Miracle-dz/UniFAR-FTUs huggingface dataset:
BMC Systems Biology

Background: Genomics research produces vast amounts of experimental data that needs to be integrated in order to understand, model, and interpret the underlying biological phenomena. Interpreting these large and complex data sets is challenging and different visualization methods are needed to help produce knowledge from the data. Results: To help researchers to visualize and interpret integrated genomics data, we present a novel visualization method and bioinformatics software tool called FORG3D that is based on real-time three-dimensional force-directed graphs. FORG3D can be used to visualize integrated networks of genome scale data such as interactions between genes or gene products, signaling transduction, metabolic pathways, functional interactions and evolutionary relationships. Furthermore, we demonstrate its utility by exploring gene network relationships using integrated data sets from a Caenorhabditis elegans Parkinson's disease model. Conclusion: We have created an open source software tool called FORG3D that can be used for visualizing and exploring integrated genome scale data.
Domain Specific e-Document Summarization Using Extractive Approach

With the rapid growth of online information availability, it becomes more and more important to find and describe textual information effectively from multiple related e-documents. Domain specific related e-documents contain information which is much relevant, similar or repeated and shares same background. Reading these all multiple relevant e-documents completely for accurate & brief contents is time-consuming, unnecessary and impossible. In this scenario multidocument summarization is useful to give an outline of a topic from multiple related source documents and allow users to zoom in for more details as per interest. We discuss here a part of research work for summarizing multiple related research papers as e-documents presented by research scholars using various approaches and techniques aiming at giving an overview of the researches of this area and describing a method for automatic summarization of sets of research papers of desired subject that may be retrieved by a digital library system or search engine in response to a user query. Many digital libraries or online services provide research papers published in journals, conferences or workshops for reference purpose to the user. Research papers contain a wealth of high quality information by specifying research objectives, research methods, evaluation of research objectives, research results and concluding remarks. However, a research paper is relatively long and browsing too many of such research papers results in information overload. Therefore, it would be helpful to summarize a set of research papers to assist users in grasping the main ideas on a desired topic in a specific area.
The sounds of culture : Maltese radio in Australia

The central concern of this study is how media personnel in one area of community based radio, namely Maltese radio broadcasters operating in Melbourne, come to understand their cultural output and their status as "cultural producers"; what is their perception of the role of "ethnic media" in a multiculturalist context and to what degree does their output in Maltese language based radio programs help to create and sustain cultural identity. What role do they think they are playing in implementing this project? In this context, this study will attempt to uncover some of the underlying cultural assumptions of what they put to air and of what they say about their fimction as "ethnic broadcasters". A key question then becomes to what extent are these media personnel serving as cultural producers who are governed by a "philosophy" of "assimilationism", "difference" or what Stuart Hall (1992, 1993) calls "hybridity".
Providing GFR Guarantees for TCP/IP Tra(cid:14)c over APON Access Systems

The paper focuses on providing bandwidth guarantees for the Guaranteed Frame Rate (GFR) service category in the context of ATM-based Passive Optical Networks (APONs). Our work builds on the research performed on ATM multiplexers and extends it with the speci(cid:12)cs of the APON access system. We study the performance of two known bu(cid:11)er management schemes (DFBA, WFBA), combined with either FIFO or per-VC queueing, when applied on virtual circuits carrying TCP/IP tra(cid:14)c. Our simulation results show that with fa irly small bu(cid:11)ers at the Optical Network Units, the schemes based on per-VC queueing yield very good performance in large number of cases. The schemes based on FIFO queueing su(cid:11)er from unfairness, although they do provide bandwidth guarantees in case the GFR bandwidth reservation is kept reasonably small. Tagging helps protecting TCP-like responsive tra(cid:14)c but does not prevent aggressive tra(cid:14)c from grabbing the non-reserved bandwidth. Tagging also increases unfairness.
A novel concurrent error detection technique for the fast Fourier transform implemented in SRAM-based FPGAs

A novel Concurrent Error Detection (CED) technique for the complex Fast Fourier Transform (FFT) implemented in SRAM based Field Programmable Gate Arrays (FPGA) is presented in this paper. This technique compares one of the inputs to a linear combination of the outputs, avoiding complex multiplications. Hence its computational complexity and resource usage are lower than that of the Parseval Sum of Squares (SoS) or other CED techniques applied to this algorithm. In order to test its performance, bit-flips have been injected in the FPGA configuration bits. The technique achieves high error detection rates with fewer unnecessary reconfigurations than SoS, which makes it suitable for applications in which circuit size and availability are critical.
A novel hybrid approach of Bayesian Logistic Regression and its ensembles for landslide susceptibility assessment

