Miracle-dz/UniFAR-FTUs huggingface dataset:
Causality-based tests to detect the influence of confounders on mobile health diagnostic applications: a comparison with restricted permutations

Machine learning practice is often impacted by confounders. Confounding can be particularly severe in remote digital health studies where the participants self-select to enter the study. While many different confounding adjustment approaches have been proposed in the literature, most of these methods rely on modeling assumptions, and it is unclear how robust they are to violations of these assumptions. This realization has recently motivated the development of restricted permutation methods to quantify the influence of observed confounders on the predictive performance of a machine learning models and evaluate if confounding adjustment methods are working as expected. In this paper we show, nonetheless, that restricted permutations can generate biased estimates of the contribution of the confounders to the predictive performance of a learner, and we propose an alternative approach to tackle this problem. By viewing a classification task from a causality perspective, we are able to leverage conditional independence tests between predictions and test set labels and confounders in order to detect confounding on the predictive performance of a classifier. We illustrate the application of our causality-based approach to data collected from mHealth study in Parkinson's disease.
Imperfect bifurcations in opinion dynamics under external fields

We investigate, through a kinetic-exchange model, the impact that an external field, like advertising and propaganda, has on opinion dynamics. We address the situations where two opposite alternatives can be selected but the possibility of indecision also exists. In this model, individuals influence each other through pairwise interactions, which can be of agreement or disagreement, and there are also external fields that can skew decision making. Two parameters are used to model the external interactions: one measures the sensitivity of the individuals to be influenced, another quantifies in which direction. We study this model in a fully connected social network scenario, by means of numerical simulations of the kinetic exchange dynamics and analytical results derived from the mean-field rate equations. We show how the external bias gives rise to imperfect bifurcations, and cusp catastrophes, allowing abrupt changes and hysteresis depending on the level of disagreement in interpersonal interactions and on the strength of the external influence.
TCT: A Cross-supervised Learning Method for Multimodal Sequence Representation

Multimodalities provide promising performance than unimodality in most tasks. However, learning the semantic of the representations from multimodalities efficiently is extremely challenging. To tackle this, we propose the Transformer based Cross-modal Translator (TCT) to learn unimodal sequence representations by translating from other related multimodal sequences on a supervised learning method. Combined TCT with Multimodal Transformer Network (MTN), we evaluate MTN-TCT on the video-grounded dialogue which uses multimodality. The proposed method reports new state-of-the-art performance on video-grounded dialogue which indicates representations learned by TCT are more semantics compared to directly use unimodality.
Relationship between entrepreneur’s traits and cloud computing adoption among malay-owned SMEs in Malaysia

[EN] Purpose of the research: This research aims to empirically examine the relationship between entrepreneurs’ personality traits and cloud computing adoption among Malay-owned SMEs in Malaysia. The research was done under theoretical framework that was developed based on the previous literature. Methodology: This study adopts non-probability sampling which is convenient sampling for data collection. Data collection was done in all major towns in West Malaysia. Hypotheses related to personality factors (determination, opportunity, independent, innovativeness, locus of control, creativity, risk taking ability and achievement) and cloud computing adoption among Malay entrepreneurial society were tested by using multiple regression analysis on survey data from a sample of 383 entrepreneurs from major towns in Malaysia. Major conclusion: The result of this study indicates that cloud computing adoption depend on the entrepreneur’s trait. This study confirms that personality traits have significant influence on cloud computing adoption. Research limitation: Like other empirical studies, this study is not without its limitations. The sample size itself is small. The study can be strengthened by increasing the sample size and including participants in other geographical areas. Originality and value: The findings of this paper serve as a reminder for the Malay-owned SMEs that they have to consider cloud computing for their business operation, particularly for managing and maintaining all their virtual resources at their own end.
Auto-configuration mechanism for sensor node integrated into Wireless Intelligent Service Highway

With the development of wireless network, more and more cities have deployed or have planned to deploy wireless city network based on WLAN technology. Since low-cost WiFi-enabled sensor nodes had been launched, much more value-added service based on sensors can be created in the wireless city network. Wireless Intelligent Service Highway is proposed to break the border of independent fixed access network with the new open access service model to embrace wireless access services and enormous sensor services. In this paper, we present the WISH solution and introduce Sensor Management Module (SMM) in Access Node (AN) of fixed network which acts as convergence point between fixed and wireless sensor technologies. The auto-configuration mechanism for sensor nodes is proposed to help SMM to manage the resource of sensor nodes and distribute the tasks to available sensor node.
Causes of interspecific variation in susceptibility to cat preda­tion on birds

