Jane sees 50 compiler errors as a challenge. John sees them as defeat. Psychology research suggests these contrasting reactions may stem from students' self-theories, or their beliefs about themselves. Jane's ...
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ISBN:
(纸本)9781605582160
Jane sees 50 compiler errors as a challenge. John sees them as defeat. Psychology research suggests these contrasting reactions may stem from students' self-theories, or their beliefs about themselves. Jane's reaction is characteristic of a growth mindset, the idea that with hard work and persistence, one's intelligence can increase. John's behavior is in line with a fixed mindset, the belief that individuals are born with a certain amount of intelligence and there is little they can do to change it. Numerous studies of self-theories have shown that students with a growth mindset perform better in academic settings;they cope more effectively with challenges, maintain higher grades, and are less susceptible to stereotype threat. In this study we attempted a "saying is believing" intervention to encourage CS1 students to adopt a growth mindset both in general and towards programming. Despite notable success of this type of intervention in a non-CS context, our results offered few statistically significant differences both from pre-survey to post-survey and between control and intervention groups. Further, the statistically significant results we did find differed in direction between institutions (some students exhibited more growth response, others less). We analyzed further evidence to explore possible confounding issues including whether our intervention even registered with students and how students interpreted the questions which we used to assess their self-theories. Copyright 2008 ACM.
This paper investigates the applicability of Gaussian processes (GP) classification for recognition of articulated and deformable human motions from image sequences. Using tensor subspace analysis (TSA), space-time hu...
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ISBN:
(纸本)9781424421749
This paper investigates the applicability of Gaussian processes (GP) classification for recognition of articulated and deformable human motions from image sequences. Using tensor subspace analysis (TSA), space-time human silhouettes (extracted from motion videos) are transformed to low-dimensional multivariate time series, based on which structure-based statistical features are calculated to summarize the motion properties. GP classification is then used to learn and predict motion categories. Experimental results on two real-world state-of-the-art datasets show that the proposed approach is effective, and outperforms support vector machine (SVM).
The purpose of this pilot study was to explore the feasibility of using hand drawn images to identify symbol components for incorporation into warning symbol design software. This software will use an interactive evol...
The purpose of this pilot study was to explore the feasibility of using hand drawn images to identify symbol components for incorporation into warning symbol design software. This software will use an interactive evolutionary computation (IEC) algorithm to generate and evolve symbols mathematically described by a set of numerical parameters. Therefore, participants (N = 100) ages 19–43 (x = 23.2) were recruited to determine these symbol design parameters. Participants were invited to hand draw warning symbols for three referents: fall from elevation, hearing protection, and hazardous atmosphere. A panel of design engineers determined 27 attributes were present in the fall from elevation, 19 in the hearing protection, and 25 in the hazardous atmosphere images. A direct clustering algorithm was used to determine which attributes, or symbol parameters, were most commonly present or conspicuously absent among the clustered image families. For the fall from elevation, hearing protection and hazardous atmosphere referents, the clustering algorithm identified six, four and four symbol parameters, respectively, primarily responsible for distinguishing one drawn symbol from another. Thus, these parameters will be included as evolvable genes in the IEC software.
In this paper, we propose an autonomic management framework (ASGrid) to address the requirements of emerging large-scale applications in hybrid grid and sensor network systems. To the best of our knowledge, we are the...
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In this paper, we propose an autonomic management framework (ASGrid) to address the requirements of emerging large-scale applications in hybrid grid and sensor network systems. To the best of our knowledge, we are the first who proposed the autonomic sensor grid system concept in a holistic manner targeted at non-trivial large applications. To bridge the gap between the physical world and the digital world and facilitate information analysis and decision making, ASGrid is designed to smooth the integration of sensor networks and grid systems and efficiently use both on demand. Under the blueprint of ASGrid, we present several building blocks that fulfill the following major features: (1) Self-configuration through content-based aggregation and associative rendezvous mechanisms; (2) Self-optimization through utility-based sensor selection and model-driven hierarchical sensing task scheduling; (3) Self-protection through ActiveKey dynamic key management and S3Trust trust management mechanisms. Experimental and simulation results on these aspects are presented.
