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Traditional teaching methodologies cover only 4 Graduate Attributes (GAs). These are GA1, GA2, GA3 and GA4. Remaining 8 Graduate Attributes will be accomplished with the new methods. In this work, activities based on active, cooperative, inductive, project, and emotional based learning methodologies are proposed. Proposed activities are designed by considering cognitive, psychomotor and affective domains of the blooms taxonomy. These activities are implemented at class level. In detail procedure to implement these methodologies in each course is presented. Hence, each course is mapped with more number Graduate Attributes. These methodologies include activities like the interactive sessions, group based assignments, case based assignments, and mini project in every course. Graduate Attributes like modern tool usage, individual/team work, communication, ethics, project management, and lifelong learning are better mapped with these new methodologies. Further, evaluation methods also proposed in this work. Experiments are performed on two classes of 60 students each. One class is taught by traditional teaching methods. Second class is taught by the proposed methodologies at course level. Performance of students in higher order thinking and lower order thinking is also assessed. Experimental results indicate that higher order thinking among students is improved. Hence, critical thinking is improved. Further, each course is mapped to 12 Graduate Attributes. Hence, possibility of attaining more Graduate Attributes is improved. Practice of proposed methodologies at class level improves the student�s attendance.

Weakly supervised semantic segmentation uses image-level labels to extract object regions. The existing methods focus on efficiently training CNN-based segmentation networks using the image-level labels. In contrast to the existing methods, this paper proposes a new fusion-based method, which first segments the foregrounds of each image by multiple group cosegmentation and then generates the semantic segmentation by combining the foregrounds. Specifically, a new CNN-based multiple group cosegmentation network is first proposed to segment foregrounds employing two cues, the discriminative cue and the local-to-global cue. Then, the fusion method is proposed to simply perform semantic segmentation based on the multiple group cosegmentation results. Experiments on the PASCAL VOC 2012 and MS COCO 2017 datasets demonstrate the effectiveness of the proposed method with mIoU values that are obviously larger than those of the existing methods.

Due to the absence of training samples and intraclass variation, the extraction of discriminative facial features and construction of powerful classifiers have bottlenecks in improving the performance of facial recognition (FR) with a single sample per data subject (SSPDS). In this paper, we propose to learn regional adaptive convolution features that are locally and globally discriminative to facial identity and robust to facial variation. Then, a novel class-level joint representation framework is presented to exploit the distinctiveness and class-level commonality of different facial features. In the proposed class-level joint representation with regional adaptive convolution features (CJR-RACF), both discriminative facial features that are robust to facial variations and powerful representations for classification with generic facial variations have been fully exploited. Furthermore, the gallery discrimination is extracted by our proposed weight-embedded supervision in the training phase (denoted by CJR-RACF w ), which is conducive to more specific features for FR with SSPDS. CJR-RACF and CJR-RACF w have been evaluated on several popular databases, including the large-scale CMU Multi-PIE, LFW, Megaface, and VGGFace datasets. Experimental results demonstrate the much higher robustness and effectiveness of the proposed methods compared to the state-of-the-art methods.

Organ transplantation has brought convenience for software reuse and evolution since it was proposed. However, studies about mature, high-quality organs are still insufficient. It is still unclear about the detailed characteristics of organs in the open-source environment. In this paper, we look deep into organs obtained from software evolution processes of the ten large-scale Java repositories hosted on GitHub, aiming at providing practical information for utilizing organs in the open-source environment. We found that: 1) commits use add as a keyword in their comments possess the most organs, occupying 38% of the total amount, but commits with the keyword fix possess the highest locating accuracy (about 57%); 2) developers prefer to add new classes when they bring new functionalities to the projects in that the proportion of class level organs is 40%, more than statement level organs' and function level organs' (35% and 25%, respectively); 3) nearly 70% of the total amount are cross-file organs with the median of the number of files each organ spans reaching three and the average of this value being around four; 4) a small number (0.55%) of organs are multi-commit; 5) more than 40% of code reuse in the open-source software can be finished by organ transplantation. Based on our findings, we highlight implications for future studies and design the mode of using organs to conduct code reuse.

