ppxscal/embedding_network huggingface dataset:
Designed Collaboration as a Scaffold for Schematic Knowledge Integration
Authors: Naomi Miyake
Venue: Proceeding of the 2006 conference on Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding (2006)

Rapid change in modern society requires higher levels of learning such as the acquisition of adaptive or “schematic” knowledge. Rather than the efficiency of simply applying what one has learned, the schematic knowledge acquisition emphasizes portability, sustainability, and dependability of learning outcomes. Schematic knowledge is expected to allow the learners to apply them to solve the wider scope of similar problems, as well as to identify new problems and create new solutions. We have been developing and testing college level learning environments to enhance the acquisition of such schematic knowledge in the domain of cognitive science, by heavily relying on understandings of how and why collaborative reflection benefits learning. In the two-year curricular we have developed, the students are first introduced to the notion of schematic learning by experiencing their own formation of schemata, and then are guided to reflect upon the process, through carefully designed collaborative activities. They will also be encouraged to integrate their experiences to technical literature through collaborative discussion in a dynamically arranged jigsaw variations. I will report on the theoretical bases of our practice, concrete learning activities, technological supports, and some results of the evaluative analyses of the learning processes and the outcomes.
A high-performance framework for analyzing massive complex networks
Authors: David A. Bader
Venue:  (2008)

Graphs are a fundamental and widely-used abstraction for representing data. We can analytically study interesting aspects of real-world complex systems such as the Internet, social systems, transportation networks, and biological interaction data by modeling them as graphs. Graph-theoretic and combinatorial problems are also pervasive in scientific computing and engineering applications. In this dissertation, we address the problem of analyzing large-scale complex networks that represent interactions between hundreds of thousands to billions of entities. We present SNAP, a new high-performance computational framework for efficiently processing graph-theoretic queries on massive datasets.Graph analysis is computationally very different from traditional scientific computing, and solving massive graph-theoretic problems on current high performance computing systems is challenging due to several reasons. First, real-world graphs are often characterized by a low diameter and unbalanced degree distributions, and are difficult to partition on parallel systems. Second, parallel algorithms for solving graph-theoretic problems are typically memory intensive, and the memory accesses are fine-grained and highly irregular. The primary contributions of this dissertation are the design and implementation of novel parallel graph algorithms for traversal, shortest paths, and centrality computations, optimized for the small-world network topology, and high-performance multithreaded architectures and multicore servers. SNAP (Small-world Network Analysis and Partitioning) is a modular, open-source framework for the exploratory analysis and partitioning of large-scale networks. With SNAP, we demonstrate the capability to process massive graphs with billions of vertices and edges, and achieve up to two orders of magnitude speedup over state-of-the-art network analysis approaches. We also design a new parallel computing benchmark for characterizing the performance of graph-theoretic problems on high-end systems; study data representations for dynamic graph problems on parallel systems; and apply algorithms in SNAP to solve real-world problems in social network analysis and systems biology.
Geometry-aware metric learning
Authors: Zhengdong Lu, Prateek Jain, Inderjit S. Dhillon
Venue: Proceedings of the 26th Annual International Conference on Machine Learning (2009)

In this paper, we introduce a generic framework for semi-supervised kernel learning. Given pair-wise (dis-)similarity constraints, we learn a kernel matrix over the data that respects the provided side-information as well as the local geometry of the data. Our framework is based on metric learning methods, where we jointly model the metric/kernel over the data along with the underlying manifold. Furthermore, we show that for some important parameterized forms of the underlying manifold model, we can estimate the model parameters and the kernel matrix efficiently. Our resulting algorithm is able to incorporate local geometry into the metric learning task; at the same time it can handle a wide class of constraints. Finally, our algorithm is fast and scalable -- unlike most of the existing methods, it is able to exploit the low dimensional manifold structure and does not require semi-definite programming. We demonstrate wide applicability and effectiveness of our framework by applying to various machine learning tasks such as semi-supervised classification, colored dimensionality reduction, manifold alignment etc. On each of the tasks our method performs competitively or better than the respective state-of-the-art method.
Dynamic analyses for understanding and optimization of enterprise java applications
Authors: Atanas Rountev
Venue:  (2008)

