Reviewerly/exHarmony huggingface dataset:
TOP-DOWN AND BOTTOM-UP STRATEGY USE AMONG GOOD AND POOR READERS IN EFL READING COMPREHENSION

This study revealed the readers’ use of top-down and bottom-up strategies in EFL learning context in Taiwan. The participants, 111 undergraduates EFL learners, were classified into good and poor readers. Quantitative and qualitative data were collected through a questionnaire and interviews. The results showed that almost no difference was confirmed between good and poor readers in bottom-up and total strategy use, whereas it was found that good readers tended to use more top-down strategies than poor readers. It is suggested that both groups of readers use bottom-up strategies to a similar degree; however, the use of top-down strategies has helped good readers advance their level of reading comprehension. Article visualizations:
Domain Adaptation for Pedestrian DCNN Detector toward a Specific Scene and an Embedded Platform

Nowadays, the analysis and the understanding of traffic scenes become a topic of great interest in several computer vision applications. Despite the presence of robust detection methods for multi-categories of objects, the performance of detectors will decrease when applied on a specific scene due to a number of constraints such as the different categories of objects, the recording time of the scene (rush hour, ordinary time), the type of traffic (simple, dense) and the type of transport infrastructure. In order to deal with this problematic, the main idea of the proposed work is to develop a domain adaptation technique to automatically adapt detectors based on deep convolutional neural network toward a specific scene and to calibrate the network parameters in order to deploy it on an embedded platform. Results are presented for the proposed adapted detector in term of global performance in mAP and execution time onto a NVIDIA Jetson TX2 board.
Body sensor networks for monitoring performances in sports: A brief overview and some new thoughts

This paper aims to review on body sensor networks (BSNs) for sports from performance monitoring point of view with some new thoughts. The focus of the paper is to show that wearable sensor is more efficient than cameras in measuring sport performance and thereby video data and video based systems can be replaced by wearable sensors. Here, the current state-of-the art in BSNs are mainly introduced relating to sports performance instead of physical activity and health/safety related issues for sports and to the best of our knowledge, this has not been done yet for different types of sports rather than a particular sport. Although the progress in BSN for sports performance is in early stage, the ultimate goal is to develop a complete training/match analysis tool using wearable sensors and various analyses techniques to monitor as well as improve performances in sports.
Cooperated Routing Problem for Ground Vehicle and Unmanned Aerial Vehicle: The Application on Intelligence, Surveillance and Reconnaissance Missions

In this paper, we present a novel Two-Echelon Ground Vehicle and Its Mounted Unmanned Aerial Vehicle Cooperated Routing Problem (2E-GUCRP). The 2E-GUCRP arises in the field of Unmanned Aerial Vehicle (UAV) Routing Problem such as those encountered in the context of city logistics. In a typical cooperated system, the UAV is launched from the Ground Vehicle (GV) and automatically flies to the designated target. Meanwhile, acting as a mobile base station, the GV can charge or change the UAV&amp;rsquo;s battery on the designated landing points to enable the UAV to continue its mission. The objective is to design efficient GV and UAV routes to minimize the total mission time while meeting the operational constraints. A Mixed Integer Programming (MIP) model, which could be solved by commercial software, is constructed to describe this problem. In order to quickly solve the medium-scale problems, two existing heuristics to solve 2E-VRP are improved. The computational experiments are set up to compare our model with the 2E-VRP. The results indicate that the 2E-GUCRP obtains a better efficiency. Further discussion of the practical instance points out that the increase in efficiency is related to the speed relationship between the GV and the UAV.
A cascading approach to training discovery

Purpose The purpose of this paper is to illustrate why libraries should develop instructional plans to further integrate Web-scale discovery services into the academy, as well as propose a three-fold delivery plan to achieve this goal. Design/methodology/approach This paper documents a strategy to integrate Web-scale discovery services into library training and instruction for multiple audiences. The strategy is informed by past analysis of discovery service search queries. Findings It presents a three-part training plan that can be applied to multiple audiences, universities/colleges and even discovery service platforms. Practical implications The strategies and practices detailed in this paper are easily adaptable to other institutions that currently subscribe to Web-scale discovery service products. Originality/value This paper introduces an innovative approach toward transforming Web-scale discovery instruction across the academy, based on search query analysis.
A Secure Communication Scheme for Corporate and Defense Community