Bug localization is an important aspect of software maintenance because it can locate modules that should be changed to fix a specific bug. Our previous study showed that the accuracy of the information retrieval (IR)-based bug localization technique improved when used in combination with code smell information. Although this technique showed promise, the study showed limited usefulness because of the small number of: 1) projects in the dataset, 2) types of smell information, and 3) baseline bug localization techniques used for assessment. This paper presents an extension of our previous experiments on Bench4BL, the largest bug localization benchmark dataset available for bug localization. In addition, we generalized the smell-aware bug localization technique to allow different configurations of smell information, which were combined with various bug localization techniques. Our results confirmed that our technique can improve the performance of IR-based bug localization techniques for the class level even when large datasets are processed. Furthermore, because of the optimized configuration of the smell information, our technique can enhance the performance of most state-of-the-art bug localization techniques., Comment: 19 pages, JSS

It is important to maintain software quality as a software system evolves. Managing code smells in source code contributes towards quality software. While metrics have been used to pinpoint code smells in source code, we present an empirical study on the correlation of code smells with class-level (micro pattern) and method-level (nano-pattern) traceable code patterns. This study explores the relationship between code smells and class-level and method-level structural code constructs. We extracted micro patterns at the class level and nano-patterns at the method level from three versions of Apache Tomcat, three versions of Apache CXF and two J2EE web applications namely PersonalBlog and Roller from Stanford SecuriBench and then compared their distributions in code smell versus noncode smell classes and methods. We found that Immutable and Sink micro patterns are more frequent in classes having code smells compared to the noncode smell classes in the applications we analyzed. On the other hand, LocalReader and LocalWriter nano-patterns are more frequent in code smell methods compared to the noncode smell methods. We conclude that code smells are correlated with both micro and nano-patterns.

Nowadays, Yoga is popular around the world. A lot of people are participating in it by themselves through watching TV/videos or teaching each other. However, it is not easy for novice people to find the incorrect parts of their Yoga poses by themselves. In this paper, we propose a Yoga pose assessment method using pose detection to help the self-learning of Yoga. The system first detects a Yoga pose using multi parts detection only with PC camera. Then, it calculates the difference of the specified body angles between the pose of an instructor and that of a user. Then, it calculates the difference of the specified body angles between the pose of an instructor and that of a user, and suggests the correction if larger than the given threshold. The total angle difference values are calculated averagely and defined as performance class level in Table 1. For evaluations, we applied the proposal to three persons with three Yoga poses of basic and easy Yoga poses for beginners and confirmed that it found the incorrect parts of each pose.

This paper presents a novel computer vision based system to support automated PACP (Pipeline Assessment Certification Program) coding for cracks. The proposed system comprises five major steps: 1) identifying forward facing view (FFV) with pipeline viewpoint detector, 2) obtaining stable edge information using structure edge detector, 3) acquiring crack segments and inner circle area of the pipeline with image binarization, 4) generating a 2D angular-diameter histogram for each frame, and 5) training a crack category classifier with support vector machine (SVM). The experimental results demonstrated that the proposed system can not only detect the cracks and categorize the crack type per PACP standard effectively but can also run about 10 frames per second (fps) in real world CCTV videos with 320 × 240 resolutions. In terms of accuracy in detecting cracks, the proposed method reaches about 91%, 88% and 90% recall for longitudinal cracks (CL), circumferential cracks (CC) and multiple cracks (CM), respectively. This paper contributes to the overall body of knowledge by providing an innovative framework that supports real-time crack identification and coding per PACP standards, which will lay a strong foundation for the development of a fully automated PACP sewer pipeline inspection system.

