nils-herrmann/clean_scirepeval huggingface dataset:
FMCW radar system with additional phase evaluation for high accuracy range detection

A radar based approach of range detection depends mainly on the utilized radar principle which allows either high accuracy or high unambiguous ranges. For the most widely used FMCW radar the entire distance is not restricted in the meter range whereas the achievable accuracy directly depends on the bandwidth. An improvement is only possible if system parameters are changed or a smart signal processing is applied. In this paper an approach is described which extends the FMCW radar with an additional phase evaluation to facilitate also high accuracy using low-cost radar components and none intensive processing methods. Therefore requirements for frequency and phase evaluation must be kept which are investigated and solved using special algorithms. The performance of these proceedings is confirmed with measurements using a developed radio frequency circuit for the K-band. Moreover the possibility to combine the frequency and phase information for range detection is described and also validated by measurements.
Using human physiology to evaluate subtle expressivity of a virtual quizmaster in a mathematical game

The aim of the experimental study described in this article is to investigate the effect of a lifelike character with subtle expressivity on the affective state of users. The character acts as a quizmaster in the context of a mathematical game. This application was chosen as a simple, and for the sake of the experiment, highly controllable, instance of human–computer interfaces and software. Subtle expressivity refers to the character’s affective response to the user’s performance by emulating multimodal human–human communicative behavior such as different body gestures and varying linguistic style. The impact of em-pathic behavior, which is a special form of affective response, is examined by deliberately frustrating the user during the game progress. There are two novel aspects in this investigation. First, we employ an animated interface agent to address the affective state of users rather than a text-based interface, which has been used in related research. Second, while previous empirical studies rely on questionnaires to evaluate the effect of life-like characters, we utilize physiological information of users (in addition to questionnaire data) in order to precisely associate the occurrence of interface events with users’ autonomic nervous system activity. The results of our study indicate that empathic character response can significantly decrease user stress see front matter r 2004  cle is a significantly revised and extended version of Prendinger et al. (2003). nding author.  dresses: helmut@nii.ac.jp (H. Prendinger),  v.t.u-tokyo.ac.jp (M. Ishizuka).
Semantic Parsing to Probabilistic Programs for Situated Question Answering

Situated question answering is the problem of answering questions about an environment such as an image or diagram. This problem requires jointly interpreting a question and an environment using background knowledge to select the correct answer. We present Parsing to Probabilistic Programs (P ), a novel situated question answering model that can use background knowledge and global features of the question/environment interpretation while retaining efficient approximate inference. Our key insight is to treat semantic parses as probabilistic programs that execute nondeterministically and whose possible executions represent environmental uncertainty. We evaluate our approach on a new, publicly-released data set of 5000 science diagram questions, outperforming several competitive classical and neural baselines.
Chemical named entities recognition: a review on approaches and applications

The rapid increase in the flow rate of published digital information in all disciplines has resulted in a pressing need for techniques that can simplify the use of this information. The chemistry literature is very rich with information about chemical entities. Extracting molecules and their related properties and activities from the scientific literature to "text mine" these extracted data and determine contextual relationships helps research scientists, particularly those in drug development. One of the most important challenges in chemical text mining is the recognition of chemical entities mentioned in the texts. In this review, the authors briefly introduce the fundamental concepts of chemical literature mining, the textual contents of chemical documents, and the methods of naming chemicals in documents. We sketch out dictionary-based, rule-based and machine learning, as well as hybrid chemical named entity recognition approaches with their applied solutions. We end with an outlook on the pros and cons of these approaches and the types of chemical entities extracted.
JouleGuard: energy guarantees for approximate applications

Energy consumption limits battery life in mobile devices and increases costs for servers and data centers. Approximate computing addresses energy concerns by allowing applications to trade accuracy for decreased energy consumption. Approximation frameworks can guarantee accuracy or performance and generally reduce energy usage; however, they provide no energy guarantees. Such guarantees would be beneficial for users who have a fixed energy budget and want to maximize application accuracy within that budget. We address this need by presenting JouleGuard: a runtime control system that coordinates approximate applications with system resource usage to provide control theoretic formal guarantees of energy consumption, while maximizing accuracy. We implement JouleGuard and test it on three different platforms (a mobile, tablet, and server) with eight different approximate applications created from two different frameworks. We find that JouleGuard respects energy budgets, provides near optimal accuracy, adapts to phases in application workload, and provides better outcomes than application approximation or system resource adaptation alone. JouleGuard is general with respect to the applications and systems it controls, making it a suitable runtime for a number of approximate computing frameworks.
Sequence of the Most Informative Joints (SMIJ): A new representation for human skeletal action recognition

