or her preferences. Different approaches for tackling the problem, have been suggested mainly using syntactic features. The “Challenge on Semantic Sentiment Analysis” aims to go beyond the word-level analysis by using semantic information. In this paper we propose a novel approach by employing the semantic information of grammatical unit called preposition. We try to derive the target of the review from the summary information, which serves as an input to identify the proposition in it. Our implementation relies on the hypothesis that the proposition expressing the target of the summary, usually containing the main polarity information

The FeatureSent System at ESWC-2018 Challenge on Semantic Sentiment Analysis

The approach described in this paper explores the use of semantic structured representation of sentences extracted from texts for multi-domain sentiment analysis purposes. The presented algorithm is built upon a domain-based supervised approach using index-like structured for representing information extracted from text. The algorithm extracts dependency parse relationships from the sentences containing in a training set. Then, such relationships are aggregated in a semantic structured together with either polarity and domain information. Such information is exploited in order to have a more fine-grained representation of the learned sentiment information. When the polarity of a new text has to be computed, such a text is converted in the same semantic representation that is used (i) for detecting the domain to which the text belongs to, and then (ii), once the domain is assigned to the text, the polarity is extracted from the index-like structure. First experiments performed by using the Blitzer dataset for training the system demonstrated the feasibility of the proposed approach