ppxscal/embedding_network huggingface dataset:
LOCK: A Highly Accurate, Easy-to-Use Location-Based Access Control System
Authors: Yongcai Wang, Junhui Zhao, Toshikazu Fukushima
Venue: Proceedings of the 4th International Symposium on Location and Context Awareness (2009)

With proliferation of ubiquitous computing, digital access is facing an increasing risk since unauthorized client located at any place may intrude a local server. Location Based Access Control (LBAC) is a promising solution that tries to protect the client's access within some user-defined secure zones. Although a lot of prior work has focused on LBAC, most of them suffer from coarse judgment resolution problem or considerable manual setting-up efforts. This paper proposes LOCK, a highly accurate, easy-to-use LBAC system, which uses autonomous ultrasound positioning devices and an access control engine to precisely characterize the secure zones and accurately judge the online access authority. Particularly, the ultrasound positioning device provides relative 3D coordinate of the mobile clients. Measurement-Free Calibration (MFC) is proposed to easily calibrate these positioning devices to transform their relative positioning results into an absolute coordinate system. In this coordinate system, secure zones are characterized by a Coherent Secure Zone Fitting (CSZF) method to compensate the disparity between manually measured secure zone and the secure zone seen by the positioning devices. Furthermore, a Round-Trip Judgment (RTJ) algorithm is designed to fast online determine the geographical relationship between the client's position and such secure zones. A prototype of LOCK system was implemented by defining a meeting table as secure zone to control the client's access to a FTP server. Experiment results show that the system can be easily set up and can control the client's access with centimeter level judgment resolution.
Physically-Constrained Diffeomorphic Demons for the Estimation of 3D Myocardium Strain from Cine-MRI
Authors: Tommaso Mansi, Jean-Marc Peyrat, Maxime Sermesant, Hervé Delingette, Julie Blanc, Younes Boudjemline, Nicholas Ayache
Venue: Proceedings of the 5th International Conference on Functional Imaging and Modeling of the Heart (2009)

Analysing heart motion provides crucial insights on the condition of the cardiac function. Tagged-MRI and 2D-strain ultrasound enable quantitative assessment of the myocardium strain. But estimating 3D myocardium strain from cine-MRI remains attractive: cine-MRI is widely available and it yields detailed 3D+t anatomical images. This paper presents an image-based method to estimate myocardium strain from clinical short-axis cine-MRI. To recover non-apparent cardiac motions, we improve the diffeomorphic demons, a non-linear registration algorithm, by adding two physical constraints. First, myocardium near-incompressibility is ensured by constraining the deformations to be divergence free. Second, myocardium elasticity is modelled using smooth vector filters. The proposed physically-constrained demons are compared with the diffeomorphic demons and evaluated in a healthy subject against tagged MRI. The method is also tested on a patient with congenital pulmonary valve regurgitations and compared with 2D-strain measurements. In both cases, obtained results correlate well with ground truth. This method may become a useful tool for cardiac function evaluation.
Combining over- and under-approximating program analyses for automatic software testing
Authors: Yannis Smaragdakis
Venue:  (2008)

This dissertation attacks the well-known problem of path-imprecision in static program analysis. Our starting point is an existing static program analysis that over-approximates the execution paths of the analyzed program. We then make this over-approximating program analysis more precise for automatic testing in an object-oriented programming language. We achieve this by combining the overapproximating program analysis with usage-observing and under-approximating analyses. More specifically, we make the following contributions. We present a technique to eliminate language-level unsound bug warnings produced by an execution-path-over-approximating analysis for object-oriented programs that is based on the weakest precondition calculus. Our technique post-processes the results of the over-approximating analysis by solving the produced constraint systems and generating and executing concrete test-cases that satisfy the given constraint systems. Only test-cases that confirm the results of the over-approximating static analysis are presented to the user. This technique has the important side-benefit of making the results of a weakest-precondition based static analysis easier to understand for human consumers. We show examples from our experiments that visually demonstrate the difference between hundreds of complicated constraints and a simple corresponding JUnit test-case. Besides eliminating language-level unsound bug warnings, we present an additional technique that also addresses user-level unsound bug warnings. This technique preprocesses the testee with a dynamic analysis that takes advantage of actual user data. It annotates the testee with the knowledge obtained from this pre-processing step and thereby provides guidance for the over-approximating analysis.We also present an improvement to dynamic invariant detection for object-oriented programming languages. Previous approaches do not take behavioral subtyping into account and therefore may produce inconsistent results, which can throw off automated analyses such as the ones we are performing for bug-finding. Finally, we address the problem of unwanted dependencies between test-cases caused by global state. We present two techniques for efficiently re-initializing global state between test-case executions and discuss their trade-offs. We have implemented the above techniques in the JCrasher, Check 'n' Crash, and DSD-Crasher tools and present initial experience in using them for automated bug finding in real-world Java programs.
Discrimination of thermal diffusivity
Authors: Wouter M. Bergmann Tiest, Astrid M. L. Kappers
Venue: Proceedings of the World Haptics 2009 - Third Joint EuroHaptics conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems (2009)

