Miracle-dz/UniFAR-FTUs huggingface dataset:
A ATR-Vis : Visual and Interactive Information Retrieval for Parliamentary Discussions in Twitter

The worldwide adoption of Twitter turned it into one of the most popular platforms for content analysis as it serves as a gauge of the public’s feeling and opinion on a variety of topics. This is particularly true of political discussions and lawmakers actions and initiatives. Yet, one common but unrealistic assumption is that the data of interest for analysis is readily available in a comprehensive and accurate form. Data need to be retrieved, but due to the brevity and noisy nature of Twitter content, it is difficult to formulate user queries that match relevant posts that use different terminology without introducing a considerable volume of unwanted content. This problem is aggravated when the analysis must contemplate multiple and related topics of interest, for which comments are being concurrently posted. This paper presents ATR-Vis, a user-driven visual approach for the retrieval of Twitter content applicable to this scenario. The method proposes a set of active retrieval strategies to involve an analyst in such a way that a major improvement in retrieval coverage and precision is attained with minimal user effort. ATR-Vis enables non-technical users to benefit from the aforementioned active learning strategies by providing visual aids to facilitate the requested supervision. This supports the exploration of the space of potentially relevant tweets, and affords a better understanding of the retrieval results. We evaluate our approach in scenarios in which the task is to retrieve tweets related to multiple parliamentary debates within a specific time span. We collected two Twitter data sets, one associated with debates in the Canadian House of Commons during a particular week in May 2014, and another associated with debates in the Brazilian Federal Senate during a selected week in May 2015. The two use cases illustrate the effectiveness of ATR-Vis for the retrieval of relevant tweets, while quantitative results show that our approach achieves high retrieval quality with a modest amount of supervision. Finally, we evaluated our tool with three external users who perform searching in social media as part of their professional work.
The advantages and disadvantages of short- and long- read metagenomics to infer bacterial and eukaryotic community composition

The first step in understanding ecological community diversity and dynamics is quantifying community membership. An increasingly common method for doing so is through metagenomics. Because of the rapidly increasing popularity of this approach, a large number of computational tools and pipelines are available for analysing metagenomic data. However, the majority of these tools have been designed and benchmarked using highly accurate short read data (i.e. illumina), with few studies benchmarking classification accuracy for long error-prone reads (PacBio or Oxford Nanopore). In addition, few tools have been benchmarked for non-microbial communities. Here we use simulated error prone Oxford Nanopore and high accuracy Illumina read sets to systematically investigate the effects of sequence length and taxon type on classification accuracy for metagenomic data from both microbial and non-microbial communities. We show that very generally, classification accuracy is far lower for non-microbial communities, even at low taxonomic resolution (e.g. family rather than genus). We then show that for two popular taxonomic classifiers, long error-prone reads can significantly increase classification accuracy, and this is most pronounced for non-microbial communities. This work provides insight on the expected accuracy for metagenomic analyses for different taxonomic groups, and establishes the point at which read length becomes more important than error rate for assigning the correct taxon.
Data‐Driven Model Uncertainty Estimation in Hydrologic Data Assimilation

The increasing availability of earth observations necessitates mathematical methods to optimally combine such data with hydrologic models. Several algorithms exist for such purposes, under the umbrella of data assimilation (DA). However, DA methods are often applied in a suboptimal fashion for complex real‐world problems, due largely to several practical implementation issues. One such issue is error characterization, which is known to be critical for a successful assimilation. Mischaracterized errors lead to suboptimal forecasts, and in the worst case, to degraded estimates even compared to the no assimilation case. Model uncertainty characterization has received little attention relative to other aspects of DA science. Traditional methods rely on subjective, ad hoc tuning factors or parametric distribution assumptions that may not always be applicable. We propose a novel data‐driven approach (named SDMU) to model uncertainty characterization for DA studies where (1) the system states are partially observed and (2) minimal prior knowledge of the model error processes is available, except that the errors display state dependence. It includes an approach for estimating the uncertainty in hidden model states, with the end goal of improving predictions of observed variables. The SDMU is therefore suited to DA studies where the observed variables are of primary interest. Its efficacy is demonstrated through a synthetic case study with low‐dimensional chaotic dynamics and a real hydrologic experiment for one‐day‐ahead streamflow forecasting. In both experiments, the proposed method leads to substantial improvements in the hidden states and observed system outputs over a standard method involving perturbation with Gaussian noise.
A Contribution to Theory Building for Mobile Marketing: Categorizing Mobile Marketing Campaigns through Case Study Research

