ppxscal/embedding_network huggingface dataset:
On Format-Compliant Iterative Encryption of JPEG2000
Authors: Thomas Stutz, Andreas Uhl
Venue: Proceedings of the Eighth IEEE International Symposium on Multimedia (2006)

Format-compliant encryption of JPEG2000 has attracted researchers for several years. Most benefits of format-compliant encryption result from the preservation of code-stream features, such as scalability. The possibility of reducing the complexity of encryption and to realize transparent encryption schemes are among the additional features of format-compliant selective encryption. Wu and Deng have proposed a format-compliant iterative encryption scheme for JPEG2000 on a CCP basis. In this paper we discuss whether it is possible to extend this approach to packet bodies and give an exact formula for the expected computational effort. The theoretical results are cross-verified by an experimental survey.
Interval Data Clustering with Applications
Authors: Wei Peng, Tao Li
Venue: Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence (2006)

Interval data is described by a group of variables, each of which contains a range of continuous values instead of the traditional single continuous or discrete value. Traditional data analysis simply replaces each interval by its representative (e.g., center or mean) and ignores the structure information of intervals. In this paper, we study the problem of clustering interval data using the modified or extended interval data dissimilarity measures. Our contributions are two-fold. First, we discuss various approaches for measuring the dissimilarities/distances between interval data, investigate the relations among them, and present a comprehensive experimental study on clustering interval data. We show that the extended interval data clustering achieves better performance than traditional ones and produces more meaningful and explanatory results. Second, we propose a two-stage approach for clustering interval data by exploiting the relations between the traditional distances and the modified distances. Experimental results show the effectiveness of our approach.
TCP performance re-visited
Authors: A. P. Foong, T. R. Huff, H. H. Hum, J. R. Patwardhan
Venue: Proceedings of the 2003 IEEE International Symposium on Performance Analysis of Systems and Software (2003)

Detailed measurements and analyses for the Linux-2.4 TCP stack on current adapters and processors are presented. We describe the impact of CPU scaling and memory bus loading on TCP performance. As CPU speeds outstrip I/O and memory speeds, many generally accepted notions of TCP performance begin to unravel. In-depth examinations and explanations of previously held TCP performance truths are provided, and we expose cases where these assumptions and rules of thumb no longer hold in modern-day implementations. We conclude that unless major architectural changes are adopted, we would be hard-pressed to continue relying on the 1 GHz/1 Gbps rule of thumb.
Automated assessment and experiences of teaching programming
Authors: Colin A. Higgins, Geoffrey Gray, Pavlos Symeonidis, Athanasios Tsintsifas
Venue: Journal on Educational Resources in Computing (JERIC) (2005)

This article reports on the design, implementation, and usage of the CourseMarker (formerly known as CourseMaster) courseware Computer Based Assessment (CBA) system at the University of Nottingham. Students use CourseMarker to solve (programming) exercises and to submit their solutions. CourseMarker returns immediate results and feedback to the students. Educators author a variety of exercises that benefit the students while offering practical benefits. To date, both educators and students have been hampered by CBA software that has been constructed to assess text-based or multiple-choice answers only. Although there exist a few CBA systems with some capability to automatically assess programming coursework, none assess Java programs and none are as flexible, architecture-neutral, robust, or secure as the CourseMarker CBA system.
Increasing deadline-constrained throughput in multi-channel ALOHA networks via non-stationary multiple-power-level transmission policies
Authors: Yitzhak Birk, Yoram Revah
Venue: Wireless Networks (2005)

