Miracle-dz/UniFAR-FTUs huggingface dataset:
Beyond Lift in Predictive Modeling: Duo-mining to Glean Strategic Insights

Coined by Tan et al. (2006), “duo-mining” recognizes the synergy produced by combining the traditional data mining techniques with the relatively new analytical methods called text mining, the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources (Hearst, n.d.). While data mining proves to be effective when the data to be analyzed is structured, attention given to text mining lately is on the rise along with the staggering growth in volume of textual data found in the public domain such as tweets and various customer reviews. Making a case for duo-mining has been done through measuring the lift in predictive modeling. Data mining is known to produce a significant lift in comparison to the baseline predictive methods such as random walk. Use of text mining also shows to create a significant lift over the efficacy of baseline prediction. When the two methods are combined, the lift is even higher than either text mining or text mining is employed alone. Customer reviews have proven to be a fertile ground to authenticate the efficacy of duo-mining by combining numerical data (star ratings) and textual data (customer review narratives). Customer reviews in popular e-commerce sites are intended to provide new or potential customers with helpful information to assist in purchase decisions (Mudambi & Schuff, 2010). However, helpfulness of such reviews suffers from the disconnect between the quantitative measures and the qualitative assessment, and studies have pointed out the disconnect between the two (Mudambi, Schuff, & Zhang, 2014; Chung 2018). The potential business value of enhanced predictive power gained through duo-mining notwithstanding, such a gain tends to be ephemeral and confined to narrow domains due to the inherent nature of data collected from social network sites. In this TREO talk, we present a novel approach of applying the duo-mining techniques to proprietary data sets of historical college admission applications, whereby not only the lift gain in the predictive power is confirmed but also strategic insights are gleaned by noting the similarities and differences in essays and recommendations, following the cluster hypothesis (van Rijsbergen, 1979).
Exploring the Impact of Information Security Practices on Competitive Advantage

While previous research has explained the effectiveness of information security investment and has put an emphasis on the need for a holistic approach to information security management, the existing contributions fail to advance such integrative view. Further, the relationship between information security and competitive advantage is rarely explored and remains largely unresearched. With this research we aim at developing an integrative model for holistic information security management, and exploring the relationship between information security and the success of organizations. We argue there are two main reasons for this low effectiveness of information security: the first is the overemphasized focus on security technology, and the second is that information security is not perceived as contributing to firm’s success. We argue that managing information security in a holistic way can contribute to the competitiveness of a company, and propose the SORP Cybersecurity Model to address both problems.
Clinician Satisfaction with In-house and Cloud-based EHR Vendors

This paper examines the factors that affect clinician satisfaction with electronic health records (EHR) vendors that provide in-house and cloud-based solutions. The study is concerned with the organizational level of different healthcare organizations. The model combines the IS Success model and the Task Technology Fit model, to best fit the health information systems. Five research hypotheses derived from the model and are going to be empirically tested using a field survey of EHR systems characteristics and its influence on the organization’s tasks achievement and mission alignment.
Factors affecting Herd Behaviour in Buying Decisions influenced by Online Communities

Online communities have developed to become an integral part of people's lives. They are used on a daily basis to communicate, share or rate. Considering herd behaviour – according to which people discount own information to imitate others – the transparency of other people's opinions by online communities might influence herd behaviour in buying decisions. Nonetheless, despite a large body of literature exploring online communities, herd behaviour and buying decisions separately, little is known about the complex interplay of the three topics. This paper seeks to identify determining factors of herd behaviour in buying decisions influenced by online communities. The factors are identified based on a qualitative content analysis of expert interviews. The findings show that there is no single factor, but rather multiple factors like drivers of online community usage, other influencing groups, segment or product specifics or online community activity. This paper facilitates future studies and adds value for practitioners.
BindMe: A thread binding library with advanced mapping algorithms

Binding parallel tasks to cores according to a placement policy is one of the key aspects to achieve good performance in multicore machines because it can reduce on‐chip communication among parallel threads. Binding also prevents operating system from migrating threads, which improves data locality. However, there is no single mapping policy that works best among all different kinds of applications and platforms because each machine has a different topology and each application exhibits different communication pattern. Determining the best policy for a given application and machine requires extra programming effort. To relieve the programmer from that burden, we introduce BindMe, a thread binding library that assists programmer to bind threads to underlying hardware. BindMe incorporates state‐of‐the‐art mapping algorithms, which use communication pattern in an application to formulate an efficient task placement policy. We also introduce ChoiceMap, a communication aware mapping algorithm that respects mutual priorities of parallel tasks and performs a fair mapping by reducing communication volume among cores. We have tested BindMe and ChoiceMap with various applications from NAS parallel benchmark and Rodinia bechmark. Our results show that choosing a mapping policy that best suits the application behavior can increase its performance and no single policy gives the best performance across different applications.
Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience

We consider the problem of transferring policies to the real world by training on a distribution of simulated scenarios. Rather than manually tuning the randomization of simulations, we adapt the simulation parameter distribution using a few real world roll-outs interleaved with policy training. In doing so, we are able to change the distribution of simulations to improve the policy transfer by matching the policy behavior in simulation and the real world. We show that policies trained with our method are able to reliably transfer to different robots in two real world tasks: swing-peg-in-hole and opening a cabinet drawer. The video of our experiments can be found at https://sites.google. com/view/simopt.
Study of Human Push Recovery

Walking and push recovery controllers for humanoid robots have advanced throughout the years while unsolved gaps leading to undesirable behaviours still exist. Because previous studies are mainly pure engineering methods, while the use of data-driven methods has made impressive achievements in the field of control, we set motivation for exploration of control laws applied by human beings. Successful findings may help fill the gaps in current engineering-based controllers and can potentially improve the performance. In this thesis, we show our complete design and implementation of a set of experiments to collect and process human data, as well as data analysis for model fitting. Using the processed motion data and force data collected, we export the position, velocity and acceleration of our participants' centres of mass to form a one-dimensional point mass model defined by the direction of pushes. As a result, we find that proportional-derivative (PD) control can describe the underlying control law that people used and different PD gains are set for different phases of a push recovery trial within the scope of our study. Our final PD control fittings have an average root mean square error below 0.1m with above 90% of a trial's data points are taken into account on average. We also explore how different error metrics that the PD control model uses influence its performance but neither of the two metrics we proposed can help improve the performance. Finally, we have statistics on how people switch push recovery strategies based on their centre of mass properties at the start of the push. We find that the further the centre of mass is from the steady-state point and the higher the velocity it has, a person is more likely to make a step for push force compensation.
A paradigm shift for bring your own device (BYOD)

The present paper aims to understand the effectiveness of the current adoption models in newly-built concept of Bring Your Own Device (BYOD) and also the role of IT departments in adopting BYOD in organizations. The paper extends the literature on adoption and combines them with the body of work on BYOD. The article compares these two concepts to build a theoretical framework in which BYOD can be examined. The paper presents the lessons learned from four different case studies in Australia, United Kingdom and Denmark. It was found that BYOD requires adaptation as opposed to adoption of technologies. Adaptation is a process in which an employee customises software in a way that allows them to do their task on their own device, at their own place and in their own time. The paper also reports that the adaptation process dictated by BYOD can lead to either innovation or errors and difficulties made by inconsistent use of technologies. The article informs IS researchers that there is a need to study the phenomena of technology adaption further. The research also rises awareness among practitioners on the significant changes that need to be implemented to run BYOD practices.
Temporal Proximity induces Attributes Similarity

Users consume their favorite content in temporal proximity of consumption bundles according to their preferences and tastes. Thus, the underlying attributes of items implicitly match user preferences, however, current recommender systems largely ignore this fundamental driver in identifying matching items. In this work, we introduce a novel temporal proximity filtering method to enable items-matching. First, we demonstrate that proximity preferences exist. Second, we present an induced similarity metric in temporal proximity driven by user tastes and third, we show that this induced similarity can be used to learn items pairwise similarity in attribute space. The proposed model does not rely on any knowledge outside users' consumption bundles and provide a novel way to devise user preferences and tastes driven novel items recommender.
An Automatic Method for Complete Brain Matter Segmentation from Multislice CT scan

Computed tomography imaging is well accepted for its imaging speed, image contrast & resolution and cost. Thus it has wide use in detection and diagnosis of brain diseases. But unfortunately reported works on CT segmentation is not very significant. In this paper, a robust automatic segmentation system is presented which is capable of segment complete brain matter from CT slices, without any lose in information. The proposed method is simple, fast, accurate and completely automatic. It can handle multislice CT scan in single run. From a given multislice CT dataset, one slice is selected automatically to form masks for segmentation. Two types of masks are created to handle nasal slices in a better way. Masks are created from selected reference slice using automatic seed point selection and region growing technique. One mask is designed for brain matter and another includes the skull of the reference slice. This second mask is used as global reference mask for all slices whereas the brain matter mask is implemented on only adjacent slices and continuously modified for better segmentation. Slices in given dataset are divided into two batches, before reference slice and after reference slice. Each batch segmented separately. Successive propagation of brain matter mask has demonstrated very high potential in reported segmentation. Presented result shows highest sensitivity and more than 96% accuracy in all cases. Resulted segmented images can be used for any brain disease diagnosis or further image analysis.
Current Advances in Molecular Basis and Mechanisms Regulating Leaf Morphology in Rice

