Large Language Models (LLMs) are widely used as conversational agents, exploiting their capabilities in various sectors such as education, law, medicine, and more.However, LLMs are often subjected to context-shifting behaviour, resulting in a lack of consistent and interpretable personality-aligned interactions.Adherence to psychological traits lacks comprehensive analysis, especially in the case of dyadic (pairwise) conversations.We examine this challenge from two viewpoints, initially using two conversation agents to generate a discourse on a certain topic with an assigned personality from the OCEAN framework (Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism) as High/Low for each trait.This is followed by using multiple judge agents to infer the original traits assigned to explore prediction consistency, intermodel agreement, and alignment with the assigned personality.Our findings indicate that while LLMs can be guided toward personalitydriven dialogue, their ability to maintain personality traits varies significantly depending on the combination of models and discourse settings.These inconsistencies emphasise the challenges in achieving stable and interpretable personality-aligned interactions in LLMs.

Abstract Online misinformation poses a significant challenge due to its rapid spread and limited supervision. To address this issue, automated rumour detection techniques are essential for countering the negative impact of false information. Previous research primarily focussed on extracting text features, which proved time-consuming and less effective. In this study, we contribute substantially to two domains: rumour detection on Twitter and the evaluation of text embeddings. We thoroughly analyse rumour detection models and compare the quality of text embeddings generated by various fine-tuned BERT-based models. Our findings indicate that our proposed models outperform existing techniques. Notably, when we test these models on combined datasets, we observe significant performance improvements with larger training and testing data sizes. We conclude that carefully considering the dataset, data splitting, and classification techniques is crucial for evaluating solution performance. Additionally, we find that differences in the quality of text embeddings between RoBERTa, BERT, and DistilBERT are insignificant. This challenges existing assumptions and highlights the need for future research to explore these nuances further.

Automatic evaluation of hashtag recommendation models is a fundamental task in Twitter. In the traditional evaluation methods, the recommended hashtags from an algorithm are first compared with the ground truth hashtags for exact correspondences. The number of exact matches is then used to calculate the hit rate, hit ratio, precision, recall, or F1-score. This way of evaluating hashtag similarities is inadequate as it ignores the semantic correlation between the recommended and ground truth hashtags. To tackle this problem, we propose a novel semantic evaluation framework for hashtag recommendation, called #REval. This framework includes an internal module referred to asBERTag, which automatically learns the hashtag embeddings. We investigate on how the #REval framework performs under different word embedding methods and different numbers of synonyms and hashtags in the recommendation using our proposed #REval-hit-ratio measure. Our experiments of the proposed framework on three large datasets show that #REval gave more meaningful hashtag synonyms for hashtag recommendation evaluation. Our analysis also highlights the sensitivity of the framework to the word embedding technique, with #REval based on BERTag more superior over #REval based on Word2Vec, FastText, and GloVe.

Interest in Generative Adversarial Networks (GANs) continues to grow, with diverse GAN variations emerging for applications across various domains. However, substantial challenges persist in advancing GANs. Effective training of deep learning models, including GANs, heavily relies on well-defined loss functions. Specifically, establishing a logical and reciprocal connection between the training image and generator is crucial. In this context, we introduce a novel GAN loss function that employs the Sugeno complement concept to logically link the training image and generator. Our proposed loss function is a composition of logical elements , and we demonstrate through analytical analysis that it outperforms an existing loss function found in the literature. This superiority is further substantiated via comprehensive experiments, showcasing the loss function’s ability to facilitate smooth convergence during training and effectively address mode collapse issues in GANs.

A wide range of respiratory diseases, such as cold and flu, asthma, and COVID-19, affect people’s daily lives worldwide. In medical practice, respiratory sounds are widely used in medical services to diagnose various respiratory illnesses and lung disorders. The traditional diagnosis of such sounds requires specialized knowledge, which can be costly and reliant on human expertise. Recently, cough audio recordings have been used to automate the process of detecting respiratory conditions. This research aims to examine various acoustic features that enhance the performance of machine learning (ML) models in detecting COVID-19 from cough signals. This study investigates the efficacy of three feature extraction techniques, including Mel Frequency Cepstral Coefficients (MFCC), Chroma, and Spectral Contrast features, on two ML algorithms, Support Vector Machine (SVM) and Multilayer Perceptron (MLP), and thus proposes an efficient COVID-19 detection system. The proposed system produces a practical solution and demonstrates higher state-of-the-art classification performance with an AUC of 0.843 on the COUGHVID dataset and 0.953 on the Virufy dataset for COVID-19 detection.

