Miracle-dz/UniFAR-FTUs huggingface dataset:
DATA MINING AND KNOWLEDGE DISCOVERY OF DROUGHT IN NEBRASKA

Drought impacts society in many ways. Studies show that economic, social, and environmental costs and losses associated with drought are increasing dramatically (Wilhite, 2000). According to the National Climatic Data Center (NCDC), the U.S. sustained 46 weather-related disasters in the period from 1980 to 1999, in which overall losses reached or exceeded $1 billion at the time of each event (Ross et al., 2000). Out of these disasters eight major droughts that occurred in the U.S. between 1980 and 1999 accounted for the largest percentage (43%) of weather-related monetary losses. The second largest percentage (30%) was due to hurricanes and tropical storms. In Nebraska, during the ten-year period from 1989 to 1998, the indemnity paid for drought losses totaled more than $92 million (USDA RMA, 1999). This implies that if droughts are frequent in the future, the losses could be much more than what has been observed in the 1980s and 1990s. Thus, since droughts are recurrent natural phenomena in Nebraska, it is obvious that proactive steps to address droughts are essential. Studying past and present droughts in relation to climatological, oceanic, and atmospheric parameters could help mitigate future drought impacts on society by improving our understanding of the drought hazard. Large historical data sets are essential to identify relationships between different climatic parameters and to distinguish patterns that may be used to predict drought. In light of this , it is essential to have an efficient way to extract information from large databases and to deliver relevant and actionable information for drought mitigation. One of the recently developed techniques relevant for such purposes is “Data Mining. ” Data mining is a process that uses a variety of data analysis tools to discover patterns and relationships of physical variables in different data sets (Two Crows, 1999). This technique is used in multidisciplinary fields bringing together techniques from machine learning, pattern recognition, statistics, databases, and visualization to address the issue of information extraction from large databases (Cabena et al., 1998). The method is used for commercial applications by many companies for designing strategic benefits to increase profitability (Groth, 1998). _______________________________________
Linked Product Data

Over the years, the Web has become a place accessible to everyone and that fact was responsible for the increasing amount of data that is available today. The AAA slogan which means that “Anyone can say Anything about Any topic” describes the open nature of the Web. In this so-called “Web of Data” the task of search is becoming harder. When someone performs a search in an engine, like Google, several web pages are returned, that are related to the search by the words given by the user, but most of those web pages are documents without a well defined data schema. This makes it difficult to reference information and to compare it accurately when it comes from different sources. The aim of this work was to develop an identity mapping engine for informational entities based on Semantic Web Technologies that can find similarities between data that is kept in different data sources and presented in different web sites, and decide which of these sources are referencing the same real world objects. With that information it is possible to create links between the different data sources and offer richer information to the users, by merging disjoint information. An application for mapping and merging product data in the Web was developed and evaluated, in different scenarios.
Improving Performance of Large Rectangular Secondary Clarifiers

Several modifications with different construction costs were proposed to enhance the performance in the large rectangular secondary clarifiers. A 3-Dimensional fully mass conservative clarifier model, based on modern computational fluid dynamics theory, was applied to evaluate proposed tank modifications and to estimate the maximum capacity of the existing and modified clarifiers. The comparison of model predicted results with the field data collected in tanks before and after modifications shows very good agreement.
Crisis detection from Arabic tweets

Social media (SM) platforms such as Twitter offer a rich source of real-time information about crises from which useful information can be extracted to support situational awareness. The task of automatically identifying SM messages related to a speciﬁc event poses many challenges, including processing large volumes of short, noisy data in real time. This paper explored the problem of extracting crisis-related messages from Arabic Twitter data. We focused on high-risk ﬂoods as they are one of the main hazards in the Middle East. In this work, we presented a gold-standard Arabic Twitter corpus for four high-risk ﬂoods that occurred in 2018. Using the annotated dataset, we investigated the performance of different classical machine learning (ML) and deep neural network (DNN) classi-ﬁers. The results showed that deep learning is promising in identifying ﬂood-related posts.
Fast Hierarchical Graph Clustering

This work studies how graph clustering methods can be used to solve other graph problems, such as hierarchical clustering and graph reordering. In this context, it explores how Faststep [1], a recent method for matrix factorization and graph clustering, can be adapted to solve these other problems. Our study develops a new method for this task, based on Faststep, and compares the proposed algorithm with state-of-the-art clustering methods: Layered Label Propagation and Lou-vain method. The results obtained are evaluated by several tests, which include comparison with ground-truth communities, networks generated by random models and direct comparison of algorithms. This study takes also advantage of Webgraph, a framework for succinct representation of graphs, and explores how clustering can be a powerful tool to improve the compression rates. We conclude that Faststep, although designed to solve a different problem, can still obtain acceptable results for this one. We also show that Louvain method, a hierarchical clustering method, can in fact obtain really promising results in the graph reordering task.
UvA-DARE (Digital Academic Repository) Distributed and Adaptive Darting Monte Carlo through Regenerations Ahn,

