Search

The ever-increasing need for securing computing systems using cryptographic algorithms is spurring interest in the efficient implementation of common algorithms. While the algorithms can be implemented in software using base instruction sets, there is considerable potential to reduce memory cost and improve speed using specialized instructions and associated hardware. However, there is a need to assess the benefits and costs of software implementations and new instructions that implement key cryptographic algorithms in fewer cycles. The primary aim of this paper is to improve the understanding of the performance and cost of implementing cryptographic algorithms for the RISC-V instruction set architecture (ISA) in two cases: software implementations of the algorithms using the rv32i instruction set and using cryptographic instructions supported by dedicated hardware in additional functional units. For both cases, we describe a RISC-V processor with cryptography hardware extensions and hand-optimized RISC-V assembly language implementations of eleven cryptographic algorithms. Compared to implementations with only the rv32i instruction set, implementations with the cryptography set extension provide a 1.5× to 8.6× faster execution speed and 1.2× to 5.8× less program memory for five of the eleven algorithms. Based on our performance analyses, a new instruction is proposed to increase the implementation efficiency of the algorithms. [ABSTRACT FROM AUTHOR]

Digital public displays installed in various locations provide valuable information for the passers-by. However, the static characteristic of the digital public display limits the consumption of the displayed content to a small area. Personal mobile devices such as smartphones are now capable of interacting with digital public displays, which enables the passers-by to “take-away” the content and consume it on-the-go. This process requires device binding, content selection, and transfer between the two devices. In this paper, we propose a device binding method which utilizes the content brightness changing pattern as a unique content ID on the public display and an illuminance sensor on the mobile to bind and transfer between two devices. We conducted performance evaluations for binding algorithm robustness in different conditions. Also comparative studies among other binding interaction methods were conducted. Our results show that our proposed method performed stably across the various conditions and overall performance in interaction completion time and error rate was similar or superior to the existing methods. [ABSTRACT FROM AUTHOR]

Certificateless aggregate signature (CLAS) is a crucial cryptosystem. It can not only compress multiple signatures into a short signature, but also ensure the validity of each signature participating in the aggregation by verifying the validity of an resulting aggregate signature. Therefore, a secure and efficient CLAS scheme is very useful for resource-constrained environments because it greatly reduces the overall length of the signature and the verifier’s computational overhead. Cheng et al. presented an efficient CLAS scheme and proved its security in the random oracle model. However, we find that their scheme has security flaws. In this paper, we demonstrate that Cheng et al.’s CLAS scheme is vulnerable to coalition attacks from internal signers. To overcome these attacks, we present an improved CLAS scheme and prove that it is existentially unforgeable under the computational Diffie-Hellman assumption. In addition, our CLAS scheme can not only resist coalition attacks but also generate a very short aggregate signature. The performance analysis results show that our improved CLAS scheme is lower than the related CLAS schemes in terms of communication overhead and computation cost. [ABSTRACT FROM AUTHOR]

The accuracy and diversity of recommendation algorithms have always been the research hotspot of recommender systems. A good recommender system should not only have high accuracy and diversity, but also have adequate robustness against spammer attacks. However, the issue of recommendation robustness has received relatively little attention in the literature. In this paper, we systematically study the influences of different spammer behaviors on the recommendation results in various recommendation algorithms. We further propose an improved algorithm by incorporating the inner-similarity of user’s purchased items in the classic KNN approach. The new algorithm effectively enhances the robustness against spammer attacks and thus outperforms traditional algorithms in recommendation accuracy and diversity when spammers exist in the online commercial systems. [ABSTRACT FROM AUTHOR]

This paper describes an approach for improving the accuracy of memory-based collaborative filtering, based on the technique for order of preference by similarity to ideal solution (TOPSIS) method. Recommender systems are used to filter the huge amount of data available online based on user-defined preferences. Collaborative filtering (CF) is a commonly used recommendation approach that generates recommendations based on correlations among user preferences. Although several enhancements have increased the accuracy of memory-based CF through the development of improved similarity measures for finding successful neighbors, there has been less investigation into prediction score methods, in which rating/preference scores are assigned to items that have not yet been selected by a user. A TOPSIS solution for evaluating multiple alternatives based on more than one criterion is proposed as an alternative to prediction score methods for evaluating and ranking items based on the results from similar users. The recommendation accuracy of the proposed TOPSIS technique is evaluated by applying it to various common CF baseline methods, which are then used to analyze the MovieLens 100K and 1M benchmark datasets. The results show that CF based on the TOPSIS method is more accurate than baseline CF methods across a number of common evaluation metrics. [ABSTRACT FROM AUTHOR]

