The increasing complexity of the smart home ecosystem necessitates effective solutions to pressing security and privacy challenges. Typically, authentication and authorization processes establish system security (i.e., system-to-user trust). Once approved, users are primarily concerned about privacy protection (i.e., user-to-system trust) when utilizing system services that require sensitive data for their functionality. We define “user-to-system trust” as the user’s confidence in data privacy protection. To establish bidirectional trust, this study enhances the Authentication Enabled Attribute-Based Access Control (AeABAC) model for user privacy protection. While traditional AeABAC focuses on system-to-user trust (authentication and authorization), it lacks mechanisms to address user-to-system trust, leaving users vulnerable to privacy risks such as opaque data handling, insufficient consent frameworks, and unmitigated disclosure risks. This study enhances the AeABAC model by integrating a risk-based privacy approach to address these gaps. The proposed Risk-Based Privacy Approach for the AeABAC model aims to build user confidence by identifying relevant privacy profile information within the smart home environment. It conducts privacy risk assessments by evaluating the likelihood of data disclosure and examining the potential harm (disclosure impact) users may face if their data is exposed. Ultimately, this approach safeguards users’ privacy by offering transparent and informative protections regarding data collection and disclosure. The key findings demonstrate that the RBP-AeABAC model enables role-specific privacy decisions (e.g., stricter controls for children), and balances usability and security through dynamic consent mechanisms. Use-case scenarios validate its practicality in real-world smart home ecosystems.

This study evaluates the performance of various machine learning models for sentiment analysis of IMDB movie reviews, focusing on Logistic Regression, Naive Bayes, Random Forest, Linear Support Vector Machine (SVM), and Decision Tree. Employing a dataset from Kaggle, the research involves data preprocessing, feature extraction using TF-IDF, and rigorous model evaluation based on accuracy, precision, recall, and F1 score. The findings indicate that Linear SVM outperforms other models, showcasing high effectiveness in sentiment classification. Logistic Regression and Naive Bayes also perform well, while Random Forest and Decision Tree show potential areas for improvement. This research underscores the significance of selecting appropriate models for accurate sentiment analysis and suggests further exploration into advanced processing techniques to enhance model performance.

Increasing popularity of Twitter in politics is subject to commercial and academic interest. To fully exploit the merits of this platform, reaching target audience with desired political leanings is critical. This paper extends the research on inferring political orientations of Twitter users to the case of 2017 Turkish constitutional referendum. After constructing a targeted dataset of tweets, we explore several types of potential features to build accurate machine learning based predictive models. In our experiments, three-class support vector machine (SVM) classifier trained on semantic features achieves the best accuracy score of 89.9%. Moreover, an SVM classifier trained on full text features performs better than an SVM classifier trained on hashtags, with respective accuracy scores of 89.05% and 85.9%. Relatively high accuracy scores obtained by full text features may point to differences in language use, which deserves further research.

Data science continues to evolve with each passing day and upgrades itself according to the exponentially increasing amount of data. The progression provides convenience to extract meaningful information from the huge amount of data from various domains including individual, public health, micro-blogging and sensors. The ability to process huge volume of data and to extract valuable information sometimes scare people especially when individual sensitive data is concerned. Many data privacy-preserving techniques are developed to overcome these fears. Over the years, these techniques are adapted to meet emerging type and increasing volume of data. For instance, to cope with today's big data we need more scalable and efficient methods. Big data platforms like Apache Hadoop and Apache Spark are highly utilized for this purpose. In this paper we study k-anonymization problem in the context of big data and develop a top-down specialization anonymization solution for Apache Spark platform. An extensive experimental evaluation has been carried out and the efficiency results are presented.

Packet-based mobile networks are increasingly carrying internet traffic mostly for data intensive-applications. Mobile internet usage, with 67% share worldwide, is very important for mobile users. Because of the explosive growth of mobile data usage demand, data speed analysis is a very essential way to characterize network quality and user experience for service providers. Key Performance Indicators (KPIs), measured with data traffic analysis on network segments by current sophisticated systems, may not correctly capture users' Quality of Experience (QoE) due to actual throughput and latency. This is simply because; the data traffic analysis is not end-to-end. End-to-end analysis is very insuperable issue to measure service quality for user experience scaling and foreseeing user complaints. In this study we develop a distributed end-to-end internet speed test and analysis system for mobile networks. The system runs speed tests with actual end-to-end usage scenarios, traces communication packets and calculates download, upload and RTTs in mobile networks from user's point of view. Test results collected as large data sets are processed and analyzed in distributed fashion on cluster of computers using MapReduce Programming Model on Hadoop. The system is also able to render regional internet speed characteristics map and proactively characterizes QoE. As a result, it provides decision makers with indicators of user experience and getting ready for possible upcoming user complaints due to service quality degradation. The system is tested with a leading mobile ISP provider of Turkey and an experimental evaluation has been presented.

