ppxscal/embedding_network huggingface dataset:
Clustered alignments of gene-expression time series data
Authors: Adam A. Smith, Aaron Vollrath, Christopher A. Bradfield, Mark Craven
Venue: Bioinformatics (2009)

Motivation: Characterizing and comparing temporal gene-expression responses is an important computational task for answering a variety of questions in biological studies. Algorithms for aligning time series represent a valuable approach for such analyses. However, previous approaches to aligning gene-expression time series have assumed that all genes should share the same alignment. Our work is motivated by the need for methods that identify sets of genes that differ in similar ways between two time series, even when their expression profiles are quite different. Results: We present a novel algorithm that calculates clustered alignments; the method finds clusters of genes such that the genes within a cluster share a common alignment, but each cluster is aligned independently of the others. We also present an efficient new segment-based alignment algorithm for time series called SCOW (shorting correlation-optimized warping). We evaluate our methods by assessing the accuracy of alignments computed with sparse time series from a toxicogenomics dataset. The results of our evaluation indicate that our clustered alignment approach and SCOW provide more accurate alignments than previous approaches. Additionally, we apply our clustered alignment approach to characterize the effects of a conditional Mop3 knockout in mouse liver. Availability: Source code is available at http://www.biostat.wisc.edu/~aasmith/catcode. Contact: aasmith@cs.wisc.edu
Computational hemodynamics in three-dimensional stenosed right coronary artery
Authors: Sandor I. Bernad, Tiberiu Barbat, Elena S. Bernad, Romeo Susan-Resiga
Venue: Proceedings of the 10th WSEAS international conference on Mathematics and computers in biology and chemistry (2009)

Transient three-dimensional laminar incompressible flow with rigid wall has been solved with a commercial 3D finite-volume numerical code. Instantaneous velocity vector and wall shear stress vector fields illustrate strong disturbed flow patterns. Implications of elevated surface contours of hemodynamic wall parameters, disturbed flows, and particle deposition sites are discussed in this paper. The purpose of this paper was to noninvasively assess hemodynamic parameters such as mass flow, wall shear stress, and wall pressure with computational fluid dynamics (CFD) in coronary arteroschlerosis using patient-specific data from computed tomography (CT) angiography.
Sleepy stack leakage reduction
Authors: Jun Cheol Park, Vincent J. Mooney
Venue: IEEE Transactions on Very Large Scale Integration (VLSI) Systems (2006)

Leakage power consumption of current CMOS technology is already a great challenge. International Technology Roadmap for Semiconductors projects that leakage power consumption may come to dominate total chip power consumption as the technology feature size shrinks. Leakage is a serious problem particularly for CMOS circuits in nanoscale technology. We propose a novel ultra-low leakage CMOS circuit structure which we call "sleepy stack." Unlike many other previous approaches, sleepy stack can retain logic state during sleep mode while achieving ultra-low leakage power consumption. We apply the sleepy stack to generic logic circuits. Although the sleepy stack incurs some delay and area overhead, the sleepy stack technique achieves the lowest leakage power consumption among known state-saving leakage reduction techniques, thus, providing circuit designers with new choices to handle the leakage power problem.
Integrating induction and deduction for finding evidence of discrimination
Authors: Dino Pedreschi, Salvatore Ruggieri, Franco Turini
Venue: Proceedings of the 12th International Conference on Artificial Intelligence and Law (2009)

Automatic Decision Support Systems (DSS) are widely adopted for screening purposes in socially sensitive tasks, including access to credit, mortgage, insurance, labor market and other benefits. While less arbitrary decisions can potentially be guaranteed, automatic DSS can still be discriminating in the socially negative sense of resulting in unfair or unequal treatment of people. We present a reference model for finding (prima facie) evidence of discrimination in automatic DSS which is driven by a few key legal concepts. First, frequent classification rules are extracted from the set of decisions taken by the DSS over an input pool dataset. Key legal concepts are then used to drive the analysis of the set of classification rules, with the aim of discovering patterns of discrimination. We present an implementation, called LP2DD, of the overall reference model integrating induction, through data mining classification rule extraction, and deduction, through a computational logic implementation of the analytical tools.
Broadcast Attacks against Lattice-Based Cryptosystems
Authors: Thomas Plantard, Willy Susilo
Venue: Proceedings of the 7th International Conference on Applied Cryptography and Network Security (2009)