Advanced Metering Infrastructure (AMI) realizes a two-way communication of electricity data through by interconnecting with a computer network as the core component of the smart grid. Meanwhile, it brings many new security threats and the traditional intrusion detection method can’t satisfy the security requirements of AMI. In this paper, an intrusion detection system based on Online Sequence Extreme Learning Machine (OS-ELM) is established, which is used to detecting the attack in AMI and carrying out the comparative analysis with other algorithms. Simulation results show that, compared with other intrusion detection methods, intrusion detection method based on OS-ELM is more superior in detection speed and accuracy. [ABSTRACT FROM AUTHOR]

A Multiple Bus Network (MBN) connects a set of processors via a set of buses. Two important parameters of an MBN are its loading (largest number of connections on a bus) and its degree (largest number of connections to a processor). These parameters determine the cost, speed, and implementability of the MBN. The smallest degree that any useful MBN can have is 2. In this paper, we study the relationship between running time, degree, and loading of degree-2 MBNs running a fundamental class of algorithms called binary tree algorithms. (A binary tree algorithm reduces 2n inputs at the leaves of a balanced binary tree to a single result at the root of the tree.) Specifically, we establish a nontrivial Ω(n/log n) loading lower bound for any degree-2 MBN running a 2n input binary tree algorithm optimally in n steps. We show that this bound does not hold if the restriction on the degree or the running time is relaxed. That is, optimal-time, degree-3, constant loading MBNs and suboptimal-time, degree-2, constant loading MBNs exist for binary tree algorithms. We also derive a lower bound on the additional time (beyond the optimal) needed to run binary tree algorithms on a degree-2, loading-L MBN, for any L⩾ 3. [ABSTRACT FROM AUTHOR]

It is desirable for the load in a distributed system to be balanced evenly. A dynamic process migration protocol is needed in order to achieve load balancing in a user transparent manner. A distributed algorithm for load balancing which is network topology independent is proposed in this paper. Different network topologies and low-level communications protocols affect the choice of only some system design parameters. The "drafting" algorithm attempts to compromise two contradictory goals: maximize the processor utilization and minimize the communication overhead. The main objective of this paper is to describe the dynamic process migration protocol based on the proposed drafting algorithm. A sample distributed system is used to further illustrate the drafting algorithm and to show how to define system design parameters. The system performance is measured by simulation experiments based on the sample system. [ABSTRACT FROM AUTHOR]

Unstructured peer-to-peer (P2P) overlay networks with two-layer hierarchy, comprising an upper layer of super-peers and an underlying layer of ordinary peers, are used to improve the performance of large-scale P2P applications like content distribution and storage. In order to deal with continuous growth of participating peers, a scalable and efficient super-peer overlay topology is essential. However, there is relatively little research conducted on constructing such super-peer overlay topology. In the existed solutions, the number of connections required to be maintained by a super-peer is in direct proportion to the total number of super-peers. For super large-scale P2P applications, i.e. the number of participating peer is over 1,000,000, these solutions are not scalable and impractical. Therefore, in this paper, we propose a scalable hierarchical unstructured P2P system in which a self-similar square network graph (SSNG) is proposed to construct and maintain the super-peer overlay topology adaptively. The SSNG topology is a constant-degree topology in which each node maintains a constant number of neighbor nodes. Moreover, a simple and efficient message forwarding algorithm is presented to ensure each super-peer to receive just one flooding message. The analytical results showed that the proposed SSNG-based overlay is more scalable and efficient than the perfect difference graph (PDG)-based overlay proposed in the literature. [ABSTRACT FROM AUTHOR]

In this paper, we describe a distributed coordination system that allows agents to seamlessly cooperate in problem solving by partially contributing to a problem solution and delegating the subproblems for which they do not have the required skills or knowledge to appropriate agents. The coordination mechanism relies on a dynamically built semantic overlay network that allows the agents to efficiently locate, even in very large unstructured networks, the necessary skills for a specific problem. Each agent performs partial contributions to the problem solution using a new distributed goal-directed version of the Graphplan algorithm. This new goal-directed version of the original Graphplan algorithm provides an efficient solution to the problem of "distraction", which most forward-chaining algorithms suffer from. We also discuss a set of heuristics to be used in the backward-search process of the planning algorithm in order to distribute this process amongst idle agents in an attempt to find a solution in less time. The evaluation results show that our approach is effective in building a scalable and efficient agent society capable of solving complex distributable problems. [ABSTRACT FROM AUTHOR]

