Reviewerly/exHarmony huggingface dataset:
Cost-Effective Resource Provisioning in Cloud Using Cooperative Coevolutionary Genetic Algorithm

Nowadays, cloud is widely used platform for intensive computing, bulk storage, and networking. Elastically scalable on demand pay as per used model is one of the major reasons for the acceptability of the cloud-based computing. Data centers with huge amount of computing devices are distributed over the cloud service provider as the back end to provide services. Computing devices are arranged bombastically in data centers to ensure 24/7 availability. Inspired from Pantriyagin’s maximization principle for optimal control and self-balancing population with predator–prey interaction in ecological environment, a model for resource provisioning with cost-effective approach and efficient resource utilization scheme is presented in this paper. We present an optimal resource provisioning model by taking into account the matter of sustainability and profitability. Cooperative coevolutionary genetic algorithm is implemented in CloudSim simulation tool to find the optimal cost per provisioning.
Forward and Backward Motion Planning for Autonomous Parking Using Smooth-feedback Bidirectional Rapidly-exploring Random Trees with Pattern Cost Penalty

Nowadays, techniques of autonomous vehicles are devoted to providing convenience for people in everyday life. However, existing applications of Advanced Driver Assistance Systems (ADAS) can just be categorized into autonomous driving level 1-4 but not full automation in level 5. With the considerations of risks and environments, autonomous parking may be the first fully autonomous applications for autonomous vehicles. A sampling based motion planner is proposed in this paper to generate a human-like and collision-free parking path efficiently with any feasible start and goal configurations for parking scenarios in everyday life. Moreover, the proposed method aims to deal with the common defects of sampling based motion planners to maintain high path quality and consistency for each execution. To verify the effects of the proposed method, the simulations are conducted in common and strict parking scenarios, such as perpendicular and parallel parking. The simulation results show the potential to deal with the various parking scenarios in everyday life.
Evaluating whole-slide imaging viewers used in digital pathology

As a Class II medical device, whole-slide imaging (WSI) systems are emerging to succeed light microscopes used by pathologists in the past decades by digitalizing histological tissue slides into millions of pixels saved in a WSI file. Unlike the standard image file formats such as JPEG or TIFF, a WSI file usually consists of hundreds of compressed images of different magnification levels and focal planes that need to be decompressed, stitched, scaled, and colormanaged to reproduce the view demanded by the user with zooming and panning operations. Currently, most WSI files are stored in proprietary file formats, due to the lack of adopting a standard WSI file format, which hinders the development of third-party WSI viewers by making it difficult to interpret WSI files faithfully. To examine the fidelity of third-party WSI viewers, in this study, three freely available viewers, Sedeen, QuPath, and ASAP, were compared with the factory viewer, NDP, at the pixel level. A software tool was developed to register and calculate the 1976 CIE color difference for each pixel between two viewers. The average color differences were found as 1.30, 18.69, and 18.79 ΔE for Sedeen, QuPath, and ASAP, respectively.
A Summary of Research Results on the Acquisition and Application of Typical Navigation Parameters of Intelligent Ship

With the rise of artificial intelligence technology, it is an inevitable trend for ships to become intelligent, and it is crucial for ships to be able to autonomously navigate with the support of intelligent technology, so the problem of obtaining various typical navigation parameters bears the brunt. For the acquisition of various typical navigation parameters, accurate and real-time acquisition of performance and status parameters such as ship platform, hull, engine room, navigation, etc. is the primary problem that ships must solve to achieve autonomous navigation. In recent years, domestic and foreign experts and scholars have carried out a lot of scientific research work. This article summarizes a large number of domestic and foreign research results on the acquisition and application of typical navigation parameters of intelligent ships, summarizes them, and discusses the acquisition methods and applications of intelligent ships and the characteristics of visualization technology, study its shortcomings, and look forward to the future development of ship intelligent technology in this direction, and put forward some new insights.
An Interactive Low-Cost Smart Assistant System: Information Kiosk as Plug &amp; Play Device

