ppxscal/embedding_network huggingface dataset:
IPP@HDL: efficient intellectual property protection scheme for IP cores
Authors: Encarnación Castillo, Uwe Meyer-Baese, Antonio García, Luis Parrilla, Antonio Lloris
Venue: IEEE Transactions on Very Large Scale Integration (VLSI) Systems (2007)

In this paper, a procedure for intellectual property protection (IPP) of digital circuits called IPP@HDL is presented. Its aim is to protect the author rights in the development and distribution of reusable modules by means of an electronic signature. The technique relies on hosting the bits of the digital signature within memory structures or combinational logic that are part of the system, at the high level description of the design. Thus, the area of the system is not increased and the signature is difficult to change or to remove without damaging the design. The technique also indudes a procedure for secure signature extraction requiring minimal modifications to the system and without interfering its normal operation. The benefits of the presented procedure are illustrated with programmable logic and cell-based application-specific integrated circuit examples with several signature lengths. These design examples show no performance degradation and a negligible area increase, while probabilistic analyses show that the proposed IPP scheme offers high resistance against attacks.
Detection of Illegal Dumping from CCTV at Recycling Centres
Authors: Naomi Harte, Andrew Rankin, Gary Baugh, Anil Kokaram
Venue: Proceedings of the International Machine Vision and Image Processing Conference (2007)

This paper describes initial work on a framework for automatic detection of illegal dumping from CCTV footage from recycle centres. Frames are seperated into foreground and background regions using a Bayesian approach that combines global motion estimates with image based information to generate a robust segmentation. The framework hence avoids explicit modelling and tracking of objects in the scene such as cars, people or rubbish bags. A feature extraction stage with diagnostics will be presented.
Analyzing the (1, λ) evolution strategy via stochastic approximation methods
Authors: G. Yin, G. Rudolph, H. -P,  Schwefel
Venue: Evolutionary Computation (1995)

The main objective of this paper is to analyze the (1, λ) evolution strategy by use of stochastic approximation methods. Both constant and decreasing step size algorithms are studied. Convergence and estimation error bounds for the (1, λ) evolution strategy are developed. First the algorithm is converted to a recursively defined scheme of stochastic approximation type. Then the analysis is carried out by using the analytic tools from stochastic approximation. In lieu of examining the discrete iterates, suitably scaled sequences are defined. These interpolated sequences are then studied in detail. It is shown that the limits of the sequences have natural connections to certain continuous time dynamical systems.
Modeling Discriminative Global Inference
Authors: Nicholas Rizzolo, Dan Roth
Venue: Proceedings of the International Conference on Semantic Computing (2007)

Many recent advances in complex domains such as Natural Language Processing (NLP) have taken a discriminative approach in conjunction with the global application of structural and domain specific constraints. We introduce LBJ, a new modeling language for specifying exact inference systems of this type, combining ideas from machine learning, optimization, First Order Logic (FOL), and Object Oriented Programming (OOP). Expressive constraints are specified declaratively as arbitrary FOL formulas over functions and objects. The language's run-time library translates them to a mathematical programming representation from which an exact solution is computed. In addition, the compiler leverages an existing OOP language: objects and functions are grounded as the OOP objects and methods that encapsulate the user's data.
A Novel Method for License Plate Localization
Authors: Hao Chen, Jisheng Ren, Huachun Tan, Jianqun Wang
Venue: Proceedings of the Fourth International Conference on Image and Graphics (2007)

A novel method for license plate localization based on texture and edge information is proposed in this paper. The whole process is divided into two parts: candidate extraction and candidate verification. In the first part, to ensure the true license plate being extracted from complex environment, several candidate areas instead of one with the max texture information are extracted. In the second part, auto-correlation based binary image and projection algorithm are used to verify the plate candidates. For a Chinese license plate, the auto-correlation curve of license plate shows approximately 13 peaks. And the real plate applied with our projection algorithm behaves about 7 areas which have the similar size to plate characters. The above characteristics combined with different binary methods to verify the Chinese license plate from all candidates. The experiments show the superiority of our proposed method.
Unconstrained Transductive Support Vector Machines
Authors: Yingjie Tian, Manfu Yan
Venue: Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02 (2007)

