Reviewerly/exHarmony huggingface dataset:
Scheduling Physical Machine Maintenance on Qualified Clouds: What if Migration is not Allowed?

Increasingly, cloud systems host IT services for critical domains such as public health and electric power. Such cloud systems need to be qualified and validated in accordance with compliance policies or government regulations. Configurations of such qualified cloud systems, including VM placement and software deployment, are limited to a small set of configurations which passed the qualification and validation process. So, on practical qualified cloud systems, on-demand VM migration or replication is not allowed. We address the problem of optimally scheduling physical machine maintenance on qualified cloud systems where VM placement cannot change dynamically. Particularly, we try to minimize the number of maintenance waves, and hence, minimize the qualified cloud systems' exposure to loss of high availability. Moreover, we study how the initial placement of service component replicas can help optimize the maintenance schedule while minimize the hardware cost, which is much higher on qualified cloud systems than regular ones. Evaluations in simulations and our real-world qualified cloud system demonstrate the effectiveness of our algorithms and justify our algorithm selection in the real qualified system. In particular, our approach eliminated impacts on service workload and availability during maintenance, while simultaneously largely reduced maintenance time.
Software-based Random Number Generators for Resource Constrained Devices

As random numbers are used in various fields such as cryptography and computer science, research on such topic is essential. In this regard, studies on efficient random number generators have been conducted, and they involve developments of both hardware and software aspect of the generator. This paper focuses on investigation of software-based random number generators, which can be applied to resource-limited devices including embedded devices. Studies of different algorithms and analysis of their strength will provide a strong basis of newly developed random generator in future studies.
ISSUES OF DESIGN OF INFORMAL LEARNING SPACES IN THE UNIVERSITY CULTURAL CENTERS

The article presents an overview and analysis of the state of research in the field of design and exploitation of modern educational spaces in universities around the world, namely areas with flexible planning, called informal learning space. According to the research conducted, the general characteristic and classification were developed and the basic principles of designing this space were revealed. The study identified several problems that arise during its exploitation, which should be taken into account in the designing process. There is described the experience of educational design of interuniversity cultural centres with the application of the developed theoretical bases of designing of informal educational space.
A novel method of combining pixel density map and SLIC for low-power display

OLED displays have different power consumption depending on R, G, and B pixel values. Therefore, if an image is segmented according to saliency and then divided according to color using a super pixel algorithm, low power can be achieved while maintaining human visual satisfaction However, if the image is segmented using saliency and then the segmented image is segmented using the super pixel algorithm, simple linear iterative clustering (SLIC), the pixels that do not have a color value because the segmented image has a different saliency level are also segmented by the super pixel algorithm. So the ability to divide color is poor at segmented image. This paper excludes pixels that do not have color values from the segmentation process when dividing an image including pixels that do not have color values by saliency criteria into super pixels. In addition, by allocating the first search position not evenly in the entire image, but focusing on the pixels with color values, the performance of the super pixel that divides the image according to color in the image divided based on saliency was improved. In terms of low power, the proposed method has similar power savings of about 38% to that of the FDM-oriented SLIC method, but the SSIM, which is structurally similar to the original image, has shown higher.
Development of a Virtual Environment Based Image Generation Tool for Neural Network Training

We present a computational tool to generate visual and descriptive data used as additional training images for neural networks involved in image recognition tasks. The work is inspired by the problem posed to acquire enough data, in order to train service robots, with the goal of improving the range of objects in the environment with which they can interact. The tool provides a framework that allows users to easily setup different environments with the visual information needed for the training, accordingly to their needs. The tool was developed with the Unity engine, and it was designed to be able to import external prefabs. These models are standardized and catalogued into lists, which are accessed to create more complex and diverse virtual environments. Another component of the tool adds an additional layer of complexity by creating randomized environments with different conditions (scale, position and orientation of objects, and environmental illumination). The performance of the created dataset was tested by training the information on the YOLO-V3 (You Only Look Once) architecture and testing on both artificial and real images.
Non-contact Digital Signage Allowing Users to Change Contents with Their Face Orientation

In today's society, digital signage is widely used in various places such as commercial facilities and train stations. Most digital signages use a liquid crystal display or LED panel. One of the advantages of digital signage is the ability to dynamically change the content of the advertisement. However, the method of change is not due to the viewer's intention, but rather to elapsed time to show, and it is merely a sequential display of static advertisements. In this study, we propose and implement a non-contact interactive digital signage system using a web camera that allows users to change the content according to their face orientation. In the implementation, we use OpenCV for showing the content and image processing, and the machine learning library, named dlib, is used for detecting user's face and identifying its orientation. The proposed system changes the layout and enlarges the content in which the user is interested. The user does not need to wear any device and to do any special action. Furthermore, the proposed system offers an intuitive interaction manner to the user. In this paper, some experimental designs are also described.
Evaluation of machine learning techniques to classify code comprehension based on developers' EEG data

