ppxscal/embedding_network huggingface dataset:
A scalable assistant librarian: hierarchical subject classification of books
Authors: Steven P. Crain, Jian Huang, Hongyuan Zha
Venue: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval (2008)

In this paper, we discuss our work in progress towards a scalable hierarchical classification system for books using the Library of Congress subject hierarchy. We examine the characteristics of this domain which make the problem very challenging, and we look at several appropriate performance measurements. We show that both Hieron and Hierarchical Support Vector Machines perform moderately well.
Performance and Availability Tradeoffs in Replicated File Systems
Authors: Jiaying Zhang, Peter Honeyman
Venue: Proceedings of the 2008 Eighth IEEE International Symposium on Cluster Computing and the Grid (2008)

Replication is a key technique for improving fault tolerance but can introduce considerable performance overhead under some circumstances. To explore the tradeoff between performance and failure resilience, we develop a calculus that takes into consideration the I/Ocharacteristics of applications and failure behavior of distributed storage nodes. With the developed evaluation model, we then prescribe a file system replication strategy that maximizes the utilization of computational resources for long-running and compute-intensive Grid applications.
A gradient-based reinforcement learning approach to dynamic pricing in partially-observable environments
Authors: David Vengerov
Venue: Future Generation Computer Systems (2008)

As more companies are beginning to adopt the e-business model, it becomes easier for buyers to compare prices at multiple sellers and choose the one that charges the best price for the same item or service. As a result, the demand for the goods of a particular seller is becoming more unstable, since other sellers are regularly offering discounts that attract large fractions of buyers. Therefore, it becomes more important for each seller to switch from static to dynamic pricing policies that take into account observable characteristics of the current demand and the state of the seller's resources. This paper presents a Reinforcement Learning algorithm that can tune parameters of a seller's dynamic pricing policy in a gradient direction (thus converging to the optimal parameter values that maximize the revenue obtained by the seller) even when the seller's environment is not fully observable. This algorithm is evaluated using a simulated Grid market environment, where customers choose a Grid Service Provider (GSP) to which they want to submit a computing job based on the posted price and expected delay information at each GSP.
Jetmax: Scalable max-min congestion control for high-speed heterogeneous networks
Authors: Yueping Zhang, Derek Leonard, Dmitri Loguinov
Venue: Computer Networks: The International Journal of Computer and Telecommunications Networking (2008)

Recent surge of interest towards congestion control that relies on single-link feedback (e.g., XCP, RCP, MaxNet, EMKC, VCP), suggests that such systems may offer certain benefits over traditional models of additive packet loss. Besides topology-independent stability and faster convergence to efficiency/fairness, it was recently shown that any stable single-link system with a symmetric Jacobian tolerates arbitrary fixed, as well as time-varying, feedback delays. Although delay-independence is an appealing characteristic, the EMKC system developed in exhibits undesirable equilibrium properties and slow convergence behavior. To overcome these drawbacks, we propose a new method called JetMax and show that it admits a low-overhead implementation inside routers (three additions per packet), overshoot-free transient and steady state, tunable link utilization, and delay-insensitive flow dynamics. The proposed framework also provides capacity-independent convergence time, where fairness and utilization are reached in the same number of RTT steps for a link of any bandwidth. Given a 1mb/s, 10gb/s, or googol (10^1^0^0) bps link, the method converges to within 1% of the stationary state in six RTTs. We finish the paper by comparing JetMax's performance to that of existing methods in ns2 simulations and discussing its Linux implementation.
MEMS based cardiac bio-enzyme detection for the acute myocardial syndrome recognition
Authors: Ion Stiharu, Subhash Rakheja, Muthukumaran Packirisamy, Amirstar Jeetender
Venue: Proceedings of the 9th WSEAS International Conference on Systems (2005)