In 1988, Håstad proposed the classical broadcast attack against public key cryptosystems. The scenario of a broadcast attack is as follows. A single message is encrypted by the sender directed for several recipients who have different public keys. By observing the ciphertexts only, an attacker can derive the plaintext without requiring any knowledge of any recipient's secret key. Håstad's attack was demonstrated on the RSA algorithm, where low exponents are used. In this paper, we consider the broadcast attack in the lattice-based cryptography, which interestingly has never been studied in the literature. We present a general method to rewrite lattice problems that have the same solution in one unique easier problem. Our method is obtained by intersecting lattices to gather the required knowledge. These problems are used in lattice based cryptography and to model attack on knapsack cryptosystems. In this work, we are able to present some attacks against both lattice and knapsack cryptosystems. Our attacks are heuristics. Nonetheless, these attacks are practical and extremely efficient. Interestingly, the merit of our attacks is not achieved by exploring the weakness of the trapdoor as usually studied in the literature, but we merely concentrate on the problem itself. As a result, our attacks have many security implications on most of the lattice-based or knapsack cryptosystems.
Traffic-aware dynamic spectrum access
Authors: Lei Yang, Lili Cao, Heather Zheng, Elizabeth Belding
Venue: Proceedings of the 4th Annual International Conference on Wireless Internet (2008)

Demand-driven spectrum allocation can drastically improve performance for WiFi access points struggling under increasing user demands. While their frequency agility makes cognitive radios ideal for this challenge, performing adaptive spectrum allocation is a complex and difficult process. In this work, we propose FLEX, an efficient spectrum allocation architecture that efficiently adapts to dynamic traffic demands. FLEX tunes network-wide spectrum allocation by access points coordinating with peers, minimizing network resets through local adaptations. Through detailed analysis and experimental evaluation, we show that FLEX converges quickly, provides users with proportional-fair spectrum usage and significantly outperforms existing spectrum allocation proposals.
A Novel Moving Object Tracking Method Using ICA-R
Authors: Xiaohong Ma, Lixin Wang, Yi Feng, Hualou Liang
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II (2009)

In this paper, the independent component analysis with reference (ICA-R) algorithm is proposed to track moving object. Taking the invariant moment of the detected object image as the reference signal, the moving object which shares the same characteristic as the reference can be extracted from the video frames through the ICA-R algorithm. Our algorithm can easily be extended to tackle the non-totally occlusion problem: the detected object image is first divided into either two or four parts and the unoccluded sub-parts can then be tracked through the ICA-R algorithm. As a result, the tracking of the whole moving object can be realized. The experimental results demonstrate the effectiveness of the proposed method.
Analyzing patterns of user content generation in online social networks
Authors: Lei Guo, Enhua Tan, Songqing Chen, Xiaodong Zhang, Yihong (Eric) Zhao
Venue: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (2009)

Various online social networks (OSNs) have been developed rapidly on the Internet. Researchers have analyzed different properties of such OSNs, mainly focusing on the formation and evolution of the networks as well as the information propagation over the networks. In knowledge-sharing OSNs, such as blogs and question answering systems, issues on how users participate in the network and how users "generate/contribute" knowledge are vital to the sustained and healthy growth of the networks. However, related discussions have not been reported in the research literature. In this work, we empirically study workloads from three popular knowledge-sharing OSNs, including a blog system, a social bookmark sharing network, and a question answering social network to examine these properties. Our analysis consistently shows that (1) users' posting behavior in these networks exhibits strong daily and weekly patterns, but the user active time in these OSNs does not follow exponential distributions; (2) the user posting behavior in these OSNs follows stretched exponential distributions instead of power-law distributions, indicating the influence of a small number of core users cannot dominate the network; (3) the distributions of user contributions on high-quality and effort-consuming contents in these OSNs have smaller stretch factors for the stretched exponential distribution. Our study provides insights into user activity patterns and lays out an analytical foundation for further understanding various properties of these OSNs.
Observational Analysis and Retrieval of Falling Snow- Using Satellite Data at High Microwave Frequencies
Authors: Yoo-Jeong Noh
Venue:  (2007)

In the mid/high latitudes during cold seasons, a substantial portion of precipitation falls in the form of snow. Falling snow has been one of the last great unkonwns for the hydrological water cycle and radiation budget analyses in climate and weather researches. Given their extensive spatial and temporal coverage, satellite-based remote sensing methods are uniquely suited for global snowfall observations. However, in spite of tremendous developments in meteological satellite technology over the past several decades, developing satellite remote sensing techniques to provide accurate snowfall estimations is still very challenging work. The main focus of this work is to develop a snowfall retrieval algorithm based on Bayes' theorem using high frequency microwave satellite data. This study also includes analyses of various observations from surface/aircraft field experments and radiative transfer modeling considering realistic nonspherical shapes of ice/snow particles, which will be very helpful to improve our knowledge and techniques of satellite remote sesning for wintertime precipitation measurements. The book is addressed to scientists and researchers in Satellite Meteorology, Climate/Weather forecasting, and Hydrology. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
Comparing trust mechanisms for monitoring aggregator nodes in sensor networks
Authors: Oly Mistry, Anil Gürsel, Sandip Sen
Venue: Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2 (2009)