Generalist mammalian predators hunt for prey in different ways than raptors, often relying on ambush attack. Such differences in predator behavior can have profound effects on susceptibility of different species of prey to predation, but can also modify the behavior of prey. We investigated susceptibility of common birds to predation by domestic cats Felis catus, relying on an extensive data set on prey abundance and prey availability as determined by standardized bird counts. Susceptibility to cat predation for different species of birds was not significantly cor- related with susceptibility to predation by sparrowhawk Accipiter nisus, but with susceptibility to goshawk Accipiter gentilis predation. Susceptibility to goshawk predation was independently pre- dicted by susceptibility to cat and sparrowhawk predation. We cross-validated our estimate of sus- ceptibility to cat predation by showing that the frequency of birds with tail loss increased strongly with susceptibility to cat predation. Male birds were disproportionately preyed upon, especially if having song posts low in the vegetation, and if having short flight distances when singing. Colo- nially breeding species were less susceptible to predation than solitary species. Bird species with high levels of innate immune defense against bacterial infections had high susceptibility to cat predation. These findings suggest that predation by cats has been an important selection pressure affecting anti-predation behavior, sexual display and immune function in common birds.
Motivate AI Class with Interactive Computer Game

This paper reports adapting traditional Pacman game with machine learning technology case-based reasoning (CBR) to provide student learning motivation in the AI subject teaching. The paper explains how the study transforms the SOAR tutorial EATER game's playing rules as a platform to experience that CBR (or AI) agent plays with human player in a shared running-map for finding the CBR agent's best solution as competition with human player and greedy agent. The advantage of AI project is that the experiment has to adopt human competition experience. There are many uncertain what CBR can achieve, which wait for the individual students to discover. The experiment results are different individually; the educators do not have to worry about the cheating in student assignments. The study found that the fun of interactive computer games would stimulate students' motivation in learning CBR concepts in AI.
The Language of Framing: A Sociolinguistic Approach to Social Movement Framing Strategies.

HOLLEY, JORDAN MCHUGH. The Language of Framing: A Sociolinguistic Approach to Social Movement Framing Strategies. (Under the direction of Dr. Thomas Shriver and Dr. Robin Dodsworth). This paper examines the link between linguistic behavior, identity practices and social movement framing strategies. Crucially, while scholars have established the link between rhetorical strategies, collective identity and social movement framing, they have not explored the ways in which linguistic variation plays a part in the identity processes involved therein. This paper offers a case study of four speeches given by William Barber II, a prominent activist in North Carolina. I perform a quantitative analysis of two linguistic variables, /ay/ monophthongization and copula absence, in order to examine how Barber’s language patterns in each of the speeches. Furthermore, I perform a qualitative content analysis and link the content and context of each of the four speeches to the patterns of linguistic variation shown by Barber. I find that Barber’s use these two linguistic variables is complex and multifaceted. While /ay/ monophthongization seems to index Barber’s identity as authentically Southern and copula absence might emphasize Barber’s black identity, the use of these linguistic variables is conditioned by a variety of social and internal linguistic factors. Nevertheless, this paper offers insight into how these variables might serve as identity moves within the context of social movement framing. © Copyright 2018 by Jordan McHugh Holley All Rights Reserved The Language of Framing: A Sociolinguistic Approach to Social Movement Framing Strategies by Jordan McHugh Holley A thesis submitted to the Graduate Faculty of North Carolina State University in partial fulfillment of the requirements for the degree of Master of Science
Adoption of Unified Communications and Collaboration from the Perspective of Diffusion of Innovation and Service Dominant Logic: A Preliminary View

Unified Communications and Collaboration (UC&C) is a platform that has become increasingly popular in recent years and used in organizations. The service could increase flexibility, interoperability, efficiency and productivity in managing business processes. As the number and variety of UC&C tools and services increases, many organizations have developed strategic plans and allocate budget to implement UC&C. However, the adoption of some UC&C tools is still below than expected. As a service that continues to evolve in tandem with new developments in technology, it is thus important to investigate the factors that impact upon the adoption of UC&C tools and services. Via a review of the literature, this paper will posit an initial model for the adoption of UC&C. This initial model is developed by adopting the views of two important theories which are the theory of Diffusion of Innovation (DOI) and service dominant logic (SDL). The DOI theory is chosen since UC&C represents evolvement in the communications technology space, whilst SDL is chosen because of its focus on value co-creation and user centeredness, which is an important factor that will influence adoption. The key factors influencing the adoption of UC&C embodied in the model will provide useful insights for managers in implementing UC&C in their organizations.
Adaptive Observer-Based Fault-Tolerant Control Design for Uncertain Systems

