ppxscal/embedding_network huggingface dataset:
Hierarchical Multi-Party Key Agreement for Wireless Networks
Authors: Sigurd Eskeland, Vladimir Oleshchuk
Venue: Proceedings of the Third International Symposium on Information Assurance and Security (2007)

A common property of practically all multi-party key agreement protocols is that they are non-hierarchical, i.e., they do not support user hierarchies. However, in real life, it is likely that members of groups and organizations differ in ranking according to their job positions so that people of higher rankings are privileged the same and higher privileged information than people of lower rankings. Hierarchical access control schemes address this issue, but they assume rather fixed scenarios based on centralized key distribution with predefined root keys and key parameters set by a trusted center. In this paper, we propose a decentralized efficient hierarchical multi-party key agreement protocol well-suited for ad-hoc wireless networks that ensures secure user hierarchies.
Performance modeling of location tracking systems
Authors: Yi-Bing Lin, Phone Lin
Venue: ACM SIGMOBILE Mobile Computing and Communications Review (1998)

A location system utilizes sensors to track the locations of mobile users with badges. A badge periodically transmits location messages to the sensor in the room. Based on a location message, the system updates or re-confirms the position of the badge. A major performance issue of the location system is the determination of the frequency that a badge transmits the location messages (or the registration message). In this paper, we propose an analytic model to study this performance issue. Our study indicates that in many cases, by decreasing the registration frequency (and consequently saving battery power), the location system may still maintain low lost-call probability. We also observe an interesting phenomenon that if the user movement pattern is highly irregular, the opportunity of losing a call becomes smaller.
Markov-Blanket Based Strategy for Translating a Bayesian Classifier into a Reduced Set of Classification Rules
Authors: Estevam R. Jr. Hruschka, M. do Carmo Nicoletti, Vilma A. de Oliveira, Glaucia M. Bressan
Venue: Proceedings of the 7th International Conference on Hybrid Intelligent Systems (2007)

Bayesian network (BN) is a formalism for representing and reasoning about uncertain domains. In BN the knowledge is represented by a combination of a graph-based structure and probability theory. A particular type of BN known as Bayesian Classifier (BC) aims at classifying a given instance into a discrete class. BCs have been extensively used for modeling knowledge in many different applications and have been the focus of many works related to data mining. Depending on the size of a BC the understandability of the knowledge it represents is not an easy task. This paper proposes an approach to help the process of understanding the knowledge represented by a BC, by translating it into a more convenient and easily understandable form of representation, that of classification rules. The proposed method named BayesRule (BR) uses the concept of Markov Blanket (MB) to obtain a reduced set of rules in respect to both, the number of rules and the number of antecedents in rules. Experiments using the ALARM network showed that the reduced set of rules extracted from the BC can be smaller than the set of rules representing a decision tree generated by C4.5, and still maintains a high accuracy rate.
Theory in the Computer Science and Engineering Curriculum: Why, What, When, and Where
Authors: B. H. Barnes
Venue: Computer (1977)

Theory plays several different roles in the undergraduate curriculum. In recognition of this, both the IEEE Computer Society1 and ACM,2,3 have recommended a substantial theoretical component in the computer science/engineering curriculum.
Analysis and Simulation of Buffered Delta Networks
Authors: D. M. Dias
Venue: IEEE Transactions on Computers (1981)

Delta networks are a class of multistage interconnection networks with gate complexity less than crossbar switches that are easy to control, and which include several networks that have been proposed in the literature as special cases. Buffered delta networks have queues of packets between the stages of the network. This paper presents analytic and simulation results for the performance of delta networks in a packet communication environment. The performance of buffered delta networks is compared with unbuffered delta networks and crossbar switches. It is demonstrated that buffering produces considerable improvement in the performance of these networks, making their performance comparable to that of crossbar switches.
A Scalable, Non-blocking Approach to Transactional Memory
Authors: Hassan Chafi, Jared Casper, Brian D. Carlstrom, Austen McDonald, Chi Cao Minh, Woongki Baek, Christos Kozyrakis, Kunle Olukotun
Venue: Proceedings of the 2007 IEEE 13th International Symposium on High Performance Computer Architecture (2007)

Transactional Memory (TM) provides mechanisms that promise to simplify parallel programming by eliminating the need for locks and their associated problems (dead-lock, livelock, priority inversion, convoying). For TM to be adopted in the long term, not only does it need to deliver on these promises, but it needs to scale to a high number of processors. To date, proposals for scalable TM have relegated livelock issues to user-level contention managers. This paper presents the first scalable TM implementation for directory-based distributed shared memory systems that is livelock free without the need for user-level intervention. The design is a scalable implementation of optimistic concurrency control that supports parallel commits with a two-phase commit protocol, uses write-back caches, and filters coherence messages. The scalable design is based on Transactional Coherence and Consistency (TCC), which supports continuous transactions and fault isolation. A performance evaluation of the design using both scientific and enterprise benchmarks demonstrates that the directory-based TCC design scales efficiently for NUMA systems up to 64 processors.
Infinitely Imbalanced Logistic Regression
Authors: Art B. Owen
Venue: The Journal of Machine Learning Research (2007)