In today's Web environment, many enterprises decide to implement and publish their applications on the Internet using Web services technology. In many cases, a single service is not sufficient to fulfill the user&...
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In today's Web environment, many enterprises decide to implement and publish their applications on the Internet using Web services technology. In many cases, a single service is not sufficient to fulfill the user's request. To solve this problem, services should be combined together. Therefore, composition of Web services is one of the recent critical issues. A number of approaches have been presented, to tackle this problem. In this paper, we categorize these approaches into four categories (workflow-based, AI-planning based, syntactic-based, and semantic-based). Then, we compare these approaches based on some criteria (like QoS, scalability, and correctness). Investigation of that classification will help researchers who are working on service composition to deliver more applicable solutions.
The Modeling in softwareengineering (MiSE) workshops are a collaboration between the ICSE and MoDELS research communities, with a focus on using models to facilitate software development.
ISBN:
(纸本)9781605580791
The Modeling in softwareengineering (MiSE) workshops are a collaboration between the ICSE and MoDELS research communities, with a focus on using models to facilitate software development.
Large-scale computing environments, such as TeraGrid, Distributed ASCI Supercomputer (DAS), and Gridpsila5000, have been using resource co-allocation to execute applications on multiple sites. Their schedulers work wi...
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Large-scale computing environments, such as TeraGrid, Distributed ASCI Supercomputer (DAS), and Gridpsila5000, have been using resource co-allocation to execute applications on multiple sites. Their schedulers work with requests that contain imprecise estimations provided by users. This lack of accuracy generates fragments inside the scheduling queues that can be filled by rescheduling both local and multi-site requests. Current resource co-allocation solutions rely on advance reservations to ensure that users can access all the resources at the same time. These coallocation requests cannot be rescheduled if they are based on rigid advance reservations. In this work, we investigate the impact of rescheduling co-allocation requests based on flexible advance reservations and processor remapping. The metascheduler can modify the start time of each job component and remap the number of processors they use in each site. The experimental results show that local jobs may not fill all the fragments in the scheduling queues and hence rescheduling co-allocation requests reduces response time of both local and multi-site jobs. Moreover, we have observed in some scenarios that processor remapping increases the chances of placing the tasks of multi-site jobs into a single cluster, thus eliminating the inter-cluster network overhead.
Since many organizations recently decide to implement and publish their applications over Internet, the number of Web services has dramatically increased. In many cases, a single service is not sufficient to respond t...
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Since many organizations recently decide to implement and publish their applications over Internet, the number of Web services has dramatically increased. In many cases, a single service is not sufficient to respond to the user’s request. In order to tackle this problem, services have to be combined together. Therefore, composition of Web services is one of the recent critical issues. Several approaches have been presented, to tackle this problem. In this paper, we classify these approaches into four categories namely Workflow-based, AI-planning based, Syntactic-based, and Ontology-based. Then, we describe and compare these approaches using some criteria (like QoS, scalability, and correctness). The overall results indicate that some AI-planning and Ontology based approaches like HTN-DL and WSMO satisfy most of the criteria.
Web services are the preferred standards-based way to realize service oriented architecture (SOA) computing. A problem that has become one of the recent critical issues is automated composition of Web services. A numb...
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Web services are the preferred standards-based way to realize service oriented architecture (SOA) computing. A problem that has become one of the recent critical issues is automated composition of Web services. A number of approaches have been proposed to resolve the problem. However, discovery and composition of Web services have been neglected by majority of those approaches. In this paper, we propose an approach called AIMO, based on AI-planning and Web service modeling ontology (WSMO), which are adapted and extended to tackle the mentioned problem. Moreover, we propose a translator to provide interaction between WSMO and AI-planning. We start with presenting the AIMO architecture which shows all components of the proposed approach. Finally, a simple case study is designed to test some components of AIMO. The preliminary results of the test demonstrate that our approach provides an applicable solution.
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