Security is one of the major concerns of modern communication systems. Users demand a secure communication environment that provides privacy to the people while they are sharing messages to anyone. Privacy is a prime concern nowadays. This paper aims to provide an optimal platform for communication between the sender and the receiver. This prototype designed in the paper will provide a better anonymous path for routing messages. It will ensure ones full privacy while he or she is using this system for communication. As the proposed system provides a secure communication environment, it is supposed to be useful for the secret communication inside different governmental and non-governmental organizations. The law enforcements can use this system for any of their operations as it will encrypt and decrypt the message to give a secure platform for communication.
Motion planner of mobile robots for obstacles in occluded areas

Motion planning for mobile robots considering occluded obstacles is a navigation challenge in dynamic environments.If an obstacle suddenly appears from the occluded area, a robot might collide with the obstacle.This is the occlusion problem.Therefore, this paper proposes a novel motion planner, Velocity Obstacle for Occlusion, VOO.The VOO is based on Velocity Obstacle, VO, which is effective for moving obstacles.In the proposed motion planner, information uncertainties about occluded obstacles, such as position, speed, and moving direction, are quantitatively addressed.Thus the robot based on the VOO is enabled to move in consideration of both observed and occluded obstacles.Through simulations and real robot experiments, we discuss the effectiveness of the VOO for the occlusion problem by comparing to the VO.
Recovering and Visualizing Deformation in 3D Aegean Sealings

Archaeological research into Aegean sealings and sigils reveals valuable insights into the Aegean socio-political organization and administration. An important question arising is the determination of authorship and origin of seals. The similarity of sealings is a key factor as it can indicate different seals with the same depiction or the same seal imprinted by different persons. Analyses of authorship and workmanship require the comparison of shared patterns and detection of differences between these artifacts. These are typically performed qualitatively by manually discovering and observing shared visual traits. In our work, we quantify and highlight visual differences, by exposing and directly matching shared features. Further, we visualize and measure the deformation of shape necessary to match sigils. The sealings used in our dataset are 3D structured light scans of plasticine and latex molds of originals. We compute four different feature descriptors on the projected surfaces and its curvature. Then, these features are matched with a rigid RANSAC estimation before a non-rigid thin-plate spline (TPS) matching is performed to fine-tune the deformation. We evaluate our approach by synthesizing artificial deformations on real world data and measuring the distance to the re-constructed deformation.
BotGraph: Web Bot Detection Based on Sitemap.

The web bots have been blamed for consuming large amount of Internet traffic and undermining the interest of the scraped sites for years. Traditional bot detection studies focus mainly on signature-based solution, but advanced bots usually forge their identities to bypass such detection. With increasing cloud migration, cloud providers provide new opportunities for an effective bot detection based on big data to solve this issue. In this paper, we present a behavior-based bot detection scheme called BotGraph that combines sitemap and convolutional neural network (CNN) to detect inner behavior of bots. Experimental results show that BotGraph achieves ~95% recall and precision on 35-day production data traces from different customers including the Bing search engine and several sites.
Boosting Transfer Learning with Survival Data from Heterogeneous Domains

Survival models derived from health care data are an important support to inform critical screening and therapeutic decisions. Most models however, do not generalize to populations outside the marginal and conditional distribution assumptions for which they were derived. This presents a significant barrier to the deployment of machine learning techniques into wider clinical practice as most medical studies are data scarce, especially for the analysis of time-to-event outcomes. In this work we propose a survival prediction model that is able to improve predictions on a small data domain of interest - such as a local hospital - by leveraging related data from other domains - such as data from other hospitals. We construct an ensemble of weak survival predictors which iteratively adapt the marginal distributions of the source and target data such that similar source patients contribute to the fit and ultimately improve predictions on target patients of interest. This represents the first boosting-based transfer learning algorithm in the survival analysis literature. We demonstrate the performance and utility of our algorithm on synthetic and real healthcare data collected at various locations.
Towards Aligning GDPR Compliance with Software Development: A Research Agenda

The General Data Protection Regulation (GDPR) caused several new legal requirements software systems in Europe have to comply to. Support for these requirements given by proprietary software systems is limited. Here, an integrative approach of combining software development with GDPR-specific demands is necessary. In this paper, we outline such an approach on the level of software source code. We illustrate how to annotate data in complex software systems and how to use such annotations for task like data visualization, data exchange standardization, and GDPR-specific consent and purpose management systems. Thereby, we outline a research agenda for subsequent efforts in aligning software development and GDPR requirements.
A Method for Obtaining a Type of Scientific Result From the Text of an Article Abstract to Improve the Quality of Recommender Systems

