Search results for: cognitive discovery

Authors: Tamanna Siddiqui, M. Afshar Alam

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Censored Production Rule is an extension of standard production rule, which is concerned with problems of reasoning with incomplete information, subject to resource constraints and problem of reasoning efficiently with exceptions. A CPR has a form: IF A (Condition) THEN B (Action) UNLESS C (Censor), Where C is the exception condition. Fuzzy CPR are obtained by augmenting ordinary fuzzy production rule “If X is A then Y is B with an exception condition and are written in the form “If X is A then Y is B Unless Z is C. Such rules are employed in situation in which the fuzzy conditional statement “If X is A then Y is B" holds frequently and the exception condition “Z is C" holds rarely. Thus “If X is A then Y is B" part of the fuzzy CPR express important information while the unless part acts only as a switch that changes the polarity of “Y is B" to “Y is not B" when the assertion “Z is C" holds. The proposed approach is an attempt to discover fuzzy censored production rules from set of discovered fuzzy if then rules in the form: A(X) ÔçÆ B(Y) || C(Z).

Keywords: Uncertainty Quantification, Fuzzy if then rules, Fuzzy Censored Production Rules, Learning algorithm.

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Authors: Fabrizio Logiurato

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Light Polarization has many technological applications, and its discovery was crucial to reveal the transverse nature of the electromagnetic waves. However, despite its fundamental and practical importance, in high school, this property of light is often neglected. This is a pity not only for its conceptual relevance, but also because polarization gives the possibility to perform many brilliant experiments with low cost materials. Moreover, the treatment of this matter lends very well to an interdisciplinary approach between art, biology and technology, which usually makes things more interesting to students. For these reasons, we have developed, and in this work, we introduce a laboratory on light polarization for high school and undergraduate students. They can see beautiful pictures when birefringent materials are set between two crossed polarizing filters. Pupils are very fascinated and drawn into by what they observe. The colourful images remind them of those ones of abstract painting or alien landscapes. With this multidisciplinary teaching method, students are more engaged and participative, and also, the learning process of the respective physics concepts is more effective.

Keywords: Light polarization, optical activity, multidisciplinary education, science and art.

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Authors: Joan Antony

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Outcome in any learning process should target three goals – propelling the underachiever’s engagement in the learning process, enhancing the drive to achieve, and modifying attitudes and beliefs in his/her capabilities. An intervention study with a three-pronged approach incorporating self-regulatory training targeting three categories of strategies – cognitive, metacognitive and motivational – was designed adopting the before and after control-experimental group design. The evaluation of the training process was based on pre- and post-intervention measures obtained through three indices of measurement – academic scores based on grades on school examinations and comprehension tests, affective variables scores and level of strategy use obtained through responses on scales and questionnaires, and content analysis of subjective responses to open-ended probes. The evaluation relied on three sources – student, teacher and parent. The t-test results for the experimental and control groups on the pre- and post-intervention measurements indicate a significant increase on comprehension tasks for the experimental group. Though statistically significant difference was not found on the school examination scores for the experimental group, there was considerable decline in performance for the control group. Analysis of covariance (ANCOVA) was applied on the scores obtained on affective variables, namely, self-esteem, personal achievement goals, personal ego goals, personal task goals, and locus of control. The experimental group showed increase in personal achievement goals and personal ego goals as compared to the control group. Responses given by the experimental group to the open-ended probes on causal attributions indicated a considerable shift from external to internal causes when moving from the pre- to post-intervention stage. ANCOVA results revealed significantly higher use of learning strategies inclusive of mental learning strategies, behavioral learning strategies, self-regulatory strategies, and an improvement in study orientation encompassing study habits and study attitudes among the experimental group students. Parents and teachers reported significant progressive transformation towards constructive engagement with study material and self-imposed regulation. The implications of this study are three-fold: firstly, strategies training (cognitive, metacognitive and motivational) should be embedded into daily classroom routine; secondly, scaffolding by teachers through activities based on curriculum will eventually enable students to rely more on their own judgements of effective strategy use; thirdly, enhanced confidence will radiate to the affective aspects with enduring effects on other domains of life as well. The cyclic nature of the interaction between utilizing one’s resources, managing effort and regulating emotions forms the foundation for academic achievement.

