ppxscal/embedding_network huggingface dataset:
Compcon Wrap-up
Authors: F. P. Mathur
Venue: Computer (1979)

The conference theme of "exploding technology" seemed particularly appropriate to Session 18, "Integrating Semiconductor Memories into Systems," which probed some of the problems attendant upon increasing chip densities. Discussing alpha particle bombardments, Michael Geilhufe of Intel described the effects of such phenomena as random, non-recurring, single-bit errors. The rate of these "soft errors" today, said Geilhufe, is an order of magnitude greater than hard error rates. For example, with an error rate of 0.1 percent per 1K hours for a 16K RAM, a 16 x 64K memory would have an MTTF of 1.75 years, whereas a 16-megabyte memory would only have a MTTF of less than four days. Memory design alternatives such as automatic error correction, periodic memory purges, and systems redundancy must be considered. Periodic memory purges may be necessary since soft errors may accumulate in infrequently accessed memory regions.
Top-Down Parameter-Free Clustering of High-Dimensional Categorical Data
Authors: Eugenio Cesario, Giuseppe Manco, Riccardo Ortale
Venue: IEEE Transactions on Knowledge and Data Engineering (2007)

A parameter-free, fully-automatic approach to clustering high-dimensional categorical data is proposed. The technique is based on a two-phase iterative procedure, which attempts to improve the overall quality of the whole partition. In the first phase, cluster assignments are given, and a new cluster is added to the partition by choosing and splitting a low-quality cluster. In the second phase, the number of clusters is fixed, and an attempt to optimize cluster assignments is done. On the basis of such features, the algorithm attempts to improve the overall quality of the whole partition and finds clusters in the data, whose number is naturally established on the basis of the inherent features of the underlying dataset, rather than being previously specified. Furthermore, the approach is parametric to the notion of cluster quality: here, a cluster is defined as a set of tuples exhibiting a sort of homogeneity. We show how a suitable notion of cluster homogeneity can be defined in the context of high dimensional categorical data, from which an effective instance of the proposed clustering scheme immediately follows. Experiments on both synthetic and real data prove that the devised algorithm scales linearly and achieves nearly-optimal results in terms of compactness and separation.
Cell Nuclei Segmentation Combining Multiresolution Analysis, Clustering Methods and Colour Spaces
Authors: Guillermo Palacios, Jose R. Beltran
Venue: Proceedings of the International Machine Vision and Image Processing Conference (2007)

In this paper a new method for medical images analysis has been proposed. It is based in a multiresolution schema in combination with a k-means clustering algorithm. The edge detection and classification schema is based on the analysis of the data obtained by a multiresolution image analysis (MRA) using Mallat and Zhong's wavelet. The edge detection and classification algorithm developed has been tested defining five contour types: step, ramp, stair, pulse and "noise'. The cell nuclei presented in medical images can be perfectly isolated with the help of the "cellular nucleus' contour, a special noise reduction achieved by means of the previous classification schema and a segmentation process provided by a k-means algorithm. We have proposed an algorithm to estimate the number of cells appearing in tissue samples, as well as the estimate of positivity levels in tumour tissues. This is part of a software tool for tumour detection and diagnosis of diseases.
Integrated multiobjective optimization and a priori preferences using genetic algorithms
Authors: Javier Sanchis, Miguel A. Martínez, Xavier Blasco
Venue: Information Sciences: an International Journal (2008)

One of the tasks of decision-making support systems is to develop methods that help the designer select a solution among a set of actions, e.g. by constructing a function expressing his/her preferences over a set of potential solutions. In this paper, a new method to solve multiobjective optimization (MOO) problems is developed in which the user's information about his/her preferences is taken into account within the search process. Preference functions are built that reflect the decision-maker's (DM) interests and use meaningful parameters for each objective. The preference functions convert these objective preferences into numbers. Next, a single objective is automatically built and no weight selection is performed. Problems found due to the multimodality nature of a generated single cost index are managed with Genetic Algorithms (GAs). Three examples are given to illustrate the effectiveness of the method.
Lightweight Distributed Topology Control Algorithms for Heterogeneous Wireless Sensor Networks
Authors: Jun Wu, Han-Chi Lin, Yung-Feng Lu
Venue: Proceedings of the 13th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (2007)

Energy efficiency remains a critical design issue for wireless sensor networks. How to maintain the network connectivity and, at the same time, to minimize the energy consumption is a very challenging problem. Different from many previous excellent results, we explore the topology control problem for heterogeneous wireless sensor networks. Our objective is to propose distributed topology control algorithms, where only local information is kept for all of the nodes. The experimental results show the strengths of the proposed algorithms in the average edge length and the average link length.
The Difficulties of Using Design Patterns among Novices: An Exploratory Study
Authors: Masita Abdul Jalil, Shahrul Azman Mohd Noah
Venue: Proceedings of the The 2007 International Conference Computational Science and its Applications (2007)