Early detection through enzymatic identification and confirmation is essential for diagnosis and prevention as in the case of Acute Myocardial Syndrome (AMS). Biochemical markers continue to be an important clinical tool for the enzymatic detection. The advent of Micro Electro Mechanical Systems (MEMS) devices can enable the use of various microstructures for the detection of enzymes. In this study, the concept of MEMS is applied for the detection of enzyme reaction, in which micro-cantilevers undergo changes in mechanical behavior that can be optically detected when enzyme molecules react on their surface, inducing geometric modifications. This paper presents the static behavior of micro-cantilevers under antigen-antibody reaction of rabbit skeletal muscle troponin C (TnC) and bee venom melittin (ME). The same troponin C is detected in the blood stream in humans a couple of hours form the acute myocardial infarction. The reported experimental results provide valuable information that will be useful in the development of MEMS sensors for enzymatic detection. The surface stress produced due to enzyme reactions results in the bending of cantilevers as similar to the influencing of laminar stress in the cantilevers. A possible design of such a system is provided in the paper.
Aggregate and Separate of Signatures in Wireless Network
Authors: Guoyan Zhang, Shaohui Wang
Venue: Proceedings of the 22nd International Conference on Advanced Information Networking and Applications - Workshops (2008)

Wireless communications offer organizations and users many benefits such as portability and flexibility, but the messages transfer is limited by the power and the bandwidth. So the compactness of messages is necessary. One of the compactness is aggregate signatures, which is a single short string that convinces any verifier that for all 1 ≤ i ≤ n, signer s_i signs message M_i, where the n signers and n messages may all be distinct. Additionally in some especial cases, it is required to induce the single signature from the aggregate signatures. Then the separating of aggregate signatures is an interesting focus. In this paper, we first present an efficient id-based aggregate signature whose security is based on RSA assumption not on the rather new anduntested assumptions related to bilinear maps. Then the notion of separation of aggregate signature is introduced and two concrete aggregate signature schemes that can be separated into single signature are presented.
Using support vector machines in data mining
Authors: Richard A. Wasniowski
Venue: Proceedings of the 4th WSEAS International Conference on Systems Theory and Scientific Computation (2004)

Multivariate data analysis techniques have the potential to improve data analysis. Support Vector Machines (SVS) are a recent addition to the family of multivariate data analysis. A brief introduction to the SVM Vector Machines technique is followed by an outline of the practical application.
The Mufflers modeling by transfer matrix method
Authors: Mihai Bugaru, Ovidiu Vasile, Nicolae Enescu
Venue: Proceedings of the 10th WSEAS International Conference on APPLIED MATHEMATICS (2006)

Mufflers are widely used for exhaust noise attenuation in vehicles, machinery and other industrial elements. Modeling procedures for accurate performance prediction had led to the development of new methods for practical muffler components in design. Plane wave based models such as the transfer matrix method (TMM) can offer fast initial prototype solutions for muffler designers. In the present paper the authors present an overview of the principles of TMM for predicting the transmission loss (TL) of a muffler. The predicted results agreed in some limits with the experimental data published in literature.
Proceedings of the 2007 Asian technology information program's (ATIP's) 3rd workshop on High performance computing in China: solution approaches to impediments for high performance computing
Venue: Conference on High Performance Networking and Computing (2007)