Materials such as wood or metal which are at equal temperatures are perceived to be of different “coldness” due to differences in thermal properties, such as the thermal diffusivity. The thermal diffusivity of a material is a parameter that controls the rate with which heat is extracted from the hand when it touches an object of that material. This rate of heat extraction is an important cue for distinguishing materials and recognising objects by means of touch. We have measured the ability of human observers to discriminate between different rates of heat extraction. This was done using a device that displayed different transient temperature profiles to the finger. In different conditions, subjects were repeatedly asked to select the faster-cooling of two stimuli. The discrimination threshold was around 43% of the extraction rate. A rate that was twice as slow also yielded twice the absolute discrimination threshold. When we halved the temperature difference between beginning and end of the stimulus, the threshold did not change as much. This shows that subjects can use the rate of heat extraction as a cue and that they can discriminate between materials if their thermal diffusivities are at least 43% apart.
iScope: personalized multi-modality image search for mobile devices
Authors: Changyun Zhu, Kun Li, Qin Lv, Li Shang, Robert P. Dick
Venue: Proceedings of the 7th international conference on Mobile systems, applications, and services (2009)

Mobile devices are becoming a primary medium for personal information gathering, management, and sharing. Managing personal image data on mobile platforms is a difficult problem due to large data set size, content diversity, heterogeneous individual usage patterns, and resource constraints. This article presents a user-centric system, called iScope, for personal image management and sharing on mobile devices. iScope uses multi-modality clustering of both content and context information for efficient image management and search, and online learning techniques for predicting images of interest. It also supports distributed content-based search among networked devices while maintaining the same intuitive interface, enabling efficient information sharing among people. We have implemented iScope and conducted in-field experiments using networked Nokia N810 portable Internet tablets. Energy efficiency was a primary design focus during the design and implementation of the iScope search algorithms. Experimental results indicate that iScope improves search time and search energy by 4.1X and 3.8X on average, relative to browsing.
Enhancing Application Identification by Means of Sequential Testing
Authors: Mohamad Jaber, Chadi Barakat
Venue: Proceedings of the 8th International IFIP-TC 6 Networking Conference (2009)

One of the most important challenges for network administrators is the identification of applications behind the Internet traffic. This identification serves for many purposes as in network security, traffic engineering and monitoring. The classical methods based on standard port numbers or deep packet inspection are unfortunately becoming less and less efficient because of encryption and the utilization of non standard ports. In this paper we come up with an online iterative probabilistic method that identifies applications quickly and accurately by only using the size of packets. Our method associates a configurable confidence level to the port number carried in the transport header and is able to consider a variable number of packets at the beginning of a flow. By verification on real traces we observe that even in the case of no confidence in the port number, a very high accuracy can be obtained for well known applications after few packets were examined.
Combined Neural Network Approach for Short-Term Urban Freeway Traffic Flow Prediction
Authors: Ruimin Li, Huapu Lu
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III (2009)

Short-term traffic flow prediction is an essential component of urban traffic management and information systems. This paper presents a new short-term freeway traffic flow prediction model based on combined neural network approach. This model consists of two modules: a self-organizing feature map neural network and an Elman neural network. The former classifies the traffic conditions in a day into six patterns, and the later specifies the relationship between input and output to provide the prediction value. The inputs of the Elman neural network model include three kinds of data: the several time series of the prediction location, the historic data of the predictive time interval in the same weekdays, the time series of the adjacent location. The performances of the combined neural network model are validated using the real observation data from 3rd ring freeway in Beijing.
Model-free reinforcement learning as mixture learning
Authors: Nikos Vlassis, Marc Toussaint
Venue: Proceedings of the 26th Annual International Conference on Machine Learning (2009)

