ppxscal/embedding_network huggingface dataset:
Flexible Immutability with Frozen Objects
Authors: K. Rustan Leino, Peter Müller, Angela Wallenburg
Venue: Proceedings of the 2nd international conference on Verified Software: Theories, Tools, Experiments (2008)

Object immutability is a familiar concept that allows safe sharing of objects. Existing language support for immutability is based on immutable classes. However, class-based approaches are restrictive because programmers can neither make instances of arbitrary classes immutable, nor can they control when an instance becomes immutable. These restrictions prevent many interesting applications where objects of mutable classes go through a number of modifications before they become immutable.This paper presents a flexible technique to enforce the immutability of individual objects by transferring their ownership to a special freezer object, which prevents further modification. The paper demonstrates how immutability facilitates program verification by extending the Boogie methodology for object invariants to immutable objects. The technique is based on Spec#'s dynamic ownership, but the concepts also apply to other ownership systems that support transfer.
Research on Asset Proposal Method Based on E-commerce
Authors: Ansh Xie, Ming Ren, Yan Ding, Wei Shang
Venue: Proceedings of the 2008 International Symposiums on Information Processing (2008)

The Asset Management Corporation (AMC)'s operational goal is to preserve the asset value and maximize the recovery value. Asset management corporation (AMC) will adapt suitable measures to specific conditions in NPLs and collateral disposition. Those measures include sale, exchange, restructuring, debt-equity swap, securitization and auction. Auction is the most popular one. But most auction processes concern with the price, which the only attribute in the decision is making process. This paper firstly presents an approach based on rough sets to solve the problem of attribute reduction and setting weight in multi-attributes decision making of asset disposal and put forward a liner program with homogeneity constrains .It will solves the problem of winner-determination problem.
Hierarchical Star Clustering Algorithm for Dynamic Document Collections
Authors: Reynaldo Gil-García, Aurora Pons-Porrata
Venue: Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications (2008)

In this paper, a new clustering algorithm called Dynamic Hierarchical Star is introduced. Our approach aims to construct a hierarchy of overlapped clusters, dealing with dynamic data sets. The experimental results on several benchmark text collections show that this method obtains smaller hierarchies than traditional algorithms while achieving a similar clustering quality. Therefore, we advocate its use for tasks that require dynamic overlapped clustering, such as information organization, creation of document taxonomies and hierarchical topic detection.
A Study Case on Domain-Driven Development, Using MDA, SOA and Web Services
Authors: Fabio Perez Marzullo, Jano Moreira de Souza, Jose Roberto Blaschek
Venue: Proceedings of the 2008 IEEE Congress on Services - Part I (2008)

This paper presents preliminary study results of a prototype architecture created with the purpose of using a domain-driven approach to shorten the development of software projects. Using a set of standards and theories such as MDA, SOA and Web Services, the proposed architecture indicates that, with the aid of standard and controlled techniques, it is possible to obtain significant gains on software scheduling and cost.
Certification of Automated Termination Proofs
Authors: Evelyne Contejean, Pierre Courtieu, Julien Forest, Olivier Pons, Xavier Urbain
Venue: Proceedings of the 6th international symposium on Frontiers of Combining Systems (2007)

Nowadays, formal methods rely on tools of different kinds: proof assistants with which the user interacts to discover a proof step by step; and fully automated tools which make use of (intricate) decision procedures. But while some proof assistants can check the soundness of a proof, they lack automation. Regarding automated tools, one still has to be satisfied with their answers Yes/No/Do not know, the validity of which can be subject to question, in particular because of the increasing size and complexity of these tools.In the context of rewriting techniques, we aim at bridging the gap between proof assistants that yield formal guarantees of reliability and highly automated tools one has to trust. We present an approach making use of both shallow and deep embeddings. We illustrate this approach with a prototype based on the CiME rewriting toolbox, which can discover involved termination proofs that can be certified by the Coq proof assistant, using the Coccinelle library for rewriting.
Collaborative Target Classification for Image Recognition in Wireless Sensor Networks
Authors: Xue Wang, Sheng Wang, Junjie Ma
Venue: Proceedings of the 3rd international conference on Advanced Data Mining and Applications (2007)