We converge the disciplines of context-aware computing, human-computer interaction and pedagogic research practice to propose an agenda for the use of embedded sensing for novel learning spaces. In this case, embedded sensing is the identification and analysis of in-the moment individual, group and class level behavioural data from students engaged in physical learning activities. Our work is motivated by the challenges and opportunities for teachers inherent in the rise of the design, development and evaluation of novel learning spaces augmented with multidevice technology. We present a framework for the use of embedded sensing, its relationship related and emerging work in the fields of social learning analytics and smart learning, and a practical illustration of SOLE (Sugata Mitra’s Self Organised Learning Environments). Our agenda addresses the conceptualization, data collection, and analysis of learning; zooming in on hard-to-identify individual-within-group learning processes. For the educational researcher, we propose a context-sensitive, dynamic and situated approach that can inform analytic frameworks and development of tools for sense-making. For the teacher-inquirer, the smart teacher, we propose that this approach directly addresses issues linked to the complexity of the ‘what and how’ of education-based evaluation and assessment of students in unstructured and multidevice learning spaces more broadly.

Due to the absence of samples with intra-class variation, extracting discriminative facial features and building powerful classifiers are the bottlenecks of improving the performance of face recognition (FR) with single sample per person (SSPP). In this paper, we propose to learn regional adaptive convolution features which are locally and globally discriminative to face identity and robust to face variation. With collected generic facial variations, a novel class-level joint representation framework is presented to exploit the distinctiveness and class-level commonality of different facial features. In the proposed class-level joint representation with regional adaptive convolution feature (CJR-RACF), both discriminative facial features robust to various facial variations and powerful representation for classification with generic facial variations that can overcome the small-sample-size problem are fully exploited. CJR-RACF has been evaluated on several popular databases, including large-scale CMU Multi-PIE and LFW databases. Experimental results demonstrate the much higher robustness and effectiveness of CJR-RACF to complex facial variations compared to the state-of-the-art methods.

Objects in images are characterized by intra-class variation, inter-class diversity, and noisy images. These characteristics pose a challenge to object localization. To address this issue, we present a novel joint Bayesian model for weakly-supervised object localization. The differences compared to previous discriminative methods are evaluated in three aspects: (1) We co-localize the similar object per class through transferring shared parts, which are pooling by modeling object, parts and features within and between-class; (2) Labels are given at class level to provide strong supervision for features and corresponding parts; (3) Noisy images are considered by leveraging a constraint on the detection of shared parts. In addition, our methods are evaluated by extensive experiments. The results indicated outperformance of the state-of-the-art approaches with almost 7% and 1.5% improvements in comparison to the previous methods on PASCAL VOC 2007 6 × 2 and Object Discovery datasets, respectively.

Modifications in any software need to be carries out on various entities. Change in one entity may force some changes in many other dependent entities. Complete understanding of entities and there dependencies are highly desirable to carry out modifications in an efficient manner. Automated summarization of classes in object oriented software can be a good step in this direction. This paper proposes a natural language summary based code summarization of those java classes which are more change prone. Code summary is generated by using concept of micro patterns and change proneness is identified by computing different kinds of dependencies among classes. A threshold is decided to identify the classes which are more sensitive to change. The empirical evaluation of some open source classes has been carried out which clearly indicates the usefulness of the proposed work.

The data model for structural experiment information formally organizes the information involved in the structural experiments before the data repository using the data model is implemented. The data model is particularly required for the data repositories for the large-scale structural experiment information and the general information for various types of experiments, such as the NEEShub Project Warehouse developed by NEES. This paper proposes criteria for evaluating the organization and the use of design model for structural experiment information. The term of AVE(attribute value existence) indicates the ratio of attributes who values exist in objects, and then used for defining the Attribute AVE for the use of an attribute, the Class AVE for a class, the Class Level AVE for a class including its lower-level classes, the Project AVE for a project including all classes at class levels, and the Data Model AVE for a data model including projects. These criteria are applied to the projects in the NEES data model, and it is successively possible to numerically describe the evaluation of the use of classes and attributes in the data model.