Earlier work has developed the underpinnings of the IC-Scheduling Theory, a framework for scheduling computations having intertask dependencies—modeled via directed acyclic graphs (DAGs)—for Internet-based computing. The goal of the schedules produced is to render tasks eligible for execution at the maximum possible rate, with the dual aim of 1) utilizing remote clients' computational resources well by always having work to allocate to an available client and 2) lessening the likelihood of a computation's stalling for lack of eligible tasks. The DAGs handled by the theory thus far are those that can be decomposed into a given collection of bipartite building block DAGs via the operation of DAG decomposition. A basic tool in constructing schedules is a relation ..., which allows one to "prioritize" the scheduling of a complex DAG's building blocks. The current paper extends the IC-Scheduling Theory in two ways: by expanding significantly the repertoire of DAGs that the Theory can schedule optimally and by allowing one sometimes to shortcut the algorithmic process required to find optimal schedules. The expanded repertoire now allows the theory to schedule optimally, among other DAGs, a large range of DAGs that are either "expansive," in the sense that they grow outward from their sources, or "reductive," in the sense that they grow inward toward their sinks. The algorithmic shortcuts allow one to "read off" an optimal schedule for a DAG from a given optimal schedule for the DAG's dual, which is obtained by reversing all arcs (thereby exchanging the roles of sources and sinks). [ABSTRACT FROM AUTHOR]

In this paper, we propose load distribution strategies for divisible loads for networked computing environments where computation and communication resource characteristics are unknown and/or vary with time. The principle on which our strategies are formulated is based on using probing loads to estimate the network characteristics and using them to determine the best possible load distribution. This work extends the adaptive strategies proposed in an earlier work to multilevel general networks. These networks, while being more challenging, also offer several opportunities that can be exploited to make the probing phase more efficient. We propose two strategies, one static, which caters to the presence of unknown parameters, and the other dynamic, which caters to both unknown as well as time-varying network parameters. The proposed strategies are robust, resilient, and easily adaptable to network fluctuations. The algorithms are also shown to have a tracking ability, a property that is important in dynamic environments. Examples are presented to illustrate the salient features of these strategies. [ABSTRACT FROM AUTHOR]

Structured peer-to-peer overlay networks, like distributed hash tables (DHTs), map data items to the network based on a consistent hashing function. Such mapping for data distribution has an inherent load balance problem. Data redistribution algorithms based on randomized matching of heavily loaded nodes with light ones can deal with the dynamics of DHTs. However, they are unable to consider the proximity of the nodes simultaneously. There are other methods that rely on auxiliary networks to facilitate locality-aware load redistribution. Due to the cost of network construction and maintenance, the locality-aware algorithms can hardly work for DHTs with churn. This paper presents a locality-aware randomized load-balancing algorithm to deal with both the proximity and network churn at the same time. We introduce a factor of randomness in the probing of lightly loaded nodes in a range of proximity. We further improve the efficiency by allowing the probing of multiple candidates (d-way) at a time. Simulation results show the superiority of the locality-aware two-way randomized algorithm in comparison with other random or locality-aware algorithms. In DHTs with churn, it performs no worse than the best churn-resilient algorithm. It takes advantage of node capacity heterogeneity and achieves good load balance effectively even in a skewed distribution of items. [ABSTRACT FROM AUTHOR]

Alternating group graphs AGn, studied by Jwo and others, Constitute a class of Cayley graphs that possess certain desirable properties compared with other regular networks considered by researchers in parallel and distributed computing. A different form, ANn, of such graphs, proposed by Youhou and dubbed alternating group networks, has been shown to possess advantages over AGn. For example, ANn has a node degree that is smaller by a factor of about 2 while maintaining a diameter comparable to that of AGn, is maximally fault-tolerant, and shares some of the positive structural attributes of the well-known star graph. In this paper, we characterize the distance between any two nodes in ANn and present an optimal (shortest-path) routing algorithm for this class of networks. [ABSTRACT FROM AUTHOR]

This paper establishes the state of the art in both deterministic and randomized online permutation routing in the POPS network. Indeed, we show that any permutation can be routed online on a POPS(d, g) network either with O(d/g log g) deterministic slots, or, with high probability, with 5c[d/g] + o(d/g) + O(log log g) randomized slots, where constant c = exp(1 + e-1) ≈ 3.927. When d = ϴ(g), which we claim to be the ‘interesting’ case, the randomized algorithm is exponentially faster than any other algorithm in the literature, both deterministic and randomized ones. This is true in practice as well. Indeed, experiments show that it outperforms its rivals even starting from as small a network as a POPS(2, 2) and the gap grows exponentially with the size of the network. We can also show that, under proper hypothesis, no deterministic algorithm can asymptotically match its performance. [ABSTRACT FROM AUTHOR]

