Reviewerly/exHarmony huggingface dataset:
Departure and Arrival Routes Optimization Near Large Airports

The bottleneck of today’s airspace is the Terminal Maneuvering Areas (TMA), where aircraft leave their routes to descend to an airport or take off and reach the en-route sector. To avoid congestion in these areas, an efficient design of departure and arrival routes is necessary. In this paper, a solution for designing departure and arrival routes is proposed, which takes into account the runway configuration, the surroundings of the airport and operational constraints such as limited slopes or turn angles. The routes consist of two parts: a horizontal path in a graph constructed by sampling the TMA around the runway, to which is associated a cone of altitudes. The set of all routes is optimized by the Simulated Annealing metaheuristic. In the process and at each iteration, each route is computed by defining adequately the cost of the arcs in the graph and then searching a path on it. The costs are chosen so as to avoid zigzag behaviors as much as possible. Two tests were performed, one on an instance taken from the literature and the other on an artificial problem designed specifically to test this approach. The obtained results are satisfying with regard to the current state of air operations management and constraints.
A Novel Frequent Pattern Mining Algorithm for Real-time Radar Data Stream

This paper attempts to improve the radar data quality and discover intrusion behaviors by mining frequent patterns of real-time data.For this purpose, the author improved the frequent pattern mining algorithm for static datasets, and proposed a maximum frequent pattern mining algorithm based on bitmap mapping.Next, a new frequent pattern mining algorithm was designed specifically for data stream with the storage structure of index pattern tree (IPT).The new algorithm was applied to process the real-time radar data stream.The application results demonstrate efficiency of our algorithm in time and space.
Segmentation and Measurement of Medical Image Quality Using K-means Clustering Algorithm

In this paper we have segmented an image by using a k-clustering algorithm, using the Gaussian Mixture Model cluster to generate the initial centroid.Many types of research have been done in the area of image segmentation using clustering especially medical images, these techniques help medical scientists in the diagnosis of diseases thereby to cure this diseases K-means clustering algorithm one of these techniques, it is an unsupervised algorithm and it is used to segment the interest area from the background.We used also partial contrast stretching to improve the quality of the original image.And the final segmented result is comparing with the k-means clustering algorithm and we can conclude that the proposed clustering algorithm has better segmentation.Finally, MSE and PSNR are checked and discovered that they have small and large value respective, which are the condition for good image segmentation quality.And comparison for MSE and PSNR are done for the proposed method and classical K-means algorithm and it is found that the proposed method has better performance result.
The Effect of Information Technology on Organizational Performance: The Mediating Role of Quality Management Capabilities

This study aims to investigate the effect of information technology on organizational performance in Jordanian pharmaceutical companies, and whether quality management capabilities mediate the effect of information technology on organizational performance.Adopting a quantitative research design, data was collected by means of a questionnaire-based survey of employees in Jordanian pharmaceutical companies. Based on 338 usable responses, the results revealed a significant effect of information technology and one of its dimensions (enterprise resource planning) on organizational performance. The Other dimension (electronic data interchange) did not contribute to quality management capabilities.Additionally, the results showed that information technology has a significant effect on quality management capabilities, and that quality management capabilities in turn affect organizational performance.The findings confirm that quality management capabilities partially mediate the effect of information technology on organizational performance. The study emphases that Jordanian pharmaceutical companies should improve and promote information technology methods and practices in order to improve their performance.Pharmaceutical companies should also consider improving their quality management capabilities, as this plays a significant role in enhancing and supporting the effect of information technology on organizational performance. This study states many recommendations for future researches, but the most important ones are to apply such a research on other sectors.
Pedagogical models of forming empathic and communicative competence of students

Н. С. Шкитина
IMPROVING STUDENTS’ WRITING SKILL ON DESCRIPTIVE TEXT BY ESTAFET STRATEGY OF THE ELEVENTH YEAR STUDENTS OF SMA 4 PALOPO

The objective of the research was to find out whether estafet strategy is effective to improve students’ writing skill on descriptive text of the eleventh year students’ of SMAN 4 Palopo. This research usedquasi experimental.The population of this research was the eleventh grade students of SMAN 4 Palopo. The number of population was 50 students. The sample were class XI PS 1 consisted of 25 students as experimental group and class XI PS 2 consisted of 25 students as control class.The sampling technique in this research was purposive sampling. The instrument of the research was writing test. The writers gave pretest and posttest to the students. The result showed that the students` mean score of posttest in experimental group was 90.44 and pretest was 65.96. The mean score of posttest was higher than the mean score of pretest (90.44&gt;65.96). While the mean score of posttest in control class was 75.76 and the mean score of pretest was 60.52. The mean score of posttest was higher than the mean score of pretest (75.76&gt;60.52). The result of statistical analysis the experimental group for level of significance 0.05 with degree of freedom (df) = 24; the probability value was smaller than α 0.00&lt;0.5 and the result of statistical analysis the control class in which the probability value was lower than α .0.00&gt;0.05. As a result, there was a significant difference in writing achievement between the students who are taught by using estafet strategy and those who are taught by non-using estafet strategy. Based on the result of this research, the writers concluded that estafet strategy upgrades the students’ writing.
Efficient Classification using Average Weighted Pattern Score with Attribute Rank based Feature Selection