The demand for a self-service device that can relay the information of an organization while maintaining privacy is quite crucial in today's world. The introduction of kiosks in such states resolves this issue but the prevailing kiosk manufacturing companies don't provide a plug & play system for kiosks. In order to nullify these issues, the design of an information kiosk as a plug & play system has been described in this paper. This kiosk can provide real-time information to the users removing the necessity of any sort of human assistance. Unlike the existing kiosks, this is a plug & play device, which makes it easier to install any specification or modification required as per the organization's requirement. It also stands out from the existing ones through the use of image processing and computer vision making it more interactive as well as user friendly. The system also possesses the capability of processing a vast amount of data. These data are collected through sensors and other means as well as conveying with high accuracy and privacy. Furthermore, the system is low-cost in comparison with the available kiosks in the market.
Probabilistic Topic Models for Enriching Ontology from Texts

The ontology enrichment process is text-based and the application domain in hand is circumscribed to the content of the related texts. However, the main challenge in ontology enrichment is its learning, since there is still a lack of relevant approach able to achieve automatic enrichment from a textual corpus or dataset of various topics. In this paper, we describe a new approach for automatic learning of terminological ontologies from textual corpus based on probabilistic models. In our approach, two topic modeling algorithms are explored, namely LDA and pLSA for learning topic ontology. The objective is to capture semantic relationships between word-topic and topic-document in terms of probability distributions to build a topic ontology and ontology graph with minimum human intervention. Experimental analysis on building a topic ontology and retrieving corresponding topic ontology for a user query demonstrates the effectiveness of the proposed approach.
500 days of Thingiverse: a longitudinal study of 30 popular things for 3D printing

Purpose The purpose of this paper is to provide insight into the way object data on Thingiverse changes over time, analyzing the relationships among views, downloads, likes, makes, remixes and comments over 500 days. Design/methodology/approach In total, 30 of the most popular things on Thingiverse were tracked between August 26, 2018 and January 7, 2020, with data collected about the different interactions at five intervals. Findings Highlights include: “#3DBenchy” became the first thing to reach one million downloads during this study. The “Xbox One controller mini wheel” achieved the highest documented download rate of 698 downloads per day. The average conversion rate from downloads to makes for all 30 things was one make for every 474 downloads at the start of the study, declining to one make for every 784 downloads by the conclusion. Research limitations/implications With over 1.6 million things on Thingiverse, this study focused on an exclusive group of things that have gained significant attention from makers and does not represent most things on the platform. Practical implications Although often considered a novelty or niche maker community, this research shows that things on Thingiverse are achieving popularity comparable to digital music, video and imagery, and a large ecosystem of things has been growing that has implications for designers, manufacturers, supply chain managers and universal popular culture. Originality/value This is the first study to track the digital behaviors of 3D printable things over time, revealing new knowledge about how people interact with content and the scale of these interactions.
Benchmarking an AI-Guided Reasoning-Based Operator Support System on the Three Mile Island Accident Scenario

Abstract In the Nuclear Power Plant (NPP) control room, the operators’ performance in emergencies is impacted by the need to monitor many indicators on the control room boards, the limited time to interact with dynamic events, and the incompleteness of the operator’s knowledge. Recent research has been directed toward increasing the level of automation in the NPP system by employing modern AI techniques that support the operator’s decisions. In previous work, the authors have employed a novel AI-guided declarative approach (namely, Answer Set Programming (ASP)) to represent and reason with human qualitative knowledge. This represented knowledge is structured to form a reasoning-based operator support system that assists the operator and compensates for any knowledge incompleteness by performing reasoning to diagnose failures and recommend executing actions in real time. A general ASP code structure has been proposed and tested against simple scenarios, e.g., diagnosis of pump failures that result in loss of flow transients and generating the needed plans for resolving the issue of stuck valves in the secondary loop. In this work, we investigate the potential of the previously proposed ASP structure by applying ASP to a realistic case study of the Three Mile Island, Unit 2 (TMI-2) accident event sequence (in particular, the first 142 minutes). The TMI scenario presents many challenges for a reasoning system, including a large number of variables, the complexity of the scenario, and the misleading readings. The capability of the ASP-based reasoning system is tested for diagnosis and recommending actions throughout the scenario. This paper is the first work to test and demonstrate the capability of an automated reasoning system by applying it to a realistic nuclear accident scenario, such as the TMI-2 accident.
An Improvement of K-Medoids Clustering Algorithm Based on Fixed Point Iteration

