Structured cases in case-based reasoning: re-using and adapting cases for time-tabling problems

In this paper, we present a case-based reasoning (CBR) approach solving educational time-tabling problems. Following the basic idea behind CBR, the solutions of previously solved problems are employed to aid finding the solutions for new problems. A list of feature-value pairs is insufficient to represent all the necessary information. We show that attribute graphs can represent more information and thus can help to retrieve re-usable cases that have similar structures to the new problems. The case base is organised as a decision tree to store the attribute graphs of solved problems hierarchically. An example is given to illustrate the retrieval, re-use and adaptation of structured cases. The results from our experiments show the effectiveness of the retrieval and adaptation in the proposed method.

Multiple-Retrieval Case-Based Reasoning for Course Timetabling Problems

The structured representation of cases by attribute graphs in a Case-Based Reasoning (CBR) system for course timetabling has been the subject of previous research by the authors. In that system, the case base is organised as a decision tree and the retrieval process chooses those cases which are sub attribute graph isomorphic to the new case. The drawback of that approach is that it is not suitable for solving large problems. This paper presents a multiple-retrieval approach that partitions a large problem into small solvable sub-problems by recursively inputting the unsolved part of the graph into the decision tree for retrieval. The adaptation combines the retrieved partial solutions of all the partitioned sub-problems and employs a graph heuristic method to construct the whole solution for the new case. We present a methodology which is not dependant upon problem specific information and which, as such, represents an approach which underpins the goal of building more general timetabling systems. We also explore the question of whether this multiple-retrieval CBR could be an effective initialisation method for local search methods such as Hill Climbing, Tabu Search and Simulated Annealing. Significant results are obtained from a wide range of experiments. An evaluation of the CBR system is presented and the impact of the approach on timetabling research is discussed. We see that the approach does indeed represent an effective initialisation method for these approaches.

Development of gene expression-based biomarkers of exposure to metals and pesticides in the freshwater amphipod Hyalella azteca

Ecological risk assessment (ERA) is a framework for monitoring risks of exposure and adverse effects of environmental stressors to populations or communities of interest. One tool of ERA is the biomarker, which is a characteristic of an organism that reliably indicates exposure to or effects of a stressor like chemical pollution. Traditional biomarkers which rely on characteristics at the tissue level and higher often detect only acute exposures to stressors. Sensitive molecular biomarkers may detect lower stressor levels than traditional biomarkers, which helps inform risk mitigation and restoration efforts before populations and communities are irreversibly affected. In this study I developed gene expression-based molecular biomarkers of exposure to metals and insecticides in the model toxicological freshwater amphipod Hyalella azteca. My goals were to not only create sensitive molecular biomarkers for these chemicals, but also to show the utility and versatility of H. azteca in molecular studies for toxicology and risk assessment. I sequenced and assembled the H. azteca transcriptome to identify reference and stress-response gene transcripts suitable for expression monitoring. I exposed H. azteca to sub-lethal concentrations of metals (cadmium and copper) and insecticides (DDT, permethrin, and imidacloprid). Reference genes used to create normalization factors were determined for each exposure using the programs BestKeeper, GeNorm, and NormFinder. Both metals increased expression of a nuclear transcription factor (Cnc), an ABC transporter (Mrp4), and a heat shock protein (Hsp90), giving evidence of general metal exposure signature. Cadmium uniquely increased expression of a DNA repair protein (Rad51) and increased Mrp4 expression more than copper (7-fold increase compared to 2-fold increase). Together these may be unique biomarkers distinguishing cadmium and copper exposures. DDT increased expression of Hsp90, Mrp4, and the immune response gene Lgbp. Permethrin increased expression of a cytochrome P450 (Cyp2j2) and decreased expression of the immune response gene Lectin-1. Imidacloprid did not affect gene expression. Unique biomarkers were seen for DDT and permethrin, but the genes studied were not sensitive enough to detect imidacloprid at the levels used here. I demonstrated that gene expression in H. azteca detects specific chemical exposures at sub-lethal concentrations, making expression monitoring using this amphipod a useful and sensitive biomarker for risk assessment of chemical exposure.

Multiple-retrieval case-based reasoning for course timetabling problems

The structured representation of cases by attribute graphs in a Case-Based Reasoning (CBR) system for course timetabling has been the subject of previous research by the authors. In that system, the case base is organised as a decision tree and the retrieval process chooses those cases which are sub attribute graph isomorphic to the new case. The drawback of that approach is that it is not suitable for solving large problems. This paper presents a multiple-retrieval approach that partitions a large problem into small solvable sub-problems by recursively inputting the unsolved part of the graph into the decision tree for retrieval. The adaptation combines the retrieved partial solutions of all the partitioned sub-problems and employs a graph heuristic method to construct the whole solution for the new case. We present a methodology which is not dependant upon problem specific information and which, as such, represents an approach which underpins the goal of building more general timetabling systems. We also explore the question of whether this multiple-retrieval CBR could be an effective initialisation method for local search methods such as Hill Climbing, Tabu Search and Simulated Annealing. Significant results are obtained from a wide range of experiments. An evaluation of the CBR system is presented and the impact of the approach on timetabling research is discussed. We see that the approach does indeed represent an effective initialisation method for these approaches.

