EEG transient event detection and classification using association rules.

In this paper, a methodology for the automated detection and classification of transient events in electroencephalographic (EEG) recordings is presented. It is based on association rule mining and classifies transient events into four categories: epileptic spikes, muscle activity, eye blinking activity, and sharp alpha activity. The methodology involves four stages: 1) transient event detection; 2) clustering of transient events and feature extraction; 3) feature discretization and feature subset selection; and 4) association rule mining and classification of transient events. The methodology is evaluated using 25 EEG recordings, and the best obtained accuracy was 87.38%. The proposed approach combines high accuracy with the ability to provide interpretation for the decisions made, since it is based on a set of association rules.     

Trap‐State Suppression and Improved Charge Transport in PbS Quantum Dot Solar Cells with Synergistic Mixed‐Ligand Treatments

The power conversion efficiency of colloidal PbS‐quantum‐dot (QD)‐based solar cells is significantly hampered by lower‐than‐expected open circuit voltage (V_OC). The V_OC deficit is considerably higher in QD‐based solar cells compared to other types of existing solar cells due to in‐gap trap‐induced bulk recombination of photogenerated carriers. Here, this study reports a ligand exchange procedure based on a mixture of zinc iodide and 3‐mercaptopropyonic acid to reduce the V_OC deficit without compromising the high current density. This layer‐by‐layer solid state ligand exchange treatment enhances the photovoltaic performance from 6.62 to 9.92% with a significant improvement in V_OC from 0.58 to 0.66 V. This study further employs optoelectronic characterization, X‐ray photoelectron spectroscopy, and photoluminescence spectroscopy to understand the origin of V_OC improvement. The mixed‐ligand treatment reduces the sub‐bandgap traps and significantly reduces bulk recombination in the devices.     

Focused crawling for the hidden web

A constantly growing amount of high-quality information resides in databases and is guarded behind forms that users fill out and submit. The Hidden Web comprises all these information sources that conventional web crawlers are incapable of discovering. In order to excavate and make available meaningful data from the Hidden Web, previous work has focused on developing query generation techniques that aim at downloading all the content of a given Hidden Web site with the minimum cost. However, there are circumstances where only a specific part of such a site might be of interest. For example, a politics portal should not have to waste bandwidth or processing power to retrieve sports articles just because they are residing in databases also containing documents relevant to politics. In cases like this one, we need to make the best use of our resources in downloading only the portion of the Hidden Web site that we are interested in. We investigate how we can build a focused Hidden Web crawler that can autonomously extract topic-specific pages from the Hidden Web by searching only the subset that is related to the corresponding area. In this regard, we present an approach that progresses iteratively and analyzes the returned results in order to extract terms that capture the essence of the topic we are interested in. We propose a number of different crawling policies and we experimentally evaluate them with data from four popular sites. Our approach is able to download most of the content in search in all cases, using a significantly smaller number of queries compared to existing approaches.     

A Usable Security Heuristic Evaluation for the Online Health Social Networking Paradigm

The security, privacy, and usability of health social networking websites remain key concerns for their successful adoption by patients. As far as it is known, no studies have developed a usable security heuristic evaluation to test these websites for the usability of their security and privacy features. This article describes the development of such a usable security heuristic evaluation in the context of the online health social networking paradigm. The tailored method approach was applied to transform themes identified from relevant literature and convert them into high-level heuristics for usable security. Checklist items were created for each high-level heuristic. The result is 13 high-level heuristics with individualized checklist items that help examine usable security. The results can identify which security and privacy features are lacking from a usability perspective and would, therefore, require design improvement. The improvements would enhance the users' experiences, which in turn can assist in the adoption of health social networking websites.     

A hierarchical feature fusion framework for adaptive visual tracking

A Hierarchical Model Fusion (HMF) framework for object tracking in video sequences is presented. The Bayesian tracking equations are extended to account for multiple object models. With these equations as a basis a particle filter algorithm is developed to efficiently cope with the multi-modal distributions emerging from cluttered scenes. The update of each object model takes place hierarchically so that the lower dimensional object models, which are updated first, guide the search in the parameter space of the subsequent object models to relevant regions thus reducing the computational complexity. A method for object model adaptation is also developed. We apply the proposed framework by fusing salient points, blobs, and edges as features and verify experimentally its effectiveness in challenging conditions.     

