Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Agents Based Software Testing As A Service On Cloud

: Software testing plays an important role to make any software project reliable and successful. Owing to human effort and deviated results, manual testing is being replaced by automated testing tools. But the automated solution is much costly due to licensing and as it still requires human interaction. Hence, such a testing solution is required, in which on demand testing services based on „pay as you go‟ model, can be provided. This framework is intended to highlight the role of intelligent agents in providing such a multi-agents based testing framework, where different agents collaborate to provide software testing as a service on cloud. Different agents‟ groups are introduced, to support different testing types within an integrated framework.
Recognizing Facial Expressions Using a Shallow Convolutional Neural Network

Generally, facial expressions could be classified into two categories: static facial expressions and micro-expressions. There are many promising applications of facial expression recognition, such as pain detection, lie detection, and babysitting. Traditional convolutional neural network (CNN)-based methods suffer from two critical problems when they are adopted to recognize micro-expressions. First, they are usually dependent on very deep architectures that overfit on small datasets. However, reliable expressions are relatively difficult to collect and relevant datasets are usually relatively small. Second, for micro-expressions, these methods usually neglect the temporal redundancy of micro-expressions which could be utilized to reduce the temporal complexity. In this paper, we propose a shallow CNN (SHCNN) architecture with only three layers to classify static expressions and micro-expressions simultaneously without big training datasets. To better explain the functionality of our SHCNN architecture, we improve the saliency maps by introducing a shrinkage factor after studying the vanishing gradient problem of existing saliency maps. Experiments are conducted on five open datasets: FER2013, FERPlus, CASME, CASME II, and SAMM. To the best of our knowledge, by comparing with other methods offering source code (or pseudo code), we believe that our method would be the best on FERPlus, CASME, and CASME II and competitive on FER2013 and SAMM.
A MOSFET-only continuous-time bandpass filter

A MOSFET-only filter is described which employs an amplifier-regulated cascode structure to realize both conductance and transconductance, while the required capacitors are realized by MOSFET gate capacitance. Distortion due to mobility degradation and MOSFET capacitor nonlinearity is analyzed, as is the effect of finite gain-bandwidth product of the regulating amplifiers on the filter's linearity and high frequency performance. A prototype second-order bandpass filter has a center frequency of 560 kHz, a third-order intermodulation of -41 dB for 0 dBm input and a dynamic range of 60 dB. It consumes 2.5 mW from a 5 V supply and occupies 0.18 mm/sup 2/ in a 1-/spl mu/m digital CMOS technology.
AN IMPROVED COHERENT POINT DRIFT METHOD FOR TLS POINT CLOUD REGISTRATION OF COMPLEX SCENES

Abstract. Processing unorganized 3D point clouds is highly desirable, especially for the applications in complex scenes (such as: mountainous or vegetation areas). Registration is the precondition to obtain complete surface information of complex scenes. However, for complex environment, the automatic registration of TLS point clouds is still a challenging problem. In this research, we propose an automatic registration for TLS point clouds of complex scenes based on coherent point drift (CPD) algorithm combined with a robust covariance descriptor. Out method consists of three steps: the construction of the covariance descriptor, uniform sampling of point clouds, and CPD optimization procedures based on Expectation-Maximization (EM algorithm). In the first step, we calculate a feature vector to construct a covariance matrix for each point based on the estimated normal vectors. In the subsequent step, to ensure efficiency, we use uniform sampling to obtain a small point set from the original TLS data. Finally, we form an objective function combining the geometric information described by the proposed descriptor, and optimize the transformation iteratively by maximizing the likelihood function. The experimental results on the TLS datasets of various scenes demonstrate the reliability and efficiency of the proposed method. Especially for complex environments with disordered vegetation or point density variations, this method can be much more efficient than original CPD algorithm.
Taxonomy and systematics of Dendrocalamus (Bambuseae; Poaceae)

THESIS 8441%%%%This thesis is concerned with the taxonomy and systematics of Dendrncalamus (Bambuseae; Poaceae). It aimed to assess phylogenetic relationships in the subfamily Bambusoideae and to focus on one highly economically, ecologically and culturally im portant genus, Dendrocalamus. It aimed to provide an account of Dendrocalamus for the Flora of Thailand project, to assess its infrageneric classification and to study the diversity and phylogeography of one of its most important species, D. membranaceus, in Thailand.
A Longitudinal Study of Early Reading Development: Letter-Sound Knowledge, Phoneme Awareness and RAN, but Not Letter-Sound Integration, Predict Variations in Reading Development