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A Modern and Sophisticated Host Based Intrusion Detection Data Set

: Cyber attacks can do great damage. Host intrusion detection systems (HIDS) can help to detect those attacks. In order to compare different HIDS and test their effectiveness, up-to-date, correct and publicly accessible data sets are required. Since all data sets available so far have serious problems, we present a new one that solves these problems and explain how it came about.
Arc Summarization of Tv Series

In this dissertation, we aim to create a system capable of generating summaries of arcs of TV series. With thousands of hours of video being uploaded and stored in videosharing websites and online streaming services, a need for video summarization appears as a necessary tool to save time and catch up with our beloved series. We propose a way to solve this problem using just subtitles information. The presented solution uses the framework of spectral graph theory to segment, find story arcs and summarize those arcs.
Brachial-Ankle Pulse Wave Velocity Predicts New-Onset Hypertension and the Modifying Effect of Blood Pressure in a Chinese Community-Based Population

Brachial-ankle pulse wave velocity (baPWV) was proven to be a prognostic indicator of cardiovascular events. However, the modifying effect of blood pressure (BP) on the longitudinal association between baPWV and new-onset hypertension is not well established. This study included 1849 non-hypertensive Chinese subjects from Shougang cohort study during December 2011 to July 2014. BaPWV was obtained using an Omron Colin BP-203RPE III device. Multivariate logistic regression models were used to evaluate associations of baPWV and outcomes. During a mean follow-up time of 2.36 years, 248 (13.41%) developed hypertension. BaPWV independently and gradably predicted the risk of incident hypertension and the SBP level at revisit (odds ratio or β (95% confidence interval) for participants with baPWV in quartile 4 vs. quartile 1: 2.72 (1.54, 4.78) for incident hypertension and 5.92 (4.26, 7.58) for SBP, P for trend: <0.001) after adjusting demonstrated risk factors. Besides, the effects of baseline baPWV on either incident hypertension or SBP at revisit were interactively modified by the level of baseline SBP; the effect size increased as the SBP level decreased. baPWV independently predicted the risk of hypertension and BP progression, modified by the level of SBP at baseline in this Chinese community-based population. The combination of baPWV and SBP can help differentiate the potential high-risk candidates who will develop hypertension quickly and benefit from early diagnosis and treatment.
Modeling Teamwork through Social Networks

Teamwork plays an important role in education, aiming for developing a set of skills only achievable through social interaction. The advances of educational data mining already allow for predicting students performance, but the accuracy of those methods is deeply impaired when teamwork results have to be estimated. The social nature of teamwork opens a window over the problem, since existing algorithms for the analysis of social networks are indeed effective. In this paper we propose to model teamwork through social networks, and make use of PageRank and its variants to improve the prediction of team results. In particular, we describe two different graph-based models to represent the interaction among students and explore three algorithms to analyze those models. Experimental results show that the more informed are the methods the better is the prediction, despite the model used.
Securing AMD SEV

AMD SEV allows for the creation of fully encrypted virtual machines. This allows cloud computing tenants’ data to be secret to the cloud computing provider. However, it has been shown that the encryption scheme used by AMD can easily be broken . The attacker can create a copy of the virtual machine, and perform some malicious operations to gain a secret value used in the encryption scheme. They can then use this value to write and read encrypted data to and from the target virtual machine. To prevent this, we propose wrapping the insecure encryption scheme with a stronger encryption scheme that is proven to be resilient to this kind of attack. In addition, we discuss other trusted execution environment vulnerabilities that have been discovered, as well as how they relate to AMD SEV and our proposed defense.
Implementing a Dashboard for Data Exchange on the REPOX Tool

The management of operational services where decisions depend on the perception of a lot of events can be supported by using interfaces with "dashboard" techniques. The usage scenarios of REPOX framework are examples of that, and therefore this thesis explores the hypothesis of a "dashboard" interface for that purpose. The REPOX platform supports the aggregation of bibliographic data for multiple channels (OAI-PMH, or even shared file Z39.50), its consolidation in local collections, and also its publication by multiple channels. In some real scenarios using REPOX, the number of these channels can reach the hundreds, with several million records. For that purpose we evaluated the various dimensions of information a manager of such a system requires, and developed and evaluated our proposals in line with this analysis.
Profiling biological applications for parallel implementation in multicore computers

