ppxscal/embedding_network huggingface dataset:
A new approach to estimating the expected first hitting time of evolutionary algorithms
Authors: Yang Yu, Zhi-Hua Zhou
Venue: Artificial Intelligence (2008)

Evolutionary algorithms (EA) have been shown to be very effective in solving practical problems, yet many important theoretical issues of them are not clear. The expected first hitting time is one of the most important theoretical issues of evolutionary algorithms, since it implies the average computational time complexity. In this paper, we establish a bridge between the expected first hitting time and another important theoretical issue, i.e., convergence rate. Through this bridge, we propose a new general approach to estimating the expected first hitting time. Using this approach, we analyze EAs with different configurations, including three mutation operators, with/without population, a recombination operator and a time variant mutation operator, on a hard problem. The results show that the proposed approach is helpful for analyzing a broad range of evolutionary algorithms. Moreover, we give an explanation of what makes a problem hard to EAs, and based on the recognition, we prove the hardness of a general problem.
Aspect-Oriented User Requirements Notation: Aspects in Goal and Scenario Models
Authors: Gunter Mussbacher
Venue: Models in Software Engineering: Workshops and Symposia at MoDELS 2007, Nashville, TN, USA, September 30 - October 5, 2007, Reports and Revised Selected Papers (2008)

Technologies based on aspects and applied at the early stages of software development allow requirements engineers to better encapsulate crosscutting concerns in requirements models. The Aspect-oriented User Requirements Notation (AoURN) extends the User Requirements Notation (URN) with aspects and thus unifies goal-oriented, scenario-based, and aspect-oriented concepts in one framework. Minimal changes to URN ensure that requirements engineers can continue working with goal and scenario models expressed in a familiar notation. At the same time, concerns in goal and scenario models, regardless of whether these concerns crosscut or not, can be managed across model types. Typical concerns in URN are non-functional requirements (NFRs), use cases, and stakeholder goals. As AoURN expresses concern composition rules with URN itself, it is possible to describe rules in a highly flexible way that is not restricted by any specific composition language. Aspects can improve the modularity, reusability, scalability, and maintainability of URN models. Considering the strong overlap between NFRs and crosscutting concerns, aspects can help bridge the gap between goals and scenarios. On the other hand, Early Aspects (EA) research can benefit from a standardized way of modeling concerns with AoURN.
Systematic QoS Class Mapping Framework over Multiple Heterogeneous Networks
Authors: Misun Ryu, Youngmin Kim, Hongshik Park
Venue: Proceedings of the 8th international conference, NEW2AN and 1st Russian Conference on Smart Spaces, ruSMART on Next Generation Teletraffic and Wired/Wireless Advanced Networking (2008)

A network defines its own Quality-Of-Service (QoS) class and has QoS support mechanisms. So, effectively to support end-to-end QoS in heterogeneous networks, a certain unified control is needed, however, it causes scalability problem as management complexity and implementation difficulty. There is a strong need to provide simple interoperability with QoS support so we present a QoS Class Mapping (QCM) framework: building blocks should be defined such as parameter mapping and class mapping. And we improve the framework, called as QCM-ASM, to support not only flawless class mapping but also fine-granular QoS in any circumstance. At last, another framework with adaptive QoS Class Selection (AQCS) mechanism, named as AQCM-ASM framework, is proposed. AQCS mechanism can prevent resource starvation of lower priority class and provide an effective resource distribution. As an experimental result, we demonstrate a performance of the proposed frameworks. The performance results show characteristics of each framework.
A Convergence Criterion for Multiobjective Evolutionary Algorithms Based on Systematic Statistical Testing
Authors: Heike Trautmann, Uwe Ligges, Jörn Mehnen, Mike Preuss
Venue: Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X (2008)

A systematic approach for determining the generation number at which a specific Multi-Objective Evolutionary Algorithm (MOEA) has converged for a given optimization problem is introduced. Convergence is measured by the performance indicators Generational Distance, Spread and Hypervolume. The stochastic nature of the MOEA is taken into account by repeated runs per generation number which results in a highly robust procedure. For each generation number the MOEA is repeated a fixed number of times, and the Kolmogorow-Smirnov-Test is used in order to decide if a significant change in performance is gained in comparison to preceding generations. A comparison of different MOEAs on a problem with respect to necessary generation numbers becomes possible, and the understanding of the algorithm's behaviour is supported by analysing the development of the indicator values. The procedure is illustrated by means of standard test problems.
Biometric and Color Features Fusion for Face Detection and Tracking in Natural Video Sequences
Authors: Juan Zapata, Ramón Ruiz
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)