Savani: A variable resolution whole-mantle model of anisotropic shear velocity variations based on multiple data sets

International audience

The microbiological signature of human cutaneous leishmaniasis lesions exhibits restricted bacterial diversity compared to healthy skin

Localised cutaneous leishmaniasis (LCL) is the most common form of cutaneous leishmaniasis characterised by single or multiple painless chronic ulcers, which commonly presents with secondary bacterial infection. Previous culture- based studies have found staphylococci, streptococci, and opportunistic pathogenic bacteria in LCL lesions, but there have been no comparisons to normal skin. In addition, this approach has strong bias for determining bacterial composition. The present study tested the hypothesis that bacterial communities in LCL lesions differ from those found on healthy skin (HS). Using a high throughput amplicon sequencing approach, which allows for better populational evaluation due to greater depth coverage and the Quantitative Insights Into Microbial Ecology pipeline, we compared the microbiological signature of LCL lesions with that of contralateral HS from the same individuals. Streptococcus , Staphylococcus , Fusobacterium and other strict or facultative anaerobic bacteria composed the LCL microbiome. Aerobic and facultative anaerobic bacteria found in HS, including environmental bacteria, were significantly decreased in LCL lesions (p < 0.01). This paper presents the first comprehensive microbiome identification from LCL lesions with next generation sequence methodology and shows a marked reduction of bacterial diversity in the lesions.

ARL1 of the Attribute c Control Chart with Estimated Parameter

International audience

CAISL: Simplification Logic for Conditional Attribute Implications

In this work, we present a sound and complete axiomatic system for conditional attribute implications (CAIs) in Triadic Concept Analysis (TCA). Our approach is strongly based on the Simplification paradigm which offers a more suitable way for automated reasoning than the one based on Armstrong’s Axioms. We also present an automated method to prove the derivability of a CAI from a set of CAI s.

Street characteristics preferred for transportation walking among older adults: a choice-based conjoint analysis with manipulated photographs

BACKGROUND: Knowledge about the relationships between micro-scale environmental factors and older adults' walking for transport is limited and inconsistent. This is probably due to methodological limitations, such as absence of an accurate neighborhood definition, lack of environmental heterogeneity, environmental co-variation, and recall bias. Furthermore, most previous studies are observational in nature. We aimed to address these limitations by investigating the effects of manipulating photographs on micro-scale environmental factors on the appeal of a street for older adults' transportation walking. Secondly, we used latent class analysis to examine whether subgroups could be identified that have different environmental preferences for transportation walking. Thirdly, we investigated whether these subgroups differed in socio-demographic, functional and psychosocial characteristics, current level of walking and environmental perceptions of their own street.

METHODS: Data were collected among 1131 Flemish older adults through an online (n = 940) or an interview version of the questionnaire (n = 191). This questionnaire included a choice-based conjoint exercise with manipulated photographs of a street. These manipulated photographs originated from one panoramic photograph of an existing street that was manipulated on nine environmental attributes. Participants chose which of two presented streets they would prefer to walk for transport.

RESULTS: In the total sample, sidewalk evenness had by far the greatest appeal for transportation walking. The other environmental attributes were less important. Four subgroups that differed in their environmental preferences for transportation walking were identified. In the two largest subgroups (representing 86% of the sample) sidewalk evenness was the most important environmental attribute. In the two smaller subgroups (each comprising 7% of the sample), traffic volume and speed limit were the most important environmental attributes for one, and the presence of vegetation and a bench were the most important environmental attributes for the other. This latter subgroup included a higher percentage of service flat residents than the other subgroups.

CONCLUSIONS: Our results suggest that the provision of even sidewalks should be considered a priority when developing environmental interventions aiming to stimulate older adults' transportation walking. Natural experiments are needed to confirm whether our findings can be translated to real environments and actual transportation walking behavior.

