nils-herrmann/clean_scirepeval huggingface dataset:
Shape and Pose Space Deformation for Subject Specific Animation

In this paper we present a framework for generating arbitrary human models and animating them realistically given a few intuitive parameters. Shape and pose space deformation (SPSD) is introduced as a technique for modeling subject specific pose induced deformations from whole-body registered 3D scans. By exploiting examples of different people in multiple poses we are able to realistically animate a novel subject by interpolating and extrapolating in a joint shape and pose parameter space. Our results show that we can produce plausible animations of new people and that greater detail is achieved by incorporating subject specific pose deformations. We demonstrate the application of SPSD to produce subject specific animation sequences driven by RGB-Z performance capture.
Spherical CNNs

Convolutional Neural Networks (CNNs) have become the method of choice for learning problems involving 2D planar images. However, a number of problems of recent interest have created a demand for models that can analyze spherical images. Examples include omnidirectional vision for drones, robots, and autonomous cars, molecular regression problems, and global weather and climate modelling. A naive application of convolutional networks to a planar projection of the spherical signal is destined to fail, because the space-varying distortions introduced by such a projection will make translational weight sharing ineffective. In this paper we introduce the building blocks for constructing spherical CNNs. We propose a definition for the spherical cross-correlation that is both expressive and rotation-equivariant. The spherical correlation satisfies a generalized Fourier theorem, which allows us to compute it efficiently using a generalized (non-commutative) Fast Fourier Transform (FFT) algorithm. We demonstrate the computational efficiency, numerical accuracy, and effectiveness of spherical CNNs applied to 3D model recognition and atomization energy regression.
A study of aircraft taxi performance for enhancing airport surface traffic control

In view of the ever-increasing air traffic, much attention in air traffic management research has been given to improving arrival and departure efficiency. As air traffic begins and ends at the airport, the issues of taxi delays and ground incursions are becoming more evident. This paper considers the surface-traffic problem at major airports and envisions a collaborative traffic and aircraft control environment where a surface traffic automation system will help coordinate surface traffic movements. Specifically, this paper studies the performance potential of highprecision taxi toward the realization of such an environment. A state-of-the-art nonlinear control system based on feedback linearization is designed for a detailed B-737 aircraft taxi model. The simulation model with the nonlinear control system is evaluated extensively in a scenario representing the demanding situation of an arrival aircraft crossing an adjacent active runway immediately following its own landing. The evaluation establishes the potential of an automated system to achieve high-precision taxi control, including the ability to comply with taxi clearances with tight time margins. Such a high-precision taxi capability reduces the time margin required for clearing taxiing aircraft to cross active runways, thus increasing the opportunity for issuing such clearances, which in turn reduces the need for aircraft to hold short at the runways to wait for the opportunity for crossing. The results from the analyses provide insight into future aircraft operational capabilities toward the design of the envisioned surface traffic automation system. Moreover, the nonlinear control design serves as a preliminary study for future auto-taxi functional development.
Recent Trends in Deep Learning Based Natural Language Processing [Review Article]

Deep learning methods employ multiple processing layers to learn hierarchical representations of data, and have produced state-of-the-art results in many domains. Recently, a variety of model designs and methods have blossomed in the context of natural language processing (NLP). In this paper, we review significant deep learning related models and methods that have been employed for numerous NLP tasks and provide a walk-through of their evolution. We also summarize, compare and contrast the various models and put forward a detailed understanding of the past, present and future of deep learning in NLP.
Diagnosing and Predicting Wind Turbine Faults from SCADA Data Using Support Vector Machines

Unscheduled or reactive maintenance on wind turbines due to component failure incurs significant downtime and, in turn, loss of revenue. To this end, it is important to be able to perform maintenance before it’s needed. To date, a strong effort has been applied to developing Condition Monitoring Systems (CMSs) which rely on retrofitting expensive vibration or oil analysis sensors to the turbine. Instead, by performing complex analysis of existing data from the turbine’s Supervisory Control and Data Acquisition (SCADA) system, valuable insights into turbine performance can be obtained at a much lower cost. In this paper, fault and alarm data from a turbine on the Southern coast of Ireland is analysed to identify periods of nominal and faulty operation. Classification techniques are then applied to detect and diagnose faults by taking into account other SCADA data such as temperature, pitch and rotor data. This is then extended to allow prediction and diagnosis in advance of specific faults. Results are provided which show recall scores generally above 80% for fault detection and diagnosis, and prediction up to 24 hours in advance of specific faults, representing significant improvement over previous techniques.
Disease detection on the leaves of the tomato plants by using deep learning