ABSTRACT It is now widely accepted that phonological language skills are a critical foundation for learning to read (decode). This longitudinal study investigated the predictive relationship between a range of key phonological language skills and early reading development in a sample of 191 children in their first year at school. The study also explored the theory that a failure to establish automatic associations between letters and speech sounds is a proximal causal risk factor for difficulties in learning to read. Our findings show that automatic letter-sound associations are established early, but do not predict variations in reading development. In contrast, phoneme awareness, letter-sound knowledge and alphanumeric RAN were all strong independent predictors of reading development. In addition, both phoneme awareness and RAN displayed a reciprocal relationship with reading, such that the growth of reading predicted improvements in these skills.
A Study on the Accuracy Improvement of Movie Recommender System Using Word2Vec and Ensemble Convolutional Neural Networks

One of the most commonly used methods of web recommendation techniques is collaborative filtering. Many studies on collaborative filtering have suggested ways to improve accuracy. This study proposes a method of movie recommendation using Word2Vec and an ensemble convolutional neural networks. First, in the user, movie, and rating information, construct the user sentences and movie sentences. It inputs user sentences and movie sentences into Word2Vec to obtain user vectors and movie vectors. User vectors are entered into user convolution model and movie vectors are input to movie convolution model. The user and the movie convolution models are linked to a fully connected neural network model. Finally, the output layer of the fully connected neural network outputs forecasts of user movie ratings. Experimentation results showed that the accuracy of the technique proposed in this study accuracy of conventional collaborative filtering techniques was improved compared to those of conventional collaborative filtering technique and the technique using Word2Vec and deep neural networks proposed in a similar study.
The Effect of Adverse Childhood Experience on Heart Rate Variability and Salivary Cortisol

Adverse childhood exposure has been discovered might alter physiological processes such as cardiovascular stress response. When the body is in a stressful condition, it triggers two primary systems that are particularly involved in adapting the body to the stress: the Autonomic Nervous System (ANS) and the Hypothalamic-Pituitary-Adrenocorticol (HPA) axis. To detect the altered stress response, biomarkers that represent both systems: ANS and HPA are proposed. Among the available biomarkers, Heart Rate Variability (HRV) has been proven as a powerful biomarker that represents ANS. Meanwhile, salivary cortisol has been suggested as a biomarker that reflects the HPA. This study will investigate the stress response on individual who have had adverse childhood experience and no adverse childhood experience by using HRV and salivary cortisol. Electrocardiograph and salivary cortisol were collected from 23 healthy participants (age, 19 to 23 years old), 12 participants who had adverse childhood experience while the remaining 11 acted as the control group. The recording session was done during a Paced Auditory Serial Addition Test (PASAT). HRV was then computed from the ECG and the HRV features were extracted. From the result, it can be seen that irregular stress response detected by HRV and salivary cortisol was found associated with adverse childhood experience with moderate classification performance; accuracy 61.7% and 59.4% respectively. To achieve a better classifier performance, an approach to the fusion method for stress response detection of adverse childhood experience is proposed for the future study.
Dysfunctional Individuation Predicts Spiritual and Religious Struggle in Emerging Adulthood

Emerging adulthood, a time of instability and growing autonomy, has been associated with a decline in religiosity and an increase in religious and spiritual (r/s) struggle. In this study we examine the relationship between r/s struggle and dysfunction in the normative developmental challenge of individuation, a question novel to the literature. A random sample of 788 emerging adults (61% female, m age = 19.5) from a private Midwestern university was assessed on indices of r/s struggle, dysfunctional individuation, perception of religious support from parents and peers, college adjustment (anxiety), spiritual quest orientation and spiritual growth mindset at three time points over the course of one school year. Latent growth curve models found that dysfunctional individuation was the most significant predictor of change in r/s struggle over time. These findings have implications for understanding the developmental sources of r/s struggle.
Mitigating Synchronous I/O Overhead in File Systems on Open-Channel SSDs

Synchronous I/O has long been a design challenge in file systems. Although open-channel solid state drives (SSDs) provide better performance and endurance to file systems, they still suffer from synchronous I/Os due to the amplified writes and worse hot/cold data grouping. The reason lies in the controversy design choices between flash write and read/erase operations. While fine-grained logging improves performance and endurance in writes, it hurts indexing and data grouping efficiency in read and erase operations. In this article, we propose a flash-friendly data layout by introducing a built-in persistent staging layer to provide balanced read, write, and garbage collection performance. Based on this, we design a new flash file system (FS) named StageFS, which decouples the content and structure updates. Content updates are logically logged to the staging layer in a persistence-efficient way, which achieves better write performance and lower write amplification. The updated contents are reorganized into the normal data area for structure updates, with improved hot/cold grouping and in a page-level indexing way, which is more friendly to read and garbage collection operations. Evaluation results show that, compared to recent flash-friendly file system (F2FS), StageFS effectively improves performance by up to 211.4% and achieves low garbage collection overhead for workloads with frequent synchronization.
Dynamic up- and down-regulation of the default (DMN) and extrinsic (EMN) mode networks during alternating task-on and task-off periods

