nils-herrmann/clean_scirepeval huggingface dataset:
Low-Power Supply-Regulation Techniques for Ring Oscillators in Phase-Locked Loops Using a Split-Tuned Architecture

A supply-regulated phase-locked loop (PLL) employs a split-tuned architecture to decouple the tradeoff between supply-noise rejection performance and power consumption. By placing the regulator in the low-bandwidth coarse loop, the proposed PLL architecture allows us to maximize its bandwidth to suppress the oscillator phase noise with neither the power supply-noise rejection nor the power dissipation of the regulator being affected. A replica-based regulator introduces a low-frequency pole in its supply-noise transfer function and avoids degradation of supply-noise rejection beyond the regulator-loop's dominant pole frequency. The prototype PLL fabricated in a 0.18 mum digital CMOS process operates from 0.5 to 2.5 GHz. At 1.5 GHz, the proposed PLL achieves 1.9 ps long-term rms jitter and a worst case supply-noise sensitivity of -28 dB (0.5 rad/V), an improvement of 20 dB over conventional solutions, while consuming 2.2 mA from a 1.8 V supply.
Autonomous robotic system for bridge deck data collection and analysis

Bridge deck inspection is conducted to identify bridge condition deterioration and, thus, to facilitate implementation of appropriate maintenance or rehabilitation procedures. In this paper, we report the development of a robotic system for bridge deck data collection and analysis. The robotic system accurately localizes itself and autonomously maneuvers on the bridge deck to collect visual images and conduct nondestructive evaluation (NDE) measurements. The developed robotic system can reduce the cost and time of the bridge deck data collection. Crack detection and mapping algorithm to build the deck crack maps is presented in detail. The electrical resistivity (ER), impact-echo (IE) and ultrasonic surface waves (USW) data collected by the robot are analyzed to generate the corrosion, delamination and concrete elastic modulus maps of the deck. The presented robotic system has been successfully deployed to inspect numerous bridges.
Development and design of a robotic manta ray featuring flexible pectoral fins

This paper showed our preliminary work in probing into fish robot with pectoral fin propulsion. The aim of this project is to develop a fish robot mimicking manta rays that swim with flexible pectoral fins in oscillation motion. Based on the analysis of motion feature and skeletal structure of pectoral fin of manta rays, a kind of flexible pectoral fin made of silicone rubber was designed. Experiments on the propulsion of fin prototype were carried out in tank. Experiment results showed that flexible pectoral fin in oscillation motion could generate great thrust and the thrust would increase with fin-beat frequency and amplitude increasing. In the end, a robot fish with flexible pectoral fins was built. The speed of robot fish could reach the maximum of 1.4 times BL (body length) per second. Experiments proved that it was feasible to develop a robot fish featuring flexible pectoral fins in oscillation motion and the method to mimic manta rays was a great idea to improve performance of underwater vehicles.
Sensor observation streams within cloud-based IoT platforms: Challenges and directions

Observation streams can be considered as a special case of data streams produced by sensors. With the growth of the Internet of Things (IoT), more and more connected sensors will produce unbounded observation streams. In order to bridge the gap between sensors and observation consumers, we have witnessed the design and the development of Cloud-based IoT platforms. Such systems raise new research challenges, in particular regarding observation collection, processing and consumption. These new research challenges are related to observation streams and should be addressed from the implementation phase by developers to build platforms able to meet other non-functional requirements later. Unlike existing surveys, this paper is intended for developers that would like to design and implement a Cloud-based IoT platform capable of handling sensor observation streams. It provides a comprehensive way to understand main observation-related challenges, as well as non-functional requirements of IoT platforms such as platform adaptation, scalability and availability. Last but not the least, it gives recommendations and compares some relevant open-source software that can speed up the development process.
Iterative Integration of Visual Insights during Scalable Patent Search and Analysis

Patents are of growing importance in current economic markets. Analyzing patent information has, therefore, become a common task for many interest groups. As a prerequisite for patent analysis, extensive search for relevant patent information is essential. Unfortunately, the complexity of patent material inhibits a straightforward retrieval of all relevant patent documents and leads to iterative, time-consuming approaches in practice. Already the amount of patent data to be analyzed poses challenges with respect to scalability. Further scalability issues arise concerning the diversity of users and the large variety of analysis tasks. With "PatViz”, a system for interactive analysis of patent information has been developed addressing scalability at various levels. PatViz provides a visual environment allowing for interactive reintegration of insights into subsequent search iterations, thereby bridging the gap between search and analytic processes. Because of its extensibility, we expect that the approach we have taken can be employed in different problem domains that require high quality of search results regarding their completeness.
Adaptive Control of an Inverted Pendulum Using Adaptive PID Neural Network

Abstract- In this paper, a new method for adaptive control of nonlinear systems using neural networks and Proportional-Integral-Derivative (PID) methodology is proposed. In this method, a PID control and adaptive linear neural network is used to control a inverted pendulum with uncertainities and external disturbances. The system consists of an inverted pole hinged on a cart which is free to move in the x direction. The performance of the proposed method is demonstrated via simulations.
Dyslexia in English as a second language.

