We introduce AI Harmonics ( AIH ), a novel metric designed to quantify the concentration of harms across stakeholder groups affected by AI systems. Unlike traditional approaches that rely on arbitrary numerical assignments to ordinal severity levels, AIH provides a principled framework grounded in inequality theory, extending concepts from the Gini index to purely ordinal data. The metric evaluates how harm is distributed among stakeholders, capturing whether severe impacts are concentrated within specific groups or more evenly spread. Experiments on annotated incident data show that the proposed metric exhibits a strong monotonic relationship with the Criticality Index (CI), preserving harm category rankings while capturing additional variation in concentration patterns. The method demonstrates high robustness, with Spearman rank correlations above 0.97 under severity perturbations and stable prioritization even under up to 80% random data removal. Political and physical harms consistently exhibit the highest concentration, indicating the need for urgent mitigation. Political harms erode public trust, while physical harms pose serious, even life-threatening risks, underscoring the real-world relevance of our approach. The AIH metric is particularly well-suited for policy-making and risk management, where only ordinal assessments are available, and it enables more informed prioritization of mitigation strategies.

The Internet Yellow Pages (IYP) aggregates information from multiple sources about Internet routing into a unified, graph-based knowledge base. However, querying it requires knowledge of the Cypher language and the exact IYP schema, thus limiting usability for non-experts. In this paper, we propose ChatIYP, a domain-specific Retrieval-Augmented Generation (RAG) system that enables users to query IYP through natural language questions. Our evaluation demonstrates solid performance on simple queries, as well as directions for improvement, and provides insights for selecting evaluation metrics that are better fit for IYP querying AI agents.

Self-supervised learning (SSL) has become the de facto training paradigm of large models, where pre-training is followed by supervised fine-tuning using domain-specific data and labels. Despite demonstrating comparable performance with supervised methods, comprehensive efforts to assess SSL's impact on machine learning fairness (i.e., performing equally on different demographic breakdowns) are lacking. Hypothesizing that SSL models would learn more generic, hence less biased representations, this study explores the impact of pre-training and fine-tuning strategies on fairness. We introduce a fairness assessment framework for SSL, comprising five stages: defining dataset requirements, pre-training, fine-tuning with gradual unfreezing, assessing representation similarity conditioned on demographics, and establishing domain-specific evaluation processes. We evaluate our method's generalizability on three real-world human-centric datasets (i.e., MIMIC, MESA, and GLOBEM) by systematically comparing hundreds of SSL and fine-tuned models on various dimensions spanning from the intermediate representations to appropriate evaluation metrics. Our findings demonstrate that SSL can significantly improve model fairness, while maintaining performance on par with supervised methods-exhibiting up to a 30% increase in fairness with minimal loss in performance through self-supervision. We posit that such differences can be attributed to representation dissimilarities found between the best- and the worst-performing demographics across models-up to x13 greater for protected attributes with larger performance discrepancies between segments. Code: https://github.com/Nokia-Bell-Labs/SSLfairness

Cancer prevalence, particularly among older individuals, imposes a significant burden on healthcare systems. However, care for older cancer patients is often insufficient and fails to address their specific needs. The LifeChamps H2020 EU project aims to develop a patient-centered digital platform to improve monitoring, anticipation, and support for complications that can deteriorate the health-related quality of life (HRQoL) of older cancer survivors. By facilitating comprehensive clinical assessments and new integrated care models, it addresses the challenges and gaps in clinical practice, promoting patient-centered care through remote digital monitoring tools that collect HRQoL data not typically captured in routine clinical practice.