Support Vector Machines have been extensively used in machine learning because of its efficiency and its theoreti- cal background. This paper focuses on Transductive Sup- port Vector Machines (TSVM) for classification and con- struct a new algorithm Unconstrained Transductive Sup- port Vector Machines (UTSVM). After researching on the special construction of primal problem in TSVM, we trans- form it to an unconstrained problem and then smooth the derived problem in order to apply usual optimization meth- ods.
Annotation-based program stepping
Authors: Glen Randolph Parker
Venue: ACM SIGPLAN Lisp Pointers (1987)

Traditionally, stepping is achieved by modifying the evaluator. In the approach presented here, stepping is achieved by annotating the user's program with calls to stepping procedures. This provides greater flexibility in the selection of which program sections the user is interested in stepping. In addition, the sections not selected are executed without overhead. Stepped program sections are visually highlighted inside the user's program editor, providing a sense of context. Stepping control mechanisms, such as the ability to execute until a certain point and then step, permit the user to navigate through a program in a precise and flexible manner. A prototype stepper based on this approach has been implemented.
Device Monitors
Authors: A. P. Ravn
Venue: IEEE Transactions on Software Engineering (1980)

A driver is the part of an I/O system used for processing of an I/O request for a specific channel. The interaction of the CPU with a channel is described through the monitor concept of Hoare and Brinch Hansen. The implementation of monitors using hardware interrupt facilities is described. The resulting device monitor is compared pared with the device processes of Wirth's Modula. The concept is illustrated through an extension to Concurrent Pascal with examples drawn from the PDP11 system. Problems of missing interrupts and power failure are also discussed.
Clustering and Embedding Using Commute Times
Authors: Huaijun Qiu, Edwin R. Hancock
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence (2007)

This paper exploits the properties of the commute time between nodes of a graph for the purposes of clustering and embedding, and explores its applications to image segmentation and multi-body motion tracking. Our starting point is the lazy random walk on the graph, which is determined by the heatkernel of the graph and can be computed from the spectrum of the graph Laplacian. We characterize the random walk using the commute time (i.e. the expected time taken for a random walk to travel between two nodes and return) and show how this quantity may be computed from the Laplacian spectrum using the discrete Green’s function. Our motivation is that the commute time can be anticipated to be a more robust measure of the proximity of data than the raw proximity matrix. In this paper, we explore two applications of the commute time. The first is to develop a method for image segmentation using the eigenvector corresponding to the smallest eigenvalue of the commute time matrix. We show that our commute time segmentation method has the property of enhancing the intra-group coherence while weakening inter-group coherence and is superior to the normalized cut. The second application is to develop a robust multi-body motion tracking method using an embedding based on the commute time. Our embedding procedure preserves commute time, and is closely akin to kernel PCA, the Laplacian eigenmap and the diffusion map. We illustrate the results both on synthetic image sequences and real world video sequences, and compare our results with several alternative methods.
On Realizations of Boolean Functions Requiring a Minimal or Near-Minimal Number of Tests
Authors: J. P. Hayes
Venue: IEEE Transactions on Computers (1971)

This paper considers the design of combinational logic circuits which require a minimal or near-minimal number of tests. Bounds on the number of tests required by various network structures are considered. It is shown that for an n-input fanout-free network, the number of single and multiple fault detection test lies between 2 vn and n + 1, while the number of fault locations tests lies between 2 vn and 2n.
Catching elephants with mice: sparse sampling for monitoring sensor networks
Authors: Sorabh Gandhi, Subhash Suri, Emo Welzl
Venue: Proceedings of the 5th international conference on Embedded networked sensor systems (2007)