Marketing experts consider the mobile device as an extremely promising marketing tool as it supports them to cope with their major challenge: getting time and attention from customers. Current mobile marketing research mostly covers success factors and acceptance analysis. Categorization, when addressed, lacks in appropriate foundation and is not linked to objectives at all. In this article we examine 55 case studies in order to identify relevant characteristics of mobile marketing campaigns. The outcome of the paper is the derivation of four mobile marketing standard types and an examination of campaign objectives that can be addressed by mobile marketing. The proposed scheme allows to unambiguously characterize any given mobile marketing campaign and to identify the respective objectives.
Drought Monitoring in the Dry Zone of Myanmar using MODIS Derived NDVI and Satellite Derived CHIRPS Precipitation Data

Drought has become an increasingly frequent phenomena around the globe causing negative impacts on ecosystems, agriculture, and socio-economic conditions. While efforts have been underway for developing effective monitoring and risk management measures, it still remains a challenge in countries like Myanmar where access to observed and near real time data is a constraint. This study therefore, tries to derive correlations between MODIS Normalized Difference Vegetation Index (NDVI) and Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data to see if some empirical relationships can be established. Statistical analysis showed that strong correlation (R² = 0.74 and 0.82) exist between NDVI and CHIRPS data indicating that vegetation stress conditions observed in the Dry Zone of Myanmar is due to insufficient precipitation conditions. The analysis also showed that the region had faced with three extreme conditions during the period from 1981-2015 with 2014 and 2015 being the extreme event. It further concluded that NDVI and CHIRPS could provide near real time information on vegetation stress situations of the Dry Zone of Myanmar.
The Adoption and Benefits of Agile Software Development Methods in Jamaica

Studies have demonstrated that most information systems projects fail due to people problems. Agile software development methods were introduced in an effort to resolve this problem. It relies on people and their creativity during system development rather than processes and the maturity of firms. Research has found that the adoption of agile methods in software development is becoming very popular. However, there is an appeal in the literature for more empirical studies of agile software development. This study examined the adoption and benefits of agile methods in Jamaican software development firms. The results showed that 53% of the firms surveyed are using agile methods, with SCRUM being the main method, and the top benefits being improved customer satisfaction, improved productivity of teams and speeding up the developmental process. The average uptake of agile methods in Jamaican firms would support the claim for a longitudinal study to track the adoption trend.
Storm Time Variation of Radiative Cooling by Nitric Oxide as Observed by TIMED‐SABER and GUVI

The variation of O/N2 (reference to N2 column density 1017 cm−2) and nitric oxide radiative emission flux exiting the thermosphere have been studied over the Northern Hemisphere during the superstorm event of 7–12 November 2004. The data have been obtained from Global Ultraviolet Imager (GUVI) and Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) on board the National Aeronautics and Space Administration (NASA)'s Thermosphere, Ionosphere, Mesosphere Energetics and Dynamics (TIMED) satellite. The NO radiative flux is observed to show an anti‐correlation with O/N2 on a global scale. Both NO radiative flux and O/N2 ratio show equatorward motion with maximum penetration in western longitude sectors. A local variation of O, O2, and N2 densities have been calculated using NRLMSISE‐00 model over a midlatitude location (55°N,180°E). On a local scale, model calculated O/O2 and O/N2 ratios are found to follow the observations made by GUVI. The collisional excitation of NO with atomic oxygen is the most dominant process for the total cooling rate. The SABER‐retrieved NO cooling rate (CR) at a local site suggests an enhancement during the storm period with the peak emission rate closely correlated to the progression of the storm. The peak emission altitude of NO CR moves upward during the main phase of the storm. The NO abundance has been calculated by using cooling rate and Nitric Oxide Empirical Model (NOEM) model. Both these suggest a vary large (3–15 times) increase in NO density during the storm, which is required to account the changes in NO radiative flux. A similar kind of enhancement in NO abundance is also noticed in Student Nitric Oxide Explorer observations during intense geomagnetic storms.
Article in Press G Model Journal of Neuroscience Methods Cluster-based Computational Methods for Mass Univariate Analyses of Event-related Brain Potentials/fields: a Simulation Study

(2014) Cluster-based computational methods for mass univariate analyses of event-related brain potentials/fields: A simulation study. Please cite this article in press as: Pernet CR, et al. Cluster-based computational methods for mass univariate analyses of event-related brain potentials/fields: A simulation study. Keywords: ERP Family-wise error rate Multiple comparison correction Cluster-based statistics Threshold free cluster enhancement Monte-Carlo simulations a b s t r a c t Background: In recent years, analyses of event related potentials/fields have moved from the selection of a few components and peaks to a mass-univariate approach in which the whole data space is analyzed. Such extensive testing increases the number of false positives and correction for multiple comparisons is needed. Method: Here we review all cluster-based correction for multiple comparison methods (cluster-height, cluster-size, cluster-mass, and threshold free cluster enhancement – TFCE), in conjunction with two computational approaches (permutation and bootstrap). Results: Data driven Monte-Carlo simulations comparing two conditions within subjects (two samples Student's t-test) showed that, on average, all cluster-based methods using permutation or bootstrap alike control well the family-wise error rate (FWER), with a few caveats. Conclusions: (i) A minimum of 800 iterations are necessary to obtain stable results; (ii) below 50 trials, bootstrap methods are too conservative; (iii) for low critical family-wise error rates (e.g. p = 1%), permutations can be too liberal; (iv) TFCE controls best the type 1 error rate with an attenuated extent parameter (i.e. power < 1).
Risk Assessment of Oil Pipeline Accidents in Special Climatic Conditions

