Miracle-dz/UniFAR-FTUs huggingface dataset:
Impulsive-Aggression, Antisocial Behaviour and Subclinical Psychopathy: Preliminary Findings From an Undergraduate Female Sample

Impulsive-aggression was investigated in a female university sample (N = 686). Four groups (impulsive-aggressive, n = 23; aggressive, n = 24; impulsive, n = 33; and control, n = 119) were selected on the basis of impulsivity scores and aggression scores. In comparison to other groups, the impulsive-aggressive women were more physically aggressive and had a greater tendency to fight or argue. They were also more likely to report a drug problem, were more sexually active, and were more likely to have faced charges as a result of antisocial activity. Due to prominent antisocial features of the impulsive-aggressive women's profile, subsequent investigation of psychopathic traits was conducted. Women from the impulsive-aggressive (n = 11), aggressive (n = 9), impulsive (n = 7), and control (n = 11) groups were individually interviewed and rated using the P-Scan (Hare & Herve, 1999). The impulsive-aggressive group (M = 6.33, SD = 3.48) scored significantly higher than all other groups (Ms < 1.70, SDs < 1.90) on total P-Scan psychopathy ratings and on the three facets of the P-Scan. Current findings suggest the use of self-report measures of impulsivity and aggressiveness may be an effective method of screening for subthreshold psychopathic traits in non-forensic samples.
UnitSelectionSynthesisin the SmartWeb Project

Thispaperdescribesthreeaspectsoftheunitselectionsynthesis usedin theSmartWeb dialogsystem. Thesynthesismodulehas beenimplementedintheIMSGermanFestivalspeechsynthesis system.First,wecompareaunitselectionstrategydevelopedin thecourseoftheprojecttoa strategydevelopedearlier. Second, we discuss our experiences with F0 smoothing and amplitude modeling, which were both devised to reduce audible discontinuities. However, the resultsare inconclusive so far. Finally, we sketch a simple mechanism that addresses the problem of languagedisambiguationfor propernames.
Cloning of Korean Morphological Analyzers using Pre-analyzed Eojeol Dictionary and Syllable-based Probabilistic Model

In this study, we verified the feasibility of a Korean morphological analyzer that uses a pre-analyzed Eojeol dictionary and syllable-based probabilistic model. For the verification, MACH and KLT2000, Korean morphological analyzers, were cloned with a pre-analyzed eojeol dictionary and syllable-based probabilistic model. The analysis results were compared between the cloned morphological analyzer, MACH, and KLT2000. The 10 million Eojeol Sejong corpus was segmented into 10 sets for cross-validation. The 10-fold cross-validated precision and recall for cloned MACH and KLT2000 were 97.16%, 98.31% and 96.80%, 99.03%, respectively. Analysis speed of a cloned MACH was 308,000 Eojeols per second, and the speed of a cloned KLT2000 was 436,000 Eojeols per second. The experimental results indicated that a Korean morphological analyzer that uses a pre-analyzed eojeol dictionary and syllable-based probabilistic model could be used in practical applications.
Stormwater control measure modeling and uncertainty analysis for total maximum daily load compliance

ii ACKNOWLEDGEMENTS . iv Chapter 1 Introduction . 1 1.1 Background . 1 1.2 Research Objectives and Expected Contribution . 3 1.3 Dissertation Outline . 4 Chapter 2: Uncertainty Analysis of a Stormwater Control Measure Model using Global Sensitivity Analysis and Bayesian Approaches . 6 2.
Spatially Superposed Highly Efficient 32APSK Transmission System