A smart web service based on the context of things

Combining the Semantic Web and the Ubiquitous Web, Web 3.0 is for things. The Semantic Web enables human knowledge to be machine-readable and the Ubiquitous Web allows Web services to serve any thing, forming a bridge between the virtual world and the real world. By using context, Web services can become smarter-that is, aware of the target things' or applications' physical environments, or situations and respond proactively and intelligently. Existing methods for implementing context-aware Web services on Web 2.0 mainly enumerate different implementations corresponding to different attribute values of the context, in order to improve the Quality of Services (QoS). However, things in the physical world are extremely diverse, which poses new problems for Web services: it is difficult to unify the context of things and to implement a flexible smart Web service for things. This article proposes a novel smart Web service based on the context of things, which is implemented using a REpresentational State Transfer for Things (Thing-REST) style, to tackle the two problems. In a smart Web service, the user's description (semantic context) and sensor reports (sensing context) are two channels for acquiring the context of things which are then employed by ontology services to make the context of things machine-readable. With guidance of domain knowledge services, event detection services can analyze things' needs particularly, well through the context of things. We then propose a Thing-REST style to manage the context of things and user context, and to mashup Web services through three structures (i.e., chain, select, and merge) to implement smart Web services. A smart plant watering-service application demonstrates the effectiveness of our method. © 2012 ACM.

Physics-Based Modeling of Power System Components for the Evaluation of Low-Frequency Radiated Electromagnetic Fields

The low-frequency electromagnetic compatibility (EMC) is an increasingly important aspect in the design of practical systems to ensure the functional safety and reliability of complex products. The opportunities for using numerical techniques to predict and analyze system’s EMC are therefore of considerable interest in many industries. As the first phase of study, a proper model, including all the details of the component, was required. Therefore, the advances in EMC modeling were studied with classifying analytical and numerical models. The selected model was finite element (FE) modeling, coupled with the distributed network method, to generate the model of the converter’s components and obtain the frequency behavioral model of the converter. The method has the ability to reveal the behavior of parasitic elements and higher resonances, which have critical impacts in studying EMI problems. For the EMC and signature studies of the machine drives, the equivalent source modeling was studied. Considering the details of the multi-machine environment, including actual models, some innovation in equivalent source modeling was performed to decrease the simulation time dramatically. Several models were designed in this study and the voltage current cube model and wire model have the best result. The GA-based PSO method is used as the optimization process. Superposition and suppression of the fields in coupling the components were also studied and verified. The simulation time of the equivalent model is 80-100 times lower than the detailed model. All tests were verified experimentally. As the application of EMC and signature study, the fault diagnosis and condition monitoring of an induction motor drive was developed using radiated fields. In addition to experimental tests, the 3DFE analysis was coupled with circuit-based software to implement the incipient fault cases. The identification was implemented using ANN for seventy various faulty cases. The simulation results were verified experimentally. Finally, the identification of the types of power components were implemented. The results show that it is possible to identify the type of components, as well as the faulty components, by comparing the amplitudes of their stray field harmonics. The identification using the stray fields is nondestructive and can be used for the setups that cannot go offline and be dismantled

The average mixing matrix signature

Laplacian-based descriptors, such as the Heat Kernel Signature and the Wave Kernel Signature, allow one to embed the vertices of a graph onto a vectorial space, and have been successfully used to find the optimal matching between a pair of input graphs. While the HKS uses a heat di↵usion process to probe the local structure of a graph, the WKS attempts to do the same through wave propagation. In this paper, we propose an alternative structural descriptor that is based on continuoustime quantum walks. More specifically, we characterise the structure of a graph using its average mixing matrix. The average mixing matrix is a doubly-stochastic matrix that encodes the time-averaged behaviour of a continuous-time quantum walk on the graph. We propose to use the rows of the average mixing matrix for increasing stopping times to develop a novel signature, the Average Mixing Matrix Signature (AMMS). We perform an extensive range of experiments and we show that the proposed signature is robust under structural perturbations of the original graphs and it outperforms both the HKS and WKS when used as a node descriptor in a graph matching task.

Non-invasive winding fault detection for induction machines based on stray flux magnetic sensors

Non-intrusive monitoring of health state of induction machines within industrial process and harsh environments poses a technical challenge. In the field, winding failures are a major fault accounting for over 45% of total machine failures. In the literature, many condition monitoring techniques based on different failure mechanisms and fault indicators have been developed where the machine current signature analysis (MCSA) is a very popular and effective method at this stage. However, it is extremely difficult to distinguish different types of failures and hard to obtain local information if a non-intrusive method is adopted. Typically, some sensors need to be installed inside the machines for collecting key information, which leads to disruption to the machine operation and additional costs. This paper presents a new non-invasive monitoring method based on GMRs to measure stray flux leaked from the machines. It is focused on the influence of potential winding failures on the stray magnetic flux in induction machines. Finite element analysis and experimental tests on a 1.5-kW machine are presented to validate the proposed method. With time-frequency spectrogram analysis, it is proven to be effective to detect several winding faults by referencing stray flux information. The novelty lies in the implement of GMR sensing and analysis of machine faults.