This study focuses on the design of the robust fault-tolerant control (FTC) system based on adaptive observer for uncertain linear time invariant (LTI) systems. In order to improve robustness, rapidity, and accuracy of traditional fault estimation algorithm, an adaptive fault estimation algorithm (AFEA) using an augmented observer is presented. By utilizing a new fault estimator model, an improved AFEA based on linear matrix inequality (LMI) technique is proposed to increase the performance. Furthermore, an observer-based state feedback fault-tolerant control strategy is designed, which guarantees the stability and performance of the faulty system. Moreover, the adaptive observer and the fault-tolerant controller are designed separately, whose performance can be considered, respectively. Finally, simulation results of an aircraft application are presented to illustrate the effectiveness of the proposed design methods.
Design and performance evaluation of a 2D-mesh Network on Chip prototype using FPGA

The network on chips (NoCs) is a promising solution for future on-chip interconnection. In this area, fast and accurate performance evaluation and design space exploration for the NoCs are critical issues. In this paper, we design a NoC prototype which consists of 4 ARM compatible cores and a router-based on-chip network, and implement it on a FPGA device. The performances of this prototype are evaluated under two real applications. Specially, we compare the performance of NoC architecture with which of hierarchy shared-bus and point-to-point architecture. The results show that the NoC architecture provides the best performance of speedup ratio with moderate area overhead.
Transmission thresholds and pupal/demographic surveys in Yogyakarta, Indonesia for developing a dengue control strategy based on targeting epidemiologically significant types of water-holding containers

All water-holding containers (ca. 3000) associated with approximately 320 residences in Yogyakarta, Indonesia, were examined for the presence of Aedes aegypti (L.), Aedes albopictus Skuse, and Culex quinquefasciatus Say pupae in four replicate surveys conducted during two dry seasons (1996 and 1998) and two wet seasons (1997 and 1999). Less than 6% of these receptacles had pupae. Ae. aegypti pupae collected were ten times more than Ae. albopictus (ca. 1600 vs. 160 respectively); Cx. quinquefasciatus was rarely encountered. From a dengue perspective, the epidemiological significance of a particular type of container is a function of the number of Ae. aegypti pupae per person – calculated simply as the ratio of total number of Ae. aegypti pupae recovered in that type and the number of residents, ca. 2800. Overall, there was an average of 0.57 Ae. aegypti pupae per person. Assuming an overall herd immunity of 33% and an average temperature of 29 °C, we estimate the transmission threshold in Yogyakarta to be approximately 0.43 Ae. aegypti pupae per person. By eliminating mosquito production in two common household containers – wells and used tyres the number of Ae. aegypti pupae would be reduced from 0.57 to 0.29 per person, below our estimate of the transmission threshold. An assessment of the effectiveness of this strategy is currently being conducted in a multi-year study in Yogyakarta using an insect growth regulator (IGR) for mosquito control.
Regularization Strategies for Discontinuity-Preserving Optical Flow Methods

The aim of this paper is to study several strategies for the preservation of flow discontinuities in variational optical flow methods. We analyze the combination of robust functionals and diffusion tensors in the smoothness assumption. Our study includes the use of tensors based on decreasing functions, which has shown to provide good results. However, it presents several limitations and usually does not perform better than other basic approaches. It typically introduces instabilities in the computed motion fields in the form of independent blobs of vectors with large magnitude. We propose two alternatives to overcome these drawbacks: first, a simple approach that combines the decreasing function with a minimum isotropic smoothing, and second, a method that looks for the best parameter configuration that preserves the important motion contours and avoid instabilities. It relies on the input images and the regularization parameter. It is fully automatic, providing a near-optimal value for many sequences, as shown in the experiments. Both proposals allow to detect the contours of the motion field and produce more stable solutions for a large range of parameters. In the experimental results, we present a detailed study and comparison of the different strategies.
Effect of haloxyfop and alloxydim applied separately and in combination with salicylic acid, diphenylamine, or norflurazon on the root growth and fatty acid composition of the selected species of grasses and dicotyledonous plants

Eight species from the grass family were studied: wheat, rye, triticale, barley, oats, maize, couch grass, rice, and two dicotyledonous species: field pea and common flax. Seedlings of the tested species were grown in hydroponic cultures with haloxyfop or alloxydim alone (graminicides) and with haloxyfop or alloxydim plus one of the tested antagonists (diphenylamine, salicylic acid, or norflurazon). All investigated species of the grass family showed similar susceptibility to the tested graminicides (measured with their effect on root growth). The tested dicotyledonous species were completely unsusceptible. Except in maize, the addition of antagonistic substances to the medium with herbicide weakened considerably the inhibitory effect of both haloxyfop and alloxydim. The tested graminicides also had a significant effect on the fatty acid composition of susceptible species (except maize, where the effect was low). In the lipids of the apical parts of roots, a significant decrease in the content of oleic and linoleic acids and a considerable increase in the content of linolenic acid were observed.
Autonomous driving in crossings using reinforcement learning