Abstract Containers have emerged recently as a cloud technology for improving and managing cloud resources. They improve resource sharing by allowing instances to run on top of the host’s operating system. Container-based virtualization runs and manages hosted instances via the host kernel. Resource sharing can cause resource contention. In addition, dependent jobs, which may be deployed across multiple hosts, require frequent communication, resulting in a high volume of network traffic and network contention. The majority of existing research focuses on load balancing, with no consideration for the fact that network contention also plays a significant role in container performance. In this research, we propose a Dependency-aware Scheduling algorithm (DAScheduler) that deploys jobs into containers while accounting for both load balancing and job dependencies. The experimental results show that DAScheduler reduces network traffic by more than half and balances the loads. In comparison to one of the existing state-of-the-art techniques, DAScheduler improves overall cloud performance.

MOTIVATION: The secondary structure of RNA is of importance to its function. Over the last few years, several papers attempted to use machine learning to improve de novo RNA secondary structure prediction. Many of these papers report impressive results for intra-family predictions but seldom address the much more difficult (and practical) inter-family problem. RESULTS: We demonstrate that it is nearly trivial with convolutional neural networks to generate pseudo-free energy changes, modelled after structure mapping data that improve the accuracy of structure prediction for intra-family cases. We propose a more rigorous method for inter-family cross-validation that can be used to assess the performance of learning-based models. Using this method, we further demonstrate that intra-family performance is insufficient proof of generalization despite the widespread assumption in the literature and provide strong evidence that many existing learning-based models have not generalized inter-family. AVAILABILITY AND IMPLEMENTATION: Source code and data are available at https://github.com/marcellszi/dl-rna. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Personalized recommendation automatically predicts the top-y hashtags to a given tweet. Most research in the literature of hashtag recommendation focused on the content of the posts such as words and topics. Although these methods have measured the performance of hashtag recommendation on large data sets, there is a lack of analysis on how these methods perform on small communities. Motivated by the well-studied research area of community detection algorithms that aggregate strongly connected users with similar interests and behaviors, in this article, we propose a community-based hashtag recommendation framework, which studies hashtag recommendation through tweet similarity task and applies it on communities detected using the Clique percolation method, Louvain algorithm, and label propagation method. The detected communities are extracted from four social network constructions based on following, mention, hashtag, and topic. Compared to the three state-of-the-art hashtag recommendation methods, our extensive experiments show that our community-based method outperforms these methods, thus giving a higher hit rate. Our in-depth analysis demonstrates that the performance of hashtag recommendation is the best when the communities are generated using the Clique percolation method (CPM) from the network of users who share similar usage of hashtags.

MOTIVATION: Predicting the secondary structure of RNA is a fundamental task in bioinformatics. Algorithms that predict secondary structure given only the primary sequence, and a model to evaluate the quality of a structure, are an integral part of this. These algorithms have been updated as our model of RNA thermodynamics changed and expanded. An exception to this has been the treatment of multi-loops. Although more advanced models of multi-loop free energy change have been suggested, a simple, linear model has been used since the 1980s. However, recently, new dynamic programing algorithms for secondary structure prediction that could incorporate these models were presented. Unfortunately, these models appear to have lower accuracy for secondary structure prediction. RESULTS: We apply linear regression and a new parameter optimization algorithm to find better parameters for the existing linear model and advanced non-linear multi-loop models. These include the Jacobson-Stockmayer and Aalberts & Nandagopal models. We find that the current linear model parameters may be near optimal for the linear model, and that no advanced model performs better than the existing linear model parameters even after parameter optimization. AVAILABILITY AND IMPLEMENTATION: Source code and data is available at https://github.com/maxhwardg/advanced_multiloops. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Social media community detection is a fundamental challenge in social data analytics, in order to understand user relationships and improve social recommendations. Although the problem has been extensively investigated, the majority of research has been based on social networks with static structures. Our findings within large social networks, such as Twitter, show that only a few users have interactions or communications at fixed time intervals. Finding active communities that demonstrate constant interactions between its members comprises a reasonable perspective. Communities examined from this perspective will provide time-variant social relationships, which may greatly improve the applicability of social data analytics. In this paper, we address the problem of temporal interaction-biased community detection using a four-step process. First, we develop a partition approach using an objective function based on clique structure, to enhance the time efficiency of our methodology. Second, we develop an influence propagation model that gives greatest weight to active edges or to inactive edges in close proximity to active edges. Third, we develop expansion-driven algorithms to efficiently find the activity-biased densest community. Finally, we verify the effectiveness of the extended community metric and the efficiency of the algorithms using three real data sets and a case study conducted on Twitter dynamic data set.

