Reviewerly/exHarmony huggingface dataset:
Teaching English through Vlogging and In-class Presentation: A Preliminary Comparative Study

This case study aims to find out the differences between in-class presentation and vlogging by comparing the scores of the five presentation assignments by high school students in South Korea. The findings revealed that the vlogging group showed higher presentation grades as the study progressed in the categories of grammar, elocution and speed, eye contact, and non-verbal communication compared to their peers who did presentations in class. However, the confidence scores were higher for the in-class presentation group in the beginning but ended up the same for both groups in the last assignment. To find out the student’s perceptions about presenting in class and vlogging, a survey was administered. All the participants regardless of the groups they were in said that they feel more confident after the presentation assignments and thought their English speaking skills have improved after the oral presentations. The students in the vlogging group seemed to prefer using cell phones to record their presentations and the in-class presentation group seemed to like giving presentations to a live audience except one student who said she prefers vlogging. Therefore, this study suggests using both methods in an English class or giving options to students to choose the form of presentation but suggests that vlogging may have better results.
Application of Image Restoration in On-Line Detection of Optical Element Damage

Abstract With the continuous development of technology, Image processing methods based on computer vision began to shine in the field of image processing. There are many easily damaged optical equipment, traditional off-line detection technology requires analysis of components in optical devices after disassembly. Therefore, offline detection technology cannot guarantee the efficiency of the device, It also affects the cost and life of the equipment. Therefore, the research on the online detection technology of optical component damage has become an urgent problem to be solved. In this paper, an online damage detection method for optical elements based on image restoration is proposed. Then, the whole image and block image are processed by this method to verify the processing effect.
A Collaborative Traffic Scheduling Mechanism for Multi-homed Networks

It is popular for a campus network to connect more than one ISP link. To schedule the egress traffic in this multi-homed network, traditional scheduling schemes consider the network topology while ignoring the diversity of network services, which easily leads to link congestion and poor scheduling effect. Motivated by that, the paper proposes a collaborative traffic scheduling mechanism (CTSM) based on network traffic identification and classification. CTSM will analyze and identify the egress traffic of the multi-homed network, and then the specific application traffic is scheduled to the link that conforms to its traffic feature. Application traffics which are sensitive to time-delay will be scheduled to the links with low delay and packet loss rate, and traffics which require a lot of bandwidth will be scheduled to the links with low load. The experiments and evaluations show that CTSM makes multi-link load more balanced and the scheduling effect more reliable.
AR Mobile Application to Enhance the Birdwatching Experience

Guard@Lis application was developed to ease the birdwatching activity. The main purpose was to support the discovery and identification of bird species along a trail, encouraging the preservation and protection of nature. To create a new bird observation perspective since augmented reality features were developed to enhance the experience of birdwatching, ensuring the chance to watch even the most elusive birds. Adding animated 3D models of some birds allows the observation of these species that, for several reasons, may not be easy to encounter in their natural habitat when the user is making the trail. The solution is composed of a multiplatform app and a back-office. It is prepared to create routes with hotspots and improve the bird species list. This first version was designed with a route in the city of Leiria, having specific points along the way, defined for the observation of certain species. Guard@Lis application intends to encourage the didactic and educational facets, so the whole development process of this application was to create a guide that allows users to be able to identify and list the number of bird species found. At this stage, the app allows its users to trail the main historical and tourist points of Leiria and collect the bird species observed.
A Robot Agent that Learns Group Interaction Through a Team-Based Virtual Reality Game Using Affective Reward Reinforcement Learning

In the near future, robots are expected to be integrated into people’s lives, interacting with them. To develop better robotics and artificial intelligence, this research focuses on the concept of teamwork. A robot agent was implemented in a virtual reality(VR) game to play the sport roundnet, a team-based sport similar to table tennis and volleyball [2]. The agent is trained with reinforcement learning with EDA skin sensor data [6] of players. The system is evaluated using a questionnaire on the player’s feeling during the experiment and compared with agents not trained with affective data. The system is implemented in Unity3D’s ML-Agents Toolkit.
Improved MSER Pedestrian Detection Algorithm based on TOF Camera

Abstract To solve the problems existing in the traditional pedestrian detection methods, such as the complexity of pedestrians wearing clothes and the change of light leading to the decrease of detection accuracy, this paper proposes an MSER pedestrian detection algorithm combined with D-NP (Double Nine-Grid). The algorithm utilizes the characteristics of high pixels of TOF (Time Of Flight) cameras and the adaptability of MSER (Maximally Stable Extreme Regions). Firstly, the depth image is subjected to hole patching, and median filtering and morphological operations are used for preprocessing. Then the MSER algorithm combined with D-NP is used to segment the image to extract candidate head regions. Finally, We perform experiments on our dataset, and the detection recognition rate of this algorithm reaches 97.26%, which can effectively reduce the impact of light, and has a good detection performance for pedestrians carrying backpacks, hats and other complex scenes.
Single Image De-raining Based on a Novel Enhanced Attentive Generative Adversarial Network