The Asian Technology Information Program (ATIP) will soon begin its fourteenth year of operations throughout Asia. ATIP is recognized as a premier source of information on Asia's most important scientific and technological trends and developments. ATIP covers a broad set of S&T topics across the Asian region, and is particularly experienced in reporting on High Performance Computing (HPC). ATIP provides timely information on Asian HPC initiatives and accomplishments and has published more than 400 reports on HPC in Asia. ATIP maintains offices and/or operations throughout the Asian region, including an office in Beijing China, and is an established activity in the Asian S&T community. HPC is a topic of particular importance in Asia, and specifically in China. ATIP has reported on China's HPC activities for more than a decade and is well known throughout the Chinese S&T community. ATIP conducted two highly successful China HPC Workshops in conjunction with the Supercomputing Conference in 2003 and in 2005. The primary goals of the HPC Workshops are to enhance Western understanding of developments in China and to foster collaborations between Chinese and Western research communities. With support from the United States National Science Foundation (NSF) ATIP's held it's 3rd Chinese HPC Workshop, "High Performance Computing in China: Solution Approaches to Impediments for High Performance Computing," in conjunction with the Supercomputing 2007 Conference and Exhibition (SC07) held in Reno, Nevada (US) from November 11-16, 2007. Additional corporate and government sponsors of the workshop included the US Office of Naval Research Global (ONRG), Sun Microsystems, Microsoft, SGI, Clearspeed, IDC, Dawning, and Shanghai Supercomputer Center. Participants of the 3rd HPC Workshop were given the opportunity to have detailed discussions with key members of the international HPC community. All academic papers submitted to the 3rd HPC Workshop were reviewed by Professor Hai Jin, Dean of the School of Computer Science and Technology at Huazhong University of Science and Technology, by Dr. Ninghui SUN, Director of the System Architecture Division at the Institute of Computing Technology, Chinese Academy of Sciences, and by Mr. Yu ZENG, Secretary General of China's High Performance Computer Standardization Committee.
Report on the Tenth ACM International Workshop on Data Warehousing and OLAP (DOLAP'07)
Authors: Torben Bach Pedersen, Il-Yeol Song
Venue: ACM SIGMOD Record (2008)

This paper presents an overview of DOLAP'07, the 10th ACM International Workshop on Data Warehousing and OLAP, held on November 9, 2007 in Lisbon, Portugal in conjunction with CIKM'07, the ACM 16th Conference on Information and Knowledge Management.
An Evaluation of In-Advance Bandwidth Scheduling Algorithms for Connection-Oriented Networks
Authors: Eun-Sung Jung, Yan Li, Sanjay Ranka, Sartaj Sahni
Venue: Proceedings of the The International Symposium on Parallel Architectures, Algorithms, and Networks (2008)

Several bandwidth management systems have been developed to reserve, in advance, dedicated connections for high-performance applications. We describe the in-advance reservation capabilities of these systems as well as the bandwidth scheduling and path computation algorithms used. An analytical and experimental evaluation of these algorithms also is provided. Our experiments indicate that for the fixed-slot problem, the minimum-hop feasible pathalgorithm proposed by us in previous research maximizes network utilization for large networks while the dynamic adaptive feasible path algorithm proposed in this paper does this for small networks.
On routing in lossy wireless networks with realistic channel models
Authors: Rong Zheng
Venue: Proceeding of the 1st ACM international workshop on Foundations of wireless ad hoc and sensor networking and computing (2008)

This paper concerns routing performance in lossy wireless networks with Rayleigh fading channel. We investigate the efficiency of a few representative deterministic and opportunistic routing protocols under light-loaded traffic conditions. This work deviates from existing work that assumes idealistic packet reception, and is thus of practical relevant. Our study shows that opportunistic routing significantly outperforms its deterministic counterparts in average effective progress in a single hop transmission. In deterministic routing schemes, tuning of parameters based on node density is critical to achieve optimal performance.
Multiresolution Rotation-Invariant Texture Classification Using Feature Extraction in the Frequency Domain and Vector Quantization
Authors: Antonella Di Lillo, Giovanni Motta, James A. Storer
Venue: Proceedings of the Data Compression Conference (2008)