We propose a scalably efficient scheme for detecting large-scale physically-correlated events in sensor networks. Specifically, we show that in a network of n sensors arbitrarily distributed in the plane, a sample of O(1/&epsilon; log 1/&epsilon;) sensor nodes (mice) is sufficient to catch any, and only those, events that affect &Omega;(&epsilon;n) nodes (elephants), for any 0 < &epsilon; < 1, as long as the geometry of the event has a bounded Vapnik-Chervonenkis (VC) dimension. In fact, the scheme is provably able to estimate the size of an event within the approximation error of &plusmn;&epsilon;n/4, which can be improved further at the expense of more mice. The detection algorithm itself requires knowledge of the event geometry (e.g. circle, ellipse, or rectangle) for the sake of computational efficiency, but the combinatorial bound on the sample size (set of mice) depends only on the VC dimension of the event class and not the precise shape geometry. While nearly optimal in theory, due to implicit constant factors, these "scale-free" bounds still prove too large in practice if applied blindly. We, therefore, propose heuristic improvements and perform empirical parameter tuning to counter the pessimism inherent in these theoretical estimates. Using a variety of data distributions and event geometries, we show through simulations that the final scheme is eminently scalable and practical for large-scale network, say, with n &ge; 1000. The overall simplicity and generality of our technique suggests that it may be well-suited for a wide class of sensornet applications, including monitoring of physical environments, network anomalies, network security, or any abstract binary event that affects a significant number of nodes in the network.
Application of a Hybrid Classifier to the Recognition of Petrochemical Odors
Authors: E. M. J. Oliveira, P. G. Campos, T. B. Ludermir, F. A. T. de Carvalho, W. R. de Oliveira
Venue: Proceedings of the 7th International Conference on Hybrid Intelligent Systems (2007)

Nowadays there are several data mining algorithms applied to the resolution of many different problems, such as the classification of patterns. However, when these algorithms are used separately to classify they usually present an inferior performance compared to the performance obtained by combined models. The Bagging and Boosting techniques combine models of the same kind in a competitive form, in other words, the output is generally provided by the winning classifier. Alternatively, Stacking usually combines different algorithms, constituting a hybrid model. Nevertheless, stacking has a high cost, due to the search for the best models that will be combined to solve a certain problem. Thus, we present a Hybrid Classifier (HC) to be applied to the recognition of gases derived from petrol at a lower cost and in a cooperative way.
Adaptive evolutionary programming based on reinforcement learning
Authors: Huaxiang Zhang, Jing Lu
Venue: Information Sciences: an International Journal (2008)

This paper studies evolutionary programming and adopts reinforcement learning theory to learn individual mutation operators. A novel algorithm named RLEP (Evolutionary Programming based on Reinforcement Learning) is proposed. In this algorithm, each individual learns its optimal mutation operator based on the immediate and delayed performance of mutation operators. Mutation operator selection is mapped into a reinforcement learning problem. Reinforcement learning methods are used to learn optimal policies by maximizing the accumulated rewards. According to the calculated Q function value of each candidate mutation operator, an optimal mutation operator can be selected to maximize the learned Q function value. Four different mutation operators have been employed as the basic candidate operators in RLEP and one is selected for each individual in different generations. Our simulation shows the performance of RLEP is the same as or better than the best of the four basic mutation operators.
Sensibility retrieval method that uses amount of physical feature of traditional craft object
Authors: Akihiro Miyakawa, Kaoru Sugita, Yoshitaka Shibata
Venue: Proceedings of the 2007 International Conference on Parallel Processing Workshops (2007)