Abstract A novel artificial intelligence approach of Bayesian Logistic Regression (BLR) and its ensembles [Random Subspace (RS), Adaboost (AB), Multiboost (MB) and Bagging] was introduced for landslide susceptibility mapping in a part of Kamyaran city in Kurdistan Province, Iran. A spatial database was generated which includes a total of 60 landslide locations and a set of conditioning factors tested by the Information Gain Ratio technique. Performance of these models was evaluated using the area under the ROC curve (AUROC) and statistical index-based methods. Results showed that the hybrid ensemble models could significantly improve the performance of the base classifier of BLR (AUROC = 0.930). However, RS model (AUROC = 0.975) had the highest performance in comparison to other landslide ensemble models, followed by Bagging (AUROC = 0.972), MB (AUROC = 0.970) and AB (AUROC = 0.957) models, respectively.
Implementation of Real Time Detection of Traffic from Twitter Stream Analysis

The social network has been newly engaged as a source of information for event discovery, with particular mention to road traffic jamming and the car accident. In this thesis, We are going to present a real-time monitor system for traffic event detection from Twitter stream study. The system fetches tweets from Twitter according to some search criterion like processes tweets, by apply text mining techniques and finally perform the categorization of tweets. The aim is to allocate the appropriate class label to each tweet as related to a traffic incident or not. The traffic detection system was engaged for real-time monitoring of several areas of the Italian road network, allowing for detection of traffic events almost in real time, often before online traffic news web sites. We employed the support vector machine as a classification model and we achieved an accuracy value of 95.75% by solving a binary classification problem (traffic versus non-traffic tweets). We were also able to discriminate if traffic is caused by an external event or not, by solving a multiclass classification problem and obtain accuracy value
Csce 970: Pattern Recognition

1 Introduction In previous lectures we talked about classiiers design where we assumed that the feature vectors were available and we didn't worry about how these features were generated or selected. In this lecture we talk about feature selection which is one of the important stages involved in the design of a classiication system. From Figure 1 we notice that feature selection is an intermediate stage between feature generation and classiier design. The goal of feature selection is to choose the best features from all the features that we get as a result of the feature generation stage, which could be in the hundreds, to use in classiier design. Figure 1: The basic stages involved in the design of a classiication system. Feature selections is important for many reasons. First, the data we get from the feature generation stage is deened by sensor data which may be deened for convenience rather than classiication discriminating power 1]. Second, to reduce computational complexity in training and classiication. Third, classiiers that use less features tend to generalize better. This is because the higher the ratio of the number of training patterns N to the number of classiier parameters l (which are aaected by the number of features), the better the generalization properties of the classiier. Fourth, classiier performance evaluation can be assessed more accurately when less features are involved. In the feature selection process it is important that the selected features take distant values in the diierent classes and close values in the same class. This means that we should get features that provide large between-class distance and small withen-class distance. Furthermore, we should drop features that do not help in the classiication process. There are three approaches to estimate the discriminating capabilities of the features. The rst is to examine the features individually and discard those with little discriminatory capabilities. The second is to examine features in combinations. The third approach is to apply linear or nonlinear transformations to a feature vector which may lead to new feature vectors with better discriminatory properties.
TO DRAW OR NOT TO DRAW: UNDERSTANDING THE TEMPORAL ORGANIZATION OF DRAWING BEHAVIOR USING FRACTAL ANALYSES

Studies on drawing often focused on spatial aspects of the finished products. Here, the drawing behavior was studied by analyzing its intermittent process, between drawing (i.e. marking a surface) and interruption (i.e. a pause in the marking gesture). To assess how this intermittence develops with age, we collected finger-drawings on a touchscreen by 185 individuals (children and adults). We measured the temporal structure of each drawing sequence to determine its complexity. To do this, we applied temporal fractal estimators to each drawing time series before combining them in a Principal Component Analysis procedure. The youngest children (3-year-old) drew in a more stereotypical way with long-range dependence detected in their alternations between states. Among older children and adults, the complexity of drawing sequences increased showing a less predictable behavior as their drawings become more detailed and figurative. This study improves our understanding of the temporal aspects of drawing behavior, and contributes to an objective understanding of its ontogeny.
Environment-aware Pedestrian Trajectory Prediction for Autonomous Driving

