1,720,980 research outputs found
Comprehensive review of depression detection techniques based on machine learning approach
No full textSpatial and temporal reasoning with granular computing and three way formal concept analysis
No full textReview of Cybersecurity Analysis in Smart Distribution Systems and Future Directions for Using Unsupervised Learning Methods for Cyber Detection
No full textIn a physical microgrid system, equipment failures, manual misbehavior of equipment, and power quality can be affected by intentional cyberattacks, made more dangerous by the widespread use of established communication networks via sensors. This paper comprehensively reviews smart grid challenges on cyber-physical and cyber security systems, standard protocols, communication, and sensor technology. Existing supervised learning-based Machine Learning (ML) methods for identifying cyberattacks in smart grids mostly rely on instances of both normal and attack events for training. Additionally, for supervised learning to be effective, the training dataset must contain representative examples of various attack situations having different patterns, which is challenging. Therefore, we reviewed a novel Data Mining (DM) approach based on unsupervised rules for identifying False Data Injection Cyber Attacks (FDIA) in smart grids using Phasor Measurement Unit (PMU) data. The unsupervised algorithm is excellent for discovering unidentified assault events since it only uses examples of typical events to train the detection models. The datasets used in our study, which looked at some well-known unsupervised detection methods, helped us assess the performances of different methods. The performance comparison with popular unsupervised algorithms is better at finding attack events if compared with supervised and Deep Learning (DL) algorithms
Verification of scope-dependent hierarchical state machines
No full textA hierarchical state machine (Hsm) is a finite state machine where a vertex can either expand to another hierarchical state machine (box) or be a basic vertex (node). Each node is labeled with atomic propositions. We study an extension of such model which allows atomic propositions to label also boxes (Shsm). We show that Shsms can be exponentially more succinct than Shsms and verification is in general harder by an exponential factor. We carefully establish the computational complexity of reachability, cycle detection, and model checking against general Ltl and Ctl specifications. We also discuss some natural and interesting restrictions of the considered problems for which we can prove that Shsms can be verified as much efficiently as Hsms, still preserving an exponential gap of succinctness
Model-checking graded computation-tree logic with finite path semantics
No full textThis paper introduces Graded Computation Tree Logic with finite path semantics (GCTLf ⁎, for short), a variant of Computation Tree Logic CTL⁎, in which path quantifiers are interpreted over finite paths and can count the number of such paths. State formulas of GCTLf ⁎ are interpreted over Kripke structures. The syntax of GCTLf ⁎ has path quantifiers of the form E≥gψ which express that there are at least g many distinct finite paths that satisfy ψ. After defining and justifying the logic GCTLf ⁎, we solve its model checking problem and establish that its computational complexity is PSPACE-complete. Moreover, we investigate GCTLf ⁎ under the imperfect information setting. Precisely, we introduce GCTLKf ⁎, an epistemic extension of GCTLf ⁎ and prove that the model checking problem also in this case is PSPACE-complete. © 2019 Elsevier B.V
Context-aware advertisment recommendation on twitter through rough sets
No full textThe main, if not the only, income for social networks is from advertising. Social media platforms like Twitter have become a main stream communication medium to disseminate information and capture the interest of potential customers. So, it is crucial that the policy implemented to decide which ads to show in proximity of which user's posts, is the most profitable one: the ads shown should be as much as possible targeted to the user's interests. In this paper, we propose a context-aware advertising recommendation system that, analyzing the users' tweets during the timeline, interpretes the personal interests of users through orthopairs (they are equivalent to rough sets) to meet ads and users' interests at the right time
Text Mining Basics in Bioinformatics
No full textBiomedical scientific literature is becoming a valuable information sourcethat includes a huge amount of novel research findings. Nevertheless, the un-structured nature of the publications stresses the importance of extracting em-bedded information to support literature-based analysis enabling the develop-ment of applications, such as Information Retrieval, Document Classification,Summarization, and so forth. There is a need to integrate several methods ad-dressing, for instance, linguistic analysis enabling the system to mine informa-tion from the text at different abstraction levels, at document level or sentence.This chapter provides an overview of the text-mining in Bioinformatics intro-ducing methods, applications, existing solutions and, finally, pointing out whatare the emerging challenges of this research area
Time-aware adaptive tweets ranking through deep learning
Full text linkGenerally, tweets about brands, news and so forth, are mostly delivered to the Twitter user in a reverse chronological order choosing among those twitted by the so-called followed users. Recently, Twitter is facing with information overload by introducing new filtering features, such as “while you are away” in order to show only a few tweets summarizing the posted ones, and ranking the tweets considering the quality, in addition to timeliness. Trivially enough we state that the strategy to rank the tweets to maximize the user engagement and, why not, augmenting the tweet and re-tweet rates, is not unique. There are several dimensions affecting the ranking, such as time, location, semantic, publisher authority, quality, and so on. We point out that the tweet ranking model should vary according to the user's context, interests and how those change along the timeline, cyclically, weekly or at specific date-time when the user logs in. In this work, we introduce a deep learning method attempting to re-adapt the ranking of the tweets by preferring those that are more likely interesting for the user. User's interests are extracted by mainly considering previous user re-tweets, replies and also the time when they occurred. We evaluate a ranking model by measuring how many tweets that will be re-tweeted in the near future were included in the top-ranked tweet list. The results of the proposed ranking model revealed good performances overcoming the methods that consider only the reverse-chronological order or user's interest score. In addition, we pointed out that in our dataset the most impacting features on the performance of proposed ranking model are: publisher authority, tweet content measures, and time-awareness
Verification of Succinct Hierarchical State Machines
No full textA hierarchical state machine (HSM) is a finite state machine where a vertex can either expand to another hierarchical state machine (box) or be a basic vertex (node). Each node is labeled with atomic propositions. We study an extension of such model which allows atomic propositions to label also boxes (SHSM). We show that SHSMs can be exponentially more succinct than HSMs, and verification is in general harder by an exponential factor. Also, we show for a subclass of SHSMs (which can still be exponentially more succinct than HSMs) the same upper bounds as for HSMs.<br/
OLAP analysis of multidimensional tweet streams for supporting advanced analytics
No full textIn this paper we propose to integrate Time-Aware Fuzzy Formal Concept Analysis theory with OLAP technology over multidimensional tweet streams in order to arrange tweets in the resulting OLAP cube within a suitable hierarchical structure of concepts (i.e., fuzzy lattice), according to their unstructured content. A microblog summarization algorithm is also introduced in order to provide subset of the tweets that best represents data of the OLAP cube according to the analysis perspective. This with the final goal of supporting advanced analytics over social media, which is becoming relevant at now. A detailed real-life and an extensive experimental analysis nicely complete our contributions
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