Ubiquitous devices, such as smartphones and wearables, are becoming increasingly popular for monitoring user behavior, health, and well-being. Through omnipresent monitoring, they aim to raise user awareness and encourage positive health behavior change. Yet, ubiquitous technologies suffer from expectation mismatch, lack of user-centric adaptiveness, and, ultimately, high abandonment rates. This work is motivated by the vital need to tailor and personalize ubiquitous technologies while dealing with the challenges arising from the lack of user profiling and the absence of relevant, self-reported user data. To this end, we show that the automatically passively collected sensing data from the wearables can be exploited to improve personalization and infer several user states relevant to demographic, physiological, psychological, and personality aspects, complementing the need for time-consuming self-reports. To accomplish this task, and enable the reproducibility and extensibility of our work; we propose an extensive benchmark suite by exploiting sensing data harvested from ubiquitous devices.Our benchmark covers a wide range of personalization tasks, including modeling gender, age, personality states, and stress, experimenting on the publicly available, newly released LifeSnaps dataset containing over 71 million rows of data capturing the daily lives of 71 participants in their naturalistic environments. The proposed benchmarking continuum showcases the strong potential for the applicability of the presented work in critical applications, such as mental healthcare monitoring, privacy preservation, and responsible artificial intelligence (AI), by fostering fairness assessments when protected attribute knowledge is unavailable.

In today’s connected society, many people rely on mHealth and self-tracking (ST) technology to help them adopt healthier habits with a focus on breaking their sedentary lifestyle and staying fit. However, there is scarce evidence of such technological interventions’ effectiveness, and there are no standardized methods to evaluate their impact on people’s physical activity and health. This work aims to help ST practitioners and researchers by empowering them with systematic guidelines and a framework for designing and evaluating technological interventions to facilitate health behavior change and user engagement, focusing on increasing physical activity and decreasing sedentariness. To this end, we conduct a literature review of 129 papers between 2008 and 2022, which identifies the core ST design principles and their efficacy, as well as the most comprehensive list to date of user engagement evaluation metrics for ST. Based on the review’s findings, we propose PAST SELF, a framework to guide the design and evaluation of ST technology that has potential applications in industrial and scientific settings. Finally, to facilitate researchers and practitioners, we complement this article with an open corpus and an online, adaptive exploration tool for the PAST SELF data.

Online social networks are actively involved in removing malicious social bots due to their role in spreading low-quality information. However, most of the existing bot detectors are supervised classifiers incapable of capturing the evolving behavior of sophisticated bots. Here we propose MulBot, an unsupervised bot detector based on multivariate time series (MTS). For the first time, we exploit multidimensional temporal features extracted from user timelines. We manage the multidimensionality with an LSTM autoencoder, which projects the MTS in a suitable latent space. Then, we perform a clustering step on this encoded representation to identify dense groups of very similar users – a known sign of automation. Finally, we perform a binary classification task achieving f1-score =0.99, outperforming state-of-the-art methods (f1-score ≤ 0.97). Not only does MulBot achieve excellent results in the binary classification task, but we also demonstrate its strengths in a novel and practically-relevant task: detecting and separating different botnets. In this multiclass classification task we achieve f1-score =0.96. We conclude by estimating the importance of the different features used in our model and by evaluating MulBot’s capability to generalize to new unseen bots, thus proposing a solution to the generalization deficiencies of supervised bot detectors.

It is indisputable that physical activity is vital for an individual’s health and wellness. However, a global prevalence of physical inactivity has induced significant personal and socioeconomic implications. In recent years, a significant amount of work has showcased the capabilities of self-tracking technology to create positive health behavior change. This work is motivated by the potential of personalized and adaptive goal-setting techniques in encouraging physical activity via self-tracking. To this end, we propose UBIWEAR, an end-to-end framework for intelligent physical activity prediction, with the ultimate goal to empower data-driven goal-setting interventions. To achieve this, we experiment with numerous machine learning and deep learning paradigms as a robust benchmark for physical activity prediction tasks. To train our models, we utilize, "MyHeart Counts", an open, large-scale dataset collected in-the-wild from thousands of users. We also propose a prescriptive framework for self-tracking aggregated data preprocessing, to facilitate data wrangling of real-world, noisy data. Our best model achieves a MAE of 1087 steps, 65% lower than the state of the art in terms of absolute error, proving the feasibility of the physical activity prediction task, and paving the way for future research.

