Miracle-dz/UniFAR-FTUs huggingface dataset:
Sentiment Analysis to Improve Emotional Health of User

The primary idea of the project is to change the mind-set of the user by providing various kinds of media or activities with the help of Sentiment Analysis. The user’s current mind-set is detected using sentiment analysis and the negative emotion is countered by showing appropriate media to the user. The input will be the emotional state of user in words and will be used to determine the exact emotion. This will allow us to show tailor-made results that are capable of overpowering the currently negative mind-set. The media response will change from user to user and will vary at different times of the day. A combination of fine sentiment analysis and intelligent profile management will be used to accomplish the results. A model is designed to learn from the user’s behavioral traits. Thus the goal of transforming the user’s mental state to achieve longer positive mentality shall be achieved. General Terms Machine Learning, Emotional Health, Emotions
Time Evolution of a Storm from X-POL in São Paulo: a ZH - ZDR and Titan Metrics Comparison

By exploring dual polarization radar measurements it was possible to identify different types of hydrometeors in storms and estimate their amount. The distribution and type of hydrometeor within a severe convective storm impact, to a certain degree, its dynamics. The identification and estimate of amount of the hydrometeors are important for many applications in research and operations. The identification of hydrometeor types, for instance, precedes the estimation of water content throughout the spatio-temporal evolution of precipitation. That estimation is highly relevant for outstanding applications such as in hydrology and NWP. While the operational frequency is a key parameter in the choice of the equipment to be used for hydrometeorological applications, there has been an ever growing interest in the shorter X-band. At this frequency radars are relatively smaller, allow that high resolution observations are made with moderate size antennas and are suitable for mobility. Such assets are to be balanced against the heavy attenuation suffered by X-band, a traditional major disadvantage. However, this disadvantage can be mitigated by using polarimetic based methods which provide correction for the incurred attenuation. This paper builds upon data obtained with the polarimetric X-band mobile radar system of the research project GLM-CHUVA. Data are from observations performed while the radar was sited at approximately 45.9°W, 23°S, at São José dos Campos, near São Paulo, during operations related to the project campaign of the 2011/2012 summer in the Paraiba Valley, one of several campaigns of the GLMCHUVA. The same set of data was used in a previous work (Calheiros et al, 2012) to generate a ZH-ZDR scatter plot which was comparable with those presented in studies developed abroad ( e.g. Marzano et al, 2010). In this paper, a cell as defined with the TITAN System (Dixon and Wiener, 1993) was selected from a complex of storms which developed on the 8 th of January, 2012 for a verification of the temporal evolution of the ZH-ZDR scatter plot. Data from PPI’s at 1° and 6.2° elevation were used for the plots.
Learning Classifier Systems Approach for Automated Discovery of Censored Production Rules

(LCS) is basically a machine learning technique which combines evolutionary computing, reinforcement learning, supervised or unsupervised learning and heuristics to produce adaptive systems. A LCS learns by interacting with an environment from which it receives feedback in the form of numerical reward. Learning is achieved by trying to maximize the amount of reward received. All LCSs models more or less, comprise four main components; a finite population of condition–action rules, called classifiers; the performance component, which governs the interaction with the environment; the credit assignment component, which distributes the reward received from the environment to the classifiers accountable for the rewards obtained; the discovery component, which is responsible for discovering better rules and improving existing ones through a genetic algorithm. The concatenate of the production rules in the LCS form the genotype, and therefore the GA should operate on a population of classifier systems. This approach is known as the 'Pittsburgh' Classifier Systems. Other LCS that perform their GA at the rule level within a population are known as 'Mitchigan' Classifier Systems. The most predominant representation of the discovered knowledge is the standard production rules (PRs) in the form of IF P THEN D. The PRs, however, are unable to handle exceptions and do not exhibit variable precision. The Censored Production Rules (CPRs), an extension of PRs, were proposed by Michalski and Winston that exhibit variable precision and supports an efficient mechanism for handling exceptions. A CPR is an augmented production rule of the form: IF P THEN D UNLESS C, where Censor C is an exception to the rule. Such rules are employed in situations, in which conditional statement IF P THEN D holds frequently and the assertion C holds rarely. By using a rule of this type we are free to ignore the exception conditions, when the resources needed to establish its presence are tight or there is simply no information available as to whether it holds or not. Thus, the IF P THEN D part of CPR expresses important information, while the UNLESS C part acts only as a switch and changes the polarity of D to ~D. In this paper Pittsburgh style LCSs approach is used for automated discovery of CPRs. An appropriate encoding scheme is suggested to represent a chromosome consisting of fixed size set of CPRs. Suitable genetic operators are designed for the set of CPRs and individual CPRs and also appropriate fitness function …
Learning Classifier Systems Approach for Automated Discovery of Crisp and Fuzzy Hierarchical Production Rules