One of the main problems in all-optical packet switched networks is the buffering of packets. A popular solution for buffering packets is to use a set of fiber delay lines attached to a switch. A priority queue is one of the most general buffering schemes that allows the packet with the highest priority to depart the buffer on a departure request and dropping of the lowest priority packet if a new packet arrives when the buffer is full. We present a recursive algorithm for constructing optical priority queues of polynomial size from a switch with linear number of inputs/outputs and fiber delay lines. The best known lower bound allows the construction of a priority queue of size 2Musing a switch of size M. However, the best known upper bound constructs a priority queue of size M3using a switch of size M. We show that it is possible to construct a priority queue of size O(MC) for a fixed constant c using a switch of size O(M).

The immense popularity and rapid growth of Online Social Networks (OSN) have attracted the interest of researchers and companies, particularly in how users group together to form communities online. While many community detection algorithms have been developed to detect communities on such OSNs, most of these algorithms are based only on topological links and researchers have observed that many topological links do not translate to actual user interaction. As such, many members of the detected communities do not communicate frequently to each other. This inactivity creates a problem in targeted advertising and viral marketing, which require the community to be highly active so as to facilitate the diffusion of product/service information. We propose an approach to detect highly interactive Twitter communities that share common interests, based on the frequency and patterns of direct tweeting among users, rather than the topological information implicit in follower/following links. Our experimental results show that communities detected by our proposed approach are more cohesive and connected within different interest groups, based on topological measures. We also show that the detected communities actively interact about the specific interests, based on the high frequency of #hashtags and @mentions related to this interest. In addition, we study the trends in their tweeting patterns such as how they follow and unfollow other users, and observe that our approach detects communities comprising users whose links are more persistent compared to those in other groups of users.

We present a framework for (automatically) classifying the relative interests of Twitter users using information from Wikipedia. Our proposed framework first uses Wikipedia to automatically classify a user's celebrity followings into various interest categories, followed by determining the relative interests of the user with a weighting compared to his/her other interests. Our preliminary evaluation on Twitter shows that this framework is able to correctly classify users' interests and that these users frequently converse about topics that reflect both their (detected) interest and a related real-life event.

This paper presents a preliminary study into the awarding of barnstars among Wikipedia editors to better understand their motivations in contributing to Wikipedia articles. We crawled the talk pages of all active Wikipedia editors and retrieved 21,299 barnstars that were awarded among 14,074 editors. In particular, we found that editors do not award and receive barnstars in equal (or similar) quantities. Also, editors were more active in editing articles before awarding or receiving barnstars.

A regression test case prioritization (TCP) technique reorders test cases for regression testing to achieve early fault detection. Most TCP techniques have been developed for regression testing of source code in an application. Most web applications rely on a database server for serving client requests. Any changes in the database result in erroneous client interactions and may bring down the entire web application. However, most prioritization techniques are unsuitable for prioritizing test suites for early detection of changes in databases. There are very few proposals in the literature for prioritization of test cases that can detect faults in the database early. We propose a new automated TCP technique for web applications that automatically identifies the database changes, prioritizes test cases related to database changes and executes them in priority order to detect faults early.

The Temporally-Ordered Routing Algorithm (TORA) is a distributed routing protocol that is based on a family of link reversal algorithms. TORA is able to provide multiple loop-free routes to any destination using the route creation, maintenance and erasure functions. TORA performs well in networks with a small number of traffic connections but poorly in networks with a large number of traffic connections. This poor performance is due to the traffic congestion caused by excessive route maintenance. This traffic congestion is further aggravated by routing overhead produced by the large number of traffic connections. We propose two modifications to improve TORA using a network localization approach and selective node participation approach. The network localization approach initializes and maintains a localized portion of the entire network while the selective node participation approach selects a subset of nodes to participate as part of the network. Benchmarks against original TORA show that our TORA modifications results in an overall performance improvement in terms of packet delivery, routing overhead and packet latency.

The IEEE 802.15.4 standard specifies the most recommended medium access control (MAC) protocol for Wireless Sensor Networks (WSNs). The contention-based access scheme of the MAC is driven by a Carrier Sense Multiple Access with Collision Avoidance (CSMA-CA) algorithm. In this paper, we evaluate the performance of the CSMA-CA algorithm for indoor WSNs by using an experimental testbed. We set up trials to test the default parameters of the IEEE 802.15.4 for saturated and unsaturated conditions of arriving packets. In particular, we demonstrate that the experimental results accurately match the predictions of an analytical model for the CSMA-CA algorithm, though the latter assumes ideal transmission over the wireless medium. Finally, we show that data delivery ratio and throughput could be improved by opting during the contention for different window sizes compared to the default values of the standard specification.