Keywords: Academic achievement, cognitive strategies, metacognitive strategies, motivational strategies.

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Authors: Tharini N. de Silva, Xiao Zhibo, Zhao Rui, Mao Kezhi

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Causal relation identification is a crucial task in information extraction and knowledge discovery. In this work, we present two approaches to causal relation identification. The first is a classification model trained on a set of knowledge-based features. The second is a deep learning based approach training a model using convolutional neural networks to classify causal relations. We experiment with several different convolutional neural networks (CNN) models based on previous work on relation extraction as well as our own research. Our models are able to identify both explicit and implicit causal relations as well as the direction of the causal relation. The results of our experiments show a higher accuracy than previously achieved for causal relation identification tasks.

Keywords: Causal relation identification, convolutional neural networks, natural Language Processing, Machine Learning.

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Authors: Nidhi Gupta, Sanjoy Das, Khushal Singh

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A Mobile Ad-hoc Network (MANET) is a self managing network consists of versatile nodes that are capable of communicating with each other without having any fixed infrastructure. These nodes may be routers and/or hosts. Due to this dynamic nature of the network, routing protocols are vulnerable to various kinds of attacks. The black hole attack is one of the conspicuous security threats in MANETs. As the route discovery process is obligatory and customary, attackers make use of this loophole to get success in their motives to destruct the network. In Black hole attack the packet is redirected to a node that actually does not exist in the network. Many researchers have proposed different techniques to detect and prevent this type of attack. In this paper, we have analyzed various routing protocols in this context. Further we have shown a critical comparison among various protocols. We have shown various routing metrics are required proper and significant analysis of the protocol.

Keywords: Black Hole, MANET, Performance Parameters, Routing Protocol.

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Authors: Zainab S. Mahmood, Aisha H. Abdalla, Wan Haslina Hassan, Farhat Anwar

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The wide use of the Internet-based applications bring many challenges to the researchers to guarantee the continuity of the connections needed by the mobile hosts and provide reliable Internet access for them. One of proposed solutions by Internet Engineering Task Force (IETF) is to connect the local, multi-hop, and infrastructure-less Mobile Ad hoc Network (MANET) with Internet structure. This connection is done through multi-interface devices known as Internet Gateways. Many issues are related to this connection like gateway discovery, handoff, address auto-configuration and selecting the optimum gateway when multiple gateways exist. Many studies were done proposing gateway selection schemes with a single selection criterion or weighted multiple criteria. In this research, a review of some of these schemes is done showing the differences, the features, the challenges and the drawbacks of each of them.

Keywords: Internet Gateway, MANET, Mobility, Selection criteria.

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Authors: Manpreet Singh, Parvinder Singh, Suman

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The data is available in abundance in any business organization. It includes the records for finance, maintenance, inventory, progress reports etc. As the time progresses, the data keep on accumulating and the challenge is to extract the information from this data bank. Knowledge discovery from these large and complex databases is the key problem of this era. Data mining and machine learning techniques are needed which can scale to the size of the problems and can be customized to the application of business. For the development of accurate and required information for particular problem, business analyst needs to develop multidimensional models which give the reliable information so that they can take right decision for particular problem. If the multidimensional model does not possess the advance features, the accuracy cannot be expected. The present work involves the development of a Multidimensional data model incorporating advance features. The criterion of computation is based on the data precision and to include slowly change time dimension. The final results are displayed in graphical form.

Keywords: Multidimensional, data precision.

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Authors: Baharak Khoshkholgh-Sima, Soroush Sardari, Jalal Izadi Mobarakeh, Ramezan Ali Khavari-Nejad

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There is an urgent need to develop novel Mycobacterium tuberculosis (Mtb) drugs that are active against drug resistant bacteria but, more importantly, kill persistent bacteria. Our study structured based on integrated analysis of metabolic pathways, small molecule screening and similarity Search in PubChem Database. Metabolic analysis approaches based on Unified weighted used for potent target selection. Our results suggest that pantothenate synthetase (panC) and and 3-methyl-2-oxobutanoate hydroxymethyl transferase (panB) as a appropriate drug targets. In our study, we used pantothenate synthetase because of existence inhibitors. We have reported the discovery of new antitubercular compounds through ligand based approaches using computational tools.

