Reviewerly/exHarmony huggingface dataset:
An Investigation into user Adoption of Personal Safety Devices in Higher Education Using the Unified Theory of Acceptance and Use of Technology (UTAUT)

Educational institutions are temples of knowledge that require the utmost safety and security. To ensure that, they are implementing safety measures, including the use of personal safety devices. Adoption of such devices is uneven and not well understood. The main focus of the current study is to investigate the adoption of Peace of Mind (POM), a personal safety device, by students at a liberal arts college in the United States. The Unified Theory of Acceptance and Use of Technology served as the theoretical framework. Findings from a sample of 405 students confirmed that performance expectancy, trusting belief, facilitating conditions, and social influence had direct effects on the students’ behavioral intention to use POM. Based on our findings, we discuss concrete implications for various stakeholders such as higher learning institutions and businesses involved in the innovation and diffusion of personal safety devices. We also make specific recommendations for future research.
Fuzzy Based Optimal QoS Constraint Services Composition in Mobile Ad Hoc Networks

In recent years, computational capability of mobile devices such as Laptops, mobile phones, PDAs, etc., are greatly improved. Implementation of SOA ("Service Oriented Architectures") in mobile ad hoc networks increases the flexibility of using mobile devices. On composing different available services to satisfy end user requirement is a critical challenge in MANETs ("Mobile Ad Hoc Network") due to dynamic topology, Resource heterogeneity, Band width constraint and highly distributed service providers. Existing composition services approaches are not suitable for MANETs due to lack of constraints consideration while choosing services. In this paper, we proposed Fuzzy based optimal QoS constrained Service Composition in MANETs. We consider Energy constraint, hop count, Response time &amp; service throughput as QoS Constraints composing optimal services. We proposed fuzzy logic based system to provide rating to the services for optimal selection of services. We also considered that each node can provide one or more services. The service composition failure rate will be reduced by selecting optimal services in available services. The simulation result demonstrates that the proposed method outperformed than the traditional AODV in terms of average packet delay, energy constraint, throughput and turnaround time.
Neural Approach for Context Scene Image Classification Based on Geometric, Texture and Color Information

Revealing the context of a scene from low-level features representation, is a challenging task for quite a long time. The classification of landscapes scenes to urban and rural categories is a preliminary task for landscapes scenes understanding. Having a global idea about the scene context (rural or urban) before investigating its details, would be an interesting way to predict the content of that scene. In this paper, we propose a novel features representation based on skyline, colour and texture, transformed by a sparse coding using Stacked Auto-Encoder. To evaluate our proposed approach; we construct a new database called SKYLINEScene Database containing 2000 images of rural and urban landscapes with a high degree of diversity. Many experiments were carried out using this database. Our approach shows it robustness in landscapes scenes classification.
Application of random survival forest for competing risks in prediction of cumulative incidence function for progression to AIDS

Objective: There has remained a need to better understanding of prognostic factors that affect the survival or risk in patients with human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS), particularly in developing countries. The aim of the present study aimed to identify the prognostic factors influencing AIDS progression in HIV positive patients in Hamadan province of Iran, using random survival forest in the presence of competing risks (death from causes not related to AIDS). This method considers all interactions between variables and their nonlinear effects.&#x0D; Method(s): A data set of 585 HIV-infected patients extracted from 1997 to 2011 was utilized. The effect of several prognostic factors on cumulative incidence function (probability) of AIDS progression and death were investigated.&#x0D; Result: The used model indicated that using antiretroviral therapy tuberculosis co-infection are two top most important variables in predicting cumulative incidence function for AIDS progression in the presence of competing risks, respectively. The patients with tuberculosis had much higher predicted cumulative incidence probability. Predicted cumulative incidence probability of AIDS progression was also higher for mother to child mode of HIV transmission. Moreover, transmission type and gender were two top most important variables for the competing event. Men and those patients with IDUS transmission mode had higher predicted risk compared to others.&#x0D; Conclusion: Considering nonlinear effects and interaction between variables, confection with tuberculosis was the most important variable in prediction of cumulative incidence probability of AIDS progression.
Punctuation-generation-inspired linguistic features for Mandarin prosody generation