Using fMRI, Hugdahl et al. (2015) reported the existence of a general-domain cortical network during active task-processing which was non-specific to the cognitive task being processed. They labelled this network the extrinsic mode network (EMN). The EMN would be predicted to be negatively, or anti-correlated with the classic default mode network (DMN), typically observed during periods of rest, such that while the EMN should be down-regulated and the DMN up-regulated in the absence of demands for task-processing, the reverse should occur when demands change from resting to task-processing. This would require alternating periods of task-processing and resting, and analyzing data continuously when demands change from active to passive periods and vice versa. We were particularly interested in how the networks interact in the critical transition points between conditions. For this purpose we used an auditory task with multiple cognitive demands in a standard fMRI block-design. Task-present (ON) blocks were alternated with an equal number of task-absent, or rest (OFF) blocks to capture network dynamics across time and changing environmental demands. To achieve this, we specified the onset of each block, and used a finite-impulse response function (FIR) as basis function for estimation of the fMRI-BOLD response. During active (ON) blocks, the results showed an initial rapid onset of activity in the EMN network, which remained throughout the period, and faded away during the first scan of the OFF-block. During OFF blocks, activity in the DMN network showed an initial time-lag where neither the EMN nor the DMN was active, after which the DMN was up-regulated. Studying network dynamics in alternating passive and active periods may provide new insights into brain network interaction and regulation.
An Incremental ACO R with Local Search for Continuous Optimization Problems

One of the most popular Ant Colony Optimization (ACO) algorithms for continuous optimization problems is ACOR. In this paper, we propose an incremental ACOR with local search (IACOR-LS) that obtains better results than the original ACOR on a number of continuous optimization problems. We first present a mechanism that improves the search diversification of ACOR. This mechanism consists of a growing solution archive with a special initialization rule applied to entrant solutions. The resulting algorithm, called IACOR, is then hybridized with a local search procedure in order to enhance its search intensification. We experiment with Powell’s conjugate directions set, Powell’s BOBYQA, and Lin-Yu Tseng’s Mtsls1 methods as local search procedures. Automatic parameter tuning results show that IACOR-LS with Mtsls1 (IACOR-Mtsls1) is not only a significant improvement over ACOR, but that it is also competitive with the state-of-the-art algorithms described in a recent special issue of the journal Soft Computing. Further experimentation with IACOR-Mtsls1 on the combined benchmark functions suite from both the special issue of Soft Computing and the IEEE 2005 Congress on Evolutionary Computation (CEC 2005) demonstrates its good performance in continuous optimization.
A global surrogate assisted CMA-ES

We explore the arguably simplest way to build an effective surrogate fitness model in continuous search spaces. The model complexity is linear or diagonal-quadratic or full quadratic, depending on the number of available data. The model parameters are computed from the Moore-Penrose pseudoinverse. The model is used as a surrogate fitness for CMA-ES if the rank correlation between true fitness and surrogate value of recently sampled data points is high. Otherwise, further samples from the current population are successively added as data to the model. We empirically compare the IPOP scheme of the new model assisted lq-CMA-ES with a variety of previously proposed methods and with a simple portfolio algorithm using SLSQP and CMA-ES. We conclude that a global quadratic model and a simple portfolio algorithm are viable options to enhance CMA-ES. The model building code is available as part of the pycma Python module on Github and PyPI.
Modern statistical methods for the analysis of capture-recapture data

Population demographics such as, population size or survival rates, can be estimated from capture–recapture data collected on individuals from the population. These estimates are of particular interest to ecologists, biologists, sociologists and epidemiologists who rely on accurate inferences so that management and conservation of the population is well maintained. Due to rapid advances in computational power and speed, a growing number of modern statistical techniques, such as penalized smoothing splines, have allowed for more flexibility to construct models from complex data. In summary, this thesis presents: (1.) the development of new and flexible methodologies using modern statistical techniques to enhance and robustify inference for capture–recapture studies, (2.) a comprehensive analysis on various real capture–recapture data sets using both classical and new capture–recapture models; (3.) the performance of new models by conducting simulations resembling real data; and (4.) a new statistical software designed for analysing capture– recapture data. The examples presented in this thesis primarily use capture–recapture data sets collected on Little Penguins Eudyptula minor from a colony located at Summerland Beach on the western end of Phillip Island, Victoria, Australia.
UNCONFINED WILDERNESS EXPERIENCES: WHAT IS IMPORTANT TO FEELING UNCONFINED WHILE VISITING THE SELWAY-BITTERROOT WILDERNESS?