A system that performs the detection and tracking of a face in real-time in real video sequences is presented in this paper. The face is detected in a complex environment by a model of human colour skin. Very good results are obtained, since the colour segmentation removes almost all the complex background and it is realized to a very high-speed, making the system very robust. On the other hand, fast and stable real-time tracking is then achieved via biometric feature extraction of face using connected components labelling. Tracking does not require a precise initial fit of the model. Therefore, the system is initialised automatically using a very simple 2D face detector based on target ellipsoidal shape. Results are presented showing a significant improvement in detection rates when the whole sequence is used instead of a single image of the face. Experiments in tracking are reported.
Robust Face Tracking with Suppressed False Positives in Smart Home Environment
Authors: Jun-Hyeong Do, Zeungnam Bien
Venue: Proceedings of the 6th international conference on Smart Homes and Health Telematics (2008)

The face-tracking systems in use often suffer from converging to a false face-like region in a smart home environment. For this, we propose a technique to reduce errors due to false positives in the estimation scheme of face movement. In the proposed method, the face movement is estimated by using the information on face-candidate blobs obtained from the current frame as well as from the previous frame. This estimated face movement information is used in the face tracker for tracking faces in video images. Our experimental result shows a conspicuous improvement in the performance of the face tracking process in terms of success rates and with robustness against interruptions from face-like blobs.
The P4 Parallel Programming System, the Linda Environment, and Some Experiences with Parallel Computation
Authors: Allan R. Larrabee
Venue: Scientific Programming (1993)

The first digital computers consisted of a single processor acting on a single stream of data. In this so-called "von Neumann" architecture, computation speed is limited mainly by the time required to transfer data between the processor and memory. This limiting factor has been referred to as the "von Neumann bottleneck". The concern that the miniaturization of silicon-based integrated circuits will soon reach theoretical limits of size and gate times has led to increased interest in parallel architectures and also spurred research into alternatives to silicon-based implementations of processors. Meanwhile, sequential processors continue to be produced that have increased clock rates and an increase in memory locally available to a processor, and an increase in the rate at which data can be transferred to and from memories, networks, and remote storage. The efficiency of compilers and operating systems is also improving over time. Although such characteristics limit maximum performance, a large improvement in the speed of scientific computations can often be achieved by utilizing more efficient algorithms, particularly those that support parallel computation. This work discusses experiences with two tools for large grain (or "macro task") parallelism.
Personalizing Threshold Values on Behavior Detection with Collaborative Filtering
Authors: Hiroyuki Yamahara, Fumiko Harada, Hideyuki Takada, Hiromitsu Shimakawa
Venue: Proceedings of the 5th international conference on Ubiquitous Intelligence and Computing (2008)

We are developing a system which assists users by collaboration between the users and environment. Our collaboration system provides services according to user behavior proactively in homes when environment detects high-level user behavior such as "leaving the home". To realize such a collaboration system, this paper proposes a method for detecting high-level user behavior. The proposed method dynamically sets values suitable for individual behavioral pattern of each user to thresholds used for detection. A conventional method determines threshold values common to all users. However, the common values are not always suitable for all users. Our method determines threshold values suitable for a user by utilizing data of other users whose characteristics are similar to the user, with collaborative filtering.
Segmentation of Web Documents and Retrieval of Useful Passages
Authors: Carlos G. Figuerola, José L. Berrocal, Angel F. Zazo Rodríguez
Venue: Advances in Multilingual and Multimodal Information Retrieval: 8th Workshop of the Cross-Language Evaluation Forum, CLEF 2007, Budapest, Hungary, September 19-21, 2007, Revised Selected Papers (2008)

This year's WebCLEF task was to retrieve snippets and pieces from documents on various topics. The extraction and the choice of the most widely used snippets can be carried out using various methods. This article illustrates the segmentation process and the choice of snippets produced in this process. It also describes the tests carried out and their results.
Design and analysis of network-on-chip (noc) architecture
Authors: Nader Bagherzadeh
Venue:  (2008)