Modern techniques in intrusion and DoS (Denial of Service) detection tend to be either supervised or semi-supervised, i.e., they require training and labelled data. In this work, we study the problem of correlating security attacks with anomalies reported at runtime by a fully unsupervised outlier detection module, i.e., a component that does not require any training at all. Through a concrete proof-of-concept case study, we demonstrate that unsupervised anomaly detection is both efficient and effective, but still, it needs to be combined with additional mechanisms to yield a complete intrusion detection and prevention solution.

Users in Online Social Networks (OSNs,) leave traces that reflect their personality characteristics. The study of these traces is important for several fields, such as social science, psychology, marketing, and others. Despite a marked increase in research on personality prediction based on online behavior, the focus has been heavily on individual personality traits, and by doing so, largely neglects relational facets of personality. This study aims to address this gap by providing a prediction model for holistic personality profiling in OSNs that includes socio-relational traits (attachment orientations) in combination with standard personality traits. Specifically, we first designed a feature engineering methodology that extracts a wide range of features (accounting for behavior, language, and emotions) from the OSN accounts of users. Subsequently, we designed a machine learning model that predicts trait scores of users based on the extracted features. The proposed model architecture is inspired by characteristics embedded in psychology; i.e, it utilizes interrelations among personality facets and leads to increased accuracy in comparison with other state-of-the-art approaches. To demonstrate the usefulness of this approach, we applied our model on two datasets, namely regular OSN users and opinion leaders on social media, and contrast both samples’ psychological profiles. Our findings demonstrate that the two groups can be clearly separated by focusing on both Big Five personality traits and attachment orientations. The presented research provides a promising avenue for future research on OSN user characterization and classification.

Aggression in online social networks has been studied mostly from the perspective of machine learning, which detects such behavior in a static context. However, the way aggression diffuses in the network has received little attention as it embeds modeling challenges. In fact, modeling how aggression propagates from one user to another is an important research topic, since it can enable effective aggression monitoring, especially in media platforms, which up to now apply simplistic user blocking techniques. In this article, we address aggression propagation modeling and minimization in Twitter, since it is a popular microblogging platform at which aggression had several onsets. We propose various methods building on two well-known diffusion models, Independent Cascade ( IC ) and Linear Threshold ( LT ), to study the aggression evolution in the social network. We experimentally investigate how well each method can model aggression propagation using real Twitter data, while varying parameters, such as seed users selection, graph edge weighting, users’ activation timing, and so on. It is found that the best performing strategies are the ones to select seed users with a degree-based approach, weigh user edges based on their social circles’ overlaps, and activate users according to their aggression levels. We further employ the best performing models to predict which ordinary real users could become aggressive (and vice versa) in the future, and achieve up to AUC = 0.89 in this prediction task. Finally, we investigate aggression minimization by launching competitive cascades to “inform” and “heal” aggressors. We show that IC and LT models can be used in aggression minimization, providing less intrusive alternatives to the blocking techniques currently employed by Twitter.

BGP prefix hijacking is a critical threat to the resilience and security of communications in the Internet. While several mechanisms have been proposed to prevent, detect or mitigate hijacking events, it has not been studied how to accurately quantify the impact of an ongoing hijack. When detecting a hijack, existing methods do not estimate how many networks in the Internet are affected (before and/or after its mitigation). In this paper, we study fundamental and practical aspects of the problem of estimating the impact of an ongoing hijack through network measurements. We derive analytical results for the involved trade-offs and limits, and investigate the performance of different measurement approaches (control/data-plane measurements) and use of public measurement infrastructure. Our findings provide useful insights for the design of accurate hijack impact estimation methodologies. Based on these insights, we design (i) a lightweight and practical estimation methodology that employs ping measurements, and (ii) an estimator that employs public infrastructure measurements and eliminates correlations between them to improve the accuracy. We validate the proposed methodologies and findings against results from hijacking experiments we conduct in the real Internet.

