nils-herrmann/clean_scirepeval huggingface dataset:
An Energy-Efficient Smart Comfort Sensing System Based on the IEEE 1451 Standard for Green Buildings

In building automation, comfort is an important aspect, and the real-time measurement of comfort is notoriously complicated. In this paper, we have developed a wireless, smart comfort sensing system. The important parameters in designing the prevalent measurement of comfort systems, such as portability, power consumption, reliability, and system cost, were considered. To achieve the target design goals, the communication module, sensor node, and sink node were developed based on the IEEE1451 standard. Electrochemical and semiconductor sensors were considered for the development of the sensor array, and the results of both technologies were compared. The sensor and sink nodes were implemented using the ATMega88 microcontroller. Microsoft Visual Studio 2013 preview was used to create the graphical user interface in C#. The sensors were calibrated after the signal processing circuit to ensure that the standard accuracy of the sensor was achieved. This paper presents detailed design solutions to problems that existed in the literature.
A non-cooperative differential game-based security model in fog computing

Fog computing is a new paradigm providing network services such as computing, storage between the end users and cloud. The distributed and open structure are the characteristics of fog computing, which make it vulnerable and very weak to security threats. In this article, the interaction between vulnerable nodes and malicious nodes in the fog computing is investigated as a non-cooperative differential game. The complex decision making process is reviewed and analyzed. To solve the game, a fictitious play-based algorithm is which the vulnerable node and the malicious nodes reach a feedback Nash equilibrium. We attain optimal strategy of energy consumption with QoS guarantee for the system, which are conveniently operated and suitable for fog nodes. The system simulation identifies the propagation of malicious nodes. We also determine the effects of various parameters on the optimal strategy. The simulation results support a theoretical foundation to limit malicious nodes in fog computing, which can help fog service providers make the optimal dynamic strategies when different types of nodes dynamically change their strategies.
A smartphone SP10T T/R switch in 180-nm SOI CMOS with 8kV+ ESD protection by co-design

This paper reports the first 8kV+ ESD-protected SP10T transmit/receive (T/R) antenna switch for quad-band (0.85/0.9/1.8/1.9-GHz) GSM and multiple W-CDMA smartphones fabricated in an 180-nm SOI CMOS. A novel physics-based switch-ESD co-design methodology is applied to ensure full-chip optimization for a SP10T test chip and its ESD protection circuit simultaneously.
Simplifying Particle Swarm Optimization

The general purpose optimization method known as Particle Swarm Optimization (PSO) has received much attention in past years, with many attempts to find the variant that performs best on a wide variety of optimization problems. The focus of past research has been with making the PSO method more complex, as this is frequently believed to increase its adaptability to other optimization problems. This study takes the opposite approach and simplifies the PSO method. To compare the efficacy of the original PSO and the simplified variant here, an easy technique is presented for efficiently tuning their behavioural parameters. The technique works by employing an overlaid meta-optimizer, which is capable of simultaneously tuning parameters with regard to multiple optimization problems, whereas previous approaches to meta-optimization have tuned behavioural parameters to work well on just a single optimization problem. It is then found that the PSO method and its simplified variant not only have comparable performance for optimizing a number of Artificial Neural Network problems, but the simplified variant appears to offer a small improvement in some cases.
An inexpensive scheme for calibration of a colour monitor in terms of CIE standard coordinates

The Commission Internationale d'Eclairage system of colorimetry is a method of measuring colours that has been standardized, and is widely used by industries involved with colour. Knowing the CIE coordinates of a colour allows it to be reproduced easily and exactly in many different media. For this reason graphics installations which utilize colour extensively ought to have the capability of knowing the CIE coordinates of displayed colours, and of displaying colours of given CIE coordinates. Such a capability requires a function which transforms video monitor gun voltages (RGB colour space) into CIE coordinates (XYZ colour space), and vice versa. The function incorporates certain monitor parameters. The purpose of this paper is to demonstrate the form that such a function takes, and to show how the necessary monitor parameters can be measured using little more than a simple light meter. Because space is limited, and because each user is likely to implement the calibration differently, few technical details are given, but principles and methods are discussed in sufficient depth to allow the full use of the system. In addition, several visual checks which can be used for quick verification of the integrity of the calibration are described.
 The paper begins with an overview of the CIE system of colorimetry. It continues with a general discussion of transformations from RGB colour space to XYZ colour space, after which a detailed step-by-step procedure for monitor calibration is presented.
Antenna Miniaturization Techniques: A Review of Topology- and Material-Based Methods

