nils-herrmann/clean_scirepeval huggingface dataset:
Massive machine-type communications in 5g: physical and MAC-layer solutions

MTC are expected to play an essential role within future 5G systems. In the FP7 project METIS, MTC has been further classified into mMTC and uMTC. While mMTC is about wireless connectivity to tens of billions of machinetype terminals, uMTC is about availability, low latency, and high reliability. The main challenge in mMTC is scalable and efficient connectivity for a massive number of devices sending very short packets, which is not done adequately in cellular systems designed for human-type communications. Furthermore, mMTC solutions need to enable wide area coverage and deep indoor penetration while having low cost and being energy-efficient. In this article, we introduce the PHY and MAC layer solutions developed within METIS to address this challenge.
Architecture and measured characteristics of a cloud based internet of things

The Internet of Things (IoT) many be thought of as the availability of physical objects, or devices, on the Internet . Given such an arrangement it is possible to access sensor data and control actuators remotely. Furthermore such data may be combined with data from other sources - e.g. with data that is contained in the Web - or operated on by cloud based services to create applications far richer than can be provided by isolated embedded systems [2,3]. This is the vision of the Internet of Things. We present a cloud-compatible open source controller and an extensible API, hereafter referred to as `IoTCloud', which enables developers to create scalable high performance IoT and sensor-centric applications. The IoTCloud software is written in Java and built on popular open source packages such as Apache Active MQ [4] and JBoss Netty . We present an overview of the IoT Cloud architecture and describe its developer API. Next we introduce the FutureGrid - a geographically distributed and heterogeneous cloud test-bed [6,7] - used in our experiments. Our preliminary results indicate that a distributed cloud infrastructure like the FutureGrid coupled with our flexible IoTCloud framework is an environment suitable for the study and development of IoT and sensor-centric applications. We also report on our initial study of certain measured characteristics of an IoTCloud application running on the FutureGrid. We conclude by inviting interested parties to use the IoTCloud to create their own IoT applications or contribute to its further development.
Ontology Engineering Methodology

In this chapter we present a methodology for introducing and maintaining ontology based knowledge management applications into enterprises with a focus on Knowledge Processes and Knowledge Meta Processes. While the former process circles around the usage of ontologies, the latter process guides their initial set up. We illustrate our methodology by an example from a case study on skills management. The methodology serves as a scaffold for Part B “Ontology Engineering” of the handbook. It shows where more specific concerns of ontology engineering find their place and how they are related in the overall process.
Exploring feedback strategies to improve public speaking: an interactive virtual audience framework

Good public speaking skills convey strong and effective communication, which is critical in many professions and used in everyday life. The ability to speak publicly requires a lot of training and practice. Recent technological developments enable new approaches for public speaking training that allow users to practice in a safe and engaging environment. We explore feedback strategies for public speaking training that are based on an interactive virtual audience paradigm. We investigate three study conditions: (1) a non-interactive virtual audience (control condition), (2) direct visual feedback, and (3) nonverbal feedback from an interactive virtual audience. We perform a threefold evaluation based on self-assessment questionnaires, expert assessments, and two objectively annotated measures of eye-contact and avoidance of pause fillers. Our experiments show that the interactive virtual audience brings together the best of both worlds: increased engagement and challenge as well as improved public speaking skills as judged by experts.
Applied ontologies and standards for service robots

Service robotics is an emerging application area for human-centered technologies. The rise of household and personal assistance robots forecasts a human–robot collaborative society. One of the robotics community’s major task is to streamline development trends, work on the harmonization of taxonomies and ontologies, along with the standardization of terms, interfaces and technologies. It is important to keep the scientific progress and public understanding synchronous, through efficient outreach and education. These efforts support the collaboration among research groups, and lead to widely accepted standards, beneficial for bothmanufacturers and users. This article describes the necessity of developing robotics ontologies and standards focusing on the past and current research efforts. In addition, the paper proposes a roadmap for service robotics ontology development. The  The efforts of theWorking group are presented here, aiming to connect the cutting edge technology with the users of these services—the general public.
Magnetic Induction for Underwater Wireless Communication Networks