In binary classification problems it is common for the two classes to be imbalanced: one case is very rare compared to the other. In this paper we consider the infinitely imbalanced case where one class has a finite sample size and the other class's sample size grows without bound. For logistic regression, the infinitely imbalanced case often has a useful solution. Under mild conditions, the intercept diverges as expected, but the rest of the coefficient vector approaches a non trivial and useful limit. That limit can be expressed in terms of exponential tilting and is the minimum of a convex objective function. The limiting form of logistic regression suggests a computational shortcut for fraud detection problems.
VHDL ?The Designer Environment
Authors: Alfred Gilman
Venue: IEEE Design Test (1986)

Part of the VHDL program is to define a support environment for the VHSIC hardware description language. This support environmentis both an open-ended integration framework for what is expected to be a growing list of tools interfaced to VHDL, and a specificset of software which is being implemented in Phase B of the program. The specific software under development as part of theDoD VHDL program includes an analyzer, simulator, simplifier, reverse analyzer, and design library manager. This article summarizesthe design and documentation capabilities offered the digital electronics designer by the VHDL toolset.
What is an analogue for the semantic web and why is having one important?
Authors: m. c. schraefel
Venue: ACM SIGWEB Newsletter (2007)

This paper postulates that for the Semantic Web to grow and gain input from fields that will surely benefit it, it needs to develop an analogue that will help people not only understand what it is, but what the potential opportunities are that are enabled by these new protocols. The model proposed in the paper takes the way that Web interaction has been framed as a baseline to inform a similar analogue for the Semantic Web. While the Web has been represented as a Page + Links, the paper presents the argument that the Semantic Web can be conceptualized as a Notebook + Memex. The argument considers how this model also presents new challenges for fundamental human interaction with computing, and that hypertext models have much to contribute to this new understanding for distributed information systems.
An Energy-efficient Multihop Cooperative Transmission Protocol Design for Sensor Networks
Authors: Pan Zhou, Wei Liu, Kanru Xu
Venue: Proceedings of the 32nd IEEE Conference on Local Computer Networks (2007)

We present a cluster-based virtual multiple-input single-output (vMISO) multihop cooperative transmission protocol in wireless sensor networks. In order to construct the cooperative transmission, we arrange each hop into two consecutive time slots: the intra-cluster slot, that accounts for data sharing within the cluster, and the inter-cluster slot for transmission between clusters. In this protocol, we investigate a novel optimal cluster formation method and best cooperative transmission routing selection base on the network energy consumption status. Also, an optimization model is developed to find the optimum number of cooperative nodes, clusters and transmission rates. Simulation results show that the protocol can save energy significantly.
Estimation of bladder muscle invasion in transitional cell carcinoma by using artificial neural networks: A study based on prebiopsy imaging findings
Authors: Mahmut Tokmakçı, Nuri Erdogan, Nurettin Sahin, Hulya Akgun, Oguz Ekmekcioglu
Venue: Expert Systems with Applications: An International Journal (2008)

Purpose: To identify the capability of ANNs in estimation of muscle invasive disease in transitional cell carcinomas (TCC). Materials and methods: In this study, we developed a MLP based ANN to detect muscle invasive disease in transitional cell carcinoma of bladder through the analysis of prebiopsy imaging data. The study includes 172 patients (116 males and 56 females; mean age, 63.92 years; range, 31-92 years) who had had the definitive diagnosis of Transitional cell carcinomas (TCC) based on biopsy results. Results: In the test group, 34 out of 35 cases were correctly classified by he MLP based Neural Network with only one false negative case. The sensitivity, specificity, positive predictive and negative predictive values calculated from the output data were 100%, 96.1%, 90%, and 100%, respectively. Conclusion: The proposed algorithm produced high sensitivity and specifity in predicting the histopathologic results, which shows that this method has a promising value in estimation of bladder muscle invasion in TCC.
Characterisation and Modelling of Voice Traffic in First Responder Networks
Authors: Nils Aschenbruck, Peter Martini, Michael Gerharz
Venue: Proceedings of the 32nd IEEE Conference on Local Computer Networks (2007)