Link Clustering (LC) is a relatively new method for detecting overlapping communities in networks. The basic principle of LC is to derive a transform matrix whose elements are composed of the link similarity of neighbor links based on the Jaccard distance calculation; then it applies hierarchical clustering to the transform matrix and uses a measure of partition density on the resulting dendrogram to determine the cut level for best community detection. However, the original link clustering method does not consider the link similarity of non-neighbor links, and the partition density tends to divide the communities into many small communities. In this paper, an Extended Link Clustering method (ELC) for overlapping community detection is proposed. The improved method employs a new link similarity, Extended Link Similarity (ELS), to produce a denser transform matrix, and uses the maximum value of EQ (an extended measure of quality of modularity) as a means to optimally cut the dendrogram for better partitioning of the original network space. Since ELS uses more link information, the resulting transform matrix provides a superior basis for clustering and analysis. Further, using the EQ value to find the best level for the hierarchical clustering dendrogram division, we obtain communities that are more sensible and reasonable than the ones obtained by the partition density evaluation. Experimentation on five real-world networks and artificially-generated networks shows that the ELC method achieves higher EQ and In-group Proportion (IGP) values. Additionally, communities are more realistic than those generated by either of the original LC method or the classical CPM method. [ABSTRACT FROM AUTHOR]

Abstract: In recent years, the research community has introduced various methods for processing skyline queries in road networks. A skyline query retrieves the skyline points that are not dominated by others in terms of static and dynamic attributes (i.e., the road distance). This paper addresses the issue of efficiently processing continuous skyline queries in road networks. Two novel and important distance-based skyline queries are presented, namely, the continuous query () and the continuous k nearest neighbor-skyline query (Cknn-SQ). A grid index is first designed to effectively manage the information of data objects and then two algorithms are proposed, the algorithm and the algorithm, which are combined with the grid index to answer the . Similarly, the Cknn-SQ algorithm and the Cknn-SQ + algorithm are developed to efficiently process the Cknn-SQ. Extensive experiments using real road network datasets demonstrate the effectiveness and the efficiency of the proposed algorithms. [Copyright &y& Elsevier]

Abstract: Large amounts of high-dimensional data are distributed with the application of networks. Distributed clustering has become an increasingly important task due to variety of real-life constrains, including bandwidth and security aspects. Many distributed clustering algorithm have been proposed, but most of them have high transmission cost and poor clustering quality. In this paper, we propose a scalable local density-based distributed clustering algorithm which can easily fit high-dimensional data sets by this method such as density attractor distance and noise factor. In order to keep a lower transmission cost, we determine suitably low factor noises to send to the server. Furthermore, Test data sets, CMC data sets and KDD-CUP-99 are used for experimental evaluation to validate the performance practically. The experimental results and theoretical analysis show that the efficiency and quality for clustering of the proposed algorithm are superior to the other distributed clustering algorithm. [Copyright &y& Elsevier]

Abstract: In this paper, we present algorithms that compute large matchings in planar graphs with fixed minimum degree. The algorithms give a guarantee on the size of the computed matching and run in linear time. Thus they are faster than the best known algorithm for computing maximum matchings in general graphs and in planar graphs, which run in and time, respectively. For the class of planar graphs with minimum degree 3, the bounds we achieve are known to be the best possible. Further, we discuss how minimum degree 5 can be used to obtain stronger bounds on the matching size. [Copyright &y& Elsevier]

Abstract: Phylogenetic networks are a restricted class of directed acyclic graphs that model evolutionary histories in the presence of reticulate evolutionary events, such as horizontal gene transfer, hybrid speciation, and recombination. Characterizing a phylogenetic network as a collection of trees and their branches has long been the basis for several methods of reconstructing and evaluating phylogenetic networks. Further, these characterizations have been used to understand molecular sequence evolution on phylogenetic networks. In this paper, we address theoretical questions with regard to phylogenetic networks, their characterizations, and sequence evolution on them. In particular, we prove that the problem of deciding whether a given tree is contained inside a network is NP-complete. Further, we prove that the problem of deciding whether a branch of a given tree is also a branch of a given network is polynomially equivalent to that of deciding whether the evolution of a molecular character (site) on a network is governed by the infinite site model. Exploiting this equivalence, we establish the NP-completeness of both problems, and provide a parameterized algorithm that runs in time , where is the total number of nodes and is the number of recombination nodes in the network, which significantly improves upon the trivial brute-force time algorithm for the problem. This reduction in time is significant, particularly when analyzing recombination hotspots. [Copyright &y& Elsevier]