Biological networks, mostly abstracted as graphs, are key to many important activities inside the cell. Similarity-based analysis is one of the techniques for understanding the role of a query network. In that context, a database consisting of biological networks is aligned with a query network and the networks having a similarity score higher and lower than a predefined cutoff value are separated. Because of the NP-complete sub-graph isomorphism problem, nontrivial similarity score calculation is computationally too expensive. To this end, several methods are proposed in the literature for an acceptable solution. Reference-based indexing methods are one of the popular solutions which indexes the network database by extracting small sized networks as references to be aligned with the query network. Based on this strategy, we propose a novel model that has methodological and heuristic improvements for fast approximate similarity search, which all turn out to be fast and accurate. We also have a high-performance implementation on Hadoop that achieved 11.42 speedup on a Hadoop cluster with 18 cores on a sample KEGG network database.

Location based queries are increasingly common in mobile applications, and the associated privacy issues have become a hot research topic in the last years. Most of the current approaches, however, do not account for the location of potentially sensitive places and for constraints on the movement of users, such as speed limits or network contraints. In this demo we present different deployment scenarios of a privacy-preserving framework for the protection of sensitive positions in real time trajectories. We assume that the sensitivity of users' positions depends on the spatial context, while the users' movement is confined to road networks and places. Further, the users are non-anonymous, as in the case of geo-social network members who agree to share their exact position whenever it does not fall within a sensitive place, e.g. a hospital. We will show that our proposal is suitable for different classes of devices and can be integrated in different kind of location based applications.

This paper presents a privacy-preserving framework for the protection of sensitive positions in real time trajectories. We assume a scenario in which the sensitivity of user's positions is space-varying, and so depends on the spatial context, while the user's movement is confined to road networks and places. Typical users are the non-anonymous members of a geo-social network who agree to share their exact position whenever such position does not fall within a sensitive place, e.g. a hospital. Suspending location sharing while the user is inside a sensitive place is not an appropriate solution because the user's stopovers can be easily inferred from the user's trace. In this paper we present an extension of the semantic location cloaking model [1] originally developed for the cloaking of non-correlated positions in an unconstrained space. We investigate different algorithms for the generation of cloaked regions over the graph representing the urban setting. We also integrate methods to prevent velocity-based linkage attacks. Finally we evaluate experimentally the algorithms using a real data set.

BACKGROUND: With the recent advances and availability of various high-throughput sequencing technologies, data on many molecular aspects, such as gene regulation, chromatin dynamics, and the three-dimensional organization of DNA, are rapidly being generated in an increasing number of laboratories. The variation in biological context, and the increasingly dispersed mode of data generation, imply a need for precise, interoperable and flexible representations of genomic features through formats that are easy to parse. A host of alternative formats are currently available and in use, complicating analysis and tool development. The issue of whether and how the multitude of formats reflects varying underlying characteristics of data has to our knowledge not previously been systematically treated. RESULTS: We here identify intrinsic distinctions between genomic features, and argue that the distinctions imply that a certain variation in the representation of features as genomic tracks is warranted. Four core informational properties of tracks are discussed: gaps, lengths, values and interconnections. From this we delineate fifteen generic track types. Based on the track type distinctions, we characterize major existing representational formats and find that the track types are not adequately supported by any single format. We also find, in contrast to the XML formats, that none of the existing tabular formats are conveniently extendable to support all track types. We thus propose two unified formats for track data, an improved XML format, BioXSD 1.1, and a new tabular format, GTrack 1.0. CONCLUSIONS: The defined track types are shown to capture relevant distinctions between genomic annotation tracks, resulting in varying representational needs and analysis possibilities. The proposed formats, GTrack 1.0 and BioXSD 1.1, cater to the identified track distinctions and emphasize preciseness, flexibility and parsing convenience.