This study focused on English as L2 in a group of Norwegian dyslexic 12 year olds, compared to an age and gender matched control group. Norwegian school children learn English from the first grades on. The subjects were assessed with a test battery of verbal and written tasks. First, they were given a comprehension task; second, a model sentence task; third, two pragmatic tasks, and fourth, three tasks of literacy. The verbal tasks were scored according to comprehension, morphology, syntax and semantics, while the literacy tasks were scored by spelling, translation and reading skills. It was hypothesized that the results of the control group and the dyslexia group would differ on all tasks, but that subgrouping the dyslexia group by comprehension skills would show heterogeneity within the dyslexia group. The data analyses confirmed these hypotheses. Significant differences were seen between the dyslexia group and the control group. However, the subgrouping revealed minor differences between the control group and the subgroup with good comprehension skills, and major differences between the control group and the subgroup with poor comprehension skills. Especially morphology and spelling were difficult for the dyslexia group. The results were tentatively discussed within the framework of biological and cognitive models of how to interpret L2 performance in dyslexia, underlining the importance of further research in L2 acquisition in dyslexia.
Phase noise modelling and mitigation techniques in ofdm communications systems

This paper addresses the analysis and mitigation of the signal distortion caused by oscillator phase noise (PN) in OFDM communications systems. Two new PN mitigation techniques are proposed, especially targeted for reducing the intercarrier interference (ICI) effects due to PN. The first proposed method is a fairly simple one, stemming from the idea of linearly interpolating between two consecutive common phase error (CPE) estimates to obtain a linearized estimate of the time-varying phase characteristics. The second technique, in turn, is an extension to the existing state-of-the-art ICI estimation methods. Here the idea is to use an additional interpolation stage to improve the phase estimation performance around the boundaries of two consecutive OFDM symbols. The paper also verifies the performance improvement of these new PN estimation techniques by comparing them to the existing state-of-the-art techniques using extensive computer simulations. To emphasize practicality, the simulations are carried out in 3GPP-LTE downlink -like system context, covering both additive white Gaussian noise (AWGN) and extended ITU-R Vehicular A multipath channel types.
Predicting spatial structure of soil physical and chemical properties of golf course fairways using an apparent electrical conductivity sensor

Soil apparent electrical conductivity (ECa) has been used to map spatial variability of soil properties in multiple cropping systems and may have applications in precision turfgrass management (PTM). The objective of this research was to determine whether ECa data could predict the spatial structure of soil properties relevant to turfgrass management. Research was conducted at the University of Georgia (UGA) and the Georgia Club (GC) golf courses in North GA during the summer of 2016. A mobile Veris Q1000 device was used to collect georeferenced ECa data from six golf course fairways (three per course). Soil samples were collected from each fairway using a georeferenced grid to determine clay content, soil pH, cation exchange capacity (CEC) and organic matter (OM). To understand the predictive relationship between ECa and soil properties, correlation coefficients and multiple linear regression models were generated for each fairway. Spatial maps were used to visually demonstrate these relationships. Though some relationships were observed between ECa and soil properties (primarily clay, soil pH and OM on the UGA course), measured parameters were insufficient to fully explain spatial variability in ECa. Findings from this study suggest that spatial variability of soil properties in turfgrass can be significant enough to warrant PTM. Though ECa may be used to partially predict clay content, CEC, OM and soil pH, additional research is required to better understand ECa variability and its applications for PTM. Future research exploring ECa for PTM should consider the roles of soil moisture, temporal variability and topography.
Meta-Learning with Latent Embedding Optimization

Gradient-based meta-learning techniques are both widely applicable and proficient at solving challenging few-shot learning and fast adaptation problems. However, they have the practical difficulties of operating in high-dimensional parameter spaces in extreme low-data regimes. We show that it is possible to bypass these limitations by learning a low-dimensional latent generative representation of model parameters and performing gradient-based meta-learning in this space with latent embedding optimization (LEO), effectively decoupling the gradientbased adaptation procedure from the underlying high-dimensional space of model parameters. Our evaluation shows that LEO can achieve state-of-the-art performance on the competitive 5-way 1-shot miniImageNet classification task.
A DNA-based implementation of YAEA encryption algorithm