A highly efficient 32 amplitude phase-shift keying (APSK) modulation satellite broadband communication system is presented here that improves the usage efficiency of the frequency resources and energy. It features a two-beam spatial superposition. The system incorporates an 8 phase-shift keying (8PSK) modulator, a quaternary phase-shift keying (QPSK) modulator, and multiple high power amplifiers (HPAs) that operate in their nonlinear region at a high level of efficiency. The lower amplitude change for the 8PSK and QPSK signals enables the HPAs in the proposed system to operate in the nonlinear high efficiency region, in which conventional systems, such as 4+12+16 APSK or 32 quadrature amplitude modulation (QAM) systems, cannot operate. After being power-amplified, their output signals are spatially superposed using a specially tailored antenna to produce a new 8x4= 32APSK signal. We investigated the performance of the new 32APSK system and found that it had a better bit error rate (BER) than the conventional 32APSK system when operated in the HPA nonlinear high efficiency region. The results from a theoretical transmission analysis helped to determine the acceptable timing delay between two modulators and the spatial superposition errors. In addition, a high gain antenna system suitable for combining the two beams is presented that satisfies the acceptable superposition errors. We also performed some experiments and proved that the spatial superposed 32APSK system is feasible and that it consumes 50% less HPA power than the 4+12+16 APSK system. Thus, the proposed system is feasible and will enable broadband transmission while more efficiently using the available amount of energy and bandwidth.
Image enhancement in simple fingerprint minutiae extraction algorithm using crossing number on valley structure

In fingerprint recognition system, fingerprint feature extraction algorithm requires good quality fingerprint images to produce good results. Therefore, one step in the preprocessing stage is image enhancement to improve the quality of poor fingerprint image, so the minutiae points can be detected with good results. In this paper, we present how this enhancement process in simple minutiae detection algorithm using crossing number on valley structure improves detection of true minutiae.
Disentanglement of Whisker Deflection Velocity and Direction

The easily identifiable structure and clearly defined function of the rodent somatosensory barrel cortex has made it a model system for study in neuroscience. The barrel consists of ~3600 regular-spiking (RS) cells that receive inhibitory inputs from <400 fast-spiking (FS) cells, both of which are excited by ~240 thalamocortical (TC) cells. RS cell population dynamics such as rate of spiking and region of increased spiking can encode velocity and directional information from the initial whisker deflection, however, behavior of any single RS cell (or small group of RS cells) may be ambiguously affected by both velocity and directional changes. This project set out to create an additional layer of RS-like cells, hereby referred to as “excitatory extensions,” whose individual behavior or small group dynamics can correctly classify velocity and directional information from the incoming input stimulus. The model shows that an architecture that takes advantage of the net scaling of RS cell layer activity can lead to EE cells that correctly classify velocity without dependency on directional input. The model also shows that an architecture that engages an inhibitory feedback system can lead to EE cells that correctly classify direction without dependency on velocity input. Further areas of study include putting two barrel systems in communication with one another while limiting the model to experimentally-supported cell dynamics to see if architecture similar to the ones discovered in
VerICS 2004: A Model Checker for Real Time and Multi-agent Systems ?

VerICS is a model checking tool for verication of timed and multi-agent systems. These systems can be represented by timed au- tomata (TA), time Petri nets (TPNs), or given as Estelle and Interme- diate Language specications. VerICS oers three verication methods: Bounded Model Checking (BMC), Unbounded Model Checking (UMC) and Splitting for properties to be specied in subsets of TCTL and CTLpK. The current version of VerICS uses also a new graphical user interface to design time Petri nets and timed automata.
Indexing Of Three Dimensions Objects Using GIST, Zernike & PCA Descriptors

In this paper, we present a new approach to object to recognition based on the combination of Zernike moments, descriptors Gist and PCA pair wise applied to color images. The recognition of objects are based on two approaches of classification the first use neural networks (NN) for learning stage and gratitude as well to the Support Vector Machines (SVM). The experimental results showed that the recognition by SVM is better than NN. We illustrate the proposed method on color images, including objects from the database COIL-100. DOI: http://dx.doi. org/10.11591/ij-ai.v2i1. 825 Full Text: PDF
Verification techniques for substrate coupling and their application to mixed-signal IC design

