ppxscal/embedding_network huggingface dataset:
On Unfolding 3D Lattice Polygons and 2D Orthogonal Trees
Authors: Sheung-Hung Poon
Venue: Proceedings of the 14th annual international conference on Computing and Combinatorics (2008)

We consider the problems of unfolding 3D lattice polygons embedded on the surface of some classes of lattice polyhedra, and of unfolding 2D orthogonal trees. During the unfolding process, all graph edges are preserved and no edge crossings are allowed. Let n be the number of edges of the given polygon or tree. We show that a lattice polygon embedded on an open lattice orthotube can be convexified in O(n) moves and time, and a lattice polygon embedded on a lattice Tower of Hanoi, a lattice Manhattan Tower, or an orthogonally-convex lattice polyhedron can be convexified in O(n 2) moves and time. The main technique in our algorithms is to fold up the lattice polygon from the end blocks of the given lattice polyhedron. On the other hand, we show that a 2-monotone orthogonal tree on the plane can be straightened in O(n 2) moves and time. We hope that our results shed some light on solving the more general conjectures, which we proposed, that a 3D lattice polygon embedded on any lattice polyhedron can always be convexified, and any 2D orthogonal tree can always be straightened.
Application of redundant computation in program debugging
Authors: Zakarya A. Alzamil
Venue: Journal of Systems and Software (2008)

Programmers spend most of their time and resources in localizing program defects. On the other hand, they commit many errors by manipulating dynamic data improperly, which may produce dynamic memory problems, such as dangling pointer, memory leaks, and inaccessible objects. Dangling pointers can occur when a function returns a pointer to an automatic variable, or when trying to access a deleted object. Inaccessible objects occur when a pointer is assigned to point to another object, leaving the original object inaccessible, either by using the new operator or regular assignment operator. Memory leaks occur when a dynamic data is allocated but never de-allocated. The existence of such dynamic memory problems causes the programs to behave incorrectly. Improper usage of dynamic data is a common defect that is easy to commit, but is difficult to diagnose and discover. In this paper, we propose a dynamic approach that detects different types of program defects including those that occur as a result of misusing the dynamic data in computer programs. Our approach uses the notion of redundant computation to identify the suspicious locations in the program that may contain defects. Redundant computation is an execution of a program statement(s) that does not contribute to the program output. The notion of redundant computation is introduced as a potential indicator of defects in programs. We investigate the application of redundant computation in debugging programs. The detection of redundant computation indicates deficiency that may represent a bug in the program. The results of the experiment show that, the redundant computation detection can help the debuggers to localize the source(s) of the program defects.
Remote Sensing and Urban Analysis
Authors: Alberta Bianchin, Laura Bravin
Venue: Proceeding sof the international conference on Computational Science and Its Applications, Part I (2008)

The paper, on the basis of a fifteen years experience in mapping urban areas from satellite images, with Landsat, Spot and IKONOS, for planning purposes, proposes a general working methodology in two phases: 1) the creation of a map of built-up areas; 2) the identification and definition of classes or typologies of built-up areas extracted in the first phase.An application to an area of the Veneto region exemplifies: 1) the variety of strategies, approaches, and processing techniques that may be used in the first phase, 2) two sets of spatial analysis techniques for the second phase, which includes computing local density of built-up areas and landscape indices. The paper discusses two general issues 1) the significance that the results of these processing assumes in different spatial scales; 2) the complex and articulate relationship between territorial descriptions and information acquisition and processing techniques.
Application of Neural Networks to Atmospheric Pollutants Remote Sensing
Authors: Esteban García-Cuesta, Susana Briz, Isabel Fernández-Gómez, Antonio J. Castro
Venue: Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II (2007)

Infrared remote sensing is an extended technique to measure "in situ" atmospheric pollutant gas concentration. However, retrieval of concentrations from the absorbance spectra provided by technique is not a straightforward problem. In this work the use of artificial neural networks to analyze infrared absorbance spectra is proposed. A summary of classical retrieval codes is presented, highlighting advantages and important drawbacks that arise when these methods are applied to spectral analysis. As an alternative, a neural network retrieval approach is suggested, based on a multi layer perceptron. This approach has been focused to the retrieval of carbon monoxide concentration, because of the great environmental importance of this gas. Absorption overlapping of atmospheric gases such as carbon dioxide, nitrous oxide or water vapour is one the most important problem in the retrieval process. The training dataset has been generated with special care to overcome this aspect and guarantee a successful training phase. Results obtained from the ANN method are very promising. However, high retrieval errors have been found when ANN method is applied to experimental spectra. This fact reveals the need of a deep study of the instrumental parameters to be included in the model.
Fast and Efficient Algorithms for Nonnegative Tucker Decomposition
Authors: Anh Huy Phan, Andrzej Cichocki
Venue: Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II (2008)