The fundamental idea behind this encryption technique is the exploitation of DNA cryptographic strength, such as its storing capabilities and parallelism in order to enforce other conventional cryptographic algorithms. In this study, a binary form of data, such as plaintext messages, and images are transformed into sequences of DNA nucleotides. Subsequently, efficient searching algorithms are used to locate the multiple positions of a sequence of four DNA nucleotides. These four DNA nucleotides represent the binary octet of a single plaintext character or the single pixel of an image within, say, a Canis Familiaris genomic chromosome. The process of recording the locations of a sequence of four DNA nucleotides representing a single plain-text character, then returning a single randomly chosen position, will enable us to assemble a file of random pointers of the locations of the four DNA nucleotides in the searched Canis Families genome. We call the file containing the randomly selected position in the searchable DNA strand for each plain text character, the ciphered text. Since there is negligible correlation between the pointers file obtained from the selected genome, with its inherently massive storing capabilities, and the plain-text characters, the method, we believe, is robust against any type of cipher attacks.
Multi-user computation offloading as Multiple Knapsack Problem for 5G Mobile Edge Computing

We present an overview of NP-hard problems and methods related to deployment, resource sharing, load balancing and fairness among multiple users in 5G mobile networks. We provide a general model of the system considering the E2E computational latency of Mobile Edge Computing (MEC) applications. An evaluation of a multi-user MEC offloading model reducible to the Multiple Knapsack Problem (MKP) is performed. We study the behaviour of an exact and a heuristic algorithm. We also show the performance results of these in a real-life small-cell deployment scenario.
Batteries and Ultracapacitors for Electric, Hybrid, and Fuel Cell Vehicles

The application of batteries and ultracapacitors in electric energy storage units for battery powered (EV) and charge sustaining and plug-in hybrid-electric (HEV and PHEV) vehicles have been studied in detail. The use of IC engines and hydrogen fuel cells as the primary energy converters for the hybrid vehicles was considered. The study focused on the use of lithium-ion batteries and carbon/carbon ultracapacitors as the energy storage technologies most likely to be used in future vehicles. The key findings of the study are as follows. 1) The energy density and power density characteristics of both battery and ultracapacitor technologies are sufficient for the design of attractive EVs, HEVs, and PHEVs. 2) Charge sustaining, engine powered hybrid-electric vehicles (HEVs) can be designed using either batteries or ultracapacitors with fuel economy improvements of 50% and greater. 3) Plug-in hybrids (PHEVs) can be designed with effective all-electric ranges of 30-60 km using lithium-ion batteries that are relatively small. The effective fuel economy of the PHEVs can be very high (greater than 100 mpg) for long daily driving ranges (80-150 km) resulting in a large fraction (greater than 75%) of the energy to power the vehicle being grid electricity. 4) Mild hybrid-electric vehicles (MHEVs) can be designed using ultracapacitors having an energy storage capacity of 75-150 Wh. The fuel economy improvement with the ultracapacitors is 10%-15% higher than with the same weight of batteries due to the higher efficiency of the ultracapacitors and more efficient engine operation. 5) Hybrid-electric vehicles powered by hydrogen fuel cells can use either batteries or ultracapacitors for energy storage. Simulation results indicate the equivalent fuel economy of the fuel cell powered vehicles is 2-3 times higher than that of a gasoline fueled IC vehicle of the same weight and road load. Compared to an engine-powered HEV, the equivalent fuel economy of the hydrogen fuel cell vehicle would be 1.66-2.0 times higher
Comparing context-aware recommender systems in terms of accuracy and diversity

Although the area of context-aware recommender systems (CARS) has made a significant progress over the last several years, the problem of comparing various contextual pre-filtering, post-filtering and contextual modeling methods remained fairly unexplored. In this paper, we address this problem and compare several contextual pre-filtering, post-filtering and contextual modeling methods in terms of the accuracy and diversity of their recommendations to determine which methods outperform the others and under which circumstances. To this end, we consider three major factors affecting performance of CARS methods, such as the type of the recommendation task, context granularity and the type of the recommendation data. We show that none of the considered CARS methods uniformly dominates the others across all of these factors and other experimental settings; but that a certain group of contextual modeling methods constitutes a reliable “best bet” when choosing a sound CARS approach since they provide a good balance of accuracy and diversity of contextual recommendations.
Gating Neural Network for Large Vocabulary Audiovisual Speech Recognition

Audio-based automatic speech recognition A-ASR systems are affected by noisy conditions in real-world applications. Adding visual cues to the ASR system is an appealing alternative to improve the robustness of the system, replicating the audiovisual perception process used during human interactions. A common problem observed when using audiovisual automatic speech recognition AV-ASR is the drop in performance when speech is clean. In this case, visual features may not provide complementary information, introducing variability that negatively affects the performance of the system. The experimental evaluation in this study clearly demonstrates this problem when we train an audiovisual state-of-the-art hybrid system with a deep neural network DNN and hidden Markov models HMMs. This study proposes a framework that addresses this problem, improving, or at least, maintaining the performance when visual features are used. The proposed approach is a deep learning solution with a gating layer that diminishes the effect of noisy or uninformative visual features, keeping only useful information. The framework is implemented with a subset of the audiovisual CRSS-4ENGLISH-14 corpus which consists of 61 h of speech from 105 subjects simultaneously collected with multiple cameras and microphones. The proposed framework is compared with conventional HMMs with observation models implemented with either a Gaussian mixture model or DNNs. We also compare the system with a multi-stream HMM system. The experimental evaluation indicates that the proposed framework outperforms alternative methods under all configurations, showing the robustness of the gating-based framework for AV-ASR.
Determinants of Information Security Awareness: An Empirical Investigation in Higher Education

