nils-herrmann/clean_scirepeval huggingface dataset:
Battery Pack Modeling , Simulation , and Deployment on a Multicore Real Time Target

Battery Management System (BMS) design is a complex task requiring sophisticated models that mimic the electrochemical behavior of the battery cell under a variety of operating conditions. Equivalent circuits are well-suited for this task because they offer a balance between fidelity and simulation speed, their parameters reflect direct experimental observations, and they are scalable. Scalability is particularly important at the real time simulation stage, where a model of the battery pack runs on a real-time simulator that is physically connected to the peripheral hardware in charge of monitoring and control. With modern battery systems comprising hundreds of cells, it is important to employ a modeling and simulation approach that is capable of handling numerous simultaneous instances of the basic unit cell while maintaining real time performance. In previous publications we presented a technique for the creation of a battery cell model that contains the electrochemical fingerprints of a battery cell based on equivalent circuit model fitting to experimental data. In this work we extend our previous model to represent a battery pack, featuring cell creation, placement, and connection using automation scripts, thus facilitating the design of packs of arbitrary size and electrical topology. In addition, we present an assessment of model partitioning schemes for real time execution on multicore targets to ensure efficient use of hardware resources, a balanced computational load, and a study of the potential impact of the calculation latencies inherent to distributed systems on solver accuracy. Prior to C code generation for real time execution, a model profiler assesses the model partitioning and helps determine the multicore configuration that results in the lowest average turnaround time, the time elapsed between task start and finish. The resulting model is useful in the generation of multiple operating scenarios of interest in the design of charging, balancing, and safety related procedures.  7(2):2014, doi:10.4271/2014-01-2217. 2014-01-2217 Published 09/16/2014  doi:10.4271/2014-01-2217 saeaero.saejournals.org A second goal is to provide a scalable methodology capable of supporting any number of battery cell components configured in series or parallel. A MATLAB script creates, places, and connects each model component, including battery cell blocks and electrical components for the battery pack load, and partitions the model.
Broadband Long Rectangular Patch Antenna With High Gain and Vertical Polarization

A new microstrip rectangular patch antenna with high gain and vertically polarized radiation at endfires is proposed. The antenna has a wide bandwidth with a very low profile. Such antenna is based on a long microstrip line with periodic shorting-vias along the longitudinal direction. When both ends of the line are also shorted by vias, a resonant type of antenna is constructed. Two resonant modes with similar radiation patterns are generated. By optimizing the separation of the resonate frequencies of the two modes, a wide bandwidth is achieved. Measured results show that the antenna with a profile of 0.03 wavelengths in free space and a length of about 1.25 wavelengths can produce a bandwidth of 12.48% and a maximum gain of about 9 dBi.
Moodle's Ontology Development from UML for Social Learning Network Analysis

The online learning called e-learning is a new learning path that offers to learners to study at their own pace and at the moments that suit them. It is in this perspective that the semantic web has known its emergence in the field of e-learning to offer platforms content more personalized and more adapted to student's and teacher's needs. Since Moodle is the most popular e-learning platform, we propose in this paper to build its OWL ontology by exploring the representative data that we collected from its UML class diagram. The choice of UML class diagram as a basis for data collection for the development of the ontology is justified by the fact that the transition from UML to OWL ontology brings ontology development process closer to the wider software engineering population. The built ontology brings also great benefit in the field of the Social Learning Network Analysis. Because it gives the opportunity to study the behavior of the platform users by giving meaning to their relationships instead of modelling them only as knots and edges.
A Method for Mapping Sensor Data to SSN Ontology

Along with the continuous development of the sensor network technology, sensors from all over the world are constantly producing sensor data. However, the sensor data from different source is hard to work together for lack of semantic. Fortunately, SSN ontology provide a way to represent sensor data semantically, but how to transform sensor data into the instance of SSN ontology conveniently is still an issue to be considered. This paper proposed a solution to map sensor data to SSN ontology automatically based on a predefined XML-based document. We design a mapping language SASML (Sensors Annotation and Semantic Mapping Language) which provide a schema to annotate sensors and sources so as to generate a XML document for mapping. Then, an algorithm (namely SDRM) is designed to automatically transform sensor data, which described by SASML, to RDF conforming to SSN ontology, according to the mapping document and the element correspondences between the SASML and SSN ontology. Further, a case study about sensor data from greenhouse is presented to illustrate our method, and a prototype is also developed to demonstrate the feasibility and effectiveness.
Immunity to Device Variations in a Spiking Neural Network With Memristive Nanodevices