The present study identifies the main accidents’ factors and causes for oil pipeline located in Siberia and operated in special climatic conditions. Various types of pipeline accident scenarios were modeled. It is showed that the most dangerous scenarios are oil spills fire and oil vapor explosion due to the loss of piping integrity (rupture) of the pipeline’s section, laying on marshlands and oil spill on the water surface due to the loss of piping integrity (puncture). The most probable scenario is oil spills fire due to the loss of piping integrity (puncture) of the pipeline’s section, laying on dry lands and marshlands. To estimate the scenarios «event tree analysis» is used. Also such risk indexes as individual, societal, public and potential risks were determined.
A Reusability Evaluation Model for OO-Based Software Components

promoted the research on software metric technology. There are metrics for identifying the quality of reusable components but the function that makes use of these metrics to find reusability of software components is still not clear. These metrics if identified in the design phase or even in the coding phase can help us to reduce the rework by improving quality of reuse of the component and hence improve the productivity due to probabilistic increase in the reuse level. CK metric suit is most widely used metrics for the object-oriented (OO) software; we critically analyzed the CK metrics, tried to remove the inconsistencies and devised the framework of metrics to obtain the structural analysis of OO-based software components. Neural network can learn new relationships with new input data and can be used to refine fuzzy rules to create fuzzy adaptive system. Hence, Neuro-fuzzy inference engine can be used to evaluate the reusability of OO-based component using its structural attributes as inputs. In this paper, an algorithm has been proposed in which the inputs can be given to Neuro-fuzzy system in form of tuned WMC, DIT, NOC, CBO , LCOM values of the OO software component and output can be obtained in terms of reusability. The developed reusability model has produced high precision results as expected by the human experts.
Strike-Slip Fault Deformation and Its Control in Hydrocarbon Trapping in Ketaling Area, Jambi Subbasin, Indonesia

Geologically, Ketaling area consists of a local high considered as flexure margin of Tempino-Kenali Asam Deep in west part and graben in east part also known as East Ketaling Deep. Numerous proven plays were established in Ketaling area with reservoir in early Miocene carbonate and middle Miocene sand. This area underwent several major deformations. Faults are developed widely, yet their geometrical features and mechanisms of formation remained so far indistinct, which limited exploration activities. With new three-dimensional seismic data acquired in 2014, this area evidently interpreted as having strike-slip mechanism. The objective of this study is to examine characteristic of strike slip fault and its affect to hydrocarbon trapping in Ketaling Area. Structural pattern and characteristic of strike slip fault deformation was examined with integration of normal seismic with variance seismic attribute analysis and the mapping of Syn-rift to Post-rift horizon. Seismic flattening on 2D seismic cross section with NW-SE direction is done to see the structural pattern related to horst (paleohigh) and graben. Typical flower structure, branching strike-slip fault system and normal fault in synrift sediment clearly showed in section. An echelon pattern identified from map view as the result of strike slip mechanism. Detail structural geology analysis show the normal fault development which has main border fault in the southern of Ketaling area dipping to the Southeast-East with NE-SW lineament. These faults related to rift system in Ketaling area. NW-SE folds with reactive NE-SW fault which act as hydrocarbon trapping in the shallow zone. This polyphase tectonic formed local graben, horst and inverted structure developed a good kitchen area (graben) and traps (horst, inverted structure). Subsequently, hydrocarbon accumulation potentials such as basement fractures, inverted syn-rift deposit and shallow zone are very interesting to explore in this area.
The E(cid:11)ect of Process Switches on Branch Prediction Accuracy

Recent superscalar processors that use deep pipelines and wide issue rates, highly depend on e(cid:14)cient branch prediction to obtain a large amount of instruction-level parallelism. Much work has been carried out in this area and the outcome of branches is very predictable. Mostly, superscalar processors are used in a multiprogram-ming environment to obtain higher throughput. In this paper, we analyze the impact of process switches on prediction accuracy using a simulator and show that the accuracy of branch prediction is still high in a multiprogramming environment where process switches occur in the order of milliseconds. However, in multithreading architectures, the prediction accuracy will be severely degraded due to the high frequency of process switches.
GPS and Discrete Kalman Filter for Indoor Robot Navigation

