Reviewerly/exHarmony huggingface dataset:
What You Cannot See Can Still Kill You: On the Use of Latent Constructs

Nursing research relies heavily on the use of latent constructs to describe and understand phenomena that cannot be measured through direct observation. In statistical models, variables representing these constructs, often operationalized and represented as scores on self-report measures, stand in as symbolic representations of real forces having an impact on patients’ experiences of living and dying. In this sense, latent constructs represent real phenomena that cannot always be seen directly.
What Are You Willing to Sacrifice to Protect Your Privacy When Using a Location-Based Service?

Today, we use location-based services on a daily basis. They provide information related to the current location of the users and are extremely helpful. The next step of location-based services is to use predicted locations of users to create new content or to improve the quality of existing ones. Today location-based services must capture a large location history of the users in order to be able to build predictive mobility models of users and forecast their future locations. This is a clear privacy issue because these services are thus able to also obtain sensitive information related to users. In this paper, we propose a system that provides future locations of users to location-based services, protects the location privacy of the users with spatio-temporal conditions and ensures the utility of the information provided by the location-based services. The user is a key actor of the system and is involved in the protection process because she indicates these spatio-temporal conditions, which express the spatio-temporal utility she is willing to sacrifice in order to protect her privacy. We evaluate the two components of the system according to two perspectives: a prediction accuracy analysis and a utility/location privacy evaluation. The proposed system provides satisfactory prediction accuracy results that exceed 70%. The utility/privacy evaluation shows that our mechanism obtains the best results in terms of utility and location privacy compared to two other common location privacy preserving mechanisms. Hence, these evaluations confirm the relevance of our system.
Using adverse outcome pathways to identify exogenous risk factors for rare diseases

Due to the large traffic congestion in modern-day, Vehicular Ad Hoc Network (VANET) becomes an important approach to improve human safety and driving comfort. The large traffic congestion increases the number of transmitted messages, which in turn increases the computations and communications overheads in VANET. Hence, VANET requires not only efficient communications, but also lightweight security mechanisms to overcome security challenges. There are many schemes provide security to VANET by using cryptography techniques such as elliptic curve technique, session key technique, public key technique, and message authentication code technique. The schemes that use the message authentication code are called MAC-based authentication schemes. They considered as the best schemes in terms of computation and communication overheads when compared with other schemes that use other cryptography techniques, but they have some limitations. This paper proposes a novel efficient and lightweight authentication scheme (ANEL). The ANEL is a MAC-based authentication scheme that offers all the advantages of the MAC-based authentication schemes and overcomes all their limitations at the same time. In addition, the proposed scheme can achieve security objectives of VANET such as authentication, privacy-preservation, non-repudiation, etc. Moreover, our scheme provides an efficient biological password login, system key update, biological password update, and other security services. Furthermore, in this proposed scheme, the Trust Authority (TA) can disclose the source driver and vehicle of each malicious message. The security analysis shows that ANEL is the first scheme that provides countermeasures on the disclosure of system key, and it can protect VANET from various well-known attacks. The comparison results of our scheme with existing MAC-based authentication schemes in terms of computation overhead, communication overhead, and security services show that the proposed scheme is more efficient and provides a high level of security. The simulation results with ONE simulator demonstrate that our scheme reduces the message authentication time and end-to-end delay when compared with previous security schemes discussed in the literature.
Analysis of Curricula of Korean Environmental and Similar Named Engineering Programs

In this study, it was investigated that the current status of universities offering environmental field studies in Korea and the curricula were analyzed among 15 universities chosen from the above environmental engineering majors. The implications and improvements of the curricula were discussed based on the proposed curriculum derived from the needs of industry in the environmental field. As of December 2018, a total of 71 universities have offered the environment-related majors nationwide. The results of curricula analysis showed that the courses related in wastewater, air pollution were excessively focused among major courses of study. This seems to be due to the placement of subjects which were too much fragmented in contents and/or which were beyond undergraduate level. Therefore, through the sharing of course information among faculty members and continuous effort to improve courses, the curriculum should be structured more densely than quantitative expansion of courses. It is necessary to increase the courses that include various environmental topics that can be used in practice. In addition, the academic institutes need to play a role to develop the standardized curriculum and contents considering the needs of the industry and the prospect of the environmental field. Key words: Environmental engineering, Curriculum analysis, Industry perspective evaluation
Programmer Selection Using Modified Fuzzy Mamdani Method