This paper presents techniques for the analysis of substrate-coupled noise in mixed-signal integrated circuits. Advantages and limitations of some commonly employed verification techniques for substrate coupling are outlined. A preprocessed boundary element method introduced in this paper utilizes precomputed z parameters to generate an analytical model for substrate impedance in a preprocessing stage. Truncated series expansions of the analytical impedance model are used to accelerate solution of the resulting boundary element equations. A methodology that applies these fast techniques to the verification of large mixed-signal circuits and results that confirm its efficiency are described. This complete methodology has been applied to the design and verification of an industrial mixed-signal video analog-to-digital converter IC for substrate noise problems.
USING DJI PHANTOM 4 RTK DRONE FOR TOPOGRAPHIC MAPPING OF COASTAL AREAS

Abstract. Imagery acquisition systems by Unmanned Aerial Vehicles (UAVs) have been rapidly evolving within the last few years. In mapping applications, it is the introduction of a considerable amount of Ground Control Points (GCPs) that enables the final reconstruction of a real-scale framed model. Since the survey of GCPs generally requires the use of total stations or GNSS receivers in Real Time Kinematic (RTK), either with or without a Network approach (NRTK), this on-site operation is particularly time consuming. In addition, the lack of clearly image-recognizable points may force the use of artificial markers (signalised GCPs) whenever no features are naturally available in the field. This implies a real waste of time for the deployment of the targets, as well as for their recovery. Recently, aircrafts’ manufacturers have integrated the on-board RTK capability on their UAVs. In such a way, the high precision GNSS system allows the 3D position detection of the camera at the time of each capture within few centimetres. In this work, we tested the DJI Phantom 4 RTK for the topographic survey of a coastal section in the Northern Adriatic Sea (Italy). The flights were performed flying at an 80 m altitude to ensure a Ground Sample Distance (GSD) of about 2 centimetres. The site extended up to 2 kilometres longitudinally. The results confirm that the on-board RTK approach really speeds up the precise mapping of coastal regions and that a single GCP may be needed to make a reliable estimation of the focal length.
Knowledge Tracing and Prediction of Future Trainee Performance

Intelligent tutoring systems seek to optimize instruction and training by adapting and individualizing the learning experience on the basis of a student model (Shute, 1995). This model represents the system’s estimate of the student’s current knowledge or skill level, established from a performance history. Knowledge tracing (Aleven & Koedinger, 2002; Anderson, Conrad, & Corbett, 1989) is a dynamic, Bayesian approach to updating the estimates of probability of skill mastery in the student model. A fundamental shortcoming of this approach is that it does not include a representation of memory decay during periods of non-practice. As a result, traditional student modeling approaches are unable to make predictions regarding knowledge and skill changes under various future training schedules or to prescribe how much training will be required to achieve specific levels of readiness at a specific future time. In this paper, we propose a new knowledge tracing equation, computationally inspired by the learning and forgetting equations in the ACT-R cognitive architecture (Anderson et al., 2004), which uses performance history to baseline student model parameters and then extrapolates knowledge state transformation to predict future performance. We explore practical issues concerning predictive models of future trainee performance and the prescription of frequency and timing of optimal learning with training systems. For instance, we investigate how much data from the training history are necessary to achieve reasonable predictive validity, and we describe the impact of data granularity through a quantitative assessment of how adequately the model can fit and predict human performance curves across aggregate-level, team-level, and individual-level resolutions. The paper ends with a discussion of the implications of this research for the future of training and education.
THE CROWN DIAMETER ESTIMATION FROM FIXED WING TYPE OF UAV IMAGERY