This paper explored the effects of in-class-level differentiation by making innovative use of an interactive whiteboard (SMARTboard) on math proficiency. Therefore, this paper evaluates the use of SMARTboard in class, in combination with teacher training, using a randomized field experiment among 199 pre-vocational students in seventh grade in the Netherlands. During 6 weeks, students in the intervention group participated in math classes in which the SMARTboard was used to apply level differentiation. The teachers of these classes received a specific training (Technological Pedagogical and Content Knowledge [TPACK]) in using the SMARTboard in class. Control classes were taught by teachers without the training, who did not use the SMARTboard in class. The results showed that level differentiation in class, which was possible because of the efficient use of the SMARTboard, significantly increased math proficiency with 0.25 points.

The original design of a software system is rarely prepared for every new requirement. Software systems should be updated frequently, which is usually accompanied by the decline in software modularity and quality. Although many approaches have been proposed to improve the quality of software, a majority of them are guided by metrics defined on the local properties of software. In this article, we propose to use a global metric borrowed from the network science to detect the moving method refactoring. First, our approach uses a bipartite network to represent classes, features (i.e., methods and fields), and their dependencies. Second, a new metric is introduced to quantify the modularity of a software system as a whole. Finally, a crossover-only evolutionary algorithm that uses the metric as its fitness function is introduced to optimize the class structure of a software system and detect the methods that should be moved. Empirical results on the benchmark Java projects show that our approach can find meaningful methods that should be moved with a high stability. The advantages of our approach are illustrated in comparison with some other approaches, specifically one refactoring approach, namely search-based refactoring approach (SBRA), and two community detection algorithms, namely a graph theoretic clustering algorithm (MCODE) and a fast algorithm for community detection (FG). Our approach provides a new way to do refactoring from the perspective of software structure.

Nowadays with the proliferation of location aware technologies and smart phones people tend to give reviews for all types of products services and place them online. It is very important to extract knowledge or information occupies in the vast amount of available text reviews. For these, user's sentiment is also monumental. If any business owners want to take decision on future planning, they must consider their clients sentiment. In this research, we proposed a noble strategy to predict user sentiment from their online reviews given for a particular business by using supervised machine learning techniques. Our proposed machine learning model will give a hand to restaurant owners to identify their customer's feedback and market positions.

Bug localization is challenging and time-consuming. Given a bug report, a developer may spend tremendous time comprehending the bug description together with code in order to locate bugs. To facilitate bug report comprehension, information retrieval (IR)-based bug localization techniques have been proposed to automatically search for and rank potential buggy code elements (i.e., classes or methods). However, these techniques do not leverage any dynamic execution information of buggy programs. In this paper, we perform the first systematic study on how dynamic execution information can help with static IR-based bug localization. More specifically, with the fixing patches and bug reports of 157 real bugs, we investigated the impact of various execution information (i.e. coverage, slicing, and spectrum) on three IR-based techniques: the baseline technique, BugLocator, and BLUiR. Our experiments demonstrate that both the coverage and slicing information of failed tests can effectively reduce the search space and improve IR-based techniques at both class and method levels. Using additional spectrum information can further improve bug localization at the method but not the class level. Some of our investigated ways of augmenting IR-based bug localization with execution information even outperform a state-of-the-art technique, which merges spectrum with an IR-based technique in a complicated way. Different from prior work, by investigating various easy-to-understand ways to combine execution information with IR-based techniques, this study shows for the first time that execution information can generally bring considerable improvement to IR-based bug localization.

This paper introduces a very important classification aspect for the analysis of huge amount of data stored in databases and other repositories. Numerous classification models are available in the literature, to predict the class of objects whose class level is unknown. Literature reveals that most of the available models are not capable in handling imperfect data. In view of this, present paper proposes a new rough set based classification model to derive the classification (IF-THEN) rules. Furthermore, developed model has been applied to handle bank-loan applications database as either safe, unsafe or risky. However, proposed model can also be used for the analysis of data from other domains.