In this paper, we propose new and efficient algorithms for nonnegative Tucker decomposition (NTD): Fast ¿-NTD algorithm which is much precise and faster than ¿-NTD [1]; and β-NTD algorithm based on the β divergence. These new algorithms include efficient normalization and initialization steps which help to reduce considerably the running time and increase dramatically the performance. Moreover, the multilevel NTD scheme is also presented, allowing further improvements (almost perfect reconstruction). The performance was also compared to other well-known algorithms (HONMF, HOOI, ALS algorithms) for synthetic and real-world data as well.
Communication synthesis of networks-on-chip (noc)
Authors: Rabi N. Mahapatra
Venue:  (2007)

The emergence of networks-on-chip (NoC) as the communication infrastructure solution for complex multi-core SoCs presents communication synthesis challenges. This dissertation addresses the design and run-time management aspects of communication synthesis. Design reuse and the infeasibility of Intellectual Property (IP) core interface redesign, requires the development of a Core-Network Interface (CNI) which allows them to communicate over the on-chip network. The absence of intelligence amongst the NoC components, entails the introduction of a CNI capable of not only providing basic packetization and depacketization, but also other essential services such as reliability, power management, recon guration and test support. A generic CNI architecture providing these services for NoCs is proposed and evaluated in this dissertation. Rising on-chip communication power costs and reliability concerns due to these, motivate the development of a peak power management technique that is both scalable to different NoCs and adaptable to varying traffic configurations. A scalable and adaptable peak power management technique - SAPP - is proposed and demonstrated. Latency and throughput improvements observed with SAPP demonstrate its superiority over existing techniques. Increasing design complexity make prediction of design lifetimes difficult. Post SoC deployment, an on-line health monitoring scheme, is essential to maintain confidence in the correct operation of on-chip cores. The rising design complexity and IP core test costs makes non-concurrent testing of the IP cores infeasible. An on-line scheme capable of managing IP core test in the presence of executing applications is essential. Such a scheme ensures application performance and system power budgets are efficiently managed. This dissertation proposes Concurrent On-Line Test (COLT) for NoC-based systems and demonstrates how a robust implementation of COLT using a Test Infrastructure-IP (TI-IP) can be used to maintain confidence in the correct operation of the SoC.
Vehicle Priority Selection Algorithm for Evacuation Planning
Authors: Marina Yusoff, Junaidah Ariffin, Azlinah Mohamed
Venue: Proceeding sof the international conference on Computational Science and Its Applications, Part I (2008)

For decades, numerous types of disasters have drawn an array of evacuation planning approaches to support evacuation processes with the objective to minimize total evacuation time. This paper focuses on the construction of optimization evacuation route planning method considering routes and capacity of vehicles. Vehicle Priority Selection (VPS) algorithm was constructed. The formulation of algorithms is based on case study for inundated areas within Kuala Lumpur, Malaysia. Experiments were done on the VPS and the improved VPS algorithm. The improved VPS with prioritize vehicle capacity demonstrates better results compared to other algorithms. It is shown to be applicable for multiple source nodes and large number of evacuees.
Learning Metrics Between Tree Structured Data: Application to Image Recognition
Authors: Laurent Boyer, Amaury Habrard, Marc Sebban
Venue: Proceedings of the 18th European conference on Machine Learning (2007)

The problem of learning metrics between structured data (strings, trees or graphs) has been the subject of various recent papers. With regard to the specific case of trees, some approaches focused on the learning of edit probabilities required to compute a so-called stochastic tree edit distance. However, to reduce the algorithmic and learning constraints, the deletion and insertion operations are achieved on entire subtrees rather than on single nodes. We aim in this article at filling the gap with the learning of a more general stochastic tree edit distance where node deletions and insertions are allowed. Our approach is based on an adaptation of the EM optimization algorithm to learn parameters of a tree model. We propose an original experimental approach aiming at representing images by a tree-structured representation and then at using our learned metric in an image recognition task. Comparisons with a non learned tree edit distance confirm the effectiveness of our approach.
Fractional Collections with Cardinality Bounds, and Mixed Linear Arithmetic with Stars
Authors: Ruzica Piskac, Viktor Kuncak
Venue: Proceedings of the 22nd international workshop on Computer Science Logic (2008)