Elements of design patterns have been incorporated in computer science syllabus over the last decade. Most of these efforts have been motivated by the benefits offered by patterns as well as positive feedback from the industry and software community. Despite various techniques and approaches suggested by researchers and educators to ensure effective learning of patterns, there is no formal reports on the actual difficulties encountered by these novices when applying patterns. Thus, we have conducted an exploratory study to identify the difficulties they have in using patterns.
Programming Language Constructs for Screen Definition
Authors: L. A. Rowe
Venue: IEEE Transactions on Software Engineering (1983)

This paper describes a screen-oriented I/O facility, designed as a part of Rigel, a high-level database programming language. The novel features of the I/O facility, called Screen Rigel, include the use of formatting techniques to lay out text and data on the screen. Applications written in Screen Rigel can directly access the database using the database constructs already in Rigel. In addition, Screen Rigel has access to the data dictionary so the programmer can use the data descriptions in the database instead of respecifying them. These features make Screen Rigel programs simple to write and highly data and terminal independent.
Statistics on views
Authors: César A. Galindo-Legaria, Milind M. Joshi, Florian Waas, Ming-Chuan Wu
Venue: Proceedings of the 29th international conference on Very large data bases - Volume 29 (2003)

The quality of execution plans generated by a query optimizer is tied to the accuracy of its cardinality estimation. Errors in estimation lead to poor performance, erratic behavior, and user frustration. Traditionally, the optimizer is restricted to use only statistics on base table columns and derive estimates bottom-up. This approach has shortcomings with dealing with complex queries, and with rich languages such as SQL: Errors grow as estimation is done on top of estimation, and some constructs are simply not handled. In this paper we describe the creation and utilization of statistics on views in SQL Server, which provides the optimizer with statistical information on the result of scalar or relational expressions. It opens a new dimension on the data available for cardinality estimation and enables arbitrary correction. We describe the implementation of this feature in the optimizer architecture, and show its impact on the quality of plans generated through a number of examples.
The eureka tool for software model checking
Authors: Alessandro Armando, Massimo Benerecetti, Dario Carotenuto, Jacopo Mantovani, Pasquale Spica
Venue: Proceedings of the twenty-second IEEE/ACM international conference on Automated software engineering (2007)

We describe EUREKA, a symbolic model checker for Linear Programs with arrays, i.e. programs where variables and array elements range over a numeric domain and expressions involve linear combinations of variables and array elements. This language fragment easily encodes a large class of programs for which, as demonstrated by our experiments, techniques based on predicate abstraction do not apply successfully.
Hybrid Computer Systems
Authors: R. M. Howe
Venue: Computer (1976)

Today's increasing emphasis on simulation of continuous dynamic systems lends a special significance to this issue of Computer. Hybrid computers, with their tremendous computing speed resulting from the all-parallel configuration of their analog subsystem, are particularly suited for dynamic system simulation. Indeed, back in the 1950's analog computers were the only game in town when it came to real-time simulation of flight vehicles. Digital computers of that era were simply too slow in the numerical solution of differential equations.
Inferring Phylogenetic Trees Using Answer Set Programming
Authors: Daniel R. Brooks, Esra Erdem, Selim T. Erdoğan, James W. Minett, Don Ringe
Venue: Journal of Automated Reasoning (2007)

We describe the reconstruction of a phylogeny for a set of taxa, with a character-based cladistics approach, in a declarative knowledge representation formalism, and show how to use computational methods of answer set programming to generate conjectures about the evolution of the given taxa. We have applied this computational method in two domains: historical analysis of languages and historical analysis of parasite-host systems. In particular, using this method, we have computed some plausible phylogenies for Chinese dialects, for Indo-European language groups, and for Alcataenia species. Some of these plausible phylogenies are different from the ones computed by other software. Using this method, we can easily describe domain-specific information (e.g., temporal and geographical constraints), and thus prevent the reconstruction of some phylogenies that are not plausible.
iGesture: A General Gesture Recognition Framework
Authors: B. Signer, U. Kurmann, M. Norrie
Venue: Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02 (2007)