As the number of integrated IP cores in the current System-on-Chips (SoCs) keeps increasing, communication requirements among cores can not be sufficiently satisfied using either traditional or multi-layer bus architectures, because of their poor scalability and bandwidth limitation on a single bus. While new interconnection techniques have been explored to overcome such a limitation, the notion of utilizing Network-on-Chip (NoC) technologies for the future generation of high performance and low power chips for myriads of applications, in particular for wireless communication and multimedia processing, has been of great importance. In order for the NoC technologies to succeed, realistic specifications such as throughput, latency, moderate design complexity, programming model, and design tools are necessary requirements. For this purpose, this dissertation covers some of the key and challenging design issues specific to the NoC architecture such as router design, network interface (NI) related issues, traffic models, and complete system-level modeling. In this dissertation, a multi-processor system platform adopting NoC techniques was proposed, called NePA (Networked Processor Array). As a component of system platform, the fundamental NoC techniques including the expandable packet formats, the associated router architectures and NI were defined and implemented adopting low power and clock efficient techniques. Using a high-level cycle-accurate simulation, various parameters relevant to their performance and systematic modeling were extracted and analyzed. In order to characterize the traffic patterns of real applications in the NoC environment and generate synthetic traffic patterns which resemble real network characteristics, a statistical traffic model with three-tupled spatio-temporal parameters was also developed. By combining various developed systematic models, the tool chain is constructed to pursue hardware/software design trade-offs necessary for better understanding of the NoC techniques. Finally, utilizing implementation of several applications on the homogeneous NePA, the feasibility and advantages of using NoC techniques were shown.
Fast Cryptographic Privacy Preserving Association Rules Mining on Distributed Homogenous Data Base
Authors: Mahmoud Hussein, Ashraf El-Sisi, Nabil Ismail
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II (2008)

Privacy is one of the most important properties of an information system must satisfy. In which systems the need to share information among different, not trusted entities, the protection of sensible information has a relevant role. A relatively new trend shows that classical access control techniques are not sufficient to guarantee privacy when data mining techniques are used in a malicious way. Privacy preserving data mining algorithms have been recently introduced with the aim of preventing the discovery of sensible information. In this paper we propose a modification to privacy preserving association rule mining on distributed homogenous database algorithm. Our algorithm is faster than old one which modified with preserving privacy and accurate results. Modified algorithm is based on a semi-honest model with negligible collision probability. The flexibility to extend to any number of sites without any change in implementation can be achieved. And also any increase doesn't add more time to algorithm because all client sites perform the mining in the same time so the overhead in communication time only. The total bit-communication cost for our algorithm is function in (N) sites.
Inventory lot-sizing with supplier selection using hybrid intelligent algorithm
Authors: Mohammad Reza Sadeghi Moghadam, Amir Afsar, Babak Sohrabi
Venue: Applied Soft Computing (2008)

In supply chain management (SCM), multi-product and multi-period models are usually used to select the suppliers. In the real world of SCM, however, there are normally several echelons which need to be integrated into inventory management. This paper presents a hybrid intelligent algorithm, based on the push SCM, which uses a fuzzy neural network and a genetic algorithm to forecast the rate of demand, determine the material planning and select the optimal supplier. We test the proposed algorithm in a case study conducted in Iran.
Can spike coordination be differentiated from rate covariation?
Authors: Benjamin Staude, Stefan Rotter, Sonja Grün
Venue: Neural Computation (2008)

There has been a long and lively debate on whether rate covariance and temporal coordination of spikes, regarded as potential origins for correlations in cortical spike signals, fulfill different roles in the cortical code. In this context, studies that report spike coordination have often been criticized for ignoring fast nonstationarities, which would result in wrongly assigned spike coordination. The underlying hypothesis of this critique is that spike coordination is essentially identical to rate covariation, only on a shorter timescale. This study investigates the validity of this critique. We provide a decomposition for the cross-correlation function of doubly stochastic point processes, where each of the components corresponds precisely to the concepts of dependence under investigation. This allows us to correct the correlation function for rate effects, which implies that spike coordination and rate covariation are statistically separable concepts of dependence. Furthermore, we present direct and intuitive model implementations of the discussed concepts and illustrate that their difference is not a matter of timescale. Analysis of data generated by our models and analytical description of the relevant estimators reveals, however, that spike coordination dramatically influences the accuracy of rate covariance estimation. As a consequence, extreme parameter combinations can lead to situations where the concept of dependence cannot be identified empirically. However, for a wide range of parameters, the concept of dependence underlying a given data set can be identified regardless of its timescale.
Modeling Peer-to-Peer Botnets
Authors: Elizabeth Van Ruitenbeek, William H. Sanders
Venue: Proceedings of the 2008 Fifth International Conference on Quantitative Evaluation of Systems (2008)

Peer-to-peer botnets are a relatively new yet rapidly growing Internet threat. In the year since its introduction in January 2007, the Storm Worm peer-to-peer botnet has become the largest botnet on the Internet. Unlike previous botnets operating over IRC channels, the Storm Worm botnet uses a decentralized peer-to-peer network to communicate among the bots and to control their computing power. While a centralized control structure can be toppled relatively easily by finding and disconnecting the head, a decentralized control structure is much harder to dismantle. Given this reality, security researchers must find new ways to defend against peer-to-peer botnets. Toward that aim, we have developed a stochastic model of peer-to-peer botnet formation to provide insight on possible defense tactics. We use the stochastic model to examine how different factors impact the growth of the botnet. Simulation results from the model evaluate the effectiveness both of prevention measures and of detection and disinfection methods. In this way, the simulation results from our peer-to-peer botnet model provide guidance for the design of future anti-malware systems.
Non-Functional Requirements Size Measurement Method (NFSM) with COSMIC-FFP
Authors: Mohamad Kassab, Olga Ormandjieva, Maya Daneva, Alain Abran
Venue: Software Process and Product Measurement: International Conference, IWSM-Mensura 2007, Palma de Mallorca, Spain, November 5-8, 2007. Revised Papers (2007)

