A comprehensive view of agent-oriented patterns

This paper presents a comprehensive framework for classifying, analyzing and describing agent-oriented software patterns. We present a two dimensional scheme for classifying agent-oriented patterns. We define agent-oriented pattern attributes and use these attributes in a process for analyzing and placing agent-oriented patterns within the classification scheme. Using the proposed classification scheme, we classify 28 patterns written by a variety of agent researchers, chosen from almost 100 patterns which we have studied.     

A distortion measure for blocking artifacts in images based onhuman visual sensitivity

A visual model that gives a distortion measure for blocking artifacts in images is presented. Given the original and reproduced image as inputs, the model output is a numerical value that quantifies the visibility of blocking error in the reproduced image. The model is derived based on the human visual sensitivity to horizontal and vertical edge artifacts that result from blocking. Psychovisual experiments have been carried out to measure the visual sensitivity to these artifacts. In the experiments, typical edge artifacts are shown to subjects and the sensitivity to them is measured with the variation of background luminance, background activity, edge length, and edge amplitude. Synthetic test patterns are used as background images in the experiments. The sensitivity measures thus obtained are used to estimate the model parameters. The final model is tested on real images, and the results show that the error visibility predicted by the model correlates well with the subjective ranking.     

Personalized trip recommendation for tourists based on user interests,points of interest visit durations and visit recency

Tour recommendation and itinerary planning are challenging tasks for tourists, due to their need to select points of interest (POI) to visit in unfamiliar cities and to select POIs that align with their interest preferences and trip constraints. We propose an algorithm called PersTour for recommending personalized tours using POI popularity and user interest preferences, which are automatically derived from real-life travel sequences based on geo-tagged photographs. Our tour recommendation problem is modeled using a formulation of the Orienteering problem and considers user trip constraints such as time limits and the need to start and end at specific POIs. In our work, we also reflect levels of user interest based on visit durations and demonstrate how POI visit duration can be personalized using this time-based user interest. Furthermore, we demonstrate how PersTour can be further enhanced by: (i) a weighted updating of user interests based on the recency of their POI visits and (ii) an automatic weighting between POI popularity and user interests based on the tourist’s activity level. Using a Flickr dataset of ten cities, our experiments show the effectiveness of PersTour against various collaborative filtering and greedy-based baselines, in terms of tour popularity, interest, recall, precision and F\(_1\)-score. In particular, our results show the merits of using time-based user interest and personalized POI visit durations, compared to the current practice of using frequency-based user interest and average visit durations.     

Streaming analysis in wireless sensor networks

Two new incremental models for online anomaly detection in data streams at nodes in wireless sensor networks are discussed. These models are incremental versions of a model that uses ellipsoids to detect first, second, and higher‐ordered anomalies in arrears. The incremental versions can also be used this way but have additional capabilities offered by processing data incrementally as they arrive in time. Specifically, they can detect anomalies ‘on‐the‐fly’ in near real time. They can also be used to track temporal changes in near real‐time because of sensor drift, cyclic variation, or seasonal changes. One of the new models has a mechanism that enables graceful degradation of inputs in the distant past (fading memory). Three real datasets from single sensors in deployed environmental monitoring networks are used to illustrate various facets of the new models. Examples compare the incremental version with the previous batch and dynamic models and show that the incremental versions can detect various types of dynamic anomalies in near real time. Copyright © 2012 John Wiley & Sons, Ltd.     

Improving performance in delay/disruption tolerant networks through passive relay points

In this paper, we study the case of a limited number of mobile nodes trying to communicate in a large geographic area, forming a delay/disruption tolerant network (DTN). In such networks the mobile nodes are disconnected for significantly long periods of time. Traditional routing protocols proposed for mobile ad hoc networks or mesh networks, which assume at least one path between each source and destination, are ineffective in DTNs. One approach to improve communication is through gossip based protocols because these protocols do not rely on a fixed path. Another approach is to control the movement of the mobile nodes and/or use special mobile nodes called ferry nodes. Others try to employ a fixed infrastructure including stationary relay points. One scheme in stationary relay point approach is to use base stations as relay points which need their own power supply. In this paper, we study a passive approach where mobile nodes deposit/retrieve messages to/ from known stationary locations in the geographic region. Messages are delivered from a source by being deposited at one or more locations that are later visited by the destination. A proposed implementation of our approach using read/writable passive Radio Frequency Identification (RFID) tags, one per point location, is considered in this work. Passive RFID technology is desirable because it operates wirelessly and without the need for attached power. Our simulation results indicate that our approach can achieve competitive message delay and delivery rates. We also demonstrate several techniques for optimizing the stationary relay node placement, namely relay pruning, probability based relay distribution and a genetic algorithm; the genetic algorithm is shown to provide the best solutions to this problem.     