Enterprise Java (J2EE) applications present significant challenges for horizontal scaling due to their large code and dataset sizes, as well as the clustering models of application servers. Our work targets a potential reimplementation of the way Enterprise Java applications are clustered. We propose three dynamic analyses, motivated by a new design for a clustered enterprise application framework, which allow better understanding of existing applications and provide support for migrating such applications to a possible future implementation of this design. The first dynamic analysis traces the flow of primary keys, which uniquely identify certain objects within the business tier of a J2EE application. It identifies the entry points of such data to the business tier, which provides crucial information for the migration of the application towards the new clustered design. We propose an algorithm to achieve this identification and implement the approach through bytecode instrumentation. Our experimental results indicate that the analysis has practical overhead and achieves excellent precision. The second dynamic analysis identifies the entry points of query parameters to the business tier of a J2EE application, as well as certain relationships in which this data participates. This is achieved through a number of enhancements to the algorithm for the first dynamic analysis. The analysis implementation exhibits reasonable overhead and achieves high-precision identification of the entry points of query parameters, as shown by our experimental studies. The third dynamic analysis identifies instances of the Data Transfer Object (DTO) design pattern within a J2EE application. It provides helpful information for the first dynamic analysis, and could be used as a preprocessing step to improve its precision and overhead. In addition, DTO identification is useful for a number of other tasks related to program comprehension, performance optimization, and software evolution. The algorithm investigates the lifecycle of certain objects within the business tier of a J2EE application, and matches that lifecycle against a state transition diagram that describes the behavior of a DTO. The algorithm was implemented through the Java Virtual Machine Tool Interface (JVMTI). Experimental results indicate that the analysis can identify the majority of DTO instances while exhibiting manageable overhead. This work is a step towards a new design for clustered enterprise applications. The proposed dynamic analyses solve some key problems for a potential future implementation of services that provide replication at the object level in enterprise application servers. Such services will solve the significant problem of horizontal scalability in Enterprise Java applications.
Transmission Network Expansion Planning Based on Mind Evolutionary Computation
Authors: Yaowu Wu, Suhua Lou, Yu Liu, Nan Zhang
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II (2009)

Mind Evolutionary Computation (MEC) is a novel kind of evolution algorithm. Employing operations of similartaxis and dissimilation, introducing information matrix and billboard, MEC possesses excellent performances due to three mechanisms: evolutionary directionality mechanism, memory mechanism and harmony mechanism between exploitation and exploration. Here, the MEC is applied to solve the transmission network expansion planning problem, and model of transmission network expansion planning based on MEC are put forward. Performances of GA and MEC are compared and the influence of several parameters and different dissimilation operations are analyzed. Simulating results show that MEC is reliable and effective for solving transmission network expansion planning problems.
Wiley Pathways Introduction to Google SketchUp, 1st edition
Authors: Aidan Chopra
Venue:  (2009)

Considerably easier to use than other 3D software, Google SketchUp has found a niche in architecture, landscape design, real estate development, furniture building, and other design professions. This book provides an accessible approach that assumes no previous 3D modeling experience and explains the basic concepts involved in 3D modeling. Wiley Pathways SketchUp shows you how to build a 3D model, print it, share it, export it to another professional design package, export it to Google Earth, and create a 3D animated tour. The book will also help you harness the power of Google SketchUp so you can populate Google Earth with 3D buildings, monuments, and other sculptures.
Performance analysis and adaptive Newton algorithms of multimodulus blind equalization criterion
Authors: Xi-Lin Li, Wen-Jun Zeng
Venue: Signal Processing (2009)

This paper studies the stationary points of the multimodulus blind equalization criterion in a noiseless communication channel and proposes two adaptive Newton algorithms. It is shown in this paper that the stationary points of the multimodulus criterion can be grouped into two categories, according to the power of equalizer output. The stationary points having the same equalizer output power with that of the transmitted symbols are desirable global minima, while the stationary points having less equalizer output power than that of the transmitted symbols are saddle points. A pseudo Newton learning algorithm and a full Newton learning algorithm minimizing the multimodulus criterion are proposed. By using the matrix inversion lemma, both Newton algorithms can be efficiently implemented with a computational complexity of O(N^2), where N is the tap length of equalizer. Computer experiment results are presented. It is found that the full Newton algorithm performs well in both static and time-varying communication channels, while the pseudo Newton algorithm performs well only in static communication channels.
Capacity of multichannel wireless networks under the protocol model
Authors: Pradeep Kyasanur, Nitin H. Vaidya
Venue: IEEE/ACM Transactions on Networking (TON) (2009)