Psychophysiological data such as brain waves have been used with machine learning techniques to classify the level of expertise and difficulty of software developers. However, little is known about the effectiveness of machine learning techniques (MLT) for classifying developers' code comprehension based on their brainwave data. This study evaluates the effectiveness of MLT's trained with EEG data to classify developers' code comprehension. Brainwave data collected from an EEG device while developers performed source code comprehension tasks was used to train the Neural Network, Support Vector Machine, Naïve Bayes and Random Forrest classifiers. The effectiveness of these techniques was analyzed using accuracy, precision and recall. The Neural Network classifier, trained with EEG data and Principal Component Analysis, obtained 84% accuracy to classify code comprehension. Thus, the application of MLT to classify developers' code comprehension based on EEG data is possible.
A Nesterov-type Acceleration with Adaptive Localized Cayley Parametrization for Optimization over the Stiefel Manifold

Despite certain singular-point issues, the Cayley parametrization (CP) has great potential to serve as a key to import many powerful strategies, developed originally for optimization over a vector space, into the task for optimization over the Stiefel manifold. In this paper, we newly present (i) a computationally efficient CP that can circumvent the singularpoint issues and (ii) a Nesterov type accelerated gradient method, based on the proposed CP, with its convergence analysis. To guarantee the convergence, we also evaluate a Lipschitz constant of the gradient of the cost function in the CP domain. Numerical experiments show excellent performance of the proposed accelerated algorithm compared with the standard algorithms, e.g., the Barzilai-Borwein method and L-BFGS method, combined with a vector transport for optimization over the Stiefel manifold as a special instance of the Riemannian manifold.
Hierarchical Reinforcement Learning Based on Continuous Subgoal Space

Multi-agents are designed at different temporal levels to decomposed a complex task into several simple ones in hierarchical reinforcement learning. At the beginning of neural network training, the great changes of the low-level policy would cause the unstable transitions of the high-level one. In this paper, we propose a hierarchical policy combined with PPO and DDPG to deal with the simultaneous training of multi-agents. To create an end-to-end policy, neural networks are employed to extract scene features in both low-level and high-level policies. In the meanwhile, a novel internal reward function is designed to enhance the goal achieving ability of low-level policy. A lightweight and fast gridworld Gym environment, MiniGrid, is used to test its validity. We found that the hierarchical policy is able to explore and plan without dense rewards. This attribute has a considerable influence on the study of robot navigation, especially in large and complex environment.
Design of a Strategy for Planning of Trajectories With Avoidance of Obstacles of a Quadrotor Drone Used in the Characterization of Geological

This article focuses on the development of an algorithm for a Quadrotor-type Drone that allows it to recognize the environment and move around in it by avoiding obstacles. This article covers the simulation phase that takes place in the MATLAB software, where algorithms for planting trajectories in known static environments were created in two stages: an artificial force field (APF) model is created for each element in the environment using sigmoid functions, the trajectories are subsequently calculated using gradient descent based strategies. Two strategies were developed, the first based on mobile points that interconnect the drone with the goal, where later each point moves towards areas free from the influence of obstacles, following the potential field, which makes paths free of obstacles. The second is based on the use of the concept of safe zones, which is used as a criterion to update the position of the points.
Feature reduction using fuzzy C-means clustering and Firefly algorithm

Feature selection refers to the elimination of many less informative features. In the proposed method, the Firefly Metaheuristic Algorithm (FMA) selects the features through other dataset features at each stage. Features data are clustered using C-means fuzzy clustering to determine clustering accuracy amounts such as Root-Mean-Square Error (RMSE) to specify how useful these features are and how much these selected features have been able to make classify correctly using clustering based on the dataset as well. Regarding this, the target class is predicted according to the selected features, where the results show the optimal performance of the proposed method. Because of using the combination of FMA and FCM clustering, the optimal centers of each cluster are found quickly, the selected feature sets known as the target class representative have the least error value, and the relationship between features are considered as well by completing the iteration of the algorithm.
Study on Agent Architecture for Context-Aware Services