Sensor nodes are often used to collect data from locations inaccessible or hazardous for humans. As they are not under normal supervision, these nodes are particularly susceptible to physical damage or remote tampering. Generally, a hierarchical data collection scheme is used by the sensors to report data to the base station. It is difficult to precisely identify and eliminate a tampered node in such a data collecting hierarchy. Most security schemes for sensor networks focuses on developing mechanism for nodes located higher in the hierarchy to monitor those located at lower levels. We propose a complementary mechanism with which the nodes at lower levels can monitor their parents in the hierarchy to detect malicious behavior. Every node maintains a reputation value of its parent and updates this at the end of every data reporting cycle. We propose a novel combination of statistical testing techniques and existing reputation management and reinforcement learning schemes to manage the reputation of a parent node. The probability that the parent node is malicious is calculated using various combination of the Q-learning algorithm and the &beta;-Reputation scheme. The input to the &beta;-Reputation scheme is a history of boolean events consisting of correct or erroneous data reporting events by the parent node. The boolean events are generated at each data reporting period using statistical tests. Our approach can be viewed as a mechanism composed of different modules for the detection of a malicious event, interpretation of the malicious event and updating node reputation value based on the interpretation. We have created different versions of our system by varying these components. We compared the effectiveness of these alternative designs in detecting different types of malicious behavior in sensor networks.
Signaling for Multimedia Conferencing in Stand-Alone Mobile Ad Hoc Networks
Authors: Chunyan Fu, Roch H. Glitho, Ferhat Khendek
Venue: IEEE Transactions on Mobile Computing (2009)

Mobile ad hoc networks (MANETs) are infrastructure-less and can be set up anywhere, anytime. They can host a wide range of applications in rescue operations, military, private, and commercial settings. Multimedia conferencing is the basis of a wealth of "killer” applications that can be deployed in MANETs. Some examples are audio/video conferencing, multiplayer games, and online public debating. Signaling is the nerve center of multimedia conferences—it establishes, modifies, and tears down conferences. This paper focuses on signaling for multimedia conferences in MANETs. We review the state of the art and propose a novel architecture based on application-level clusters. Our validation employed SIP as the implementation technology and OPNET as our simulation tool. Our clusters are constructed dynamically and the nodes that act as cluster heads are elected based on their capabilities. The capabilities are published and discovered using a simple application-level protocol. The architectural principles and the clustering operations are discussed. Our SIP-based implementation is also presented along with the performance evaluation.
Towards support in building qualitative knowledge models
Authors: Vania Bessa Machado, Roland Groen, Bert Bredeweg
Venue: Proceeding of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology (2005)

Qualitative Reasoning (QR) formalisms provide ontological primitives for capturing conceptual knowledge. Recently QR-based diagrammatic tools are being developed to support learners in creating concept maps as means to acquire such knowledge. However, QR formalisms are complex which hampers their usability. While other approaches address this by simplifying the formalism they use, we seek the solution in providing a set of agents that can support the learner. Based on a previously reported study on using QR modelling tools, we have developed a multi-agent approach to support the QR modelling process. The agents provide different kinds of help, such as general information on the formalisms and tailored feedback addressing the individual needs of a learner. Agents thus have scope, provide context-sensitive help, and are personified according to the type of support they provide. An evaluation study shows that the help-system is well accepted by learners.
Design and Implementation of a Scalable General High Performance Remote Sensing Satellite Ground Processing System on Performance and Function
Authors: Jingshan Li, Dingsheng Liu
Venue: Proceedings of the 9th International Conference on Computational Science (2009)

This paper discusses design and implementation of a scalable high performance remote sensing satellite ground processing system using a variety of advanced hardware and software application technology on performance and function. These advanced technologies include the network, parallel file system, parallel programming, job schedule, workflow management, design patterns,etc, which make performance and function of remote sensing satellite ground processing system scalable enough to fully meet the high performance processing requirement of multi-satellite, multi-tasking, massive remote sensing satellite data. The "beijing-1" satellite remote sensing ground processing system is introduced as an instance.
A study on traffic modeling in ubiquitous cloud
Authors: Shuji Kawasaki, Masakazu Higuchi, Kento Hirano, Hitomi Murakami
Venue: Proceedings of the 8th Wseas international conference on Telecommunications and informatics (2009)

