Miracle-dz/UniFAR-FTUs huggingface dataset:
Mobile Technology Adoption and the Effects of Cultural Factors

Sanakulov, Nodir Mobile Technology Adoption and the Effects of Cultural Factors Jyväskylä: University of Jyväskylä, 2018, 63 p. (JYU Dissertations, ISSN 2489-9003; 55) ISBN 978-951-39-7676-7 (PDF) Mobile technologies are an important part of human life in both the personal and professional realms. Many services (banking, news, health, education, etc.) that traditionally offered either face-to-face, telephone, or other conventional platforms and technologies are now establishing services on mobile platforms as well. Mobile gadgets are now also being used by company employees as sales automation tools. The importance of culture in technology adoption and usage has been well-established. The adoption of any technology includes a set of factors that determine if the technology is either accepted or rejected, and the individual strengths of these determining factors may vary greatly, depending on the importance placed on them by an individual. Because culture affects human behavior, there will be obvious differences in technology adoption between two groups with distinct cultural differences. This dissertation aims to contribute to the technology adoption literature by conducting a cross-cultural study of consumer smartphone adoption to determine adoption levels in general and the differences between the antecedents of adoption in the included groups in particular. It will also examine salespersons ́ adoption and usage of mobile sales customization tools. The findings will confirm the importance of cultural factors in technology adoption, identify primary variables for adoption within each group, offer conceptualization of MSCT adoption, compile suggestions for further development of the sales tool, and make recommendations that will help increase efficiency in usage and sales.
Randomized Admission Policy for Efficient Top-k, Frequency, and Volume Estimation

Network management protocols often require timely and meaningful insight about per flow network traffic. This paper introduces <italic>Randomized Admission Policy (RAP)</italic> –a novel algorithm for the <italic>frequency</italic>, <italic>top-k</italic>, and byte volume estimation problems, which are fundamental in network monitoring. We demonstrate space reductions compared to the alternatives, for the frequency estimation problem, by a factor of up to 32 on real packet traces and up to 128 on heavy-tailed workloads. For top-<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> identification, RAP exhibits memory savings by a factor of between 4 and 64 depending on the workloads’ skewness. These empirical results are backed by formal analysis, indicating the asymptotic space improvement of our probabilistic admission approach. In Addition, we present <italic>d-way RAP</italic>, a hardware friendly variant of RAP that empirically maintains its space and accuracy benefits.
Word2Sense: Sparse Interpretable Word Embeddings

We present an unsupervised method to generate Word2Sense word embeddings that are interpretable — each dimension of the embedding space corresponds to a fine-grained sense, and the non-negative value of the embedding along the j-th dimension represents the relevance of the j-th sense to the word. The underlying LDA-based generative model can be extended to refine the representation of a polysemous word in a short context, allowing us to use the embedings in contextual tasks. On computational NLP tasks, Word2Sense embeddings compare well with other word embeddings generated by unsupervised methods. Across tasks such as word similarity, entailment, sense induction, and contextual interpretation, Word2Sense is competitive with the state-of-the-art method for that task. Word2Sense embeddings are at least as sparse and fast to compute as prior art.
Learning regular and irregular examples separately

Oka and Yoshida (1992) proposed GLLL, a hybrid neural network architecture-of a global and a local learning module, and demonstrated its high accuracy and efficiency. In this paper the authors analyze learning in GLLL directing their attention to separation of examples between the two modules. The authors' findings are: 1) regular and irregular examples are distinguished and learned separately by the two modules; 2) learning progresses in three stages, and overgeneralization occurs in the second stage; 3) outputs for highly unusual inputs are produced as if they are members of regular examples. These findings fit qualitatively human learning data reported by Marcus et al. (1990) and Pinker (1991).
Statistical models for land mobile and fixed satellite communications at Ka band

This paper describes statistical models in terms of signal envelope and signal phase probability distribution for both fixed and mobile satellite communications channels at Ka band (20/30 GHz). Statistical models for eleven different weather conditions are given for the fixed satellite communications channels. Also, statistical models for light to medium shadowing, medium to heavy shadowing and heavy shadowing are given for the land mobile satellite communications channels at Ka band. The results described should be useful for the design of fixed and mobile satellite communications at Ka band.
Graphical models for high dimensional genomic data

