Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated Test Data Generation Based On Individual Constraints and Boundary Value

Testing is an important activity in software development. Unfortunately till today testing is done manually by most of the industry due to high cost and complexity of automation. Automated testing can reduce the cost of software significantly. Automated Software Test Data Generation is an activity that in the course of software testing automatically generates test data for the software under test. Most of the automated test data generation uses constraint solver to generate test data. But it cannot generate test data when the constraints are not solvable. Although method can be found to generate test data even if the constraints are unsolvable, but it is poor in terms of code coverage. In this paper, we propose a test data generation method to improve test coverage and to avoid the unsolvable constraints problem. Our method is based on the individual constraints and same or dependent variable to create the path table which holds the information about the path traversed by various input test data. For generating unique test data for all the linearly independent feasible path we created equivalence class from the path table on the basis of path traversed by the various input test data. The input data is taken based on individual constraints or boundary value. Our results are compared with cyclomatic complexity and number of possible infeasible paths. The
Trust-based Relay Selection in Relay-based Networks

It has been demonstrated that choosing an appropriate relay node can improve the transmission rate for the system. However, such system improvement brought by the relay selection may be degraded with the presence of the malicious relay nodes, which are selected but refuse to cooperate for transmissions deliberately. In this paper, we formulate the relay selection issue as a restless bandit problem with the objective to maximize the average rate, while considering the credibility of each relay node, which may be different at each time instant. Then the optimization problem is solved by using the priority-index heuristic method effectively. Furthermore, a low complexity algorithm is offered in order to facilitate the practical implementations. Simulation results are conducted to demonstrate the effectiveness of the proposed trust-based relay selection scheme.
Queue Dynamic Workpackage Creation Dynamic Workitem Creation Work Aggregation Event Occurrence Parallel Procedure Execution

In this paper we present Softshell, a novel execution model for devices composed of multiple processing cores operating in a single instruction, multiple data fashion, such as graphics processing units (GPUs). The Softshell model is intuitive and more flexible than the kernel-based adaption of the stream processing model, which is currently the dominant model for general purpose GPU computation. Using the Softshell model, algorithms with a relatively low local degree of parallelism can execute efficiently on massively parallel architectures. Softshell has the following distinct advantages: (1) work can be dynamically issued directly on the device, eliminating the need for synchronization with an external source, i.e., the CPU; (2) its three-tier dynamic scheduler supports arbitrary scheduling strategies, including dynamic priorities and real-time scheduling; and (3) the user can influence, pause, and cancel work already submitted for parallel execution. The Softshell processing model thus brings capabilities to GPU architectures that were previously only known from operating-system designs and reserved for CPU programming. As a proof of our claims, we present a publicly available implementation of the Softshell processing model realized on top of CUDA. The benchmarks of this implementation demonstrate that our processing model is easy to use and also performs substantially better than the state-of-the-art kernel-based processing model for problems that have been difficult to parallelize in the past. CR Categories: I.3. 6 [Computer Graphics]: Methodology and Techniques—Languages; I.3. 1 [Computer Graphics]: Hardware Architecture—Parallel processing;
Learning Virtual Community Loyalty Behavior From a Perspective of Social Cognitive Theory

Drawing upon social cognitive theory (SCT), this research postulates several personal and environmental factors as key drivers of virtual community loyalty behavior in online settings. An empirical testing of this model, by investigating undergraduate students' participation in communities of online games, reveals the applicability of SCT in virtual communities. The study's test results show that the influences of both affective commitment and social norms on community loyalty behavior are significant, whereas the influences of both exchange ideology and social support on community loyalty behavior are insignificant. This research contributes to the online community literature by assessing critical antecedent factors to the unexplored area of community loyalty behavior, by validating idiosyncratic drivers of community loyalty behavior and by performing an operationalization of affective commitment and social norms in a virtual world. Last, managerial implications and limitations of this research are provided.
Inferring the function performed by a recurrent neural network

A central goal in systems neuroscience is to understand the functions performed by neural circuits. Previous top-down models addressed this question by comparing the behaviour of an ideal model circuit, optimised to perform a given function, with neural recordings. However, this requires guessing in advance what function is being performed, which may not be possible for many neural systems. To address this, we propose an inverse reinforcement learning (RL) framework for inferring the function performed by a neural network from data. We assume that the responses of each neuron in a network are optimised so as to drive the network towards ‘rewarded’ states, that are desirable for performing a given function. We then show how one can use inverse RL to infer the reward function optimised by the network from observing its responses. This inferred reward function can be used to predict how the neural network should adapt its dynamics to perform the same function when the external environment or network structure changes. This could lead to theoretical predictions about how neural network dynamics adapt to deal with cell death and/or varying sensory stimulus statistics.
Guided Wave Propagation and Damage Interaction in Isotropic and Composite Structures.