Keywords: In Silico, Ligand-based Virtual Screening, Metabolic Pathways, Mycobacterium tuberculosis

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Authors: Mohammad N. Alanazi, David A. Gustafson

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In this article, we introduce a new approach for analyzing UML designs to detect the inconsistencies between multiple state diagrams and sequence diagrams. The Super State Analysis (SSA) identifies the inconsistencies in super states, single step transitions, and sequences. Because SSA considers multiple UML state diagrams, it discovers inconsistencies that cannot be discovered when considering only a single UML state diagram. We have introduced a transition set that captures relationship information that is not specifiable in UML diagrams. The SSA model uses the transition set to link transitions of multiple state diagrams together. The analysis generates three different sets automatically. These sets are compared to the provided sets to detect the inconsistencies. SSA identifies five types of inconsistencies: impossible super states, unreachable super states, illegal transitions, missing transitions, and illegal sequences.

Keywords: Modeling Languages, Object-Oriented Analysis, Sequence Diagrams, Software Models, State Diagrams, UML.

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Authors: Chia-Feng Lu, Yuh-Jen Wang, Shin Teng, Yu-Te Wu, Sui-Hing Yan

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Brain functional networks based on resting-state EEG data were compared between patients with mild Alzheimer’s disease (mAD) and matched patients with amnestic subtype of mild cognitive impairment (aMCI). We integrated the time–frequency cross mutual information (TFCMI) method to estimate the EEG functional connectivity between cortical regions and the network analysis based on graph theory to further investigate the alterations of functional networks in mAD compared with aMCI group. We aimed at investigating the changes of network integrity, local clustering, information processing efficiency, and fault tolerance in mAD brain networks for different frequency bands based on several topological properties, including degree, strength, clustering coefficient, shortest path length, and efficiency. Results showed that the disruptions of network integrity and reductions of network efficiency in mAD characterized by lower degree, decreased clustering coefficient, higher shortest path length, and reduced global and local efficiencies in the delta, theta, beta2, and gamma bands were evident. The significant changes in network organization can be used in assisting discrimination of mAD from aMCI in clinical.

Keywords: EEG, functional connectivity, graph theory, TFCMI.

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Authors: Mohammad Jafariahangari, Marjan Habibi, Miresmaeil Mirnabibaboli, Mirza Hassan Hosseini

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Self-service technologies (SSTs) make an important contribution to the daily life of people nowadays. However, the introduction of SST does not lead to its usage. Thereby, this paper was an attempt on discovery of the most preferred SST in the customers’ point of view. To fulfill this aim, the Analytical Hierarchy Process (AHP) was applied based on Saaty’s questionnaire which was administered to the customers of e-banking services located in Golestan providence, northern Iran. This study used qualitative factors in association with the intention of consumers’ usage of SSTs to rank three SSTs: ATM, mobile banking and internet banking. The results showed that mobile banking get the highest weight in consumers’ point of view. This research can be useful both for managers and service providers and also for customers who intend to use e-banking.

Keywords: Analytical Hierarchy Process, Decision-making, Ebanking, Iran, Self-service technologies.

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Authors: A. Kardi, R. Zagrouba, M. Alqahtani

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The Internet of Everything (IoE) presents today a very attractive and motivating field of research. It is basically based on Wireless Sensor Networks (WSNs) in which the routing task is the major analysis topic. In fact, it directly affects the effectiveness and the lifetime of the network. This paper, developed from recent works and based on extensive researches, proposes a taxonomy of routing protocols in WSNs. Our main contribution is that we propose a classification model based on nine classes namely application type, delivery mode, initiator of communication, network architecture, path establishment (route discovery), network topology (structure), protocol operation, next hop selection and latency-awareness and energy-efficient routing protocols. In order to provide a total classification pattern to serve as reference for network designers, each class is subdivided into possible subclasses, presented, and discussed using different parameters such as purposes and characteristics.

Keywords: WSNs, sensor, routing protocols, survey.