People’s safety is a primary concern in autonomous driving. There exist eﬃcient methods for identifying static obstacles. However, the prediction of future trajectories of moving elements, such as pedes-trians crossing a street, is a much more challenging problem. A promising direction of research is the use of machine learning algorithms with location bias maps. Our goal was to further explore this idea by training an interchangeable location bias map, a location-speciﬁc feature that is added into the middle of a convolutional neural network. For diﬀerent locations, we used diﬀerent location bias maps to allow the network to learn from diﬀerent setting contexts without overﬁtting to a speciﬁc setting. Using pre-annotated video footage of pedestrians moving around in crowded areas, we implemented a pedestrian behavior encoding scheme to generate input and output volumes for the neural network. Using this encoding scheme, we trained our neural network and interchangeable location bias map. Our research demonstrates that the network with an interchangeable location bias map can predict realistic pedestrian trajectories even when trained simultaneously in multiple settings.
Control Strategies for Parsing with Freer Word-order Languages Organizers Chair: Workshop Program Session: Constraints and Language Processing Control Strategies for Parsing with Freer Word-order Languages Constraints in Language Processing: Do Grammars Count?

One of the central assumptions of Optimality Theory is the hypothesis of strict domination among constraints. A few studies have suggested that this hypothesis is too strong and should be abandoned in favor of a weaker cumulativity hypothesis. If this suggestion is correct, we should be able to find evidence for cumulativity in the comprehension of Gapping sentences, which lack explicit syntactic clues in the form of the presence of a finite verb. On the basis of a comparison between several computational models of constraint evaluation, we conclude that the comprehension of Gapping sentences does not yield compelling evidence against the strict domination hypothesis.
ANN Based Broken Rotor Bar Fault Detection in LSPMS Motors

Line Start Permanent Magnet Synchronous Motors (LSPMSMs) combine the high efficiency of the permanent magnet synchronous motors (PMSM) with the ease of use, simplicity in design and high starting capability of the induction motors (IMs). Due to the rapidly growing usage of this relatively new motor, proposing a diagnostics method for broken rotor bar fault is necessity. In this paper, a diagnostics technique based on Artificial Neural Network (ANN) was developed to detect the broken bars fault in LSPMSM using Singular Value Decomposition (SVD) in order to extract distinguishing features from the stator phase current. This distinguishing attributes were proposed to be the inputs to the built neural network.
NO2 Seasonal and Interannual Variability in Ukrainian Industrial Cities

Abstract The paper aims to define the main features and principles of seasonal and interannual NO2 variations in Ukrainian industrial cities. Using ground-based measurements for 15-year period, it shows weak NO2 seasonal variability that could intensify in case of three regularities. These regularities depend on impact of natural conditions during anthropogenic emissions growth and redistribution between emission sources. Most industrial cities are characterized by positive trends even if stationary industrial emissions fall. NO2 interannual changes forms under variety of fluctuations. However, 6.2- and 9.3-year periods have the biggest impact and might be explained by low-frequent lunar tidal forces through its influence on meteorological conditions.
A Novel Face Detection and Facial Feature Detection Algorithm using Skin Colour and Back Propagation Neural Network

Face detection is an important topic in the world of computer science and information technology. Face detection is a computer technology that determines the locations and sizes of human faces in arbitrary (digital) images [1]. It detects facial features and ignores anything else. In this paper a unique method to identify faces is given using backpropagation neural network.
CONTENT-BASED CLUSTERING AND VISUALIZATION OF SOCIAL MEDIA TEXT MESSAGES

Although Twitter has been around for more than ten years, crisis management agencies and first response personnel are not able to fully use the information this type of data provides during a crisis or natural disaster. This thesis addresses clustering and visualizing social media data by textual similarity, rather than by only time and location, as a tool for first responders. This thesis presents a tool that automatically clusters geotagged text data based on their content and displays the clusters and their locations on the map. It allows at-a-glance information to be displayed throughout the evolution of a crisis. For accurate clustering, we used silhouette coefficients to determine the number of clusters automatically. To visualize the topics (i.e., frequent words) within each cluster, we used the word cloud. This tool could be easily used by first response and official management personnel to quickly determine when a crisis is occurring, where it is concentrated, and what resources to best deploy to stabilize the situation.
Scholarship at UWindsor Scholarship at UWindsor