The process of K-medoids algorithm is that it first selects data randomly as initial centers to form initial clusters. Then, based on PAM (partitioning around medoids) algorithm, centers will be sequential replaced by all the remaining data to find a result has the best inherent convergence. Since PAM algorithm is an iterative ergodic strategy, when the data size or the number of clusters are huge, its expensive computational overhead will hinder its feasibility. The authors use the fixed-point iteration to search the optimal clustering centers and build a FPK-medoids (fixed point-based K-medoids) algorithm. By constructing fixed point equations for each cluster, the problem of searching optimal centers is converted into the solving of equation set in parallel. The experiment is carried on six standard datasets, and the result shows that the clustering efficiency of proposed algorithm is significantly improved compared with the conventional algorithm. In addition, the clustering quality will be markedly enhanced in handling problems with large-scale datasets or a large number of clusters.
An Experimental Study of the Accuracy vs Inference Speed of RGB-D Object Recognition in Mobile Robotics

This paper presents a study in terms of accuracy and inference speed using RGB-D object detection and classification for mobile platform applications. The study is divided in three stages. In the first, eight state-of-the-art CNN-based object classifiers (AlexNet, VGG16-19, ResNet1850-101, DenseNet, and MobileNetV2) are used to compare the attained performances with the corresponding inference speeds in object classification tasks. The second stage consists in exploiting YOLOv3/YOLOv3-tiny networks to be used as Region of Interest generator method. In order to obtain a real-time object recognition pipeline, the final stage unifies the YOLOv3/YOLOv3-tiny with a CNN-based object classifier. The pipeline evaluates each object classifier with each Region of Interest generator method in terms of their accuracy and frame rate. For the evaluation of the proposed study under the conditions in which real robotic platforms navigate, a nonobject centric RGB-D dataset was recorded in Institute of Systems and Robotics facilities using a camera on-board the ISR-InterBot mobile platform. Experimental evaluations were also carried out in Washington and COCO datasets. Promising performances were achieved by the combination of YOLOv3tiny and ResNet18 networks on the embedded hardware Nvidia Jetson TX2.
Discovering Similarity Across Heterogeneous Features

The analysis of both continuous and categorical attributes generating a heterogeneous mix of attributes poses challenges in data clustering. Traditional clustering techniques like k-means clustering work well when applied to small homogeneous datasets. However, as the data size becomes large, it becomes increasingly difficult to find meaningful and well-formed clusters. In this paper, the authors propose an approach that utilizes a combined similarity function, which looks at similarity across numeric and categorical features and employs this function in a clustering algorithm to identify similarity between data objects. The findings indicate that the proposed approach handles heterogeneous data better by forming well-separated clusters.
Data Visualization with NumPy and Matplotlib

Chapter 4 introduced the basics of NumPy. You learned how to install it and how to create ndarrays. In this chapter, we continue working with NumPy by looking at a few ndarray creation routines. We will also get started with the data visualization library of the scientific computing ecosystem, Matplotlib. We will use the NumPy ndarray creation routines to demonstrate visualizations with Matplotlib. This is a detailed chapter with emphasis on coding and visualizations. The following topics are covered in this chapter:
Recommender Systems Based on Resonance Relationship of Criteria With Choquet Operation

Recommender systems are becoming increasingly important in every aspect of life for the diverse needs of users. One of the main goals of the recommender system is to make decisions based on criteria. It is thus important to have a reasonable solution that is consistent with user requirements and characteristics of the stored data. This paper proposes a novel recommendation method based on the resonance relationship of user criteria with Choquet Operation for building a decision-making model. It has been evaluated on the multirecsys tool based on R language. Outputs from the proposed model are effective and reliable through the experiments. It can be applied in appropriate contexts to improve efficiency and minimize the limitations of the current recommender systems.
Opportunities and challenges of <scp>self‐tracking</scp> technologies: Understanding information, technology, and users through the lens of information science

Abstract Health self‐tracking technologies are used for various reasons, from tracking of physical activity (e.g., counting steps) and supporting weight loss to managing chronic illness and monitoring fertility. In many cases, the technology is characterized as a facilitator, the user as the main actor, and the information as a foundation for achieving the desired outcome. Apart from obvious benefits such as gaining awareness of one's body and health, users also face a range of challenges when using various self‐tracking technologies. This panel focuses the discussion on opportunities and challenges of self‐tracking technologies from the perspective of information science. Particularly, the panelists will address the importance of the information provided by self‐tracking technologies, which is a critical component of the conceptual triad (information, technology, and users) to comprehensively understand the nature of human interactions with such technologies.
Event representations omit stretches of time