The design of methods in the pharmaceutical compendia for the laboratory-based evaluation of orally inhaled product (OIP) performance is intentionally aimed for simplicity and robustness in order to achieve the high degree of accuracy and precision required for the assurance of product quality in a regulated environment. Consequently, performance of the inhaler when used or even misused by the patient or care-giver has often not been assessed. Indeed, patient-use-based methodology has been developed in a somewhat piecemeal basis when a need has been perceived by the developing organization. There is, therefore, a lack of in-use test standardization across OIP platforms, and often important details have remained undisclosed beyond the sponsoring organization. The advent of international standards, such as ISO 20072:2009, that focus specifically on the OIP development process, together with the need to make these drug delivery devices more patient-friendly as an aid to improving compliance, is necessitating that clinically appropriate test procedures be standardized at the OIP class level. It is also important that their capabilities and limitations are well understood by stakeholders involved in the process. This article outlines how this process might take place, drawing on current examples in which significant advances in methodology have been achieved. Ideally, it is hoped that such procedures, once appropriately validated, might eventually become incorporated into the pharmacopeial literature as a resource for future inhaler developers, regulatory agencies, and clinicians seeking to understand how these devices will perform in use to augment ongoing product quality testing which is adequately served by existing methods.

In this paper, we quantify the existence of concept drift in patent data, and examine its impact on classification accuracy. When developing algorithms for classifying incoming patent applications with respect to their category in the International Patent Classification (IPC) hierarchy, a temporal mismatch between training data and incoming documents may deteriorate classification results. We measure the effect of this temporal mismatch and aim to tackle it by optimal selection of training data. To illustrate the various aspects of concept drift on IPC class level, we first perform quantitative analyses on a subset of English abstracts extracted from patent documents in the CLEF-IP 2011 patent corpus. In a series of classification experiments, we then show the impact of temporal variation on the classification accuracy of incoming applications. We further examine what training data selection method, combined with our classification approach yields the best classifier; and how combining different text representations may improve patent classification. We found that using the most recent data is a better strategy than static sampling but that extending a set of recent training data with older documents does not harm classification performance. In addition, we confirm previous findings that using 2-skip-2-grams on top of the bag of unigrams structurally improves patent classification. Our work is an important contribution to the research into concept drift for text classification, and to the practice of classifying incoming patent applications.

Mutation testing is a standard technique to evaluate the quality of a test suite. Due to its computationally intensive nature, many approaches have been proposed to make this technique feasible in real case scenarios. Among these approaches, uniform random mutant selection has been demonstrated to be simple and promising. However, works on this area analyze mutant samples at project level mainly on projects with adequate test suites. In this paper, we fill this lack of empirical validation by analyzing random mutant selection at class level on projects with non-adequate test suites. First, we show that uniform random mutant selection underachieves the expected results. Then, we propose a new approach named weighted random mutant selection which generates more representative mutant samples. Finally, we show that representative mutant samples are larger for projects with high test adequacy., EASE 2016, Article 11 , 10 pages

In this paper, we investigate the impact of the accident reporting strategy of drivers, within a Bonus-Malus system. We exhibit the induced modification of the corresponding class level transition matrix and derive the optimal reporting strategy for rational drivers. The hunger for bonuses induces optimal thresholds under which, drivers do not claim their losses. Mathematical properties of the induced level class process are studied. A convergent numerical algorithm is provided for computing such thresholds and realistic numerical applications are discussed.

One of the most useful aspects of descriptors is their ability to encapsulate data so well. With descriptors, you have a simple way of accessing an attribute via attribute access notation (a.x) while allowing more complex actions to happen in the background.

Although learning with Intelligent Tutoring Systems (ITS) has been well studied, little research has investigated what role teachers can play, if empowered with data. Many ITSs provide student performance reports, but they may not be designed to serve teachers’ needs well, which is important for a well-designed dashboard. We investigated what student data is most helpful to teachers and how they use data to adjust and individualize instruction. Specifically, we conducted Contextual Inquiry interviews with teachers and used Interpretation Sessions and Affinity Diagramming to analyze the data. We found that teachers generate data on students’ concept mastery, misconceptions and errors, and utilize data provided by ITSs and other software. Teachers use this data to drive instruction and remediate issues on an individual and class level. Our study uncovers how data can support teachers in helping students learn and provides a solid foundation and recommendations for designing a teacher’s dashboard.