Analysing and modelling traffic is one important step in the performance evaluation of communication systems. In this paper we focus on first responder (FR) networks. The goal is to figure out models that can be used to generate realistic synthetic push to talk voice traffic for single talk groups to be used in network simulation. Our work is based on an empirical long-time measurement of one FR channel. The analysis of the trace shows significant short- and long-range correlations as well as variations of load over time. As the characteristics analysed are similar to ones observed in disaster area (DA) traces, we consider n-state and 3-state (Semi)-Markov models. After fitting the parameters of these models to the traces, synthetic traffic streams for the different models are generated and finally evaluated by both visual and statistical analysis.
Cooperative efforts in individualized education
Authors: Kenneth L. Modesitt
Venue: ACM SIGCAS Computers and Society (1977)

Computer-based education (CBE) and personalized systems of instruction (PSI) are both remarkably successful tools for instructional delivery in content-based courses. However, CBE and PSI are both primarily designed to minister to the intellect of the student. That is appropriate within the university, but it is not sufficient.
Forecasting of the Reactive Dyes K/S Value Based on BP Nerve Network
Authors: HuiYu Jiang, Min Dong, Feng Yang
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 02 (2007)

The traditional determination method of K/S value is testing the CIEL*a*b* value of each dyeing fabric fistly by the color chromatic aberration SC-80, and then, based on the CIEL*a*b* value, they calculate the K/S value scoping 10nm within 400nm-700nm the visible region. These are finished with the help of the software Hyper Choshoku-Senka TX. In this paper, authors estimate the K/S value of the dyed dyes is estimated by using the nerve network method. The correlation coefficient reachs 0.995 between the estimated value and tested one. It shows the feasibility of the artificial nerve network applied to the reactive dyes dyeing.
Probabilistic decision diagrams for exact probabilistic analysis
Authors: Afshin Abdollahi
Venue: Proceedings of the 2007 IEEE/ACM international conference on Computer-aided design (2007)

A decision diagram based framework is proposed for representing the probabilistic behavior of circuits with faulty gates. We introduce Probabilistic decision diagrams (PDD) as an exact computational tool which along with vast expressive power holds many other useful properties such as space efficiency (on average) and efficient manipulation algorithms (polynomial in size.) An algorithm for constructing the PDD for a circuit is proposed. Useful information about probabilistic behavior of the circuit (such as output error probability for arbitrary input probability distribution) can be directly extracted from the PDD representation. Experimental results demonstrate the effectiveness and applicability of the proposed approach.
Perfect hash functions for large dictionaries
Authors: Amjad M. Daoud
Venue: Proceedings of the ACM first workshop on CyberInfrastructure: information management in eScience (2007)

We describe a new practical algorithm for finding perfect hash functions with no specification space at all, suitable for key sets ranging in size from small to very large. The method is able to find perfect hash functions for various sizes of key sets in linear time. The perfect hash functions produced are optimal in terms of time (perfect) and require at most computation of h1(k) and h2(k); two simple auxiliary pseudorandom functions.
The Effect of GA Redundancy on the Design of Reverse Logistics Network
Authors: Tzong-Heng Chi, Fang-Cheng Hsu, Ching-Kai Lin
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 05 (2007)

Genetic algorithm's approach has been one of the most important optimization techniques though the related problems of selection pressure still exist. Diversity is the key issue. For this reason, we explored the implication of redundancy in both genetic algorithm's overrepresentation and multi-explanation. Our research result supports the view of cross- competition but not diversity loss when considering uniform coding redundancy with expressive elitism and the tolerance of infeasible solutions. On the other side, we developed new decoding procedure based on the inborn searchability of the small world. In designing the network of the reverse logistics having multiple lower-dimensional constraints, with the help of multiple overlapped fitness functions, the performance of genetic algorithms can truly be improved.
Adaptive Backoff Algorithm Based on Network Congestion in Multi-hop Wireless Ad hoc Networks
Authors: Hailun Xia, Zhimin Zeng, Wei Ding
Venue: Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 03 (2007)

In IEEE802.11-based wireless multi-hop Ad hoc networks nodes need to contend for the shared wireless channel, which could result in congestions and waste the limited wireless bandwidth resource. A novel adaptive backoff algorithm based on network congestion--NCBAB (Network-Congestion Based Adaptive Backoff) in media access control (MAC) layer is proposed. In this algorithm, the node adjusts its backoff mechanism according to the feedback of its next-hop's congestion level during the frame transmission sequence. It increases its minimum contention window of the backoff algorithm when its next-hop gets more severe congestion, and its own contention window decreases with the increasing of backoff stage when it encounters high-level congestion and its next-hop encounters no congestion. The simulation results show that the proposed algorithm can adjust the congested node's packet receiving and transmitting rate, and can remarkably increase the network end-to-end saturation throughput.
High-performance public-key cryptoprocessor for wireless mobile applications
Authors: Kazuo Sakiyama, Lejla Batina, Bart Preneel, Ingrid Verbauwhede
Venue: Mobile Networks and Applications (2007)