A large body of research has linked the ‘event structure’ of experience to core cognitive processes such as attention. For example, when subjects step through the frames of a movie in a self-paced slideshow, they dwell longer on certain frames, and these moments of greater attentional engagement turn out to be the same moments that subjects later mark as the ‘boundaries’ between successive events. An assumption in this method—and indeed, in most past discussions of event perception—is that event representations are perfectly contiguous, with each passing moment reflected in one representation or another. By contrast, here we asked whether event representations might actually omit stretches of time. Subjects placed markers on a movie timeline to indicate not the boundaries between events, but rather the start and end times of each event. Although subjects were encouraged to mark every event that they saw, large portions of each movie’s timeline (21%; around 8s of a 40s movie) were not included in any event interval. Why should this be? In a slideshow (completed prior to the event marking task), subjects dwelled less on frames that they later omitted from any interval, compared to frames that they later marked as starts, suggesting lower-information moments may be omitted. We also investigated another form of event segmentation—comic artists’ depictions of these same movies as sequences of drawn images. A second group of subjects indicated the first and last frames that each comic panel represented, again by placing markers on a timeline. This revealed that comics are a similarly sparse form of event representation, omitting on average 33% of their target movie, with omitted frames again occasioning lower dwell times than frames corresponding to the start of an event/panel. We conclude that event representations may omit time to a much greater extent than is typically acknowledged.
Research on the Development of Computer-aided Higher Vocational Physical Education Teaching in the Network Information Age

Abstract This research analyzes the development direction of PE teaching in the future from the development trend of computer aided in the network information age. This research analyzes the development direction of PE teaching in the future from the development trend of computer aided in the network information age. The author points out the significance of network information to the development of physical education teaching through the discussion of network information, and compares the network information-assisted physical education teaching mode with the traditional teaching mode in many aspects and angles, so as to draw a more accurate and fair conclusion for the reference of higher vocational physical education teaching researchers.
A Framework for a Motorcycle Design Computer-Based Intelligent Tool

Abstract Motorcycle riders’ road experience, attitude and position have a large impact on safety. Besides these aspects, a motorcycle designer has to consider the aesthetical emotional value of such artefacts. This paper contributes a novel framework architecture supports designers to develop a rider-centred, safer motorcycle design, while at the same time considering human factors and the emotional values of such artefacts. This paper explores the requirements for this framework through a validated mixed method approach, gaining input from interviewed designers, stakeholders as well as surveyed riders. The proposed framework takes a user-centred approach, placing designers and riders at the framework’s core. Riders are an essential aspect as they assist in generating the knowledge which is critical to the operation of the framework. The framework acts as a support to the motorcycle designer, where it couples the intellectual resources of the designer with the knowledge capabilities of the framework to proactively support motorcycle design decision making. The proposed framework is driven by a harmonisation engine, where the aesthetic, ergonomic, persona and market trend domains are harmonised to achieve a balanced motorcycle design solution. The framework architecture will be employed to implement an intelligent computer-based motorcycle design support tool, in future work.
Improved CNN-Based Marker Labeling for Optical Hand Tracking

Hand tracking is essential in many applications reaching from the creation of CGI movies to medical applications and even real-time, natural, physically-based grasping in VR. Optical marker-based tracking is often the method of choice because of its high accuracy, the support for large workspaces, good performance, and there is no wiring of the user required. However, the tracking algorithms may fail in case of hand poses where some of the markers are occluded. These cases require a subsequent reassignment of labels to reappearing markers. Currently, convolutional neural networks (CNN) show promising results for this re-labeling because they are relatively stable and real-time capable. In this paper, we present several methods to improve the accuracy of label predictions using CNNs. The main idea is to improve the input to the CNNs, which is derived from the output of the optical tracking system. To do so, we propose a method based on principal component analysis, a projection method that is perpendicular to the palm, and a multi-image approach. Our results show that our methods provide better label predictions than current state-of-the-art algorithms, and they can be even extended to other tracking applications.
A Unifying Framework for Dynamic Monitoring and a Taxonomy of Optimizations

Reducing the performance overhead of run-time monitoring is crucial for making it affordable to enforce more complex requirements than simple security or safety properties. Optimizations for reducing the overhead are becoming increasingly sophisticated themselves, which makes it mandatory to verify that they preserve what shall be enforced. In this article, we propose a taxonomy for such optimizations and use it to develop a classification of existing optimization techniques. Moreover, we propose a semantic framework for modeling run-time monitors that provides a suitable basis both, for verifying that optimizations preserve reliable enforcement and for analytically assessing the performance gain.