In this paper, we propose a Kansei* retrieval method based on the design pattern of traditional Japanese crafting object to provide a user with the desired presentation space in digital traditional Japanese crafting system. The visual quantitative feature values are extracted by using Visual Pattern Image Coding (VPIC). These values include the total number, the frequency, the dispersion rate and the deviation rate for different edges. The quantitative feature values for traditional Japanese crafting objects are registered in the multimedia database and the relation between Kansei words and the visual feature of traditional Japanese crafting objects are analyzed by using the questionnaire. Then, the visual features are compared with the quantitative feature values. Through the above process, we can find the relation between the design pattern components and edge types using VPIC. By finding this relation, the Kansei retrieval method can be realized.
BlueSniff: Eve meets Alice and Bluetooth
Authors: Dominic Spill, Andrea Bittau
Venue: Proceedings of the first USENIX workshop on Offensive Technologies (2007)

Much of Bluetooth's data remains confidential in practice due to the difficulty of eavesdropping it. We present mechanisms for doing so, therefore eliminating the data confidentiality properties of the protocol. As an additional security measure, devices often operate in "undiscoverable mode" in order to hide their identity and provide access control. We show how the full MAC address of such master devices can be obtained, therefore bypassing the access control of this feature. Our work results in the first open-source Bluetooth sniffer.
Chromosome classification using dynamic time warping
Authors: Benoít Legrand, C. S. Chang, S. H. Ong, Soek-Ying Neo, Nallasivam Palanisamy
Venue: Pattern Recognition Letters (2008)

A classifier based on dynamic time warping (DTW) has been developed to perform the classification of human chromosomes. DTW is used in speech recognition applications to compare two time-sequences. This paper describes a method to adapt the DTW technique in order to deal with the length and the density profile, which are common features used in classifying chromosomes. The DTW classifier is able to compare chromosomes with different elongations. Since chromosomes are non-rigid objects, the proposed classifier has the main advantage of requiring only a small training set in comparison with the conventional methods based on Bayesian classifiers or neural networks. For the same classification accuracy of 81.0%, we achieve a reduction of 88% of the size of the training set in comparison with a Bayesian classifier.
Information systems project post-mortems: Insights from an attribution perspective
Authors: Gary Pan, Shan L. Pan, Michael Newman
Venue: Journal of the American Society for Information Science and Technology (2007)

Information systems (IS) project failure is a costly and common problem. This is despite advances in development tools and technologies. In this article, we argue that one reason for this is the failure of project post-mortems to generate constructive &ldquo;lessons learned&rdquo; from previous projects. Over time, the ineffective practices would persist in the organization, rendering it resistant to change. The attribution theory literature serves as one of the few promising theoretical bases to explain why project post-mortems fail. A case study of a project post-mortem undertaken for an abandoned electronic procurement system project is discussed and analyzed. We identify five antecedent conditions of attribution error: the presence of self-appointed mindsets, the general persistence of negative beliefs, memory decay, selective recall of project events and the influence of power dynamics within the organization. We discuss the research and practical implications of these findings and suggest how the problem of attribution error may be minimized in project post-mortems. &copy; 2007 Wiley Periodicals, Inc.
Extending Multicore Architectures to Exploit Hybrid Parallelism in Single-thread Applications
Authors: Hongtao Zhong, Steven A. Lieberman, Scott A. Mahlke
Venue: Proceedings of the 2007 IEEE 13th International Symposium on High Performance Computer Architecture (2007)

Chip multiprocessors with multiple simpler cores are gaining popularity because they have the potential to drive future performance gains without exacerbating the problems of power dissipation and complexity. Current chip multiprocessors increase throughput by utilizing multiple cores to perform computation in parallel. These designs provide real benefits for server-class applications that are explicitly multi-threaded. However, for desktop and other systems where single-thread applications dominate, multicore systems have yet to offer much benefit. Chip multiprocessors are most efficient at executing coarse-grain threads that have little communication. However, general-purpose applications do not provide many opportunities for identifying such threads, due to frequent use of pointers, recursive data structures, if-then-else branches, small function bodies, and loops with small trip counts. To attack this mismatch, this paper proposes a multicore architecture, referred to as Voltron, that extends traditional multicore systems in two ways. First, it provides a dual-mode scalar operand network to enable efficient inter-core communication and lightweight synchro synchronization. Second, Voltron can organize the cores for execution in either coupled or decoupled mode. In coupled mode, the cores execute multiple instruction streams in lock-step to collectively function as a wide-issue VLIW. In decoupled mode, the cores execute a set of fine-grain communicating threads extracted by the compiler. This paper describes the Voltron architecture and associated compiler support for orchestrating bi-modal execution.
The virtual sandbox
Authors: Iris A. Junglas, Douglas J. Steel
Venue: ACM SIGMIS Database (2007)