The Temporally-Ordered Routing Algorithm (TORA) is a highly adaptive distributed routing algorithm used in Mobile Ad hoc Networks (MANET) that provides multiple loop-free routes to a destination. TORA is very dependent on the services provided by the Internet MANET Encapsulation Protocol (IMEP) to carry out its three main functions: route creation, route maintenance, and route erasure. In this paper, we study how certain behaviour of IMEP leads to the poor performance of TORA and narrow down the problems of IMEP. We also proposed two modifications to IMEP that resulted in an overall performance improvement of TORA in terms of packet delivery, routing overhead and average packet latency. The original TORA and TORA based on our two IMEP modifications were also benchmarked against two other on-demand routing protocols: the Dynamic Source Routing (DSR) protocol and Ad-hoc On-demand Distance Vector (AODV) protocol.

Many community detection algorithms have been developed to detect communities on Online Social Networks (OSN). However, these algorithms are based only on topological links and researchers have observed that many topological links do not translate to actual user interaction. As such, many members of the detected communities do not communicate frequently to each other. This inactivity creates a problem in targeted advertising and viral marketing which requires the community to be highly active so as to allow the diffusion of product/service information. We propose an approach to detect highly interactive Twitter communities that share common interests, based on the frequency and patterns of direct tweeting among users, rather than the topological information implicit in follower/following links. From a topological aspect, we show that our method detects communities that are more cohesive and connected within different interest groups. We also show that the detected communities interact actively about the specific interests, based on the high frequency of #hash tags and @mentions related to this interest. In addition, we study the trends in their tweeting patterns such as how they follow and unfollow other users.

MOTIVATION: Laboratory RNA structure determination is demanding and costly and thus, computational structure prediction is an important task. Single sequence methods for RNA secondary structure prediction are limited by the accuracy of the underlying folding model, if a structure is supported by a family of evolutionarily related sequences, one can be more confident that the prediction is accurate. RNA pseudoknots are functional elements, which have highly conserved structures. However, few comparative structure prediction methods can handle pseudoknots due to the computational complexity. RESULTS: A comparative pseudoknot prediction method called DotKnot-PW is introduced based on structural comparison of secondary structure elements and H-type pseudoknot candidates. DotKnot-PW outperforms other methods from the literature on a hand-curated test set of RNA structures with experimental support. AVAILABILITY: DotKnot-PW and the RNA structure test set are available at the web site http://dotknot.csse.uwa.edu.au/pw. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Multidimensional aggregation is one of the most important computational building blocks and hence also a potential performance bottleneck in Online Analytic Processing (OLAP). In order to deliver fast query responses for interactive operations such as slicing, dicing, roll-up and drill-down, it is essential that aggregates along the relevant dimensions of a data cube can be calculated as efficiently as possible. General-purpose computing on graphics processing units (GPGPU) is a recent trend used in many computing domains with the potential for tremendous speedups through the massively data-parallel computation available on such devices. We present a GPU-based cube data structure and algorithms for fast multidimensional aggregation, implemented using Nvidia's CUDA framework. Our experimental tests show a substantial speedup over state-of-the-art sequential algorithms. Moreover, the performance gain is particularly high in cases exposing the weaknesses of traditional algorithms, i.e. when the number of base cells involved in an aggregation is large.

FlexiTP is a novel TDMA protocol that offers a synchronized and loose slot structure. Nodes in the network can build, modify, or extend their scheduled number of slots during execution, based on their local information. Nodes wake up for their scheduled slots; otherwise, they switch into power-saving sleep mode. This flexible schedule allows FlexiTP to be strongly fault tolerant and highly energy efficient. FlexiTP is scalable for a large number of nodes because its depth-first-search schedule minimizes buffering, and it allows communication slots to be reused by nodes outside each other's interference range. Hence, the overall scheme of FlexiTP provides end-to-end guarantees on data delivery (throughput, fair access, and robust self-healing) while also respecting the severe energy and memory constraints of wireless sensor networks. Simulations in ns-2 show that FlexiTP ensures energy efficiency and is robust to network dynamics (faults such as dropped packets and nodes joining or leaving the network) under various network configurations (network topology and network density), providing an efficient solution for data-gathering applications. Furthermore, under high contention, FlexiTP outperforms 2-MAC in terms of energy efficiency and network performance.