With the emergence of digital pen and paper interfaces, there is a need for gesture recognition tools for digital pen input. While there exists a variety of gesture recognition frameworks, none of them addresses the issues of support- ing application developers as well as the designers of new recognition algorithms and, at the same time, can be inte- grated with new forms of input devices such as digital pens. We introduce iGesture, a Java-based gesture recognition framework focusing on extensibility and cross-application reusability by providing an integrated solution that includes tools for gesture recognition as well as the creation and management of gesture sets for the evaluation and optimi- sation of new or existing gesture recognition algorithms. In addition to traditional screen-based interaction, iGesture provides a digital pen and paper interface.
Bluetooth Hopping BER Testing Methodologies on a Production Test Platform
Authors: David Bement, David Karr
Venue: Proceedings of the 16th Asian Test Symposium (2007)

Bluetooth devices are required to meet specific levels for Bit Error Rate (BER) performance. Two approaches to implementing a Bluetooth BER test with Frequency Hopping in a production ATE environment will be discussed.
Diagnostic Test Generation Targeting Equivalence Classes
Authors: Irith Pomeranz, Sudhakar M. Reddy
Venue: Proceedings of the 16th Asian Test Symposium (2007)

We describe a diagnostic test generation procedure that targets the equivalence classes of the test set as it is being generated, instead of considering one fault pair at a time (an equivalence class contains faults that are indistinguished by the test set). When an equivalence class is targeted, all the fault pairs in the equivalence class are targeted simultaneously. This reduces the number of test generation targets, and as a result, it reduces the number of tests in the final test set as well as the test generation time. The implementation of the diagnostic test generation procedure is based on a test elimination process that can accommodate equivalence classes of any size.
Single neuron computation: From dynamical system to feature detector
Authors: Sungho Hong, Blaise Agüera y Arcas, Adrienne L. Fairhall
Venue: Neural Computation (2007)

White noise methods are a powerful tool for characterizing the computation performed by neural systems. These methods allow one to identify the feature or features that a neural system extracts from a complex input and to determine how these features are combined to drive the system's spiking response. These methods have also been applied to characterize the input-output relations of single neurons driven by synaptic inputs, simulated by direct current injection. To interpret the results of white noise analysis of single neurons, we would like to understand how the obtained feature space of a single neuron maps onto the biophysical properties of the membrane, in particular, the dynamics of ion channels. Here, through analysis of a simple dynamical model neuron, we draw explicit connections between the output of a white noise analysis and the underlying dynamical system. We find that under certain assumptions, the form of the relevant features is well defined by the parameters of the dynamical system. Further, we show that under some conditions, the feature space is spanned by the spike-triggered average and its successive order time derivatives.
A Scalable Search Algorithm on Unstructured P2P Networks
Authors: Fuyong Yuan, Jian Liu, Chunxia Yin
Venue: Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 02 (2007)

Gnutella is one of the prominent unstructured P2P networks. In the unstructured P2P networks, the network topology and the placement of files are largely unconstrained. The primitive search algorithm in Gnutella is flooding-based. The algorithm produces too much traffic though with limited search region which is constrained by TTL value. To improve search efficiency and reduce unnecessary traffic in Gnutella, this paper proposes an algorithm to unstructured P2P network, and it consists of ranked neighbor caching scheme and queryhit caching scheme. The proposed algorithm can extend the search region but reduce the search traffic, and also balance the network load, so that acquires the whole networks scalable.
Automated Lip Contour Detection Using the Level Set Segmentation Method
Authors: Abu Sayeed Md. Sohail, Prabir Bhattacharya
Venue: Proceedings of the 14th International Conference on Image Analysis and Processing (2007)

This paper describes a fully automated technique of detecting lip contours from static face images. Face detection is performed first on the input image using a variation of the AdaBoost classifier trained with Haarlike features extracted from the face. A second trained classifier is applied over this extracted face region for isolating the mouth section. The detection of lip contour is then performed from this isolated mouth region using the level set method of image segmentation. A new variational formulation of level set method, proposed by Li et al. (CVPR-2005), has been applied here that forces the level set function to be close to a signed distance function and therefore completely eliminates the need of the costly reinitialization procedure. The proposed method has been tested on three different face databases that contain images of both neutral faces as well as facial expressions, and a maximum successful lip contour detection rate of 91.08% has been achieved.
OR-ATP: An Operation Refinement Approach As a Process of Automatic Theorem Proving
Authors: Shuaiqiang Wang, Jiancheng Wan, Jinkui Hou
Venue: Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 03 (2007)

