ppxscal/embedding_network huggingface dataset:
On The Detection of Test Smells: A Metrics-Based Approach for General Fixture and Eager Test
Authors: Bart Van Rompaey, Bart Du Bois, Serge Demeyer, Matthias Rieger
Venue: IEEE Transactions on Software Engineering (2007)

As a testing method, white box testing has been demonstrated to be very efficient in early defect detection. However, white box testing introduces test co-evolution as an additional burden to software development. To mitigate the effects of co-evolution, tests should be written in a manner that makes them easy to change. Fortunately, we are able to concretely express what a good test is by exploiting the specific principles underlying white box testing. Analogous to the concept of code smells, violations of these principles are termed test smells. In this paper, we present a formal description of test smells, and propose metrics to support their detection. We validate the feasibility of detecting two test smells, General Fixture and Eager Test, by comparison with human evaluation. We demonstrate the effectiveness of the detection in the case the assessment is agreed upon by evaluators. For the General Fixture, a qualitative investigation showed that an ambiguous test smell definition prohibits the detection by metrics and suggests disentangling its definition. On the bright side, test evolvability can be more concretely expressed than general evolvability due to the exploitation of the specific principles underlying white box testing. In particular, adherence to a rigid setup-stimulate-verify-teardown cycle has been reported an essential characteristic of evolvable tests. In this article, we propose to incorporate structural characteristics of tests in the definition of test smells, thereby providing an objective means to detect test evolution obstacles. We validate the feasibility of detecting test evolution obstacles using such test smells, thereby contributing the first step to the mitigation of the cost of test co-evolution.
An Algebraic Method to Determine If a Point is on a Spline
Authors: David Fredricks
Venue: IEEE Computer Graphics and Applications (1984)

For this task a method that is algebraic in nature should be faster than the usual approximate analytical techniques.
A Data Structure and an Algorithm for the Nearest Point Problem
Authors: I. Kalantari
Venue: IEEE Transactions on Software Engineering (1983)

In this paper we present a tree structure for storing points from a normed space whose norm is effectively computable. We then give an algorithm for finding the nearest point from the tree to a given query point. Our algorithm searches the tree and uses the triangle inequality to eliminate searching of the entirety of some branches of the tree whenever a certain predicate is satisfied. Our data structure uses 0(n) for storage. Empirical data which we have gathered suggest that the expected complexity for preprocessing and the search time are, respectively, 0(nlogn) and 0(logn).
fMRI Brain Image Retrieval Based on ICA Components
Authors: Bing Bai, Paul Kantor, Ali Shoukoufandeh, Deborah Silver
Venue: Proceedings of the Eighth Mexican International Conference on Current Trends in Computer Science (2007)

This manuscript proposes a retrieval system for fMRI brain images. Our goal is to find a similaritymetric to enable us to support queries for "similar tasks" for retrieval on a large collection of brain experiments. The system uses a novel similarity measure between the result of probabilistic independent component analysis (PICA) of brain images. Specifically, the times series of an fMRI dataset will be represented using a number of ICA components as high level taskrelated features. The similarity between two datasets is the value of the maximum weight bipartite matching defined on the component-wise similarities. The component-wise similarities are calculated based on the size of the overlap between the "highly activated" regions in the corresponding activation maps. We evaluated the performance of the proposed method on a moderate size fMRI image database with considerable variety. The ICA-based component selection in combination with bipartite matching similarity measure outperforms several other component selection methods and similarity measurements. The results also suggest that there is a direct correlation between the involvement of ICA components in cognitive processes and their time course spectrum. Along with other heuristics, this property can be for fMRI image retrieval and classification.
A general local reconstruction approach based on a truncated Hilbert transform
Authors: Yangbo Ye, Hengyong Yu, Yuchuan Wei, Ge Wang
Venue: Journal of Biomedical Imaging (2007)

Exact image reconstruction from limited projection data has been a central topic in the computed tomography (CT) field. In this paper, we present a general region-of-interest/volume-of-interest (ROI/VOI) reconstruction approach using a truly truncated Hilbert transform on a line-segment inside a compactly supported object aided by partial knowledge on one or both neighboring intervals of that segment. Our approach and associated new data sufficient condition allows the most flexible ROI/VOI image reconstruction from the minimum account of data in both the fan-beam and cone-beam geometry. We also report primary numerical simulation results to demonstrate the correctness and merits of our finding. Our work has major theoretical potentials and innovative practical applications.
Automating Mappings between Use Case Diagrams and Feature Models for Software Product Lines
Authors: Alexandre Braganca, Ricardo J. Machado
Venue: Proceedings of the 11th International Software Product Line Conference (2007)