Machine learning techniques such as artificial neural networks have recently shown very promising results for decision control tasks when combined with reinforcement learning. This thesis presents an applicable approach using longitudinal acceleration control for autonomous vehicles driving through crossings in a simulated traffic environment. An Acceleration Regulator, which controls the autonomous vehicle, is trained using reinforcement learning and attempts to take advantage of gaps between cars, while following a pre-planned path along its lane. The results show that policies can be trained to successfully drive comfortably through a crossing, avoiding collision with other cars and being too passive. Comfortability is achieved by constraining jerk. The Acceleration Regulator generalize over different types of traffic crossings and driver behaviors. In 95% of all attempts, the learned policy is able to handle traffic situations with a varied number of cars having the same behavior on four types of crossings, involving several lanes and turns. When other cars vary between four different behaviors, driving in only one fixed crossing, the policy is successful in 97.2% of all attempts. Deep Q-learning is used to find a policy for the Acceleration Regulator, that can solve this task. This learning algorithm does not infer information over time. To enable this, a Deep Recurrent Q-Network is tested and compared to the Deep Q-learning approach. Results show that a Deep Recurrent Q-Network succeeds in three out of four attempts where a Deep Q-Network fails.
European Conference on Information Systems ( ECIS ) 2000 Transforming Healthcare in Australia : The PeCC Initiative

Many of the current crises in contemporary healthcare management centre on issues concerning information management and costs. Electronic commerce (or e-commerce) activity, grounded in the development of the Internet, is challenging traditional management models and providing new paradigms and possible solutions for improved health care management. Australia’s health industry, like other economic sectors here and globally, is grasping the need to use IT and telecommunications with e-commerce strategies for improved cost-effective services to its key stakeholders. This paper addresses the changes occurr ing in Australia’s health care industry influenced by trends in information systems. While the Federal government’ s recent repor t, From Telehealth to E-Health: The Unstoppable Rise of EHealth [3], outlines a diverse range of projects and practices, here the authors focus on Australia’s first Internet trading community, The Project Electronic Commerce and Communication for Healthcare, otherwise known as PeCC. This study is suppor ted by an ARC Collaborative Grant. The Industry par tner is IBM.
A Critical Consideration of Harsanyi’s Preference Utilitarianism: The Double-Profile Approach

This paper examines Harsanyi’s preference utilitarianism by giving a doubleprofile approach. In this approach, each individual has two kinds of preference relations: personal private preferences and personal ethical preferences. Moreover, the social ethnical observer has its own social preferences. We assume that those preference relations all satisfy the von Neumann-Morgenstern axioms. Using Harsanyi’s axioms, personal private preferences are aggregated into personal ethical preferences, and then personal ethical preferences are aggregated into the social preferences. We give certain axioms on those aggregation steps to explain the difference between the two steps. We construct this approach so that we can study how interpersonal utility comparisons are involved in those steps.
Pollution Heaven: Be Honest

Most of the developing countries are facing a dilemma either to have a better control over the environmental policy that may lead to less foreign direct investment (FDI) or less control on the environmental policy but enjoys more FDI through which economic may grow faster. Motivated by this paradox, it is necessary to empirically examine the dynamic relationship between FDI, pollution control and corruption to suggest a mechanism that may be useful to get rid of pollution heaven malaise. Using dynamic panel GMM estimation for 110 countries from 2005 to 2012, the finding conforms that the stringency in environmental control alone has negative effect on FDI and at the same time, high level of corruption indeed has attracted FDI inflows. Interestingly, in contrast to previous finding, our result shows that high stringency in environmental control coupled with low level of corruption has significantly attracted more FDI inflows. In other words, not corrupt or honest institutions could nullify the negative effect of stringency in pollution control to FDIs. The finding, besides it’s robustness to various environmental stringencies measures, is a potential answer to pollution heaven predicament for developing countries.
Estimating Option-Implied Distributions in Illiquid Markets and Implementing the Ross Recovery Theorem

We describe how forward-looking information on the statistical properties of an asset can be extracted directly from options market data and how this can be used practically in portfolio management. Although the extraction of a forward-looking risk-neutral distribution is well-established in the literature, the issue of estimation in an illiquid market is not. We use the deterministic SVI volatility model to estimate weekly risk-neutral distribution surfaces. The issue of calibration with sparse and noisy data is considered at length and a simple but robust fitting algorithm is proposed. Furthermore, we attempt to extract real-world implied information by implementing the recovery theorem introduced by Ross (2015). Recovery is an ill-posed problem that requires careful consideration. We describe a regularization methodology for extracting real-world implied distributions and implement this method on a history of SVI volatility surfaces. We analyse the first four moments from the implied risk-neutral and real-world implied distributions and use them as signals within a simple tactical asset allocation framework, finding promising results.