We present a high-speed public-key cryptoprocessor that exploits three-level parallelism in Elliptic Curve Cryptography (ECC) over GF(2n). The proposed cryptoprocessor employs a Parallelized Modular Arithmetic Logic Unit (P-MALU) that exploits two types of different parallelism for accelerating modular operations. The sequence of scalar multiplications is also accelerated by exploiting Instruction-Level Parallelism (ILP) and processing multiple P-MALU instructions in parallel. The system is programmable and hence independent of the type of the elliptic curves and scalar multiplication algorithms. The synthesis results show that scalar multiplication of ECC over GF(2163) on a generic curve can be computed in 20 and 16 µs respectively for the binary NAF (Non-Adjacent Form) and the Montgomery method. The performance can be accelerated furthermore on a Koblitz curve and reach scalar multiplication of 12 µs with the TNAF (τ-adic NAF) method. This fast performance allows us to perform over 80,000 scalar multiplications per second and to enhance security in wireless mobile applications.
Distributed probabilistic inferencing in sensor networks using variational approximation
Authors: Sourav Mukherjee, Hillol Kargupta
Venue: Journal of Parallel and Distributed Computing (2008)

This paper considers the problem of distributed inferencing in a sensor network. It particularly explores the probabilistic inferencing problem in the context of a distributed Boltzmann machine-based framework for monitoring the network. The paper offers a variational mean-field approach to develop communication-efficient local algorithm for variational inferencing in distributed environments (VIDE). It compares the performance of the proposed approximate variational technique with respect to the exact and centralized techniques. It shows that the VIDE offers a much more communication-efficient solution at very little cost in terms of the accuracy. It also offers experimental results in order to substantiate the scalability of the proposed algorithm.
A query-aware document ranking method for geographic information retrieval
Authors: Bo Yu, Guoray Cai
Venue: Proceedings of the 4th ACM workshop on Geographical information retrieval (2007)

Geographically oriented search must consider both the thematic and geographic dimensions of relevance when matching documents to queries. We propose a dynamic document ranking scheme to combine the thematic and geographic relevance measures on a per-query basis. Query specificity is introduced to determine the relative weights of different sources of ranking evidence for each query. A preliminary evaluation comparing with human judgment shows that our method to distinguish different types of geo-referenced queries based on query specificity is promising to address the issue of relevance combination in GIR document ranking. In addition, we explore the possibility of using Dempster-Shafer's theory to combine the two different sources of ranking evidence.
Current Testable Design of Resistor String DACs
Authors: Masaki Hashizume, Yutaka Hata, Tomomi Nishida, Hiroyuki Yotsuyanagi, Yukiya Miura
Venue: Proceedings of the 16th Asian Test Symposium (2007)

In this paper, a DFT method of resistor string digital-to-analog converters (DACs) is proposed so as to be tested fully by supply current testing. Targeted defects are opens and shorts in the DACs. Testability of a testable designed DAC is examined experimentally. The results show that shorts and opens in a testable designed DAC will be detected with a smaller number of test vectors by supply current testing.
A simulation test bed for hypotheses of genome evolution
Authors: Robert G. Beiko, Robert L. Charlebois
Venue: Bioinformatics (2007)

Motivation: Microbial genomes undergo evolutionary processes such as gene family expansion and contraction, variable rates and patterns of sequence substitution and lateral genetic transfer. Simulation tools are essential for both the generation of data under different evolutionary models and the validation of analytical methods on such data. However, meaningful investigation of phenomena such as lateral genetic transfer requires the simultaneous consideration of many underlying evolutionary processes. Results: We have developed EvolSimulator, a software package that combines non-stationary sequence and gene family evolution together with models of lateral genetic transfer, within a customizable birth–death model of speciation and extinction. Here, we examine simulated data sets generated with EvolSimulator using existing statistical techniques from the evolutionary literature, showing in detail each component of the simulation strategy. Availability: Source code, manual and other information are freely available at www.bioinformatics.org.au/evolsim Contact: beiko@cs.dal.ca Supplementary information: Supplementary data are available at Bioinformatics online.
Geo-tagging for imprecise regions of different sizes
Authors: Robert C. Pasley, Paul D. Clough, Mark Sanderson
Venue: Proceedings of the 4th ACM workshop on Geographical information retrieval (2007)

Extracting geographical information from various web sources is likely to be important for a variety of applications. One such use for this information is to enable the study of vernacular regions: informal places referred to on a day-to-day basis, but with no official entry in geographical resources, such as gazetteers. Past work in automatically extracting geographical information from the web to support the creation of vernacular regions has tended to focus on larger regions (e.g. "The British Midlands" and "The South of France"). In this paper we report the results of preliminary work to investigate the success of using a simple geo-tagging approach and resources of varying granularity from the Ordnance Survey to extract geographical information from web pages. We find that the data gathered for smaller regions (compared with larger ones) is more "fine-grained" which has an effect on the type of resource most useful for geo-tagging and its success.