Abstract With rapid development of deep learning in artificial intelligence and computer vision, generative adversarial network plays an important role in single image de-raining. Attentive generative adversarial network (AttGAN) has problems about complicated network structure and distortion of background color. Considering the relative complex background in the real image with raindrops, a new single image de-raining algorithm based on enhanced attentive generative adversarial network (EAttGAN) is proposed to retain the original background of the blurred image. In order to restore more complete background and accelerate network training, a generator enhanced by residual scaling and a Markovian discriminator are fused effectively in the network. Compared with AttGAN, experimental results indicate that EAttGAN can not only achieve higher sharpness of a single image, but also take less time in the training process.
Kulakov’s Method of Bilateral Remote Control of a Space Manipulation Robots

The article describes the approach to remote control of a space robot with force feedback developed by F. M. Kulakov. This method allows to minimize the effect of delays in signal transmission due to features in the control algorithm. Using of feedback on force is necessary for the interaction space robot with objects having holonomic constraints, which is typical for assembly operations. The article gives a mathematical description of the elements of the bilateral control system and also presents a special case of the motion of the robotic system.
Design of Trajectory Tracking Algorithm of Isolated Observation Point Automatic Distribution Vehicle based on Visual Servo

Abstract The medical isolation observation point for epidemic prevention and control is an important part of the epidemic prevention and control link. With the attention paid by all countries to the epidemic prevention and control, the design and construction of the medical isolation observation point for epidemic prevention and control have put forward higher requirements. This design uses a visual servo-based automatic delivery car for trajectory tracking and research on the tracking algorithm to realize the automatic delivery car for the regular delivery of medicines, food and other daily necessities for isolated personnel. The application of automatic trolleys in the epidemic prevention and control medical isolation observation point can effectively alleviate the pressure on staff, avoid infection, and improve the efficiency of epidemic prevention. Aiming at the complexity of its motion trajectory, a tracking algorithm of vision and information fusion is used to implement the trajectory tracking of the smart car. The trend of the target trajectory is determined by the least square method. The image information collected by the image binarization algorithm is added to a certain anti-jamming program. The PID algorithm is used to optimize the control of the steering gear and the drive motor, improve the operating efficiency and system stability, and meet the actual application.
Nonlinear Geometric Framework for Software Defect Prediction

Humans use the software in every walk of life thus it is essential to have the best quality software. Software defect prediction models assist in identifying defect prone modules with the help of historical data, which in turn improves software quality. Historical data consists of data related to modules /files/classes which are labeled as buggy or clean. As the number of buggy artifacts as less as compared to clean artifacts, the nature of historical data becomes imbalance. Due to this uneven distribution of the data, it difficult for classification algorithms to build highly effective SDP models. The objective of this study is to propose a new nonlinear geometric framework based on SMOTE and ensemble learning to improve the performance of SDP models. The study combines the traditional SMOTE algorithm and the novel ensemble Support Vector Machine (SVM) is used to develop the proposed framework called SMEnsemble. SMOTE algorithm handles the class imbalance problem by generating synthetic instances of the minority class. Ensemble learning generates multiple classification models to select the best performing SDP model. For experimentation, datasets from three different software repositories that contain both open source as well as proprietary projects are used in the study. The results show that SMEnsemble performs better than traditional methods for identifying the minority class i.e. buggy artifacts. Also, the proposed model performance is better than the latest state of Art SDP model- SMOTUNED. The proposed model is capable of handling imbalance classes when compared with traditional methods. Also, by carefully selecting the number of ensembles high performance can be achieved in less time.
Hairy Slices II: Depth Cues for Visualizing 3D Streamlines Through Cutting Planes

Abstract Visualizing 3D vector fields is challenging because of occlusion problems and the difficulty of providing depth cues that adequately support the perception of direction of flow lines in 3D space. One of the depth cues that has proven most valuable for the perception of other kinds of 3D data, notably 3D networks and 3D point clouds, is structure‐from‐motion (also called the Kinetic Depth Effect); another powerful depth cue is stereoscopic viewing. We carried out an experiment of the perception of direction for short streamlines passing through a cutting plane. The conditions included viewing with and without structure‐from‐motion and with and without stereoscopic depth. Conditions also include comparing streamtubes to lines. The results show that for this particular task, stereo provided an effective depth cue, but structure‐from‐motion did not. Ringed streamtubes and streamcones provided good 3D direction information, even without stereoscopic viewing. We conclude with guidelines for viewing slices through vector fields.
Derivation of Earth Geoid Model for Mosul City in Iraq