Abstract Due to their confidence and dominance, narcissistic leaders oftentimes can be perceived favorably by followers, in particular during times of uncertainty. In this study, we propose and examine the relationship between narcissistic leaders and followers who are prone to experience uncertainty intensely and frequently in general, namely highly anxious followers. We do so by applying machine learning algorithms to account for personality traits in a large sample of leaders and followers on Twitter. We find that highly anxious followers are more likely to interact with narcissistic leaders in general, and male narcissistic leaders in particular. Finally, we also examined these interactions in the context of highly popular leaders and found that as leaders become more popular, they begin to attract less anxious followers, regardless of leader gender. We interpret and discuss these findings in relation to previous work and outline limitations and future research recommendations based on our approach.

Popular microblogging platforms (such as Twitter) offer a fertile ground for open communication among humans, however, they also attract many bots and automated accounts "disguised" as human users. Typically, such accounts favor malicious activities such as phishing, public opinion manipulation and hate speech spreading, to name a few. Although several AI driven bot detection methods have been implemented, the justification of bot classification and characterization remains quite opaque and AI decisions lack in ethical responsibility. Most of these approaches operate with AI black-boxed algorithms and their efficiency is often questionable. In this work we propose Bot-Detective, a web service that takes into account both the efficient detection of bot users and the interpretability of the results as well. Our main contributions are summarized as follows: i) we propose a novel explainable bot-detection approach, which, to the best of authors' knowledge, is the first one to offer interpretable, responsible, and AI driven bot identification in Twitter, ii) we deploy a publicly available bot detection Web service which integrates an explainable ML framework along with users feedback functionality under an effective crowdsourcing mechanism; iii) we build the proposed service under a newly created annotated dataset by exploiting Twitter's rules and existing tools. This dataset is publicly shared for further use. In situ experimentation has showcased that Bot-Detective produces comprehensive and accurate results, with a promising service take up at scale.

Cyberbullying and cyberaggression are increasingly worrisome phenomena affecting people across all demographics. More than half of young social media users worldwide have been exposed to such prolonged and/or coordinated digital harassment. Victims can experience a wide range of emotions, with negative consequences such as embarrassment, depression, isolation from other community members, which embed the risk to lead to even more critical consequences, such as suicide attempts. In this work, we take the first concrete steps to understand the characteristics of abusive behavior in Twitter, one of today’s largest social media platforms. We analyze 1.2 million users and 2.1 million tweets, comparing users participating in discussions around seemingly normal topics like the NBA, to those more likely to be hate-related, such as the Gamergate controversy, or the gender pay inequality at the BBC station. We also explore specific manifestations of abusive behavior, i.e., cyberbullying and cyberaggression, in one of the hate-related communities (Gamergate). We present a robust methodology to distinguish bullies and aggressors from normal Twitter users by considering text, user, and network-based attributes. Using various state-of-the-art machine-learning algorithms, we classify these accounts with over 90% accuracy and AUC. Finally, we discuss the current status of Twitter user accounts marked as abusive by our methodology and study the performance of potential mechanisms that can be used by Twitter to suspend users in the future.

Recent advancements in information and communication technologies resulted an increase on data produced by the cities. These data are either produced on social media networks or collected by public services and are usually enriched with useful geospatial information. This influx of information coupled with the urge of urban designers to make cities smarter, led to the development of intelligent applications designed to facilitate the everyday lives of citizens. In this demo we present an online platform, Bostonhood, which is responsible for neighborhood recommendation and sustainability assessment. Data came from the official open data hub for the city of Boston, the location-based social network Foursquare and the Airbnb platform. The methodology that we followed combines two different Multi-Criteria Decision Analysis algorithms (COPRAS and TOPSIS) and creates a mutual ranking using the Borda count method. This platform is useful not only to citizens and tourists who want to find the best neighborhood to live in according to selected criteria but also to urban designers who want to improve Boston's sustainability.

This demo showcases some of the capabilities of Diligent, a platform for collecting and analysing data from Online Social Networks and is still under development. Diligent relies on microservices and reactive streams, which optimize the time spent (t), to the resources used (r), ratio (t/r). The proposed demo will present: - The vast hardware utilization margins produced by using both blocking and reactive I/O approaches. - The performance gap between using blocking I/O and Reactive I/O clients. Both experiments highlight the added benefits of using reactive approaches in online social network data processing systems.