Features have been widely used by the product line community to model variability. They represent the common and variable characteristics of the members of a product line. They are very well suited for the configuration of product line members. Outside the product line community, use cases are also widely used to model the functionality of systems at a similar level of abstraction but from a user perspective. Significant work has been done by several authors regarding the possible relationship between these two perspectives of a system. Nonetheless, this has been done in an informal way. In this paper we explore the relationships between these two perspectives and describe a possible approach to automate the transformation from UML use case to feature models.
Ink Discrimination Based on Co-occurrence Analysis of Visible and Infrared Images
Authors: V. Kokla, A. Psarrou, V. Konstantinou
Venue: Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02 (2007)

Inks found in Byzantine manuscripts are semi- transparent pigments and their examination and analysis provide an invaluable source of information on the au- thenticity and dating of manuscripts and the number of authors involved. However, inks are difficult to characterize because their intensity depends on the amount of liquid spread during scripting and the reflective properties of the support. Most existing methods for the analysis of ink materials are based on destructive testing techniques that require the physicochemical sampling of data. Such methods cannot be widely used because of the historical and cultural value of the manuscripts. In this work we show that manuscript inks can be represented through a mixture of Gaussian functions and can be characterised using co-occurrence matrices.
WCDMA multiservice uplink capacity of highways cigar-shaped microcells
Authors: Bazil Taha-Ahmed, Miguel Calvo Ramon
Venue: EURASIP Journal on Wireless Communications and Networking (2007)

The multiservice uplink capacity and the interference (intracellular and intercellular) statistics (mean and variance) of the sectors of cigar-shaped wideband code-division multiple access (WCDMA) microcell are studied using a model of 5 highway microcells in rural zone. The two-slope propagation loss model with lognormal shadowing is used in the analysis. The capacity and the interference statistics of the microcell are studied for different sector ranges, antenna side lobe levels, standard deviation of the power control error, breakpoint distance, and different intersites correlation coefficient. It is shown that reducing the antenna side lobe level increases the sector capacity. Also, it is shown that the sector range that gives the quasi the maximum sector capacity is in the order of 800 to 1200 m.
Specification and execution of composite trading activities
Authors: Yain-Whar Si, David Edmond, Marlon Dumas, Arthur H. Hofstede
Venue: Electronic Commerce Research (2007)

With the growing number of trading opportunities available online, software tools designed to act on behalf of human traders are increasingly being used to automate trading activities. The next logical step in this evolution is the automation of composite trading activities designed to fulfill complex user goals and requirements. In this paper, we describe a model for specifying composite trading activities involving concurrent and interrelated negotiations with multiple parties and heterogeneous protocols. The model supports the specification of several types of constraints, such as the number of required successful negotiations, the limit price for the items to be traded, and the temporal constraints imposed by all trading parties. In order to guide the execution of the trading activities, we describe a novel planning and execution model for composite trading activities which is designed to maximize the expected utility of the user.
Research on Lossless Image Compression Algorithms Using Fermat Number Transform
Authors: Zhang Hong, Cai Zhengxing, Yang Fan, Wei Wei
Venue: Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 02 (2007)

This paper studies the Fermat Number Transform ,and proposes 1D and 2D invariance nature and cumulative nature of FNT(Fermat Number Transform), and combines with features of gray images , and establishes the encoding/decoding model for gray image and designs corresponding algorithm. The result show that the model proposed in this paper has good performance whose compression is about 87.93% of JPEG-LS. The coding model in this paper adopts a new theoretical foundation, and applicates FNT to gray image compression for the first time which can be improved and optimized greatly.
A Learner Model for Learning-by-Example Context
Authors: Yuan Fan Zhang, Laurence Capus, Nicole Tourigny
Venue: Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 03 (2007)

Nowadays learning environments put more and more accent on the intelligence of the system. The intelligence of a learning environment is largely attributed to its ability of adapting to a specific learner during the learning process. The adaptation depends on individual learner's knowledge of the subject to be learned, and other relevant characteristics of the learner. The knowledge and the relevant information about the learner are maintained in the learner model. A learner model can be defined as structured information about the learning process; and this structure contains some values of the learner's characteristics. This paper proposes a new learner model, which is based on the consideration of what is appropriate to the learning-by-example context. The model records five categories of information about the learner: personal data, learner's characteristics, learning state, learner's interactions with the system, and learner's knowledge. This model is being integrated in Sphinx, an educational environment based on learning by means of examples.
An evolutionary approach to cancer chemotherapy scheduling
Authors: Gabriela Ochoa, Minaya Villasana, Edmund K. Burke
Venue: Genetic Programming and Evolvable Machines (2007)

