Using MOEA/D for optimizing ontology alignments

This paper proposes a novel approach which uses a multi-objective evolutionary algorithm based on decomposition to address the ontology alignment optimization problem. Comparing with the approach based on Genetic Algorithm (GA), our method can simultaneously optimize three goals (maximizing the alignment recall, the alignment precision and the f-measure). The experimental results shows that our approach is able to provide various alignments in one execution which are less biased to one of the evaluations of the alignment quality than GA approach, thus the quality of alignments are obviously better than or equal to those given by the approach based on GA which considers precision, recall and f-measure only, and other multi-objective evolutionary approach such as NSGA-II approach. In addition, the performance of our approach outperforms NSGA-II approach with the average improvement equal to 32.79  \(\%\) . Through the comparison of the quality of the alignments obtained by our approach with those by the state of the art ontology matching systems, we draw the conclusion that our approach is more effective and efficient.     

A comparison of standard spell checking algorithms and a novel binary neural approach

In this paper, we propose a simple, flexible, and efficient hybrid spell checking methodology based upon phonetic matching, supervised learning, and associative matching in the AURA neural system. We integrate Hamming Distance and n-gram algorithms that have high recall for typing errors and a phonetic spell-checking algorithm in a single novel architecture. Our approach is suitable for any spell checking application though aimed toward isolated word error correction, particularly spell checking user queries in a search engine. We use a novel scoring scheme to integrate the retrieved words from each spelling approach and calculate an overall score for each matched word. From the overall scores, we can rank the possible matches. We evaluate our approach against several benchmark spellchecking algorithms for recall accuracy. Our proposed hybrid methodology has the highest recall rate of the techniques evaluated. The method has a high recall rate and low-computational cost.     

Stimulated recall methodology for assessing work system barriers and facilitators in family-centered rounds in a pediatric hospital

Human factors and ergonomics methods are needed to redesign healthcare processes and support patient-centered care, in particular for vulnerable patients such as hospitalized children. We implemented and evaluated a stimulated recall methodology for collective confrontation in the context of family-centered rounds. Five parents and five healthcare team members reviewed video records of their bedside rounds, and were then interviewed using the stimulated recall methodology to identify work system barriers and facilitators in family-centered rounds. The evaluation of the methodology was based on a survey of the participants, and a qualitative analysis of interview data in light of the work system model of 30 and 35. Positive survey feedback from the participants was received. The stimulated recall methodology identified barriers and facilitators in all work system elements. Participatory ergonomics methods such as the stimulated recall methodology allow a range of participants, including parents and children, to participate in healthcare process improvement.     

Helicopter control law based on sliding mode with model following

The design of a control law adapted to a process characterized by great parametric variations and strong couplings is considered here using variable structure methodology and approximate model following. It is shown that, even in the case of partial state availability, this approach leads to a much better robustness than modal control while allowing more versatility with respect to control objectives (a modification of the reference model does not imply a modification of the switching surface); furthermore these increased performances do not require a higher level of control activity than in modal-based schemes. After a brief recall of the methodology used, the paper is mostly concerned with a specific application; numerous simulation results are given.     

Automatic identification of relevant places from cellular network data

We present a methodology to automatically identify users’ relevant places from cellular network data.¹ In this work we used anonymized Call Detail Record (CDR) comprising information on where and when users access the cellular network. The key idea is to effectively cluster CDRs together and to weigh clusters to determine those associated to frequented places. The approach can identify users’ home and work locations as well as other places (e.g., associated to leisure and night life). We evaluated our approach threefold: (i) on the basis of groundtruth information coming from a fraction of users whose relevant places were known, (ii) by comparing the resulting number of inhabitants of a given city with the number of inhabitants as extracted by the national census. (iii) Via stability analysis to verify the consistency of the extracted results across multiple time periods. Results show the effectiveness of our approach with an average 90% precision and recall.     

A comparison of a novel neural spell checker and standard spell checking algorithms

In this paper, we propose a simple and flexible spell checker using efficient associative matching in the AURA modular neural system. Our approach aims to provide a pre-processor for an information retrieval (IR) system allowing the user's query to be checked against a lexicon and any spelling errors corrected, to prevent wasted searching. IR searching is computationally intensive so much so that if we can prevent futile searches we can minimise computational cost. We evaluate our approach against several commonly used spell checking techniques for memory-use, retrieval speed and recall accuracy. The proposed methodology has low memory use, high speed for word presence checking, reasonable speed for spell checking and a high recall rate.     