data that takes mined flat rules as input and discovers crisp as well as fuzzy hierarchical structures using Learning Classifier System approach. Learning Classifier System (LCS) is basically a machine learning technique that combines evolutionary computing, reinforcement learning, supervised or unsupervised learning and heuristics to produce adaptive systems. A LCS learns by interacting with an environment from which it receives feedback in the form of numerical reward. Learning is achieved by trying to maximize the amount of reward received. Crisp description for a concept usually cannot represent human knowledge completely and practically. In the proposed Learning Classifier System initial population is constructed as a random collection of HPR–trees (related production rules) and crisp / fuzzy hierarchies are evolved. A fuzzy subsumption relation is suggested for the proposed system and based on Subsumption Matrix (SM), a suitable fitness function is proposed. Suitable genetic operators are proposed for the chosen chromosome representation method. For implementing reinforcement a suitable reward and punishment scheme is also proposed. Experimental results are presented to demonstrate the performance of the proposed system.
Interdental papillae height assessment in the aesthetic zone of the maxilla

Open gingival embrasure occurs as a result of a deficiency of papilla apically to the contact point. Open gingival embrasures are a complex aesthetic and functional problem. Several risk factors lead to the development of an open gingival embrasure: aging; periodontal disease; ratio between the interdental bone and the interproximal contact point between adjacent teeth, length of the embrasure area, root angulations, and triangular-shaped crowns. The study included 1088 interdental papillae in the aesthetic zone of the maxilla of 220 individuals. The height of the interdental papillae was measured according to the classification of Nordland & Tarnow (1998). The analysis of the results showed that the frequency and severity of height loss of interdental papillae increased with age.
EXPLORING SUBTLE FOOT PLANTAR-BASED GESTURES USING SOCK-PLACED PRESSURE SENSORS

We use our hands to control a computing device (e.g. personal computers and mobile devices). However, our hands often get busy in our life, such as during cooking and carrying bags. Though a number of interaction techniques using foot-based gestures have been proposed to solve this problem, users need to move their feet largely; these gestures are not private and sometimes annoyance to perform in a public space. In this thesis, we propose subtle foot-based gestures named foot plantar-based (FPB) gestures that are used with sock-placed pressure sensors. In this system, the user can control a computing device by changing his or her foot plantar distributions, e.g., pressing the floor with his or her toe. Because such foot movement is subtle, it is suitable for use especially in a public space such as a crowded train. In this work, we focus on a userdefined gesture that is designed by the end-users, not developers of this system. We first conduct a guessability study that asks people what is the appropriate gesture for a specific command to control the computing device. Then, we implement a gesture recognizer with a machine learning technique. To avoid unexpected gesture activations, we also collect foot plantar pressure patterns made during daily activities such as walking, as negative training data. Additionally, we evaluate the unobservability of FPB gestures by using crowdsourcing. 909 crowd workers are participated in this study and we confirm FPB gesture are less observable than existing foot-based gestures. Finally, we conclude with several applications to further illustrate the utility of FPB gestures.
Attacking Software-Defined Networks : The First Feasibility Study [ Extended

In this work, we demonstrate an effective and efficient attack against software-defined networks with the knowledge of some basic characteristics of the SDN technology. Essentially, since the control plane is separated from the data plane in a SDN network, the data plane will typically ask the control plane to obtain flow rules when the data plane sees new network packets that it does not know how to handle. By exploiting this key property, our proposed attack can first fingerprint whether a given network uses SDN/OpenFlow switches and then generate specifically crafted flow requests from the data plane to the control plane. This has two effects: (i) it can make the (logically centralized singlepoint) control plane hard to handle all requests (i.e., control plane resource consumption or DoS attack); (ii) the generated fake flow requests can produce many useless flow rules that need to be held by the data plane, thus making the data plane hard to store flow rules for normal network flows (data plane resource consumption or DoS attack). To demonstrate the feasibility of such attack, we create a new SDN network scanning prototype tool (named as SDN scanner) to remotely fingerprint networks that deploy SDN, and this method can be easily operated by mod-
Mitigating Routing Misbehaviors Using Subjective Trust Model in Mobile Ad Hoc Networks