Abstract. The forest inventory is an important instrument for sustainable forest management. Canopy Height Model (CHM) and Digital Surface Model (DSM) created from high-resolution UAV (unmanned aerial vehicle) imagery provide possibility to determine tree crown diameters for the whole stand at fast. The goal of this paper is to identify the influence of tree species on the accuracy of estimation of crown diameter from high-resolution UAV imagery. In Plot 1 with coniferous tree species we identified 21 trees from total of 22 trees that leads to a detection rate of 95%. In Plot 1 with deciduous trees species we identified 24 trees from total 34 trees that leads to a detection rate of 71%. The RMSE errors calculated between the reference crown diameters and estimated crown diameters by IWS on Plot 1and Plot 2 were calculated as 0.80 m (RMSE% = 21.85) and 1.89 m (RMSE% = 21.54), respectively. The results didn’t show the significant influence of tree species on the accuracy of estimation of crown diameter from high-resolution UAV imagery. However, result showed the significant influence of tree species on the detection number trees on the plot. The detection of number trees on the plot by method Inverese Watersed Segmentation in software ArcGis is higher for coniferous tree species. It is mainly due to the overlapping crowns.
IMAGE RETRIEVAL USING BIT -PLANE PIXEL DISTRIBUTION

Users needing is to store and index the image data and retrieved the image on feature vector derived by the user. Content Based Image Retrieval (CBIR) is search engine to retrieving the desired image automatically from the large image database having different categories. Retrieving the relevant images from the database by using feature vector is the challenging and important task. It is also need to retrieve the images from variety of the domain that is the application of CBIR that domains are medicine, crime prevention ,Biometrics, architecture, Fashion and publishing. This paper present the method developed to search and retrieve the similar image using bit plane image. Bit plane images are formed by using threshold and using bit plane slicing. Mean, standard deviation and third moment of row and column pixel distribution of bit plane image is used as a feature vector. We use simple Euclidean distance to compute the similarity measures of images for Content Based Image Retrieval application. The average precision and average recall of each image category and over all precision and recall is considered for the performance measure.
Discovering the Representative Subset with Low Redundancy for Hyperspectral Feature Selection

In this paper, a novel unsupervised band selection (BS) criterion based on maximizing representativeness and minimizing redundancy (MRMR) is proposed for selecting a set of informative bands to represent the whole hyperspectral image cube. The new selection criterion is denoted as the MRMR selection criterion and the associated BS method is denoted as the MRMR method. The MRMR selection criterion can evaluate the band subset’s representativeness and redundancy simultaneously. For one band subset, its representativeness is estimated by using orthogonal projection (OP) and its redundancy is measured by the average of the Pearson correlation coefficients among the bands in this subset. To find the satisfactory subset, an effective evolutionary algorithm, i.e., the immune clone selection (ICS) algorithm, is applied as the subset searching strategy. Moreover, we further introduce two effective tricks to simplify the computation of the representativeness metric, thus the computational complexity of the proposed method is reduced significantly. Experimental results on different real-world datasets demonstrate that the proposed method is very effective and its selected bands can obtain good classification performances in practice.
Matching and scheduling in a generalized optimal selection theory

Studies a model for matching and scheduling a task structure onto a heterogeneous suite of computers. The paper first generalizes optimal selection theory (OST) and its later augmentations to include general dependency graphs, which represent dependencies between subtasks, and presents the generalized OST (GOST) model. The GOST model allows nonoptimal choices of machines, as in augmented OST (AOST), and heterogeneous code blocks, as in heterogeneous OST (HOST), and incorporates communication time, system reconfiguration time, and data reformatting time (needed when data is exchanged between heterogeneous processors). Under the GOST model, we address the problem of matching/scheduling dependency graphs on a heterogeneous suite of computers. For arbitrary dependency graphs, matching/scheduling is NP-complete. But for the special instances of trees, and, more importantly, in the case of series-parallel dependency graphs we present polynomial time algorithms for optimal matching/scheduling. <<ETX>>
Literacy Coaching Candidates: Motivation, Perceptions, and Expectations.