The process of discovering relevant patterns holding in a database was first indicated as a threat to database security by O'Leary in. Since then, many different approaches for knowledge hiding have emerged over the years, mainly in the context of association rules and frequent item sets mining. Following many real-world data and application demands, in this paper, we shift the problem of knowledge hiding to contexts where both the data and the extracted knowledge have a sequential structure. We define the problem of hiding sequential patterns and show its NP-hardness. Thus, we devise heuristics and a polynomial sanitization algorithm. Starting from this framework, we specialize it to the more complex case of spatiotemporal patterns extracted from moving objects databases. Finally, we discuss a possible kind of attack to our model, which exploits the knowledge of the underlying road network, and enhance our model to protect from this kind of attack. An exhaustive experiential analysis on real-world data sets shows the effectiveness of our proposal.

Preserving individual privacy when publishing data is a problem that is receiving increasing attention. According to the fc-anonymity principle, each release of data must be such that each individual is indistinguishable from at least k - 1 other individuals. In this paper we study the problem of anonymity preserving data publishing in moving objects databases. We propose a novel concept of k-anonymity based on co-localization that exploits the inherent uncertainty of the moving object's whereabouts. Due to sampling and positioning systems (e.g., GPS) imprecision, the trajectory of a moving object is no longer a polyline in a three-dimensional space, instead it is a cylindrical volume, where its radius delta represents the possible location imprecision: we know that the trajectory of the moving object is within this cylinder, but we do not know exactly where. If another object moves within the same cylinder they are indistinguishable from each other. This leads to the definition of (k,delta) -anonymity for moving objects databases. We first characterize the (k, delta)-anonymity problem and discuss techniques to solve it. Then we focus on the most promising technique by the point of view of information preservation, namely space translation. We develop a suitable measure of the information distortion introduced by space translation, and we prove that the problem of achieving (k,delta) -anonymity by space translation with minimum distortion is NP-hard. Faced with the hardness of our problem we propose a greedy algorithm based on clustering and enhanced with ad hoc pre-processing and outlier removal techniques. The resulting method, named NWA (Never Walk .Alone), is empirically evaluated in terms of data quality and efficiency. Data quality is assessed both by means of objective measures of information distortion, and by comparing the results of the same spatio-temporal range queries executed on the original database and on the (k, delta)-anonymized one. Experimental results show that for a wide range of values of delta and k, the relative error introduced is kept low, confirming that NWA produces high quality (k, delta)-anonymized data.

BACKGROUND: Computational discovery of regulatory elements is an important area of bioinformatics research and more than a hundred motif discovery methods have been published. Traditionally, most of these methods have addressed the problem of single motif discovery - discovering binding motifs for individual transcription factors. In higher organisms, however, transcription factors usually act in combination with nearby bound factors to induce specific regulatory behaviours. Hence, recent focus has shifted from single motifs to the discovery of sets of motifs bound by multiple cooperating transcription factors, so called composite motifs or cis-regulatory modules. Given the large number and diversity of methods available, independent assessment of methods becomes important. Although there have been several benchmark studies of single motif discovery, no similar studies have previously been conducted concerning composite motif discovery. RESULTS: We have developed a benchmarking framework for composite motif discovery and used it to evaluate the performance of eight published module discovery tools. Benchmark datasets were constructed based on real genomic sequences containing experimentally verified regulatory modules, and the module discovery programs were asked to predict both the locations of these modules and to specify the single motifs involved. To aid the programs in their search, we provided position weight matrices corresponding to the binding motifs of the transcription factors involved. In addition, selections of decoy matrices were mixed with the genuine matrices on one dataset to test the response of programs to varying levels of noise. CONCLUSION: Although some of the methods tested tended to score somewhat better than others overall, there were still large variations between individual datasets and no single method performed consistently better than the rest in all situations. The variation in performance on individual datasets also shows that the new benchmark datasets represents a suitable variety of challenges to most methods for module discovery.