Classification is found to be an important field of research for many applications such as medical diagnosis, credit risk and fraud analysis, customer segregation, and business modeling.The main intention of classification is to predict the class labels for the unlabeled test samples using a labelled training set accurately.Several classification algorithms exist to classify the test samples based on the trained samples.However, they are not suitable for many real world applications since even a small performance degradation of classification algorithms may lead to substantial loss and crucial implications.In this paper, a simple classification method using the average weighted pattern score with attribute rank based feature selection has been proposed.Feature selection is carried out by computing the attribute score based ranking and the classification is performed using average weighted pattern computation.Experiments have been performed with 40 standard datasets and the results are compared with other classifiers.The outcome of the analysis shows the good performance of the proposed method with higher classification accuracy.
PRIORITY-AWARE JOB SCHEDULING ALGORITHM IN CLOUD COMPUTING: A MULTI-CRITERIA APPROACH

Job scheduling is one of the most problematic theoretical issues in the area of cloud computing.The existing scheduling methods attempt to consider only a few criteria of scheduling without covering other sufficient criteria.Since, cloud computing faces a large scale resource for allocating to a large number of jobs, due to optimizing the users' requirements; therefore, a suitable cloud-based job scheduling method must satisfy a wide range of criteria.Besides, in cloud computing, the jobs come with different priorities.Thus, in the cloud environment, a suitable job scheduling algorithm should be able to combine several priorities.This paper proposes a new multi-criteria priority-aware job scheduling algorithm in cloud computing.Experimental results indicate that the proposed method is able to consider different criteria for scheduling.
From third-person to first-person cartographies with immersive virtual environments

Abstract. Recent cognitive research indicates that immersive virtual reality (VR) systems can increase the impact of visualization products through the formation of spatial presence, defined as a sense of “being there” in a virtual place. These findings make VR a highly interesting tool for cartography, but challenge the subject’s self-conception in different regards. The present article aims at highlighting the fundamental characteristics of geovisualization with immersive environments. We will approach the challenge of 1:1 representation with a typology borrowed from video game theory, where players can experience games from a first-person or third-person perspective. These two categories provide a useful framework to describe the basic difference between non-/low- and high-immersive geovisualization. In order to project the first- vs. third-person metaphor from a gaming to a cartographic mapping context, we will try to semiotically express the general process of map use in form of a triadic sequence, where the representation mediates between users and geospatial phenomena. Compared with common cartographic products, this mediation process is fundamentally different in VR systems, as immersive applications merge map user and map space. A set of future research questions and further considerations on first-person cartography will close the text. These considerations on first- vs. third-person visualization shall facilitate a conceptually better integration of IVE into current cartographic theory and practice.
GAN-based pencil drawing learning system for art education on large-scale image datasets with learning analytics

We design a generative adversarial network (GAN)-based pencil drawing learning system for art education on large image datasets to help students study how to draw pencil drawings for images and scenes. The system generates a pencil drawing result for a natural image based on GAN. The GAN network is trained on pencil drawing big datasets containing image pairs of natural images and their corresponding pencil drawings. Using the pencil drawing learning system, students can paint pencil drawings whenever they want and for whatever they like by uploading an image of the content they want to draw and getting a pencil drawing example of the uploaded image from the system. With the returned pencil drawing, students will see the pencil drawing effect of natural scenes clearly and realize how to draw the pencil drawing for the natural scene. Besides, with students using the pencil drawing learning system, it will be convenient for teachers assigning homework to students. Teachers can know the learning demands of students by evaluating the hand-in homework and update the content correspondingly. We have conducted two user studies for evaluating the practicality of the system, and the result of the two user studies demonstrated the applicability and practicality of the system.
Decentralized Ensemble Learning Based on Sample Exchange among Multiple Agents