This paper proposes two novel linguistic features extracted from text input for prosody generation in a Mandarin text-to-speech system. The first feature is the punctuation confidence (PC), which measures the likelihood that a major punctuation mark (MPM) can be inserted at a word boundary. The second feature is the quotation confidence (QC), which measures the likelihood that a word string is quoted as a meaningful or emphasized unit. The proposed PC and QC features are influenced by the properties of automatic Chinese punctuation generation and linguistic characteristic of the Chinese punctuation system. Because MPMs are highly correlated with prosodic–acoustic features and quoted word strings serve crucial roles in human language understanding, the two features could potentially provide useful information for prosody generation. This idea was realized by employing conditional random-field-based models for predicting MPMs, quoted word string locations, and their associated confidences—that is, PC and QC—for each word boundary. The predicted punctuations and their confidences were then combined with traditional linguistic features to predict prosodic–acoustic features for performing speech synthesis using multilayer perceptrons. Both objective and subjective tests demonstrated that the prosody generated with the proposed linguistic features was superior to that generated without the proposed features. Therefore, the proposed PC and QC are identified as promising features for Mandarin prosody generation.
Use of Bivariate Lifetime Distributions Assuming Continuous or Discrete Data Applied to Patients with Breast Cancer

Breast cancer is the second most common type of cancer in the world (World Cancer Report, 2014 a, b).The evolution of breast cancer treatment usually allows a longer life of patients as well in many cases a relapse of the disease.Usually medical researchers are interested to analyze data denoting the time until the occurrence of an event of interest such as the time of death by cancer in presence of right censored data and some covariates.In some situations, we could have two lifetimes associated to the same patient, as for example, the time free of the disease until recurrence and the total lifetime of the patient.In this case, it is important to assume a bivariate lifetime distribution which describes the possible dependence between the two observations.We consider as an application, different parametric bivariate lifetime distributions to analyze a breast cancer data set considering continuous or discrete data.Inferences of interest are obtained under a statistical Bayesian approach.We get the posterior summaries of interest using existing MCMC (Markov Chain Monte Carlo) methods.The main goal of the study, is to compare the bivariate continuous and discrete distributions that better describes the breast cancer lifetimes.
BNSP: an R Package for Fitting Bayesian Semiparametric Regression Models and Variable Selection

The R package BNSP provides a unified framework for semiparametric location-scale regression and stochastic search variable selection. The statistical methodology that the package is built upon utilizes basis function expansions to represent semiparametric covariate effects in the mean and variance functions, and spike-slab priors to perform selection and regularization of the estimated effects. In addition to the main function that performs posterior sampling, the package includes functions for assessing convergence of the sampler, summarizing model fits, visualizing covariate effects and obtaining predictions for new responses or their means given feature/covariate vectors.
Fast Near-Optimal Path Planning on State Lattices with Subgoal Graphs

Subgoal graphs can be constructed on top of graphs during a preprocessing phase to speed-up shortest path queries. They have an undominated query-time/memory trade-off in the Grid-Based Path Planning Competitions. While grids are useful for path planning, other kinds of graphs, such as state lattices, have to be used for motion planning. While state lattices are regular graphs like grids, subgoal graphs improve query times by a much smaller factor on state lattices than on grids. In this paper, we present a new version of subgoal graphs that forfeits its optimality guarantee for smaller query times. It guarantees completeness, and our experimental results on state lattices suggest that it can find paths that are close to optimal.
Using Quaternion Fourier Transform in Steganography Systems

steganography is the discipline of exchanging information messages in such way that no one, other than the intended recipient, suspects the existence of the message. The transmitted message can be in textual or multimedia form (audio, image or video) and can be hidden within cover media. Moreover, the hidden message can be in either plain or cipher form. In steganography, the majority of hiding techniques are implemented either in spatial domain or in frequency domain of the cover media. The current contribution introduces a new a steganography technique for hiding a textual message within a cover image. Both the message and the cover image is converted to quaternion form and then only the quaternion message is converted to the frequency domain using Quaternion Fast Fourier Discrete Transform (QFFDT) technique. Simple quaternion mathematics are used to combine the message (in quaternion frequency domain) within the cover image (in quaternion form). Conversely, the hidden message can be revealed at the receiver using simple quaternion mathematics in presence of the original cover image. The proposed method allows hiding a huge amount of data and it is much complicated against steganalysis compared to the traditional methods. The method is assessed using the known performance metrics and the obtained results show that it is robust and more secure against steganalysis attacks without affecting the consumed bandwidth of the communication channel.
Estimating the Number of Correct Matches Using Only Spatial Order