Guided Wave Propagation and Damage Interaction in Isotropic and Composite Structures by Matthew Bridger Obenchain Chair: Professor Carlos E. S. Cesnik Guided wave structural health monitoring methods offer many of the capabilities needed to move from a schedule-based maintenance paradigm to a more cost-effective condition-based system. This dissertation explores several key aspects of guided wave propagation and damage interaction in both isotropic and composite structures. First, a reliable method of computing displacement time histories from guided wave excitation is presented. This formulation, based on the Global Matrix Method, is directly applicable to composite laminates. It improves upon previous methods that were unable to properly separate inbound and outbound wave solutions. Second, a comprehensive wave propagation simulation tool is presented that combines the best features of the Global Matrix Method and the recently developed local interaction simulation approach (LISA). This LISA hybrid model accurately captures guided wave generation from both piezoceramic and piezocomposite actuators. Wave propagation results from the new model compare favorably with semi-analytical models for both isotropic plates and composite laminates. Following that, the dissertation describes the application of the LISA hybrid model to examine guided xviii wave interaction with holes in plate structures. Simulations are used to analyze the influence of various damage parameters, such as hole radius and depth, and the results are compared with experimental measurements. The effect of hole orientation relative to fiber direction in composite laminates is also explored. Subsequently, the dissertation examines guided wave interaction with low-velocity impact damage in composite laminates. Diagnostic imagery of laboratory-produced impact damage is presented to help characterize the size, shape, and composition of the damage. Experimental results from guided wave interrogation of the damage region are also presented. Together, these are used to evaluate various methods to model the impact damage in LISA. Finally, this dissertation introduces a damage characterization tool based on the matching pursuit method. The new algorithm uses a library of LISA simulations that capture the effects of various damage sizes and locations. The ability of the algorithm to locate damage is demonstrated in both 1-D and 2-D scenarios.
Scholarship@Western Scholarship@Western

How do populations of neurons represent a variable of interest? The notion of feature spaces is a useful concept toapproachthisquestion:Accordingtothismodel,theactivationpatternsacrossaneuronalpopulationarecom-posed of different pattern components. The strength of each of these components varies with one latent feature, which together are the dimensions along which the population represents the variable. Here we propose a new method to determine the number of feature dimensions that best describes the activation patterns. The method is based on Gaussian linear classi ﬁ ers that use only the ﬁ rst d most important pattern dimensions. Using cross-validation, we can identify the classi ﬁ er that best matches the dimensionality of the neuronal representation. We test this method on two datasets of motor cortical activation patterns measured with functional magnetic resonance imaging (fMRI), during (i) simultaneous presses of all ﬁ ngers of a hand at different force levels and (ii) presses of different individual ﬁ ngers at a single force level. As expected, the new method showsthattherepresentationofforceislow-dimensional;theneuralactivationfordifferentforcelevelsisscaled versionsofeachother.Incomparison,individual ﬁ ngerpressesarerepresentedinafull,four-dimensionalfeature space.The approachcanbeusedtodetermine animportant characteristicofneuronal population codeswithout knowing the form of the underlying features. It therefore provides a novel tool in the building of quantitative models of neuronal population activity as measured with fMRI or other approaches. © 2013 Elsevier Inc.
A new deep-sea elasmobranch fauna from the Central Paratethys (Neuhofener Beds, Mitterdorf, near Passau, Germany, Early Miocene, Middle Burdigalian)

In this study, a diverse fauna of fossil elasmobranch teeth from the Early Miocene (Middle Burdigalian) is analysed. The fossil diversity strongly resembles extant deep-water shark and ray assemblages. The fossils were collected from the Upper Marine Molasse of the lower Ottnangian in the Neuhofener Beds location, Mitterdorf, Germany. The collection site is a clay pit in between the Lower Bavarian villages Fürstenzell and Schmidham. The sample revealed 14 shark and four ray species. We present the first record of fossils assigned to taxa Nanocetorhinus tuberculatus, Deania and Apristurus from Germany. In addition, we describe a hitherto unknown genus and species of shark, Pseudoapristurus nonstriatus gen. et sp. nov., based on fossil teeth. The documented diversity is compared to both extant and fossil records of neoselachian deep-water diversities, and it is evident that this Miocene fauna is very similar in composition to indo-pacific deep-water assemblages.
Mining : Extraction of Interesting Association Rule with Frequent Itemsets Mining for Korean Language from Unstructured Data