Packet Alters are rules for classifying packets based on their header fields. In order to avoid ambiguities in packet classification, the set of filters in a router table must be conflict-free, i.e. for any incoming packet p, there must be a unique best matching filter which applies to p. We present the first output-sensitive solution for the offline version of the conflict detection and resolving problem for 1-dimensional arbitrary range filters under the most-specific tie breaking rule. The algorithm achieves a worst case time complexity of O(n log n) and uses space O(n). Instead of reporting and resolving all pairs of conflicting filters, we straight away report those resolve filters that are necessary to make the set conflict-free.

In this paper, we propose a fault-tolerant node scheduling scheme to solve the coverage problem in sensor networks.

In vehicular ad hoc networks (VANETs) vehicles communicate with each other without any fixed infrastructure (inter-vehicular communication). VANETs have received wide attention in the research community in recent years. Wireless routing protocols (e.g. DSR or AODV) were studied in freeway scenarios. But whereas recent research dealt with different vehicle density rates (number of vehicles per km) the distance and especially the long distance between the source and the destination vehicles has not been tackled well in the published literature. We studied the performance of the AODV protocol considering different distances between the source and destination vehicles. The mobility traces were generated by a microscopic vehicular traffic generator based on the intelligent-driver model. We addressed the limitations of other protocols in this scenario by implementing some improvements to increase the packet delivery rate and the throughput, and to decrease the normalized routing load. For these improvements we used mobility prediction based on neighborhood knowledge to guarantee more reliable intermediate nodes and therefore a more stable route to the destination. The reliability of the intermediate nodes gets more and more important the longer the routes are, as we show in this paper.

This paper addresses the problem of Generic Object Recognition by modeling the perceptual capability of human beings. In contrast to the traditional approaches, we have approached the recognition problem by proposing a framework which involves two stages of processing. First, an intelligent generic recognizer based on independent component analysis (ICA) is employed to reduce the search space to a few rank-ordered samples. It is shown that ICA captures the appearance characteristics of objects. Shape cues (distance transform based matching) are then used to verify the result of the appearance-based classifier and identify the correct object class and pose. Experiments were conducted using objects with complex appearance and shape characteristics. Sensitivity of recognition to the number of independent components and number of learning samples is analyzed on COIL-100 database. The performance of the generic classifier using ICA with and without shape matching is also analyzed.

The use of wireless sensor networks for gathering environmental and safety-critical data in real time is increasing at a rapid rate. Some of the main criteria in designing sensor network architectures are energy-efficiency, self-management and self-healing. However, most protocols for data gathering and routing in sensor networks implicitly assume a regular rate of data gathering by individual nodes. While this is sufficient for sensing parameters that change slowly over time, individual nodes in a small part of a network may need to increase the rate of data gathering considerably for reporting important data in real-time. Though CSMA protocols can report increasing amount of data in theory, increased contention for the wireless medium in a small part of the network usually results in increased message collision and retransmission. In this paper, we present a novel TDMA protocol for transferring time slots from one part of the network to another part to support non-uniform and reactive sensing in different parts of a network. We discuss the design of this protocol and show that it performs much better compared to CSMA protocols both in terms of energy-efficiency and supporting nonuniform sensing.

Existing TDMA-based MAC protocols for wireless sensor networks are not specifically built to consider the harsh conditions of industrial environments where the communication channel is prone to signal fading. We propose a TDMA-based MAC protocol for wireless sensor networks built for industrial applications that uses link state dependent scheduling. In our approach, nodes gather samples of the channel quality and generate prediction sets from the sample sets in independent slots. Using the prediction sets, nodes only wake up to transmit/receive during scheduled slots that are predicted to be clear and sleep during scheduled slots that may potentially cause a transmitted signal to fade. We simulate our proposed protocol and compare its performance with a general non-link state dependent TDMA protocol and a CSMA protocol. We found that our protocol significantly improves packet throughput as compared to both the general non-link state dependent TDMA protocol and CSMA protocol. We also found that in conditions which are not perfect under our assumptions, the performance of our protocol degrades gracefully.

Ad hoc networks, due to their improvised nature, are frequently established in insecure environments and hence become susceptible to attacks. These attacks are launched by participating malicious nodes against different network services. Routing protocols, which act as the binding force in these networks, are a common target of these nodes. A number of secure routing protocols have recently been proposed, which make use of cryptographic algorithms to secure the routes. However, in doing so, these protocols entail a number of prerequisites during both the network establishment and operation phases. In contrast, trust-based routing protocols locate trusted rather than secure routes in the network by observing the sincerity in participation by other nodes. These protocols thus permit rapid deployment along with a dynamically adaptive operation, which conforms with the current network situation. In this paper, we evaluate the performance of three trust-based reactive routing protocols in a network with varying number of malicious nodes. With the help of exhaustive simulations, we demonstrate that the performance of the three protocols varies significantly even under similar attack, traffic, and mobility conditions. However, each trust-based routing protocol has its own peculiar advantage making it suitable for application in a particular extemporized environment.