Antenna miniaturization has been the subject of numerous studies for almost 70 years [1]-[4]. Early studies showed that a decrease in the size of an antenna results in a direct reduction in its bandwidth and efficiency (hr) , . The size limitation translates into a lower boundary on the achievable radiation quality factor (Q factor) and consequently on the maximum achievable impedance bandwidth. Recently, many new investigations have been conducted to reduce the form factor (or the overall size) of different types of antennas while trying to maintain acceptable matching properties and operating bandwidth. These miniaturization techniques are generally related to changing the electrical and physical properties of an antenna.
Online Contrastive Divergence with Generative Replay: Experience Replay without Storing Data

Conceived in the early 1990s, Experience Replay (ER) has been shown to be a successful mechanism to allow online learning algorithms to reuse past experiences. Traditionally, ER can be applied to all machine learning paradigms (i.e., unsupervised, supervised, and reinforcement learning). Recently, ER has contributed to improving the performance of deep reinforcement learning. Yet, its application to many practical settings is still limited by the memory requirements of ER, necessary to explicitly store previous observations. To remedy this issue, we explore a novel approach, Online Contrastive Divergence with Generative Replay (OCDGR), which uses the generative capability of Restricted Boltzmann Machines (RBMs) instead of recorded past experiences. The RBM is trained online, and does not require the system to store any of the observed data points. We compare OCDGR to ER on 9 real-world datasets, considering a worst-case scenario (data points arriving in sorted order) as well as a more realistic one (sequential random-order data points). Our results show that in 64.28% of the cases OCDGR outperforms ER and in the remaining 35.72% it has an almost equal performance, while having a considerably reduced space complexity (i.e., memory usage) at a comparable time complexity.
Asymmetrical voltage-cancellation control for full-bridge series resonant inverters

This paper presents and analyzes the asymmetrical voltage-cancellation (AVC) control, a generalized control technique for resonant inverters. It is applied to the popular full-bridge series resonant inverter. The proposed control technique achieves better efficiency performances than conventional fixed-frequency control strategies, while considering zero-voltage-switching operation, output power and load variations. The theoretical results are verified experimentally, using a prototype for an induction-heating cooking appliance.
Who is tweeting on Twitter: human, bot, or cyborg?

Twitter is a new web application playing dual roles of online social networking and micro-blogging. Users communicate with each other by publishing text-based posts. The popularity and open structure of Twitter have attracted a large number of automated programs, known as bots, which appear to be a double-edged sword to Twitter. Legitimate bots generate a large amount of benign tweets delivering news and updating feeds, while malicious bots spread spam or malicious contents. More interestingly, in the middle between human and bot, there has emerged cyborg referred to either bot-assisted human or human-assisted bot. To assist human users in identifying who they are interacting with, this paper focuses on the classification of human, bot and cyborg accounts on Twitter. We first conduct a set of large-scale measurements with a collection of over 500,000 accounts. We observe the difference among human, bot and cyborg in terms of tweeting behavior, tweet content, and account properties. Based on the measurement results, we propose a classification system that includes the following four parts: (1) an entropy-based component, (2) a machine-learning-based component, (3) an account properties component, and (4) a decision maker. It uses the combination of features extracted from an unknown user to determine the likelihood of being a human, bot or cyborg. Our experimental evaluation demonstrates the efficacy of the proposed classification system.
Toward a modular soft sensor-embedded glove for human hand motion and tactile pressure measurement

The ability to measure human hand motions and interaction forces is critical to improving our understanding of manual gesturing and grasp mechanics. This knowledge serves as a basis for developing better tools for human skill training and rehabilitation, exploring more effective methods of designing and controlling robotic hands, and creating more sophisticated human-computer interaction devices which use complex hand motions as control inputs. This paper presents work on the design, fabrication, and experimental validation of a soft sensor-embedded glove which measures both hand motion and contact pressures during human gesturing and manipulation tasks. We design an array of liquid-metal embedded elastomer sensors to measure up to hundreds of Newtons of interaction forces across the human palm during manipulation tasks and to measure skin strains across phalangeal and carpal joints for joint motion tracking. The elastomeric sensors provide the mechanical compliance necessary to accommodate anatomical variations and permit a normal range of hand motion. We explore methods of assembling this soft sensor glove from modular, individually fabricated pressure and strain sensors and develop design guidelines for their mechanical integration. Experimental validation of a soft finger glove prototype demonstrates the sensitivity range of the designed sensors and the mechanical robustness of the proposed assembly method, and provides a basis for the production of a complete soft sensor glove from inexpensive modular sensor components.
Head Up Games: combining the best of both worlds by merging traditional and digital play