Memristive nanodevices can feature a compact multilevel nonvolatile memory function, but are prone to device variability. We propose a novel neural network-based computing paradigm, which exploits their specific physics, and which has virtual immunity to their variability. Memristive devices are used as synapses in a spiking neural network performing unsupervised learning. They learn using a simplified and customized “spike timing dependent plasticity” rule. In the network, neurons’ threshold is adjusted following a homeostasis-type rule. We perform system level simulations with an experimentally verified model of the memristive devices’ behavior. They show, on the textbook case of character recognition, that performance can compare with traditional supervised networks of similar complexity. They also show that the system can retain functionality with extreme variations of various memristive devices’ parameters (a relative standard dispersion of more than 50% is tolerated on all device parameters), thanks to the robustness of the scheme, its unsupervised nature, and the capability of homeostasis. Additionally the network can adjust to stimuli presented with different coding schemes, is particularly robust to read disturb effects and does not require unrealistic control on the devices’ conductance. These results open the way for a novel design approach for ultraadaptive electronic systems.
Point & Teleport Locomotion Technique for Virtual Reality

With the increasing popularity of virtual reality (VR) and new devices getting available with relatively lower costs, more and more video games have been developed recently. Most of these games use first person interaction techniques since it is more natural for Head Mounted Displays (HMDs). One of the most widely used interaction technique in VR video games is locomotion that is used to move user's viewpoint in virtual environments. Locomotion is an important component of video games since it can have a strong influence on user experience. In this study, a new locomotion technique we called "Point & Teleport" is described and compared with two commonly used VR locomotion techniques of walk-in-place and joystick. In this technique, users simply point where they want to be in virtual world and they are teleported to that position. As a major advantage, it is not expected to introduce motion sickness since it does not involve any visible translational motion. In this study, two VR experiments were designed and performed to analyze the Point & Teleport technique. In the first experiment, Point & Teleport was compared with walk-in-place and joystick locomotion techniques. In the second experiment, a direction component was added to the Point & Teleport technique so that the users could specify their desired orientation as well. 16 users took part in both experiments. Results indicated that Point & Teleport is a fun and user friendly locomotion method whereas the additional direction component degraded the user experience.
Neural scene representation and rendering

Scene representation—the process of converting visual sensory data into concise descriptions—is a requirement for intelligent behavior. Recent work has shown that neural networks excel at this task when provided with large, labeled datasets. However, removing the reliance on human labeling remains an important open problem. To this end, we introduce the Generative Query Network (GQN), a framework within which machines learn to represent scenes using only their own sensors. The GQN takes as input images of a scene taken from different viewpoints, constructs an internal representation, and uses this representation to predict the appearance of that scene from previously unobserved viewpoints. The GQN demonstrates representation learning without human labels or domain knowledge, paving the way toward machines that autonomously learn to understand the world around them.
Grasping Without Squeezing: Design and Modeling of Shear-Activated Grippers

Grasping objects that are too large to envelop is traditionally achieved using friction that is activated by squeezing. We present a family of shear-activated grippers that can grasp such objects without the need to squeeze. When a shear force is applied to the gecko-inspired material in our grippers, adhesion is turned on; this adhesion in turn results in adhesion-controlled friction, a friction force that depends on adhesion rather than a squeezing normal force. Removal of the shear force eliminates adhesion, allowing easy release of an object. A compliant shear-activated gripper without active sensing and control can use the same light touch to lift objects that are soft, brittle, fragile, light, or very heavy. We present three grippers, the first two designed for curved objects, and the third for nearly any shape. Simple models describe the grasping process, and empirical results verify the models. The grippers are demonstrated on objects with a variety of shapes, materials, sizes, and weights.
Medical Sentiment Analysis using Social Media: Towards building a Patient Assisted System

With the enormous growth of Internet, more users have engaged in health communities such as medical forums to gather health-related information, to share experiences about drugs, treatments, diagnosis or to interact with other users with similar condition in communities. Monitoring social media platforms has recently fascinated medical natural language processing researchers to detect various medical abnormalities such as adverse drug reaction. In this paper, we present a benchmark setup for analyzing the sentiment with respect to users’ medical condition considering the information, available in social media in particular. To this end, we have crawled the medical forum website ‘patient.info’ with opinions about medical condition self narrated by the users. We constrained ourselves to some of the popular domains such as depression, anxiety, asthma, and allergy. The focus is given on the identification of multiple forms of medical sentiments which can be inferred from users’ medical condition, treatment, and medication. Thereafter, a deep Convolutional Neural Network (CNN) based medical sentiment analysis system is developed for the purpose of evaluation. The resources are made available to the community through LRE map for further research.
Modeling secrecy and deception in a multiple-period attacker-defender signaling game

