BACKGROUND: Antigen receptors are characterized by an extreme diversity of specificities, which poses major computational and analytical challenges, particularly in the era of high-throughput immunoprofiling by next generation sequencing (NGS). The T cell Receptor/Immunoglobulin Profiler (TRIP) tool offers the opportunity for an in-depth analysis based on the processing of the output files of the IMGT/HighV-Quest tool, a standard in NGS immunoprofiling, through a number of interoperable modules. These provide detailed information about antigen receptor gene rearrangements, including variable (V), diversity (D) and joining (J) gene usage, CDR3 amino acid and nucleotide composition and clonality of both T cell receptors (TR) and B cell receptor immunoglobulins (BcR IG), and characteristics of the somatic hypermutation within the BcR IG genes. TRIP is a web application implemented in R shiny. RESULTS: Two sets of experiments have been performed in order to evaluate the efficiency and performance of the TRIP tool. The first used a number of synthetic datasets, ranging from 250k to 1M sequences, and established the linear response time of the tool (about 6 h for 1M sequences processed through the entire BcR IG data pipeline). The reproducibility of the tool was tested comparing the results produced by the main TRIP workflow with the results from a previous pipeline used on the Galaxy platform. As expected, no significant differences were noted between the two tools; although the preselection process seems to be stricter within the TRIP pipeline, about 0.1% more rearrangements were filtered out, with no impact on the final results. CONCLUSIONS: TRIP is a software framework that provides analytical services on antigen receptor gene sequence data. It is accurate and contains functions for data wrangling, cleaning, analysis and visualization, enabling the user to build a pipeline tailored to their needs. TRIP is publicly available at https://bio.tools/TRIP_-_T-cell_Receptor_Immunoglobulin_Profiler .

Item clustering is commonly used for dimensionality reduction, uncovering item similarities and connections, gaining insights of the market structure and recommendations. Hierarchical clustering methods produce a hierarchy structure along with the clusters that can be useful for managing item categories and sub-categories, dealing with indirect competition and new item categorization as well. Nevertheless, baseline hierarchical clustering algorithms have high computational cost and memory usage. In this paper we propose an innovative scalable hierarchical clustering framework, which overcomes these limitations. Our work consists of a binary tree construction algorithm that creates a hierarchy of the items using three metrics, a) Identity, b) Similarity and c) Entropy, as well as a branch breaking algorithm which composes the final clusters by applying thresholds to each branch of the tree. The proposed framework is evaluated on the popular MovieLens 20M dataset achieving significant reduction in both memory consumption and computational time over a baseline hierarchical clustering algorithm.

This paper describes a multimodal graph-based approach to address the problem of event detection and summarization in large scale image collections. A first version of our system was presented in the Yahoo-Flickr Event Summarization Challenge of ACM Multimedia 2015 [6]. The objective of the approach is to automatically detect events within millions of photos and summarizing them efficiently for user consumption. The presented approach uses a moving time window over the collection of multimedia items to build a same-event image graph and applies graph clustering to detect events. In addition, it makes use of a graph-based diversity-oriented ranking algorithm to summarize instances of the detected events. A demo of the system is online at: http://mklab.iti.gr/acmmm2015-gc/.

Due to the increasing popularity of microblogging platforms, the amount of messages (posts) related to public events, especially posts encompassing multimedia content, is steadily increasing. The inclusion of images can convey much more information about the event, compared to their text, which is typically very short (e.g., tweets). Although such messages can be quite informative regarding different aspects of the event, there is a lot of spam and redundancy making it challenging to extract pertinent insights. In this work, we describe a summarization framework that, given a set of social media messages about an event, aims to select a subset of images derived from them, that, at the same time, maximizes the relevance of the selected images and minimizes their redundancy. To this end, we propose a topic modeling technique to capture the relevance of messages to event topics and a graph-based algorithm to produce a diverse ranking of the selected high-relevance images. A user-centered evaluation on a large Twitter dataset around several real-world events demonstrates that the proposed method considerably outperforms a number of state-of-the-art summarization algorithms in terms of result relevance, while at the same time it is also highly competitive in terms of diversity. Namely, we get an improvement of 25% in terms of precision compared to the second best result, and 7% in terms of diversity.

The paper describes a multimodal graph-based system for addressing the Yahoo-Flickr Event Summarization Challenge of ACM Multimedia 2015. The objective is to automatically uncover structure within a collection of 100 million photos/videos in the form of detecting and identifying events, and summarizing them succinctly for consumer consumption. The presented system uses a sliding window over the stream of multimedia items to build and maintain a multimodal same-event image graph and applies a graph clustering algorithm to detect events. In addition, it makes use of a graph-based diversity-oriented ranking approach and a versatile event retrieval mechanism to access summarized instances of the events of interest. A demo of the system is online at http://mklab.iti.gr/acmmm2015-gc/.