Social media dominance largely affects multi-store brands success potential. How can a brand choose the optimal place to locate its stores, given the social media pulse? Which are the suitable metrics to guide such challenging decisions? This work addresses such crucial problems by a novel location casting approach which extracts and integrates knowledge from open data and social media, providing specific indicators and a systematic pipeline for effective locations casting. Emphasis is placed on how the derived knowledge will assess the particular characteristics of accessibility, interest, and centrality to identify fine grained urban indicators. The proposed pipeline predicts the success potential of a chain store's location, under individual or combined such indicators individually. The experimentation under qualitative tests, indicates that the proposed approach provides reliable estimations of brand's locations suitability, and also outperforms existing similar state-of-the-art approaches.

Highly distributed applications dominate today's software industry posing new challenges for novel software architectures capable of supporting real time processing and analytics. The proposed framework, so called REAXICS, is motivated by the fact that the demand for aggregating current and past big data streams requires new software methodologies, platforms and services. The proposed framework is designed to tackle with data intensive problems in real time environments, via services built dynamically under a fully scalable and elastic Lambda based architecture. REAXICS proposes a multi-layer software platform, based on the lambda architecture paradigm, for aggregating and synchronizing real time and batch processing. The proposed software layers and adaptive components support quality of experience, along with community driven software development. Flexibility and elasticity are targeted by hiding the complexity of bootstrapping and maintaining a multi level architecture, upon which the end user can drive queries over input data streams. REAXICS proposes a flexible and extensible software architecture that can capture users preference at the front-end and adapt the appropriate distributed technologies and processes at the back-end. Such a model enables real time analytics in large-scale data driven cloud-based systems.

Microblogging has become commonplace and created new methods of communication, contributing significantly to information sharing. This holds since microblogging focuses on sharing content while building social relations among people who share the same interests and/or activities. In this context, people's perception and emotions towards a specific subject is a valuable piece of information and sentiment and affective analysis play an important role. In this paper, an affective analysis methodology is proposed, which is a lexicon-based technique, for capturing the wisdom of crowds, as well as the social pulse and the trends, through the more accurate assessment of human emotion states. The methodology adopted involves the monitoring of the emotions' intensity, i.e. how strong or weak the emotional states of the published information are. The results suggest that the proposed approach manages to efficiently capture people's emotions as these were recorded in datasets derived from Twitter.

In this paper, we present a contribution to IR modeling. We propose an approach that computes on the fly, a Personalized Social Document Representation (PSDR) of each document per user based on his social activities. The PSDRs are used to rank documents with respect to a query. This approach has been intensively evaluated on a large public dataset, showing significant benefits for personalized search.

Social networks drive todays opinions and content diffusion. Large scale, distributed and unpredictable social data streams are produced and such evolving data production offers the ground for the data mining and analysis tasks. Such social data streams embed human reactions and inter-relationships and affective and emotional analysis has become rather important in todays applications. This work highlights the major data structures and methodologies used in evolving social data mining and proceeds to the relevant affective analysis techniques. A particular framework is outlined along with indicative applications which employ evolving social data analysis with emphasis on the seminal criteria of topic, location and time. Such mining and analysis overview is beneficial for various scientific and enterpreneural audiences and communities in the social networking area.

The Web as a graph can be embedded in a low-dimensional space where its geometry can be visualized and studied in order to mine interesting patterns such as web communities. The existing algorithms operate on small-to-medium-scale graphs; thus, we propose a close to linear time algorithm called Mani-Web suitable for large-scale graphs. The result is similar to the one produced by the manifold-learning technique Laplacian eigenmap that is tested on artificial manifolds and real web-graphs. Mani-Web can also be used as a general-purpose manifold-learning/dimensionality-reduction technique as long as the data can be represented as a graph.

We present a technical demonstration of an online city exploration application that helps users identify interesting spots in a city by use of spatio-temporal analysis and clustering of user contributed photos. Our framework analyzes the spatial distribution of large city-centered collections of user contributed photos at different time scales in order to index the most popular spots of a city in a time-aware manner. Subsequently, the photo sets belonging to the same spatiotemporal context are clustered in order to extract representative photos for each spot. The resulting application enables users to obtain flexible summaries of the most important spots in a city given a temporal slice (time of the day, month, season). The demonstration will be based on a photo dataset covering major European cities.