We had previously proposed an outstanding evolutionary method, Automatically Defined Groups (ADG), for generating heterogeneous cooperative agents, and then we had developed a rule extraction algorithm from computer log files using ADG. In this algorithm, agents search multiple errordetection rules cooperatively based on the difference between normal state log files and abnormal state log files. The more frequent applicable and the more accurate the error-detection rule is, the more agents are allocated for searching the rule. Therefore, the number of agents allocated for each rule can represent the important degree of the rule. However, when the rule extraction method was applied to the large scale log files, which may have a number of latent rules, a problematic situation on the number of agents could be observed. In the previous proposed method, the number of agents is not adaptive, therefore the number of agents may be lack for evaluating the each rule's importance minutely. In this paper, we propose an improved method, where the number of agents is adaptively increased depending on the discovered rules. As a result, the importance of respective rules could be evaluated minutely by increasing the number of agents. In addition, the proposed method could acquire more rules than those by the method with the fixed number of agents. [ABSTRACT FROM AUTHOR]

Abstract: The effectiveness of a distributed system hinges on the manner in which tasks and data are assigned to the underlying system resources. Moreover, today''s large-scale distributed systems must accommodate heterogeneity in both the offered load and in the makeup of the available storage and compute capacity. The ideal resource assignment must balance the utilization of the underlying system against the loss of locality incurred when individual tasks or data objects are fragmented among several servers. In this paper we describe this locality-maximizing placement problem and show that an optimal solution is NP-hard. We then describe a polynomial-time algorithm that generates a placement within an additive constant of two from optimal. [Copyright &y& Elsevier]

The article reports on the discussion and analysis of the Distributed Interactive Engineering Toolbox (DIET) architecture and the hierarchical scheduling approach used by DIET agents and servers. The extensions made to the standard DIET scheduling approach is presented. An alternative model founded on active management of the flow of requests throughout the system is in consideration. Supports are added on (1) limitation on the number of concurrent requests on interactive servers, (2) server and agent level queues, and (3) window-based scheduling algorithms.

In this paper, we consider the problem of constructing a multicast tree in star interconnection networks under the single-port communication model. Unlike previous schemes for constructing space-efficient multicast trees, we adopt the completion time of each multicast as the objective function to be minimized. In particular, we study a special case of the problem in which all destination vertices are immediate neighbors of the source vertex, and propose a multicast scheme of [formula] time units for the star graph of dimension n. [ABSTRACT FROM AUTHOR]

We model a systolic array as a network of, mostly identical, communicating finite state machines that exchange messages over one-to-one, unbounded, FIFO channels. Each machine has a cyclic behavior; in each cycle, a machine first receives one message from each of its input channels, then sends one message to each of its output channels. If in a cycle a machine does not have any data message to send to one of its output channels, it sends a null message instead; thus, machines exchange two types of messages, data and null. We characterize the liveness and termination properties for such networks, and discuss two algorithms that can be used to decide these properties for any given network. We apply these algorithms to establish the liveness and termination properties of four systolic array examples. These examples include a linear matrix-vector multiplier, a linear priority queue, and a search tree. [ABSTRACT FROM AUTHOR]

Network clustering is a very popular topic in the network science field. Its goal is to divide (partition) the network into groups (clusters or communities) of “topologically related” nodes, where the resulting topology-based clusters are expected to “correlate” well with node label information, i.e., metadata, such as cellular functions of genes/proteins in biological networks, or age or gender of people in social networks. Even for static data, the problem of network clustering is complex. For dynamic data, the problem is even more complex, due to an additional dimension of the data—their temporal (evolving) nature. Since the problem is computationally intractable, heuristic approaches need to be sought. Existing approaches for dynamic network clustering (DNC) have drawbacks. First, they assume that nodes should be in the same cluster if they are densely interconnected within the network. We hypothesize that in some applications, it might be of interest to cluster nodes that are topologically similar to each other instead of or in addition to requiring the nodes to be densely interconnected. Second, they ignore temporal information in their early steps, and when they do consider this information later on, they do so implicitly. We hypothesize that capturing temporal information earlier in the clustering process and doing so explicitly will improve results. We test these two hypotheses via our new approach called ClueNet. We evaluate ClueNet against six existing DNC methods on both social networks capturing evolving interactions between individuals (such as interactions between students in a high school) and biological networks capturing interactions between biomolecules in the cell at different ages. We find that ClueNet is superior in over 83% of all evaluation tests. As more real-world dynamic data are becoming available, DNC and thus ClueNet will only continue to gain importance. [ABSTRACT FROM AUTHOR]