The prediction of gene function from genome sequences is one of the main issues in Bioinformatics. Most computational approaches are based on the similarity between sequences to infer gene function. However, the availability of several fully sequenced genomes has enabled alternative approaches, such as phylogenetic profiles. Phylogenetic profiles are vectors which indicate the presence or absence of a gene in other genomes. The main concept of phylogenetic profiles is that proteins participating in a common structural complex or metabolic pathway are likely to evolve in a correlated fashion. In this paper, a multi level clustering algorithm of phylogenetic profiles is presented, which aims to detect inter- and intra-genome gene clusters.

The Reinforcement Learning (RL) paradigm is an appropriate formulation for agent, goal-directed, sequential decision making. In order though for RL methods to perform well in difficult, complex, real-world tasks, the choice and the architecture of an appropriate function approximator is of crucial importance. This work presents a method of automatically discovering such function approximators, based on a synergy of ideas and techniques that are proven to be working on their own. Using Echo State Networks (ESNs) as our function approximators of choice, we try to adapt them, by combining evolution and learning, for developing the appropriate ad-hoc architectures to solve the problem at hand. The choice of ESNs was made for their ability to handle both non-linear and non-Markovian tasks, while also being capable of learning online, through simple gradient descent temporal difference learning. For creating networks that enable efficient learning, a neuroevolution procedure was applied. Appropriate topologies and weights were acquired by applying the NeuroEvolution of Augmented Topologies (NEAT) method as a meta-search algorithm and by adapting ideas like historical markings, complexification and speciation, to the specifics of ESNs. Our methodology is tested on both supervised and reinforcement learning testbeds with promising results.

Web 2.0 provided internet users with a dynamic medium, where information is updated continuously and anyone can participate. Though preliminary analysis exists, there is still little understanding on what exactly stimulates users to actively participate, create and share content in online communities. In this paper we present a methodology that aspires to identify and analyze those events that trigger web user activity, content creation and sharing in Web 2.0. Our approach is based on user personality and motivation, and on the occurrence of events with a personal or global impact. The proposed methodology was applied on data collected from Flickr and analysis was performed through the use of statistics and data mining techniques.

In this paper, a novel transform-based, blind and robust 3-D mesh watermarking scheme is presented. The 3-D surface of the mesh is firstly divided into a number of discrete continuous regions, each of which is successively sampled and mapped onto oblate spheroids, using a novel surface parameterization scheme. The embedding is performed in the spheroidal harmonic coefficients of the spheroids, using a novel embedding scheme. Changes made to the transform domain are then reversed back to the spatial domain, thus forming the watermarked 3-D mesh. The embedding scheme presented herein resembles, in principal, the ones using the multiplicative embedding rule (inherently providing high imperceptibility). The watermark detection is blind and by far more powerful than the various correlators typically incorporated by multiplicative schemes. Experimental results have shown that the proposed blind watermarking scheme is competitively robust against similarity transformations, connectivity attacks, mesh simplification and refinement, unbalanced resampling, smoothing and noise addition, even when juxtaposed to the informed ones.

Semantic annotation and querying is currently applied on a number of versatile disciplines, providing the added-value of such an approach and, consequently the need for more elaborate - either case-specific or generic - tools. In this context, we have developed Eikonomia: an integrated semantically-aware tool for the description and retrieval of Byzantine artwork Information. Following the needs of the ORMYLIA Art Diagnosis Center for adding semantics to their legacy data, an ontology describing Byzantine artwork based on CIDOC-CRM, along with the interfaces for synchronization to and from the existing RDBMS have been implemented. This ontology has been linked to a reasoning tool, while a dynamic interface for the automated creation of semantic queries in SPARQL was developed. Finally, all the appropriate interfaces were instantiated, in order to allow easy ontology manipulation, query results projection and restrictions creation.

Knowledge extraction and acute forecasting are among the most challenging issues concerning the use of computational intelligence (CI) methods in real world applications. Both aspects are essential in cases where decision making is required, especially in domains directly related to the quality of life, like the quality of the atmospheric environment. In the present paper we emphasize on short term Air Quality (AQ) forecasting as a key constituent of every AQ management system, and we apply various CI methods and tools for assessing PMio concentration values. We report our experimental strategy and preliminary results that reveal interesting interrelations between AQ and various city operations, while performing satisfactory in predicting concentration values.