Target classification, especially visual target classification, in complex situations is challenging for image recognition in wireless sensor networks (WSNs). The distributed and online learning for target classification is significant for highly-constrained WSNs. This paper presents a collaborative target classification algorithm for image recognition in WSNs, taking advantages of the collaboration for the data mining between multi-sensor nodes. The proposed algorithm consists of three steps, target detection and feature extraction are based on single-sensor node processing, whereas target classification is implemented by collaboration between multi-sensor nodes using collaborative support vector machines (SVMs). For conquering the disadvantages of inevitable missing rate and false rate in target detection, the proposed collaborative SVM adopts a robust mechanism for adaptive sample selection, which improves the incremental learning of SVM by just fusing the information from a selected set of wireless sensor nodes. Furthermore, a progressive distributed framework for collaborative SVM is also introduced for enhancing the collaboration between multi-sensor nodes. Experimental results demonstrate that the proposed collaborative target classification algorithm for image recognition can accomplish target classification quickly and accurately with little congestion, energy consumption and execution time.
Composing RESTful Services and Collaborative Workflows: A Lightweight Approach
Authors: Florian Rosenberg, Francisco Curbera, Matthew J. Duftler, Rania Khalaf
Venue: IEEE Internet Computing (2008)

The use of RESTful Web services has gained momentum in the development of distributed applications based on traditional Web standards such as HTTP. In particular, these services can integrate easily into various applications, such as mashups. Composing RESTful services into Web-scale workflows requires a lightweight composition language that's capable of describing both the control and data flow that constitute a workflow. The authors address these issues with Bite, a lightweight and extensible composition language that enables the creation of Web-scale workflows and uses RESTful services as its main composable entities.
Epiphyses Extraction Method Using Shape Information for Left Hand Radiography
Authors: Jong-Min Lee, Whoi-Yul Kim
Venue: Proceedings of the 2008 International Conference on Convergence and Hybrid Information Technology (2008)

This paper details a epiphyseal/metaphyseal region of interest (EMROI) extraction method used for automated TW2 RUS. Several methods for automated bone age assessment using image processing have been developed but they have some limitations in assisting physicians efficiently. Also, a method is required that can extract the feature region robustly to gray value variation of background and the position and orientation of the hand. In order to overcome the limitation of previous methods and achieve robust feature region extraction, we propose an EMROI extraction method which contains the procedures from background removal to EMROI extraction. Each procedure is designed to resolve problems which may occur by variation of the position and orientation of the hand and bones. Through experiments, the proposed method resulted in over 91% correct extraction for TW2 RUS from 296 left hand radiograph data.
A Framework for Tolerance Analysis in Collaborative Engineering Mechanical Design
Authors: Carles Riba-R., Roberto Pérez-R., Luis W. Hernández-G., Joaquim Ciurana-G.
Venue: Proceedings of the 5th international conference on Cooperative Design, Visualization, and Engineering (2008)

Nowadays, the global competitive environment has a special impact on small and medium enterprises (SMEs). The development of computer and network technology has provided opportunities for researchers to construct and build systems to support collaborative engineering design in a concurrent manner. A special issue related to the development of design tools to improve the quality and productivity is the tolerance analysis evaluation through the lifecycle of the product. In response to this context, the tools should be collaborative and flexible. This paper shows the development of a framework to concurrent tolerance analysis evaluation through the lifecycle of the product in collaborative engineering mechanical design.
Maximizing connectivity and performance in mobile ad hoc networks using mobile agents
Authors: Alice E. Smith
Venue:  (2007)

Mobile wireless ad hoc networks are instantaneous, autonomous telecommunication networks that provide service to users wherever and whenever the service is needed. The communication depends on wireless links that are formed between the users. A link is formed between two users if they are within each other&rsquo;s wireless communication range. The mobility in these networks can cause links to disconnect, disrupting communications. A new strategy is proposed which controls the movements of some mobile agents to maintain network connectivity. The main objective of these mobile agents is to maximize network data flow, which is formulated as an all-pair maximum flow problem. This is accomplished by optimizing the movements of the agents to their next locations as the user nodes travel freely in the field. The representation of ad hoc network performance in terms of an all-pair maximum flow problem is novel as is dynamically optimizing the agent nodes using heuristic algorithms integrated with network flow algorithms. Two evolutionary inspired, population based heuristic algorithms; a genetic algorithm and a particle swarm are developed along with an approximate linear programming model as optimizer tools. The results show the advantage of employing heuristic algorithms due to the complexity of the problem. While the approximate linear model could only solve small static and medium dynamic problems with poor results, the heuristics performed successfully for problems two to four times larger. These heuristic approaches will enable robust and physically self organizing networks with superior connectivity properties. The approach proposed in this research can be applied to static scenarios and dynamic situations. This is important because there are practical static applications of ad hoc networks, mainly in sensor networks. The novel models and algorithms developed should enable new research and commercial opportunities in ad hoc wireless networking.
Coordinated Control of Reaching and Grasping During Prehension Movement
Authors: Masazumi Katayama, Hirokazu Katayama
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part I (2007)