In this paper, the authors present a method to deriving type of scientific results from the texts of scientific articles and implementing these results into recommender systems to enhance their output. Type of scientific results and text data are divided into corresponding classes, which are based on hypothetical user needs. The classes indicate the type of a scientific result in article. The proposed approach involves the determination of meaningful collocations for scientific article. Topic modeling is applied to the received collocation sets from the text of an article abstract. The topics (classes) obtained show the relationship between a scientific article and the type of scientific result.
The Simultaneous Identification Capacity of the Classical--Quantum Multiple Access Channel with Stochastic Encoders for Transmission

In [1], it is shown that the simultaneous identification capacity region for the discrete, memoryless, classical-quantum multiple access channel is equal to the transmission capacity region for codes using a deterministic encoding scheme. Here, we use transmission codes with a stochastic encoding scheme and show that the analogous capacity theorem holds. Further, by using stochastic encoders, the proof of achievability is highly simplified.
Developing supplementary reading materials for grade 11 students at a multimedia study program

This research is aimed at developing a supplementary reading material to fulfill students' need in one of the multimedia study program at Mangelang, Indonesia, for learning reading. The design of this research is Research and Development. This research was oriented to the product development, and it was conducted in two main stages; (1) exploration stage and (2) product development stage. The exploration stage includes (1) literature review, (2) field study, and (3) need analysis. Meanwhile, the product development stage describes (1) the prototype development, (2) experts’ judgment, and (3) pilot. This research was conducted in a vocational secondary school involving an English teacher, two material experts, and students. The findings show that the existing textbook used to teach reading contains general English material and it is less specific to be used for the students at the program. Therefore, it implies that there should be supplementary reading materials for the teacher to develop reading to meet the needs of these students.
ANALISIS SISTEM INFORMASI AKUNTANSI PENGGAJIAN DAN PENGUPAHAN DALAM UPAYA PENGENDALIAN INTERNAL PADA PT. GEMILANG EMAS INDONESIA

Payroll and wages accounting information system are important in the enterprise systems that help in decision making and implementation of the payroll and wage employees. Internal control system is a system that protects wealth and enterprise accounting data. This study aims to determine how the payroll accounting information system and wage PT. Gemilang Emas Indonesia and whether the system supports the company's internal control efforts. Methods analysis used in this research is descriptive analysis. The results showed payroll accounting information system and wages in PT. Gemilang Emas Indonesia does not yet support the internal control efforts. Still a lack of segregation of duties in the organizational structure and practices of healthy companies.
Incremental Learning of Discrete Planning Domains from Continuous Perceptions

We propose a framework for learning discrete deterministic planning domains. In this framework, an agent learns the domain by observing the action effects through continuous features that describe the state of the environment after the execution of each action. Besides, the agent learns its perception function, i.e., a probabilistic mapping between state variables and sensor data represented as a vector of continuous random variables called perception variables. We define an algorithm that updates the planning domain and the perception function by (i) introducing new states, either by extending the possible values of state variables, or by weakening their constraints; (ii) adapts the perception function to fit the observed data (iii) adapts the transition function on the basis of the executed actions and the effects observed via the perception function. The framework is able to deal with exogenous events that happen in the environment.
On the linear complexity of new generalized cyclotomic binary sequences of period p <sup> n </sup> q <sup> m </sup>

Abstract Sequences with high linear complexity are of importance in different applications. These sequences can be derived from generalized cyclotomic classes. In this paper, we construct new families of binary sequences of period p n q m using new generalized cyclotomic classes, as well as study the linear complexity of these sequences. We obtain the estimate of the linear complexity of new sequences and show that they have high linear complexity for m + n &gt; 2.
Comma Analysis and Processing for Improving Translation Quality of Long Sentences in Rule-based English-Korean Machine Translation

Current English-Korean machine translation system cannot provide practical translation quality mainly due to the difficulties in long sentence parsing. Long sentences generally include commas, resulting in lots of different possible sentence structures. It is very difficult to accurately parse the long sentences that have commas. The roles of the commas in constructing sentences have to be identified and then the syntactic analysis should be performed according to the roles of the commas for accurate parsing of the long sentences. This paper presents the analysis results of the comma usages and the comma processing methods for each comma usage. And it also proposes the comma usage classification method using machine learning technique. In experiment, some improved translation results, by identifying comma usage and processing the commas, are also presented.
Circular Fringe Projection Method for 3D Profiling of High Dynamic Range Objects