In fall 2008, students from first-year Composition I and upper-level classes at Florida Gulf Coast University participated in a citation analysis study. The citation pages of their research papers revealed that the students used more books, more types of sources, and more overall sources when a librarian provided instruction. When these results were compared to those produced by students in upper-level classes (all of whom received instruction), it was discovered that, as the class level increased, the number of citations and the percentage of scholarly citations generally increased and there was a high preference for books from all disciplines, especially history.

Most of the object-oriented cohesion metrics proposed in the literature define static cohesion at class level. Measurement of object-level dynamic cohesion however gives better insight into the behavioural aspects of the system. In this paper, dynamic cohesion metrics are introduced which provide scope of cohesion measurement up to object level and take into account important and widely used object-oriented features such as inheritance, polymorphism and dynamic binding during measurement. A theoretical framework is introduced before defining the measures and a theoretic validation of the proposed measures is carried out to make them more meaningful. A dynamic analyser tool is developed using aspect-oriented programming (AOP) to perform dynamic analysis of Java applications for the purpose of collecting run-time data for computation of the proposed dynamic cohesion measures. Further, an experiment is carried out for the proposed dynamic cohesion metrics using 20 Java programs and this study shows that the proposed dynamic cohesion metrics are more accurate and useful in comparison to the existing cohesion metrics. Moreover, the proposed dynamic cohesion metrics are validated empirically using source code APIs of Java Development Kit (JDK) and the proposed metrics are found to be better indicators of change-proneness of classes than the existing cohesion metrics.

Date Presented 4/19/2018 This study assessed the feasibility of embedded multitiered support services using the Zones of Regulation® curriculum, which is focused on self-regulation skills. The study provides scientific evidence and tools for interdisciplinary implementation of self-regulation programming in the classroom. Primary Author and Speaker: Erin McQuaid

Programming measurements was utilized for foreseeing issue in modules of programming ventures. Convenient forecast of flaws enhances programming quality and subsequently its dependability. In this paper, a framework towards subspace grouping of large data set was pro-posed at class level to minimize the error. We composed an iterative calculation for grouping of high dimensional datasets for improvement of a target work. At that point the bunched data sets were examined utilizing Step-Wise Linear Regression to investigate the relationship among a structure variable and the autonomous factors in order to anticipate of damaged and non-faulty classes. To evaluate the supportive-ness of the model, we drove a practical learning on the Attitude Survey Data. The proposed strategy specifically managed blunder variables and consequently gave precise fault prediction least standard error (0.003) when contrasted with the current technique (4.687). Root mean square error which measures the distinction between the assessed error and the real error was (0.8) in the proposed technique. The results demonstrated that the forecast models based on subspace clustering were essentially predominant to the current techniques.

To increase efficacy in traditional classroom courses as well as in Massive Open Online Courses (MOOCs), automated systems supporting the instructor are needed. One important problem is to automatically detect students that are going to do poorly in a course early enough to be able to take remedial actions. Existing grade prediction systems focus on maximizing the accuracy of the prediction while overseeing the importance of issuing timely and personalized predictions. This paper proposes an algorithm that predicts the final grade of each student in a class. It issues a prediction for each student individually, when the expected accuracy of the prediction is sufficient. The algorithm learns online what is the optimal prediction and time to issue a prediction based on past history of students' performance in a course. We derive a confidence estimate for the prediction accuracy and demonstrate the performance of our algorithm on a dataset obtained based on the performance of approximately 700 UCLA undergraduate students who have taken an introductory digital signal processing over the past 7 years. We demonstrate that for 85% of the students we can predict with 76% accuracy whether they are going do well or poorly in the class after the 4th course week. Using data obtained from a pilot course, our methodology suggests that it is effective to perform early in-class assessments such as quizzes, which result in timely performance prediction for each student, thereby enabling timely interventions by the instructor (at the student or class level) when necessary., 15 pages, 15 figures