BACKGROUND: Computational discovery of motifs in biomolecular sequences is an established field, with applications both in the discovery of functional sites in proteins and regulatory sites in DNA. In recent years there has been increased attention towards the discovery of composite motifs, typically occurring in cis-regulatory regions of genes. RESULTS: This paper describes Compo: a discrete approach to composite motif discovery that supports richer modeling of composite motifs and a more realistic background model compared to previous methods. Furthermore, multiple parameter and threshold settings are tested automatically, and the most interesting motifs across settings are selected. This avoids reliance on single hard thresholds, which has been a weakness of previous discrete methods. Comparison of motifs across parameter settings is made possible by the use of p-values as a general significance measure. Compo can either return an ordered list of motifs, ranked according to the general significance measure, or a Pareto front corresponding to a multi-objective evaluation on sensitivity, specificity and spatial clustering. CONCLUSION: Compo performs very competitively compared to several existing methods on a collection of benchmark data sets. These benchmarks include a recently published, large benchmark suite where the use of support across sequences allows Compo to correctly identify binding sites even when the relevant PWMs are mixed with a large number of noise PWMs. Furthermore, the possibility of parameter-free running offers high usability, the support for multi-objective evaluation allows a rich view of potential regulators, and the discrete model allows flexibility in modeling and interpretation of motifs.

There are several methods for scoring a set of upstream DNA sequences against a given motif. Typically, significance of raw scores are based on p-values, measured by statistical hypothesis testing. As an extension, multiple hypothesis testing is adopted in cases where there are multiple motifs to be evaluated in parallel. In this way significant motifs are identified for a given significance level. However, a set of significantly identified motifs can contain false positives. In this work, we introduce a false discovery rate estimation problem for significantly predicted motifs. An explorative method for this problem is presented. We test the method using TRANSFAC and JASPAR motif libraries on several upstream DNA subsets of S.cerevisiae. The results show the effectiveness of the method.

Spatio-temporal, geo-referenced datasets are growing rapidly, and will be more in the near future. This phenomenon is mostly due to the daily collection of telecommunication data from mobile phones and other location-aware devices and is expected to enable novel classes of applications based on the extraction of behavioral patterns from mobility data. Such patterns could be used for instance in traffic and sustainable mobility management (e.g., to study the accessibility to services), urban planning, environmental monitoring, and collaborative location-based services. Clearly, in these applications privacy is a concern, since some knowledge may be sensitive, or an over-specific pattern may reveal the behaviour of groups of few individual. In this paper we focus on automated privacy-preserving methods we developed for extracting and sharing user- consumable forms of knowledge from large amounts of raw data referenced in space and in time.

BACKGROUND: An important step in annotation of sequenced genomes is the identification of transcription factor binding sites. More than a hundred different computational methods have been proposed, and it is difficult to make an informed choice. Therefore, robust assessment of motif discovery methods becomes important, both for validation of existing tools and for identification of promising directions for future research. RESULTS: We use a machine learning perspective to analyze collections of transcription factors with known binding sites. Algorithms are presented for finding position weight matrices (PWMs), IUPAC-type motifs and mismatch motifs with optimal discrimination of binding sites from remaining sequence. We show that for many data sets in a recently proposed benchmark suite for motif discovery, none of the common motif models can accurately discriminate the binding sites from remaining sequence. This may obscure the distinction between the potential performance of the motif discovery tool itself versus the intrinsic complexity of the problem we are trying to solve. Synthetic data sets may avoid this problem, but we show on some previously proposed benchmarks that there may be a strong bias towards a presupposed motif model. We also propose a new approach to benchmark data set construction. This approach is based on collections of binding site fragments that are ranked according to the optimal level of discrimination achieved with our algorithms. This allows us to select subsets with specific properties. We present one benchmark suite with data sets that allow good discrimination between positive and negative instances with the common motif models. These data sets are suitable for evaluating algorithms for motif discovery that rely on these models. We present another benchmark suite where PWM, IUPAC and mismatch motif models are not able to discriminate reliably between positive and negative instances. This suite could be used for evaluating more powerful motif models. CONCLUSION: Our improved benchmark suites have been designed to differentiate between the performance of motif discovery algorithms and the power of motif models. We provide a web server where users can download our benchmark suites, submit predictions and visualize scores on the benchmarks.

