nils-herrmann/clean_scirepeval huggingface dataset:
Soft compression and the origins of nonlinear behavior of GaN HEMTs

This work responds to the commonly formulated question in PA design of “Why do GaN HEMTs' nonlinear behavior seems so distinct from the one of their Si LDMOS, GaAs MESFETs or HEMTs counterparts?”. Starting from some recent results on the origin of AM/AM and AM/PM distortion in power amplifiers made for these two device types, we demonstrate that there is no fundamental reason why the devices should be different, except that the formers suffer from a noticeable low-frequency dispersion. Then, we dig into this particular aspect of GaN HEMT operation, to show that this charge trapping-related phenomenon is responsible for a severe self-biasing capable of inducing soft-compression of an otherwise almost flat AM/AM gain plot - when measured with static CW tests - which, in fact, corresponds to a severe gain expansion when the AM/AM is assessed with more realistic dynamic tests performed with real communication signals. And it is this class-C PA like AM/AM that is responsible for the recognized GaN HEMT nonlinearity.
Semantically Guided Depth Upsampling

We present a novel method for accurate and efficient upsampling of sparse depth data, guided by high-resolution imagery. Our approach goes beyond the use of intensity cues only and additionally exploits object boundary cues through structured edge detection and semantic scene labeling for guidance. Both cues are combined within a geodesic distance measure that allows for boundary-preserving depth interpolation while utilizing local context. We model the observed scene structure by locally planar elements and formulate the upsampling task as a global energy minimization problem. Our method determines globally consistent solutions and preserves fine details and sharp depth boundaries. In our experiments on several public datasets at different levels of application, we demonstrate superior performance of our approach over the state-of-the-art, even for very sparse measurements.
A new model for Machine Comprehension via multi-perspective context matching and bidrectional attention flow

To answer a question about a context paragraph, there needs to be a complex model for interactions between these two. Previous Machine Comprehension (MC) where either not large enough to train end-to-end deep neural networks, or not hard to learn. Recently, after the release of SQuAD dataset dataset, several adept models have been proposed for the task of MC. In this work we try to combine the ideas of two state-of-the-art models (BiDAF and MPCM) with our new ideas to obtain a new model for question answering task. Promising experimental results on the test set of SQuAD encourages us to continue working on the proposed model.
Innovative enhancement of the Caesar cipher algorithm for cryptography

The Caesar Cipher algorithm for cryptography is one of the oldest algorithms. Now much newer algorithms have arrived that are much more secure, however in terms of speed of execution Caesar cipher algorithm is still the fastest owing to its simplicity. However the algorithm is extremely easy to crack. This is because in this algorithm each character of a message is always replaced by the same fixed character that has been predetermined. To improve the algorithm and enhance it's security feature, a few changes can be added. This paper proposes an enhancement to the existing algorithm by making use first of a simple Diffie-Hellman key exchange scenario to obtain a secret key and later using simple mathematics to ensure the encryption of data is much more safer. Once a private shared key is obtained by making use of the Diffie-Hellman method, the key is subject to the mod operation with 26 to obtain a value less than or equal to 26, then the current character is taken and to this the key value obtained is added to obtain a new character. For any character in the `x' position the key is simply first multiplied with `x' and then mod is done to obtain the encrypted character. So 2nd character of the message is multiplied with 2, third character with 3 and so on. This enhances the security and also does not increase the time of execution by a large margin.
The Intellectual Structure of Human Computer Interaction Research

Although human-computer interaction (HCI) has been developing for thirty years, the field has advanced slowly due to a lack of knowledge about HCI structure. This research thus seeks to explore and clarify HCI intellectual structure. Data collected from the ISI Web of Knowledge totaled 1,168 HCI related articles, and 33,724 references were identified. There were 75 highly cited research articles identified using a citation analysis and then implementing co-citation analyses, including factor analysis, multidimensional scaling and cluster analysis. According to cluster analysis, twelve core knowledge domains of HCI were identified: System capability, User interface design, Task of the HCI system, Evaluation of interaction, User acceptance of technology, Personalized system design, Interface analysis, Performance measurement and improvement, Development of interaction, Human reaction to technology, Facial expression, and Effective interaction. Among these topics, two of them, “Personalized system design” and “Facial expression”, are new intellectual cores which did not appear in previous related researches. The findings of this study provide an intellectual structure and research directions for researchers and practitioners interested in the HCI field.
A 5-DOF rotation-symmetric parallel manipulator with one unconstrained tool rotation