Texture identification can be a key component in content based image retrieval systems. Although formal definitions of texture vary in the literature, it is commonly accepted that textures are naturally extracted and recognized as such by the human visual system, and that this analysis is performed in the frequency domain. The vast majority of the methods proposed in the literature provide good characterization of texture in controlled environments. In order to better describe textures, features must capture the nature of the texture, invariant to rotational, shift, and scale transformations. In this work, a rotation-invariant feature extraction technique is presented, extending our previous work [6], which was not rotation-invariant. The technique demonstrated here similarly employs a discrete Fourier transform in the polar space followed by a dimensionality reduction, but achieves rotational invariance by incorporating an additional transform into the process. Selected features are then processed with vector quantization for the classification of textures. Experiments performed on a standard test suite show that this method improves over previous methods.
Image Compression by Visual Pattern Vector Quantization (VPVQ)
Authors: Feng Wu, Xiaoyan Sun
Venue: Proceedings of the Data Compression Conference (2008)

This paper proposes a new image compression scheme by introducing visual patterns to nonlinear interpolative vector quantization (IVQ). Input images are first distorted by a generic down-sampling so that some details are removed before compression. Then, the distorted images are compressed lossly by traditional image coding scheme and transmitted to the decoder. In the decoder side, VQ indices are extracted from the decoded images to reproduce the removed details from a pre-trained codebook. One of main contributions in this paper is, we introduce visual patterns on designing the codebook, where only removed details that contain visual patterns and their original counterparts as pairs are trained. Experimental results show: (1) visual pattern blocks are easy to form clusters than original blocks; (2) the proposed scheme achieves much better performance over JPEG in terms of visual quality and PSNR.
Shape modeling and matching in identifying 3D protein structures
Authors: Sasakthi Abeysinghe, Tao Ju, Matthew L. Baker, Wah Chiu
Venue: Computer-Aided Design (2008)

In this paper, we describe a novel geometric approach in the process of recovering 3D protein structures from scalar volumes. The input to our method is a sequence of @a-helices that make up a protein, and a low-resolution protein density volume where possible locations of @a-helices have been detected. Our task is to identify the correspondence between the two sets of helices, which will shed light on how the protein folds in space. The central theme of our approach is to cast the correspondence problem as that of shape matching between the 3D volume and the 1D sequence. We model both shapes as attributed relational graphs, and formulate a constrained inexact graph matching problem. To compute the matching, we developed an optimal algorithm based on the A*-search with several choices of heuristic functions. As demonstrated in a suite of synthetic and authentic inputs, the shape-modeling approach is capable of identifying helix correspondences in noise-abundant volumes at high accuracy with minimal or no user intervention.
A New Object-Based System for Fractal Video Sequences Compression
Authors: Kamel Belloulata, Shiping Zhu
Venue: Proceedings of the Data Compression Conference (2008)

A novel object-based fractal monocular and stereo video compression scheme with quadtree-based motion and disparity compensation is proposed in this paper. Fractal coding is adopted and each object is encoded independently by a prior image segmentation alpha plane, which is defined exactly as in MPEG-4. The first n frames of right video sequence are encoded by using the Circular Prediction Mapping (CPM) and the remaining frames are encoded by using the Non Contractive Interframe Mapping (NCIM). The CPM and NCIM methods accomplish the motion estimation/compensation of right video sequence. According to the different coding or user requirements, the spatial correlations between the left and right frames can be explored by partial or full affine transformation quadtree-based disparity estimation/ compensation, or simply by applying CPM/NCIM on left video sequence.
Getting Chipped: To Ban or Not to Ban
Authors: Maya Gadzheva
Venue: Information and Communications Technology Law (2007)

Human Radio Frequency Identification (RFID) implants are currently being marketed as a way to limit access to secure areas or as anti-kidnapping devices, as well as means to track and quickly identify in emergency situations individuals with serious medical conditions or mental disabilities or to serve as cashless payment devices with a wave of the hand. While in certain instances, such as in some medical applications, this technology can have positive and benign effects there are also serious privacy implications. Although at present, technological and financial challenges prevent the wide take-up of these highly controversial devices, privacy advocates fear the technology might be abused by governments and big corporations and used to constantly locate and profile people. A detailed critique of the complex privacy, security, ethical and health implications in the light of this emerging technology is still lacking and this article attempts to contribute to this process.
Compensation technique for DC and transient instability of thin film transistor circuits for large-area devices
Authors: G. Reza Chaji, Nader Safavian, Arokia Nathan
Venue: Analog Integrated Circuits and Signal Processing (2008)

