Miracle-dz/UniFAR-FTUs huggingface dataset:
A MACHINE LEARNING BASED APPROACH FOR DETECTING NON-DETERMINISTIC TESTS AND ITS ANALYSIS IN MOBILE APPLICATION TESTING

Hundreds of different mobile devices are on the market, produced by different vendors, and with different software features and hardware components. Mobile applications, while running on different devices, may behave differently due to variations in the hardware or O.S. components. Since mobile applications are expected to be deployed and executed on diverse mobile platforms, they must be validated on different mobile platforms and devices. Due to the peculiarities of mobile application development, there is a need for a quality assurance approach that focuses on its challenges. Moreover, mobile test executions take a long time because all the tests were executed in different environments and developers had to create complex tear down procedures. Such procedures were lengthy and far from perfect, leading to unpredictable failures. Regression testing is a crucial part of Mobile app development and it checks that software changes do not break existing functionality. An important assumption of regression testing is that test outcomes are deterministic and tests are expected to either always pass or always fail for the same code under test. But unfortunately, in real projects multiple release cycles, some tests— often called flaky tests—have non-deterministic outcomes. These tests undermine the regression testing cycle as they make it difficult to rely on test results. These results significantly reduced the trust in the tests and thus undermined the whole mobile app test automation effort. We trained machine learning classifiers separately on each test result dataset and compared performance across datasets. The proposed model predicts result types as Non-Deterministic or Deterministic tests from the regression suite results executed in various release cycles.
Social Transfer Cross Domain Transfer Learning From Social Streams for Media Applications

Recommender systems can suffer from data scarcity and cold start issues. However, social networks, which enable users to build relationships and create different types of items, present an unprecedented opportunity to alleviate these issues. In this paper, we represent a social network as a star-structured hybrid graph centered on a social domain, which connects with other item domains. With this innovative representation, useful knowledge from an auxiliary domain can be transferred through the social domain to a target domain. Various factors of item transferability, including popularity and behavioral consistency, are determined. We propose a novel Hybrid Random Walk (HRW) method, which incorporates such factors, to select transferable items in auxiliary domains, bridge cross-domain knowledge with the social domain, and accurately predict user-item links in a target domain. Extensive experiments on a real social dataset demonstrate that HRW signiﬁcantly outperforms existing approaches.
Prevention or Treatment of Hunan Ear Pain, Itch or Vertigo (Dizziness) Caused by Cerumen (Earwax) Impaction and Ear Hairs

Introduction: Cerumen impaction occurs in up to 6% of the general population, affecting 10% of children and over 30% of the elderly and cognitively impaired populations. Here, I proposed theoretical models of clinical medicine to prevent or treat the ear pain, itch or vertigo (dizziness) caused by cerumen impaction and ear hairs. I also reported a clinical case to support the theoretical models in the discussion of this paper. Method: I used published clinical data and scientific theories in the modeling study. A 63 years old male subject participated in the clinical case investigation. The participant had complained his ear pain and itch for two years without any hearing loss and ear wounds; found cerumen often and discoverd rich and long hairs two years ago, in his ear canals; heard his left temporomandibular joint sound recently when eating; and had a history of arthritis symptoms. Following the nonprescription instruction, the entrant applied sterile cotton swabs, medical H 2 O 2 (3%) and alcohol (75%) to clean his ear canals as well as used warm running water to massage his ear auricles and roots, temporomandibular joints and temporal points twice daily. Results: Modeling results illustrate how a cerumen impaction inducing pain, itch or vertigo is formed by ear hairs and how abnormal oscillations in semicircular canals leading to the vertigos are produced by stimulations. The clinical results show treatment methods are 90% effective in this case report. Conclusion: Keeping our whole bodies clean and hygienic are often key points to prevent all of the diseases. Cleaning cerumen is as important as cleaning eye crusts and nasal discharges (mucus). Therefore, ideally I suggest everyone, especially elderly, to clean external ears using methods in this paper (or similar methods) every day. The treatment methods are safe (almost no side effect) and economical. Discussion: I suggest using earphones outside of ear canals (auricles), instead of inside of ear canals. I believe arthritis of temporomandibular joints may lead to ear pain or itch sometimes. I also believe the lymphatic motion and oscillation in the inner ears is the same as or similar to that of cardiovascular and nervous systems in principle. The moving or oscillating model of the vestibular (physics) plasma in semicircular canals in this study could also provide a navigational mechanism for bird or other animal migration.
Interactive Techniques for High-level Spacetime Editing of Human Locomotion