This paper introduces a novel 5-DOF parallel manipulator with a rotation-symmetric arm system. The manipulator is unorthodox since one degree of freedom of its manipulated platform is unconstrained. Such a manipulator is still useful in a wide range of applications utilizing a rotation-symmetric tool. The manipulator workspace is analyzed for singularities and collisions. The rotation-symmetric arm system leads to a large positional workspace in relation to the footprint of the manipulator. With careful choice of structural parameters, the rotational workspace of the tool is also sizeable.
Evaluation of the impact of plug-in electric vehicle loading on distribution system operations

Electric transportation has many attractive features in today's energy environment including decreasing greenhouse gas emissions from the transportation sector, reducing dependence on imported petroleum, and potentially providing consumers a lower cost alternative to gasoline. Plug-in hybrid Electric (PHEV) vehicles represent the most promising approach to electrification of a significant portion of the transportation sector. Electric power utilities recognize this possibility and must analyze the associated impacts to electric system operations. This paper provides details of analytical framework developed to evaluate the impact of PHEV loading on distribution system operations as part of a large, multi-utility collaborative study. This paper also summarizes partial results of the impact of PHEVs on one utility distribution feeder.
A Smart-Power Synchronous Rectifier by CMOS Process

The usage of power MOSFET functioning as a synchronous rectifier is a much preferred choice in switched-mode converters, especially for those of high-frequency and high-power density with low output voltage, for minimization of power loss and efficient energy management. However, its scope of application and integration as a replacement of diode rectifier is handicapped by the compulsory requirement of external sensing and gating circuits needed for precise on/off timing control and the tedious incorporation to be a dependent part of the overall converter circuit. We report a unique smart-power synchronous rectifier (SPSR) that is a two-terminal diode-alike power-integrated circuit building block that provides ultralow on-state voltage rectification without the need of adding any external sensing and gating control. Therefore, a direct replacement of diode rectifier by the SPSR in any converter circuit is made possible. In this paper, the design and fabrication of a fully CMOS-process compatible SPSR device is presented. The fabricated device achieved a low forward voltage of 0.14 V in comparison with 0.75 V of the body diode at the same rated-current density. It has a low specific input capacitance of 11.8 nF/cm2, which makes it suitable for megahertz switching operations. This reported SPSR is CMOS-fabrication-process compatible and can be directly integrated with any power converter circuits on the silicon substrate.
A Survey on Detection of Reasons Behind Infant Cry Using Speech Processing

Infant cry analysis is necessary to detect the reasons behind the cry. Infants can communicate through their cry only. They express their physical and emotional needs through their cry. The crying of an infant can be stopped if the reason behind their cry is known. Therefore, identifying the reason behind infant cry is very essential for medical follow up. In this paper, a literature survey is done to study, analyze, compare the existing approaches and their limitations. This survey helped us to propose a new approach for detecting the reasons behind Infant's cry. The proposed model will use input as the cry signal and from these signal patterns the unique features are extracted using MFCCs, LPCCs and Pitch, etc. These features will help discriminating the patterns from one another. These extracted features will be used to train Neural Network Multilayer classifier. This classifier will be used to identify multiple classes of reasons behind infant cry such as hunger, pain, sleep, discomfort, etc. Efforts will be made for achieving good accuracy results through this approach.
Acute Sexual Assault in the Pediatric and Adolescent Population.

Children and adolescents are at high risk for sexual assault. Early medical and mental health evaluation by professionals with advanced training in sexual victimization is imperative to assure appropriate assessment, forensic evidence collection, and follow-up. Moreover, continued research and outreach programs are needed for the development of preventative strategies that focus on this vulnerable population. In this review we highlight key concepts for assessment and include a discussion of risk factors, disclosure, sequelae, follow-up, and prevention.
Social Internet of Things: The Potential of the Internet of Things for Defining Human Behaviours

The potential of the artificial intelligence, data mining and the so-called Big Data to build solutions based on the understanding of human behaviours is being extended with the capabilities of the Future Internet and the Internet of Things (IoT). The described potential of data and networks have been analyzed by the complex network theory, offering a specialized branch, the defined as Human Dynamics. The goal with the Internet of Things in the social area is to describe in real-time the human behaviours and activities. These goals are starting to be feasible through the quantity of data provided by the personal devices such as smart phone, and the smart environments such as Smart cities that makes more intelligent the environment and offer a smart space that sense our movements, actions, and the evolution of the ecosystem. This work analyses the ecosystem defined through the triangle formed by Big Data, Smart Cities and Personal/Wearable Computing to determinate human behaviours and human dynamics. For this purpose, the life-cycle of human dynamics determinations in Smart Cities have been analysed in order to determinate the current status of the technology, challenges, and opportunities.
Learning Symbolic Models of Stochastic Domains