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Authors: Muhammad Y.K. Barozai, Ifthikhar A. Baloch, M. Din

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MicroRNAs (miRNAs) are small, non-coding and regulatory RNAs about 20 to 24 nucleotides long. Their conserved nature among the various organisms makes them a good source of new miRNAs discovery by comparative genomics approach. The study resulted in 21 miRNAs of 20 pre-miRNAs belonging to 16 families (miR156, 157, 158, 164, 165, 168, 169, 172, 319, 390, 393, 394, 395, 400, 472 and 861) in evergreen spruce tree (Picea). The miRNA families; miR 157, 158, 164, 165, 168, 169, 319, 390, 393, 394, 400, 472 and 861 are reported for the first time in the Picea. All 20 miRNA precursors form stable minimum free energy stem-loop structure as their orthologues form in Arabidopsis and the mature miRNA reside in the stem portion of the stem loop structure. Sixteen (16) miRNAs are from Picea glauca and five (5) belong to Picea sitchensis. Their targets consist of transcription factors, growth related, stressed related and hypothetical proteins.

Keywords: BLAST, Comparative Genomics, Micro-RNAs, Spruce

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Authors: Jing Lu, Malcolm Keech, Weiru Chen

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Web usage mining is an interesting application of data mining which provides insight into customer behaviour on the Internet. An important technique to discover user access and navigation trails is based on sequential patterns mining. One of the key challenges for web access patterns mining is tackling the problem of mining richly structured patterns. This paper proposes a novel model called Web Access Patterns Graph (WAP-Graph) to represent all of the access patterns from web mining graphically. WAP-Graph also motivates the search for new structural relation patterns, i.e. Concurrent Access Patterns (CAP), to identify and predict more complex web page requests. Corresponding CAP mining and modelling methods are proposed and shown to be effective in the search for and representation of concurrency between access patterns on the web. From experiments conducted on large-scale synthetic sequence data as well as real web access data, it is demonstrated that CAP mining provides a powerful method for structural knowledge discovery, which can be visualised through the CAP-Graph model.

Keywords: concurrent access patterns (CAP), CAP mining and modelling, CAP-Graph, web access patterns (WAP), WAP-Graph, Web usage mining.

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Authors: Ya-Fen Lee, Yun-Yao Chi

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This paper aims to quantify the impact of natural disaster on tourism by the change of annual visitors to scenic spots. The data of visitors to Alishan, Sun Moon Lake, Sitou and Palace Museum in Taiwan during 1986 to 2012 year is collected, and the trend analysis is used to predict the annual visitors to these scenic spots. The findings show that 1999 Taiwan earthquake had significant effect on the visitors to Alishan, Sun Moon Lake and Sitou with an average impact of 55.75% during 1999 to 2000 year except for Palace Museum. The impact was greater as closer epicenter of 1999 earthquake. And the discovery period of visitors is about 2 to 9 years. Further, the impact of heavy rainfall on Alishan, Taiwan is estimated. As the accumulative rainfall reaches to 500 mm, the impact on visitors can be predicted. 

Keywords: Impact, Natural disaster, tourism, visitors.

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Authors: Rani Astya, S.C. Sharma

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The wireless adhoc network is comprised of wireless node which can move freely and are connected among themselves without central infrastructure. Due to the limited transmission range of wireless interfaces, in most cases communication has to be relayed over intermediate nodes. Thus, in such multihop network each node (also called router) is independent, self-reliant and capable to route the messages over the dynamic network topology. Various protocols are reported in this field and it is very difficult to decide the best one. A key issue in deciding which type of routing protocol is best for adhoc networks is the communication overhead incurred by the protocol. In this paper STAR a table driven and DSR on demand protocols based on IEEE 802.11 are analyzed for their performance on different performance measuring metrics versus varying traffic CBR load using QualNet 5.0.2 network simulator.

Keywords: Adhoc networks, wireless networks, CBR, routingprotocols, route discovery, simulation, performance evaluation, MAC, IEEE 802.11, STAR, DSR

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Authors: Sharan Badiger, Prema T. Akkasaligar, Deepak Kadeli, M. Vishok