This paper discusses the implementation of the Kalman Filter along with the Global Positioning System (GPS) for indoor robot navigation. Two dimensional coordinates is used for the map building, and refers to the global coordinate which is attached to the reference landmark for position and direction information the robot gets. The Discrete Kalman Filter is used to estimate the robot position, project the estimated current state ahead in time through time update and adjust the projected estimated state by an actual measurement at that time via the measurement update. The navigation test has been performed and has been found to be robust. Keywords—Global positioning System, kalman filter, robot navigation.
The metric backbone preserves community structure and is a primary transmission subgraph in contact networks

The structure of social networks strongly a ects how di erent phenomena spread in human society, from the transmission of information to the propagation of contagious diseases. It is well-known that heterogeneous connectivity strongly favors spread, but a precise characterization of the redundancy present in social networks and its e ect on the robustness of transmission is still lacking. This gap is addressed by the recently introduced metric backbone, a subgraph that is su cient to compute all shortest paths of weighted graphs. We show that the metric backbones of nine contact networks obtained from proximity sensors in a variety of social contexts are generally very small, 49% of the original graph for one and ranging from about 6% to 20% for the others. This reflects a surprising amount of redundancy and reveals that shortest paths on these networks are very robust to random attacks and failures. We show that the metric backbone preserves the community structures of the full contact networks and is a primary subgraph in epidemic transmission. The metric backbone, thus, is a principled graph reduction technique that does not a ect shortest path connectivity for any nodes in a network. It is an important subgraph to characterize epidemic spread, the robustness of social networks, and any communication dynamics that depend on complex network shortest paths. keywords: complex networks, network backbone, community structure, epidemic spreading, contact networks.
Not Typing but Writing: Eye-based Text Entry Using Letter-like Gestures

September 3-4, 2007 Leicester, UK Not Typing but Writing: Eye-based Text Entry Using Letter-like Gestures Jacob O. Wobbrock, James Rubinstein, Michael Sawyer, and Andrew T. Duchowski The Information School University of Washington Seattle, WA 98195 USA wobbrock@u.washington. edu Department of Psychology Department of Industrial Engineering School of Computing Clemson University Clemson, SC USA 29634 {jrubins, msawyer, andrewd}@clemson.edu
A Fuzzy Kwan–Cai Neural Network for Determining Image Similarity and for the Face Recognition

Similarity is a crucial issue in Image Retrieval [1–4]. It is relevant both for unsupervised clustering [5, 6] and for supervised classification [7]. In this study, we aim to provide an effective method for learning image similarity. To reach this aim we start of from the Fuzzy Kwan–Cai Neural Network (FKCNN) [8] and turn it into a supervised method for learning similarity. Unlike the classical unsupervised FKCNN [8], in which a class is represented by a single output neuron, the supervised FKCNN has more output neurons that each designate a class. These give a better performance than their unsupervised counterparts as in the case of the classical unsupervised FKCNN, a class is represented by a single output neuron while the supervised FKCNN has more output neurons that each designate a class. This concept is similar with the idea of replacing the binary decision about the membership (nonmembership) of a pattern to a class, with the introduction of a membership degree, between 0 and 1. In order to evaluate the performance of our proposed neural network, it is compared with two baseline methods: Self-organizing Kohonen maps (SOKM) and k-Nearest Neighbors (k-NN). The feasibility of the presented methods for similarity learning has been successfully evaluated on the Visual Object Classes (VOC) database [9], that consists of 10102 images in 20 object classes. The concept of similarity is important not only in almost every scientific field but it has [10, 11] deep roots in philosophy and psychology. Our work however deals more with the measure of similarity in computer science domain (Information Retrieval to be more specific, that has focused on images, video, and to some extent audio). “Measuring image similarity is an important task for various multimedia applications. ”1
Toward Verified Artificial Intelligence

I L L U S T R A T I O N B Y P E T E R C R O W T H E R A S S O C I A T E S software, cyber-attacks, and AI system safety.4 Therefore, the question of verification and validation of AI systems, and, more broadly, of achieving trustworthy AI, has begun to demand the attention of the research community. We define “verified AI” as the goal of designing AI systems that have strong, ideally provable, assurances of correctness with respect to mathematically specified requirements. How can we achieve this goal? In this article, we consider the challenge of verified AI from the perspective of formal methods, a field of computer science and engineering concerned with the rigorous mathematical specification, design, and verification of systems. At its core, formal methods is about proof: formulating specifications that form proof obligations; designing systems to meet those obligations; and verifying, via algorithmic proof search, that the systems indeed meet their specifications. A spectrum of formal methods, from specificationdriven testing and simulation to model checking and theorem proving, are routinely used in the computer-aided design of integrated circuits (ICs) and have been widely applied to find bugs in software, analyze cyber-physical systems (CPS), and find security vulnerabilities. We review the way for-