Simulating the psycho-societal aspects of a human community is an issue always intriguing and challenging, aspiring us to help better understand, macroscopically, the way(s) humans behave. The mathematical models that have extensively been used for the analytical study of the various related phenomena prove inefficient, since they cannot conceive the notion of population heterogeneity, a parameter highly critical when it comes to community interactions. Following the more successful paradigm of artificial societies, coupled with multi-agent systems and other Artificial Intelligence primitives, and extending previous epidemiological research work, we have developed GeneCity: an extended agent community, where agents live and interact under the veil of a hereditary epidemic. The members of the community, which can be either healthy, carriers of a trait, or patients, exhibit a number of human-like social (and medical) characteristics: wealth, acceptance and influence, fear and knowledge, phenotype and reproduction ability. GeneCity provides a highly-configurable interface for simulating social environments and the way they are affected with the appearance of a hereditary disease, either Autosome or X-linked. This paper presents an analytical overview of the work conducted and examines a testhypothesis based on the spreading of Thalassaemia major.

Although search engines traditionally use spiders for traversing and indexing the Web, there has not yet been any methodological attempt to model, deploy and test learning spiders. The flourishing of the semantic Web provides understandable information that may improve the accuracy of search engines. In this paper, we introduce BioSpider, an agent-based simulation framework for developing and testing autonomous, intelligent, semantically-focused Web spiders. BioSpider assumes a direct analogy of the problem at hand with a multi-variate ecosystem, where each member is self-maintaining. The population of the ecosystem comprises cooperative spiders incorporating communication, mobility and learning skills, striving to improve efficiency. Genetic algorithms and classifier rules have been employed for spider adaptation and learning. A set of experiments has been performed in order to qualitatively test the efficacy and applicability of the proposed approach

The study of distributed computational systems issues, such as heterogeneity, concurrency, control, and coordination, has yielded a number of models and architectures, which aspire to provide satisfying solutions to each of the above problems. One of the most intriguing and complex classes of distributed systems are computational ecosystems, which add an "ecological" perspective to these issues and introduce the characteristic of self-organization. Extending previous research work on self-organizing communities, we have developed Biotope, which is an agent simulation framework, where each one of its members is dynamic and self-maintaining. The system provides a highly configurable interface for modeling various environments as well as the "living" or computational entities that reside in them, while it introduces a series of tools for monitoring system evolution. Classifier systems and genetic algorithms have been employed for agent learning, while the dispersal distance theory has been adopted for agent replication. The framework has been used for the development of a characteristic demonstrator, where Biotope agents are engaged in well-known vital activities-nutrition, communication, growth, death-directed toward their own self-replication, just like in natural environments. This paper presents an analytical overview of the work conducted and concludes with a methodology for simulating distributed multiagent computational systems.

With the ever-expanding size of multimedia databases it is important to seek new technologies that improve the performance of systems that manage these databases. One such technology is holographic memory which has some very interesting attributes such as massive parallelism, high speed, and content searching. A volume holographic database system (VHDS) serves as a building block for a postulated terabyte electro-optical computer architecture (EOCA). Data mining and image management applications are considered for the EOCA. Using the associative processing capabilities of the holographic memory a number of queries can be completed in a matter of seconds for the entire terabyte database. These queries could take hours on sequential computers. However, the state of the art in holographic memory lags far behind electronic computing and thus considerably more research and development must be performed before these systems can realize their vast potential.

In this paper we propose a full text search system based on optics. The storage and processing of the textual data are performed by an optical back-end system to an electronic computer. In this way we can take advantage of the speed and parallelism of digital optical processing. Using the proposed configuration we show how one might implement a set of text processing operations using lasers, spatial light modulators and photodetectors.

Storage, interconnection, and processing are discussed. Various types of optical disks and page-oriented holographic memories are considered. It is shown that optical storage is advancing rapidly and holds the potential of hundreds of megabytes per second data rates from a single storage unit, which can provide many new opportunities for supercomputing. Module-to-module, board-to-board, and chip-to-chip interconnection and gate-to-gate communication are discussed. It is concluded that optical interconnection is, in many cases, superior to electronic interconnection and holds the key to the development of future electrooptic systems. Optical computing devices are discussed and various application areas where optical processing as well as storage and interconnection are expected to play a role in the future are considered. The authors believe that optical processing, while holding considerable promise, lags behind its electronic counterpart primarily due to the fact that digital optical device development is in its infancy. They predict near-term systems will be electrooptic, with each technology providing its strength to the problem at hand.>

The authors assess the possible impact of optics on database and knowledge base systems, focusing on storage, interconnection, and processing. Various types of optical disks and page-oriented holographic memories are discussed. In the interconnection section, data communication is discussed at a variety of levels. Under processing, applications involving optical content addressable memories, optical data/knowledge base machines, and optics applied to full text processing will be optoelectronic, with easy technology providing its strength to the problem under consideration. It is noted that optical interconnection is superior to electronic interconnection in many cases and that the rapid advance of optical storage holds the potential of hundreds of megabytes per second data rates from a single storage unit. It is noted that optical processing holds considerable promise but lags behind primarily because digital optical device development is in its infancy.>