Detecting emotional state of a child in a conversational computer game

The automatic recognition of user’s communicative style within a spoken dialog system framework, including the affective aspects, has received increased attention in the past few years. For dialog systems, it is important to know not only what was said but also how something was communicated, so that the system can engage the user in a richer and more natural interaction. This paper addresses the problem of automatically detecting “frustration”, “politeness”, and “neutral” attitudes from a child’s speech communication cues, elicited in spontaneous dialog interactions with computer characters. Several information sources such as acoustic, lexical, and contextual features, as well as, their combinations are used for this purpose. The study is based on a Wizard-of-Oz dialog corpus of 103 children, 7–14 years of age, playing a voice activated computer game. Three-way classification experiments, as well as, pairwise classification between polite vs. others and frustrated vs. others were performed. Experimental results show that lexical information has more discriminative power than acoustic and contextual cues for detection of politeness, whereas context and acoustic features perform best for frustration detection. Furthermore, the fusion of acoustic, lexical and contextual information provided significantly better classification results. Results also showed that classification performance varies with age and gender. Specifically, for the “politeness” detection task, higher classification accuracy was achieved for females and 10–11 years-olds, compared to males and other age groups, respectively.     

Activity sequence modelling and dynamic clustering for personalized e-learning

Monitoring and interpreting sequential learner activities has the potential to improve adaptivity and personalization within educational environments. We present an approach based on the modeling of learners?? problem solving activity sequences, and on the use of the models in targeted, and ultimately automated clustering, resulting in the discovery of new, semantically meaningful information about the learners. The approach is applicable at different levels: to detect pre-defined, well-established problem solving styles, to identify problem solving styles by analyzing learner behaviour along known learning dimensions, and to semi-automatically discover learning dimensions and concrete problem solving patterns. This article describes the approach itself, demonstrates the feasibility of applying it on real-world data, and discusses aspects of the approach that can be adjusted for different learning contexts. Finally, we address the incorporation of the proposed approach in the adaptation cycle, from data acquisition to adaptive system interventions in the interaction process.     

Multimodal and ontology-based fusion approaches of audio and visual processing for violence detection in movies

In this paper we present our research results towards the detection of violent scenes in movies, employing advanced fusion methodologies, based on learning, knowledge representation and reasoning. Towards this goal, a multi-step approach is followed: initially, automated audio and visual analysis is performed to extract audio and visual cues. Then, two different fusion approaches are deployed: (i) a multimodal one that provides binary decisions on the existence of violence or not, employing machine learning techniques, (ii) an ontological and reasoning one, that combines the audio-visual cues with violence and multimedia ontologies. The latter reasons out not only the existence of violence or not in a video scene, but also the type of violence (fight, screams, gunshots). Both approaches are experimentally tested, validated and compared for the binary decision problem of violence detection. Finally, results for the violence type identification are presented for the ontological fusion approach. For evaluation purposes, a large dataset of real movie data has been populated.     

The welfare costs of non-marginal water pricing: evidence from the water only companies in England and Wales

The evaluation of the economic efficiency of regulatory schemes is essential for regulators and utilities. In this study it is analysed for the first time the welfare costs of non-marginal cost pricing in the water supply in England and Wales, by computing the deadweight loss of the water only companies (WoCs) that existed over the period of 1993–2009. The results indicate that the current price schemes can have substantial efficiency costs. Our estimates show that the loss of efficiency for the WoCs lies between 15 and 60 million GBP over the period 1993–2009. These amounts could have been redistributed either to the companies in terms of profits or to the consumers via price reductions. The methodology and results of this study are of great interest for both regulators and water utilities managers to evaluate the effectiveness of price regulation and make informed decisions.