In this paper, we investigate the employment of evolutionary algorithms as a search mechanism in a decision support system for designing chemotherapy schedules. Chemotherapy involves using powerful anti-cancer drugs to help eliminate cancerous cells and cure the condition. It is given in cycles of treatment alternating with rest periods to allow the body to recover from toxic side-effects. The number and duration of these cycles would depend on many factors, and the oncologist would schedule a treatment for each patient's condition. The design of a chemotherapy schedule can be formulated as an optimal control problem; using an underlying mathematical model of tumour growth (that considers interactions with the immune system and multiple applications of a cycle-phase-specific drug), the objective is to find effective drug schedules that help eradicate the tumour while maintaining the patient health's above an acceptable level. A detailed study on the effects of different objective functions, in the quality and diversity of the solutions, was performed. A term that keeps at a minimum the tumour levels throughout the course of treatment was found to produce more regular treatments, at the expense of imposing a higher strain on the patient's health, and reducing the diversity of the solutions. Moreover, when the number of cycles was incorporated in the problem encoding, and a parsimony pressure added to the objective function, shorter treatments were obtained than those initially found by trial and error.
Gabor Saliency Map for Face Recognition
Authors: Raffaele Cappelli, Annalisa Franco, Dario Maio
Venue: Proceedings of the 14th International Conference on Image Analysis and Processing (2007)

In this work a novel feature extraction approach for face recognition is proposed. The method is based on the application of Gabor filters to the most significant image locations, determined during the training stage on the basis of a saliency map. The experiments, carried out on well-known datasets, confirm the efficacy of the approach.
Introduction to SPLM
Authors: R. Hokom, A. Grebert, F. Gerbstadt, R. Bock
Venue: Computer (1973)

This is an introduction to, and an overview of, the Space Programming Language Machine (SPLM), which is an architecture for a class of machines designed for efficient execution of on-board, aerospace computation functions. It directly executes software written in the SPLM Language (SPLML), which has been specially constructed as a complete, concise notation for the application area. The goal of the SPLM development effort is to design machines that are smaller, faster and easier to program than currently available aerospace computers. The specific features and characteristics of the SPLM may have general applicability, although this was not a design criterion.
A Hybrid Escalating Evolutionary Algorithm for Multi-objective Flow-Shop Scheduling
Authors: Ruifeng Shi, Yiming Zhou, Hong Zhou
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 04 (2007)

A hybrid escalating evolutionary algorithm, which aims at solving multi-objective flow-shop scheduling problems, is proposed in this paper. The new algorithm takes an escalating evolutionary structure, which helps it escaping from premature, and an elitism strategy is introduced to improve its convergence, besides, a problem-dependent metaheuristic variable local search strategy is adopted to enhance its local search ability. To assess the performance and demonstrate the effectiveness of the new algorithm, a series of standard bi-objective test problems and a typical tri-objective case study are employed. Empirical results have shown that, our new algorithm has outperformed some well-known algorithms like NSGA-II, ENGA and MOGLS.
Toward measuring network security using attack graphs
Authors: Lingyu Wang, Anoop Singhal, Sushil Jajodia
Venue: Proceedings of the 2007 ACM workshop on Quality of protection (2007)

In measuring the overall security of a network, a crucial issue is to correctly compose the measure of individual components. Incorrect compositions may lead to misleading results. For example, a network with less vulnerabilities or a more diversified configuration is not necessarily more secure. To obtain correct compositions of individual measures, we need to first understand the interplay between network components. For example, how vulnerabilities can be combined by attackers in advancing an intrusion. Such an understanding becomes possible with recent advances in modeling network security using attack graphs. Based on our experiences with attack graph analysis, we propose an integrated framework for measuring various aspects of network security. We first outline our principles andmethodologies. We then describe concrete examples to buildintuitions. Finally, we present our formal framework. It is our belief that metrics developed based on the proposed framework will lead to novel quantitative approaches to vulnerability analysis, network hardening, and attack response.
A Generalized Query-by-Example Data Manipulation Language Based on Database Logic
Authors: B. E. Jacobs
Venue: IEEE Transactions on Software Engineering (1983)

The purpose of this paper is to introduce a Generalized-Query-By-Example (GQBE) data manipulation language (DML) that can be built on top of most existing databases (ie., relational, hierarchical, and network). The data manipulation language supports retrieval, insertion, deletion, and update operations and has a formal semantics based on database logic. It is also seen that GQBE can by used as a DML on external views of an integrated database. We also show the advantages of GQBE on heterogeneous databases over Zloof's QBE on relational external views.
AS2M4SoC: An Autonomic Service Scheduling Model and Strategy for SoC Based on Evidence Theory
Authors: LiNa Ni, JinQuan Zhang, ChangJun Jiang
Venue: Proceedings of the Sixth International Conference on Grid and Cooperative Computing (2007)