With the evolution of the web services paradigm, there is a need for an effective mechanism to offer a better service experience to users in a dynamic adaptation process. In particular, the context knowledge can be adapted to dynamic adaptation taking into account the context of the user and the context of the device. Creating context-sensitive services today is a complex task because there is no adequate infrastructure support in a ubiquitous IT environment. This paper we proposed a service-oriented contextual architecture for creating fast contextual services. The objective of this study is to develop an intelligent agent platform that applies the developed intelligent agent platform to the field of medical applications. The results describe the general process of a situational awareness system and the design considerations for the situational awareness system. In addition, it summarizes the characteristics of the existing situational awareness system and presents a trend analysis of the system. In conclusion, we propose future work for a better situational awareness system.
Leveraging Linguistically-aware Object Relations and NASNet for Image Captioning

Image captioning is a challenging vision-to-language task, which has garnered a lot of attention over the past decade. The introduction of Encoder-Decoder based architectures expedited the research in this area and provided the backbone of the most recent systems. Moreover, leveraging relationships between objects for holistic scene understanding, which in turn improves captioning, has recently sparked interest among researchers. Our proposed model encodes the spatial and semantic proximity of object pairs into linguistically-aware relationship embeddings. Moreover, it captures the global semantics of the image using NASNet. This way, true semantic relations that are not apparent in visual content of an image can be learned, such that the decoder can attend to the most relevant object relations and visual features to generate more semantically-meaningful captions. Our experiments highlight the usefulness of linguistically-aware object relations as well as NASNet visual features for image captioning.
Autonomous Perching of an Omni-Directional Unmanned Aerial Vehicle

A novel omni-directional multi-rotor prototype with an attached gripper mechanism called the OmniRaptor is presented. The OmniRaptor has the capability to land on inclined members in a controlled, stable manner. The control system for fully autonomous waypoint guided omnidirectional flight is presented. The results of test flights are shown demonstrating autonomous take-off, flying, and landing with secure grasping for both a level and an inclined beam. The results show that the OmniRaptor's design and controller allow it to fly and land at arbitrary attitudes which is not possible for traditional quad-rotors.
Annotating Documents using Active Learning Methods for a Maintenance Analysis Application

The aircraft cargo industry still maintains vast amounts of the maintenance history of aircraft components in electronic (i.e. scanned) but unsearchable images. For a given supplier, there can be hundreds of thousands of image documents only some of which contain useful information. Using supervised machine learning techniques has been shown to be effective in recognising these documents for further information extraction. A well known deficiency of supervised learning approaches is that annotating sufficient documents to create an effective model requires valuable human effort. This paper first shows how to obtain a representative sample from a supplier's corpus. Given this sample of unlabelled documents an active learning approach is used to select which documents to annotate first using a normalised certainty measure derived from a soft classifier's prediction distribution. Finally the accuracy of various selection approaches using this certainty measure are compared along each iteration of the active learning cycle. The experiments show that a greedy selection method using the uncertainty measure can significantly reduce the number of annotations required for a certain accuracy. The results provide valuable information for users and more generally illustrate an effective deployment of a machine learning application.
V-PCC Component Synchronization for Point Cloud Reconstruction

For a V-PCC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> system to be able to reconstruct a single instance of the point cloud one V-PCC unit must be transferred to the 3D point cloud reconstruction module. It is however required that all the V-PCC components i.e. occupancy map, geometry, atlas and attribute to be temporally aligned. This, in principle, could pose a challenge since the temporal structures of the decoded sub-bitstreams are not coherent across V-PCC sub-bitstreams. In this paper we propose an output delay adjustment mechanism for the decoded V-PCC sub-bitstreams to provide synchronized V-PCC components input to the point cloud reconstruction module.
Website-Based Information System for Mapping Restaurants or Eating Places in DKI Jakarta Using Google Maps

Abstract Technology continues to develop very rapidly, especially in the field of information both in print and electronic media that provides information in the form of text, sound and images. Currently there are many restaurants or other eating places that use the internet to promote themselves. Therefore, this website-based application program is created to map restaurants or eating places using Google maps. The purpose of this program is to make it easier for users to get information about restaurants or eating places that exist today.
Mobile-Edge Cooperative Multi-User 360° Video Computing and Streaming