We cast model-free reinforcement learning as the problem of maximizing the likelihood of a probabilistic mixture model via sampling, addressing both the infinite and finite horizon cases. We describe a Stochastic Approximation EM algorithm for likelihood maximization that, in the tabular case, is equivalent to a non-bootstrapping optimistic policy iteration algorithm like Sarsa(1) that can be applied both in MDPs and POMDPs. On the theoretical side, by relating the proposed stochastic EM algorithm to the family of optimistic policy iteration algorithms, we provide new tools that permit the design and analysis of algorithms in that family. On the practical side, preliminary experiments on a POMDP problem demonstrated encouraging results.
On Reducing the Pre-release Failures of Web Plug-In on Social Networking Site
Authors: Xingliang Yu, Jing Li, Hua Zhong
Venue: Proceedings of the International Conference on Software Process: Trustworthy Software Development Processes (2009)

In recent years, web plug-ins have been flourishing social networking sites. Web plug-in is successful since it results in unique user experience, and promotes the fast-pace innovation of web technologies. However, the plug-ins developed by end users also introduces many new problems to both networking and software engineering fields. One of the key problems is pre-release failure. In other words, the failures that we can avoid before software release are usually found after the release. However, existing methods fail to avoid the pre-release failures of web plug-ins. To do this, this paper introduces an experimental technology, namely release-waiting farm. It not only maintains the free and creative environment of end user development, encouraging them to deliver plug-ins, but effectively formalizes their development process, thus provide long-term benefit to both end users and social networking sites.
Leakage-aware task scheduling for partially dynamically reconfigurable FPGAs
Authors: Ping-Hung Yuh, Chia-Lin Yang, Chi-Feng Li, Chung-Hsiang Lin
Venue: ACM Transactions on Design Automation of Electronic Systems (TODAES) (2009)

As technology continues to shrink, reducing leakage power of Field-Programmable Gate Arrays (FPGAs) becomes a critical issue for the practical use of FPGAs. In this article, we address the leakage issue of partially dynamically reconfigurable FPGA architectures with sleep transistors embedded into FPGA fabrics. In particular, we focus on eliminating leakage waste due to the delay between reconfiguration and execution time of a task. For partially dynamically reconfigurable FPGAs, the configuration prefetching technique is commonly used to hide runtime reconfiguration overhead. With prefetching, the configuration of a task is loaded into FPGAs as early as possible. Therefore, there is often a delay between reconfiguration and execution time of a task. In this period of time, the SRAM cells allocated to a task cannot be turned off even though they are not utilized. In this article, we propose a two-stage task scheduling methodology to reduce leakage waste due to the delay between reconfiguration and execution time of a task without sacrificing performance. In the first stage, a performance-driven task scheduler that targets at minimizing the schedule length is invoked to generate an initial placement. In the second stage, a postplacement leakage-aware task scheduling is applied to refine the initial placement such that leakage waste is minimized provided that the schedule length is not increased. To solve the postplacement leakage optimization problem, we propose two algorithms. The first one is an optimal algorithm based on Integer Linear Programming (ILP). The second algorithm is a heuristic approach that iteratively refines the placement to reduce leakage waste. Experimental results on real and synthetic designs show that the efficiency and effectiveness of the proposed postplacement leakage reduction techniques.
Context life cycle management in smart space environments
Authors: Wan-rong Jih, Chi-Chia Huang, Jane Yung-jen Hsu
Venue: Proceedings of the 3rd workshop on Agent-oriented software engineering challenges for ubiquitous and pervasive computing (2009)

Life cycle management plays an essential role in context-aware systems, it needs to aware surrounding contexts and adapt to changing contexts in highly dynamic environments. Consequently, context management is different from traditional approaches due to the characteristics of contextual information are dynamic, transient, and fallible. Understanding the life cycle of a context is the key issue to maintain consistent contextual information. Accordingly, we propose an ontology-based model for representing, deducing, and managing consistent contextual information. In addition, an agent-based architecture supports the process of context life cycle management.
Structure and Interpretation of Computer Programs in Visual Language Zed
Authors: Zelah Hutchinson
Venue:  (2007)

ZED is a highly readable yet concise language, somewhat resembling Scheme. It is a small language, allowing for complete mastery, and remains simple without sacrificing expressiveness. ZED is truly general purpose and elegantly handles: high level, low level, multi-threaded, functional, imperative, object oriented, and logic style programming! (The last two will be covered thoroughly in a future edition of the book). ZED's simplicity and versatility are partially due to innovations in the textual elements of the language and partially due to the visual elements of the language which are modifications of what are known as 'structured flowcharts'. Programs written in ZED are beautiful to look at and may be executed efficiently by machine. This book is a tutorial introduction to ZED, arranged as a single, long, but well documented visual program. The book, in combination with Sussman/Abelson provides a basis for the construction of this new language. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
The Top Ten Algorithms in Data Mining, 1st edition
Authors: Xindong Wu, Vipin Kumar
Venue: Chapman & Hall/Crc Data Mining And Knowledge Discovery (2009)