A wireless network (WN) is a distributed system where each node is a small hand-held commodity device called a station. Wireless sensor networks have received increasing interest in recent years due to their usage in monitoring and data collection in a wide variety of environments like remote geographic locations, industrial plants, toxic locations, or even office buildings. Two of the most important issues related to a WN are their energy constraints and their potential for developing faults. A station is usually powered by a battery which cannot be recharged while on a mission. Hence, any protocol run by a WN should be energy-efficient. Moreover, it is possible that all stations deployed as part of a WN may not work perfectly. Hence, any protocol designed for a WN should work well even when some of the stations are faulty. The permutation routing problem is an abstraction of many routing problems in a wireless network. In an instance of the permutation routing problem, each of the p-stations in the network is the sender and recipient of n/p packets. The task is to route the packets to their correct destinations. We consider the permutation routing problem in a single-hop wireless network, where each station is within the transmission range of all other stations. We design a protocol for permutation routing on a WN which is both energy efficient and fault tolerant. We present both theoretical estimates and extensive simulation results to show that our protocol is efficient in terms of energy expenditure at each node even when some of the nodes are faulty. Moreover, we show that our protocol is also efficient for the unbalanced permutation routing problem when each station is the sender and recipient of an unequal number of packets.

In this paper, we identify a general paradigm for portal-based rendering and present an image-space algorithm for rendering complex portals. Our general paradigm is an abstraction of portal-based rendering that is independent of scene geometry. It provides a framework for flexible and dynamic scene composition by connecting cells with transformative portals. Our rendering algorithm maintains a visible volume in image-space and uses fragment culling to discard fragments outside of this volume. We discuss our implementation in OpenGL and present results that show it provides correct rendering of complex portals at interactive rates on current hardware. We believe that our work will be useful in many applications that require a means of creating dynamic and meaningful visual connections between different sets of data.

We present a user interface, based on parallel coordinates, that facilitates exploration of volume data. By explicitly representing the visualization parameter space, the interface provides an overview of rendering options and enables users to easily explore different parameters. Rendered images are stored in an integrated history bar that facilitates backtracking to previous visualization options. Initial usability testing showed clear agreement between users and experts of various backgrounds (usability, graphic design, volume visualization, and medical physics) that the proposed user interface is a valuable data exploration tool.

An ad-hoc network is built, operated and maintained exclusively through the cooperative behaviour of its constituent wireless nodes. Precision in routing demands that all network nodes portray persistent benevolent behaviour. This is however, difficult to achieve and so a number of malicious nodes participate in the route discovery process only to sabotage the network. In This work we present a novel and pragmatic mechanism for establishing trust in ad-hoc networks using swarm intelligence. The proposed mechanism is based upon maintaining and sharing pheromone (trust) levels so as to find trustworthy routes. We believe that our model is most suited to ad-hoc networks that can be created on-the-fly without any trust infrastructure.

SUMMARY: Multiple sequence alignment is the NP-hard problem of aligning three or more DNA or amino acid sequences in an optimal way so as to match as many characters as possible from the set of sequences. The popular sequence alignment program ClustalW uses the classical method of approximating a sequence alignment, by first computing a distance matrix and then constructing a guide tree to show the evolutionary relationship of the sequences. We show that parallelizing the ClustalW algorithm can result in significant speedup. We used a cluster of workstations using C and message passing interface for our implementation. Experimental results show that speedup of over 5.5 on six processors is obtainable for most inputs. AVAILABILITY: The software is available upon request from the second author.

A radio network (RN) is a distributed system where each station or node is a small hand-held commodity device called a station. Typically, each station has access to a few channels for transmitting and receiving messages. By RN(p, k), we denote a radio network with p stations, where each station has access to k channels. In a single-hop RN, every station is within the transmission range of every other station. Each station consumes power while transmitting or receiving a message, even when it receives a message that is not destined for it. It is extremely important that the stations consume power only when it is necessary since it is not possible to recharge batteries when the stations are on a mission. We are interested in designing an energy-efficient protocol for permutation routing, which is one of the most fundamental problems in any distributed system. An instance of the permutation routing problem involves p stations of an RN, each storing n/p items. Each item has a unique destination address which is the identity of the destination station to which the item should be sent. The goal is to route all the items to their destinations while consuming as little energy as possible. We show that the permutation routing problem of n packets on an RN(p, k) can be solved in 2n/k+(p/k)/sup 2/+p+2k/sup 2/ slots and each station needs to be awake for at most 6n/p+2p/k+8k slots. When k/spl Lt/p/spl Lt/n, our protocol is more efficient, both in terms of total number of slots and the number of slots each station is awake compared to a previously published protocol by Nakano et al. (2001).