Text mining is a specific method to extract knowledge from structured and unstructured data. This extracted knowledge from text mining process can be used for further usage and discovery. This paper presents the method for extraction information from unstructured text data and the importance of Association Rules Mining, specifically for of Korean language (text) and also, NLP (Natural Language Processing) tools are explained. Association Rules Mining (ARM) can also be used for mining association between itemsets from unstructured data with some modifications. Which can then, help for generating statistical thesaurus, to mine grammatical rules and to search large data efficiently. Although various association rules mining techniques have successfully used for market basket analysis but very few has applied on Korean text. A proposed Korean language mining method calculates and extracts meaningful patterns (association rules) between words and presents the hidden knowledge. First it cleans and integrates data, select relevant data then transform into transactional database. Then data mining techniques are used on data source to extract hidden patterns. These patterns are evaluated by specific rules until we get the valid and satisfactory result. We have tested on Korean news corpus and results have shown that it has worked well, and the results were adequate enough to research further.
Reply to comment by David A. DiCarlo on “A phase field model of unsaturated flow”

[1] In a recent series of papers [Cueto‐Felgueroso and Juanes, 2008, 2009a, 2009b, 2009c], we put forward a framework for modeling multiphase flow in porous media, which recognizes the presence of macroscopic diffuse interfaces (displacement fronts). The proposed framework is based on a phase field approach, which originated in the 1950s in the field of solidification [Cahn and Hilliard, 1958]. It is built on the idea that the free energy of the system is nonlocal, therefore involving the gradient of the fluid saturation. This leads naturally to higher‐order terms in the mass conservation equations. The model guarantees thermodynamically admissible solutions, with positive energy dissipation and entropy production. We have applied this formalism to the process of water infiltration into dry soil, a dynamic imbibition problem which displays a well‐known instability: preferential flow in the form of “gravity fingers. ” Our phase field model contains a new term, a fourth‐order spatial derivative of saturation, the magnitude of which is set by a proposed scaling. Our model reproduces the key features of unsaturated flow: a nonmonotonic saturation profile, and the formation and persistence of gravity fingers, both in homogeneous and heterogeneous media. It shows good quantitative agreement with experiments in terms of finger tip saturation, tip velocity and finger width. A rigorous linear stability analysis also confirms the experimental observation that saturation overshoot is a prerequisite for gravity fingering. [2] In his comment, DiCarlo [2010] argues that our model of unsaturated flow leads to unphysical results when used to predict gravity‐capillary equilibrium saturation profiles. Here we show that this is not the case, and that in fact excellent agreement with static saturation profiles can be obtained with monotonic capillary pressure functions. We also reply to other questions raised in the comment which, contrary to DiCarlo’s exposition, further validate our model.
GasHis-Transformer: A Multi-scale Visual Transformer Approach for Gastric Histopathological Image Classiﬁcation

In this paper, a multi-scale visual transformer model, referred as GasHisTransformer, is proposed for Gastric Histopathological Image Classiﬁcation (GHIC), which enables the automatic classiﬁcation of microscopic gastric images into abnormal and normal cases. GasHis-Transformer model consists of two key modules, which are designed to extract global and local information using a position-encoded transformer model and a convolutional neural network with local convolution, respectively. A publicly available hematoxylin and eosin (H&E) stained gastric histopathological image dataset is used in the experiment. Moreover, a series of contrast and extended experiments veriﬁed the robustness and extensibility of GasHis-Transformer. In conclu-∗
Evaluation of Mouse and Touch Input for a Tabletop Display Using Fitts' Reciprocal Tapping Task

User performance with a tabletop display was tested using touch-based and mouse-based interaction in a traditional pointing task. Dependent variables were throughput, error rate, and movement time. In a study with 12 participants, touch had a higher throughput with average of 5.53 bps compared to 3.83 bps for the mouse. Touch also had a lower movement time on average, with block means ranging from 403 ms to 1051 ms vs. 607 ms to 1323 ms with the mouse. Error rates were lower for the mouse at 2.1%, compared to 9.8% for touch. The high error rates using touch were attributed to problems in selecting small targets with the finger. It is argued that, overall, touch input is a preferred and efficient input technique for tabletop displays, but that more research is needed to improve touch selection of small targets.
Relationship of Agronomic Practices to Soil Nitrogen Dynamics

Soil nitrogen (N) dynamics are a major concern of soil nutrient status and its supply for crop uptake and growth. They are a central focus of agroecosystems. Agronomic practices play a central role in regulating soil N dynamics; the methodologies for investigating soil N mineralization are diverse, but debatable. This chapter discusses the pros and cons of different methods for measuring soil N mineralization, including laboratory, in-situ, and modeling procedures. This chapter illustrates the influence of agronomic practices on root architecture that potentially affects crop nutrient uptake. The relationship between agronomic practices and soil N dynamics were fully discussed, which can substantially inform soil fertility and crop nutrition management.
Flow, mixing, and displacement in using a data‐based hydrochemical model to predict conservative tracer data

