PURPOSE: The article presents DiscMycoVir, an elegant and user-friendly platform for discovering mycoviruses in fungal transcriptomes. DiscMycoVir is a pipeline of established tools for next-generation sequencing analysis and database searching, incorporated in an interface that facilitates accessibility even for users that have no programming skills and expertise. A comprehensive and detailed result report enhances user experience. DiscMycoVir can be accessed online for reviewing purposes at: https://discmycovir.imslab.gr:8000 and the source code is located at https://github.com/abompotas/DiscMycoVir . We recommend using the GitHub repository, as the online platform may lack the necessary resources to ensure uninterrupted service especially on large files. METHODS-RESULTS: We employed state-of-the-art technologies in the design and implementation phase of the platform. We present the application of the platform in screening RNA-seq data from the yeast Candida auris for mycoviruses, demonstrating its efficiency and simplicity in use. CONCLUSIONS: DiscMycoVir serves as a user-friendly platform for identifying mycoviruses in RNA-seq data. Our tool was successfully implemented to discover mycoviruses in a C. auris isolate and could be adapted to detect viruses in transcriptomes from other organisms as well.

This paper presents a straightforward yet novel approach to enhance graph-based information retrieval models , by calibrating the relationships between node terms, leading to better evaluation metrics at the retrieval phase , and by reducing the total size of the graph. This is achieved by integrating spectral clustering , embedding-based graph pruning and term re-weighting. Spectral clustering assigns each term to a specific cluster, allowing us to propose two pruning methods: out-cluster and in-cluster pruning based on node similarities. In-cluster pruning refers to pruning edges between terms within the same cluster, while out-cluster pruning refers to edges that connect different clusters. Both methods utilize spectral embeddings to assess node similarities, resulting in more manageable clusters termed concepts . These concepts are likely to contain semantically similar terms, with each term’s concept defined as the centroid of its cluster. We show that this graph pruning strategy significantly enhances the performance and effectiveness of the overall model, reducing, at the same time, its graph sparsity . Moreover, during the retrieval phase , the conceptually calibrated centroids are used to re-weight terms generated by user queries, and the precomputed embeddings enable efficient query expansion through a k-Nearest Neighbors (K-NN) approach, offering substantial enhancement with minimal additional time cost. To the best of our knowledge, this is the first application of spectral clustering and embedding-based conceptualization to prune graph-based IR models. Our approach enhances both retrieval efficiency and performance while enabling effective query expansion with minimal additional computational overhead. Our proposed technique is applied across various graph-based information retrieval models , improving evaluation metrics and producing sparser graphs.

The AlphaFold2 introduces an entirely new era in the field of computational biology, achieving phenomenal results in protein structure predictions. Despite its remarkable performance, the model’s prediction pipeline is highly complex, and the Deep Learning model itself is a black box, leaving researchers uncertain about how specific predictions are made. Explainable AI (XAI) offers a promising avenue for improving the transparency and trustworthiness of such models, making their predictions understandable and trustworthy. This research presents how DeepSHAP, an advanced XAI method, can be applied to explain AlphaFold2’s predictions through the OpenFold and ColabFold frameworks. DeepSHAP is designed to integrate Shapley Additive exPlanations (SHAP) values and Deep Learning Important Features (DeepLIFT) with a complex deep learning model to interpret the contribution of individual input features for a specific prediction. This study depicts that certain amino acids play key roles in the formation of distinct protein structures. Specifically, by calculating SHAP values and integrating them with DeepLIFT, it investigates several protein structures and identifies key amino acids that are most influential in AlphaFold2’s prediction process. The results demonstrate the promising potential of this approach to enhance interpretability and provide insights for the transparency of AI-based protein structure prediction models.

Abstract The purpose of this paper is to present a set of algorithmic improvements upon an extension of the Set-Based Model [14] that focuses on the dependence among document terms employing graph representations. A graph-based approach to document representation can positively affect the information retrieval process due to the ability of graphs to capture the syntactic notion and, in some cases, the semantic relationship among document terms. The aforementioned model generates complete graphs; thus, each document term will be interdependent with the rest. Consequently, an interdependent segment or multiple parts of a document called a window is defined, in which the graph creation algorithms are applied. The proposed methods aim to approximate the window size by exploiting the document length. Moreover, an attempt to create multiple windows is made, considering the relationship between a sentence and a paragraph, which is reflected in the semantic importance of nodes and edges. An attempt to tackle the stop-word detection problem on bridge nodes is made by implementing algorithmic schemes that use core decomposition [41] to identify the importance of such nodes in a sample of the corpus collection. Finally, a simple reranking scheme and an ensemble voting technique are implemented to enhance model performance on queries where the proposed approach lacks performance. The experimental analysis made on multiple document collections exhibits performance improvements and in some queries, our approach outperforms even state-of-the-art models such as BM25 [36] and ColBERT [17].