Ensemble learning aims to train and combine multiple base learners in the hope of improving the overall performance. Existing ensemble algorithms rely on a centralized framework where each base learner has access to the entire training dataset. We combine the technology of blockchains which is mainly used for data validation with ensemble learning and propose a decentralized framework where data are distributed among multiple base learners, who exchange their respective data to improve the collective predictive abilities. We develop two realizations of this framework, based on static and dynamic decision trees, respectively. We evaluate our methods over 20 real-world datasets and compare them against other centralized ensemble methods. Experimental results show that the proposed method obtains improved accuracy scores through sample exchange and achieves competitive performance with state-of-the-art ensemble methods whereas the base learners store only a small fraction of the samples.
Exposing Agents as Web Services in JADE

The objective of this chapter is to revisit and explore how intelligent agents can be used in conjunction with modern Web technologies. We use JADE and BDI4JADE to expose cognitive agents using a BDI architecture as web services that can be integrated with different modern cloud-based services, such as Amazon AWS services and Google Home.
Adopting an Omnichannel Approach to Improve User Experience in Online Enrolment at an E-learning University

In this research article, we present the initial results of our case study on improving user experience (UX) in a fully online enrolment process at an e-learning university, the Universitat Oberta de Catalunya (UOC). Our main conclusion is that an omnichannel approach is necessary for improving prospective students' UX. To obtain our results, we made use of three different data sources, which allowed us to map a current and future customer journey. These sources were: (1) quantitative data on the main stages in the process at which prospective students drop out; (2) field diaries compiled by prospective students over the course of the enrolment process and a co-creation workshop with the same prospective students; and (3) the UOC's perspective, gathered from workshops and interviews with University staff responsible for the enrolment process. Comparing the current journey with the ideal journey, we will demonstrate that significant improvements can be made to prospective students' UX in online enrolment via an omnichannel approach. We discovered a need for improved channel coordination (e.g. information transfer between channels), greater availability and a wider range of channels (inbound and outbound, telephone vs. chatbot), personalization and adaptation of channel content according to the stage prospective students have reached in the process, and the ability to choose the channel that best meets their needs (chatbot for commonly asked questions vs. real people for more complex or specific queries). In view of our results, we would especially highlight technological and organizational changes as the next steps to take.
ICAuth: Implicit and Continuous Authentication When the Screen Is Awake

Implicit authentication has become increasingly popular over recent years due to the fact that it relieves users from explicit actions such as remembering and entering passwords. This paper puts forward ICAuth, a general and simple implicit authentication method for mobile devices, to authenticate the current user implicitly and continuously when the screen is awake. Distinct from existing implicit user authentication methods which only focus on behavioral characteristics and ignore contextual information, ICAuth is devised to understand different behaviors in various contexts. We investigate the correlations between the behavioral characteristics and contextual information via sensors on mobile devices and observe that user's behavioral characteristics are strongly related to the context. These sensors are divided into two kinds, including fine-grained sensors and coarse-grained sensors, where fine-grained sensor data represent behavioral features and the coarse-grained depict contextual information. ICAuth provides continuous authentication without the involvement of users. It promotes security via authenticating the current user continuously and improves the usability via eliminating the limitation of specific behaviors. We evaluate ICAuth comprehensively with a large dataset including 340842 samples collected from 142 subjects. Our approach achieves an accuracy of 96.85%, FNR of 2.95%, and FPR of 4.01%. Security analysis is also conducted to demonstrate that ICAuth is resilient against common smartphone authentication threats. Finally, we show the low power consumption and authentication latency with 2.2 seconds of ICAuth.
Readout of a weakly coupled qubit through the use of an auxiliary mode

The dispersive coupling between a qubit and a cavity mode is widely used for performing non-destructive readout of the qubit state. In this approach, it is typically required that the dispersive strong coupling regime is achieved. Here we show that the use of an auxiliary cavity mode reduces by orders of magnitude the required value of the dispersive coupling, for a given decay rate of the cavity mode. The analysis is performed within the input-output formalism, in terms of the photon scattering matrix elements and of the signal-to-noise ratio. We derive simple analytical expressions for the optimal parameters and recover the standard single-mode result as a limiting case. The present results can also be applied to the qubit readout based on longitudinal cavity-qubit interactions, and to any sensing scheme where the cavity frequency is used as a probe to estimate some physical parameter of interest.
A representation theorem for measurable relation algebras with cyclic groups

A relation algebra is measurable if the identity element is a sum of atoms, and the square $x;1;x$ of each subidentity atom $x$ is a sum of non-zero functional elements. These functional elements form a group $G_x$. We prove that a measurable relation algebra in which the groups $G_x$ are all finite and cyclic is completely representable. A structural description of these algebras is also given.
Towards Efficient, Work-Conserving, and Fair Bandwidth Guarantee in Cloud Datacenters