In a partitioned optical passive stars (POPS) network, n=dg processors are divided into g groups of d processors each, and such a POPS network is denoted by POPS(d,g). There is an optical passive star (OPS) coupler between every pair of groups. Hence, a POPS(d,g) requires g/sup 2/ couplers. It is likely that, in a practical system, the number of couplers will be less than the number of processors, i.e., d>/spl radic/n>g and the number of groups will be smaller than the number of processors in a group. Hence, it is important to design fast algorithms for basic operations on such POPS networks with large group size. We present fast algorithms for data sum, prefix sum, and permutation routing on a POPS(d,g) such that d>/spl radic/n>g. Our data sum and prefix sum algorithms improve upon the best known algorithms for these problems designed by Sahni (2000). Permutation routing can be solved on a POPS network by simulating a hypercube sorting algorithm. Our algorithm for permutation routing is more efficient compared to this simulated hypercube sorting algorithm.

We present an O((log log N)/sup 2/) -time algorithm for computing the distance transform of an N /spl times/ N binary image. Our algorithm is designed for the common concurrent read concurrent write parallel random access machine (CRCW PRAM) and requires O(N/sup 2+/spl epsi///log log N) processors, for any /spl epsi/ such that 0 < /spl epsi/ < 1. Our algorithm is based on a novel deterministic sampling scheme and can be used for computing distance transforms for a very general class of distance functions. We also present a scalable version of our algorithm when the number of processors is available p/sup 2+/spl epsi///log log p for some p < N. In this case, our algorithm runs in O((N/sup 2//p/sup 2/)+(N/p) log log p + (log log p)/sup 2/) time. This scalable algorithm is more practical since usually the number of available processors is much less than the size of the image.

This paper proposes a novel approach to automatic recognition of handprinted Bangla (an Indian script) numerals. A modified Topology Adaptive Self-Organizing Neural Network is proposed to extract a vector skeleton from a binary numeral image. Simple heuristics are considered to prune artifacts, if any, in such a skeletal shape. Certain topological and structural features like loops, junctions, positions of terminal nodes, etc. are used along with a hierarchical tree classifier to classify handwritten numerals into smaller subgroups. Multilayer perceptron (MLP) networks are then employed to uniquely classify the numerals belonging to each subgroup. The system is trained using a sample data set of 1800 numerals and we have obtained 93.26% correct recognition rate and 1.71% rejection on a separate test set of another 7760 samples. In addition, a validation set consisting of 1440 samples has been used to determine the termination of the training algorithm of the MLP networks. The proposed scheme is sufficiently robust with respect to considerable object noise.

A self-organizing neural network model that computes the smallest circle (also called minimum spanning circle) enclosing a finite set of given points was proposed by Datta.3 In the article,3 the algorithm is stated and it is demonstrated by simulation that the center of the smallest circle can be achieved with a given level of accuracy. No rigorous proof was given in support of the simulation results. In this paper, we make a rigorous analysis of the model and mathematically prove that the model converges to the desired center of the minimum spanning circle. A suitable neural network architecture is also designed for parallel implementation of the proposed model. Time complexity of the algorithm is worked out under the proposed architecture. Extension of the proposed model to higher dimensions is discussed and demonstrated with some applications.

We present two fast algorithms for sorting on a linear array with a reconfigurable pipelined bus system (LARPBS), one of the recently proposed parallel architectures based on optical buses. In our first algorithm, we sort N numbers in O(log N log log N) worst-case time using N processors. In our second algorithm, we sort N numbers in O((log log N)/sup 2/) worst-case time using N/sup 1+/spl epsi// processors, for any fixed /spl epsi/ such that 0 < /spl epsi/ < 1. Our algorithms are based on a novel deterministic sampling scheme for merging two sorted arrays of length N each in O(log log N) time on an LARPBS with N processors. To our knowledge, the previous best sorting algorithm on this architecture has a running time of O((log N)/sup 2/) using N processors.