Multi-channel Slotted ALOHA is currently used primarily in satellite-based networks for transaction processing (e.g., credit card payments at cash registers). For these applications, maximization of attainable throughput while adhering to a maximum-delay constraint with a required probability reflects both the user's requirements, the network owner's desires, and the non-deterministic nature of ALOHA. This paper explores the judicious use of multiple power levels as a priority mechanism; e.g., the last transmission attempt uses higher power. It focuses on the practical and relevant range of three transmission attempts, up to three power levels, and maximum values of the permissible probability of missing the deadline (Pe) in the range 10-5 < Pe < 102. Our scheme increases the attainable delay-constrained throughput by 84-355% (two power levels) and 140-762% (three levels) over classical slotted ALOHA. An optimized combination of multiple copies and two power levels outperforms classical slotted ALOHA by 144-1240%. The smaller Pe, the greater the improvement. The benefit of our schemes is thus dramatic, and far exceeds the contribution of power capture to (unconstrained) capacity of ALOHA.
Feature Selection for Evaluating Fluorescence Microscopy Images in Genome-Wide Cell Screens
Authors: Vassili Kovalev, Nathalie Harder, Beate Neumann, Michael Held, Urban Liebel, Holger Erfle, Jan Ellenberg, Roland Eils, Karl Rohr
Venue: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (2006)

We investigate different approaches for efficient feature space reduction and compare different methods for cell classification. The application context is the development of automatic methods for analysing fluorescence microscopy images with the goal to identify those genes that are involved in the mitosis of human cells (cell division). We distinguish four cell classes comprising interphase cells, mitotic cells, apoptotic cells, and cells with clustered nuclei. Feature space reduction was performed using the Principal Component Analysis and Independent Component Analysis methods. Six classification methods were examined including unsupervised clustering algorithms such as K-means, Hard Competitive Learning, and Neural Gas as well as Hierarchical Clustering, Support Vector Machines, and Random Forests classifiers. Detailed results on the cell image classification accuracy and computational efficiency achieved using different feature sets and different classification methods are reported.
Introduction to AVS audio
Authors: Hao-Jun Ai, Shui-Xian Chen, Rui-Min Hu
Venue: Journal of Computer Science and Technology (2006)

This paper describes a general audio coding algorithm which has been recently standardized by AVS, China. The algorithm is based on a perceptual coding technique. The codec delivers near CD-quality audio at 128kb/s. This paper describes the coder structure in detail and discusses the reasons for specific design methods. A summary of the subjective test results are presented for the prototype codec. Comparison Mean Opinion Score (CMOS) test indicates that the quality of the AVS audio coder is comparable with MPEG Layer-3 audio coder. A real-time decoder was used for the characterization test, which is based on a 16-bit fixed-point DSP. The performance of the DSP solution was demonstrated, including computational complexity and storage characteristics.
Two-level mapping based cache index selection for packet forwarding engines
Authors: Kaushik Rajan, R. Govindarajan
Venue: Proceedings of the 15th international conference on Parallel architectures and compilation techniques (2006)

Packet forwarding is a memory-intensive application requiring multiple accesses through a trie structure. The efficiency of a cache for this application critically depends on the placement function to reduce conflict misses. Traditional placement functions use a one-level mapping that naively partitions trie-nodes into cache sets. However, as a significant percentage of trie nodes are not useful, these schemes suffer from a non-uniform distribution of useful nodes to sets. This in turn results in increased conflict misses. Newer organizations such as variable associativity caches achieve flexibility in placement at the expense of increased hit-latency. This makes them unsuitable for L1 caches.We propose a novel two-level mapping framework that retains the hit-latency of one-level mapping yet incurs fewer conflict misses. This is achieved by introducing a secondlevel mapping which reorganizes the nodes in the naive initial partitions into refined partitions with near-uniform distribution of nodes. Further as this remapping is accomplished by simply adapting the index bits to a given routing table the hit-latency is not affected. We propose three new schemes which result in up to 16% reduction in the number of misses and 13% speedup in memory access time. In comparison, an XOR-based placement scheme known to perform extremely well for general purpose architectures, can obtain up to 2% speedup in memory access time.
CoMMA: a framework for integrated multimedia mining using multi-relational associations
Authors: Ankur M. Teredesai, Muhammad A. Ahmad, Juveria Kanodia, Roger S. Gaborski
Venue: Knowledge and Information Systems (2006)