In this paper, we apply game theory to model strategies of secrecy and deception in a multiple-period attacker–defender resource-allocation and signaling game with incomplete information. At each period, we allow one of the three possible types of defender signals—truthful disclosure, secrecy, and deception. We also allow two types of information updating—the attacker updates his knowledge about the defender type after observing the defender’s signals, and also after observing the result of a contest (if one occurs in any given time period). Our multiple-period model provides insights into the balance between capital and expense for defensive investments (and the effects of defender private information, such as defense effectiveness, target valuations, and costs), and also shows that defenders can achieve more cost-effective security through secrecy and deception (possibly lasting more than one period), in a multi-
Microcontroller Based Wireless Temperature And Heart Beat Read-Out

In this paper, we propose a Simple Wireless transmission System using common approach Sensor Platform called The wireless based Patient Sensor platform (WSP, Sensor Node) which has remote access capability. The goals of the WSP are to establish: Standard sensor node (System on module), a common software .The proposed platform architecture (Sensor Node) offers flexibility, easy customization for different vital parameter collecting and sending. An prototype has been established based on wireless communication channel. Wireless lan (IEEE .802.15.4)) has been used as communication channel on our prototype (Sensor node). Desire sensor information (vital parameter) can be viewed remotely, and also vital parameter can be adjusted to meet demand.
The Cloud Adoption Toolkit: Supporting Cloud Adoption Decisions in the Enterprise

Cloud computing promises a radical shift in the provisioning of computing resource within the enterprise. This paper describes the challenges that decision makers face when assessing the feasibility of the adoption of cloud computing in their organisations, and describes our Cloud Adoption Toolkit, which has been developed to support this process. The toolkit provides a framework to support decision makers in identifying their concerns, and matching these concerns to appropriate tools/techniques that can be used to address them. Cost Modeling is the most mature tool in the toolkit, and this paper shows its effectiveness by demonstrating how practitioners can use it to examine the costs of deploying their IT systems on the cloud. The Cost Modeling tool is evaluated using a case study of an organization that is considering the migration of some of its IT systems to the cloud. The case study shows that running systems on the cloud using a traditional „always on‟ approach can be less cost effective, and the elastic nature of the cloud has to be used to reduce costs. Therefore, decision makers have to be able to model the variations in resource usage and their systems‟ deployment options to obtain accurate cost estimates.
{\Omega}-Net (Omega-Net): Fully Automatic, Multi-View Cardiac MR Detection, Orientation, and Segmentation with Deep Neural Networks

Pixelwise segmentation of the left ventricular (LV) myocardium and the four cardiac chambers in 2-D steady state free precession (SSFP) cine sequences is an essential preprocessing step for a wide range of analyses. Variability in contrast, appearance, orientation, and placement of the heart between patients, clinical views, scanners, and protocols makes fully automatic semantic segmentation a notoriously difficult problem. Here, we present Ω-Net (Omega-Net): a novel convolutional neural network (CNN) architecture for simultaneous localization, transformation into a canonical orientation, and semantic segmentation. First, an initial segmentation is performed on the input image; second, the features learned during this initial segmentation are used to predict the parameters needed to transform the input image into a canonical orientation; and third, a final segmentation is performed on the transformed image. In this work, Ω-Nets of varying depths were trained to detect five foreground classes in any of three clinical views (short axis, SA; four-chamber, 4C; two-chamber, 2C), without prior knowledge of the view being segmented. This constitutes a substantially more challenging problem compared with prior work. The architecture was trained on a cohort of patients with hypertrophic cardiomyopathy (HCM, N = 42) and healthy control subjects (N = 21). Network performance, as measured by weighted foreground intersection-over-union (IoU), was substantially improved for the best-performing Ω-Net compared with U-Net segmentation without localization or orientation (0.858 vs 0.834). In addition, to be comparable with other works, Ω-Net was retrained from scratch on the publicly available 2017 MICCAI Automated Cardiac Diagnosis Challenge (ACDC) dataset. The Ω-Net outperformed the state-of-the-art method in segmentation of the LV and RV bloodpools, and performed slightly worse in segmentation of the LV myocardium. We conclude that this architecture represents a substantive advancement over prior approaches, with implications for biomedical image segmentation more generally.
Towards Generating Real-life Datasets for Network Intrusion Detection