Although acoustic waves are the most versatile and widely used physical layer technology for underwater wireless communication networks (UWCNs), they are adversely affected by ambient noise, multipath propagation, and fading. The large propagation delays, low bandwidth, and high bit error rates of the underwater acoustic channel hinder communication as well. These operational limits call for complementary technologies or communication alternatives when the acoustic channel is severely degraded. Magnetic induction (MI) is a promising technique for UWCNs that is not affected by large propagation delays, multipath propagation, and fading. In this paper, the MI communication channel has been modeled. Its propagation characteristics have been compared to the electromagnetic and acoustic communication systems through theoretical analysis and numerical evaluations. The results prove the feasibility of MI communication in underwater environments. The MI waveguide technique is developed to reduce path loss. The communication range between source and destination is considerably extended to hundreds of meters in fresh water due to its superior bit error rate performance.
Control parameter optimization for a microgrid system using particle swarm optimization

Microgrids are new concept of electric power networks consisting of distributed generators, renewable energy sources and sensitive loads. The goal of microgrid operation is to provide reliable and high-quality electric power regardless of faults or abnormal operating conditions. This paper presents control schemes for coordination of multiple microgrid generators, especially with voltage source inverter type interface, for both grid-connected and autonomous modes. To maintain required control performance and power quality during severe conditions, excessive trial-and-error-based repeated tuning process is required. This paper proposes an effective optimal control parameter-tuning method using the particle swarm optimization (PSO) algorithm. Requirements of power quality and load following performance during the island operation are considered in the PSO tuning.
Understanding Human-Computer Interaction for Information Systems Design

Over the past 35 years, information technology has permeated every business activity. This growing use of information technology promised an unprecedented increase in end-user productivity. Yet this promise is unfulfilled, due primarily to a lack of understanding of end-user behavior. End.user productivity is tied directly to functionality and ease of learning and use. Furthermore , system designers lack the necessary guidance and tools to apply effectively what is known about human-computer interaction (HCI) during systems design. Software developers need to expand their focus beyond functional requirements to include the behavioral needs of users. Only when system functions fit actual work and the system is easy to learn and use will the system be adopted by office workers and business professionals. The large, interdisciplinary body of research literature suggest HCI's importance as well as its complexity. This article is the product of an extensive effort to integrate the diverse body of HCI literature into a comprehensible framework that provides guidance to system designers: HCI design is divided into three major divisions: system model, action language, and presentation language. The system model is a conceptual depiction of system objects and functions. The basic premise is that the selection of a good system model provides dtrection for designing action and presentation languages that determine the system's look and feel. Major design recommendations in each division are identified along with current research trends and future research issues.
A Machine Learning Based Framework for Sub-Resolution Assist Feature Generation

Sub-Resolution Assist Feature (SRAF) generation is a very important resolution enhancement technique to improve yield in modern semiconductor manufacturing process. Model- based SRAF generation has been widely used to achieve high accuracy but it is known to be time consuming and it is hard to obtain consistent SRAFs on the same layout pattern configurations. This paper proposes the first ma- chine learning based framework for fast yet consistent SRAF generation with high quality of results. Our technical con- tributions include robust feature extraction, novel feature compaction, model training for SRAF classification and pre- diction, and the final SRAF generation with consideration of practical mask manufacturing constraints. Experimental re- sults demonstrate that, compared with commercial Calibre tool, our machine learning based SRAF generation obtains 10X speed up and comparable performance in terms of edge placement error (EPE) and process variation (PV) band.
Critical Thinking , Cognitive Presence , and Computer Conferencing in Distance Education

This article describes a practical approach to judging the nature and quality of critical discourse in a computer conference. A model of a critical community of inquiry frames the research. A core concept in defining a community of inquiry is cognitive presence. In turn, the practical inquiry model operationalizes cognitive presence for the purpose of developing a tool to assess critical discourse and reflection. Encouraging empirical findings related to an attempt to create an efficient and reliable instrument to assess the nature and quality of critical discourse and thinking in a textbased educational context are presented. Finally, it is suggested that cognitive presence (i.e., critical, practical inquiry) can be created and supported in a computer conference environment with appropriate teaching and social presence.
An Improved Watershed Image Segmentation Technique using MATLAB

Watershed Transformation in mathematical morphology is a powerful tool for image segmentation. Watershed transformation based segmentation is generally marker controlled segmentation. This paper purposes a novel method of image segmentation that includes image enhancement and noise removal techniques with the Prewitt’s edge detection operator. The proposed method is evaluated and compared to existing method. The results show that the proposed method could effectively reduce the over segmentation effect and achieve more accurate segmentation results than the existing method.
Cloud computing adoption by higher education institutions in Saudi Arabia: an exploratory study