The aim of this work is to detect diseases that occur on plants in tomato fields or in their greenhouses. For this purpose, deep learning was used to detect the various diseases on the leaves of tomato plants. In the study, it was aimed that the deep learning algorithm should be run in real time on the robot. So the robot will be able to detect the diseases of the plants while wandering manually or autonomously on the field or in the greenhouse. Likewise, diseases can also be detected from close-up photographs taken from plants by sensors built in fabricated greenhouses. The examined diseases in this study cause physical changes in the leaves of the tomato plant. These changes on the leaves can be seen with RGB cameras. In the previous studies, standard feature extraction methods on plant leaf images to detect diseases have been used. In this study, deep learning methods were used to detect diseases. Deep learning architecture selection was the key issue for the implementation. So that, two different deep learning network architectures were tested first AlexNet and then SqueezeNet. For both of these deep learning networks training and validation were done on the Nvidia Jetson TX1. Tomato leaf images from the PlantVillage dataset has been used for the training. Ten different classes including healthy images are used. Trained networks are also tested on the images from the internet.
Articulatory , positional and coarticulatory characteristics for clear / l / and dark / l / : evidence from two Catalan dialects

Electropalatographic and acoustic data reported in this study show differences in closure location and degree, dorsopalatal contact size, closure duration, relative timing of events and formant frequency between clear /l/ and dark /l/ in two dialects of Catalan (Valencian and Majorcan). The two Catalan dialects under investigation differ also regarding degree of darkness but essentially not regarding coarticulatory resistance at the word edges, i.e. the alveolar lateral is equally dark word-initially and word-finally in Majorcan, and clearer in the former position vs. than the latter in Valencian, and more resistant to vowel effects in the two positions than intervocalically in both dialects. With reference to data from the literature, it appears that languages and dialects may differ as to whether /l/ is dark or clear in all word positions or whether or not initial /l/ is clearer than final /l/, and that articulatory strengthening occurs not only wordand utterance-initially but wordand utterance-finally as well. These and other considerations confirm the hypothesis that degree of darkness in /l/ proceeds gradually rather than categorically from one language to another.
The Multi-User Security of Authenticated Encryption: AES-GCM in TLS 1.3

We initiate the study of multi-user (mu) security of authenticated encryption (AE) schemes as a way to rigorously formulate, and answer, questions about the “randomized nonce” mechanism proposed for the use of the AE scheme GCM in TLS 1.3. We (1) Give definitions of mu ind (indistinguishability) and mu kr (key recovery) security for AE (2) Characterize the intent of nonce randomization as being improved mu security as a defense against mass surveillance (3) Cast the method as a (new) AE scheme RGCM (4) Analyze and compare the mu security of both GCM and RGCM in the model where the underlying block cipher is ideal, showing that the mu security of the latter is indeed superior in many practical contexts to that of the former, and (5) Propose an alternative AE scheme XGCM having the same efficiency as RGCM but better mu security and a more simple and modular design.
A UHF class E2 DC/DC converter using GaN HEMTs

In this paper, the design of a class E2 resonant DC/DC converter, operating at UHF band, is proposed. Combining the use of GaN HEMT devices, both for the inverter and the synchronous rectifier, with high Q lumped-element multi-harmonic matching networks, a peak efficiency value of 72% has been obtained at 780 MHz with a 10.3 W output power. By means of a Pulse Width Modulation (PWM) over the gate driving envelope, the output voltage may be controlled while keeping low switching losses, with an estimated small-signal bandwidth (BW) and a slew rate of 11 MHz and 630 V/µSeg, respectively.
An empirical approach to the experience of architectural space in virtual reality — exploring relations between features and affective appraisals of rectangular indoor spaces

In the presented exploratory study, quantitative relations between the experience of architectural spaces and physical properties were investigated using virtual reality (VR) simulations. Geometric properties from a component-based description of rectangular rooms were tested on their suitability as predictor variables for experiential qualities. In a virtual-reality-based perceptual experiment, qualities of 16 vacant rectangular interiors were rated in eight principal categories by 16 participants using the semantic differential scaling technique. The simulated scenes were automatically generated by means of a custom made utility that also provided for the component-based room descriptions. The data analysis revealed strong correlations between several scene features and the averaged rated experience. For example, a preference for ratios near to the golden section could be observed for spatial proportions, which are not directly perceivable. Altogether, a set of five widely independent factors (openness, two room proportions, room area, and balustrade height) turned out to be most effective for describing the observed variance in the attributed experiential qualities. The combination of realistic virtual reality simulations and psychophysical data raising methods proved to be an effective means for basic architectural research. Several quantitative relations between physical properties and the emotional experience of architectural space could be demonstrated. D 2004 Elsevier B.V. All rights reserved.
RGBD Salient Object Detection: A Benchmark and Algorithms