Bandwidth guarantee is a critical feature to enable performance predictability in cloud datacenters. This process is expected to achieve three requirements: work conservation, fairness, and simplicity. However, the distributed nature of datacenters raises significant challenges to attaining those requirements at the same time. In this paper, we propose an efficient approach that can satisfy the three requirements simultaneously. Our scheme takes advantage of multipath TCP (MPTCP) to generate explicit bandwidth guarantee (BG) traffic and work conservation (WC) traffic. We further prioritize the BG traffic over the WC traffic in the network fabric. Due to the priority setting, WC cannot harm bandwidth guarantees and thus is effectively supported. We show that the MPTCP fits this direction well but presents some new issues when the WC subflows own a low priority. We thus adapt the MPTCP to handle these issues through a customized scheduler (which strictly prioritizes BG subflow during packet scheduling) and adopting a large receive buffer. In addition, we enable tenants to share unused bandwidth fairly by managing the overall aggressiveness of the WC traffic. The proposed system can be easily implemented with commercial off-the-shelf servers and switches. We have implemented with the Linux kernel MPTCP for experiments. The extensive experiments in a small cluster (including one MapReduce experiment) and trace-driven simulations show that our scheme achieves the design goals effectively.
Teaching by Recycling; Transferring the Individually-Obtained Knowledge by a Single Student to All Other Students

In computer-based courses such as multimedia and digital design courses, being fully conversant with a computer application is almost unachievable within a single academic semester. Therefore, it is common for students to seek additional help and information from their lecturers either during office hours or through emails. It is also common for students to acquire skills from online tutorials, books, friends, or private tutors, which may cause the student to possess more advanced computer skills than others, including their lecturers. The knowledge that is individually acquired by the student from the teacher or from other sources is usually limited to that single learner. Therefore, this action research proposes a new teaching method; Teaching by Recycling (TBR) whereby the effort of teaching a single student is recycled into the practice of teaching all students together. The main aim is to transfer the individually-obtained knowledge from the teacher or from other sources to other students. This research explores the implications of TBR and investigates its effectiveness in augmenting the pedagogical efficiency and scope of teaching students individually through expanding this scope to include teaching all students collectively.
DSNet: Deep and Shallow Feature Learning for Efficient Visual Tracking

In recent years, Discriminative Correlation Filter (DCF) based tracking methods have achieved great success in visual tracking. However, the multi-resolution convolutional feature maps trained from other tasks like image classification, cannot be naturally used in the conventional DCF formulation. Furthermore, these high-dimensional feature maps significantly increase the tracking complexity and thus limit the tracking speed. In this paper, we present a deep and shallow feature learning network, namely DSNet, to learn the multi-level same-resolution compressed (MSC) features for efficient online tracking, in an end-to-end offline manner. Specifically, the proposed DSNet compresses multi-level convolutional features to uniform spatial resolution features. The learned MSC features effectively encode both appearance and semantic information of objects in the same-resolution feature maps, thus enabling an elegant combination of the MSC features with any DCF-based methods. Additionally, a channel reliability measurement (CRM) method is presented to further refine the learned MSC features. We demonstrate the effectiveness of the MSC features learned from the proposed DSNet on two DCF tracking frameworks: the basic DCF framework and the continuous convolution operator framework. Extensive experiments show that the learned MSC features have the appealing advantage of allowing the equipped DCF-based tracking methods to perform favorably against the state-of-the-art methods while running at high frame rates.
A Smart Home Environment to Support Risk Monitoring for the Elderly Living Independently

Elderly people prefer to live independently despite being vulnerable to age-related challenges. Constant monitoring is required in cases where the elderly are living alone. The home environment can be a dangerous environment for the elderly due to adverse events that can occur at any time. The potential risks for the elderly living independently can be categorised as injury in the home, home environmental risks, and inactivity due to unconsciousness.&#x0D; &#x0D; The aim of this paper is to discuss the development of a low-cost Smart Home Environment (SHE) that can support risk and safety monitoring for the elderly living independently. An unobtrusive and low cost SHE prototype that uses a Raspberry Pi 3 model B, a Microsoft Kinect Sensor and an Aeotec 4-in-1 Multisensor was designed and implemented. An experimental evaluation was conducted to determine the accuracy with which the prototype SHE detected abnormal events. The results show that the prototype has a mean accuracy, sensitivity and specificity of 94%, 96.92% and 88.93% respectively. The sensitivity shows that the chance of the prototype missing a risk situation is 3.08%, and the specificity shows that the chance of incorrectly classifying a non-risk situation is 11.07%.