Academic study of cloud computing is an emerging research field in Saudi Arabia. Saudi Arabia represents the largest economy in the Arab Gulf region, which makes it a potential market of cloud computing technologies. This cross-sectional exploratory empirical research is based on technology–organization–environment (TOE) framework, targeting higher education institutions. In this study, the factors that affect the cloud adoption by higher education institutions were identified and tested using SmartPLS software, a powerful statistical analysis tool for structural equation modeling. Three factors were found significant in this context. Relative advantage, complexity and data concern were the most significant factors. The model explained 47.9 % of the total adoption variance. The findings offer education institutions and cloud computing service providers with better understanding of factors affecting the adoption of cloud computing.
Three-dimensional multipass SAR focusing: experiments with long-term spaceborne data

Synthetic aperture radar (SAR) interferometry is a modern efficient technique that allows reconstructing the height profile of the observed scene. However, apart for the presence of critical nonlinear inversion steps, particularly crucial in abrupt topography scenarios, it does not allow one to separate different scattering mechanisms in the elevation (height) direction within the ground pixel. Overlay of scattering at different elevations in the same azimuth-range resolution cell can be due either to the penetration of the radiation below the surface or to perspective ambiguities caused by the side-looking geometry. Multibaseline three-dimensional (3-D) SAR focusing allows overcoming such a limitation and has thus raised great interest in the recent research. First results with real data have been only obtained in the laboratory and with airborne systems, or with limited time-span and spatial-coverage spaceborne data. This work presents a novel approach for the tomographic processing of European Remote Sensing satellite (ERS) real data for extended scenes and long time span. Besides facing problems common to the airborne case, such as the nonuniformly spaced passes, this processing requires tackling additional difficulties specific to the spaceborne case, in particular a space-varying phase calibration of the data due to atmospheric variations and possible scene deformations occurring for years-long temporal spans. First results are presented that confirm the capability of ERS multipass tomography to resolve multiple targets within the same azimuth-range cell and to map the 3-D scattering properties of the illuminated scene.
Multi-atlas segmentation with augmented features for cardiac MR images

Multi-atlas segmentation infers the target image segmentation by combining prior anatomical knowledge encoded in multiple atlases. It has been quite successfully applied to medical image segmentation in the recent years, resulting in highly accurate and robust segmentation for many anatomical structures. However, to guide the label fusion process, most existing multi-atlas segmentation methods only utilise the intensity information within a small patch during the label fusion process and may neglect other useful information such as gradient and contextual information (the appearance of surrounding regions). This paper proposes to combine the intensity, gradient and contextual information into an augmented feature vector and incorporate it into multi-atlas segmentation. Also, it explores the alternative to the K nearest neighbour (KNN) classifier in performing multi-atlas label fusion, by using the support vector machine (SVM) for label fusion instead. Experimental results on a short-axis cardiac MR data set of 83 subjects have demonstrated that the accuracy of multi-atlas segmentation can be significantly improved by using the augmented feature vector. The mean Dice metric of the proposed segmentation framework is 0.81 for the left ventricular myocardium on this data set, compared to 0.79 given by the conventional multi-atlas patch-based segmentation (Coupé et al., 2011; Rousseau et al., 2011). A major contribution of this paper is that it demonstrates that the performance of non-local patch-based segmentation can be improved by using augmented features.
Driving behavior analysis with smartphones: insights from a controlled field study

We evaluate a mobile application that assesses driving behavior based on in-vehicle acceleration measurements and gives corresponding feedback to drivers. In the insurance business, such applications have recently gained traction as a viable alternative to the monitoring of drivers via "black boxes" installed in vehicles, which lacks interaction opportunities and is perceived as privacy intrusive by policyholders. However, pose uncertainty and other noise-inducing factors make smartphones potentially less reliable as sensor platforms. We therefore compare critical driving events generated by a smartphone with reference measurements from a vehicle-fixed IMU in a controlled field study. The study was designed to capture driver variability under real-world conditions, while minimizing the influence of external factors. We find that the mobile measurements tend to overestimate critical driving events, possibly due to deviation from the calibrated initial device pose. While weather and daytime do not appear to influence event counts, road type is a significant factor that is not considered in most current state-of-the-art implementations.
Cutting the Cord: Static and Dynamic Inductive Wireless Charging of Electric Vehicles