In this paper, we investigate coordinated control of human reaching and grasping during the prehension movements. The interaction between reaching and grasping has been investigated by using visual perturbations that unexpectedly change size or position of an object when executing the movement. Those studies have reported that both of reaching and grasping interact each other. However, the interaction may include some properties in visual information processing for physical properties of an target object. Thus, the interaction should be examined without the visual perturbations because the detail of the interaction between reaching and grasping is still unclear. From this point of view, the influence from reaching to grasping have been directly investigated by using mechanical perturbations. While, the influence from grasping to reaching have never been examined. In this study, we examined the influence that grasping affects reaching by giving mechanical perturbations to the finger tips during the movement. As a result, we found that humans adjust the speed of reaching based on the changes in grasping perturbed by the mechanical perturbations. Moreover, both the movement times of grasping and reaching are highly correlated even when grasping was perturbed by the perturbations. Consequently, we confirmed the temporally-coordinated adjustment from grasping to reaching during the prehension movement. This result indicates that the previously proposed control schemes can not explain our findings. This finding is quite important for building a computational model of the coordinated control of human reaching and grasping.
Learning Decision Trees for Unbalanced Data
Authors: David A. Cieslak, Nitesh V. Chawla
Venue: Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I (2008)

Learning from unbalanced datasets presents a convoluted problem in which traditional learning algorithms may perform poorly. The objective functions used for learning the classifiers typically tend to favor the larger, less important classes in such problems. This paper compares the performance of several popular decision tree splitting criteria --- information gain, Gini measure, and DKM --- and identifies a new skew insensitive measure in Hellinger distance. We outline the strengths of Hellinger distance in class imbalance, proposes its application in forming decision trees, and performs a comprehensive comparative analysis between each decision tree construction method. In addition, we consider the performance of each tree within a powerful sampling wrapper framework to capture the interaction of the splitting metric and sampling. We evaluate over this wide range of datasets and determine which operate best under class imbalance.
Extracting and Verifying Hyponymy Relations Based on Multiple Patterns and Features
Authors: Lei Liu, Sen Zhang, Lu Hong Diao, Shu Ying Yan, Cun Gen Cao
Venue: Proceedings of the 31st annual German conference on Advances in Artificial Intelligence (2008)

Hyponymy relations play a crucial role in various natural language processing systems. Automatic acquisition and verification of hyponymy relations is a basic problem in knowledge acquisition from text. We present a method that acquires and verifies hyponymy relations based on multiple patterns and features. It initially obtains a set of removable patterns using Chinese lexico-syntactic patterns. Then the concepts of constituting hyponymy relation are acquired with removable patterns. Finally, hyponymy relations are verified with space structure features, semantic features and context features. Experimental results demonstrate good performance of the method.
Protecting data on mobile devices: A taxonomy of security threats to mobile computing and review of applicable defenses
Authors: Jon Friedman, Daniel V. Hoffman
Venue: Information-Knowledge-Systems Management (2008)

Mobile devices such as laptops, PDAs and cell phones have become essential tools for enterprise productivity, but they are in fact significantly more vulnerable to attack than desktop computers. This paper provides a broad overview of threats to mobile devices and the data that resides on them, as well as available defenses. It provides a taxonomy that divides threats to mobile devices into seven categories: malware, phishing and social engineering, direct attack by hackers, data communication interception and spoofing, loss and theft of devices, malicious insider actions, and user policy violations. It then discusses security technologies that can be applied against each of these threat types, including firewalls, anti-virus and zero day anti-malware software, intrusion prevention systems, virtual private networks, data encryption, device control and data leak prevention technologies. It suggests how to assess priorities among the different threats and defenses, and concludes with suggestions for further research.
Universal active current filter using single second-generation current controlled conveyor
Authors: Chun-Ming Chang, Tzu-Hao Huang, Shu-Hui Tu, Chun-Li Hou, Jiun-Wei Horng
Venue: Proceedings of the 7th WSEAS International Conference on Applied Computer and Applied Computational Science (2008)

The realization of the second-order universal (low-pass, band-pass, high-pass, band-reject, and all-pass) active current filter using a single second-generation current controlled conveyor (CCCII), two grounded capacitors, and one resistor is proposed in this paper. Three input signals and the bias current of the active element determine the filtering type of the output signals. H-Spice simulations with 0.35µm process are used to validate the theoretical predictions of the filtering signal, very low sensitivities, and the correction for the deviation of the output signals using the result of the sensitivity.
A Novel Method of Constructing ANN
Authors: Xiangping Meng, Quande Yuan, Yuzhen Pi, Jianzhong Wang
Venue: Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks (2007)