Decentralized multi-dimensional alert correlation for collaborative intrusion detection

The growth in coordinated network attacks such as scans, worms and distributed denial-of-service (DDoS) attacks is a profound threat to the security of the Internet. Collaborative intrusion detection systems (CIDSs) have the potential to detect these attacks, by enabling all the participating intrusion detection systems (IDSs) to share suspicious intelligence with each other to form a global view of the current security threats. Current correlation algorithms in CIDSs are either too simple to capture the important characteristics of attacks, or too computationally expensive to detect attacks in a timely manner. We propose a decentralized, multi-dimensional alert correlation algorithm for CIDSs to address these challenges. A multi-dimensional alert clustering algorithm is used to extract the significant intrusion patterns from raw intrusion alerts. A two-stage correlation algorithm is used, which first clusters alerts locally at each IDS, before reporting significant alert patterns to a global correlation stage. We introduce a probabilistic approach to decide when a pattern at the local stage is sufficiently significant to warrant correlation at the global stage. We then implement the proposed two-stage correlation algorithm in a fully distributed CIDS. Our experiments on a large real-world intrusion data set show that our approach can achieve a significant reduction in the number of alert messages generated by the local correlation stage with negligible false negatives compared to a centralized scheme. The proposed probabilistic threshold approach gains a significant improvement in detection accuracy in a stealthy attack scenario, compared to a naive scheme that uses the same threshold at the local and global stages. A large scale experiment on PlanetLab shows that our decentralized architecture is significantly more efficient than a centralized approach in terms of the time required to correlate alerts.     

One isoform for one task? The second hydrogenase of Chlamydomonas reinhardtii prefers hydrogen uptake

Gaining knowledge about the algal hydrogen metabolism is prerequisite for the biotechnological exploitation of photosynthetic H₂ production. Model organism Chlamydomonas reinhardtii encodes two [FeFe]-hydrogenases, which are individually capable of catalysing the reversible reduction of protons to molecular hydrogen. While physiological results indicated that HYDA1, connected to the photosynthetic electron transfer pathway via plant-type ferredoxin PETF, is accountable for a major part of algal H₂ evolution, the role of HYDA2 in the algal metabolism is not understood yet. Herein, a comprehensive screening of enzymatic attributes was conducted, revealing that the two enzymes differ in their affinity to oxidised PETF. Notably, utilising protein film voltammetry, a higher catalytic preference of HYDA2 to consume H₂ was observed. Site directed exchange mutagenesis revealed the contribution of a hydroxyl group in place of threonine 226, present in HYDA1, but not in HYDA2, potentially influencing the electronic properties of the active site, thereby fine-tuning catalytic function.     

Middleware 2007 Works in Progress

We propose a Web-service-based P2P architecture for VoIP called service-oriented VoIP. SOVoIP provides interoperability between protocols from both telephony and data networks using the converging behavior of Web services while ensuring security, extendability, and mobility. We also address other critical issues related to VoIP such as network address translation and firewall traversal, Enhanced 911, and the Communication Assistance for Law Enforcement Act. SOVoIP also provides modularity and reusability, making client development easier. Extendability in the Web service architecture is transparent to the client, so frequent client updates aren't required in order to consume new features. This reduces the time and money spent on upgrades.     

An efficient hyperellipsoidal clustering algorithm for resource-constrained environments

Clustering has been widely used as a fundamental data mining tool for the automated analysis of complex datasets. There has been a growing need for the use of clustering algorithms in embedded systems with restricted computational capabilities, such as wireless sensor nodes, in order to support automated knowledge extraction from such systems. Although there has been considerable research on clustering algorithms, many of the proposed methods are computationally expensive. We propose a robust clustering algorithm with low computational complexity, suitable for computationally constrained environments. Our evaluation using both synthetic and real-life datasets demonstrates lower computational complexity and comparable accuracy of our approach compared to a range of existing methods.     

Optimizing checkpoint-based fault-tolerance in distributed stream processing systems: Theory to practice

Fault-tolerance is an essential part of a stream processing system that guarantees data analysis could continue even after failures. State-of-the-art distributed stream processing systems use checkpointing to support fault-tolerance for stateful computations where the state of the computations is periodically persisted. However, the frequency of performing checkpoints impacts the performance (utilization, latency, and throughput) of the system as the checkpointing process consumes resources and time that can be used for actual computations. In practice, systems are often configured to perform checkpoints based on crude values ignoring factors such as checkpoint and restart costs, leading to suboptimal performance. In our previous work, we proposed a theoretical optimal checkpoint interval that maximizes the system utilization for stream processing systems to minimize the impact of checkpointing on system performance. In this article, we investigate the practical benefits of our proposed theoretical optimal by conducting experiments in a real-world cloud setting using different streaming applications; we use Apache Flink, a well-known stream processing system for our experiments. The experiment results demonstrate that an optimal interval can achieve better utilization, confirming the practicality of the theoretical model when applied to real-world applications. We observed utilization improvements from 10% to 200% for a range of failure rates from 0.3 failures per hour to 0.075 failures per minute. Moreover, we explore how performance measures: latency and throughput are affected by the optimal interval. Our observations demonstrate that significant improvements can be achieved using the optimal interval for both latency and throughput.