Interactive Techniques for High-Level Spacetime Editing of Human Locomotion Noah Lockwood Doctor of Philosophy Graduate Department of Computer Science University of Toronto 2016 In recent years, computer animated characters have become commonplace, often appearing extremely lifelike in movies, television, and video games. Unfortunately, animating digital characters remains difficult and time-consuming, pursued mostly by dedicated professionals, because conventional animation methods require careful control and coordination of many different low-level parameters which affect the believability of a character’s pose and timing. In this thesis, we present techniques to allow high-level control of motions for animated human characters, by interactively editing existing locomotive motions. These techniques prioritize interactivity by allowing the user to manipulate more general or high-level aspects of the motion, such as a motion path. These techniques also have low data and computation requirements, as they operate on a single input motion without expensive optimization or simulation, allowing for instant feedback. The foundation of this work is a novel path-based editing algorithm based on simple and well-established biomechanical and physical principles of motion. After user manipulation of a motion path, the poses of the character along the path are automatically adjusted to match the new path, and an automatic timewarping procedure adjusts the timing of the new motion depending on its context, in order to remain consistent with the original even after large changes. Our two performance techniques utilize familiar input methods to control the path-based editing algorithm in different ways. First, based on a study exploring how users can express motion using their hands, our “finger walking” technique allows control of motion editing by mimicking full-body motion on a touch-sensitive tabletop. A further study was conducted to test automatic identification of the motion type of the performance, and to evaluate user satisfaction with the generated motions. The second technique uses handheld manipulation of
Loose fusion based on SLAM and IMU for indoor environment

The simultaneous localization and mapping (SLAM) method based on the RGB-D sensor is widely researched in recent years. However, the accuracy of the RGB-D SLAM relies heavily on correspondence feature points, and the position would be lost in case of scenes with sparse textures. Therefore, plenty of fusion methods using the RGB-D information and inertial measurement unit (IMU) data have investigated to improve the accuracy of SLAM system. However, these fusion methods usually do not take into account the size of matched feature points. The pose estimation calculated by RGB-D information may not be accurate while the number of correct matches is too few. Thus, considering the impact of matches in SLAM system and the problem of missing position in scenes with few textures, a loose fusion method combining RGB-D with IMU is proposed in this paper. In the proposed method, we design a loose fusion strategy based on the RGB-D camera information and IMU data, which is to utilize the IMU data for position estimation when the corresponding point matches are quite few. While there are a lot of matches, the RGB-D information is still used to estimate position. The final pose would be optimized by General Graph Optimization (g2o) framework to reduce error. The experimental results show that the proposed method is better than the RGB-D camera’s method. And this method can continue working stably for indoor environment with sparse textures in the SLAM system.
Large scale image classification and object detection

Significant advancement of research on image classification and object detection has been achieved in the past decade. Deep convolutional neural networks have exhibited superior performance in many visual recognition tasks including image classification, object detection, and scene labeling, due to their large learning capacity and resistance to overfit. However, learning a robust deep CNN model for object recognition is still quite challenging because image classification and object detection is a severely unbalanced large-scale problem. In this dissertation, we aim at improving the performance of image classification and object detection algorithms by taking advantage of deep convolutional neural networks by utilizing the following strategies: We introduce Deep Neural Pattern, a local feature densely extracted from an image with arbitrary resolution using a well trained deep convolutional neural network. We propose a latent CNN framework, which will automatically select the most discriminate region in the image to reduce the effect of irrelevant regions. We also develop a new combination scheme for multiple CNNs via Latent Model Ensemble to overcome the local minima problem of CNNs. In addition, a weakly supervised CNN framework, referred to as Multiple Instance Learning Convolutional Neural Networks is developed to alleviate strict label requirements. Finally, a novel residual-network architecture, Residual networks of Residual networks, is constructed to improve the optimization ability of very deep convolutional neural networks. All the proposed algorithms are validated by thorough experiments and have shown solid accuracy on large scale object detection and recognition benchmarks.
Antecedents and Outcomes of Positive Disconfirmation after Service Failure and Recovery

The main purpose of this study was to identify the precursors of disconfirmation and examine the influence of disconfirmation on customer satisfaction in the airline industry of an emerging economy. The target population was airline travellers who had previously experienced service failure with a South African airline. Data was collected from a total of 300 respondents and analysed using structural equation modelling (SEM). The results revealed that positive disconfirmation is influenced by service recovery expectations and the perceived quality of recovery performance; that recovery expectation is positively related to the perceived quality of recovery performance; that service failure severity influences customer service recovery expectations; and that disconfirmation influences customer recovery satisfaction, which in turn affects customer loyalty. The study recommends that airlines establish in advance their customer expectations after service failure so that they can craft appropriate service recovery strategies.
Structure sharing for resource effective solutions: Improving measures to account for importance and quality of functions