This paper aims to identify the determinants that affect information security awareness (ISA) of college students. With the prevalence and continuous growing of information technology (IT) in our daily life, we are experiencing immense benefits of the computerintensive business environment. However, information security is a critical issue at the same time. It is necessary to examine the ISA and intention to take action to reduce security risks. ISA refers to what degree that users understand the risks of using information systems (IS) and the corresponding methods to protect them from being compromised. It is demanded to understand what factors affecting users’ ISA that will directly affect their behaviors. We will collect the data by sending out survey through college students. They are a special group of users of IS due to their young age and their academic environment. The expected contributions and implications are discussed.
Statistics: a brief overview.

The Accreditation Council for Graduate Medical Education sets forth a number of required educational topics that must be addressed in residency and fellowship programs. We sought to provide a primer on some of the important basic statistical concepts to consider when examining the medical literature. It is not essential to understand the exact workings and methodology of every statistical test encountered, but it is necessary to understand selected concepts such as parametric and nonparametric tests, correlation, and numerical versus categorical data. This working knowledge will allow you to spot obvious irregularities in statistical analyses that you encounter.
Big data visualization: Review of techniques and datasets

In the last 20 years the term of Big Data took strength which refers to datasets that in size exceed the ability of typical database tools to capture store manage and analyze. Big Data visual analysis is a new field that is emerging as a powerful tool for extracting useful information. This paper discusses the revision of 83 articles on visualization techniques for Big Data of the last six years for the future realization of a comparative analysis of these techniques and to determine which are the most optimistic when analyzing Big Data.
Small-Size Hybrid Loop/Open-Slot Antenna for the LTE Smartphone

A small-size hybrid loop/open-slot antenna for the LTE smartphone is presented. The antenna is disposed in a small notch of 7 × 40 mm2 in the system ground plane of the smartphone and has a thickness of 5.8 mm to be capable of embedding within most modern smartphones. The antenna mainly comprises a capacitively fed loop antenna and a ground strip embedded therein to create an open-slot antenna so as to generate additional resonant modes for bandwidth enhancement. The antenna can provide two wide operating bands to cover the LTE low band (698-960 MHz) and the LTE middle/high bands (1710-2690/3400-3800 MHz). In this study, the creation of the open-slot antenna using a ground strip embedded in the ground clearance region for the coupled-fed loop antenna is addressed. The proposed antenna is also fabricated and tested. Both the simulation study and the experimental results are presented.
A 324-Element Vivaldi Antenna Array for Radio Astronomy Instrumentation

This paper presents a 324-element 2-D broadside array for radio astronomy instrumentation which is sensitive to two mutually orthogonal polarizations. The array is composed of cruciform units consisting of a group of four Vivaldi antennas arranged in a cross-shaped structure. The Vivaldi antenna used in this array exhibits a radiation intensity characteristic with a symmetrical main beam of 87.5° at 3 GHz and 44.2° at 6 GHz. The measured maximum side/backlobe level is 10.3 dB below the main beam level. The array can operate at a high frequency of 5.4 GHz without the formation of grating lobes.
Modeling tone and intonation in Mandarin and English as a process of target approximation.

This paper reports the development of a quantitative target approximation (qTA) model for generating F(0) contours of speech. The qTA model simulates the production of tone and intonation as a process of syllable-synchronized sequential target approximation [Xu, Y. (2005). "Speech melody as articulatorily implemented communicative functions," ]. It adopts a set of biomechanical and linguistic assumptions about the mechanisms of speech production. The communicative functions directly modeled are lexical tone in Mandarin and lexical stress in English and focus in both languages. The qTA model is evaluated by extracting function-specific model parameters from natural speech via supervised learning (automatic analysis by synthesis) and comparing the F(0) contours generated with the extracted parameters to those of natural utterances through numerical evaluation and perceptual testing. The F(0) contours generated by the qTA model with the learned parameters were very close to the natural contours in terms of root mean square error, rate of human identification of tone, and focus and judgment of naturalness by human listeners. The results demonstrate that the qTA model is both an effective tool for research on tone and intonation and a potentially effective system for automatic synthesis of tone and intonation.