Yield is majorly affected by photosynthetic efficiency. Leaves are essential structure for photosynthesis and their morphology especially size and shape in a plant canopy can affect the rate of transpiration, carbon fixation and photosynthesis. Leaf rolling and size are considered key agronomic traits in plant architecture that can subsidize yield parameters. In last era, a number of genes controlling leaf morphology have been molecularly characterized. Despite of several findings, our understanding toward molecular mechanism of leaf rolling and size are under-developed. Here, we proposed a model to apprehend the physiological basis of different genes organized in a complex fashion and govern the final phenotype of leaf morphology. According to this leaf rolling is mainly controlled by regulation of bulliform cells by SRL1, ROC5, OsRRK1, SLL2, CLD1, OsZHD1/2, and NRL1, structure and processes of sclerenchyma cells by SLL1 and SRL2, leaf polarity by ADL1, RFS and cuticle formation by CFL1, and CLD1. Many of above mentioned and several other genes interact in a complex manner in order to sustain cellular integrity and homeostasis for optimum leaf rolling. While, leaf size is synchronized by multifarious interaction of PLA1, PLA2, OsGASR1, and OsEXPA8 in cell division, NAL1, NAL9, NRL1, NRL2 in regulation of number of veins, OsCOW1, OsPIN1, OsARF19, OsOFP2, D1 and GID in regulation of phytohormones and HDT702 in epigenetic aspects. In this review, we curtailed recent advances engrossing regulation and functions of those genes that directly or indirectly can distress leaf rolling or size by encoding different types of proteins and genic expression. Moreover, this effort could be used further to develop comprehensive learning and directing our molecular breeding of rice.
From Signs to Insights: the Moderating Role of the Absorptive Capacity between Strategic Foresight and the Firm's Sustainability

With new strategic challenges arising from the process of digital transformation in Latin America, both the political-economic landscape of uncertainty and the growing corporate death rates have brought to light signs of a new dynamic of competitiveness in changing environments. In this sense, this research in progress is aimed at investigating how organizations would achieve the long-term performance by monitoring signs of market changes that might be turned into actionable insights for the corporate strategy. Drawing on the literature of information systems and strategic management, we developed a model that outlines the moderating role of the absorptive capacity (CA) in strengthening the relationship between strategic foresight (SF) and the sustained competitive advantage (VCS). To evaluate this model, a test of the hypotheses originated from the research model will be conducted using covariance-based structural equation modeling (CB-SEM), based on survey data collected from businesses within the digital context in Brazil.
Linalool Odor-Induced Anxiolytic Effects in Mice

In folk medicine, it has long been believed that odorous compounds derived from plant extracts can have anxiolytic effects. Among them, linalool, one of the terpene alcohols in lavender extracts, has been reported to have the anxiolytic effects. However, the anxiolytic nature of the linalool odor itself as well as its potential action through the olfactory system has not been thoroughly examined. In this study, we examined the anxiolytic effects of linalool odor with light/dark box test and with elevated plus maze (EPM), and found that linalool odor has an anxiolytic effect without motor impairment in mice. The effect was not observed in anosmic mice, indicating that it was triggered by olfactory input evoked by linalool odor. Furthermore, the effect was antagonized by flumazenil, indicating that the linalool odor-induced anxiolytic effect was mediated by γ-aminobutyric acid (GABA)ergic transmission via benzodiazepine (BDZ)-responsive GABAA receptors. These results provide information about the potential central neuronal mechanisms underlying the odor-induced anxiolytic effects and the foundation for exploring clinical application of linalool odor in anxiety treatments.
Patient Subtyping with Disease Progression and Irregular Observation Trajectories