Since it is too difficult to develop a feasible tool to execute the stepwise refinement automatically, the applications of formal methods have mainly been limited to safety critical domains. With the development of the theory and practice of modeling by the integration of UML and formal methods, formal methods usually play a role of representing the behaviormodels. Thanks to the information provided by the architecture models, such as the concrete data structure, limit conditions, invariants and so on, the automatic refinement tools become possible. This paper presents an automatic operation refinement approach of formal methods, which bases on the theorem of Automatic Theorem Proving (ATP). Plenty of rules and patterns have been already defined or can be defined by the users, which relate to the concrete data structure and context. Driven by these rules and patterns, and even the users' manual direction, the refinement results can be finally obtained in the form of an operation sequence.
The Evidential Reasoning Approach for Multi-attribute Decision Making Problem with Incomplete Decision Matrix
Authors: Bengang Gong, Zhongsheng Hua
Venue: Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02 (2007)

In this paper, a new evidential reasoning approach is developed for a MADM problem with incomplete decision matrix that includes qualitative, quantitative and missing values. The approach first converts quantitative values into qualitative values and determines all possible focal elements based on the decision matrix, and then it calculates the basic utility assignment of each focal element and the utility interval of each decision alternative. Ranking of decision alternative is at last obtained by comparing their utility intervals. A numerical example is presented to demonstrate the implementation of this approach. Index Terms: Multi-attribute decision making; Incomplete decision matrix; Evidential reasoning; Utility
Research of Information Fusion Evaluation Methodology Based on Dominance Relation
Authors: Yunxiang Liu, Weichang Wu, Jie Lin
Venue: Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 01 (2007)

The theories and methods of performance evaluation for multi-sensor data fusion system based on rough set theory and A Fuzzy Interval are discussed. A information fusion performance criteria system is founded with C^{3}I applications as background, the evaluation process is similar to the decision process, A function g_{j}(a) represents an evaluation of object a for attribute j and is called a criteria. Each criterion induces a particular ordering of the object, and it is very significant to find a procedure by which to construct one overall preference ordering. Proposing the idea of translating imprecision or ambiguity in measurement of an alternative for a given point of view with the use of a fuzzy set in R which is a set of ordered pairs {x, \mu _j^a (x)} where \mu_j^a (x) is termedthe grade of membership of x" for alternative a related to j. This offered theoretical foundation for aggregation based on all evaluations from attributes.
Fusion of ICA Spatial, Temporal and Localized Features for Face Recognition
Authors: Jiajin Lei, Chao Lu
Venue: Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 02 (2007)

Independent Component Analysis (ICA) has found its application in face recognition successfully [1,2]. In practice several ICA representations can be derived. Particularly they include spatial ICA, spatiotemporal ICA, and localized spatiotemporal ICA, which respectively extract features of face images in terms of space domain, time-space domain, and local region. Our work has shown that while spatiotemporal ICA outperforms other ICA representations, further improvement can be made by a fusion of variety of ICA features. However, simply combining all features will not work as well as expected. For this reason an optimization method for feature selection and combination is proposed in this paper. We present here an optimizing process of feature selection about which features and how many features from each individual ICA feature set are selected. The experimental results show that feature fusion method can improve face recognition rate up to 94.62% compared with that of 86.43% by using spatiotemporal ICA alone.
An Automatic Target Detection Post-Processing Algorithm Based on Swarm Intelligence
Authors: Chen Zhuo, Liu Xiang-shuang, Zhuang Xiao-dong
Venue: Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 02 (2007)

Presently, most target detection post-processing algorithms are short of self-adapting, which make them have many limitations in automatic target detection from the digital images. Therefore this paper provides a new type of automatic target detection post-processing algorithm (CASI) which adopts clustering algorithm based on swarm intelligence to post-process the detected target pixels. Because of the characteristics of self-organization, robustness, expandability and simplicity, CASI can self-adaptively restore the target pixels, divide them into different targets and delete the false alarm targets. Experimental results conform that the target detection post-processing effect of this new type of algorithm is much better than the ones of morphological transformation in common use.
Quantitative software security risk assessment model
Authors: Idongesit Mkpong-Ruffin, David Umphress, John Hamilton, Juan Gilbert
Venue: Proceedings of the 2007 ACM workshop on Quality of protection (2007)

Risk analysis is a process for considering possible risks and determining which are the most significant for any particular effort. Determining which risks to address and the optimum strategy for mitigating said risks is often an intuitive and qualitative process. An objective view of the risks inherent in a development effort requires a quantitative risk model. Quantitative risk models used in determining which risk factors to focus on, tend to use a traditional approach of annualized loss expectancy (ALE). This research uses empirical data that reflects the security posture of each vulnerability to calculate Loss Expectancy; a risk impact estimator. Data from open source vulnerability databases and results of predicted threat models are used as input to the risk model. Security factors that take into account the innate characteristics of each vulnerability are incorporated into the calculation of the risk model; resulting in an empirical assessment of the potential threats to a development effort based on the risk metric calculation.