Service-oriented resource management and application are important issues. The usability of selected resources for a service is a primary factor to computing performance. This paper proposes an architectural service management model(AS2M4SoC), which realizes autonomic service efficiently through mapping a group of services to the user service registry via resource's QoS appraisal and user's credit appraisal strategy. Because of the uncertainty of heterogeneous resources, the resource usability appraisal strategy and the resources allocating method based on D-S evidence theory are established to realize the binding of service with high usability resources, the selecting of QoS-guaranteed resources and the guaranteeing of the users' tasks QoS. Service scheduling algorithm is presented to realize that high QoS-guaranteed services can priorly map to user's registry with high credit degree. Simulation experiment demonstrates that our service scheduling model can not only improve the QoS and service efficiency, but also effectively relieve the workload of GSM.
Overview on Low Power SoC Design Technology
Authors: Kimiyoshi Usami
Venue: Proceedings of the 2007 Asia and South Pacific Design Automation Conference (2007)

So far, low power design for SoC has mainly focused on techniques to reduce dynamic power and standby leakage power. In further scaled devices, design technology to reduce active leakage power at the operation mode becomes indispensable. This is because the share of leakage power in the total operation power continues to increase as the device gets scaled. This paper gives a brief overview on the conventional leakage reduction techniques and describes novel approaches to use run-time power gating for active leakage reduction.
Autonomic runtime manager for adaptive distributed applications
Authors: Jingmei Yang, Huoping Chen, Salim Hariri
Venue: Proceedings of the High Performance Distributed Computing, 2005. HPDC-14. Proceedings. 14th IEEE International Symposium (2005)

For adaptive distributed applications, the computational complexity associated with each computational region varies continuously and dramatically both in space and time throughout the life cycle of the application execution. Consequently, static scheduling techniques are inefficient for such applications. In this paper, we present an autonomic runtime manager (ARM) that uses the application spatial and temporal characteristics as well as resource status as the main criteria to self optimize the execution of distributed applications at runtime. We applied the ARM system to a wildfire simulation and our experimental results show that the performance of the wildfire simulation has been improved by 45% when compared with a static partitioning algorithm. We also evaluate the performance of ARM using two partitioning strategies: natural regions (NR) approach and a graph partitioning approach.
Fuzzy Mixed Controller Design to an Aircraft
Authors: Chunning Yang, Jihong Zhu, Yong Fan
Venue: Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02 (2007)

This paper addresses the design of output-feedback H and fuzzy mixed controllers that satisfy global stability, the controllers are robust to deal with the external disturbance of the plant. To an aircraft which is one of the complex nonlinear systems with uncertainty in large flight envelop (LFE), through representing the aircraft plant by a fuzzy dynamic model, so as to design flight control law by applying the H and fuzzy mixed output feedback approach. The simulation results show that the designed fuzzy controllers are better to resist the external disturbance, and robust to parameter perturbation. This method is possible for designing less number and better performance controllers in LFE.
Combination Methodologies of Text Classifier: Design and Implementation
Authors: Rujiang Bai, Xiaoyue Wang
Venue: Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 04 (2007)

Support Vector Machines, one of the most population techniques for classification, have been widely used in many application areas. The kernel parameters setting for SVM in a training process impacts on the classification accuracy. Feature selection is another factor that impacts classification accuracy .The objective of this work is to reduce the dimension of feature vectors, optimizing the parameters to improve the SVM classification accuracy and speed. We present rough set method for feature reduce and a genetic algorithm approach for feature selection and parameters optimization to solve this kind of problem. We tried Reuters 21578 using the proposed method. Experimental results indicate, compared with the traditional methods, our proposed method significantly improves the classification accuracy and has fewer input features for support vector machines.
AI Optimization of a Billiard Player
Authors: Jean-François Landry, Jean-Pierre Dussault
Venue: Journal of Intelligent and Robotic Systems (2007)

In this paper, we take a look back at the problem of creating an intelligent billiard player on a computer simulated table by using optimization techniques. We setup the problem by first defining the equations that describe the ball movements on a pool table, and go on to create a function that will be minimised to give us a player with the ability to reposition itself almost anywhere on a given table. We then look at possible strategies that the player can use to play intelligently while at the same time only looking one shot in advance. Pre-computed tables are introduced in the context of finding good starting points for the optimization model, thus reducing execution time and allowing the possibility of looking more than one shot ahead.