An adaptive label propagation algorithm (ALPA) is proposed to detect and monitor communities in dynamic networks. Unlike the traditional methods by re-computing the whole community decomposition after each modification of the network, ALPA takes into account the information of historical communities and updates its solution according to the network modifications via a local label propagation process, which generally affects only a small portion of the network. This makes it respond to network changes at low computational cost. The effectiveness of ALPA has been tested on both synthetic and real-world networks, which shows that it can successfully identify and track dynamic communities. Moreover, ALPA could detect communities with high quality and accuracy compared to other methods. Therefore, being low-complexity and parameter-free, ALPA is a scalable and promising solution for some real-world applications of community detection in dynamic networks. [ABSTRACT FROM AUTHOR]

Time series are recorded values of an interesting phenomenon such as stock prices, household incomes, or patient heart rates over a period of time. Time series data mining focuses on discovering interesting patterns in such data. This article introduces a wavelet-based time series data analysis to interested readers. It provides a systematic survey of various analysis techniques that use discrete wavelet transformation (DWT) in time series data mining, and outlines the benefits of this approach demonstrated by previous studies performed on diverse application domains, including image classification, multimedia retrieval, and computer network anomaly detection. [ABSTRACT FROM AUTHOR]

Wireless self-organizing networks (WSONs) have attracted considerable attention from the network research community; however, the key for their success is the rigorous validation of the properties of the network protocols. Applications of risk or those demanding precision (like alert-based systems) require a rigorous and reliable validation of deployed network protocols. While the main goal is to ensure the reliability of the protocols, validation techniques also allow the establishment of their correctness regarding the related protocols' requirements. Nevertheless, even if different communities have carried out intensive research activities on the validation domain, WSONs still raise new issues for and challenging constraints to these communities. We thus, advocate the use of complementary techniques coming from different research communities to efficiently address the validation of WSON protocols. The goal of this tutorial is to present a comprehensive review of the literature on protocol engineering techniques and to discuss difficulties imposed by the characteristics of WSONs on the protocol engineering community. Following the formal and nonformal classification of techniques, we provide a discussion about components and similarities of existing protocol validation approaches. We also investigate how to take advantage of such similarities to obtain complementary techniques and outline new challenges. [ABSTRACT FROM AUTHOR]

Computing inference from a given knowledgebase is one of the key competences of computer science. Therefore, numerous formalisms and specialized inference routines have been introduced and implemented for this task. Typical examples are Bayesian networks, constraint systems or different kinds of logic. It is known today that these formalisms can be unified under a common algebraic roof called valuation algebra. Based on this system, generic inference algorithms for the processing of arbitrary valuation algebras can be defined. Researchers benefit from this high level of abstraction to address open problems independently of the underlying formalism. It is therefore all the more astonishing that this theory did not find its way into concrete software projects. Indeed, all modern programming languages for example provide generic sorting procedures, but generic inference algorithms are still mythical creatures. NENOK breaks a new ground and offers an extensive library of generic inference tools based on the valuation algebra framework. All methods are implemented as distributed algorithms that process local and remote knowledgebases in a transparent manner. Besides its main purpose as software library, NENOK also provides a sophisticated graphical user interface to inspect the inference process and the involved graphical structures. This can be used for educational purposes but also as a fast prototyping architecture for inference formalisms. [ABSTRACT FROM AUTHOR]

Data mining applications place special requirements on clustering algorithms including: the ability to find clusters embedded in subspaces of high dimensional data, scalability, end-user comprehensibility of the results, non-presumption of any canonical data distribution, and insensitivity to the order of input records. We present CLIQUE, a clustering algorithm that satisfies each of these requirements. CLIQUE identifies dense clusters in subspaces of maximum dimensionality. It generates clusters descriptions in the form of DNF expression that are minimized for ease of comprehension. It produces identical results irrespective of the order in which input records are presented and does not presume any specific mathematical form for data distribution. Through experiments, we show that CLIQUE efficiently finds accurate clusters in large high dimensional datasets. [ABSTRACT FROM AUTHOR]