Introductory courses offer several opportunities to inspire students to pursue a degree in STEM majors. Attracting and retaining students involves engagement and enjoyment at the individual, team, and class level. We explored the effects of these factors with a gamified approach to programming practice in an introductory computer science course. Gamification involves the use of game design elements in a non-game context to engage and motivate students. We present a novel learning game and the results of play-testing with 12 CS1 students. Students were surveyed prior to and after the gamified programming session. Positive feedback was obtained from the students and the educators observed high participation, interaction, and successful team programming efforts. We objectively report student satisfaction with the game, lessons learned by the educators, and suggest future work for stimulating students in introductory STEM courses.

The present research explores the challenges of testing deep word knowledge of the vocabulary of students of English as a Foreign/Second Language (EFL/ESL) at higher education. A productive test modelled on the Lex30 test developed by Meara and Fitzpatrick (2000) was presented to the participants. Results indicate that (i) ESL students outperform their EFL counterparts of comparable class level, (ii) aspects of deep word knowledge among both higher education EFL and ESL students develop in the order of analytic relations, paradigmatic relations, and collocations; and (iii) aspects of deep word knowledge among both higher education EFL and ESL students grow alongside one another and correlate significantly with overall deep word knowledge; the strength of which may reflect the extent to which they contribute to it. On the basis of these findings, we conclude that Lex30 may constitute a good measure of deep word knowledge at the productive level, which is the path we suggest should be followed in order to test deep word knowledge of vocabulary at higher education. Furthermore, teaching implications aimed to foster deep word knowledge growth are discussed. Keywords: Vocabulary Deep Word Knowledge, Testing Deep Word Knowledge Productively, Vocabulary Dimensions, English as a Foreign/Second Language (EFL/ESL)

As empirically demonstrated by the Word Sense Disambiguation (WSD) tasks of the last SensEval/SemEval exercises, assigning the appropriate meaning to words in context has resisted all attempts to be successfully addressed. Many authors argue that one possible reason could be the use of inappropriate sets of word meanings. In particular, WordNet has been used as a de-facto standard repository of word meanings in most of these tasks. Thus, instead of using the word senses defined in WordNet, some approaches have derived semantic classes representing groups of word senses. However, the meanings represented by WordNet have been only used for WSD at a very fine-grained sense level or at a very coarse-grained semantic class level (also called SuperSenses). We suspect that an appropriate level of abstraction could be on between both levels. The contributions of this paper are manifold. First, we propose a simple method to automatically derive semantic classes at intermediate levels of abstraction covering all nominal and verbal WordNet meanings. Second, we empirically demonstrate that our automatically derived semantic classes outperform classical approaches based on word senses and more coarse-grained sense groupings. Third, we also demonstrate that our supervised WSD system benefits from using these new semantic classes as additional semantic features while reducing the amount of training examples. Finally, we also demonstrate the robustness of our supervised semantic class-based WSD system when tested on out of domain corpus. This work has been partially supported by the NewsReader project (ICT-2011-316404), the Spanish project SKaTer (TIN2012-38584-C06-02).

Filter is a critical component in ahydraulic system for maintaining the cleanliness of the fluid to required class level. InMarine applications very high reliable filter is required to operate continuously in saline environment. Design anddevelopment of high pressure hydraulic filter for Marine application is a challenging task. The design involves selection of special materialsandstringent qualification tests as per International standards. The present paper describes various stages of design and development of high pressure hydraulic filter for Marine application.

This paper discusses the need to establish a contingency plan at the university class level to mitigate the impacts of a disaster. It places the class room plan in context of the contingency plans of higher organizational units. It related the authors' experience with multiple natural disasters and how those experiences can be used to establish a contingency plan that is appropriate to the reader's environment.