MIN JIN HA: Graphical Models for High Dimensional Genomic Data (Under the direction of Dr. Wei Sun) Graphical models study the relations among a set of random variables. In a graph, vertices represent variables and edges capture relations among the variables. We have developed three statistical methods for graphical model construction using high dimensional genomic data. We first focus on estimating a high-dimensional partial correlation matrix. It is estimated by ridge penalty followed by hypothesis testing. The null distribution of the test statistics derived from penalized partial correlation estimates has not been established. We address this challenge by estimating the null distribution from the empirical distribution of the test statistics of all the penalized partial correlation estimates. The performance of our method is systematically evaluated in simulation and application studies. Next, we consider estimating Directed Acyclic Graph (DAG) models for multivariate Gaussian random variables. The skeleton of a DAG is an undirected graphical model, which is constructed by removing the directions of all the edges in the DAG. Given observational data, not all the directions of the edges of a DAG are identifiable; however the skeleton of the DAG is identifiable. We propose a novel method named PenPC to estimate the skeleton of a high dimensional DAG by a two-step approach. We first estimate an undirected graph by selecting the non-zero entries of the partial correlation matrix, then remove false connections in this undirected graph to obtain the skeleton. We systematically study the asymptotic property of PenPC on high dimensional problems. Both simulations and real data analysis suggest that our method have substantially higher sensitivity and specificity to estimate network skeleton than existing methods.
A framework for reasoning about Erlang code

We present a framework for formal reasoning about the behaviour of software written in Erlang, a functional programming language with prominent support for process based concurrency, message passing communication and distribution. The framework contains the following key ingredients: a specification language based on the mu-calculus and first-order predicate logic, a hierarchical small-step structural operational semantics of Erlang, a judgement format allowing parameterised behavioural assertions, and a Gentzen style proof system for proving validity of such assertions. The proof system supports property decomposition through a cut rule and handles program recursion through well-founded induction. An implementation is available in the form of a proof assistant tool for checking the correctness of proof steps. The tool offers support for automatic proof discovery through higher--level rules tailored to Erlang. As illustrated in several case
Performance Analysis of Sobel Edge Filter on Heterogeneous System Using OpenCL

The fundamental task required for any image or Video processing applications like video surveillance, medical imaging is Edge detection. Any of the filters available can be used to detect the edges. In this paper Sobel Edge filter is used for comparing the performance analysis on CPUs and GPUs and from this study it is found that the performance analysis on GPUs is much higher as compared to CPUs. Also it is seen that parallel execution time is much less as compared to sequential execution for the heterogeneous systems.
Annotating and Analyzing Semantic Role of Elementary Units and Relations in Online Persuasive Arguments

For analyzing online persuasions, one of the important goals is to semantically understand how people construct comments to persuade others. However, analyzing the semantic role of arguments for online persuasion has been less emphasized. Therefore, in this study, we propose a novel annotation scheme that captures the semantic role of arguments in a popular online persuasion forum, so-called ChangeMyView. Through this study, we have made the following contributions: (i) proposing a scheme that includes five types of elementary units (EUs) and two types of relations. (ii) annotating ChangeMyView which results in 4612 EUs and 2713 relations in 345 posts. (iii) analyzing the semantic role of persuasive arguments. Our analyses captured certain characteristic phenomena for online persuasion.
Hierarchical Multi-label Classification of Text with Capsule Networks

Capsule networks have been shown to demonstrate good performance on structured data in the area of visual inference. In this paper we apply and compare simple shallow capsule networks for hierarchical multi-label text classification and show that they can perform superior to other neural networks, such as CNNs and LSTMs, and non-neural network architectures such as SVMs. For our experiments, we use the established Web of Science (WOS) dataset and introduce a new real-world scenario dataset, the BlurbGenreCollection (BGC). Our results confirm the hypothesis that capsule networks are especially advantageous for rare events and structurally diverse categories, which we attribute to their ability to combine latent encoded information.
Motivations to engage in word-of-mouth behavior on social network sites

The main objective of this study was to enhance the understanding of users’ motivation to engage in word-of-mouth behavior on social network sites. This study conducted a hierarchical regression analysis to examine the motivations of social and personal constructs that may influence users’ engagement in word-of-mouth. The results posited that not only social constructs (tie strength, expressiveness, social enhancement, relationship management, normative influence, and informational influence), but also personal constructs (altruism, narcissism, image building, and achievement) positively affect users’ engagement in word-of-mouth. These findings can provide useful implications for theory in relation to the motives to disseminate word-of-mouth, and be a useful resource and reference for marketing practice.
Enhancing Pre-Trained Language Representations with Rich Knowledge for Machine Reading Comprehension

Machine reading comprehension (MRC) is a crucial and challenging task in NLP. Recently, pre-trained language models (LMs), especially BERT, have achieved remarkable success, presenting new state-of-the-art results in MRC. In this work, we investigate the potential of leveraging external knowledge bases (KBs) to further improve BERT for MRC. We introduce KT-NET, which employs an attention mechanism to adaptively select desired knowledge from KBs, and then fuses selected knowledge with BERT to enable context- and knowledge-aware predictions. We believe this would combine the merits of both deep LMs and curated KBs towards better MRC. Experimental results indicate that KT-NET offers significant and consistent improvements over BERT, outperforming competitive baselines on ReCoRD and SQuAD1.1 benchmarks. Notably, it ranks the 1st place on the ReCoRD leaderboard, and is also the best single model on the SQuAD1.1 leaderboard at the time of submission (March 4th, 2019).
Coverage Performance in MIMO-ZFBF Dense HetNets with Multiplexing and LOS/NLOS Path-Loss Attenuation