In this article, we work towards the goal of developing agents that can learn to act in complex worlds. We develop a probabilistic, relational planning rule representation that compactly models noisy, nondeterministic action effects, and show how such rules can be effectively learned. Through experiments in simple planning domains and a 3D simulated blocks world with realistic physics, we demonstrate that this learning algorithm allows agents to effectively model world dynamics.
Learning effective Gait features using LSTM

Human gait is an important biometric feature for person identification in surveillance videos because it can be collected at a distance without subject cooperation. Most existing gait recognition methods are based on Gait Energy Image (GEI). Although the spatial information in one gait sequence can be well represented by GEI, the temporal information is lost. To solve this problem, we propose a new feature learning method for gait recognition. Not only can the learned feature preserve temporal information in a gait sequence, but it can also be applied to cross-view gait recognition. Heatmaps extracted by a convolutional neutral network (CNN) based pose estimate method are used to describe the gait information in one frame. To model a gait sequence, the LSTM recurrent neural network is naturally adopted. Our LSTM model can be trained with unlabeled data, where the identity of the subject in a gait sequence is unknown. When labeled data are available, our LSTM works as a frame to frame view transformation model (VTM). Experiments on a gait benchmark demonstrate the efficacy of our method.
A 0.003 mm$^{2}$ 10 b 240 MS/s 0.7 mW SAR ADC in 28 nm CMOS With Digital Error Correction and Correlated-Reversed Switching

This paper describes a single-channel, calibration-free Successive-Approximation-Register (SAR) ADC with a resolution of 10 bits at 240 MS/s. A DAC switching technique and an addition-only digital error correction technique based on the non-binary search are proposed to tackle the static and dynamic non-idealities attributed to capacitor mismatch and insufficient DAC settling. The conversion speed is enhanced, and the power and area of the DAC are also reduced by 40% as a result. In addition, a switching scheme lifting the input common mode of the comparator is proposed to further enhance the speed. Moreover, the comparator employs multiple feedback paths for an enhanced regeneration strength to alleviate the metastable problem. Occupying an active area of 0.003 mm 2 and dissipating 0.68 mW from 1 V supply at 240 MS/s in 28 nm CMOS, the proposed design achieves an SNDR of 57 dB with low-frequency inputs and 53 dB at the Nyquist input. This corresponds to a conversion efficiency of 4.8 fJ/c.-s. and 7.8 fJ/c.-s. respectively. The DAC switching technique improves the INL and DNL from +1.15/-1.01 LSB and +0.92/-0.28 LSB to within +0.55/-0.45 LSB and +0.45/-0.23 LSB, respectively. This ADC is at least 80% smaller and 32% more power efficient than reported state-of-the-art ADCs of similar resolutions and Nyquist bandwidths larger than 75 MHz.
The Impact of Clinical Empathy on Patients and Clinicians : Understanding Empathy ' s Side Effects

Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content.
A Review and Analysis of Commercial User Modeling Servers for Personalization on the World Wide Web

The aim of this article is to present and discuss selected commercial user modeling systems against the background of deployment requirements in real-world environments. Following the recent trend towards personalization on the World Wide Web, these systems are mainly aimed at supporting e-commerce including customer relationship management. In order to guide and structure our review, we define a requirements catalogue that comprises the main dimensions of functionality, data acquisition, representation, extensibility and flexibility, integration of external user-related information, compliance with standards, concern for privacy, and system architecture. Apart from the novelty of such a comparison both inside and outside the classical user modeling literature, a presentation of the core features of these commercial systems may provide a source of information and inspiration for the design, implementation, and deployment of future user modeling systems in research and commercial environments.
Logic and Artificial Intelligence

Nilsson, N.J., Logic and artificial intelligence, Artificial Intelligence 47 (1990) 31-56. The theoretical foundations of the logical approach to artificial intelligence are presented. Logical languages are widely used for expressing the declarative knowledge needed in artificial intelligence systems. Symbolic logic also provides a clear semantics for knowledge representation languages and a methodology for analyzing and comparing deductive inference techniques. Several observations gained from experience with the approach are discussed. Finally, we confront some challenging problems for artificial intelligence and describe what is being done in an attempt to solve them.
Transfer learning for time series classification