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Neurological disorders are the most debilitating of manifestations seen in patients infected with HIV. The clinical profile of neurological manifestations in HIV patients has undergone a shift in recent years with opportunistic infections being controlled with combination anti-retroviral therapy and the advent of drugs which have higher central nervous system penetrability. The aim of this paper is to study the clinical, investigation profile and various neurological disorders in HIV patients on anti‐retroviral therapy. Fifty HIV patients with neurological manifestations were studied. A complete neurological examination including neurocognitive functioning using Montreal Cognitive Assessment and HIV Dementia scale were assessed. Apart from relevant investigations, CD4 count, cerebrovascular fluid analysis, computed tomography (CT) and magnetic resonance imaging (MRI) of brain were done whenever required. Neurocognitive disorders formed the largest group with 42% suffering from HIV associated Neurocognitive Disorders. Among them, asymptomatic neurocognitive impairment was seen in 28%; mild neurocognitive disorder in 12%, and 2% had HIV‐associated dementia. Opportunistic infections of the nervous system accounted for 32%, with meningitis being the most common. Four patients had space occupying lesions of central nervous system; four tuberculomas, and one toxoplasmosis. With the advent of highly active retroviral therapy, HIV patients have longer life spans with suppression of viral load leading to decrease in opportunistic infections of the nervous system. Neurocognitive disorders are now the most common neurological dysfunction seen and thus neurocognitive assessment must be done in all patients with HIV.

Keywords: Anti retroviral therapy, cognitive dysfunction, dementia, neurological manifestations, opportunistic infections.

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Authors: Walid Cherif

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Data mining has, over recent years, seen big advances because of the spread of internet, which generates everyday a tremendous volume of data, and also the immense advances in technologies which facilitate the analysis of these data. In particular, classification techniques are a subdomain of Data Mining which determines in which group each data instance is related within a given dataset. It is used to classify data into different classes according to desired criteria. Generally, a classification technique is either statistical or machine learning. Each type of these techniques has its own limits. Nowadays, current data are becoming increasingly heterogeneous; consequently, current classification techniques are encountering many difficulties. This paper defines new measure functions to quantify the resemblance between instances and then combines them in a new approach which is different from actual algorithms by its reliability computations. Results of the proposed approach exceeded most common classification techniques with an f-measure exceeding 97% on the IRIS Dataset.

Keywords: Data mining, knowledge discovery, machine learning, similarity measurement, supervised classification.

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Authors: Vladimir Rastunkov, Jae-Eun Park, Abhijit Mitra, Brian Quanz, Steve Wood, Christopher Codella, Heather Higgins, Joseph Broz

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Quantum Support Vector Machines (QSVM) have become an important tool in research and applications of quantum kernel methods. In this work we propose a boosting approach for building ensembles of QSVM models and assess performance improvement across multiple datasets. This approach is derived from the best ensemble building practices that worked well in traditional machine learning and thus should push the limits of quantum model performance even further. We find that in some cases, a single QSVM model with tuned hyperparameters is sufficient to simulate the data, while in others - an ensemble of QSVMs that are forced to do exploration of the feature space via proposed method is beneficial.

Keywords: QSVM, Quantum Support Vector Machines, quantum kernel, boosting, ensemble.

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Authors: M. Cheraghi, B. Lorestani, N. Yousefi

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Contamination of heavy metals represents one of the most pressing threats to water and soil resources as well as human health. Phytoremediation can be potentially used to remediate metalcontaminated sites. A major step towards the development of phytoremediation of heavy metal impacted soils is the discovery of the heavy metal hyperaccumulation in plants. In this study, the several established criteria to define a hyperaccumulator plant were applied. The case study was represented by a mining area in Hamedan province in the central west part of Iran. Obtained results showed that the most of sampled species were able to grow on heavily metal-contaminated soils and also were able to accumulate extraordinarily high concentrations of some metals such as Zn, Mn, Cu, Pb and Fe. Using the most common criteria, Euphorbia macroclada and Centaurea virgata can be classified as hyperaccumulators of some measured heavy metals and, therefore, they have suitable potential for phytoremediation of contaminated soils.

Keywords: Enrichment factor, Heavy metals, Hyperaccumulator, Phytoremediation, Translocation factor

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Authors: Hung-Ming Lai, Andreas Albrecht, Kathleen Steinhöfel

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Cancers could normally be marked by a number of differentially expressed genes which show enormous potential as biomarkers for a certain disease. Recent years, cancer classification based on the investigation of gene expression profiles derived by high-throughput microarrays has widely been used. The selection of discriminative genes is, therefore, an essential preprocess step in carcinogenesis studies. In this paper, we have proposed a novel gene selector using information-theoretic measures for biological discovery. This multivariate filter is a four-stage framework through the analyses of feature relevance, feature interdependence, feature redundancy-dependence and subset rankings, and having been examined on the colon cancer data set. Our experimental result show that the proposed method outperformed other information theorem based filters in all aspect of classification errors and classification performance.