Structured sharing is believed to improve resource effectiveness of design solutions by allowing multiple functions to be achieved from the same shared structure. The current measures for assessing structure sharing and resource effectiveness account for the number of functions and structures in the solutions, but they do not take into account some of the basic aspects of functions from the customer and user perspective, which may determine whether the structure shared solution is desirable or not. This paper addresses this gap by improving the existing measures of structure sharing and resource effectiveness to take into account: (1) Relative importance of the different functions performed by the product (2) Quality of functions, which answers how well are the functions in the structure shared products performed when compared to a typical non-structured product serving the same function, and (3) emergent negative functions in the shared product that did not exist in the non-shared counterparts. The paper presents the derived equation, findings from the empirical tests conducted to test and validate the developed measures, and implications and recommendations for future research.
Stability of cubic and quartic rho-functional inequalities in fuzzy normed spaces

In this paper, we solve the following cubic ρ-functional inequality N(f(2x+ y) + f(2x− y)− 2f(x+ y)− 2f(x− y)− 12f(x) (1) −ρ ( 4f ( x+ y 2 ) + 4f ( x− y 2 ) − f(x+ y)− f(x− y)− 6f(x) ) , t) ≥ t t+ φ(x, y) and the following quartic ρ-functional inequality N(f(2x+ y) + f(2x− y)− 4f(x+ y)− 4f(x− y)− 24f(x) + 6f(y) (2) −ρ ( 8f ( x+ y 2 ) + 8f ( x− y 2 ) − 2f(x+ y)− 2f(x− y)− 12f(x) + 3f(y) ) , t) ≥ t t+ φ(x, y) in fuzzy normed spaces, where ρ is a fixed real number with ρ 6= 2. Using the direct method, we prove the Hyers-Ulam stability of the cubic ρ-functional inequality (1) and the quartic ρ-functional inequality (2) in fuzzy Banach spaces. c ©2016 All rights reserved.
Applying and Augmenting Deep Reinforcement Learning in Serious Games through Interaction

Serious games belong to the most important future e-learning trends and are frequently used in recruitment and training. Their development, however, is still a demanding and tedious process, especially when regarding reasonable non-player character behaviour. Serious games can generally profit from diverse, adaptive behaviour to increase learning effectiveness. Deep reinforcement learning has already shown considerable results in automatically generating successful AI behaviour, but its past applications were mainly focused on optimization and short-horizon games. To expand the underlying ideas to serious games, we introduce a new approach of augmenting the application of deep reinforcement learning methods by interactively making use of domain experts’ knowledge to guide the learning process. Thereby, we aim to establish a synergistic combination of experts and emergent cognitive systems to create adaptive and more human behaviour. We call this approach interactive deep reinforcement learning and point out important aspects regarding realization within a novel framework.
Usability testing on intelligent mobile web pre-fetching of cloud storage scheme

Mobile device and Cloud Storage (CS) represent the trends of technology usage of the last few years. However, the difficulty in managing the data when there are too many simultaneous uses of cloud storage services at the same time that can cause latency or delayed time. This paper evaluates mobile cloud storage services using usability testing, which is intended to access by multiple of Cloud Storage Services (CSS) with the proposed Intelligent Mobile Web Pre-fetching of Cloud Storage Scheme (MOBICS). The results show most of the respondents with 95.65% agreeing that MOBICS system was very practical and has enhanced the speed in accessing and storing data by Mobile Cloud Storage (MCS). Besides, MOBICS reduces time of interaction up to 19.28% for the local pre-fetching and 18.80% for the intelligent pre-fetching.
Automatic Extraction of Component Collaboration in Java Web Applications by Using Servlet Filters and Wrappers

As web apps have evolved faster and become more complex, their validation and verification have become essential for their development and maintenance. Efficient validation and verification require understanding of how web components collaborate with each other to meet user requests. Thus, this paper proposes a new approach to automatically extracting such collaboration when a user issues a request for a new page. The approach is dynamic and less sensitive to web development languages and technologies, compared to static extraction approaches. It considers an orignal web app as a black-box and does not change the app’s behavior. The empirical evaluation shows that our approach can be applicable to extract component collaboration and understand the behavior of open source web apps.
Model Reduction and Nonlinear Model Predictive Control of Large-Scale Distributed Parameter Systems with Applications in Solid Sorbent-Based CO2 Capture