In this paper we address the problem of Web Mashups full completion which consists of predicting the most suitable set of (combined) services that successfully meet the goals of an end-user Mashup, given the current service (or composition of services) initially supplied. We model full completion as a frequent sequence mining problem and we show how existing algorithms can be applied in this context. To overcome some limitations of the frequent sequence mining algorithms, e.g., efficiency and recommendation granularity, we propose FESMA, a new and efficient algorithm for computing frequent sequences of services and recommending completions. FESMA also integrates a social dimension, extracted from the transformation of user-service interactions into user-user interactions, building an implicit graph that helps to better predict completions of services in a fashion tailored to individual users. Evaluations show that FESMA is more efficient outperforming the existing algorithms even with the consideration of the social dimension. Our proposal has been implemented in a prototype, SoCo, developed at Bell Labs.

Getting an overview of a large online social net-work and deciding which communities to join is a challenging task for a new user. We propose a method that maps a large network into a smaller graph with two kinds of nodes: a node of the first kind is representative of a community, a node of the second kind is neighbor to a representative and rejects the semantics of that community. Our approach encompasses a learning and ranking algorithm that derives this smaller graph from the original one, and a visualization algorithm that returns a graph layout to the observer. We report on our results on inspecting the network of a folksonomy.

LDAP directories have proliferated as the appropriate storage framework for various and heterogeneous data sources, operating under a wide range of applications and services. Due to the increased amount and heterogeneity of the LDAP data, there is a requirement for appropriate data organization schemes. The LPAIR & LMERGE (LP-LM) algorithm, presented in this article, is a hierarchical agglomerative structure-based clustering algorithm which can be used for the LDAP directory information tree definition. A thorough study of the algorithm’s performance is provided, which designates its efficiency. Moreover, the Relative Link as an alternative merging criterion is proposed, since as indicated by the experimentation, it can result in more balanced clusters. Finally, the LP and LM Query Engine is presented, which considering the clustering-based LDAP data organization, results in the enhancement of the LDAP server’s performance.

The wide adoption of photo sharing applications such as Flickr©and the massive amounts of user-generated content uploaded to them raises an information overload issue for users. An established technique to overcome such an overload is to cluster images into groups based on their similarity and then use the derived clusters to assist navigation and browsing of the collection. In this paper, we present a community detection (i.e. graph-based clustering) approach that makes use of both visual and tagging features of images in order to efficiently extract groups of related images within large image collections. Based on experiments we conducted on a dataset comprising publicly available images from Flickr©, we demonstrate the efficiency of our method, the added value of combining visual and tag features and the utility of the derived clusters for exploring an image collection.

We present a technical demonstration of an online city exploration application that helps users identify interesting spots in a city by use of photo clusters corresponding to landmarks and events. Our application, called ClustTour, is based on an efficient landmark and event detection scheme for tagged photo collections. The proposed scheme relies on the combination of a graph-based photo clustering algorithm, making use of both visual and tag information of photos, with a cluster classification and merging module. ClustTour creates a map-based visualization of the identified photo clusters that are classified in prominent categories and are filterable by time and tag. We believe that such an application can greatly facilitate the task of knowing a city through its landmarks and events. So far, the demo has been based on a large photo dataset focused on Barcelona, and it is gradually expanding to contain photo clusters of several major cities of Europe. Furthermore, an Android application is developed that complements the web-based version of ClustTour.