The performance of multiple-input multiple-output (MIMO) multiplexing heterogenous cellular networks are often analyzed using a single-exponent path-loss model. Thus, the effect of the expected line-of-sight (LOS) propagation in densified settings is unaccounted for, leading to inaccurate performance evaluation and/or inefficient system design. This is due to the complexity of LOS/non-LOS models in the context of MIMO communications. We address this issue by developing an analytical framework based on stochastic geometry to evaluate the coverage performance. We focus on the zero-forcing beamforming where the maximum signal-to-interference ratio is used for cell association. We analytically derive the coverage. We then investigate the cross-stream interference correlation, and develop two approximations of the coverage: Alzer Approximation (A-A) and Gamma Approximation (G-A). The former is often used in the single antenna and single-stream MIMO. We extend A-A to a MIMO multiplexing system and evaluate its utility. We show that the inverse interference is well-fitted by a Gamma random variable, where its parameters are directly related to the system parameters. The accuracy and robustness of G-A is higher than that of A-A. We observe that depending on the multiplexing gain, it is possible to attain the best coverage probability by proper densification.
Water at molecular interfaces : structure and dynamics near biomolecules and amorphous silica.

Water, the fundamental constituent of life, has been found to have a critical role at both organic and inorganic surfaces. The properties of water near surfaces, is known to be different from water far away from the interface. This dissertation explores the degree to which inorganic materials such as amorphous silica (glass) and biomolecular surfaces change the properties of water. Of particular interest is the interplay between biological molecules proteins and nucleic acids and their aqueous environment, and how this determines biological function. The mobility of water near protein surfaces has been of considerable recent interest. There have been many reports in the literature postulating that interfacial water is incapable of undergoing rapid rotational motions due to strong electrostatic forces from the protein surface. This has led to confusing and conflicting interpretations on the molecular origin of the slow features observed in certain experiments that probe protein surfaces. Our theoretical studies resolve the conflicts and show that the slow dynamics observed, originates from the protein and water jostling in a concerted fashion. Our studies support a change in the paradigm for the function of proteins to include both the protein and the surrounding water as active participants in biological function.
A Working Memory Model for Task-oriented Dialog Response Generation

Recently, to incorporate external Knowledge Base (KB) information, one form of world knowledge, several end-to-end task-oriented dialog systems have been proposed. These models, however, tend to confound the dialog history with KB tuples and simply store them into one memory. Inspired by the psychological studies on working memory, we propose a working memory model (WMM2Seq) for dialog response generation. Our WMM2Seq adopts a working memory to interact with two separated long-term memories, which are the episodic memory for memorizing dialog history and the semantic memory for storing KB tuples. The working memory consists of a central executive to attend to the aforementioned memories, and a short-term storage system to store the “activated” contents from the long-term memories. Furthermore, we introduce a context-sensitive perceptual process for the token representations of dialog history, and then feed them into the episodic memory. Extensive experiments on two task-oriented dialog datasets demonstrate that our WMM2Seq significantly outperforms the state-of-the-art results in several evaluation metrics.
Sparse tensor dimensionality reduction with application to clustering of functional connectivity

Functional connectivity (FC) is a graph-like data structure commonly used by neuroscientists to study the dynamic behaviour of the brain activity. However, these analyses rapidly become complex and time-consuming. In this work, we present complementary empirical results on two tensor decomposition previously proposed named modified High Order Orthogonal Iteration (mHOOI) and High Order sparse Singular Value Decomposition (HOsSVD). These decompositions associated to k-means were shown to be useful for the study of multi trial functional connectivity dataset.
Design and validation of novel methods for long-term road traffic forecasting