We extend the data‐based hydrochemical model of Iorgulescu et al. (2005), able to simulate discharge and reactive chemical tracer concentrations (silica and calcium) in streamflow for subcatchments of the Haute‐Mentue research basin (Switzerland), to the prediction of additional δ18O values treated as a conservative tracer. The hydrochemical model is based on a parameterization of three runoff components (direct precipitation (DP), acid soil water (AS), and deep groundwater (GW)) in a chemical mixing model. Each component is modeled through an identical structure consisting of a nonlinear gain and a linear transfer function with two reservoirs (fast/slow) in parallel having a constant partition between them. We formulate a set of hypotheses concerning the isotope characterization of each component to provide additional information about how new rainfall inputs are processed in the hydrological response of the catchment. In particular, the AS component is modeled through a nested structure of hypotheses (models) of increasing complexity. It will be shown that hydrological processes in the hillslope associated with the DP, AS, and GW components are especially effective in filtering of higher‐frequency fluctuations in precipitation isotopic ratios at the intraevent, interevent/seasonal, and annual/multiannual timescales. The highly nonlinear and nonstationary AS component represents predominantly “recent” water stored in the upper decimeters of the soil profile. Results also suggest that subsurface pathways are significant for the DP component. A local flow path mechanism is proposed for explaining the large fluxes of subsurface flows.
Analyzing and modelling drivers’ deceleration behaviour from normal driving

Most research in vehicle automation has mainly focused on the safety aspect with only limited studies on occupants’ discomfort. In order to facilitate their rapid uptake and penetration, autonomous vehicles (AVs) should ensure that occupants are both safe and comfortable. Recent research however revealed that people felt uncomfortable when AVs braked. This may be due to their robot-like braking performance. Existing studies on drivers’ braking behaviour investigated data either from controlled experiments or driving simulators. There is a dearth of research on braking behaviour in normal driving. The objective of this paper is therefore to examine drivers’ braking behaviours by exploiting naturalistic driving data from the Pan-European TeleFOT (Field Operational Tests of Aftermarket and Nomadic Devices in Vehicles) project. On a fixed route of 16.5km long, 16 drivers were asked to drive an instrumented vehicle. A total of about eleven million observations were analysed to identify the profile, value and duration of deceleration events. Since deceleration events are nested within trips and trips within drivers, multilevel mixed-effects linear models were employed to develop relationships between deceleration value and duration and the factors influencing them. The results indicate that the most used profile of the deceleration behaviour follows a hard braking at the beginning when detecting a danger and then becomes smoother. Furthermore, they suggest that the speed, the reason for braking and the deceleration profile mostly affect the deceleration events. Findings from this study should be considered in examining the braking behaviour of AVs .
eScholarship@UMMS eScholarship@UMMS

New genome assemblies have been arriving at a rapidly increasing pace, thanks to decreases in sequencing costs and improvements in third-generation sequencing technologies 1–3 . For example, the number of vertebrate genome assemblies currently in the NCBI (National Center for Biotechnology Information) database 4 increased by more than 50% to 1,485 assemblies in the year from July 2018 to July 2019. In addition to this influx of assemblies from different species, new human de novo assemblies 5 are being produced, which enable the analysis of not only small polymorphisms, but also complex, large-scale structural differences between human individuals and haplotypes. This coming era and its unprecedented amount of data offer the opportunity to uncover many insights into genome evolution but also present challenges in how to adapt current analysis methods to meet the increased scale. Cactus 6 , a reference-free multiple genome alignment program, has been shown to be highly accurate
Parental involvement in reducing learner absenteeism in selected primary schools of Kazungula district in the Southern Province of Zambia

iv DEDICATION. v ACKNOWLEDGEMENT . vi LIST OF FIGURES . x LIST OF APPENDICES . xi ACRONYMS . xii CHAPTER ONE: INTRODUCTION . 1 1.1 Overview . 1 1.2 Background . 1 1.3 Statement of the Problem. 3 1.4 Purpose of the Study . 3 1.5 Research Objectives . 3 1.6 Research Questions . 3 1.7 Significance of the Study . 4 1.8 Theoretical Foundation . 4 1.9 Limitations of the Study . 8 1.10 Delimitation of the Study . 8 1.12 Summary . 9 CHAPTER TWO: LITERATURE REVIEW . 10 2.1 Overview . 10 2.2 Ways Through which Parents are Involved in Reducing Learner Absenteeism. 10