Selection of candidate of the programmer is a complex and tiring process. Software development manager must work hard to guarantee that only qualified candidates will be selected. This study the parameters needed by the programmer are proper and adequate knowledge, skills, attitudes, and productivity. Knowledge, skills, attitudes, and productivity are the four competencies that every programmer must-have. The four components above are very important in developing an IT company. This study proposes a classification model of programmer selection based on certain criteria, parameters, and attributes. This study modifies the Fuzzy Mamdani Method as the approach for determining the feasibility of the programmer. The proposed model has satisfied result of percent of accuracy with 75.57% level. The result indicates that the proposed model has produced a sufficient solution to be used in the real situation for selecting the feasible programmer.&#x0D;
Mendeley: red social, descubrimiento de información y gestión de referencias. Guía para estudiantes de máster y doctorado

Mendeley is an academic information manager. This guide for PhD students shows the main features of the Mendeley web portal version. The guide focuses on the functions of academic social web and information discovery, but also includes aspects of bibliographic reference management. Finally, basic aspects of using the desktop application are also presented.
Sistem Informasi Kegiatan Briefing Akademik Study Kasus Universitas Bina Darma Palembang

ABSTRACTBina Darma University is one of the private universities in Palembang that has various technology facilities, from the University website, SISFO Academic, Student E-Learning, and others. At Bina Darma University has a variety of activity schedules starting from the teaching and learning schedule, off-campus activity schedules to the evaluation schedule, commonly referred to as briefing activities, for the briefing schedule, Bina Darma University is usually done every Monday morning starting from 8 to finished , which is attended by all lecturers, employees and the entire academic community. Even though the activities have been scheduled but there are still some lecturers, employees or academics who cannot attend the evaluation due to certain reasons, which results in the failure of what is discussed in the briefing, in addition to evaluation activities that are only a few hours, resulting in lack of flexibility in conducting joint discussions about what was discussed in the briefing activities. Therefore, so that all can know what is discussed in the briefing and can participate in the study, a knowledge system in the form of a Knowledge Management System is needed as a means for lecturers, employees and other academics to acquire new knowledge, and conduct study.
Generating Diverse Story Continuations with Controllable Semantics

We propose a simple and effective modeling framework for controlled generation of multiple, diverse outputs. We focus on the setting of generating the next sentence of a story given its context. As controllable dimensions, we consider several sentence attributes, including sentiment, length, predicates, frames, and automatically-induced clusters. Our empirical results demonstrate: (1) our framework is accurate in terms of generating outputs that match the target control values; (2) our model yields increased maximum metric scores compared to standard n-best list generation via beam search; (3) controlling generation with semantic frames leads to a stronger combination of diversity and quality than other control variables as measured by automatic metrics. We also conduct a human evaluation to assess the utility of providing multiple suggestions for creative writing, demonstrating promising results for the potential of controllable, diverse generation in a collaborative writing system.
ANALYSING THE INFLUENCE OF DEMOGRAPHICS ON DEPOSITOR BEHAVIOUR

Banks are exposed to operational risk events on a daily basis and constitute a large portion of a bank's risk exposure. The most important role of a bank is to establish who their key stakeholders are and to prioritise responsibilities according to these stakeholder characteristics, needs, perceptions and behaviour. This paper made use of hypothetical operational risk events within a bank in order to test how likely depositors will be to withdraw funds from their bank accounts. A non-parametric Spearman correlation and analysis of variation was used to test the relationship between demographical factors and depositors' likelihood to withdraw funds. Concerning the level of education, a negative relationship exists between the level of education and depositors' likelihood to withdraw funds. A negative relationship also exists between the income level and depositors' likelihood to withdraw funds.
FORECASTING SAHAM SYARIAH DENGAN MENGGUNAKAN LSTM

Islamic stocks as one of the many stocks listed on the JCI are a barometer of the Islamic market in Indonesia. One approach in predicting stock prices is by using machine learning. The purpose of this study is to make a model that is used to predict JII shares using the LSTM approach. The data used amounted to 1402 records related to the Jakarta Islamic Index (JII) stock from March 4, 2014 - January 2, 2019. Model making uses 3 Epochs (1, 10 and 20). The results showed the best model was to use 20 Epochs. The increase in Epoch affects the value of MSE and RMSE which are getting smaller. For Epoch 20, the values of MSE and RMSE are ~ 0.00019 and ~ 0.014, respectively.
Taxonomy and Ontology Management Tools: A General Explanation