Generating captions or annotations automatically for still images is a challenging task. Traditionally, techniques involving higher-level (semantic) object detection and complex feature extraction have been employed for scene understanding. On the basis of this understanding, corresponding text descriptions are generated for a given image. In this paper, we pose the auto-annotation problem as that of multi-relational association rule mining where the relations exist between image-based features, and textual annotations. The central idea is to combine low-level image features such as color, orientation, intensity, etc. and corresponding text annotations to generate association rules across multiple tables using multi-relational association mining. Subsequently, we use these association rules to auto-annotate test images.In this paper we also present a multi-relational extension to the FP-tree algorithm to accomplish the association rule mining task effectively. The motivation for using multi-relational association rule mining for multimedia data mining is to exhibit the potential accorded by multiple descriptions for the same image (such as multiple people labeling the same image differently). Moreover, multi-relational association rule mining can also benefit the auto-annotation process by pruning the number of trivial associations that are generated if text and image features were combined in a single table through a join. In this paper, we discuss these issues and the results of our auto-annotation experiments on different test sets. Another contribution of this paper is highlighting a need to develop robust evaluation metrics for the image annotation task. We propose several applicable scoring techniques and then evaluate the performance of the different algorithms to study the utility of these techniques. A detailed analysis of the datasets used and the performance results are presented to conclude the paper.
Efficient Bottom-Up Image Segmentation Using Region Competition and the Mumford-Shah Model for Color and Textured Images
Authors: Yongsheng Pan, J. Douglas Birdwell, Seddik M. Djouadi
Venue: Proceedings of the Eighth IEEE International Symposium on Multimedia (2006)

Curve evolution implementations [3][23] [25] of the Mumford-Shah functional [16] are of broad interest in image segmentation. These implementations, however, have initialization problems [6] [25]. A mathematical analysis of the initialization problem for the Chan-Vese implementation [3] [25] is provided in this paper. The initialization problem is a result of the non-convexity of the Mumford- Shah functional and the top-down hierarchy of the model's use of global region information in the image. Based on the analysis, efficient implementation methods are proposed for the Chan-Vese models [3] [25]. The proposed methods do not have to solve PDEs and thus work fast. The advantages of level set methods, such as automatic handling of topological changes, are preserved. These methods work well for images without strong noise. Initialization problems, however, still exist. A bottom-up image segmentation method is proposed that alleviates the initialization problem, based on region competition and the Mumford Shah functional [16]. This algorithm extends the method in [15], and is able to automatically and efficiently segment objects in complicated images. Using a bottom-up hierarchy, the method avoids the initialization problem in the Chan-Vese model and works for images with multiple junctions and color images. It is then extended to textured images using Gabor filters and fractal methods. Experimental results show that the proposed method works well and is robust to the effects of noise.
Practical Attack Graph Generation for Network Defense
Authors: Kyle Ingols, Richard Lippmann, Keith Piwowarski
Venue: Proceedings of the 22nd Annual Computer Security Applications Conference (2006)

Attack graphs are a valuable tool to network defenders, illustrating paths an attacker can use to gain access to a targeted network. Defenders can then focus their efforts on patching the vulnerabilities and configuration errors that allow the attackers the greatest amount of access. We have created a new type of attack graph, the multiple-prerequisite graph, that scales nearly linearly as the size of a typical network increases. We have built a prototype system using this graph type. The prototype uses readily available source data to automatically compute network reachability, classify vulnerabilities, build the graph, and recommend actions to improve network security. We have tested the prototype on an operational network with over 250 hosts, where it helped to discover a previously unknown configuration error. It has processed complex simulated networks with over 50,000 hosts in under four minutes.
Lamb Meat Quality Assessment by Support Vector Machines
Authors: Paulo Cortez, Manuel Portelinha, Sandra Rodrigues, Vasco Cadavez, Alfredo Teixeira
Venue: Neural Processing Letters (2006)