Keywords: Colon cancer, feature interdependence, feature subset selection, gene selection, microarray data analysis.

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Authors: P. Jomsri

Abstract:

Nowadays social media are important tools for web resource discovery. The performance and capabilities of web searches are vital, especially search results from social research paper bookmarking. This paper proposes a new algorithm for ranking method that is a combination of similarity ranking with paper posted time or CSTRank. The paper posted time is static ranking for improving search results. For this particular study, the paper posted time is combined with similarity ranking to produce a better ranking than other methods such as similarity ranking or SimRank. The retrieval performance of combination rankings is evaluated using mean values of NDCG. The evaluation in the experiments implies that the chosen CSTRank ranking by using weight score at ratio 90:10 can improve the efficiency of research paper searching on social bookmarking websites.

Keywords: combination ranking, information retrieval, time, similarity ranking, static ranking, weight score

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Authors: Nittaya Kerdprasop, Kittisak Kerdprasop

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Frequent patterns are patterns such as sets of features or items that appear in data frequently. Finding such frequent patterns has become an important data mining task because it reveals associations, correlations, and many other interesting relationships hidden in a dataset. Most of the proposed frequent pattern mining algorithms have been implemented with imperative programming languages such as C, Cµ, Java. The imperative paradigm is significantly inefficient when itemset is large and the frequent pattern is long. We suggest a high-level declarative style of programming using a functional language. Our supposition is that the problem of frequent pattern discovery can be efficiently and concisely implemented via a functional paradigm since pattern matching is a fundamental feature supported by most functional languages. Our frequent pattern mining implementation using the Haskell language confirms our hypothesis about conciseness of the program. The performance studies on speed and memory usage support our intuition on efficiency of functional language.

Keywords: Association, frequent pattern mining, functionalprogramming, pattern matching.

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Authors: M. Gunasekaran, K. Premalatha

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Privacy and Security have emerged as an important research issue in Mobile Ad Hoc Networks (MANET) due to its unique nature such as scarce of resources and absence of centralized authority. There are number of protocols have been proposed to provide privacy and security for data communication in an adverse environment, but those protocols are compromised in many ways by the attackers. The concept of anonymity (in terms of unlinkability and unobservability) and pseudonymity has been introduced in this paper to ensure privacy and security. In this paper, a Secure Onion Throat (SOT) protocol is proposed to provide complete anonymity in an adverse environment. The SOT protocol is designed based on the combination of group signature and onion routing with ID-based encryption for route discovery. The security analysis demonstrates the performance of SOT protocol against all categories of attacks. The simulation results ensure the necessity and importance of the proposed SOT protocol in achieving such anonymity.

Keywords: Routing, anonymity, privacy, security and MANET.

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Authors: Filiz Ersoz, Taner Ersoz, Erkin Guler

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This paper addresses the issues and technique for textile industry using data mining techniques. Data mining has been applied to the stitching of garments products that were obtained from a textile company. Data mining techniques were applied to the data obtained from the CHAID algorithm, CART algorithm, Regression Analysis and, Artificial Neural Networks. Classification technique based analyses were used while data mining and decision model about the production per person and variables affecting about production were found by this method. In the study, the results show that as the daily working time increases, the production per person also decreases. In addition, the relationship between total daily working and production per person shows a negative result and the production per person show the highest and negative relationship.

Keywords: Data mining, textile production, decision trees, classification.