Modern virtual environments are three-dimensional, simulated worlds. Inside of them, avatars live the equivalent of a human life: they walk, talk, and interact with other avatars, thus exposing what is considered to be social behavior. As a result, virtual worlds lend themselves to the study of social behaviors in a laboratory setting. In fact, their technical capabilities provide a number of advantages over real life experimental laboratories. On the downside, however, virtual worlds also add complexity to experiments by clouding the identities of the people who "play" the role of their avatars. Despite these issues, virtual environments can contribute to future experimental research by addressing three generic needs that information systems support: increasing social presence, offering an elaborate visualization tool, and providing a safe haven for simulation, exploration, and innovation.
Corrections and Comments on "On Models for Diagnosable Systems and Probabilistic Fault Diagnosis"
Authors: S. N. Maheshwari
Venue: IEEE Transactions on Computers (1978)

Dr. H. Fujiwara of Osaka University, Osaka, Japan, has brought to our attention two errors in the above paper.1
Service Description Based on CW for Pervasive Service Discovery
Authors: Dexin Zhao, Qing Yu, Wenjie Li
Venue: Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 01 (2007)

Current existing service discovery mechanisms are lack of representation about imprecise or vague information of service on semantic level. This paper proposes a novel approach that service advertisements or requests can be expressed in natural language involving linguistic variables, which are considered as one of the essential factors to achieve the vision of pervasive computing. This new service description method has three features by comparing with existing architectures: First, service description is on semantic level, especially allows users to express their requests involving imprecise or vague data in a natural way. Second, the approach is based on the theory of Computing with Words (CW), which is defined to be an extension of fuzzy logic. Third, the architecture has ontology support which makes the service descriptions be understandable by all the participants.
Research on the Forecasting Model of Sand-Dust Storm Based on the Grid Field
Authors: Zhiying Lu, Jianhui Dai, Yufeng Yang, Huanzhu Liu
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 03 (2007)

The sand-dust storm data set can be characterized by high field distribution, high dimensionality and huge data volume, which explains why forecasting results of sandstorms are hardly satisfactory. BP neural network provides a tutor style of learning. Meanwhile, GA is a parallel algorithm based on natural selection and genetic rules, so it is often used in global searching and global optimization. In this paper, a sand-storm forecasting model is constructed and implemented using BP neural network together with GA algorithm. The result of the experiment shows that the GA-ANN approach has higher performances in stability, accuracy and the running speed.
Superheated Steam Temperature Cascade Control System Based on Fuzzy-Immune PID
Authors: Keming Xie, Wei Hao, Jun Xie
Venue: Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02 (2007)

The object of superheated steam temperature in fossil- fired power plants has some characters,such as long time- delay, large inertia and variation with time. However, con- ventional cascade control systems have been criticized for their bad control performance. In this paper, we propose a new Fuzzy-Immune PID cascade control system. The new cascade control system includes a basic cascade control system and a Fuzzy-Immune PID controller inspired from the immune feedback mechanism of biological immune sys- tems.And we take the 600 MW units in fossil-fired power plant as a practical example . Simulation results indicate that the performance of the new cascade control system is better than regular PID cascade control system and fuzzy PID cascade control system and it has some great features such as excellent stabilization, speediness and strong ro- bustness.