Current pervasive games are mostly location-aware applications, played on handheld computing devices. Considering pervasive games for children, it is argued that the interaction paradigm existing games support limits essential aspects of outdoor play like spontaneous social interaction, physical movement, and rich face-to-face communication. We present a new genre of pervasive games conceived to address this problem, that we call “Head Up Games” (HUGs) to underline that they liberate players from facing down to attend to screen-based interactions. The article discusses characteristics of HUG and relates them to existing genres of pervasive games. We present lessons learned during the design and evaluation of three HUG and chart future challenges.
A 6.3 GHz BFSK Transmitter with On-Chip Antenna for Self-Powered Medical Sensor Applications

This paper presents a completely integrated, low-power 6.3 GHz oscillator transmitter which includes an on-chip antenna suitable for short-range medical sensor applications. The transmitter, implemented in a 1.2 V 0.13 mum CMOS process, utilizes open-loop direct VCO modulation for BFSK data at a rate of 300 kbps. For communicating a 1 kbit packet once per second, an average power consumption of 14 muW is achieved. During a packet transmission, the power consumption of the transmitter is 4.25 mW, enabling a self-powered design using integrated ultracapacitors for an SoC solution. With a radiated power of 0 dBm, the transmitter has a communication range of 2 m.
Far-Field On-Chip Antennas Monolithically Integrated in a Wireless-Powered 5.8-GHz Downlink/UWB Uplink RFID Tag in 0.18-$\mu{\hbox {m}}$  Standard CMOS

This paper discusses two antennas monolithically integrated on-chip to be used respectively for wireless powering and UWB transmission of a tag designed and fabricated in 0.18-μm CMOS technology. A multiturn loop-dipole structure with inductive and resistive stubs is chosen for both antennas. Using these on-chip antennas, the chip employs asymmetric communication links: at downlink, the tag captures the required supply wirelessly from the received RF signal transmitted by a reader and, for the uplink, ultra-wideband impulse-radio (UWB-IR), in the 3.1-10.6-GHz band, is employed instead of backscattering to achieve extremely low power and a high data rate up to 1 Mb/s. At downlink with the on-chip power-scavenging antenna and power-management unit circuitry properly designed, 7.5-cm powering distance has been achieved, which is a huge improvement in terms of operation distance compared with other reported tags with on-chip antenna. Also, 7-cm operating distance is achieved with the implemented on-chip UWB antenna. The tag can be powered up at all the three ISM bands of 915 MHz and 2.45 GHz, with off-chip antennas, and 5.8 GHz with the integrated on-chip antenna. The tag receives its clock and the commands wirelessly through the modulated RF powering-up signal. Measurement results show that the tag can operate up to 1 Mb/s data rate with a minimum input power of -19.41 dBm at 915-MHz band, corresponding to 15.7 m of operation range with an off-chip 0-dB gain antenna. This is a great improvement compared with conventional passive RFIDs in term of data rate and operation distance. The power consumption of the chip is measured to be just 16.6 μW at the clock frequency of 10 MHz at 1.2-V supply. In addition, in this paper, for the first time, the radiation pattern of an on-chip antenna at such a frequency is measured. The measurement shows that the antenna has an almost omnidirectional radiation pattern so that the chip's performance is less direction-dependent.
Online and Offline Domain Adaptation for Reducing BCI Calibration Effort

Many real-world brain–computer interface (BCI) applications rely on single-trial classification of event-related potentials (ERPs) in EEG signals. However, because different subjects have different neural responses to even the same stimulus, it is very difficult to build a generic ERP classifier whose parameters fit all subjects. The classifier needs to be calibrated for each individual subject, using some labeled subject-specific data. This paper proposes both online and offline weighted adaptation regularization (wAR) algorithms to reduce this calibration effort, i.e., to minimize the amount of labeled subject-specific EEG data required in BCI calibration, and hence to increase the utility of the BCI system. We demonstrate using a visually evoked potential oddball task and three different EEG headsets that both online and offline wAR algorithms significantly outperform several other algorithms. Moreover, through source domain selection, we can reduce their computational cost by about  $\text{50}\%$, making them more suitable for real-time applications.
Electroactive polymer actuators as artificial muscles: are they ready for bioinspired applications?