Fringe projection profilometry is a widely used active optical method for 3D profiling of real-world objects. Linear fringes with sinusoidal intensity variations along the lateral direction are the most commonly used structured pattern in fringe projection profilometry. The structured pattern, when projected onto the object of interest gets deformed in terms of phase modulation by the object height profile. The deformed fringes are demodulated using methods like Fourier transform profilometry for obtaining the wrapped phase information, and the unwrapped phase provides the 3D profile of the object. One of the key challenges with the conventional linear fringe Fourier transform profilometry (LFFTP) is that the dynamic range of the object height that can be measured with them is very limited. In this paper we propose a novel circular fringe Fourier transform profilometry (CFFTP) method that uses circular fringes with sinusoidal intensity variations along the radial direction as the structured pattern. A new Fourier transform-based algorithm for circular fringes is also proposed for obtaining the height information from the deformed fringes. We demonstrate that, compared to the conventional LFFTP, the proposed CFFTP based structure assessment enables 3D profiling even at low carrier frequencies, and at relatively much higher dynamic ranges. The reasons for increased dynamic range with circular fringes stem from the non-uniform sampling and narrow band spectrum properties of CFFTP. Simulation results demonstrating the superiority of CFFTP over LFFTP are also presented.
An Exact Auxiliary Variable Gibbs Sampler for a Class of Diffusions

Stochastic differential equations (SDEs) or diffusions are continuous-valued continuous-time stochastic processes widely used in the applied and mathematical sciences. Simulating paths from these processes is usually an intractable problem, and typically involves time-discretization approximations. We propose an exact Markov chain Monte Carlo sampling algorithm that involves no such time-discretization error. Our sampler is applicable to the problem of prior simulation from an SDE, posterior simulation conditioned on noisy observations, as well as parameter inference given noisy observations. Our work recasts an existing rejection sampling algorithm for a class of diffusions as a latent variable model, and then derives an auxiliary variable Gibbs sampling algorithm that targets the associated joint distribution. At a high level, the resulting algorithm involves two steps: simulating a random grid of times from an inhomogeneous Poisson process, and updating the SDE trajectory conditioned on this grid. Our work allows the vast literature of Monte Carlo sampling algorithms from the Gaussian process literature to be brought to bear to applications involving diffusions. We study our method on synthetic and real datasets, where we demonstrate superior performance over competing methods.
Impact of network delays on Hyperledger Fabric

Blockchain has become one of the most attractive technologies for applications, with a large range of deployments such as production, economy, or banking. Under the hood, Blockchain technology is a type of distributed database that supports untrusted parties. In this paper we focus Hyperledger Fabric, the first blockchain in the market tailored for a private environment, allowing businesses to create a permissioned network. Hyperledger Fabric implements a PBFT consensus in order to maintain a non forking blockchain at the application level. We deployed this framework over an area network between France and Germany in order to evaluate its performance when potentially large network delays are observed. Overall we found that when network delay increases significantly (i.e. up to 3.5 seconds at network layer between two clouds), we observed that the blocks added to our blockchain had up to 134 seconds offset after 100 th block from one cloud to another. Thus by delaying block propagation, we demonstrated that Hyperledger Fabric does not provide sufficient consistency guaranties to be deployed in critical environments. Our work, is the fist to evidence the negative impact of network delays on a PBFT-based blockchain.
Application of R Software in Life Sciences

R programming a perfect choice to execute and analyze life science data, as there are vast developers working and coming up with new packages of R programming.R software is worth its popularity worldwide and it is going to scale further.R software allows a wide variety of statistical techniques like classical statistical tests, modeling (linear and nonlinear), classification, time series analysis, cluster analysis, as well as the graphical visualization of data.Besides, R software is highly extensible and an easy to learn language making this software an ideal choice for manipulating big data and life science data.
Modelling Graph Errors: Towards Robust Graph Signal Processing.

The first step for any graph signal processing (GSP) procedure is to learn the graph signal representation, i.e., to capture the dependence structure of the data into an adjacency matrix. Indeed, the adjacency matrix is typically not known a priori and has to be learned. However, it is learned with errors. A little attention has been paid to modelling such errors in the adjacency matrix, and studying their effects on GSP methods. However, modelling errors in the adjacency matrix will enable both to study the graph error effects in GSP and to develop robust GSP algorithms. In this paper, we therefore introduce practically justifiable graph error models. We also study, both analytically when possible and numerically, the graph error effect on the performance of GSP methods in different types of problems such as filtering of graph signals and independent component analysis of graph signals (graph decorrelation).
Implementing ‘PATRIOT’ As an Integrated Model of Instruction to Rebuild the Culture of Entrepreneurship in Higher Education

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