We present decision procedures for logical constraints involving collections such as sets, multisets, and fuzzy sets. Element membership in our collections is given by characteristic functions from a finite universe (of unknown size) to a user-defined subset of rational numbers. Our logic supports standard operators such as union, intersection, difference, or any operation defined pointwise using mixed linear integer-rational arithmetic. Moreover, it supports the notion of cardinality of the collection, defined as the sum of occurrences of all elements. Deciding formulas in such logic has applications in software verification.Our decision procedure reduces satisfiability of formulas with collections to satisfiability of formulas in an extension of mixed linear integer-rational arithmetic with a "star" operator. The star operator computes the integer cone (closure under vector addition) of the solution set of a given formula. We give an algorithm for eliminating the star operator, which reduces the problem to mixed linear integer-rational arithmetic. Star elimination combines naturally with quantifier elimination for mixed integer-rational arithmetic. Our decidability result subsumes previous special cases for sets and multisets. The extension with star is interesting in its own right because it can encode reachability problems for a simple class of transition systems.
The Effect of Auditory Cues on the Audiotactile Roughness Perception: Modulation Frequency and Sound Pressure Level
Authors: M. Ercan Altinsoy
Venue: Proceedings of the 3rd international workshop on Haptic and Audio Interaction Design (2008)

Scraping a surface with the finger tip is a multimodal event. We obtain information about the texture, i.e. roughness of the surface, at least through three different sensory channels, i.e. auditory, tactile and visual. People are highly skilled in using touch-produced sounds to identify texture properties. Sound pressure level, modulation frequency and pitch of the touch-induced scraping sounds are the important psychoacoustical determinants of the texture roughness perception. In this study, psychophysical experiments were conducted to investigate what are the relative contributions of the auditory and tactile sensory modalities to the multimodal (audiotactile) roughness percept?, what are the effects of the perceptual discrepancy between the modalities on the multimodal roughness judgment and how different modulation frequency and loudness conditions affect the subjects' roughness perception.
Dynamic Link Matching between Feature Columns for Different Scale and Orientation
Authors: Yasuomi D. Sato, Christian Wolff, Philipp Wolfrum, Christoph Malsburg
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part I (2007)

Object recognition in the presence of changing scale and orientation requires mechanisms to deal with the corresponding feature transformations. Using Gabor wavelets as example, we approach this problem in a correspondence-based setting. We present a mechanism for finding feature-to-feature matches between corresponding points in pairs of images taken at different scale and/or orientation (leaving out for the moment the problem of simultaneously finding point correspondences). The mechanism is based on a macro-columnar cortical model and dynamic links. We present tests of the ability of finding the correct feature transformation in spite of added noise.
A novel adaptive lattice filtering algorithm for alpha-stable processes
Authors: Zhijin Zhao, Junna Shang, Kehai Dong
Venue: Digital Signal Processing (2008)

This paper generalizes Burg's formula to be suitable for alpha-stable distribution and proposes a novel adaptive lattice algorithm for adaptive parameter estimation of alpha-stable AR processes. The performance is compared to that of other algorithms with lattice structure for alpha-stable AR processes for different @a parameter. Simulations studies indicate that the proposed algorithm shows superior convergence speed over existing well-known lattice algorithms in parameter estimation of alpha-stable processes.
Emotion Based Control Architecture for Robotics Applications
Authors: Jochen Hirth, Tim Braun, Karsten Berns
Venue: Proceedings of the 30th annual German conference on Advances in Artificial Intelligence (2007)

Assistance and service systems are one of the main research topics in robotics today. A major problem for creating these systems is that they have to work and navigate in the real world. Because this world is too complex to model, these robots need to make intelligent decisions and create an intelligent behavior without knowing everything about the current situation. For these aspects, the importance of emotion increases, because the emotional influence helps human beings as well as animals to make their decisions. To enable a robot to use emotions, a concept for an emotion based control architecture was designed. The basis of this architecture is a behavior based approach. This paper presents the developed architecture. Furthermore two application possibilities are presented, where parts of the architecture were already tested and implemented.
Issues and methodologies in designing databases in distributed environments -- privacy, quality, resources
Authors: Sharad Mehrotra
Venue:  (2007)