Artificial Neural Networks (ANNs) are powerful computational and modeling tools, however there are still some limitations in ANNs. In this paper, we give a new method to construct artificial neural network, which based on multi-agent theory and Reinforcement learning algorithm. All nodes in this new neural networks are presented as agents, and these agents have learning ability via implementing reinforcement learning algorithm. The experiment results show this method is effective.
Clustering Streaming Time Series Using CBC
Authors: Weimin Li, Liangxu Liu, Jiajin Le
Venue: Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007 (2007)

Clustering streaming time series is a difficult problem. Most traditional algorithms are too inefficient for large amounts of data and outliers in them. In this paper, we propose a new clustering method, which clusters Bi-clipped (CBC) stream data. It contains three phrases, namely, dimensionality reduction through piecewise aggregate approximation (PAA), Bi-clipped process that clipped the real valued series through bisecting the value field, and clustering. Through related experiments, we find that CBC gains higher quality solutions in less time compared with M-clipped method that clipped the real value series through the mean of them, and unclipped methods. This situation is especially distinct when streaming time series contain outliers.
Power Consumption Modeling and Characterization of the TI C6201
Authors: Nathalie Julien, Johann Laurent, Eric Senn, Eric Martin
Venue: IEEE Micro (2003)

This new approach characterizes power dissipation on complex DSPs. Its processor model relies on an initial functional-level power analysis of the target processor together with a characterization that qualifies the more significant architectural and algorithmic parameters for power dissipation. These parameters come from a simple profiling of the assembly code. This functional model accounts for deeply pipelined, superscalar, and hierarchical memory architectures.
Measuring System for Furrow Opener Based on Virtual Instrument Technology
Authors: Shucai Xu, Jinhuan Zhang, Jianqiao Li
Venue: Proceedings of the 2008 Second International Conference on Genetic and Evolutionary Computing (2008)

Now measure instruments and electronic technology developed quickly, however, as for traditional measure instruments, even if digital instruments and intelligent instruments which heighten the accuracy and function of the traditional measurement and analysis system, they still can not change their some shortcomings, such as single function, handle manipulating, large and ponderous volume, fixed channel number and high cost to develop functions etc. Aimed at the above demerits, a kind of virtual instrument that is developed with PC and correlative software emerged. A single channel stress-strain virtual measuring system, which was designed by graphical programming language DASYLab, was successfully substituted for the traditional stress-strain measuring system on the experiment of the horizontal force acting on the furrow opener surface by disturbed soil. The stress-strain virtual measuring system indicates more merits than the traditional stress-strain measuring system on many aspects including data real-time display, data real-time storage, data memory format, data processing and replay, and operation convenience. It shows much higher ratio of performance to price.
Acyclic Edge Colouring of Outerplanar Graphs
Authors: Rahul Muthu, N. Narayanan, C. R. Subramanian
Venue: Proceedings of the 3rd international conference on Algorithmic Aspects in Information and Management (2007)

An acyclic edge colouring of a graph is a proper edge colouring having no 2-coloured cycle, that is, a colouring in which the union of any two colour classes forms a linear forest. The acyclic chromatic index of a graph is the minimum number k such that there is an acyclic edge colouring using k colours and is usually denoted by a¿(G). Determining a¿(G) exactly is a very hard problem (both theoretically and algorithmically) and is not determined even for complete graphs. We show that a¿(G) ≤ Δ(G) + 1, if G is an outerplanar graph. This bound is tight within an additive factor of 1 from optimality. Our proof is constructive leading to an $O\!\left({n \log \Delta}\right)$ time algorithm. Here, Δ = Δ(G) denotes the maximum degree of the input graph.
Towards a Group-Based RBAC Model and Decentralized User-Role Administration
Authors: Qi Li, Mingwei Xu, Xinwen Zhang
Venue: Proceedings of the 2008 The 28th International Conference on Distributed Computing Systems Workshops (2008)

Role-based Access Control (RBAC) has been widely deployed in many distributed systems in recent years. However, in many large-scale enterprise environments, it is difﬁcult to manage RBAC because of the huge number of users and roles, and complex interrelationships between them. Moreover, with the development of information and communication technologies, many temporal and ad hoc collaborations between groups and departments are emerging, which require dynamic user-role and permission-role assignments. In thesescenarios it is infeasible, if not impossible, for few security ofﬁcers to administrate the assignment for various applications. In this paper, we propose a novel RBAC model for decentralized and distributed systems. As one of the main contributions, we also propose a decentralizedadministration model to address the management issues in traditional RBAC systems, Our model can be used for group-based applications with dynamic assignments where typically local (group-level) administrators take charge of the dynamic assignments. In this way, many administrativetasks for different applications can spread over many different local administrators, and a ﬁne-grained administration model of RBAC based on the local administration policies is realized. As a proof-of-conceptsystem, we implemented a secure Spread prototype based on our proposed model to show the feasibility in the real applications.