Non-functional requirements (NFRs) of software systems are an important source of uncertainty in effort estimation. Furthermore, quantitatively approaching NFR early in a project is difficult. This paper makes a step towards reducing the impact of uncertainty due to NFRs. It offers a new generic classification of the NFRs, and a NFR size measurement method (NFSM) that incorporates NFRs into the functional size quantification process. We chose the NFR framework as a vehicle to integrate NFRs into the requirements modeling process and to apply quantitative assessment procedures. Our solution proposal also rests on the functional size measurement method, COSMIC-FFP, adopted in 2003 as the ISO/IEC 19761 standard. We discuss the advantages of our approach and the open questions related to its design as well.
CycleTalk: Data Driven Design of Support for Simulation Based Learning
Authors: Carolyn P. Rosé, Rohit Kumar, Vincent Aleven, Allen Robinson, Chih Wu
Venue: International Journal of Artificial Intelligence in Education (2006)

In this article, we discuss the motivation for a novel style of tutorial dialogue system that emphasizes reflection in a simulation based exploratory learning environment called CyclePad, which was developed in order to offer students practice at design and optimization of thermodynamic cycles. We argue that while typical forms of exploration support offered in simulation based learning environments are meant to encourage the development of good exploration process skills, the instantiation of these typical forms of support in CyclePad are not sufficient, primarily because students don't choose to use them. We present a preliminary cognitive task analysis of design exploration tasks using CyclePad. Using this cognitive task analysis, we analyze data collected in two waves of formal data collection. Finally, we conclude with some system desiderata derived from our analysis as well as discussion of directions for continued experimental investigations related to tutor style.
An Evaluation of a Hybrid Language Understanding Approach for Robust Selection of Tutoring Goals
Authors: Carolyn Rosé, Kurt Vanlehn
Venue: International Journal of Artificial Intelligence in Education (2005)

In this paper, we explore the problem of selecting appropriate interventions for students based on an analysis of their interactions with a tutoring system. In the context of the WHY2 conceptual physics tutoring system, we describe CarmelTC, a hybrid symbolic/statistical approach for analysing conceptual physics explanations in order to determine which Knowledge Construction Dialogues (KCDs) students need for the purpose of encouraging them to include important points that are missing. We briefly describe our tutoring approach. We then present a model that demonstrates a general problem with selecting interventions based on an analysis of student performance in circumstances where there is uncertainty with the interpretation, such as with speech or text based natural language input, complex and error prone mathematical or other formal language input, graphical input (i.e., diagrams, etc.), or gestures. In particular, when student performance completeness is high, intervention selection accuracy is more sensitive to analysis accuracy, and increasingly so as performance completeness increases. In light of this model, we have evaluated our CarmelTC approach and have demonstrated that it performs favourably in comparison with the widely used LSA approach, a Naive Bayes approach, and finally a purely symbolic approach.
EpiSimdemics: an efficient algorithm for simulating the spread of infectious disease over large realistic social networks
Authors: Christopher L. Barrett, Keith R. Bisset, Stephen G. Eubank, Xizhou Feng, Madhav V. Marathe
Venue: Proceedings of the 2008 ACM/IEEE conference on Supercomputing (2008)

Preventing and controlling outbreaks of infectious diseases such as pandemic influenza is a top public health priority. We describe EpiSimdemics - a scalable parallel algorithm to simulate the spread of contagion in large, realistic social contact networks using individual-based models. EpiSimdemics is an interaction-based simulation of a certain class of stochastic reaction-diffusion processes. Straightforward simulations of such process do not scale well, limiting the use of individual-based models to very small populations. EpiSimdemics is specifically designed to scale to social networks with 100 million individuals. The scaling is obtained by exploiting the semantics of disease evolution and disease propagation in large networks. We evaluate an MPI-based parallel implementation of EpiSimdemics on a mid-sized HPC system, demonstrating that EpiSimdemics scales well. EpiSimdemics has been used in numerous sponsor defined case studies targeted at policy planning and course of action analysis, demonstrating the usefulness of EpiSimdemics in practical situations.