In recent years social media data analytics allow enterprises to adopt a data driven approach to manage their processes. Although social media provide a plethora of data, still much work needs to be done to transform these data into services that businesses can use and make insightful decisions. In this work, we address one of the most important problems in business, the relationship with the customers and more precisely the identification of loyal customers. We use behavioral analytics to model and process customer actions. We extract user behavior based on our unified crawling approach and collect data from three different social media namely Reddit, Twitter and YouTube. We are extracting for each user three different behaviors namely communication, sentiment and product mix and convert them into a 3-D time-series. We use shapelet clustering to determine the loyal users. To verify our approach, we develop a set of metrics based on trust, commitment and engagement and we show that our approach results in differentiating the loyal users successfully. Moreover, we validate our results presenting a word cloud visualization. We extend our methodology introducing a semantic data transformation algorithm where we use the topic extraction, reducing the time-series to a more relevant one. Our experiments show that based on the verification metrics, our transformation increases the accuracy of the clustering results.

The identification of social media communities has recently been of major concern, since users participating in such communities can contribute to viral marketing campaigns. In this work, we focus on users' communication considering personality as a key characteristic for identifying communicative networks i.e., networks with high information flows. We describe the Twitter Personality based Communicative Communities Extraction (T-PCCE) system that identifies the most communicative communities in a Twitter network graph considering users' personality. We then expand existing approaches in users' personality extraction by aggregating data that represent several aspects of user behavior using machine learning techniques. We use an existing modularity based community detection algorithm and we extend it by inserting a post-processing step that eliminates graph edges based on users' personality. The effectiveness of our approach is demonstrated by sampling the Twitter graph and comparing the communication strength of the extracted communities with and without considering the personality factor. We define several metrics to count the strength of communication within each community. Our algorithmic framework and the subsequent implementation employ the cloud infrastructure and use the MapReduce Programming Environment. Our results show that the T-PCCE system creates the most communicative communities.

Digital influence and trust are central research topics in social media analysis with a plethora of applications ranging from social login to geolocation services and community structure discovery. In the evolving and diverse microblogging sphere of Twitter verified accounts reinforce digital influence through trust. These typically correspond either to an organization or to a person of high social status or to netizens who have been consistently proven to be highly influential. This conference paper presents a framework for estimating the probability that the next mention of any account will be to a verified account, an important metric of digital influence. At the heart of this framework lies a convolutional neural network (CNN) implemented in keras over TensorFlow. The training features are extracted from a dataset of tweets regarding the presentation of the Literature Nobel prize to Bob Dylan collected with the Twitter Streaming API and stored in MongoDB. In order to demonstrate the performance of the CNN, the results obtained by applying logistic regression to the same training features are shown in the form of statistical metrics computed from the corresponding contingency matrices, which are obtained using the pandas Python library.

In this paper we analyze and present an important aspect of Twitter platform concerning the Influence of a user. Building on existing work regarding influential users, we propose different Influence Metrics that take into account Twitter analytics as well as the behavior of each User's Followers. Twitter enables users so as to read and send short 140-character messages called "tweets" while helps them understand what is happening in the world. Exceeding 241 million active users, 500 million Tweets and 2.1 billion searches per day, every user and particularly online marketers have an actively informed audience to engage with. The flow of information has been implicitly filtered and annotated with meta-information such as reputation and influence. Social Influence can be described as the power or even the ability of a person to yet influence the thoughts and actions of other users. So, User Influence stands as a value that depends on the interest of the Followers (via Replies, Mentions, Retweets, Favorites). Additionally we look into the potential quantity of Influence these specific Followers have.

In this paper, we describe two novel frameworks for improving search results. Both of them organize relevant documents into clusters utilizing a new soft clustering method and language models. The first framework is query-independent and takes into account only the inter-document lexical or semantic similarities in order to form clusters. Also, we try to locate the duplicated content inside the formed clusters. The second framework is query-dependent and uses a query expansion technique for the cluster formation. The experimental evaluation demonstrates that the proposed method performs well in the majority of the results.

Over the last years, the advent of social networks has changed the way of human communication giving users the ability to express their thoughts and opinions. In this paper, we present a work on analyzing human communication and interaction in Twitter. The aim is to get indicative factors about user's behavior as well as public stance and attitude towards various events around the globe. The methodology initially analyzes users' tweets and determines their emotional content based on Ekman emotional scale. Then, user's characteristics and behavior in Twitter are analyzed and their influence in the network is calculated. Based on tweets emotional content as well as user's influence, the conversation emotional graphs are developed to model and represent user's emotional interactions. Furthermore, we introduce a prediction method using machine learning techniques in order to discover the changes of topic emotional content during users' discussion.

In this paper, we address the problem of handling weighted sequences. This is by taking advantage of the inverted files machinery and targeting text processing applications, where the involved documents cannot be separated into words (such as texts representing biological sequences) or word separation is difficult and involves extra linguistic knowledge (texts in Asian languages). Besides providing a handling of weighted sequences using n-grams, we also provide a study of constructing space efficient n-gram inverted indexes. The proposed techniques combine classic straightforward n-gram indexing, with the recently proposed twolevel n-gram inverted file technique. The final outcomes are new data structures for n-gram indexing, which perform better in terms of space consumption than the existing ones. Our experimental results are encouraging and depict that these techniques can surely handle n-gram indexes more space efficiently than already existing

In this article, we propose new word sense disambiguation strategies for resolving the senses of polysemous query terms issued to Web search engines, and we explore the application of those strategies when used in a query expansion framework. The novelty of our approach lies in the exploitation of the Web page PageRank values as indicators of the significance the different senses of a term carry when employed in search queries. We also aim at scalable query sense resolution techniques that can be applied without loss of efficiency to large data sets such as those on the Web. Our experimental findings validate that the proposed techniques perform more accurately than do the traditional disambiguation strategies and improve the quality of the search results, when involved in query expansion.