This dissertation deals with some computational and analytic challenges for dynamic process operations using first-principles models. For processes with significant spatial variations, spatially distributed first-principles models can provide accurate physical descriptions, which are crucial for offline dynamic simulation and optimization. However, the large amount of time required to solve these detailed models limits their use for online applications such as nonlinear model predictive control (NMPC). To cope with the computational challenge, we develop computationally efficient and accurate dynamic reduced order models which are tractable for NMPC using temporal and spatial model reduction techniques. Then we introduce an input and state blocking strategy for NMPC to further enhance computational efficiency. To improve the overall economic performance of process systems, one promising solution is to use economic NMPC which directly optimizes the economic performance based on first-principles dynamic models. However, complex process models bring challenges for the analysis and design of stable economic NMPC controllers. To solve this issue, we develop a simple and less conservative regularization strategy with focuses on a reduced set of states to design stable economic NMPC controllers. In this thesis, we study the operation problems of a solid sorbent-based CO2 capture system with bubbling fluidized bed (BFB) reactors as key components, which are described by a large-scale nonlinear system of partial-differential algebraic equa-
Minimally invasive gastrectomy and the risk of pancreas-related morbidity

For most of the last century, open gastrectomy (OG) has been the standard approach for gastric cancer. However, in recent years there has been growing acceptance of laparoscopic gastrectomy (LG), first performed by Kitano in 1994 (1), as an alternative and equally effective approach.
Spam Filter Improvement Through Measurement

This work supports the thesis that sound quantitative evaluation for spam filters leads to substantial improvement in the classification of email. To this end, new laboratory testing methods and datasets are introduced, and evidence is presented that their adoption at Text REtrieval Conference (TREC)and elsewhere has led to an improvement in state of the art spam filtering. While many of these improvements have been discovered by others, the best-performing method known at this time – spam filter fusion – was demonstrated by the author. This work describes four principal dimensions of spam filter evaluation methodology and spam filter improvement. An initial study investigates the application of twelve open-source filter configurations in a laboratory environment, using a stream of 50,000 messages captured from a single recipient over eight months. The study measures the impact of user feedback and on-line learning on filter performance using methodology and measures which were released to the research community as the TREC Spam Filter Evaluation Toolkit. The toolkit was used as the basis of the TREC Spam Track, which the author cofounded with Cormack. The Spam Track, in addition to evaluating a new application (email spam), addressed the issue of testing systems on both private and public data. While streams of private messages are most realistic, they are not easy to come by and cannot be shared with the research community as archival benchmarks. Using the toolkit, participant filters were evaluated on both, and the differences found not to substantially confound evaluation; as a result, public corpora were validated as research tools. Over the course of TREC and similar evaluation efforts, a dozen or more archival benchmarks – some private and some public – have become available. The toolkit and methodology have spawned improvements in the state of the art every year since its deployment in 2005. In 2005, 2006, and 2007, the spam track yielded new best-performing systems based on sequential compression models, orthogonal sparse bigram features, logistic regression and support vector machines. Using the TREC participant filters, we develop and demonstrate methods for on-line filter fusion that outperform all other reported on-line personal spam filters.
Robôs Evolutivos para a Investigação do Comportamento de Ratos no Teste de Campo Aberto

O teste do campo aberto é um experimento bastante utilizado para teste de nı́veis de atividade motora e ansiedade em roedores, permitindo o es- tudo de compostos farmacológicos ansiolı́ticos ou ansiogênicos. Neste traba- lho, foi desenvolvido um modelo computacional para ratos virtuais baseados em robôs evolutivos para o teste de campo aberto. O rato virtual é controlado por uma rede neural recorrente otimizada por algoritmos genéticos (AGs). A função de fitness utilizada no AG corresponde ao conflito entre medo e caráter exploratório do rato no campo aberto. Simulações e um experimento com o robô real indicam que o rato virtual otimizado tem comportamentos similares aos de ratos reais no teste do campo aberto.
T
-branes, string junctions, and 6D SCFTs

Recent work on 6D superconformal field theories (SCFTs) has established an intricate correspondence between certain Higgs branch deformations and nilpotent orbits of flavor symmetry algebras associated with T-branes. In this paper, we return to the stringy origin of these theories and show that many aspects of these deformations can be understood in terms of simple combinatorial data associated with multi-pronged strings stretched between stacks of intersecting 7-branes in F-theory. This data lets us determine the full structure of the nilpotent cone for each semi-simple flavor symmetry algebra, and it further allows us to characterize symmetry breaking patterns in quiver-like theories with classical gauge groups. An especially helpful feature of this analysis is that it extends to "short quivers" in which the breaking patterns from different flavor symmetry factors are correlated.