Electroactive polymer (EAP) actuators are electrically responsive materials that have several characteristics in common with natural muscles. Thus, they are being studied as 'artificial muscles' for a variety of biomimetic motion applications. EAP materials are commonly classified into two major families: ionic EAPs, activated by an electrically induced transport of ions and/or solvent, and electronic EAPs, activated by electrostatic forces. Although several EAP materials and their properties have been known for many decades, they have found very limited applications. Such a trend has changed recently as a result of an effective synergy of at least three main factors: key scientific breakthroughs being achieved in some of the existing EAP technologies; unprecedented electromechanical properties being discovered in materials previously developed for different purposes; and higher concentration of efforts for industrial exploitation. As an outcome, after several years of basic research, today the EAP field is just starting to undergo transition from academia into commercialization, with significant investments from large companies. This paper presents a brief overview on the full range of EAP actuator types and the most significant areas of interest for applications. It is hoped that this overview can instruct the reader on how EAPs can enable bioinspired motion systems.
Dynamic Target Tracking and Obstacle Avoidance using a Drone

This paper focuses on tracking dynamic targets using a low cost, commercially available drone. The approach presented utilizes a computationally simple potential field controller expanded to operate not only on relative positions, but also relative velocities. A brief background on potential field methods is given, and the design and implementation of the proposed controller is presented. Experimental results using an external motion capture system for localization demonstrate the ability of the drone to track a dynamic target in real time as well as avoid obstacles in its way.
Norms with feeling: towards a psychological account of moral judgment

There is a large tradition of work in moral psychology that explores the capacity for moral judgment by focusing on the basic capacity to distinguish moral violations (e.g. hitting another person) from conventional violations (e.g. playing with your food). However, only recently have there been attempts to characterize the cognitive mechanisms underlying moral judgment (e.g. Cognition 57 (1995) 1; Ethics 103 (1993) 337). Recent evidence indicates that affect plays a crucial role in mediating the capacity to draw the moral/conventional distinction. However, the prevailing account of the role of affect in moral judgment is problematic. This paper argues that the capacity to draw the moral/conventional distinction depends on both a body of information about which actions are prohibited (a Normative Theory) and an affective mechanism. This account leads to the prediction that other normative prohibitions that are connected to an affective mechanism might be treated as non-conventional. An experiment is presented that indicates that "disgust" violations (e.g. spitting at the table), are distinguished from conventional violations along the same dimensions as moral violations.
Compiling probabilistic logic programs into sentential decision diagrams

Knowledge compilation algorithms transform a probabilistic logic program into a circuit representation that permits efficient probability computation. Knowledge compilation underlies algorithms for exact probabilistic inference and parameter learning in several languages, including ProbLog, PRISM, and LPADs. Developing such algorithms involves a choice, of which circuit language to target, and which compilation algorithm to use. Historically, Binary Decision Diagrams (BDDs) have been a popular target language, whereas recently, deterministicDecomposable Negation Normal Form (d-DNNF) circuits were shown to outperform BDDs on these tasks. We investigate the use of a new language, called Sentential Decision Diagrams (SDDs), for inference in probabilistic logic programs. SDDs combine desirable properties of BDDs and d-DNNFs. Like BDDs, they support bottom-up compilation and circuit minimization, yet they are a more general and flexible representation. Our preliminary experiments show that compilation to SDD yields smaller circuits and more scalable inference, outperforming the state of the art in ProbLog inference.
An Adaptive PMU Based Fault Detection / Location Technique for Transmission Lines Part I : Theory and Algorithms

An adaptive fault detection/location technique based on Phasor Measurement Unit (PMU) for an EHV/UHV transmission line is presented in this two paper set. This paper is Part I of this set. A fault detection/location index in terms of Clarke components of the synchronized voltage and current phasors is derived. The line parameter estimation algorithm is also developed to solve the uncertainty of parameters caused by aging of transmission lines. This paper also proposes a new Discrete Fourier Transform (DFT) based algorithm (termed as Smart Discrete Fourier Transform, SDFT) to eliminate system noise and measurement errors such that extremely accurate fundamental frequency components can be extracted for calculation of fault detection/location index. The EMTP was used to simulate a high voltage transmission line with faults at various locations. To simulate errors involved in measurements, Gaussian-type noise has been added to the raw output data generated by EMTP. Results have shown that the new DFT based method can extract exact phasors in the presence of frequency deviation and harmonics. The parameter estimation algorithm can also trace exact parameters very well. The accuracy of both new DFT based method and parameter estimation algorithm can achieve even up to 99.999% and 99.99% respectively, and will be presented in Part II. The accuracy of fault location estimation by the proposed technique can achieve even up to 99.9% in the performance evaluation, which is also presented in Part II.
Failure identification and analysis for high voltage induction motors in petrochemical industry

The study gives a synopsis over condition monitoring methods both as a diagnostic tool and as a technique for failure identification in high voltage induction motors in industry. New running experience data for 483 motor units with 6135 unit years are registered and processed statistically, to reveal the connection between motor data, protection and condition monitoring methods, maintenance philosophy and different types of failures. The different types of failures are further analyzed to failure-initiators, -contributors and -underlying causes. The results have been compared with those of a previous survey,  Survey of Industrial and Commercial Installations, 1985. In the present survey the motors are in the range of 100 to 1300 kW, 47% of them between 100 and 500 kW.