Road traffic management is a critical aspect for the design and planning of complex urban transport networks for which vehicle flow forecasting is an essential component. As a testimony of its paramount relevance in transport planning and logistics, thousands of scientific research works have covered the traffic forecasting topic during the last 50 years. In the beginning most approaches relied on autoregressive models and other analysis methods suited for time series data. During the last two decades, the development of new technology, platforms and techniques for massive data processing under the Big Data umbrella, the availability of data from multiple sources fostered by the Open Data philosophy and an ever-growing need of decision makers for accurate traffic predictions have shifted the spotlight to data-driven procedures. Even in this convenient context, with abundance of open data to experiment and advanced techniques to exploit them, most predictive models reported in literature aim for shortterm forecasts, and their performance degrades when the prediction horizon is increased. Long-termforecasting strategies are more scarce, and commonly based on the detection and assignment to patterns. These approaches can perform reasonably well unless an unexpected event provokes non predictable changes, or if the allocation to a pattern is inaccurate.The main core of the work in this Thesis has revolved around datadriven traffic forecasting, ultimately pursuing long-term forecasts. This has broadly entailed a deep analysis and understanding of the state of the art, and dealing with incompleteness of data, among other lesser issues. Besides, the second part of this dissertation presents an application outlook of the developed techniques, providing methods and unexpected insights of the local impact of traffic in pollution. The obtained results reveal that the impact of vehicular emissions on the pollution levels is overshadowed
Perceived Workload Is Associated with Cabin Crew Fatigue on Ultra-Long Range Flights

ABSTRACT Objective: This study aimed to determine whether on ultra-long range (ULR) flights, perceived workload is an independent predictor of cabin crew fatigue at top-of-descent (TOD) and if so, to what degree it is associated with cabin crew fatigue relative to sleep-related factors. Background: Current ULR scheduling for cabin crew is predominantly based on flight crew data. However, cabin crew workload is very different in nature to that of flight crew. Method: Fifty-five cabin crew wore an actigraph and completed a sleep/duty diary to monitor sleep during a ULR trip. At TOD, crewmembers completed a 5-min Psychomotor Vigilance Task (PVT), rated their sleepiness (Karolinska Sleepiness Scale) and fatigue (Samn-Perelli Crew Status Check), and after landing their workload (NASA Task Load Index). Results: When workload was perceived as higher, crewmembers felt more sleepy and fatigued and had more PVT lapses at TOD. The effect of workload on sleepiness was larger (Cohen’s ƒ2 = .27) than the duration of wakefulness (Cohen’s ƒ2 = .14), but the effect of workload on fatigue (Cohen’s ƒ2 = .17) was smaller than the duration of wakefulness (Cohen’s ƒ2 = .24). Lapses were not associated with sleep history, whereas workload had a small effect (Cohen’s ƒ2 = .14). Conclusion: Workload as a fatigue factor for cabin crew warrants ongoing monitoring. This can be achieved by including a workload question in fatigue reports as an essential component in Fatigue Risk Management Systems.
Deep learning frameworks for functional and structural medical image analysis

Medical image analysis plays an important role for understanding both human psychology and physiology. Human body functional and structural information can be recorded using different medical imaging techniques. As for functional image analysis, understanding the organizational architecture of human brain function has been of intense interest since the inception of human neuroscience. In-vivo Functional Magnetic Resonance Imaging (fMRI) technology enabled the investigation of the human brain functional mechanism and the decomposition of the brain functional network components. However, it is a great challenge to conduct simultaneous spatial-temporal decomposition analysis due to the 4D nature of the fMRI data. Existing methods, i.e., general linear model, independent component analysis, sparse representation, have been proposed for purely functional network decomposition on either spatial or temporal feature of fMRI data. Spatial functional network analysis after fMRI decomposition is still an open question. The major challenge is the lack of ability to effectively describe spatial volume maps of brain networks exposed to extensive individual variability. Besides, the 4D nature of fMRI data has not been fully investigated due to the traditional methodology limitation. To address the abovementioned challenges, we proposed a series of deep learning frameworks for spatial functional network map descriptor and 4D spatial temporal analysis of fMRI data. Applications include both task-evoked fMRI, resting state fMRI and also brain disease fMRI data. As for structural image analysis, magnetic resonance (MR) images and computed tomography (CT) are both primary structural imaging modalities for solving and analyzing various medical problems. Understanding of human body structure is advanced by medical imaging technologies, while medical images analysis is advanced by the development of new digital analysis techniques from computer science community. To provide better solutions for some existing challenging medical problems, we proposed deep learning frameworks to facilitate cross modality synthesis and landmark detection tasks, both are widely used and eagerly needed in healthcare industry. INDEX WORDS: functional magnetic resonance imaging analysis, structural medical imaging analysis, spatial temporal analysis, cross modality analysis, deep learning DEEP LEARNING FRAMEWORKS FOR FUNCTIONAL AND STRUTURAL MEDICAL IMAGE ANALYSIS
Is Word Segmentation Child’s Play in All Languages?

When learning language, infants need to break down the flow of input speech into minimal word-like units, a process best described as unsupervised bottom-up segmentation. Proposed strategies include several segmentation algorithms, but only cross-linguistically robust algorithms could be plausible candidates for human word learning, since infants have no initial knowledge of the ambient language. We report on the stability in performance of 11 conceptually diverse algorithms on a selection of 8 typologically distinct languages. The results consist evidence that some segmentation algorithms are cross-linguistically valid, thus could be considered as potential strategies employed by all infants.