Correctly matching feature points in a pair of images is an important preprocessing step for many computer vision applications. In this paper we propose an efficient method for estimating the number of correct matches without explicitly computing them. To this end, we propose to analyze the set of matches using the spatial order of the features, as projected to the x-axis of the image. The set of features in each image is thus represented by a sequence, and analyzed using the Kendall and Spearman Footrule distance metrics between permutations. This result is interesting in its own right. Moreover, we demonstrate three useful applications of our method: (i) a new halting condition for RANSAC based epipolar geometry estimation methods, (ii) discarding spatially unrelated image pairs in the Structure-from-Motion pipeline, and (iii) computing the probability that a given match is correct based on the rank of the features within the sequences. Our experiments on a large number of synthetic and real data demonstrate the effectiveness of our method. For example, the running time of the image matching stage in the Structure-from-Motion pipeline may be reduced by about 90 percent while preserving about 85 percent of the image pairs with spatial overlap.
Control Method of Differential Power Steering for Port Locomotive Obstacle Avoidance under Emergency

Tian, J.; Liu, C. and Chen, N., 2018. Control method of differential power steering for port locomotive obstacle avoidance under emergency. In: Liu, Z.L. and Mi, C. (eds.), Advances in Sustainable Port and Ocean Engineering. Journal of Coastal Research, Special Issue No. 83, pp. 531–539. Coconut Creek (Florida), ISSN 0749-0208.At present, there is not much space in the port area and the differential power steering control method is often used in locomotive obstacle avoidance under emergency. The method does not take into account the effect of tire slip and actuator torque limitation on locomotive drive braking during the avoidance under emergency, which only relies on the differential control, that is limited in improving steering stability. Based on the establishment of the dynamic model of the controlled object as well as the nonlinear tire model, the constraints of the locomotive driven by the ground characteristics of the port area, the tire slip and the actuator torque are considered, and the design is based on the differential Power steering controller. According to the deviation of yaw rate and yaw rate of center of mass, the related control characteristic quantity is determined, and the result is the control target to improve the steering performance of the locomotive. The actual locomotive test results showed that the proposed controller design method reduces the brake deceleration of the port locomotive in the process of emergency braking, which achieves the best slip ratio effect, shortens the braking distance, improves the braking efficiencyof the locomotives and improves the port traffic safety.
Active learning with simultaneous subject and variable selections

Training data are essential for learning classification models. Therefore, if only a limited number of labeled subjects are available for use as training samples, whereas a considerable amount of unlabeled data already exists, then it is always desirable enlarging the training set by labeling more subjects in order to ameliorate classification models. When it is costly in time and capital to label unlabeled subjects, it is crucial to know how many labeled subjects are necessary for training a satisfactory classification model. Although, active learning methods can gradually recruit new unlabeled subjects and disclose their label information to enlarge the size of the training set, there is a lack of discussion about the size of training samples in the literature. Hence, when/how to appropriately stop an active learning procedure is studied in this paper. Since the sequential subject recruiting strategy is used in active learning procedures, it is natural to adopt the idea of sequential analysis to dynamically and adaptively determine the training sample size for learning. In this study, we propose a stopping criterion for a linear model-based active learning procedure, such that this learning process will asymptotically achieve its best possible empirical performance, in terms of the area under receiver the operating characteristic curve (ROC), when the procedure is stopped. Other statistical properties of the proposed procedure, including estimation consistency and variable selection, are also studied. The numerical results using both synthesized and a real example are reported.
Removing fences from sweep motion videos using global 3D reconstruction and fence-aware light field rendering

Diminishing the appearance of a fence in an image is a challenging research area due to the characteristics of fences (thinness, lack of texture, etc.) and the need for occluded background restoration. In this paper, we describe a fence removal method for an image sequence captured by a user making a sweep motion, in which occluded background is potentially observed. To make use of geometric and appearance information such as consecutive images, we use two well-known approaches: structure from motion and light field rendering. Results using real image sequences show that our method can stably segment fences and preserve background details for various fence and background combinations. A new video without the fence, with frame coherence, can be successfully provided.
Comprehensibility of Classification Trees–Survey Design

Comprehensibility is the decisive factor for application of classifiers in practice.However, most algorithms that learn comprehensible classifiers use classification model size as a metric that guides the search in the space of all possible classifiers instead of comprehensibility -which is ill-defined.Several surveys have shown that such simple complexity metrics do not correspond well to the comprehensibility of classification trees.This paper therefore suggests a classification tree comprehensibility survey in order to derive an exhaustive comprehensibility metrics better reflecting the human sense of classifier comprehensibility and obtain new insights about comprehensibility of classification trees.
EA Reader: Enhance Attentive Reader for Cloze-Style Question Answering via Multi-Space Context Fusion