A reliable driving scheme that can compensate for the inherent instability of hydrogenated amorphous silicon (a-Si:H) thin film transistors (TFTs) is essential for implementation of large-area devices including displays and sensor arrays for bio-imaging applications. In particular, for high precision and high-resolution devices, the technique should be accurate and fast. A new driving scheme is presented that enables control of the DC and transient shift in the threshold voltage (V T ) and gate voltage of drive/amplifier TFT, while fulfilling the timing requirements for the different applications. The transient shift in the gate voltage has been known to contribute as much as 10% error in controlling the DC shift in the V T whereas it is less than 0.5% for the driving scheme presented here.
Theoretical Advantages of Lenient Learners: An Evolutionary Game Theoretic Perspective
Authors: Liviu Panait, Karl Tuyls, Sean Luke
Venue: The Journal of Machine Learning Research (2008)

This paper presents the dynamics of multiple learning agents from an evolutionary game theoretic perspective. We provide replicator dynamics models for cooperative coevolutionary algorithms and for traditional multiagent Q-learning, and we extend these differential equations to account for lenient learners: agents that forgive possible mismatched teammate actions that resulted in low rewards. We use these extended formal models to study the convergence guarantees for these algorithms, and also to visualize the basins of attraction to optimal and suboptimal solutions in two benchmark coordination problems. The paper demonstrates that lenience provides learners with more accurate information about the benefits of performing their actions, resulting in higher likelihood of convergence to the globally optimal solution. In addition, the analysis indicates that the choice of learning algorithm has an insignificant impact on the overall performance of multiagent learning algorithms; rather, the performance of these algorithms depends primarily on the level of lenience that the agents exhibit to one another. Finally, the research herein supports the strength and generality of evolutionary game theory as a backbone for multiagent learning.
Computational complexity investigations for high dimensional model representation algorithms used in multivariate interpolation problems
Authors: M. Alper Tunga, Metin Demiralp
Venue: Proceedings of the 12th WSEAS International Conference on Applied Mathematics (2007)

In multivariate interpolation problems, increase in both the number of independent variables of the sought function and the number of nodes appearing in the data set cause computational and mathematical difficulties. It may be a better way to deal with less variate partitioned data sets instead of an N-dimensional data set in a multivariate interpolation problem. New algorithms such as High Dimensional Model Representation (HDMR), Generalized HDMR, Factorized HDMR, Hybrid HDMR are developed or rearranged for these types of problems. Up to now, the efficiency of the methods in mathematical sense were discussed in several papers. In this work, the efficiency of these methods in computational sense will be discussed. This investigation will be done by using several numerical implementations.
The Mobile Sensing Platform: An Embedded Activity Recognition System
Authors: Tanzeem Choudhury, Gaetano Borriello, Sunny Consolvo, Dirk Haehnel, Beverly Harrison, Bruce Hemingway, Jeffrey Hightower, Predrag "Pedja" Klasnja, Karl Koscher, Anthony LaMarca, James A. Landay, Louis LeGrand, Jonathan Lester, Ali Rahimi, Adam Rea, Danny Wyatt
Venue: IEEE Pervasive Computing (2008)

The Mobile Sensing Platform (MSP) is a small-form-factor wearable device designed for embedded activity recognition. The MSP aims broadly to support context-aware ubiquitous computing applications. It incorporates multimodal sensing, data processing and inference, storage, all-day battery life, and wireless connectivity into a single 4 oz (115 g) wearable unit. Several design iterations and real-world deployments over the last four years have identified a set of core hardware and software requirements for a mobile inference system. This article presents findings and lessons learned in the course of designing, improving and using this system. This article is part of a special issue on activity-based computing.