Ad hoc networks have been targeted for great interest now-a-days. The vision of nomadic computing with its ubiquitous access has stimulated much interest in the MANET technology. The absence of infrastructure, the nature of the envisioned applications and the resource constrained environment pose some new challenges in securing the protocols in the MANET environments. In this paper, we tend to overcome the deficiencies in existing security mechanisms of various traditional routing protocols by proposing a novel secure routing protocol for MANET based on subjective trust model. The proposed protocol TBDSR (Trust Based DSR) extends the widely used DSR (Dynamic Source Routing) protocol and employs the idea of a subjective trust model to mitigate routing misbehaviors in the network layer of MANETs.
Statement of originality

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Quantifying the Impact of Climate Change and Human Activities on Streamflow in a Semi-Arid Watershed with the Budyko Equation Incorporating Dynamic Vegetation Information

Understanding hydrological responses to climate change and land use and land cover change (LULCC) is important for water resource planning and management, especially for water-limited areas. The annual streamflow of the Wuding River Watershed (WRW), the largest sediment source of the Yellow River in China, has decreased significantly over the past 50 years at a rate of 5.2 mm/decade. Using the Budyko equation, this study investigated this decrease with the contributions from climate change and LULCC caused by human activities, which have intensified since 1999 due to China’s Grain for Green Project (GFGP). The Budyko parameter that represents watershed characteristics was more reasonably configured and derived to improve the performance of the Budyko equation. Vegetation changes were included in the Budyko equation to further improve its simulations, and these changes showed a significant upward trend due to the GFGP based on satellite data. An improved decomposition method based on the Budyko equation was used to quantitatively separate the impact of climate change from that of LULCC on the streamflow in the WRW. Our results show that climate change generated a dominant effect on the streamflow and decreased it by 72.4% in the WRW. This climatic effect can be further explained with the drying trend of the Palmer Severity Drought Index, which was calculated based only on climate change information for the WRW. In the meantime, although human activities in this watershed have been very intense, especially since 1999, vegetation cover increase contributed a 27.6% decline to the streamflow, which played a secondary role in affecting hydrological processes in the WRW.
What Makes a Helpful Online Review? A Study of Customer Reviews on Amazon.com

Customer reviews are increasingly available online for a wide range of products and services. They supplement other information provided by electronic storefronts such as product descriptions, reviews from experts, and personalized advice generated by automated recommendation systems. While researchers have demonstrated the benefits of the presence of customer reviews to an online retailer, a largely uninvestigated issue is what makes customer reviews helpful to a consumer in the process of making a purchase decision. Drawing on the paradigm of search and experience goods from information economics, we develop and test a model of customer review helpfulness. An analysis of 1,587 reviews from Amazon.com across six products indicated that review extremity, review depth, and product type affect the perceived helpfulness of the review. Product type moderates the effect of review extremity on the helpfulness of the review. For experience goods, reviews with extreme ratings are less helpful than reviews with moderate ratings. For both product types, review depth has a positive effect on the helpfulness of the review, but the product type moderates the effect of review depth on the helpfulness of the review. Review depth has a greater positive effect on the helpfulness of the review for search goods than for experience goods. We discuss the implications of our findings for both theory and practice.
Generation and Evaluation of Business Continuity Processes using Algebraic Graph Transformation and the 'mCRL2' Process Algebra

Critical business processes can fail. Therefore, continuity processes are needed as back-up solutions. Today, those continuity processes are set up and maintained manually. They are mostly based on best practices that focus on specific continuity scenarios, Nevertheless, failures can occur in new and unforeseen combinations. As a consequence, a given business continuity plan needs to handle such situations as well. For this purpose, we present a technique for the generation and validation of the universe of continuity processes given a critical business process at Credit Suisse. The presented approach uses a combination of formal methods in the area of algebraic graph transformation and process algebra encompassing modal logic. The overall approach prepares for a sound evaluation of the effectiveness and efficiency of such plans. It uses formal tools, not standard software engineering solutions, to benefit from formal guarantees that facilitate the implementation of local and global security requirements.
Deciding with the Crowd Wisdom : An Overview of Issues Facing the Corporate Application of Prediction Markets