Morphological query expansion and language-filtering words for improving Basque web retrieval

The experience of a user of major search engines or other web information retrieval services looking for information in the Basque language is far from satisfactory: they only return pages with exact matches but no inflections (necessary for an agglutinative language like Basque), many results in other languages (no search engine gives the option to restrict its results to Basque), etc. This paper proposes using morphological query expansion and language-filtering words in combination with the APIs of search engines as a very cost-effective solution to build appropriate web search services for Basque. The implementation details of the methodology (choosing the most appropriate language-filtering words, the number of them, the most frequent inflections for the morphological query expansion, etc.) have been specified by corpora-based studies. The improvements produced have been measured in terms of precision and recall both over corpora and real web searches. Morphological query expansion can improve recall up to 47 % and language-filtering words can raise precision from 15 % to around 90 %, although with a loss in recall of about 30–35 %. The proposed methodology has already been successfully used in the Basque search service Elebila (http://www.elebila.eu) and the web-as-corpus tool CorpEus (http://www.corpeus.org), and the approach could be applied to other morphologically rich or under-resourced languages as well.     

A component recommender for bug reports using Discriminative Probability Latent Semantic Analysis

ContextThe component field in a bug report provides important location information required by developers during bug fixes. Research has shown that incorrect component assignment for a bug report often causes problems and delays in bug fixes. A topic model technique, Latent Dirichlet Allocation (LDA), has been developed to create a component recommender for bug reports.ObjectiveWe seek to investigate a better way to use topic modeling in creating a component recommender.MethodThis paper presents a component recommender by using the proposed Discriminative Probability Latent Semantic Analysis (DPLSA) model and Jensen–Shannon divergence (DPLSA-JS). The proposed DPLSA model provides a novel method to initialize the word distributions for different topics. It uses the past assigned bug reports from the same component in the model training step. This results in a correlation between the learned topics and the components.ResultsWe evaluate the proposed approach over five open source projects, Mylyn, Gcc, Platform, Bugzilla and Firefox. The results show that the proposed approach on average outperforms the LDA-KL method by 30.08%, 19.60% and 14.13% for recall @1, recall @3 and recall @5, outperforms the LDA-SVM method by 31.56%, 17.80% and 8.78% for recall @1, recall @3 and recall @5, respectively.ConclusionOur method discovers that using comments in the DPLSA-JS recommender does not always make a contribution to the performance. The vocabulary size does matter in DPLSA-JS. Different projects need to adaptively set the vocabulary size according to an experimental method. In addition, the correspondence between the learned topics and components in DPLSA increases the discriminative power of the topics which is useful for the recommendation task.     

Automatic building of an ontology on the basis of text corpora in Thai

This paper presents a methodology for automatic learning of ontologies from Thai text corpora, by extraction of terms and relations. A shallow parser is used to chunk texts on which we identify taxonomic relations with the help of cues: lexico-syntactic patterns and item lists. The main advantage of the approach is that it simplify the task of concept and relation labeling since cues help for identifying the ontological concept and hinting their relation. However, these techniques pose certain problems, i.e. cue word ambiguity, item list identification, and numerous candidate terms. We also propose the methodology to solve these problems by using lexicon and co-occurrence features and weighting them with information gain. The precision, recall and F-measure of the system are 0.74, 0.78 and 0.76, respectively.

Pedestrian navigation using the sense of touch

Haptics is a feedback technology that takes advantage of the human sense of touch by applying forces, vibrations, and/or motions to a haptic-enabled user device such as a mobile phone. Historically, human–computer interaction has been visual, data, or images on a screen. Haptic feedback can be an important modality in Mobile Location-Based Services like – knowledge discovery, pedestrian navigation and notification systems. In this paper we describe a methodology for the implementation of haptics in four distinct prototypes for pedestrian navigation. Prototypes are classified based on the user’s navigation guidance requirements, the user type (based on spatial skills), and overall system complexity. Here haptics is used to convey location, orientation, and distance information to users using pedestrian navigation applications. Initial user trials have elicited positive responses from the users who see benefit in being provided with a “heads up” approach to mobile navigation. We also tested the spatial ability of the user to navigate using haptics and landmark images based navigation. This was followed by a test of memory recall about the area. Users were able to successfully navigate from a given origin to a Destination Point without the use of a visual interface like a map. Results show the users of haptic feedback for navigation prepared better maps (better memory recall) of the region as compared to the users of landmark images based navigation.     