This paper studies the capacity of n a node static wireless network with c channels and m radio interfaces per node under the protocol model of interference. In their seminal work, Gupta and Kumar have determined the capacity of a single channel network (c = 1, m = 1). Their results are also applicable to multichannel networks provided each node has one interface per channel (m = c). However, in practice, it is often infeasible to equip each node with one interface per channel. Motivated by this observation, we establish the capacity of general multichannel networks (m ≤ c). Equipping each node with fewer interfaces than channels in general reduces network capacity. However, we show that one important exception is a random network with up to O(log n) channels, where there is no capacity degradation even if each node has only one interface. Our initial analysis assumes that the interfaces are capable of switching channels instantaneously, but we later extend our analysis to account for interface switching delays seen in practice. Furthermore, some multichannel protocols proposed so far rarely require interfaces to switch, and therefore, we briefly study the capacity with fixed interfaces as well.
CourseRank: a social system for course planning
Authors: Benjamin Bercovitz, Filip Kaliszan, Georgia Koutrika, Henry Liou, Zahra Mohammadi Zadeh, Hector Garcia-Molina
Venue: Proceedings of the 35th SIGMOD international conference on Management of data (2009)

Special-purpose social sites can offer valuable services to well-defined, closed, communities, e.g., in a university or in a corporation. The purpose of this demo is to show the challenges, special features and potential of a focused social system in action through CourseRank, a course evaluation and planning social system.
Generating Sufficiency-based Non-Synthetic Perturbed Data
Authors: Krishnamurty Muralidhar, Rathindra Sarathy
Venue: Transactions on Data Privacy (2008)

The mean vector and covariance matrix are sufficient statistics when the underlying distribution is multivariate normal. Many type of statistical analyses used in practice rely on the assumption of multivariate normality (Gaussian model). For these analyses, maintaining the mean vector and covari-ance matrix of the masked data to be the same as that of the original data implies that if the masked data is analyzed using these techniques, the results of such analysis will be the same as that using the original data. For numerical confidential data, a recently proposed perturbation method makes it possi-ble to maintain the mean vector and covariance matrix of the masked data to be exactly the same as the original data. However, as it is currently proposed, the perturbed values from this method are consid-ered synthetic because they are generated without considering the values of the confidential variables (and are based only on the non-confidential variables). Some researchers argue that synthetic data re-sults in information loss. In this study, we provide a new methodology for generating non-synthetic perturbed data that maintains the mean vector and covariance matrix of the masked data to be exactly the same as the original data while offering a selectable degree of similarity between original and per-turbed data.
Combining Facial Skin Mark and Eigenfaces for Face Recognition
Authors: Zhi Zhang, Sergey Tulyakov, Venu Govindaraju
Venue: Proceedings of the Third International Conference on Advances in Biometrics (2009)

In this paper we investigate the use of facial skin mark information for biometric person verification. We performed statistical analysis of the facial skin mark information. The position, size and color intensity of the skin marks are considered as features for skin mark based face matching. Developed facial skin mark matcher has good performance, but can not be applied to faces with no detected skin marks. Due to non-universality of skin mark information, a novel combination algorithm of traditional Eigenfaces matcher with skin mark matcher has been proposed. The resulting combined face matcher has universality property and delivers better performance than either single matcher. The AR Face Database was used in experiments.
On the degree distance of a graph
Authors: P. Dankelmann, I. Gutman, S. Mukwembi, H. C. Swart
Venue: Discrete Applied Mathematics (2009)

If G is a connected graph with vertex set V, then the degree distance of G, D^'(G), is defined as @?"{"u","v"}"@?"V(degu+degv)d(u,v), where degw is the degree of vertex w, and d(u,v) denotes the distance between u and v. We prove the asymptotically sharp upper bound D^'(G)@?14nd(n-d)^2+O(n^7^/^2) for graphs of order n and diameter d. As a corollary we obtain the bound D^'(G)@?127n^4+O(n^7^/^2) for graphs of order n. This essentially proves a conjecture by Tomescu [I. Tomescu, Some extremal properties of the degree distance of a graph, Discrete Appl. Math. (98) (1999) 159-163].
Solution counting algorithms for constraint-centered search heuristics
Authors: Alessandro Zanarini, Gilles Pesant
Venue: Constraints (2009)