Patient subtyping based on temporal observations can lead to significantly nuanced subtyping that acknowledges the dynamic characteristics of diseases. Existing methods for subtyping trajectories treat the evolution of clinical observations as a homogeneous process or employ data available at regular intervals. In reality, diseases may have transient underlying states and a state-dependent observation pattern. In our paper, we present an approach to subtype irregular patient data while acknowledging the underlying progression of disease states. Our approach consists of two components: a probabilistic model to determine the likelihood of a patient's observation trajectory and a mixture model to measure similarity between asynchronous patient trajectories. We demonstrate our model by discovering subtypes of progression to hemodynamic instability (requiring cardiovascular intervention) in a patient cohort from a multi-institution ICU dataset. We find three primary patterns: two of which show classic signs of decompensation (rising heart rate with dropping blood pressure), with one of these showing a faster course of decompensation than the other. The third pattern has transient period of low heart rate and blood pressure. We also show that our model results in a 13% reduction in average cross-entropy error compared to a model with no state progression when forecasting vital signs.
Go Back to Your Room: Designing a Scalable In-Class Required College Course

Our work is motivated by the need to scale an introductory information systems course to prepare about 1,000 business college freshmen per year. The point of departure for our work is the centrality of human interactions in learning environments. Thus, we leverage IT to design a required in-class introductory college course that can scale to large numbers of students, under resource constraint. We adopt a design science research approach, grounding the work in intervention theory, and we report the results of the evaluation from the first instantiation of the artifact in a semester course.
Genetic Diversity and Conservation Units: Dealing With the Species-Population Continuum in the Age of Genomics

Current approaches to biodiversity conservation are largely based on geographic areas, ecosystems, ecological communities and species, with less attention ongenetic diversity and the evolutionary continuum from populations to species. Conservation management generally rests on discrete categories, such as identified species, and, for threated taxa, intraspecific units. Species, in particular, provide a common measure of biodiversity yet in both theory and nature, speciation is typically a protracted process progressing from connected populations to unambiguous species with variable rates of phenotypic, ecological and genetic divergence. Thus, most recognised species are not genetically uniform and are sometimes highly structured into historically isolated populations worthy of consideration as intraspecific units that represent unique genetic diversity for conservation. Genome screens offer unprecedented resolution of structure across taxonomic boundaries in species complexes, and have the potential to oversplit species if not interpreted conservatively. This highlights the blurred line between populations and species, and can confound simple dichotomies of ‘species’ versus ‘not species’. At the same time, like plants, there is increasing evidence than even distantly related animal species can hybridize and exchange genes. A review of conservation legislation reveals that legal definitions of ‘species’ are quite flexible and can accommodate a range of infra-specific taxa and divergent populations, as well as taxonomically recognised species. For example, the legislative definition of a species around the world can include: species, subspecies, varieties, and geographically and/or genetically distinct populations. In principle, this flexibility allows for protection of genetic diversity and maintenance of evolutionary processes at a broad range of infra-specific levels. However, evolutionary biologists often fail to adequately justify and then translate their evidence for genetically defined units into categories suited to assessment under local legislation. We recommend that (i). genomic data should be interpreted conservatively when formally naming species, (ii). concomitantly, there should be stronger impetus and a more uniform approach to identifying clearly justified intraspecific units, (iii). guidelines be developed for recognising and labelling intraspecific data that align with best scientific practice, and (iv). that the more nuanced view of species and speciation emerging from genomic analyses is communicated more effectively by scientists to decision makers.
MS-BACO: A new Model Selection algorithm using Binary Ant Colony Optimization for neural complexity and error reduction

Stabilizing the complexity of Feedforward Neural Networks (FNNs) for the given approximation task can be managed by defining an appropriate model magnitude which is also greatly correlated with the generalization quality and computational efficiency. However, deciding on the right level of model complexity can be highly challenging in FNN applications. In this paper, a new Model Selection algorithm using Binary Ant Colony Optimization (MS-BACO) is proposed in order to achieve the optimal FNN model in terms of neural complexity and cross-entropy error. MS-BACO is a meta-heuristic algorithm that treats the problem as a combinatorial optimization problem. By quantifying both the amount of correlation exists among hidden neurons and the sensitivity of the FNN output to the hidden neurons using a sample-based sensitivity analysis method called, extended Fourier amplitude sensitivity test, the algorithm mostly tends to select the FNN model containing hidden neurons with most distinct hyperplanes and high contribution percentage. Performance of the proposed algorithm with three different designs of heuristic information is investigated. Comparison of the findings verifies that the newly introduced algorithm is able to provide more compact and accurate FNN model.
Digital Entrepreneurship: A Path to a More Inclusive Digital Future

Digital innovation has created unprecedented wealth in recent decades, but not always the same degree of new opportunity. Technology sector wealth creation has been concentrated in a relatively small group of companies, founders, and executives (Allen, 2017). The technology industries have not earned a reputation of being inclusive in their workplaces, in their educational processes, or in their venture funding.