Although depth information plays an important role in the human vision system, it is not yet well-explored in existing visual saliency computational models. In this work, we first introduce a large scale RGBD image dataset to address the problem of data deficiency in current research of RGBD salient object detection. To make sure that most existing RGB saliency models can still be adequate in RGBD scenarios, we continue to provide a simple fusion framework that combines existing RGB-produced saliency with new depth-induced saliency, the former one is estimated from existing RGB models while the latter one is based on the proposed multi-contextual contrast model. Moreover, a specialized multi-stage RGBD model is also proposed which takes account of both depth and appearance cues derived from low-level feature contrast, mid-level region grouping and high-level priors enhancement. Extensive experiments show the effectiveness and superiority of our model which can accurately locate the salient objects from RGBD images, and also assign consistent saliency values for the target objects.
Functional test generation using design and property decomposition techniques

Functional verification of microprocessors is one of the most complex and expensive tasks in the current system-on-chip design methodology. Simulation using functional test vectors is the most widely used form of processor validation. A significant bottleneck in the validation of such systems is the lack of automated techniques for directed test generation. While existing model checking--based approaches have proposed several promising ideas for automated test generation, many challenges remain in applying them to industrial microprocessors. The time and resources required for test generation using existing model checking--based techniques can be prohibitively large. This article presents an efficient test generation technique using decompositional model checking. The contribution of the article is the development of both property and design decomposition procedures for efficient test generation of pipelined processors. Our experimental results using a multi-issue MIPS processor and an industrial processor based on Power Architecture#8482; Technology demonstrate several orders-of-magnitude reduction in validation effort by drastically reducing both test generation time and test program length.
Adas on Cots with OpenCL: A Case Study with Lane Detection

The concept of autonomous cars is driving a boost for car electronics and the size of automotive electronics market is foreseen to double by 2025. How to benefit from this boost is an interesting question. This article presents a case study to test the feasibility of using OpenCL as the programming language and Cots components as the underlying computing platforms for Adas development. For representative Adas applications, a scalable lane detection is developed that can tune the trade-off between detection accuracy and speed. Our OpenCL implementation is tested on 14 video streams from different data-sets with different road scenarios on 5 Cots platforms. We demonstrate that the Cots platforms can provide more than sufficient computing power for the lane detection in the meanwhile our OpenCL implementation can exploit the massive parallelism provided by the Cots  platforms.
Soft Robotic Blocks: Introducing SoBL, a Fast-Build Modularized Design Block

This article presents a new, modularized design concept based on a bottom-up approach to assembly. This concept enables the structures and motions of soft robots to be rapidly revised to create new designs that can accomplish different tasks. We designed three basic types of pneumatically actuated soft modules, called soft robotic blocks (SoBL), that implement a single motion each (translation, bending, or twisting), which, when assembled, create structures capable of various motions. We introduce three types of connection mechanisms-screw thread, push fitting, and bistable junction-that can be used with any of the modules and that are designed to make for easy assembly and disassembly. Units were fabricated by multimaterial threedimensional (3-D) printing or silicone molding.
Interaction-Aware Probabilistic Behavior Prediction in Urban Environments

Planning for autonomous driving in complex, urban scenarios requires accurate prediction of the trajectories of surrounding traffic participants. Their future behavior depends on their route intentions, the road-geometry, traffic rules and mutual interaction, resulting in interdependencies between their trajectories. We present a probabilistic prediction framework based on a dynamic Bayesian network, which represents the state of the complete scene including all agents and respects the aforementioned dependencies. We propose Markovian, context-dependent motion models to define the interaction-aware behavior of drivers. At first, the state of the dynamic Bayesian network is estimated over time by tracking the single agents via sequential Monte Carlo inference. Secondly, we perform a probabilistic forward simulation of the network's estimated belief state to generate the different combinatorial scene developments. This provides the corresponding trajectories for the set of possible, future scenes. Our framework can handle various road layouts and number of traffic participants. We evaluate the approach in online simulations and real-world scenarios. It is shown that our interaction-aware prediction outperforms interaction-unaware physics- and map-based approaches.
Coverage of Author Identifiers in Web of Science and Scopus