Today's web commercial applications demand more powerful recommendation systems due to the rapid increase in the number of both consumers and available products. Searching for the best algorithm with the highest accuracy and realistic complexity is, most of the time, a very costly process in terms of both time and resources. In this paper we suggest an alternative framework called Hydra which enables the virtual fusion of any and as many currently available recommendation algorithms in such a distributed manner that algorithms' complexities are not summarized but parallelized. Therefore, we utilize the available algorithms and technologies aiming to achieve better accuracy in order to surpass even the most state of the art algorithms. In addition, Hydra can be used to find how algorithms interact with each other in order to estimate the resulting accuracy towards inventing a more precise algorithm diminishing the risk of a failed investment. Hydra can be adjusted and integrated in any recommendation application while it is also open to new functionalities which can be embedded easily and in a transparent manner.

Artificial graphs are commonly used for the evaluation of community mining and clustering algorithms. Each artificial graph is assigned a pre-specified clustering, which is compared to clustering solutions obtained by the algorithms under evaluation. Hence, the pre-specified clustering should comply with specifications that are assumed to delimit a good clustering. However, existing construction processes for artificial graphs do not set explicit specifications for the pre-specified clustering. We call these graphs, randomly clustered graphs. Here, we introduce a new class of benchmark graphs which are clustered according to explicit specifications. We call them optimally clustered graphs. We present the basic properties of optimally clustered graphs and propose algorithms for their construction. Experimentally, we compare two community mining algorithms using both randomly and optimally clustered graphs. Results of this evaluation reveal interesting insights both for the algorithms and the artificial graphs.

Content distribution networks (CDNs) balance costs and quality in services related to content delivery. Devising an efficient content outsourcing policy is crucial since, based on such policies, CDN providers can provide client-tailored content, improve performance, and result in significant economical gains. Earlier content outsourcing approaches may often prove ineffective since they drive prefetching decisions by assuming knowledge of content popularity statistics, which are not always available and are extremely volatile. This work addresses this issue, by proposing a novel self-adaptive technique under a CDN framework on which outsourced content is identified with no a-priori knowledge of (earlier) request statistics. This is employed by using a structure-based approach identifying coherent clusters of "correlated" Web server content objects, the so-called Web page communities. These communities are the core outsourcing unit and in this paper a detailed simulation experimentation has shown that the proposed technique is robust and effective in reducing user-perceived latency as compared with competing approaches, i.e., two communities-based approaches, Web caching, and non-CDN.

Under social tagging systems, a typical Web 2.0 application, users label digital data sources by using freely chosen textual descriptions (tags). Poor retrieval in the aforementioned systems remains a major problem mostly due to questionable tag validity and tag ambiguity. Earlier clustering techniques have shown limited improvements, since they were based mostly on tag co-occurrences. In this paper, a co-clustering approach is employed, that exploits joint groups of related tags and social data sources, in which both social and semantic aspects of tags are considered simultaneously. Experimental results demonstrate the efficiency and the beneficial outcome of the proposed approach in correlating relevant tags and resources.

Web users' clustering is a crucial task for mining information related to users' needs and preferences. Up to now, popular clustering approaches build clusters based on usage patterns derived from users' page preferences. This paper emphasizes the need to discover similarities in users' accessing behavior with respect to the time locality of their navigational acts. In this context, we present two time-aware clustering approaches for tuning and binding the page and time visiting criteria. The two tracks of the proposed algorithms define clusters with users that show similar visiting behavior at the same time period, by varying the priority given to page or time visiting. The proposed algorithms are evaluated using both synthetic and real data sets and the experimentation has shown that the new clustering schemes result in enriched clusters compared to those created by the conventional non-time-aware user clustering approaches. These clusters contain users exhibiting similar access behavior in terms not only of their page preferences but also of their access time.

Web caching and content replication techniques emerged to solve performance problems related to the Web. We propose a generic non-parametric heuristic method that integrates both techniques under a CDN. We provide experimentation showing that our method outperforms the so far separate implementations of Web caching and content replication. Moreover, we show that the performance improvement compared with an existing algorithm is significant. We test all these techniques in a simulation environment under a flash crowd event and a workload of a typical light-weighted CDN operation