Clustering is particularly useful in problems where there is little prior information about the data under analysis. This is usually the case when attempting to evaluate a software system's maintainability, as many dimensions must be taken into account in order to reach a conclusion. On the other hand partitional clustering algorithms suffer from being sensitive to noise and to the initial partitioning. In this paper we propose a novel partitional clustering algorithm, k-Attractors. It employs the maximal frequent itemset discovery and partitioning in order to define the number of desired clusters and the initial cluster attractors. Then it utilizes a similarity measure which is adapted to the way initial attractors are determined. We apply the k-Attractors algorithm to two custom industrial systems and we compare it with WEKA 's implementation of K-Means. We present preliminary results that show our approach is better in terms of clustering accuracy and speed.

A multirepeat in a string is a substring (factor) that appears a predefined number of times. A multirepeat is maximal if it cannot be extended either to the right or to the left and produce a multirepeat. In this paper, we present algorithms for two different versions of the problem of finding maximal multirepeats in a set of strings. In the case of arbitrary gaps, we propose an algorithm with O ( σN2n + α ) time complexity. When the gap is bounded in a small range c , we propose an algorithm with O (( c2 + σ2 ) mN2n log( Nn ) + α ) time complexity. Here, N is the number of strings, n the mean length of each string, m the multiplicity of the multirepeat and α the number of reported occurrences. Our results extend previous work by considering sets of strings as well as by generalizing pairs to multirepeats.

Late developments in Web Service discovery approaches mainly focus on the incorporation of the QoS concept in the selection process. In this paper, we present a generalized design and implementation of a QoS enabled Web Service discovery mechanism. We describe a mechanism extending the propositions of [1] in a generalized environment that consists of dynamic groups/ categories of different Web Services. A UDDI search wrapper is introduced to facilitate the traditional WS discovery by taking into account QoS characteristics in transparent way. Our aim is the combination of the classic Web Service discovery, at design or execution time, with the benefit of QoS characteristics without affecting the existing UDDI search facilities. To evaluate the functionality, feasibility and effectiveness of the mechanism proposed, a number of experimental applications have been developed. All applications have been available for evaluation online in laboratory.

We develop a new finger search tree with worst-case constant update time in the Pointer Machine (PM) model of computation. This was a major problem in the field of Data Structures and was tantalizingly open for over twenty years while many attempts by researchers were made to solve it. The result comes as a consequence of the innovative mechanism that guides the rebalancing operations combined with incremental multiple splitting and fusion techniques over nodes.

Describes a system offering a solution to the problem of ambulance management and emergency incident handling in the prefecture of Attica in Greece. It is based on a geographic information system (GIS) coupled with GPS (Global Positioning System) and GSM (Global System for Mobile Communication) technologies. The system's operation is expected to minimize the ambulances' response time. Consequently, there will be a drastic improvement in the way emergency incidents are being handled. This fact will thus significantly affect the quality of health services offered to citizens.

We present simple algorithms for three problems belonging to the class of intersection query problems. The first algorithm deals with the static rectangle enclosure problem and can easily be extended to d dimensions, the second algorithm copes with the generalized c-oriented polygon intersection searching problem in two dimensions, while the third solves the static 2-dimensional dominance searching problem with respect to a set of obstacles. All algorithms are simple, are based on persistence and improve previous bounds. Also, as a corollary of the first algorithm, we present a result for the static d-dimensional range searching problem.

Let Q 1 be a set of'red' geometric objects and Q 2 a set of 'blue' ones. The objects in Q 1 and Q 2 are of arbitrarily large description size but each one is the union of simpler constant size components. We consider the problem of reporting all intersections between objects of Q 1 and objects of Q 2 in a time that depends on the size of the output. This is equivalent to painting the constant size objects in Q 1 and Q 2 and reporting colour intersections between Q 1 and Q 2 . We present a technique that yields simple output-sensitive algorithms for many kinds of geometric objects. We also show that, using the same technique, it is possible to report all intersecting pairs in a set of objects in an output-sensitive manner.

The retrieval capabilities of the signature file access method have become very attractive for data processing applications dealing with both formatted and unformatted data. Multimedia databases should also take advantage of this method, provided convenient storage structures for organizing signature files are available. In this paper a high performance dynamic signature file organization is proposed, combining the latest developments in storage structures (dynamic partitioning schemes) with the classical multilevel organizations in an efficient and effective way. We call the method the parametric weighted filter (PWF) and the performance analysis shows its superiority over previous methods. Finally, refinements are proposed that can further improve the scheme and that are worth further investigation.