Biological sequence databases keep growing every day, which represents a constant challenge in the context of the hardware and software architectures specially developed for high performance sequence alignment procedures. The main objective of this work is to propose a set of parallelization models for a restricted set of dynamic programming sequence alignment algorithms. A simulation environment is also proposed, making it possible to study the solution’s behavior on a common GPP multi-core architecture. The Smith-Waterman algorithm was implemented in the context of the presented framework. The proposed parallelization model, based both on fine and coarse-grained parallelization approaches, obtained a rather flat performance curve in several different platforms, scaling almost linearly with the number of cores in the architecture. The algorithm’s parallel implementation reached speedup values of over 480 times faster than the vanilla version of the same algorithm, which corresponds to a performance of more than 77 GCUPS obtained by using 32 parallel worker threads, which is almost three times faster than Rognes’ SWIPE implementation running in the exact same conditions. The presented study supports the conceptual design of a dedicated heterogeneous architecture, which will be developed in the context of the HELIX project. Keywords-Bioinformatic algorithms; Heterogeneous architectures; Parallel computing; SIMD.
Abstract. microRNA-188 (miR-188) acts as a tumor suppressor in various types of human cancer, including glioma, oral squamous cell carcinoma and hepatocellular carcinoma. However, the function and mechanism of miR-188 in pediatric osteosarcoma

microRNA-188 (miR-188) acts as a tumor suppressor in various types of human cancer, including glioma, oral squamous cell carcinoma and hepatocellular carcinoma. However, the function and mechanism of miR-188 in pediatric osteosarcoma (OS) have yet to be investigated. In the present study reverse transcription-quantitative polymerase chain reaction revealed that miR‐188 expression was significantly downregulated in pediatric OS tissues and cell lines. miR-188 overexpression markedly suppressed OS cell proliferation, migration and invasion, and induced cellular apoptosis. An in vivo assay demonstrated that miR-188 overexpression inhibited tumor growth. miR-188 targeted SOX4 to regulate its expression. miR-188 expression was inversely correlated with SOX4 in pediatric OS tissues. SOX4 restoration abrogated the inhibitory effects of miR-188 on OS cells. The results of the present study indicated that miR-188 suppressed pediatric OS progression by targeting SOX4.
AcTinG: Accurate Freerider Tracking in Gossip

Gossip-based content dissemination protocols are a scalable and cheap alternative to centralized content sharing systems. However, it is well known that these protocols suffer from rational nodes, i.e., nodes that aim at downloading the content without contributing their fair share to the system. While the problem of rational nodes that act individually has been well addressed in the literature, colluding rational nodes is still an open issue. Indeed, LiFTinG, the only existing gossip protocol addressing this issue, yields a high ratio of false positive accusations of correct nodes. In this paper, we propose AcTinG, a protocol that prevents rational collusions in gossip-based content dissemination protocols, while guaranteeing zero false positive accusations. We assess the performance of AcTinG on a testbed comprising 400 nodes running on 100 physical machines, and compare its behaviour in the presence of colluders against two state-of-the-art protocols: BAR Gossip that is the most robust protocol handling non-colluding rational nodes, and LiFTinG, the only existing gossip protocol that handles colluding nodes. The performance evaluation shows that AcTinG is able to deliver all messages despite the presence of colluders, whereas LiFTinG and BAR Gossip, both suffer heavy message losses. Finally, using simulations involving up to a million nodes, we show that AcTinG exhibits similar scalability properties as standard gossip-based dissemination protocols.
Deep Learning in Visual Recognition and Planning

Task quality is an important metric to have in consideration when we execute a task. One way to help increase this metric is to have a supervisor. If this supervisor could be a system that would help the user perform the task, that would be a very interesting problem in Artiﬁcial Intelligence. The main challenge with this problem is to recognize a scene and understand in which step of the task we are and how to proceed to achieve a solution. Therefore this thesis proposes a system that captures a scene and given the information returns an action for the user. This system includes a retrain Deep Learning Model that is based on Inception Module and a Hidden Markov Model to help complement the information given by the Deep Learning Model. Besides this, it has a module to transmit the best next action based on the state our model give us. Finally, we present user studies that reveal the performance levels of this system, and future work that could be developed.
Learning the Irreducible Representations of Commutative Lie Groups

We present a new probabilistic model of compact commutative Lie groups that produces invariant-equivariant and disentangled representations of data. To define the notion of disentangling, we borrow a fundamental principle from physics that is used to derive the elementary particles of a system from its symmetries. Our model employs a newfound Bayesian conjugacy relation that enables fully tractable probabilistic inference over compact commutative Lie groups - a class that includes the groups that describe the rotation and cyclic translation of images. We train the model on pairs of transformed image patches, and show that the learned invariant representation is highly effective for classification.
Where Have I Been - Visualizing Personal Geolocation Data