Identifying some of the most influential algorithms that are widely used in the data mining community, this volume provides a description of each algorithm, discusses the impact of the algorithms, and reviews current and future research on the algorithms. Thoroughly evaluated by independent reviewers, each chapter focuses on a particular algorithm and is written by either the original authors of the algorithm or world-class researchers who have extensively studied the respective algorithm. The book concentrates on the following important algorithms: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART.
Preface for the Joint Workshop on Tools for Program Development and Analysis in Computational Science and Software Engineering for Large-Scale Computing
Authors: Andreas Knüpfer, Arndt Bode, Dieter Kranzlmüller, Daniel Rodrìguez, Roberto Ruiz, Jie Tao, Roland Wismüller, Jens Volkert
Venue: Proceedings of the 9th International Conference on Computational Science (2009)

Today, computers and computational methods are increasingly important and powerful tools for science and engineering. Yet, using them effectively and efficiently requires both, expert knowledge of the respective application domain as well as solid experience applying the technologies. Only the combination allows new and faster advancement in the area of application. The same is true for establishing new computational concepts as regular methods in the field of application. This applies to either quantitative improvement (e.g. by parallel scalability) or by qualitative progress (e.g. by better algorithms).
Performance modeling and optimization techniques in the presence of random process variations to improve parametric yield vlsi circuits
Authors: Ranga Vemuri
Venue:  (2008)

As semiconductor industry continues to follow Moore&rsquo;s Law of doubled device count every 18 months, it is challenged by the rising uncertainties in the manufacturing process for nanometer technologies. Manufacturing defects lead to a random variation in physical parameters like the dopant density, critical dimensions and oxide thickness. These physical defects manifest themselves as variations in device process parameters like threshold voltage and effective channel length of transistors. The randomness in process parameters affect the performance of VLSI circuits which leads to a loss in parametric yield. Conventional design methodologies, with corner case based analysis techniques fail to predict the performance of circuits reliably in the presence of random process variations. Moreover, the analysis techniques for detection of defects in the later stages of the design cycle result in significant overhead in cost due to re-spins. In recent times, VLSI computer aided design methodologies have shifted to statistical analysis techniques for performance measurements with specific yield targets. However, the adoption of statistical techniques in commercial design flows has been limited by the complexity of their usage and the need for generating specially characterized models. This also makes them unsuitable in repeated loops during the synthesis process. In this dissertation, we present an alternate approach to model and optimize the performance of digital and analog circuits in the presence of random process variations. Our work is targeted for a bottom-up methodology providing incremental tolerance to the circuits under the impact of random process variations. The methodologies presented, can be used to generate fast evaluating accurate macromodels to compute the bounds of performance due to the underlying variations in device parameters. The primary goal of our methodology is to capture the statistical aspects of variation in the lower levels of abstraction, while aiding deterministic analysis during the top level design optimization. We also attempt to build our solutions as a wrapper around a conventional design flow, without the requirement for special characterization. The modeling and optimization techniques are perfectly scalable across technology generations and can find practical usage during variation-tolerant synthesis of VLSI circuit performance.
Building a MARC-to-OLAC crosswalk: repurposing library catalog data for the language resources community
Authors: Christopher Hirt, Gary Simons, Joan Spanne
Venue: Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries (2009)

The Open Language Archives Community (OLAC) is an international partnership of institutions which are building a network of interoperating repositories and services to create a worldwide virtual library of language resources (that is, resources that document, describe, or develop the more than 7,000 known languages of the world). OLAC uses a community-specific refinement of qualified Dublin Core [http://www.language-archives.org/OLAC/metadata.htm] along with a community-specific refinement of the OAI Protocol for Metadata Harvesting [http://www.language-archives.org/OLAC/repositories.htm] to maintain an aggregated catalog of the holdings of the 35 participating archives. OLAC recognizes that the language resources of interest to the community come not only from sources within the community but also from many sources outside the community. This poster describes one approach we have developed for addressing this issue, namely, a crosswalk that transforms the MARC21 catalog for a library or archive into an OAI static repository that holds an OLAC metadata record for each MARC record identified as describing a language resource.