Constraints have played a central role in cp because they capture key substructures of a problem and efficiently exploit them to boost inference. This paper intends to do the same thing for search, proposing constraint-centered heuristics which guide the exploration of the search space toward areas that are likely to contain a high number of solutions. We first propose new search heuristics based on solution counting information at the level of individual constraints. We then describe efficient algorithms to evaluate the number of solutions of two important families of constraints: occurrence counting constraints, such as alldifferent, and sequencing constraints, such as regular. In both cases we take advantage of existing filtering algorithms to speed up the evaluation. Experimental results on benchmark problems show the effectiveness of our approach.
A visual interface to a music database
Authors: Robert St. Amant, James E. Blair, Patrick Barry, Yinon Bentor, Christopher G. Healey
Venue: Proceedings of the working conference on Advanced visual interfaces (2002)

This paper describes a system for exploring and selecting entries from a music database through a visualization interface. The system is designed for deployment in situations in which the user's attention is a tightly limited resource. The system combines research topics in intelligent user interfaces, visualization techniques, and cognitive modeling. Informal evaluation of the system has given us useful insights into the design tradeoffs that developers may face when building visual interfaces for off-the-desktop applications.
A methodology for developing self-explaining agents for virtual training
Authors: Maaike Harbers, Karel van den Bosch, John-Jules Meyer
Venue: Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2 (2009)

Virtual training systems are used to train people for complex, dynamic tasks in which fast decision making is required, e.g. those in command in a crisis, military mission or fire incident. During a training session, trainees have to fulfill a task or mission for which they have to interact with other players, such as team-members, opponents, or colleagues from other domains. By using intelligent agents to generate the behavior of these virtual players, trainees can train at any place and time, reducing costs.
Deliveries optimization by exploiting production traceability information
Authors: Simon Tamayo, Thibaud Monteiro, Nathalie Sauer
Venue: Engineering Applications of Artificial Intelligence (2009)

The recent product traceability requirements, particularly in food production chains, demonstrate an industrial need to improve traceability systems. Having real-time access to traceability information allows its exploitation, which is the aim of this work. In this paper, the problem of minimizing the cost of products recall is treated. First the raw material dispersion problem is analyzed, in order to determine a risk-level criterion or ''production criticality''. This criterion is used subsequently to optimize deliveries dispatch with the purpose of minimizing the number of batch recalls in case of crisis. This is achieved by implementing decision-making aid tools based on operational research and artificial intelligence.
Type-Directed Compilation for Multicore Programming
Authors: Kohei Honda, Vasco T. Vasconcelos, Nobuko Yoshida
Venue: Electronic Notes in Theoretical Computer Science (ENTCS) (2009)

In this paper, we outline a general picture of our ongoing work on a compilation and execution framework for a class of multicore CPUs [Gelsinger, P., P. Gargini, G. Parker and A. Yu, Microprocessors circa 2000, IEEE SPectrum (1989), pp. 43-47; Olukotun, K., B. A. Nayfeh, L. Hammond, K. G. Wilson and K. Chang, The case for a single-chip multiprocessor, in: ASPLOS, 1996, pp. 2-11; Pham, D. et al., The design and implementation of a first-generation cell processor, in: ISSCC Dig. Tech. Papers (2005), pp. 184-185]. Our focus is to harness the power of concurrency and asynchrony in one of the major forms of multicore CPUs based on distributed, noncoherent memory [Pham, D. et al., The design and implementation of a first-generation cell processor, in: ISSCC Dig. Tech. Papers (2005), pp. 184-185], using the well-known technology of type-directed compilation [Morrisett, G., D. Walker, K. Crary and N. Glew, From System F to typed assembly language, ACM Trans. Program. Lang. Syst. 21 (1999), pp. 527-568]. The key idea is to regard explicit asynchronous data transfer among local caches as typed communication among processes. By typing imperative processes with a variant of session types [Honda, K., V. T. Vasconcelos and M. Kubo, Language primitives and type disciplines for structured communication-based programming, in: ESOP'98, LNCS 1381 (1998), pp. 22-138; Takeuchi, K., K. Honda and M. Kubo, An interaction-based language and its typing system, in: PARLE'94, LNCS 817 (1994), pp. 398-413], we obtain both type-safe and efficient compilation into processes distributed over multiple cores with local memories.