The correct assessment of meat quality (i.e., to fulfill the consumer's needs) is crucial element within the meat industry. Although there are several factors that affect the perception of taste, tenderness is considered the most important characteristic. In this paper, a Feature Selection procedure, based on a Sensitivity Analysis, is combined with a Support Vector Machine, in order to predict lamb meat tenderness. This real-world problem is defined in terms of two difficult regression tasks, by modeling objective (e.g. Warner---Bratzler Shear force) and subjective (e.g. human taste panel) measurements. In both cases, the proposed solution is competitive when compared with other neural (e.g. Multilayer Perceptron) and Multiple Regression approaches.
Collaborative example selection in an intelligent example-based programming environment
Authors: Peter Brusilovsky, Gerhard Weber
Venue: Proceedings of the 1996 international conference on Learning sciences (1996)

This paper discusses problems related to finding relevant examples in educational example-based programming environments. We consider several approaches to organizing example selection in such environments. The paper defends the collaborative approach to example selection which implies that the user and the system collaborate in the process of example selection. The search process is driven by the user and the system supports the user with all available knowledge. To demonstrate possible ways of implementing this approach we describe how several forms of collaborative example selection were implemented in ELM-PE, an intelligent programming environment for learning LISP.
Temperature dependence of GaN Schottky diodes I-V characteristics
Authors: J. Osvald, J. Kuzmik, G. Konstantinidis, P. Lobotka, A. Georgakilas
Venue: Microelectronic Engineering (2005)

We have studied the temperature dependence of I-V curves of GaN Schottky diodes with two crystal polarities. Relatively large barrier height differences have been observed between Ga- and N-face GaN polarities which we ascribed to the possible influence of the surface fixed charge caused by the opposite spontaneous polarization in GaN with Ga- and N-polarities. Large differences between barrier height calculated from I-V and C-V curves may also be caused by the fixed surface polarization charge. Barrier heights decrease with decreasing temperature for both polarities and ideality factors increase. Low ideality factors and no excess leakage currents indicate thermionic transport mechanism for Ga-face diodes at temperatures over 200 K. On the contrary large ideality factors and reverse leakage currents for N-face polarity diodes are probably connected with other form of current transport-tunneling.
Computing Similarity of Square Matrices by Eigenconjugation
Authors: V. Ortega-Gonzalez
Venue: Proceedings of the Fifth Mexican International Conference on Artificial Intelligence (2006)

In computer sciences, matrices are widely used for representing different kinds of information. Measuring similarity among square matrices is an interesting open problem in computer sciences. Furthermore, eigenvalues and eigenvectors are a powerful way for representing and characterizing square matrices. In this paper we introduce a new similarity measure among two square data matrices of the same class; the idea is based on evaluating the effect of conjugate the eigenvalues and eigenvectors of one matrix with the other matrix, and vice versa. Some experimental results are showed in order to analyze and exemplify the Eigenconjugation as an approach for the problem of similarity of matrices.
Sockets Direct Protocol over InfiniBand in clusters: is it beneficial?
Authors: P. Balaji, S. Narravula, K. Vaidyanathan, S. Krishnamoorthy, J. Wu, D. K. Panda
Venue: Proceedings of the 2004 IEEE International Symposium on Performance Analysis of Systems and Software (2004)