Transfer learning for deep neural networks is the process of first training a base network on a source dataset, and then transferring the learned features (the network’s weights) to a second network to be trained on a target dataset. This idea has been shown to improve deep neural network’s generalization capabilities in many computer vision tasks such as image recognition and object localization. Apart from these applications, deep Convolutional Neural Networks (CNNs) have also recently gained popularity in the Time Series Classification (TSC) community. However, unlike for image recognition problems, transfer learning techniques have not yet been investigated thoroughly for the TSC task. This is surprising as the accuracy of deep learning models for TSC could potentially be improved if the model is fine-tuned from a pre-trained neural network instead of training it from scratch. In this paper, we fill this gap by investigating how to transfer deep CNNs for the TSC task. To evaluate the potential of transfer learning, we performed extensive experiments using the UCR archive which is the largest publicly available TSC benchmark containing 85 datasets. For each dataset in the archive, we pre-trained a model and then fine-tuned it on the other datasets resulting in 7140 different deep neural networks. These experiments revealed that transfer learning can improve or degrade the models predictions depending on the dataset used for transfer. Therefore, in an effort to predict the best source dataset for a given target dataset, we propose a new method relying on Dynamic Time Warping to measure inter-datasets similarities. We describe how our method can guide the transfer to choose the best source dataset leading to an improvement in accuracy on 71 out of 85 datasets.
Generating Adversarial Driving Scenarios in High-Fidelity Simulators

In recent years self-driving vehicles have become more commonplace on public roads, with the promise of bringing safety and efficiency to modern transportation systems. Increasing the reliability of these vehicles on the road requires an extensive suite of software tests, ideally performed on highfidelity, photorealistic simulators, where multiple vehicles and pedestrians interact with the self-driving vehicle. It is therefore of critical importance to ensure that self-driving software is assessed against a wide range of challenging simulated driving scenarios. The state of the art in driving scenario generation, as adopted by the frontrunners of the self-driving car industry, still relies on human input . In this paper we propose to automate the process using Bayesian Optimization to generate adversarial self-driving scenarios that expose poorly-engineered or poorly-trained self-driving policies, and increase the risk of collision with simulated pedestrians and vehicles. We show that by incorporating the generated scenarios into the training set of the self-driving policy, and by fine-tuning the policy using vision-based imitation learning we obtain safer self-driving behavior.
A 2.4-GHz ZigBee Transmitter Using a Function-Reuse Class-F DCO-PA and an ADPLL Achieving 22.6% (14.5%) System Efficiency at 6-dBm (0-dBm) $P_{\mathrm {out}}$

This paper describes a sub-1-V 2.4-GHz ZigBee transmitter (TX) with scalable output power (<inline-formula> <tex-math notation="LaTeX">$P_{\mathrm {out}}$ </tex-math></inline-formula>) and system efficiency. It features a function-reuse class-F topology unifying the digital-controlled oscillator (DCO) and power amplifier (PA), designated as DCO-PA. Unlike the existing current-reuse topologies that rely on transistor stacking, here the power consumption of the DCO and PA-driver is absorbed into the DCO-PA without losing the voltage headroom, while allowing a low supply voltage. The DCO-PA also benefits from a six-port transformer with customized coupling coefficients and turn ratios to jointly perform the functions of resonant tank and output matching network, saving the chip area. A fractional-N all-digital phase-locked loop (ADPLL) realizes a two-point data modulation. Its phase-interpolated time-to-digital converter prevents time-consuming calibration. The entire TX fabricated in 65-nm CMOS occupies a 0.39-mm<inline-formula> <tex-math notation="LaTeX">$^{\mathrm { {2}}}$ </tex-math></inline-formula> active area. The standalone DCO-PA shows a peak efficiency of 26.2% at a 6-dBm <inline-formula> <tex-math notation="LaTeX">$P_{\mathrm {out}}$ </tex-math></inline-formula>, and a back-off efficiency of 17.7% at a −4.3 dBm <inline-formula> <tex-math notation="LaTeX">$P_{\mathrm {out}}$ </tex-math></inline-formula> under a scalable supply voltage (0.3–0.7 V). The system efficiency, including the ADPLL, is 22.6% (14.5%) at 6-dBm (0-dBm) <inline-formula> <tex-math notation="LaTeX">$P_{\mathrm {out}}$ </tex-math></inline-formula>. The HS-OQPSK modulated output complies with the ZigBee spectral mask with an adequate margin and the error vector magnitude is 2.29%.
Design and implementation of low-profile contactless battery charger using planar printed circuit board windings as energy transfer device

This paper paper presents the practical details involved in the design and implementation of a contactless battery charger that employs a pair of neighboring printed circuit board (PCB) windings as a contactless energy transfer device. A prototype contactless battery charger developed for application with cellular phones is used as an example to address the design considerations for the PCB windings and energy transfer circuit, plus demonstrates the performance of the contactless charger adapted to a practical application system.