The World Wide Web is the result of a radical new way of thinking about sharing information. This idea seems familiar now, as the web itself has become pervasive. But this radical new way of thinking has even more profound ramifications when it is applied to a web of data like the semantic web. These ramifications have driven many of the design decisions for the semantic web standards and have a strong influence on the craft of producing quality semantic web applications. Until several years ago, the semantic web was primarily in a research phase. The new implementations of it were mainly to demonstrate the potential of the idea. While much of the activity related to semantic technology still takes place within the academic community, there are now real-world examples of the technology to use as a model. This paper has selected the matured level open-source tools for management of taxonomy and ontology in semantic web environment. Apart from this it also presents some important snapshot available in an online environment for designing and developing the taxonomy and ontology. This is very helpful to the users in using the concept of science and technology in cloud computing and online and offline environment.
The contour image style-transfer-based convolutional neural network

The aim of style transfer is giving the style from one picture to another. The application of neural network in image processing separates the high level features and low level features of the image in the process of style transfer, and derives a variety of methods and optimization for style processing. The style transfer generates new images by separating and recombining the content and style of original images. In this process, various factors such as color and illumination will affect the result. The traditional algorithm only focuses on continuous pixels and the whole image, this paper will extend the process object to the contour of the image, and improves the detail processing from the existing style transfer examples. From the contour of images, the target image retains the contour feature of style image and the content of original image, in other word, gives the contour style of style image to original image. Finally, the style transfer effect based on the original image contour is obtained with some defects. The work can be easily extended to the aspects of video and 3D images.
Generic Construction of Bounded-Collusion IBE via Table-Based ID-to-Key Map

In this paper, we give a new generic construction of bounded-collusion identity-based encryption (BC-IBE) scheme from public key encryption (PKE) scheme. Our construction significantly reduces the number of public parameters to O(t), where t is a collusion parameter. Especially, we present the first construction in which the number of public parameters is independent from the total number of identities in the system. To achieve this, we propose a novel table-based ID-to-key map, and a method of deriving key pair from two (public and secret) parameter tables by using a cryptographic hash function. We provide a security proof of our construction in random oracle model.
Exploring 3 R's of Long-term Tracking: Re-detection, Recovery and Reliability

Recent works have proposed several long term tracking benchmarks and highlight the importance of moving towards long-duration tracking to bridge the gap with application requirements. The current evaluation methodologies, however, do not focus on several aspects that are crucial in a long term perspective like Re-detection, Recovery, and Reliability. In this paper, we propose novel evaluation strategies for a more in-depth analysis of trackers from a long-term perspective. More specifically, (a) we test re-detection capability of the trackers in the wild by simulating virtual cuts, (b) we investigate the role of chance in the recovery of tracker after failure and (c) we propose a novel metric allowing visual inference on the ability of a tracker to track contiguously (without any failure) at a given accuracy. We present several original insights derived from an extensive set of quantitative and qualitative experiments.
Adaptive updating correlation filter based on multi features

Traditional discriminant correlation filters fuse hand-crafted features and deep features fusion for tracking, which will cause features redundancy too much and lead to model overfitting when parameters are updated. Moreover, it is difficult to deal with various complex problems in video by using t he same label map for different features. In addition, the model updating strategy of the traditional method is relatively single, the model is updated by one frame or several frames. Different from the traditional methods of response map fusion, this paper proposes a multi-layer features correlation filter algorithm to estimate the target model from multiple perspectives. The corresponding label maps are used for different features, and an adaptive model updating strategy is proposed. The proposed tracker achieving leading performance in OTB2013, OTB2015 and VOT2016 datasets.
Simulation of Boolean Query Implementation Strategy in Lucene

Boolean query is an important method to achieve accurate data retrieval. The deeply study of Boolean queryâ€™s implementation strategy is conducive to flexible application of Boolean query, optimization of query expression and improvement of execution efficiency. Starting with the implementation strategy of Boolean query in Lucene framework, this paper studies the representation, execution sequence diagram, logical operation rules and sub-query merging algorithm. According to the implementation strategy, the simplification rules of complex Boolean query with depth of 2 are proposed, and their correctness is proved theoretically. Finally, the validity of these laws is verified by experimental simulation.
BLCU-NLP at COIN-Shared Task1: Stagewise Fine-tuning BERT for Commonsense Inference in Everyday Narrations