This study examined the motivation, perceptions/future plans, and expectations of postgraduate students (referred to as literacy coaching candidates) enrolled in the first course of a Literacy Coaching Endorsement program at a large comprehensive institution of higher education. Results from data focus to: What motivates a licensed Reading Specialist to enroll in a post graduate program to obtain a Literacy Coaching Endorsement; what the students’ expectations of the program and of coaching are; and students’ perceptions/future plans at the end of the semester and plans for the rest of the cohort program. The data revealed the need for literacy coaching candidates to build confidence, look to new careers paths, become reflective practitioners, and consider the workload of the program. Collaboration with the professor and peers in the program was also considered.
Maximum Likelihood Estimator

Advanced statistical models rely on maximum likelihood (ML) estimators to estimate unknown parameters. Given the complexity and highly technical nature of the numerical approaches embedded in ML, textbooks typically offer oversimplified descriptions of ML, omitting important details from the discussion. These untold stories about ML create barriers, anxieties, and uncertainties among users, and increase the risk that poorly informed users might misinterpret study findings. Taking a simple logistic regression as an example, this methodological note describes the basic ideas and detailed steps of running the Newton-Raphson algorithm (i.e., the most popular ML method). An Excel spreadsheet illustrates the iterative procedure that aims to maximize sample likelihood. Implications for discussing the ML procedure, particularly caveats and tips for application, are summarized.
AUTOMATIC DETECTION OF FOREST-ROAD DISTANCES TO IMPROVE CLEARING OPERATIONS IN ROAD MANAGEMENT

Abstract. There is a complex relation between roads and fires. Several major wildfires were ignited near to roads (Morrison 2007) and how they progressed is an important role to understand the importance to forest management in this environment. Nowadays, a sustainable forest management is necessary both for environment and politics. One of the reasons of road management is that these infrastructures provide an effective firewall in case of forest fires and an escape route for the population. Forest management optimization in road surroundings would improve wildfires prevention and mitigate their effects. One of the main indicators of road safety is the distance between road and vegetation.The aim of this work is to develop a methodology to determine what areas do not obey current laws about safety distances between forest and roads. The acquisition of LiDAR data is done by Lynx Mobile Mapper System from University of Vigo. The methodology is automated using LiDAR data processing. The developed algorithms are based in height and length segmentation of the road. The objective is classifying vegetation groups by height and calculate the distance to the edges of road. The vegetation is divided in groups of height of 5, 10, 15 and 30 m. The minimum distance calculation is 2 m, for the vegetation of 5 m height and a maximum of 60 m for vegetation 30 m height. The height of vegetation has a directly relation with the distance separation with the road.
Coarse to Fine Approach for Object Category Detection using Appearance of Local Features

Sliding window is the most popular approach to detect position of an object in an image. However it is well known that primates pay selective attention to saliecncy point as a candidate of object area. In this paper, we propose coarse to fine approach to detect object using apearance of local feature without sliding window. We adopted C2 feature of ”standard model of visual cortex” proposed by Serre, and select object candidate using the appearance of these features. We evaluated our model by UIUC Car dataset, and confirmed its value.
Integrating technologies into ‘‘authentic’’ assessment design: an affordances approach

Current pressures in higher education around student employability are driving new initiatives for change. Assessment is also a topic of debate, as it is a key driver of student behaviour, yet often falls behind other metrics in national surveys. In addition, increasing focus on digital literacies is catalysing new appreciations of what emerging digital culture might mean for both students and staff. These three highly topical challenges were jointly explored by the University of Exeter’s Collaborate project, which aimed to create employability-focused assessments enhanced by technology. By combining existing research on assessment with grounded data derived from local stakeholders, the project has developed a model for assessment design which embeds employability directly into the curriculum. Digital technologies have been aligned with this model using a “top trump” metaphor, where key affordances of technologies are highlighted in the context of the model. This paper explores the design-based research approach taken to develop this model and associated “top trumps”, along with results from the first practical iteration. Results suggest that the model is effective in supporting the design of an “authentic” assessment and that a targeted affordances approach can support the alignment of specific technologies with a particular pedagogic design. Keywords: employability; assessment; authentic; affordance; evaluation (Published: 6 September 2013) Citation: Research in Learning Technology 2013, 21 : 21986 - http://dx.doi. org/10.3402/rlt.v21i0. 21986