Query-document semantic interactions are essential for the success of many cloze-style question answering models. Recently, researchers have proposed several attention-based methods to predict the answer by focusing on appropriate subparts of the context document. In this paper, we design a novel module to produce the query-aware context vector, named Multi-Space based Context Fusion (MSCF), with the following considerations: (1) interactions are applied across multiple latent semantic spaces; (2) attention is measured at bit level, not at token level. Moreover, we extend MSCF to the multi-hop architecture. This unified model is called Enhanced Attentive Reader (EA Reader). During the iterative inference process, the reader is equipped with a novel memory update rule and maintains the understanding of documents through read, update and write operations. We conduct extensive experiments on four real-world datasets. Our results demonstrate that EA Reader outperforms state-of-the-art models.
Analysis of Joint Multilingual Sentence Representations and Semantic K-Nearest Neighbor Graphs

Multilingual sentence and document representations are becoming increasingly important. We build on recent advances in multilingual sentence encoders, with a focus on efficiency and large-scale applicability. Specifically, we construct and investigate the k-nn graph over the joint space of 566 million news sentences in seven different languages. We show excellent multilingual retrieval quality on the UN corpus of 11.3M sentences, which extends to the zero-shot case where we have never seen a language. We provide a detailed analysis of both the multilingual sentence encoder for twenty-one European languages and the learned graph. Our sentence encoder is language agnostic and supports code switching.
Unsupervised Meta-Learning of Figure-Ground Segmentation via Imitating Visual Effects

This paper presents a “learning to learn” approach to figureground image segmentation. By exploring webly-abundant images of specific visual effects, our method can effectively learn the visual-effect internal representations in an unsupervised manner and uses this knowledge to differentiate the figure from the ground in an image. Specifically, we formulate the meta-learning process as a compositional image editing task that learns to imitate a certain visual effect and derive the corresponding internal representation. Such a generative process can help instantiate the underlying figure-ground notion and enables the system to accomplish the intended image segmentation. Whereas existing generative methods are mostly tailored to image synthesis or style transfer, our approach offers a flexible learning mechanism to model a general concept of figure-ground segmentation from unorganized images that have no explicit pixel-level annotations. We validate our approach via extensive experiments on six datasets to demonstrate that the proposed model can be end-to-end trained without ground-truth pixel labeling yet outperforms the existing methods of unsupervised segmentation tasks.
Knockoff filter‐based feature selection for discrimination of non‐small cell lung cancer in CT image

Lung cancer is one of the deadliest diseases worldwide and the classification of different types of lung cancers in computed tomography (CT) images is also one of the most significant issues in computer-aided diagnosis. It remains a tough task since various image features could be extracted from one single image while part of the features is irrelevant to the final diagnosis results. In this study, a knockoff filter-based approach is proposed to produce the optimal feature set and to minimise the irrelevancy of the output features for the classification of lung cancer in CT images. The proposed feature selection strategy not only can generate the optimal feature subset but also constrain the false discovery rate of the irrelevant features under a specified parameter setting. Ten-fold leave-one-out cross-validation and the area under the receiver operating characteristic curve are both adopted in the experiments to evaluate the performance of the proposed method. The areas under curve of is achieved when the support vector machine classifier is trained on the features determined by the proposed feature selection strategy. The experimental results demonstrate that the presented approach is potentially valuable for lung cancer diagnosis.
MOTIVASI MAHASISWA BERORGANISASI DI KAMPUS

The aim of this study is to describe the student’s motivation in participating in student organizations at Universitas Jenderal Achmad Yani. This study uses self-determination theory to explain the motivation of students to involve in student organizations. Self-determination theory explained that human motivations were important sources in the development of personality and self-regulation. The motivation involved the reasons and the purposes in performing an action. Motivation moves from extrinsic motivations to intrinsic motivations. The method of this study was quantitative descriptive research. Participant of this research were college students who participating in students organizations. The result showed that most of the students participated in student organizations had integration extrinsic motivation. Students involved in the student organizations because they interested and it gived them some benefits. Draw the benefits in students organizations become the majority reasons of the students in participating in the student organizations.