With exponential growth in the number of computer applications and the sizes of networks, the potential damage that can be caused by attacks launched over the Internet keeps increasing dramatically. A number of network intrusion detection methods have been developed with respective strengths and weaknesses. The majority of network intrusion detection research and development is still based on simulated datasets due to non-availability of real datasets. A simulated dataset cannot represent a real network intrusion scenario. It is important to generate real and timely datasets to ensure accurate and consistent evaluation of detection methods. In this paper, we propose a systematic approach to generate unbiased fullfeature real-life network intrusion datasets to compensate for the crucial shortcomings of existing datasets. We establish the importance of an intrusion dataset in the development and validation process of detection mechanisms, identify a set of requirements for effective dataset generation, and discuss several attack scenarios and their incorporation in generating datasets. We also establish the effectiveness of the generated dataset in the context of several existing datasets.
Reflections on craft: probing the creative process of everyday knitters

Crafters today blend age-old techniques such as weaving and pottery with new information and communication technologies such as podcasts, online instructions, and blogs. This intersection of tradition and modernity provides an interesting site for understanding the adoption of new technology. We present a qualitative study of seven knitters introduced to Spyn - a system that enables the association of digitally recorded messages with physical locations on knit fabric. We gave knitters Spyn in order to elicit their reflections on their craft practices and learn from their interactions with material, people, and technology. While creating artifacts for friends and loved ones, knitters expanded the creative and communicative potential of their craftwork: knitters envisioned travel journals in knitted potholders and sung lullabies in knitted hats. We describe how these unusual craft activities provide a useful lens onto contemporary technological appropriation.
AIS-Based Evaluation of Target Detectors and SAR Sensors Characteristics for Maritime Surveillance

This paper studies the performances of different ship detectors based on adaptive threshold algorithms. The detection algorithms are based on various clutter distributions and assessed automatically with a systematic methodology. Evaluation using large datasets of medium resolution SAR images and AIS (automatic identification system) data as ground truths allows to evaluate the efficiency of each detector. Depending on the datasets used for testing, the detection algorithms offer different advantages and disadvantages. The systematic method used in discriminating real detected targets and false alarms in order to determine the detection rate, allows us to perform an appropriate and consistent comparison of the detectors. The impact of SAR sensors characteristics (incidence angle, polarization, frequency and spatial resolution) is fully assessed, the vessels' length being also considered. Experiments are conducted on Radarsat-2 and CosmoSkymed ScanSAR datasets and AIS data acquired by coastal stations.
Off the Radar: Comparative Evaluation of Radial Visualization Solutions for Composite Indicators

A composite indicator (CI) is a measuring and benchmark tool used to capture multi-dimensional concepts, such as Information and Communication Technology (ICT) usage. Individual indicators are selected and combined to reflect a phenomena being measured. Visualization of a composite indicator is recommended as a tool to enable interested stakeholders, as well as the public audience, to better understand the indicator components and evolution overtime. However, existing CI visualizations introduce a variety of solutions and there is a lack in CI's visualization guidelines. Radial visualizations are popular among these solutions because of CI's inherent multi-dimensionality. Although in dispute, Radar-charts are often used for CI presentation. However, no empirical evidence on Radar's effectiveness and efficiency for common CI tasks is available. In this paper, we aim to fill this gap by reporting on a controlled experiment that compares the Radar chart technique with two other radial visualization methods: Flowercharts as used in the well-known OECD Betterlife index, and Circle-charts which could be adopted for this purpose. Examples of these charts in the current context are shown in Figure 1. We evaluated these charts, showing the same data with each of the mentioned techniques applying small multiple views for different dimensions of the data. We compared users' performance and preference empirically under a formal task-taxonomy. Results indicate that the Radar chart was the least effective and least liked, while performance of the two other options were mixed and dependent on the task. Results also showed strong preference of participants toward the Flower chart. Summarizing our results, we provide specific design guidelines for composite indicator visualization.
Charging algorithms of lithium-ion batteries: An overview

This paper presents the overview of charging algorithms for lithium-ion batteries, which include constant current-constant voltage (CC/CV), variants of the CC/CV, multistage constant current, pulse current and pulse voltage. The CC/CV charging algorithm is well developed and widely adopted in charging lithium-ion batteries. It is used as a benchmark to compare with other charging algorithms in terms of the charging time, the charging efficiency, the influences on battery life and other aspects, which can serve as a convenient reference for future work in developing a charger for lithium-ion battery.