Big volumes of data are being produced with the large number of portable devices that exist nowadays and are capable of collecting GPS data. Despite the fact people track their mobility, most approaches on the analysis of spatiotemporal data are too complex and technical. It is very easy and common to collect spatio-temporal data but it is diﬃcult to analyze it and extract things with personal relevance. To make the analysis of personal geolocation data easier, we devised a visual language for accessing and querying that data, including support for personal semantics of locations. This visual language was validated, before developing the system, to see if users could use and understand it. We then implemented a system that integrated our visual language with result viewing and map interaction. An evaluation showed users could use and understand the whole system, thus certifying our initial objectives.
MGM: A Significantly More Global Matching for Stereovision

Semi-global matching (SGM) is among the top-ranked stereovision algorithms. SGM is an efficient strategy for approximately minimizing a global energy that comprises a pixel-wise matching cost and pair-wise smoothness terms. In SGM the two-dimensional smoothness constraint is approximated as the average of one-dimensional line optimization problems. The accuracy and speed of SGM are the main reasons for its widespread adoption, even when applied to generic problems beyond stereovision. This approximate minimization, however, also produces characteristic low amplitude streaks in the final disparity image, and is clearly suboptimal with respect to more comprehensive minimization strategies. Based on a recently proposed interpretation of SGM as a min-sum Belief Propagation algorithm, we propose a new algorithm that allows to reduce by a factor five the energy gap of SGM with respect to reference algorithms for MRFs with truncated smoothness terms. The proposed method comes with no compromises with respect to the baseline SGM, no parameters and virtually no computational overhead. At the same time it attains higher quality results by removing the characteristic streaking artifacts of SGM.
Spatial Memory of Paths Using Circular Probability Distributions: Theoretical Properties, Navigation Strategies and Orientation Cue Combination

Abstract We propose a mathematical model of the Path Integration (PI) process. Its core assumption is that orientations of a path are summarized by circular probability distributions. We compare our model with classical, deterministic models of PI and find that, although they are indistinguishable in terms of information encoded, the probabilistic model is more parsimonious when considering navigation strategies. We show how sensory events can enrich the probability distributions memorized, resulting in a continuum of navigation strategies, from PI to stimulus-triggered response. We analyze the combination of circular probability distributions (e.g., multicue fusion), and demonstrate that, contrary to the linear case, adding orientation cues does not always increase reliability of estimates. We discuss experimental predictions entailed by our model.
G ENERATING C ONTROL P OLICIES FOR A UTONOMOUS V EHICLES U SING N EURAL ODE S

The problem of robot control often requires solving a system of ordinary differential equations (ODEs). Traditionally this has been accomplished by using iterative ODE solvers. These solvers start with an initial guess, which is iteratively improved to converge to a correct solution. However, traditional solvers can be slow and do not combine well with other systems since they are not differentiable. In response, some researchers have proposed using neural networks in an end-to-end system that directly maps perceptual inputs to control actions. Because of their differentiablity, end-to-end approaches can be composed with other modules more readily than traditional ODE solvers. However the end-to-end approach no longer carries the guarantee that the solution obeys the required dynamics. We propose a framework for using Neural ODE to combine the ﬂexibility of the end-to-end approach with the guarantees of traditional solvers. In our approach a neural network is used to provide the initial guess to a differentiable ODE solver. The ODE solver then yields a solution trajectory. We use this trajectory to improve the guesses of the neural network. This framework allows the neural network to learn initial guesses that are close to the correct solution, improving overall system performance while ensuring that dynamics constraints are always satisﬁed. We demonstrate the utility of this framework in the case of robot control, where we use it to solve a family of boundary value problems that are essential for steering an autonomous vehicle to a goal state.
Evaluating Information Systems Security using a Multicriteria Decision Analysis approach

In the last decade, organizations are increasingly becoming dependent on Information Systems (IS) which brings a corresponding increase of the security abuses on IS. Many security methodologies were created to evaluate the security level in organizations but often those methodologies are very generic and can’t manage the real security concerns. Currently, organizations before acquiring new IS need to evaluate its security to guarantee that is according to the organization’s internal policies, but the existing methodologies don’t permit to decision maker’s making an adapted decision for their organization. To reduce this problem, we propose the definition of a set of security evaluation criteria that will help decision maker’s to make the decision for one specific IS, using Multiple Criteria Decision Analysis (MCDA). We’ll use Design Science Research (DSRM) to guide our work and to evaluate the results we are going to use interviews, feedback from the scientific community, Moody and Shanks Quality Framework, and Östrele principles.