基于梯度提升决策树的变形宏病毒检测

宏病毒在高级持续性威胁中被广泛运用.其变形成本低廉且方式灵活,导致传统的基于病毒规则库的反病毒系统难于有效对抗.提出一种基于梯度提升决策树的变形宏病毒检测方法.该方法以病毒专家经验为指导,实施大规模特征工程,基于词法分析对变形宏病毒做细粒度建模,并使用海量样本训练模型.实验表明,该方法能够准确检测企业级用户网络中传播的...     

A knowledge based reliability engineering approach to manage product safety and recalls

PurposeManaging processed food products’ safety and recall is a challenge for industry and governments. Contaminated food items can create a significant public health hazard with potential for acute and chronic food borne illnesses. This industry study examines the challenges companies face while managing a processed food recall situation and devise a responsive and reliable knowledge management framework for product safety and recall supply chain for the focal global manufacturing and distribution enterprise.MethodDrawing upon the knowledge management and product safety and recall literature and reliability engineering theory, this study uses a holistic single case based approach to develop a knowledge management framework with Failure Mode Effects and Criticality Analysis (FMECA) decision model. This knowledge management decision framework facilitates analysis of the root causes for each potential major recall issue and assesses the reliability of the product safety and recall supply chain system and its critical components.ResultsThe main reasons highlighted for a recall and associated failure modes in a knowledge management framework are to devise appropriate deployment of resources, technology and procedures to recall supply chain. This study underscores specific information described by managers of a global processed food manufacturer and their perspectives about the product safety and recall process, and its complexities. Full scale implementation of product safety and recall supply chain in the proposed knowledge management framework after the current pilot study will be carried out eventually through expert systems. This operational system when fully implemented will capture the essence of decision making environments comprising goals and objectives, courses of action, resources, constraints, technology and procedures.ImplicationsThe study recognizes the significance of communication, integration, failsafe knowledge management process design framework, leveraging technology such as Radio Frequency Identification (RFID) within all levels of supply chain for product traceability and the proactive steps to help companies successfully manage a recall process and also reestablish trust among the consumers. The proposed knowledge management framework can also preempt product recall by acting as an early warning system. A formal knowledge management framework will enable a company’s knowledge be cumulative for product safety and recall and serve as an important integrating and coordinating role for the organization.     

Continuous activity recognition in a maintenance scenario: combining motion sensors and ultrasonic hands tracking

We describe the design and evaluation of pattern analysis methods for the recognition of maintenance-related activities. The presented work focuses on the spotting of subtle hand actions in a continuous stream of data based on a combination of body-mounted motion sensors and ultrasonic positioning. The spotting and recognition approach is based on three core ideas: (1) the use of location information to compensate for the ambiguity of hand motions, (2) the use of motion data to compensate for the slow sampling rate and unreliable signal of the low cost ultrasonic positioning system, and (3) an incremental, multistage spotting methodology. The proposed methods are evaluated in an elaborate bicycle repair experiment containing nearly 10 h of data from six subjects. The evaluation compares different strategies and system variants and shows that precision and recall rates around 90% can be achieved.     

A Spatio-Temporal malware and country clustering algorithm: 2012 IIJ MITF case study

A huge number of botnet malware variants can be downloaded by zombie personal computers as secondary injections and upgrades according to their botmasters to perform different distributed/coordinated cyber attacks such as phishing, spam e-mail, malicious Web sites, ransomware, DDoS. In order to generate a faster response to new threats and better understanding of botnet activities, grouping them based on their malicious behaviors has become extremely important. This paper presents a Spatio-Temporal malware clustering algorithm based on its (weekly-hourly-country) features. The dataset contains more than 32 million of malware download logs from 100 honeypots set up by Malware Investigation Task Force (MITF) of Internet Initiative Japan Inc. (IIJ) from 2011 to 2012. The Top-20 malware clustering results coincidentally correspond to Conficker.B and Conficker.C with relatively high precision and recall rates up to 100.0, 88.9 % and 91.7, 100.0 %, respectively. On the other hand, the resulting two clusters of Top-20 countries are comparable to those with high and low growth rates recently reported in 2015 by Asghari et al. Therefore, our approach can be validated and evaluated to yield precision and recall of up to 75.0 and 86.7 %, respectively.     