We investigate a novel communications system that integrates scalable multi-layer 360° video tiling, viewport-adaptive rate-distortion optimal resource allocation, and VR-centric edge computing and caching, to enable future high-quality untethered VR streaming. Our system comprises a collection of 5G small cells that can pool their communication, computing, and storage resources to collectively deliver scalable 360° video content to mobile VR clients at much higher quality. Our major contributions are rigorous design of multi-layer 360° tiling and related models of statistical user navigation, and analysis and optimization of edge-based multi-user VR streaming that integrates viewport adaptation and server cooperation. We also explore the possibility of network coded data operation and its implications for the analysis, optimization, and system performance we pursue here. We demonstrate considerable gains in delivered immersion fidelity, featuring much higher 360° viewport peak signal to noise ratio (PSNR) and VR video frame rates and spatial resolutions.
Vision based distance estimation from single RGB camera using field of view and magnification measurements –an AI based non triangulation technique for person distance estimation in surveillance areas

Depth data from conventional cameras in monitoring fields provides a thorough assessment of human behavior. In this context, the depth of each viewpoint must be calculated using binocular stereo, which requires two cameras to retrieve 3D data. In networked surveillance environments, this drives excess energy and also provides extra infrastructure. We launched a new computational photographic technique for depth estimation using a single camera based on the ideas of perspective projection and lens magnification property. The person to camera distance (or depth) is obtained from understanding the focal length, field of view and magnification characteristics. Prior to finding distance, initially real height is estimated using Human body anthropometrics. These metrics are given as inputs to the Gradient-Boosting machine learning algorithm for estimating Real Height. And then magnification and Field of View measurements are extracted for each sample. The depth (or distance) is predicted on the basis of the geometrical relationship between field of view, magnification and camera at object distance. Using physical distance and height measurements taken in real time as ground truth, experimental validation is performed and it is inferred that with in 3m–7 m range, both in indoor and outdoor environments, the camera to person distance (Preddist) anticipated from field of view and magnification is 91% correlated with actual depth at a confidence point of 95% with RMSE of 0.579.
On Visualization and Quantification of Lesion Margin in CT Liver Images

Cancer is the one of the leading causes of death worldwide, and cancer incidence increases every year. The analysis of lesion margin is quite important to diagnose malignant and benign masses and to detect the presence and the stage of tumor invasion in case of cancer. Accordingly, the aim of the study is to visualize and quantify margin of lesions on radiological images by means of a digital computer. In this study, computed tomography (CT) images of liver have been employed for analysis because the liver has crucial tasks in our body and liver cancer-related deaths is ranked as the forth among the cancer-related deaths. The proposed method consisted of four main steps: image cropping and smoothing, specification of target lesion, the boundary detection of target lesion, and visualization and quantification of margin. First, the images were converted to gray scale. The blank regions surrounding the liver in the CT images were removed before specification of target lesion, and further were smoothed with a bilateral filter. Next, the target region was specified roughly by drawing it manually. The boundary of lesion was more precisely determined with the active contour method employing the sketched borderline as the initial curve. Next, the properties of the target region: the centroid, major axis length, and the orientation values were computed. The intensities along a line passing through the center of the tumor were obtained for eighteen different rotation angles. A pulse model was fit to each of the intensity signal corresponding to a rotation. Then, the intensity change, margin sharpness and width were acquired from the pulse approximation associated to each rotation angle. The level difference provided the intensity change, the slope of edges gave the margin sharpness, and distance between the start and end points of the pulse edge represented margin width. Besides, the inner (core) and outer diameter with respect to angle were also displayed.
Dialog Driven Face Construction using GANs

This paper presents an end-to-end pipeline using Generative Adversarial Networks (GANs) for face construction based on speech-based descriptions, and iterative editing of the generated image to arrive at a close approximation of the expected face. We propose a dialog-based interaction with the system where the user and system take turns providing descriptions and generating images respectively. A rule-based Natural Language (NL) Parser is used to extract facial attribute descriptors from textual descriptions, MSG-Style GAN (Multi-Scale Gradient Style GAN) for face generation, and Attribute GAN (AttGAN) for facial attribute manipulation.
A Novel Algorithm Checking for the Line Layout in a Factory

For Industry 4.0, changing lines in a factory is happen very frequency. In this case, sometimes we make a missing layout of processing machines on the line. We can know the layout information which is the machine's location and orientation in advance. And we can get the relative location information between machines from the image file taken by the camera. In this paper, we propose a novel algorithm in order to find a missing layout from these information using the augmented reality technology. We consider the RoboCup Logistics League which is one of the autonomous mobile robot competitions which assume the factory automation. We make the 1/10 size field of this robot competition in order to show the performance of our prototype system. By our proposed algorithm, we can change the layout of lines more frequency and easier.