The Ubiquitous Cloud is a concept of large-scale information service network as a social infra-structure. It is featured by real-world context information extraction, user information profiling and self-cofiguration/-control of network. The objective of the research is to evaluate the network traffic theoretically and thus give a guideline of network design. In this paper, we especially consider those traffic factors that are related to movement of mobile users and rea-time services, and thus discuss some necessary requirements for the network specification.
A learning style classification mechanism for e-learning
Authors: Yi-Chun Chang, Wen-Yan Kao, Chih-Ping Chu, Chiung-Hui Chiu
Venue: Computers Education (2009)

With the growing demand in e-learning, numerous research works have been done to enhance teaching quality in e-learning environments. Among these studies, researchers have indicated that adaptive learning is a critical requirement for promoting the learning performance of students. Adaptive learning provides adaptive learning materials, learning strategies and/or courses according to a student's learning style. Hence, the first step for achieving adaptive learning environments is to identify students' learning styles. This paper proposes a learning style classification mechanism to classify and then identify students' learning styles. The proposed mechanism improves k-nearest neighbor (k-NN) classification and combines it with genetic algorithms (GA). To demonstrate the viability of the proposed mechanism, the proposed mechanism is implemented on an open-learning management system. The learning behavioral features of 117 elementary school students are collected and then classified by the proposed mechanism. The experimental results indicate that the proposed classification mechanism can effectively classify and identify students' learning styles.
System performance of self-organizing network algorithm in WiMAX femtocells
Authors: Hui Zeng, Chenxi Zhu, Wei-Peng Chen
Venue: Proceedings of the 4th Annual International Conference on Wireless Internet (2008)

Femtocell is an emerging idea in the next generation wireless networks to enhance the indoor coverage. One of challenges of femtocell is how to configure femto base stations (fBSs) in an autonomous and optimal manner. In this paper, we evaluate performance of the WiMAX (802.16e) femtocell systems, in terms of the network coverage and the system capacity. Particularly we are interested in the performance of Mobile Stations (MSs) in the indoor environment. The single-segment systems, where all fBSs are assigned the same frequency segment, are considered at first. Through extensive simulation, we investigate the effect of the TX power at fBSs, cell radius and loading factor. Furthermore, several heuristic frequency assignment schemes are proposed and compared along with the random assignment scheme that randomly assigns one of three segments for each fBS. Finally, Least Interference Power (LIP) scheme, which for an fBS picks the segment with the least total interference power received at this fBS, is recommended due to its complete locality, practical metric, the least computation and close-to-optimum performance.
Simulations of active vibration isolation with fuzzy logic device
Authors: Silviu Nastac
Venue: Proceedings of the 10th WSEAS international conference on Mathematical and computational methods in science and engineering (2008)

The methods used at present for vibration isolation provided from different sources - technological, human, random activities - leaves from the basic hypothesis of the isolator working parameters perfect determination. Comparing with the human practice, when the protective actions have done according to the self-senses and the subjective and potential imminence of high admissible limit passing through, the fixed and determined algorithms for vibration isolation drivers seems to be more complicated at the same performances level. Hereby, the fuzzy logic theory and its algorithms could be solve this apparent problem. And this is possible by using of fuzzy logic basic controller on active vibration isolation driver. The initial rules for fuzzy controller could be obtained from the human operator experiences or from initial tests with manual controller of isolation driver. Then it could be possible to develop a neuronal network which assure the necessary adjustments. Or, based on the working observations, it could be tunning the fuzzy controller manually. As a final remark, it could be said that the most difficult on a fuzzy logic utilization is the initial settlement of the fuzzification rules. In this paper it is briefly described a basic way to fuzzy logic unit utilization on vibration isolation device driver development.
Macro-level software evolution: a case study of a large software compilation
Authors: Jesus M. Gonzalez-Barahona, Gregorio Robles, Martin Michlmayr, Juan José Amor, Daniel M. German
Venue: Empirical Software Engineering (2009)

Software evolution studies have traditionally focused on individual products. In this study we scale up the idea of software evolution by considering software compilations composed of a large quantity of independently developed products, engineered to work together. With the success of libre (free, open source) software, these compilations have become common in the form of `software distributions', which group hundreds or thousands of software applications and libraries into an integrated system. We have performed an exploratory case study on one of them, Debian GNU/Linux, finding some significant results. First, Debian has been doubling in size every 2 years, totalling about 300 million lines of code as of 2007. Second, the mean size of packages has remained stable over time. Third, the number of dependencies between packages has been growing quickly. Finally, while C is still by far the most commonly used programming language for applications, use of the C++, Java, and Python languages have all significantly increased. The study helps not only to understand the evolution of Debian, but also yields insights into the evolution of mature libre software systems in general.
Dejean's conjecture holds for n≥30
Authors: James Currie, Narad Rampersad
Venue: Theoretical Computer Science (2009)

We extend Carpi's results by showing that Dejean's conjecture holds for n>=30.