基于视觉注意特征和SVM的镜头边界检测算法

镜头边界检测是视频分析的基础。借鉴心理学中有关视觉注意的研究成果,提出了一种采用符合人类视觉注意的特征,并利用支持矢量机进行视频镜头边界检测的算法。通过对TRECVID2007数据库进行实验的结果表明,该算法在查全率和查准率方面都获得了较好的性能。     

Multiobjective optimization for classifier ensemble and feature selection: an application to named entity recognition

In this paper, the concept of finding an appropriate classifier ensemble for named entity recognition is posed as a multiobjective optimization (MOO) problem. Our underlying assumption is that instead of searching for the best-fitting feature set for a particular classifier, ensembling of several classifiers those are trained using different feature representations could be a more fruitful approach, but it is crucial to determine the appropriate subset of classifiers that are most suitable for the ensemble. We use three heterogenous classifiers namely maximum entropy, conditional random field, and support vector machine in order to build a number of models depending upon the various representations of the available features. The proposed MOO-based ensemble technique is evaluated for three resource-constrained languages, namely Bengali, Hindi, and Telugu. Evaluation results yield the recall, precision, and F-measure values of 92.21, 92.72, and 92.46%, respectively, for Bengali; 97.07, 89.63, and 93.20%, respectively, for Hindi; and 80.79, 93.18, and 86.54%, respectively, for Telugu. We also evaluate our proposed technique with the CoNLL-2003 shared task English data sets that yield the recall, precision, and F-measure values of 89.72, 89.84, and 89.78%, respectively. Experimental results show that the classifier ensemble identified by our proposed MOO-based approach outperforms all the individual classifiers, two different conventional baseline ensembles, and the classifier ensemble identified by a single objective?Cbased approach. In a part of the paper, we formulate the problem of feature selection in any classifier under the MOO framework and show that our proposed classifier ensemble attains superior performance to it.     

A Fuzzy Ontological Knowledge Document Clustering Methodology

This correspondence presents a novel hierarchical clustering approach for knowledge document self-organization, particularly for patent analysis. Current keyword-based methodologies for document content management tend to be inconsistent and ineffective when partial meanings of the technical content are used for cluster analysis. Thus, a new methodology to automatically interpret and cluster knowledge documents using an ontology schema is presented. Moreover, a fuzzy logic control approach is used to match suitable document cluster(s) for given patents based on their derived ontological semantic webs. Finally, three case studies are used to test the approach. The first test case analyzed and clustered 100 patents for chemical and mechanical polishing retrieved from the World Intellectual Property Organization (WIPO). The second test case analyzed and clustered 100 patent news articles retrieved from online Web sites. The third case analyzed and clustered 100 patents for radio-frequency identification retrieved from WIPO. The results show that the fuzzy ontology-based document clustering approach outperforms the K-means approach in precision, recall, F-measure, and Shannon's entropy.     

To simplify or not? Facilitating English L2 users' comprehension and processing of open educational resources in English using text simplification

Open Educational Resources (OER) aim to provide equal access to education. Yet, as the language level used in OER presents a barrier to many learners, there is a need to make these resources more comprehensible. This study combined eye-tracking methodology and comprehension assessment to explore the effect of text simplification on English second language (L2) users, while also accounting for text organizational structure and individual predispositions. A total of 37 adult English L2 users took part in the study. They had to read either an authentic narrative, authentic expository OER or their linguistically simplified versions. The analysis showed that simplification led to better text comprehension, and text narrativity facilitated text recall, particularly at lower English proficiency levels. Eye-tracking measures revealed that text simplification led to an increase in processing time during the initial reading of the text and a decrease in processing time during text re-inspection. These findings have strong practical applications for online teaching with OER.     

减少多种子内建自测试方法硬件开销的有效途径

提出一个基于重复播种的新颖的BIST方案，该方案使用侦测随机向量难测故障的测试向量作为种子，并利用种子产生过程中剩余的随意位进行存储压缩；通过最小化种子的测试序列以减少测试施加时间．实验表明，该方案需要外加硬件少，测试施加时间较短，故障覆盖率高，近似等于所依赖的ATPG工具的故障覆盖率．在扼要回顾常见的确定性BIST方案的基础上，着重介绍了文中的压缩存储硬件的方法、合成方法和实验结果．