Abstract The quality and efficiency of geoid models have been developed rapidly to provide elevations instead of traditional surveying. The high cost and the long measurement time of surveying instrumentation cause an urgent need for deriving local Earth Geoid Model (EGM). Hundred control points are selected on the study area topographic map. The study area is located in Mosul city in Iraq. Multiple earth geoid models have been derived using different interpolation methods available in ArcGIS environment. The results of statistical analysis determine the best interpolation method. The percentage root mean square errors (RMSEs) for the adapted interpolation methods are 26.54 %, 28.43 %, 30.13 %, 34.79 %, 36.75 %, and 37.28 % in kriging, Inverse Distance Weighted (IDW), Trend, Natural Neighbor, Spline, and Topo To Raster, respectively. Kriging interpolation gives minimum standard deviation (2.226 m) and RMSE (2.654 m) so it is the preferable method. The results indicate that the geoid is departed from GPS Visualizer data by the mean value of minus (1.405 m) in the study area.
Natural Disaster Predictions of Whirlwinds in Cilacap Region of Central Java Using Adaptive Neighborhood Modified Backpropagation (ANMBP)

BNPB’s data shows that in the last 10 years, the province of Central Java was the most whirlwind region if compared with other provinces in Indonesia which amounted to 1281 incidents and whirlwinds most often in Cilacap which amounted to 202 incidents. The purpose of this research was to predict whirlwind in Cilacap region of Central Java using meteorological parameters as predictive parameters and adaptive neighborhood modified backpropagation (ANMBP) parameters as a prediction method. In the preprocessing process, the input data variable was reduced from seven input variables to five input variables by using the principal component analysis (PCA) method. The prediction process using ANMBP, and trial and error processes are carried out repeatedly by changing the hidden layer values, learning rate, and the amount of training and testing data. The best performance is obtained from the distribution of training and testing data at 60–40%, it produced an MSE value of 0.0004, and accuracy is 85.59% using two hidden layers with values of 110 and 95 and learning rate of 0.1.
Achieving secure and convenient WLAN sharing in personal

The authors analyse the security threats caused by personal wireless local area network (WLAN) sharing, propose schemes under two different conditions, and evaluate the performance of their schemes. WLAN is a widely used low-cost wireless networking technology. Most personal WLANs use the Wi-Fi-protected access II (WPA2)-personal to ensure robust security. Exposing the passphrase of WLAN is the only way to share it. Passphrase exposure can cause three threats, i.e. eavesdropping, evil twin attack, and resource abuse. This study addresses these threats by proposing two schemes under different device upgrade difficulties. For devices that are difficult to upgrade, their scheme only upgrades wireless routers. All WPA2-personal certified user devices can address these threats without any changes. For easy-upgrade and new devices, their scheme uses the attribute-based key exchange to address threats and provide ease of use, anonymity, and fine-grained access control. To solve the problem practically, they propose a mutual authentication method based on trust-on-first-use and a convenient attribute assignment method based on the existence of social information. The attribute authority already has numerous social information to provide services and cannot obtain more private information from participants in their scheme. The analysis shows that these proposed schemes are secure and practical.
Multi-resource Low-latency Cluster Scheduling without Execution Time Estimation

Cluster scheduling based on the prior estimation of job execution time is vulnerable to inaccurate estimates. To avoid performance degradation due to this misestimation, recent studies have proposed cluster schedulers that do not rely on prior estimation. However, they do not assume tasks with multitype heterogeneous computing resource demands, resulting in high job latency in real environments. Unfortunately, the optimal scheduling of such tasks is inherently difficult. In this paper, we present a cluster scheduler that heuristically handles multi-type heterogeneous resource demands without prior estimation. To reduce job latency, especially that of short jobs, our scheduler employs two techniques: (1) distributing tasks to nodes based on the similarity between resource demands and availability to simultaneously run as many tasks as possible, and (2) finding a suitable set of tasks for preemption in a node to minimize the number of task preemptions. Experimental evaluations using a real cluster and practical workloads confirm that our scheduler reduced the 90th percentile of slowdown rates by 6.4% and the 99th percentile by 29% compared to a naive extension of Kairos, an existing non-estimation-based scheduler. The experimental results also demonstrate that our scheduler is more effective when workloads have higher heterogeneity in resource demands.
Adaptive &amp; Learning-aware Orchestration of Content Delivery Services