As digital collections of scientific literature are widespread and used frequently in knowledge-intense working environments, it has become a challenge to identify author names correctly. The treatment of homonyms is crucial for the reliable resolution of author names. Apart from varying handling of first, middle and last names, vendors as well as the digital library community created tools to address the problem of author name disambiguation. This technical report focuses on two widespread collections of scientific literature, Web of Science (WoS) and Scopus, and the coverage with author identification information such as Researcher ID, ORCID and Scopus Author Identifier in the period 1996 2014. The goal of this study is to describe the significant differences of the two collections with respect to overall distribution of author identifiers and its use across different subject domains. We found that the STM disciplines show the best coverage of author identifiers in our dataset of 6,032,000 publications which are both covered by WoS and Scopus. In our dataset we found 184,823 distinct ResearcherIDs and 70,043 distinct ORCIDs. In the appendix of this report we list a complete overview of all WoS subject areas and the amount of author identifiers in these subject areas.
A Temporal Logic for Reasoning About Processes and Plans

A common disclaimer by an AI author is that he has neglected temporal considerations to avoid complication. The implication is nearly made that adding a temporal dimension to the research (on engineering, medical diagnosis, etc.) would be a familiar but tedious exercise that would obscure the new material presented by the author. Actually, of course, no one has ever dealt with time correctly in an AI program, and there is reason to believe that doing it would change everything. Because time has been neglected, medical diagnosis programs cannot talk about the course of a disease. Story understanding programs have trouble with past events. Problem solvers have had only the crudest models of the future, in spite of the obvious importance of future events. Many researchers have compensated by modeling the course of external time with the program’s own internal time, changing the world model to reflect changing reality. This leads to a confusion between correcting a mistaken belief and updating an outdated belief. Most AI data bases have some sort of operator for removing formulas. (e.g., ERASE in PLANNER, Hewitt, 1972) This operator has tended to be used for two quite different purposes: getting rid of tentative or hypothetical assertions that turned out not to be true, and noting that an assertion is no longer true. The confusion is natural, since some of the same consequences must follow in either case. For example, if ‘‘The car is drivable’’ follows from ‘‘There is gas in the car,’’ then the former statement must be deleted when the latter is, whether you have discovered there to be no gas after all, or the gas has been used up. But in many cases, the two behave quite differently, and efforts to make them the same have resulted in awkward, inextensible programs. For example, from ‘‘x is beating his wife,’’ you are entitled to infer, ‘‘x is a bad man.’’ But if x pauses to catch his breath, only the former statement must be deleted from the data base. Clearly, the proper inference is from ‘‘If x has beat his wife recently, he is a bad man,’’ and ‘‘x is beating his wife,’’ to ‘‘For the next year or so, x will have beaten his wife recently,’’ and hence to
Context-aware application scheduling in mobile systems: what will users do and not do next?

Usage patterns of mobile devices depend on a variety of factors such as time, location, and previous actions. Hence, context-awareness can be the key to make mobile systems to become personalized and situation dependent in managing their resources. We first reveal new findings from our own Android user experiment: (i) the launching probabilities of applications follow Zipf's law, and (ii) inter-running and running times of applications conform to log-normal distributions. We also find context-dependency in application usage patterns, for which we classify contexts in a personalized manner with unsupervised learning methods. Using the knowledge acquired, we develop a novel context-aware application scheduling framework, CAS that adaptively unloads and preloads background applications in a timely manner. Our trace-driven simulations with 96 user traces demonstrate the benefits of CAS over existing algorithms. We also verify the practicality of CAS by implementing it on the Android platform.
An Alpha-wave-based binaural beat sound control system using fuzzy logic and autoregressive forecasting model

We are developing a new real-time control system for customizing auditory stimulus (the binaural beat sound) by judging user alpha waves to entrain a userpsilas feeling in the most relaxed way. Since brainwave activity provides the necessary predictive information for arousal states, we use an autoregressive forecasting model to estimate the frequency response series of the alpha frequency bands and the inverted-U concept to determine the userpsilas arousal state. A fuzzy logic controller is also employed to regulate the binaural beat control signal on a forecasting error signal. Our system allows comfortable user self-relaxation. The results of experiments confirm the constructed systempsilas effectiveness and necessity.
A Boosting-based Deep Neural Networks Algorithm for Reinforcement Learning

In this paper, a new boosting-based deep neural networks algorithm is designed for improving the performance of model-free reinforcement learning structures. Based on theoretical proof and performance analysis, it is going to demonstrate that the new approach gives a faster convergence speed and a better return compared to existing deep neural network based RL approaches, like deep $Q$ network (DQN)[30], [13] and etc. A complete and detailed exploration of the new algorithm will be given in the paper as well. Also, simulation studies are conducted and compared with several other RL algorithms on the RL benchmark experiment tasks as given in . The results demonstrate a great performance improvement on $Q$ learning by using our new boosting-based deep neural networks algorithm.