In this paper, we study the control problem and note that it is multi-objective by nature, and thus we develop an optimal multi-objective approach. Our approach includes formalizing components and identifying dimensions, resulting in few cases for concrete problem formulation. For a selected case, namely the finite control case, a single-objective from the literature and our multi-objective solutions are presented. It is demonstrated that the multi-objective solution avoids drawbacks of the single-objective solution, particularly the need for defining single objective out of many

The problem of identifying significantly differentially expressed genes for replicated microarray experiments is accepted as significant and has been tackled by several researchers. Patterns from Gene Expression (PaGE) and q-values are two of the well-known approaches developed to handle this problem. This paper proposes a powerful approach to handle this problem. We first propose a method for estimating the prior probabilities used in the first version of the PaGE algorithm. This way, the problem definition of PaGE stays intact and we just estimate the needed prior probabilities. Our estimation method is similar to Storey's estimator without being its direct extension. Then, we modify the problem formulation to find significantly differentially expressed genes and present an efficient method for finding them. This formulation increases the power by directly incorporating Storey's estimator. We report the preliminary results on the BRCA data set to demonstrate the applicability and effectiveness of our approach.

MOTIVATION: It is important to consider finding differentially expressed genes in a dataset of microarray experiments for pattern generation. RESULTS: We developed two methods which are mainly based on the q-values approach; the first is a direct extension of the q-values approach, while the second uses two approaches: q-values and maximum-likelihood. We present two algorithms for the second method, one for error minimization and the other for confidence bounding. Also, we show how the method called Patterns from Gene Expression (PaGE) (Grant et al., 2000) can benefit from q-values. Finally, we conducted some experiments to demonstrate the effectiveness of the proposed methods; experimental results on a selected dataset (BRCA1 vs BRCA2 tumor types) are provided. CONTACT: [email protected].

This paper addresses the control formulation process for probabilistic boolean genetic networks. It is a major problem that has not been investigated enough yet. We argue that a monitoring stage is necessary after the control stage for providing guidance about the evolution of the investigated state. For this purpose, we developed methods for generating optimal control policies for each of the following five cases: finite control, infinite control, finite control-infinite monitoring, finite control-finite monitoring, and repeated finite control-finite monitoring. Our initial proposal was based on using action cost functions in the process. In this study, we propose Markov decision processes as an alternative to the action cost functions approach. We conducted experiments on two simple illustrative examples to demonstrate that the considered five cases are necessary, effective and really matter while developing optimal control policies; the obtained results are promising.

Cluster validity investigates whether generated clusters are true clusters or due to chance. This is usually done based on subsampling stability analysis. Related to this problem is estimating true number of clusters in a given dataset. There are a number of methods described in the literature to handle both purposes. In this paper, we propose three methods for estimating confidence in the validity of clustering result. The first method validates clustering result by employing supervised classifiers. The dataset is divided into training and test sets and the accuracy of the classifier is evaluated on the test set. This method computes confidence in the generalization capability of clustering. The second method is based on the fact that if a clustering is valid then each of its subsets should be valid as well. The third method is similar to second method; it takes the dual approach, i.e., each cluster is expected to be stable and compact. Confidence is estimated by repeating the process a number of times on subsamples. Experimental results illustrate effectiveness of the proposed methods.

The paper presents two new multi-agent based domain independent coordination mechanisms for reinforcement learning. The first mechanism allows agents to learn coordination information from state transitions and the second one from the observed reward distribution. In this way, the latter mechanism tends to increase region-wide joint rewards. The selected experimented domain is Adversarial Food-Collecting World (AFCW), which can be configured both as single and multi-agent environments. Experimental results show the effectiveness of these mechanisms.

Learning in a partially observable and nonstationary environment is still one of the challenging problems in the area of multiagent (MA) learning. Reinforcement learning is a generic method that suits the needs of MA learning in many aspects. This paper presents two new multiagent based domain independent coordination mechanisms for reinforcement learning; multiple agents do not require explicit communication among themselves to learn coordinated behavior. The first coordination mechanism is the perceptual coordination mechanism, where other agents are included in state descriptions and coordination information is learned from state transitions. The second is the observing coordination mechanism, which also includes other agents in state descriptions and additionally the rewards of nearby agents are observed from the environment. The observed rewards and agent's own reward are used to construct an optimal policy. This way, the latter mechanism tends to increase region-wide joint rewards. The selected experimented domain is adversarial food-collecting world (AFCW), which can be configured both as single and multiagent environments. Function approximation and generalization techniques are used because of the huge state space. Experimental results show the effectiveness of these mechanisms.