Many media services undergo a varying workload, showing periodic usage patterns or unexpected traffic surges. As cloud and NFV services are increasingly softwarized, they enable a fully dynamic deployment and scaling behaviour. At the same time, there is an increasing need for fast and efficient mechanisms to allocate sufficient resources with the same elasticity, only when they are needed. This requires adequate performance models of the involved services, as well as awareness of those models in the involved orchestration machinery. In this paper we present how a scalable content delivery service can be deployed in a resource- and time-efficient manner, using adaptive machine learning models for performance profiling. We include orchestration mechanisms which are able to act upon the profiled knowledge in a dynamic manner. Using an offline profiled performance model of the service, we are able to optimize the online service orchestration, requiring fewer scaling iterations.
Integrated Analysis and Hypothesis Testing for Complex Spatio-Temporal Data

Analysis of unstructured, complex data is a challenging task that requires a combination of various data analysis techniques, including, among others, deep learning, statistical analysis, and interactive methods. A simple use of individual data analysis techniques addresses only a part of the overall data exploration and analysis challenge. The visual exploration process also requires exploration of what-if scenarios, a continuous and iterative process of generating and testing hypotheses. We describe a comprehensive approach to exploration of complex data that combines automatic and interactive data analysis and hypotheses testing techniques. The proposed approach is illustrated on a publicly available spatio-temporal data set, a collection of bird songs recorded over an extended period of time. Convolutional Neural Network is used to identify and classify bird species from the bird songs data. In addition, two new interactive views, integrated within a coordinated multiple views setup, are introduced: the what-if view and the spectrogram view. The proposed approach is used to develop a unified tool for exploration of bird songs data, called Bird Song Explorer.
Double Door Authentication for Mobile Devices using Personalised Lock (pins)

As the usage of mobile devices has been enormously increased, the role of authentication became very prominent. Usually explicit authentication techniques such as passwords are employed. However the devices would be infracted if the passwords are stolen without the user's knowledge. Therefore, there is an adverse search for finding out the ways for improvising the authentication techniques for mobile platforms and services. Double door authentication for mobile devices is the method that provides privacy with enhanced security feature. This paper explores the main objective of providing an authentication facility that supports the users to manage all the confidential information with the feature of personalized locking(2 pins, hand-in-hand simultaneously).In a realistic manner, the personalized locking feature can exclude the devices from third-party security installations and interferences such as sure-lock, vault-hide etc. Here the primary and secondary users are considered to be the key players. Especially, the primary user has an extensive role of sensitive applications selection and restricted access implementation.
Efficient EK-means: Extended K-means Clustering for Categorical data with High Processing Speed

Abstract The typical representative of the hard clustering algorithm, K-means, is one of the fastest processing algorithms with good scalability. However, it cannot deal with categorical attributes, which is one of the important indicators to measure the pros and cons. Due to the lack of processing capabilities on categorical attributes, k-means has a large limit on data processing capabilities. This paper proposes a clustering algorithm extends from K-means. This algorithm introduces the concept of a Pseudo-mean distance calculation formula and a counting-table so that categorical attributes can be processed while reducing the time cost as much as possible. Experimental results illustrate the proposed Pseudo-means can extend the processing range of k-means to category-type data, and the counting table also effectively reduces the time cost.
Fuzzy Interval Number K-Means Clustering for Region Division of Pork Market

This article proposes a new clustering algorithm named FINK-means. First, this article converts original data into a fuzzy interval number (FIN). Second, it proves the F that denotes the collection of FINs is a lattice. Finally, it introduces a novel metric distance on the lattice F. The contrast experiments about FINK-means, k-means, and FCM algorithm are carried out on two simulated datasets and four public datasets. The results show that the FINK-means algorithm has better clustering performance on three evaluation indexes including the purity, loss cost, and silhouette coefficient. FINK-means is applied to the task of region division of pork market in China based on the daily data of pork price for different provinces of China from August 9, 2017 to August 9, 2018. The results show that regions of pork market in China was divided into five categories, namely very low, low, medium, high, and very high. Every category has been discussed as well. At last, an additional experiment about region division in Canada was carried out to prove the efficiency of FINK-means further.
Unambiguous discrimination between linearly dependent symmetric states with multiple copies

We investigate the unambiguous discrimination (UD) among three linearly dependent (LD) symmetric states with [Formula: see text] copies. The optimal discrimination probability is derived. Our result shows that a set of linearly dependent symmetric states can be perfectly discriminated as the number of copies goes to infinity.