The Sockets Direct Protocol (SDP) had been proposed recently in order to enable sockets based applications to take advantage of the enhanced features provided by InfiniBand architecture. In this paper, we study the benefits and limitations of an implementation of SDP. We first analyze the performance of SDP based on a detailed suite of micro-benchmarks. Next, we evaluate it on two different real application domains: (1) A multitier data-center environment and (2) A Parallel Virtual File System (PVFS). Our micro-benchmark results show that SDP is able to provide up to 2.7 times better bandwidth as compared to the native sockets implementation over InfiniBand (IPoIB) and significantly better latency for large message sizes. Our experimental results also show that SDP is able to achieve a considerably higher performance (improvement of up to 2.4 times) as compared to IPoIB in the PVFS environment. In the data-center environment, SDP outperforms IPoIB for large file transfers inspite of currently being limited by a high connection setup time. However, this limitation is entirely implementation specific and as the InfiniBand software and hardware products are rapidly maturing, we expect this limitation to be overcome soon. Based on this, we have shown that the projected performance for SDP, without the connection setup time, can outperform IPoIB for small message transfers as well.
Bridging real world semantics to model world semantics for taxonomy based knowledge representation system
Authors: Ju-Hum Kwon, Chee-Yang Song, Chang-Joo Moon, Doo-Kwon Baik
Venue: Journal of Computer Science and Technology (2005)

As a mean to map ontology concepts, a similarity technique is employed. Especially a context dependent concept mapping is tackled, which needs contextual information from knowledge taxonomy. Context-based semantic similarity differs from the real world similarity in that it requires contextual information to calculate similarity. The notion of semantic coupling is introduced to derive similarity for a taxonomy-based system. The semantic coupling shows the degree of semantic cohesiveness for a group of concepts toward a given context. In order to calculate the semantic coupling effectively, the edge counting method is revisited for measuring basic semantic similarity by considering the weighting attributes from where they affect an edge's strength. The attributes of scaling depth effect, semantic relation type, and virtual connection for the edge counting are considered. Furthermore, how the proposed edge counting method could be well adapted for calculating context-based similarity is showed. Thorough experimental results are provided for both edge counting and context-based similarity. The results of proposed edge counting were encouraging compared with other combined approaches, and the context-based similarity also showed understandable results. The novel contributions of this paper come from two aspects. First, the similarity is increased to the viable level for edge counting. Second, a mechanism is provided to derive a context-based similarity in taxonomy-based system, which has emerged as a hot issue in the literature such as Semantic Web, MDR, and other ontology-mapping environments.
Intelligent Software Measurement System for Automating the Goal-Question-Metrics Process
Authors: Junling Huang, Behrouz H. Far
Venue: Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence (2006)

Intelligent Software Measurement System (ISMS) has been developed to generate a software measurement plan towards a user's initial business goal. The ISMS is based on adapting two methodologies: (1) the normalized 10- step goal-driven software measurement process within the Goal /Question /Metrics (GQM) paradigm; and (2) the software measurement knowledge base building based on the ISMS ontology. In order to take full advantages of these two methodologies, ISMS is designed to be a multiagent system (MAS). In this paper, the goal-driven software measurement process, the design and implementation of the ISMS knowledge base and the design and implementation of the MAS for ISMS are discussed.
Intrinsic Evolution of Predictable Behavior Evolvable Hardware in Dynamic Environment
Authors: Peter Tawdross, Senthil K. Lakshmanan, Andreas Konig
Venue: Proceedings of the Sixth International Conference on Hybrid Intelligent Systems (2006)

Sensor electronics performance is susceptible to static and dynamic deviations. Even laser trimming still can't deal with all the deviations. Recently, analog reconfigurable electronics offers a solution to compensate these effects. The state of the art uses genetic algorithm (GA) to find an arbitrary topology to fulfill the given specifications, which can cause hardware with unpredictable behavior. In case of any environmental change, the state of the art starts the evolution from scratch. Considering the robustness of the reconfiguration approach, we used the particle swarm optimization (PSO) [13] as an alternative to GA for reconfiguration of programmable sensor electronics. In this paper, we extend our work to investigate the PSO methods for dynamic environment in which the hardware can track the environmental change without starting from scratch. We run the algorithm on a real hardware (intrinsic evolution). Our hardware was designed in such a way that its performance is predictable by employing standard circuit topologies.