Prediction markets (PM) have emerged as a major tool for contemporary decision making tools within economic, financial and psychological literature. It is arguably one of the most efficient markets in history. Although applicable within financial markets, the principles of PM can also be applied within political frameworks, corporate environments public enterprise management. That is why since its discovery; other areas of human endeavour have applied prediction markets for organization-wide decision making with different outcomes and recommendations for improvement in prediction markets. This review examines the history of prediction markets, its ontology, epistemology and paradigm. It also examines the early applications of prediction market to institutional decision making and how early challenges have strengthened modern prediction markets. The key issues facing prediction market as a decision making tool is revisited in the last section before summarizing our findings and future research direction.
Knowledge Driven Variable Selection ( KDVS ) – a new approach to enrichment analysis of gene signatures obtained from high – throughput data

Background: High–throughput (HT) technologies provide huge amount of gene expression data that can be used to identify biomarkers useful in the clinical practice. The most frequently used approaches first select a set of genes (i.e. gene signature) able to characterize differences between two or more phenotypical conditions, and then provide a functional assessment of the selected genes with an a posteriori enrichment analysis, based on biological knowledge. However, this approach comes with some drawbacks. First, gene selection procedure often requires tunable parameters that affect the outcome, typically producing many false hits. Second, a posteriori enrichment analysis is based on mapping between biological concepts and gene expression measurements, which is hard to compute because of constant changes in biological knowledge and genome analysis. Third, such mapping is typically used in the assessment of the coverage of gene signature by biological concepts, that is either score–based or requires tunable parameters as well, limiting its power. Results: We present Knowledge Driven Variable Selection (KDVS), a framework that uses a priori biological knowledge in HT data analysis. The expression data matrix is transformed, according to prior knowledge, into smaller matrices, easier to analyze and to interpret from both computational and biological viewpoints. Therefore KDVS, unlike most approaches, does not exclude a priori any function or process potentially relevant for the biological question under investigation. Differently from the standard approach where gene selection and functional assessment are applied independently, KDVS embeds these two steps into a unified statistical framework, decreasing the variability derived from the threshold–dependent selection, the mapping to the biological concepts, and the signature coverage. We present three case studies to assess the usefulness of the method. Conclusions: We showed that KDVS not only enables the selection of known biological functionalities with accuracy, but also identification of new ones. An efficient implementation of KDVS was devised to obtain results in a fast and robust way. Computing time is drastically reduced by the effective use of distributed resources. Finally, integrated visualization techniques immediately increase the interpretability of results. Overall, KDVS approach can be considered as a viable alternative to enrichment–based approaches.
MOCHA: A Model CheckingTool that Exploits DesignStructur e

1 INTR ODUCTION Modelcheckingis emergingasapracticaltool for automated debuggingof embeddedsoftware(see[7] for a survey, and [12, 11] for samplemodelcheckers, and[8] for applications to softwareanalysis). In model checking,a high-level descriptionof a systemis comparedagainsta logical correctnessrequirementto discover inconsistencies.Sincemodel checkingis basedon exhausti vestate-spacexploration,and the sizeof the statespaceof a designgrows exponentially with the sizeof the description,scalabilityremainsa challenge. The goal of our researchis to develop techniques for exploiting modulardesignstructureduringmodelchecking, andthemodelcheckerMOCHA is basedonthis themeof exploiting modularity. Insteadof manipulatingunstructured state-transitiongraphs,it supportsthehierarchicalmodeling framework of ReactiveModules[3]. The hierarchyis exploited by the tool in threeways. First, verification tasks suchas refinementcheckingcan be decomposedinto subgoalsusingassume-guarantee rules[10]. Second,insteadof traditionaltemporallogics suchasCTL, it usesAlternating Temporal Logic (ATL), a game-basedtemporallogic that is designedto specifycollaborati veaswell asadversarialinteractionsbetweendifferentcomponents[4]. Third, theMOCHA algorithmsincorporateoptimizationsbasedonthehierarchical reductionof sequences of internaltransitions[5].
Stability Analysis of the Particle Swarm Optimization Algorithm Considering Interaction between Particles

The purpose of this paper is to analyze stability of the so-called particle swarm optimization (PSO) algo-rithm by modeling it as a Markov jump linear system with multiplicative noise, so that we can take into account the interaction between particles. A stability condition is represented in terms of linear matrix inequalities which is checked eﬃciently by using a convex optimization solver. A condition on the decay rate of the PSO algorithm is also shown. Stability region and contours of the decay rate of the PSO algorithm are shown by using the proposed analysis methods. Consequently, we provide more precise understandings of the PSO algorithm when the interaction between particles is considered.