Electronic Product Code has been widely used to identify items with RFID tags. To the best of our knowledge, however, few works focus on exploring the value of textural data in the EPC Network that pertains to EPC. In this paper we propose the concept of the content-based query for tagged items by applying information retrieval method to the EPC network resources. The objective of the content-based query is to discover a set of class-level tagged items of interest which correlate with the user-specific keyword or EPC in question. Accordingly, we describe the strategies in terms of content integration, modelling and similarity measurement for the content-based queries. Our experimental results demonstrate the effectiveness of our approach.

Hypovolemia is a dangerous and acute disease for mankind which results form a decreased level of blood volume or a loss of blood. In extreme case, it can cause an irreversible cellular damage which can cause death. In this paper, a fuzzy control system is proposed to detect this hypovolemia class level. The four classes of hypovolemia can be detected by this proposed system for adult persons. This system analyzes five physiological data which come from the devices that are used for monitoring the patients and regarding all these data it'll give the hypovolemia class level of the patients. It'll empower the doctors to take quick decisions in case of surgeries and seizures. This fuzzy system can be embedded using FPGAs or CPLDs so that can also be used to give treatment to remote place patients.

Based on approximately 25,000 Student Course Evaluation Questionnaires (SCEQ) covering 16 semesters from Spring 2005 through Fall 2012 and publicly available at the University of Maryland, Baltimore County’s website http://www.umbc.edu/oir/sceq/index.html, we analyze the effect of class level and discipline on student responses. We compare the results obtained and conclusions drown with those already reported in the literature.

We have presented a classification framework that combines multiple heterogeneous classifiers in the presence of class label noise. An extension ofm-Mediods based modeling is presented that generates model of various classes whilst identifying and filtering noisy training data. This noise free data is further used to learn model for other classifiers such as GMM and SVM. A weight learning method is then introduced to learn weights on each class for different classifiers to construct an ensemble. For this purpose, we applied genetic algorithm to search for an optimal weight vector on which classifier ensemble is expected to give the best accuracy. The proposed approach is evaluated on variety of real life datasets. It is also compared with existing standard ensemble techniques such as Adaboost, Bagging, and Random Subspace Methods. Experimental results show the superiority of proposed ensemble method as compared to its competitors, especially in the presence of class label noise and imbalance classes.

This paper will share with the members of the conference the findings from the sixth study that related to Class level. This study contains five semesters of analyzed data which compares the retention of students, final grades for students, the grades for five specific tasks that were given in blended in-class courses and in the totally online courses and a comparison of data by Class. All courses were American Politics PLSC 111. Each semester one or two American Politics courses were conducted in the classroom and one American Politics distance education course was conducted totally online. Each time the courses were given, it was during the same semester and by the same professor who was the researcher.

This paper describes our experiments and analysis of utilizing class-level features to predict student performance for retention tests. There are two aspects that make this paper interesting. First, instead of focusing on short-team performance, we investigated student performance after a delay of at least 7 days. Second, we explored several class-level features that can be captured in intelligent tutoring systems (ITS), and we showed that some of them have encouraging predictive power. With the help of class-level features, the prediction result indicated an improvement from an R² of 0.183 with a normal feature set to an R² value of 0.224.

The work summarizes and discusses the basic conditions and rules for the use of the deductive method of syntaxonomic classification. This method develops and supplements classification procedures of the Braun-Blanquet approach. It enables to classify the communities which cannot be characterized by their own character- and differential-species. The deductive method incorporates into the inductively constructed system of abstract units of the Braun-Blanquet approach (classes, orders, alliances and associations) really existing communities on the basis of the presence or absence of diagnostic characters (character-and differential-species) of these abstract units. Some communities are incorporable only at the class level, others at the level of lower units. To objectify the process of incorporation of the communities represented by the respective phytocoenological releves, an algorithm of the deductive method was constructed which uniquely incorporates individual releves and groups of releves into the phytocoenological system on the basis of the presence, coverage and diagnostic value of species. The work includes a detailed description of this algorithm and examples of its application on concrete phytocoenological material. Examples of hitherto use of the deductive method are also discussed and its nomenclatural rules briefly explained.