The principal aim of this study was to increase our understanding of an “unconfined type of recreation”. This management objective is mandated by the Wilderness Act of 1964 and has received little empirical focus within visitor experience research. A 20-item survey research scale was developed and found to be valid and reliable when considering what is important to feeling unconfined in the Selway-Bitterroot Wilderness of Idaho and Montana. The scale included four components or factors that were labeled, “Free Choice”, “Untethering from Responsibility”, “Making Own Plans”, and “Exploring”. In addition, the Perceived Freedom in Leisure Scale was administered and showed that the majority of respondents display high leisure functioning. Three clusters of respondents were formed based on the 20-item unconfined component scores, but these groups were not found to differ by any trip or visitor characteristic. Therefore, when agencies manage for outstanding opportunities for an unconfined type of recreation they should not target any one type of wilderness visitor or trip characteristic.
ADVANCED ENCRYPTION STANDARD

Advanced Encryption Standard (AES) is the current standard for secret key encryption. AES was created by two Belgian cryptographers, Vincent Rijmen and Joan Daemen, replacing the old Data Encryption Standard (DES). The Federal Information Processing Standard 197 used a standardized version of the algorithm called Rijndael for the Advanced Encryption Standard. The algorithm uses a combination of Exclusive-OR operations (XOR), octet substitution with an S-box, row and column rotations, and a MixColumn. It was successful because it was easy to implement and could run in a reasonable amount of time on a regular computer.
End-to-End Full-Page Handwriting Recognition

End-to-End Full-Page Handwriting Recognition Curtis Michael Wigington Department of Computer Science, BYU Master of Science Despite decades of research, offline handwriting recognition (HWR) of historical documents remains a challenging problem, which if solved could greatly improve the searchability of online cultural heritage archives. Historical documents are plagued with noise, degradation, ink bleed-through, overlapping strokes, variation in slope and slant of the writing, and inconsistent layouts. Often the documents in a collection have been written by thousands of authors, all of whom have significantly different writing styles. In order to better capture the variations in writing styles we introduce a novel data augmentation technique. This methods achieves state-of-the-art results on modern datasets written in English and French and a historical dataset written in German. HWR models are often limited by the accuracy of the preceding steps of text detection and segmentation. Motivated by this, we present a deep learning model that jointly learns text detection, segmentation, and recognition using mostly images without detection or segmentation annotations. Our Start, Follow, Read (SFR) model is composed of a Region Proposal Network to find the start position of handwriting lines, a novel line follower network that incrementally follows and preprocesses lines of (perhaps curved) handwriting into dewarped images, and a CNN-LSTM network to read the characters. SFR exceeds the performance of the winner of the ICDAR2017 handwriting recognition competition, even when not using the provided competition region annotations.
Computational reinforcement learning.

Reinforcement learning (RL) refers to the scientific study of how animals and machines adapt their behavior in order to maximize reward. e history of RL research can be traced to early work in psychology on instrumental learning behavior. However, the modern field of RL is a highly interdisciplinary area which lies that the intersection of ideas in computer science, machine learning, psychology, and neuroscience. is chapter summarizes the key mathematical ideas underlying this field including the exploration/exploitation dilemma, temporal-difference (TD) learning, Q-learning, andmodelbased versus model-free learning. In addition, a broad survey of open questions in psychology and neuroscience are reviewed.
Design of scalable and efficient information retrieval systems

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Doing Better Statistics in Human-Computer Interaction

Each chapter of this book covers specific topics in statistical analysis, such as robust alternatives to t-tests or how to develop a questionnaire. They also address particular questions on these topics that are commonly asked by HCI researchers when planning or completing the analysis of their data. The book presents the current best practice in statistics, drawing on the state-of-the-art literature that is rarely presented in HCI. This is achieved by providing strong arguments that support good statistical analysis without relying on mathematical explanations. It additionally offers some philosophical underpinning for statistics, so that readers can see how statistics fit with experimental design and the fundamental goal of discovering new HCI knowledge.