In this article, we have reviewed the state of the art of IPT systems and have explored the suitability of the technology to wirelessly charge battery powered vehicles. the review shows that the IPT technology has merits for stationary charging (when the vehicle is parked), opportunity charging (when the vehicle is stopped for a short period of time, for example, at a bus stop), and dynamic charging (when the vehicle is moving along a dedicated lane equipped with an IPT system). Dynamic wireless charging holds promise to partially or completely eliminate the overnight charging through a compact network of dynamic chargers installed on the roads that would keep the vehicle batteries charged at all times, consequently reducing the range anxiety and increasing the reliability of EVs. Dynamic charging can help lower the price of EVs by reducing the size of the battery pack. Indeed, if the recharging energy is readily available, the batteries do not have to support the whole driving range but only supply power when the IPT system is not available. Depending on the power capability, the use of dynamic charging may increase driving range and reduce the size of the battery pack.
Detection of Chinese Word Usage Errors for Non-Native Chinese Learners with Bidirectional LSTM

Selecting appropriate words to compose a sentence is one common problem faced by non-native Chinese learners. In this paper, we propose (bidirectional) LSTM sequence labeling models and explore various features to detect word usage errors in Chinese sentences. By combining CWINDOW word embedding features and POS information, the best bidirectional LSTM model achieves accuracy 0.5138 and MRR 0.6789 on the HSK dataset. For 80.79% of the test data, the model ranks the groundtruth within the top two at position level.
Deep Learning Quadcopter Control via Risk-Aware Active Learning

Modern optimization-based approaches to control increasingly allow automatic generation of complex behavior from only a model and an objective. Recent years has seen growing interest in fast solvers to also allow real-time operation on robots, but the computational cost of such trajectory optimization remains prohibitive for many applications. In this paper we examine a novel deep neural network approximation and validate it on a safe navigation problem with a real nano-quadcopter. As the risk of costly failures is a major concern with real robots, we propose a risk-aware resampling technique. Contrary to prior work this active learning approach is easy to use with existing solvers for trajectory optimization, as well as deep learning. We demonstrate the efficacy of the approach on a difficult collision avoidance problem with non-cooperative moving obstacles. Our findings indicate that the resulting neural network approximations are least 50 times faster than the trajectory optimizer while still satisfying the safety requirements. We demonstrate the potential of the approach by implementing a synthesized deep neural network policy on the nano-quadcopter microcontroller.
A Comprehensive Survey on Fog Computing: State-of-the-Art and Research Challenges

Cloud computing with its three key facets (i.e., Infrastructure-as-a-Service, Platform-as-a-Service, and Software-as-a-Service) and its inherent advantages (e.g., elasticity and scalability) still faces several challenges. The distance between the cloud and the end devices might be an issue for latency-sensitive applications such as disaster management and content delivery applications. Service level agreements (SLAs) may also impose processing at locations where the cloud provider does not have data centers. Fog computing is a novel paradigm to address such issues. It enables provisioning resources and services outside the cloud, at the edge of the network, closer to end devices, or eventually, at locations stipulated by SLAs. Fog computing is not a substitute for cloud computing but a powerful complement. It enables processing at the edge while still offering the possibility to interact with the cloud. This paper presents a comprehensive survey on fog computing. It critically reviews the state of the art in the light of a concise set of evaluation criteria. We cover both the architectures and the algorithms that make fog systems. Challenges and research directions are also introduced. In addition, the lessons learned are reviewed and the prospects are discussed in terms of the key role fog is likely to play in emerging technologies such as tactile Internet.
A Long-Pulse Generator Based on Tesla Transformer and Pulse-Forming Network

An approach for producing a long pulse up to 100 ns is presented. The generator based on this approach consists of a Tesla transformer and a set of pulse-forming networks (PFNs). The Tesla transformer is used to charge pulse-forming lines (PFLs) and PFNs which are in parallel. When the voltage increases to a certain value, the main switch will close, and the PFLs and PFNs will discharge rapidly to the load. Therefore, a high-voltage long pulse is formed on the load. The amplitude of this pulse is dependent only on the charging voltage and the matching state between the load and the PFL (PFN). The pulsewidth is determined by the transmission time of the PFL and PFN. The rise time is determined by the working state of the main switch and the impedance of the PFL and is independent of the parameters of the PFN. The PFN is multistage and assembled in series. The single-stage PFN is formed with ceramic capacitors placed between two unclosed annular plates. The total series impedance is equal to the sum of every single-stage PFN's impedance. A nine-stage PFN is used in the generator, and the total impedance is 40 Omega. Experimental results show that a high voltage of an amplitude of 300 kV, current of 6.9 kA, and duration of 110 ns is obtained at a repetition rate of 10 Hz, with a rise time of approximately 7 ns.