A considerable effort has been recently devoted to the development of database management systems (DBMS) which guarantee high assurance security and privacy. An important component of any strong security solution is represented by intrusion detection (ID) systems, able to detect anomalous behavior by applications and users. To date, however, there have been very few ID mechanisms specifically tailored to database systems. In this paper, we propose such a mechanism. The approach we propose to ID is based on mining database traces stored in log files. The result of the mining process is used to form user profiles that can model normal behavior and identify intruders. An additional feature of our approach is that we couple our mechanism with role based access control (RBAC). Under a RBAC system permissions are associated with roles, usually grouping several users, rather than with single users. Our ID system is able to determine role intruders, that is, individuals that while holding a specific role, have a behavior different from the normal behavior of the role. An important advantage of providing an ID mechanism specifically tailored to databases is that it can also be used to protect against insider threats. Furthermore, the use of roles makes our approach usable even for databases with large user population. Our preliminary experimental evaluation on both real and synthetic database traces show that our methods work well in practical situations

Nowadays, the amount of multimedia data is increasing rapidly, and hence, there is an increasing need for efficient methods to manage the multimedia content. This paper proposes a framework for the description and retrieval of multimedia data. The data are represented at both the syntactic (structure, metadata and low level features) and semantic (the meaning of the data) levels. We use the MPEG-7 standard, which provides a set of tools to describe multimedia content from different viewpoints, to represent the syntactic level. However, due to its XML schema based representation, MPEG-7 is not suitable to represent the semantic aspect of the data in a formal and concise way. Moreover, inferential mechanisms are not provided. To alleviate these limitations, we propose to extend MPEG-7 with a domain ontology, formalized using a logical formalism. Then, the semantic aspect of the data is described using the ontology's vocabulary, as a set of logical expressions. We enhance the ontology by a rules layer, to describe more complex constraints between domain concepts and relations. User's queries may concern the syntactic and/or semantic features. The syntactic constraints are expressed using XQuery language and evaluated using an XML query engine; whereas the semantic query constraints are expressed using a rules language and evaluated using a specific resolution mechanism.

Efficient detection of plagiarism in programming assignments of students is of a great importance to the educational procedure. This paper presents a clustering oriented approach for facing the problem of source code plagiarism. The implemented software, called PDetect, accepts as input a set of program sources and extracts subsets (the clusters of plagiarism) such that each program within a particular subset has been derived from the same original. PDetect proposes the use of an appropriate measure for evaluating plagiarism detection performance and supports the idea of combining different plagiarism detection schemes. Furthermore, a cluster analysis is performed in order to provide information beneficial to the plagiarism detection process. PDetect is designed such that it may be easily adapted over any keyword-based programming language and it is quite beneficial when compared with earlier (state-of-the-art) plagiarism detection approaches.

Lately, Web-accessed resources have superceded the resources accessed by local or wide-area networks. Therefore, new mechanisms should be implemented for protecting resources from unknown clients. Attribute Certificates is a quite new technology offering such functionality. Those certificates are issued by Attribute Authorities validating the attributes of the owner of the certificate. Based on this technology an XML-based access control mechanism is introduced for protecting any kind of resources (from both known and unknown clients). The proposed model is ultimately role-based since both clients and protected resources are organized into roles. Moreover, an XML-based language is introduced to express roles, authorizations, delegation rules, hierarchies and certificates.

In our project, we are adopting a new approach for handling video data. We view the video as a well-defined data type with its own description, parameters and applicable methods. The system is based on PREDATOR, an open-source object-relational DBMS. PREDATOR uses Shore as the underlying storage manager. Supporting video operations (storing, searching-by-content and streaming) and new query types (query-by-example and multi-feature similarity searching) requires major changes in many of the traditional system components. More specifically, the storage and buffer manager has to deal with huge volumes of data with real-time constraints. Query processing has to consider the video methods and operators in generating, optimizing and executing the query plans.

A client/server model which employs a polling policy as its access strategy is considered. We propose a learning-automata-based approach for polling in order to improve the throughput-delay performance of the system. Each client has an associated queue and the server performs selective polling such that the next client to be served is identified by a learning automaton. The learning automaton updates each client's choice probability according to the feedback information. Simulation results have shown that the proposed polling policy is beneficial in comparison to the conventional round-robin polling when operating under bursty traffic conditions.