This paper describes our system for COIN Shared Task 1: Commonsense Inference in Everyday Narrations.To inject more external knowledge to better reason over the narrative passage, question and answer, the system adopts a stagewise fine-tuning method based on pre-trained BERT model.More specifically, the first stage is to fine-tune on additional machine reading comprehension dataset to learn more commonsense knowledge.The second stage is to fine-tune on target-task (MCScript2.0) with MCScript (2018) dataset assisted.Experimental results show that our system achieves significant improvements over the baseline systems with 84.2% accuracy on the official test dataset.
Enhancing Nutrition Learning Using Interactive Tools

Nowadays, obesity is a serious health problem worldwide. Its occurrence has exponentially increased in both children and adults, especially in developing countries. Since overweight and obese children are more likely to be overweight or obese adults, it is important to control and prevent childhood obesity. Moreover, overweight and obesity can put children at higher risk for various health problems. Recently, several information and communication technology tools have been designed to promote good health habits, physical activity, nutrition education, and weight loss. Most of these tools, however, target at an adult audience and fail to capture the attention of children. To address this gap, we present two child-friendly prototypes as nutrition education tools for preventing obesity. The prototypes rely on computational techniques such as augmented reality and serious games to provide children a more user-friendly experience. We also present the designs and evaluation of the prototypes and provide key guidelines for developers.
Finding Waldo: Two Routes for Processing Visual Search in Complex Scenic Images

This paper reviews the cognitive mechanisms underlying visual search in complex visual images.Visual search is a type of task that we are constantly engaged in our everyday lives.Here, the current paper introduces the current literature that suggests that there are two different processing routes that support visual search in human observers: Bottom-up and Top-down processing.Bottom-up processing allows the visual system to process the sensory information from the visual image as it is, whereas top-down processing guides the visual system to efficiently allocate attention to a part of the image to facilitate the search process.The two processing routes via bottom-up and topdown processing work in concert during complex visual search, guiding our search behavior to be more efficient and adaptive.In closing remark, this review also briefly discusses how the current knowledge about the roles of bottom-up and top-down processing in visual search can contribute to development of computer vision and artificial intelligent.
DINE: A Framework for Deep Incomplete Network Embedding

Network representation learning (NRL) plays a vital role in a variety of tasks such as node classification and link prediction. It aims to learn low-dimensional vector representations for nodes based on network structures or node attributes. While embedding techniques on complete networks have been intensively studied, in real-world applications, it is still a challenging task to collect complete networks. To bridge the gap, in this paper, we propose a Deep Incomplete Network Embedding method, namely DINE. Specifically, we first complete the missing part including both nodes and edges in a partially observable network by using the expectation-maximization framework. To improve the embedding performance, we consider both network structures and node attributes to learn node representations. Empirically, we evaluate DINE over three networks on multi-label classification and link prediction tasks. The results demonstrate the superiority of our proposed approach compared against state-of-the-art baselines.
Lecturer’s beliefs and practice in integrating digital literacy into lesson

This is case study at University level, Padang Indonesia, aimed to examining lecturer's beliefs and practice in integrating digital literacy into lesson.The data taken base on lecturers' interviewed following observations in class to explore lecturer's practice in using digital literacy.The participants of this study were four lecturers who teach English reading and writing.The data were analyzed qualitatively by explaining lecturers' beliefs of integrating digital literacy and their practice.As the result, the lecturers who teach writing and reading at university have a strong belief that integrating digital literacy into the lesson is essencial to do.Unfortunately, in practices, the lecturers use digital literacy limitedly on e-learning that must be used in the university as the requirement.It suggested that the lecturers should balance the reflection of their beliefs and practice in integrating digital literacy into lesson.
An Advanced Least Squares Twin Multi-class Classification Support Vector Machine for Few-Shot Classification

In classification tasks, deep learning methods yield high performance. However, owing to lack of enough annotated data, deep learning methods often underperformed. Therefore, we propose an advance version of least squares twin multi-class classification support vector machine (ALST-KSVC) which leads to low computational complexity and comparable accuracy based on LST-KSVC for few-shot classification. In ALST-KSVC, we modified optimization problems to construct a new “1-versus-1-versus-1” structure, proposed a new decision function, and constructed smaller number of classifiers than our baseline LST-KSVC. We empirically demonstrate that the proposed method has better classification accuracy than LST-KSVC. Especially, ALST-KSVC achieves the state-of-the-art performance on MNIST, USPS, Amazon, Caltech image datasets and Iris, Teaching evaluation, Balance, Wine, Transfusion UCI datasets.
IGMPv3/MLDv2 Message Extension

IGMP and MLD protocols are extensible, but no extensions have beendefined so far. This document provides a well-defined way of extendingIGMP and MLD, using a list of TLVs (Type, Length and Value).