We present efficient algorithms for solving several fundamental graph theoretic problems on a Linear Array with a Reconfigurable Pipelined BUS System (LARPBS), one of the recently proposed models of computation based on optical buses. Our algorithms include finding connected components, minimum spanning forest, biconnected components, bridges and articulation points for an undirected graph. We complete the connected components and minimum spanning forest of a graph in O(log n) time using O(m+n) processors where m and n are the number of edges and vertices in the graph and m=O(n/sup 2/) for a dense graph. Both the processor and time complexities of these two algorithms match the complexities of algorithms on the Arbitrary and Priority CRCW PRAM models which are two of the strongest PRAM models. The previous best algorithms by Li et al. for these two problems on the LARPBS require O(log n) time and O(n/sup 3//log n) processors. Our algorithms for computing biconnected components, bridges and articulation points of an undirected graph run in O(log n) time on an LARPBS with O(n/sup 2/) processors. No previous algorithms were known for these latter problems on the LARPBS.

The Euclidean distance transform (EDT) is an operation to convert a binary image consisting of black and white pixels to a representation where each pixel has the Euclidean distance of the nearest black pixel. The EDT has many applications in computer vision and image processing. In this paper, we present an algorithm for computing the EDT of a binary image on the Linear Array with a Reconfigurable Pipelined Bus System (LARPBS), a recently proposed architecture based on optical buses. Our algorithm is deterministic and runs in time for a binary image on an LARPBS with processors, for any fixed , . We also show that our algorithm is highly scalable. If the LARPBS has only processors for , our algorithm runs in time. The algorithm

A self-organizing neural-network model is proposed for computation of the convex-hull of a given set of planar points. The network evolves in such a manner that it adapts itself to the hull-vertices of the convex-hull. The proposed network consists of three layers of processors. The bottom layer computes some angles which are passed to the middle layer. The middle layer is used for computation of the minimum angle (winner selection). These information are passed to the topmost layer as well as fed back to the bottom layer. The network in the topmost layer self-organizes by labeling the hull-processors in an orderly fashion so that the final convex-hull is obtained from the topmost layer. Time complexity of the proposed model is analyzed and is compared with existing models of similar nature.

The study of the filamentous objects in waste water has gained momentum due to its significant effect in environmental pollution. The paper describes a neural network based skeleton extraction technique for volumetric images of these biofilm objects. These objects require huge computer storage space. One way to economize the storage space is to represent such images in the form of a vector skeleton (a piecewise linear approximation). Such a skeleton preserves the essential structure of the object. The proposed neural network does not start with a predefined net topology. The topology evolves during the learning process on the basis of the input. The present technique has certain advantages over the conventional 3-D thinning techniques. It achieves data reduction at a higher rate. Also, the proposed technique is highly robust to noise and arbitrary rotations of an image.

We consider the navigation problem of a robot from a starting point S to a target point T inside an unknown polygonal environment. The robot knows the directions of the points S and T and it can detect the walls of the environment through its tactile sensors. We present a competitive strategy for the robot to reach the target T. The path length generated by the robot is /spl les/4.5d(/spl kappa/-1)(/spl kappa/+2)/spl times/d, where, /spl kappa/=3/sup (d/2-1)/ and d is the L/sub 1/ distance between S and T.

Extraction of skeletal shape from a 2D dot pattern is discussed. We use a self-organizing neural network model to get a piecewise linear approximation of a skeleton of the pattern. It is found that even without a proper definition of a skeleton, the proposed algorithm is able to produce skeletons that are quite close to what we intuitively feel it should be. In Kohonen's self-organizing model, the set of processors and their neighbourhoods are fixed. We suggest here some modifications of it in which the set of processors and their neighbourhoods change adaptively.

We consider the problem of a robot which has to find a path in an unknown simple polygon from one point s to another point t, based only on what it has seen so far. A Street is a polygon for which the two boundary chains from s to t are mutually weakly visible, and the set of streets was the only class of polygons for which a competitive search algorithm was known.

We present efficient parallel algorithms for some geometric bipartitioning problems. Our algorithms are designed to run in the CREW PRAM model of parallel computation. These bipartition problems are the following. Given a planar point set S (left| S right| = n), a measure mu acting on S and a pair of values fmu_1 and mu_2, does there exist a bipartition S = S_1 cup S_2 such that mu(S_{1}) leqslant mu_i for i = 1,2? We present efficient parallel algorithms for several measures like diameter under L_infty and L_1 metric; area, perimeter or length of diagonal of the smallest enclosing axes-parallel rectangle and the side length of the smallest enclosing axes-parallel square. All our parallel algorithms run in O(logn) time using O{n) processors in the CREW PRAM. The work done (processor-time product) by our algorithms matches the time complexity of the best known sequential algorithms for most of these problems. As a by product of our algorithms, we can perform report mode orthogonal range queries in optimal O(logn) time using 0(1 + k/logn) processors, where k is the number of points inside the query range. The processor-time product for this range reporting algorithm is optimal.