Traditional data management systems store data at a central location, with well structured indices. However, the emergence and fast development of network technologies have forced data management into a new horizon. Data is not centrally stored any more, but generated and stored at distributed sources. Even more, data may not exist at all and can only be produced upon request. The data sources are situated in and are accessed through some network environments, such as Internet, cellular network, or wireless sensor networks. With the dynamic nature of most networks, precise data collection and query processing are hard to achieve. How to collect necessary data from the sources to answer user queries becomes a distinct challenge in data management. My contribution with this thesis is to address the issues with adaptive techniques. Adaptation is explored in two perspectives. Firstly, data quality is adapted to meet the final query requirements, which are approximate. We look into a broad range of quality definitions which include numerical precision, query response time, and user privacy needs. Secondly, the network resources are adapted as well in order to achieve querying goals such as long network lives and timeliness. The resources to be adapted include power sources of network nodes and the communication infrastructure and media. In this thesis, I first explore quality adaptation and present a middleware framework for data collection and query processing which allows flexible conversion between user quality requirements and the data collection precision. The framework is instantiated and validated with an application of mobile target tracking and a data archiving application. It is shown that this is sometimes a complicated task, especially when probabilistic measures are involved. I further investigate resource adaptation in data collection and query processing techniques which fully exploits the interaction and collaboration between active network nodes. One application studied is a fully in-network query processing technique which allows a full array of adaptations to be implemented. Another application takes into account the operation status of network nodes into consideration and solves the problem of state scheduling for energy-efficient fast data aggregation. With the ever-increasing importance of privacy preservation, I studied the technique on data privacy, winch can be viewed as a different quality measure. The investigated application domain is data collection in location based services and trajectory data anonymization. It is shown that maintaining desired user privacy while collecting appropriate data for efficient query processing is an very challenging issue.
Rough set-based software quality models and quality of data
Authors: Taghi M. Khoshgoftaar
Venue:  (2008)

In this dissertation we address two significant issues of concern. These are software quality modeling and data quality assessment. Software quality can be measured by software reliability. Reliability is often measured in terms of the time between system failures. A failure is caused by a fault which is a defect in the executable software product. The time between system failures depends both on the presence and the usage pattern of the software. Finding faulty components in the development cycle of a software system can lead to a more reliable final system and will reduce development and maintenance costs. The issue of software quality is investigated by proposing a new approach, rule-based classification model (RBCM) that uses rough set theory to generate decision rules to predict software quality. The new model minimizes over-fitting by balancing the Type I and Type II misclassification error rates. We also propose a model selection technique for rule-based models called rule-based model selection (RBMS). The proposed rule-based model selection technique utilizes the complete and partial matching rule sets of candidate RBCMs to determine the model with the least amount of over-fitting. In the experiments that were performed, the RBCMs were effective at identifying faulty software modules, and the RBMS technique was able to identify RBCMs that minimized over-fitting.
Evaluating the Reference and Representation of Domain Concepts in APIs
Authors: Daniel Ratiu, Jan Juerjens
Venue: Proceedings of the 2008 The 16th IEEE International Conference on Program Comprehension (2008)

As libraries are the most widespread form of software reuse, the usability of their APIs substantially influences the productivity of programmers in all software development phases. In this paper we develop a framework to characterize domain-specific APIs along two directions: 1) how can the API users reference the domain concepts implemented by the API; 2) how are the domain concepts internally represented in the API. We define metrics that allow the API developer for example to assess the conceptual complexity of his API and the non-uniformity and ambiguities introduced by the API's internal representations of domain concepts, which makes developing and maintaining software that uses the library difficult and error-prone. The aim is to be able to predict these difficulties already during the developmentof the API, and based on this feedback be able to develop better APIs up front, which will reduce the risks of these difficulties later.
A Layered Architecture for Detecting Malicious Behaviors
Authors: Lorenzo Martignoni, Elizabeth Stinson, Matt Fredrikson, Somesh Jha, John C. Mitchell
Venue: Proceedings of the 11th international symposium on Recent Advances in Intrusion Detection (2008)

We address the semantic gap problem in behavioral monitoring by using hierarchical behavior graphs to infer high-level behaviors from myriad low-level events. Our experimental system traces the execution of a process, performing data-flow analysis to identify meaningful actions such as "proxying", "keystroke logging", "data leaking", and "downloading and executing a program" from complex combinations of rudimentary system calls. To preemptively address evasive malware behavior, our specifications are carefully crafted to detect alternative sequences of events that achieve the same high-level goal. We tested eleven benign programs, variants from seven malicious bot families, four trojans, and three mass-mailing worms and found that we were able to thoroughly identify high-level behaviors across this diverse code base. Moreover, we effectively distinguished malicious execution of high-level behaviors from benign by identifying remotely-initiated actions.
A Propositional Dynamic Logic for CCS Programs
Authors: Mario R. Benevides, L. Menasché Schechter
Venue: Proceedings of the 15th international workshop on Logic, Language, Information and Computation (2008)

This work presents a Propositional Dynamic Logic in which the programs are CCS terms (CCS-PDL). Its goal is to reason about properties of concurrent systems specified in CCS. CCS is a process algebra that models the concurrency and interaction between processes through individual acts of communication. At a first step, we consider only CCS processes without constants and give a complete axiomatization for this logic, which is very similar to *-free PDL. Then, we proceed to include CCS processes with constants. In this case, we impose some restrictions on the form of the recursive equations that can be built with those constants. We also give an axiomatization for this second logic and prove its completeness using a Fischer-Ladner construction. Unlike Concurrent PDL (with channels) [1,2], our logic has a simple Kripke semantics, a complete axiomatization and the finite model property.