Exposure to maternally derived glucocorticoids during embryonic development impacts offspring phenotype. Although many of these effects appear to be transiently ‘negative’, embryonic exposure to maternally derived stress hormones is hypothesized to induce preparative responses that increase survival prospects for offspring in low-quality environments; however, little is known about how maternal stress inﬂuences longer-term survival-related performance traits in free-living individuals. Using an experimental elevation of yolk corticosterone (embryonic signal of low maternal quality), we examined potential impacts of embryonic exposure to maternally derived stress on ﬂight performance, wing loading, muscle morphology and muscle physiology in juvenile European starlings ( Sturnus vulgaris ). Here we report that ﬂedglings exposed to experimentally increased corticosterone in ovo performed better during ﬂight performance trials than control ﬂedglings. Consistent with differences in performance, individuals exposed to elevated embryonic corticosterone ﬂedged with lower wing loading and had heavier and more functionally mature ﬂight muscles compared with control ﬂedglings. Our results indicate that the positive effects on a survival-related trait in response to embryonic exposure to maternally derived stress hormones may balance some of the associated negative developmental costs that have recently been reported. Moreover, if embryonic experience is a good predictor of the quality or risk of future environments, a preparative phenotype associated with exposure to apparently negative stimuli during development may be adaptive.
Modeling Advertiser Bidding Behaviors in Google Sponsored Search with a Mirror Attention Mechanism

In this paper, we present a data-driven approach to predicting advertiser’s bid prices in a sponsored search system, like Google Search Ads, with a new type of attention-based sequence learning model. Instead of characterizing the advertisers’ bidding behaviors with explicit assumptions (e.g., rationality models), as done in previous work [15], we treat their bid adjustments as response to observable metrics (e.g., impression count, click-through rate) and directly predict the bid prices using recurrent neural networks combined with a novel attention mechanism. The proposed model consists of two recurrent neural networks, for capturing the dynamics of metric sequence and bid sequence respectively, connected by a mirror attention layer formulation that transfers location information from metrics to bids. We evaluate the performance of the proposed model, along with other baselines, on advertiser bidding history data extracted from Google Search Ads system logs. We also demonstrate the generality of the new mechanism by experimenting on another domain: air quality prediction. Our empirical results show the effectiveness of the modeling approach and the new mechanism — we see a significant improvement over the baseline models for both advertiser bid prediction and air quality prediction tasks.
Continuation Methods for Qualitative Analysis of Aircraft Dynamics

ABSTRACTA class of numerical methods for constructing bifurcation curves for systems of coupled, non-linear ordinary differential equations is presented. Foundations are discussed, and several vari-ations are outlined along with their respective capabilities. Appropriate background materialfrom dynamical systems theory is presented. 1 INTRODUCTIONDevelopment of a model for the macroscopic motion of aircraft begins with the rigid body equa-tions of motion, a system of coupled, nonlinear ordinary differential equations (ODEs). The ﬁnalmodel, called the aircraft equations of motion, is of the formx_ = f(x;u) (1)Both x and u are multi–dimensional, and the exact number of components depends on the aircraftand on the reference frame in which the model is developed. In any case, components of xarecommonly referred to as the state variables, and components of uas the control variables.Quantitative analysis of the equations of motion often involves numerical solution of a simpli-ﬁed version of (1). A typical approach is to assume that the aircraft’s motion is limited to smallvariations from a reference, or trim, condition. Mathematically, this is equivalent to linearizingthe equations of motion, or assuming that the state variables are of the form x
in Computer Science and Software Engineering

— Document Annotation is the task of adding metadata information in the document which is useful in information extraction. Document annotation has emerged as a different stream in data mining. Majority of algorithms are concentrated on query workload. This paper uses Probing algorithm with Bayesian approach which identifies the attribute based on query workload, text frequency and content of the previous text annotation such as content value. This method has been implemented in datasets that facilitates data annotation and prioritizes the values of the attributes by ranking scheme. Query cost is also low when compared to other approach. The experimental analysis shows a better performance while comparing with other methods because probability theory provides a principled foundation for such reasoning under uncertainty.