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Authors: B. Parchem, M. Watanabe, D. Modrakovic, L. Mathew, A. Franklin, M. Cao, R. E. Broudy

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Objective: Executive functioning (EF) deficits underlie several mental health diagnoses including ADHD, anxiety, and depression. Community mental health clinics face extensive waitlists for services with many referrals involving EF deficits. A pilot trial of a four-week group therapy was developed using key components from Cognitive-Behavioral Therapy (CBT), Dialectical Behavior Therapy (DBT), and mindfulness with an aim to improve EF skills and offer low-fee services. Method: Eight adults (M = 34.5) waiting for services at a community clinic were enrolled in a four-week group therapy at an in-house training clinic for doctoral trainees. Baseline EF, pre-/post-intervention ADHD and distress symptoms, group satisfaction, and curriculum helpfulness were assessed. Results: Downward trends in ADHD and distress symptoms pre/post-intervention were not significant. Favorable responses on group satisfaction and helpfulness suggest clinical utility. Conclusion: Preliminary pilot data from a brief group therapy to improve EF may be an efficacious, acceptable, and feasible intervention for adults waiting for services at community mental health and training clinics where there are high demands and limits to services and staffs.

Keywords: Executive functioning, cognitive-behavioral therapy, dialectical behavior therapy, mindfulness, adult group therapy.

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Authors: Shigeaki Sakurai, Ken Ueno, Ryohei Orihara

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This paper proposes a method that discovers time series event patterns from textual data with time information. The patterns are composed of sequences of events and each event is extracted from the textual data, where an event is characteristic content included in the textual data such as a company name, an action, and an impression of a customer. The method introduces 7 types of time constraints based on the analysis of the textual data. The method also evaluates these constraints when the frequency of a time series event pattern is calculated. We can flexibly define the time constraints for interesting combinations of events and can discover valid time series event patterns which satisfy these conditions. The paper applies the method to daily business reports collected by a sales force automation system and verifies its effectiveness through numerical experiments.

Keywords: Text mining, sequential mining, time constraints, daily business reports.

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Authors: Nittaya Kerdprasop, Kittisak Kerdprasop

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The state of the art in instructional design for computer-assisted learning has been strongly influenced by advances in information technology, Internet and Web-based systems. The emphasis of educational systems has shifted from training to learning. The course delivered has also been changed from large inflexible content to sequential small chunks of learning objects. The concepts of learning objects together with the advanced technologies of Web and communications support the reusability, interoperability, and accessibility design criteria currently exploited by most learning systems. These concepts enable just-in-time learning. We propose to extend theses design criteria further to include the learnability concept that will help adapting content to the needs of learners. The learnability concept offers a better personalization leading to the creation and delivery of course content more appropriate to performance and interest of each learner. In this paper we present a new framework of learning environments containing knowledge discovery as a tool to automatically learn patterns of learning behavior from learners' profiles and history.

Keywords: Knowledge mining, Web-based learning, Learning environments.

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Authors: Eiad Yafi, Ahmed Sultan Al-Hegami, M. A. Alam, Ranjit Biswas

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Association rules are an important problem in data mining. Massively increasing volume of data in real life databases has motivated researchers to design novel and incremental algorithms for association rules mining. In this paper, we propose an incremental association rules mining algorithm that integrates shocking interestingness criterion during the process of building the model. A new interesting measure called shocking measure is introduced. One of the main features of the proposed approach is to capture the user background knowledge, which is monotonically augmented. The incremental model that reflects the changing data and the user beliefs is attractive in order to make the over all KDD process more effective and efficient. We implemented the proposed approach and experiment it with some public datasets and found the results quite promising.

Keywords: Knowledge discovery in databases (KDD), Data mining, Incremental Association rules, Domain knowledge, Interestingness, Shocking rules (SHR).

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Authors: Quoc-Nam Tran

Abstract:

The theory of Groebner Bases, which has recently been honored with the ACM Paris Kanellakis Theory and Practice Award, has become a crucial building block to computer algebra, and is widely used in science, engineering, and computer science. It is wellknown that Groebner bases computation is EXP-SPACE in a general polynomial ring setting. However, for many important applications in computer science such as satisfiability and automated verification of hardware and software, computations are performed in a Boolean ring. In this paper, we give an algorithm to show that Groebner bases computation is PSPACE in Boolean rings. We also show that with this discovery, the Groebner bases method can theoretically be as efficient as other methods for automated verification of hardware and software. Additionally, many useful and interesting properties of Groebner bases including the ability to efficiently convert the bases for different orders of variables making Groebner bases a promising method in automated verification.

Keywords: Algorithm, Complexity, Groebner basis, Applications of Computer Science.

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