Much of the existing work on action recognition combines simple features (e.g., joint angle trajectories, optical flow, spatio-temporal video features) with somewhat complex classifiers or dynamical models (e.g., kernel SVMs, HMMs, LDSs, deep belief networks). Although successful, these approaches represent an action with a set of parameters that usually do not have any physical meaning. As a consequence, such approaches do not provide any qualitative insight that relates an action to the actual motion of the body or its parts. For example, it is not necessarily the case that clapping can be correlated to hand motion or that walking can be correlated to a specific combination of motions from the feet, arms and body. In this paper, we propose a new representation of human actions called Sequence of the Most Informative Joints (SMIJ), which is extremely easy to interpret. At each time instant, we automatically select a few skeletal joints that are deemed to be the most informative for performing the current action. The selection of joints is based on highly interpretable measures such as the mean or variance of joint angles, maximum angular velocity of joints, etc. We then represent an action as a sequence of these most informative joints. Our experiments on multiple databases show that the proposed representation is very discriminative for the task of human action recognition and performs better than several state-of-the-art algorithms.
Automatic Annotation and Evaluation of Error Types for Grammatical Error Correction

Until now, error type performance for Grammatical Error Correction (GEC) systems could only be measured in terms of recall because system output is not annotated. To overcome this problem, we introduce ERRANT, a grammatical ERRor ANnotation Toolkit designed to automatically extract edits from parallel original and corrected sentences and classify them according to a new, dataset-agnostic, rulebased framework. This not only facilitates error type evaluation at different levels of granularity, but can also be used to reduce annotator workload and standardise existing GEC datasets. Human experts rated the automatic edits as “Good” or “Acceptable” in at least 95% of cases, so we applied ERRANT to the system output of the CoNLL-2014 shared task to carry out a detailed error type analysis for the first time.
Evaluating Display Fidelity and Interaction Fidelity in a Virtual Reality Game

In recent years, consumers have witnessed a technological revolution that has delivered more-realistic experiences in their own homes through high-definition, stereoscopic televisions and natural, gesture-based video game consoles. Although these experiences are more realistic, offering higher levels of fidelity, it is not clear how the increased display and interaction aspects of fidelity impact the user experience. Since immersive virtual reality (VR) allows us to achieve very high levels of fidelity, we designed and conducted a study that used a six-sided CAVE to evaluate display fidelity and interaction fidelity independently, at extremely high and low levels, for a VR first-person shooter (FPS) game. Our goal was to gain a better understanding of the effects of fidelity on the user in a complex, performance-intensive context. The results of our study indicate that both display and interaction fidelity significantly affect strategy and performance, as well as subjective judgments of presence, engagement, and usability. In particular, performance results were strongly in favor of two conditions: low-display, low-interaction fidelity (representative of traditional FPS games) and high-display, high-interaction fidelity (similar to the real world).
Attentional modulation of visual processing.

Single-unit recording studies in the macaque have carefully documented the modulatory effects of attention on the response properties of visual cortical neurons. Attention produces qualitatively different effects on firing rate, depending on whether a stimulus appears alone or accompanied by distracters. Studies of contrast gain control in anesthetized mammals have found parallel patterns of results when the luminance contrast of a stimulus increases. This finding suggests that attention has co-opted the circuits that mediate contrast gain control and that it operates by increasing the effective contrast of the attended stimulus. Consistent with this idea, microstimulation of the frontal eye fields, one of several areas that control the allocation of spatial attention, induces spatially local increases in sensitivity both at the behavioral level and among neurons in area V4, where endogenously generated attention increases contrast sensitivity. Studies in the slice have begun to explain how modulatory signals might cause such increases in sensitivity.
The economy of brain network organization

The brain is expensive, incurring high material and metabolic costs for its size — relative to the size of the body — and many aspects of brain network organization can be mostly explained by a parsimonious drive to minimize these costs. However, brain networks or connectomes also have high topological efficiency, robustness, modularity and a 'rich club' of connector hubs. Many of these and other advantageous topological properties will probably entail a wiring-cost premium. We propose that brain organization is shaped by an economic trade-off between minimizing costs and allowing the emergence of adaptively valuable topological patterns of anatomical or functional connectivity between multiple neuronal populations. This process of negotiating, and re-negotiating, trade-offs between wiring cost and topological value continues over long (decades) and short (millisecond) timescales as brain networks evolve, grow and adapt to changing cognitive demands. An economical analysis of neuropsychiatric disorders highlights the vulnerability of the more costly elements of brain networks to pathological attack or abnormal development.
Intrinsically Switched Varactor-Tuned Filters and Filter Banks

Intrinsically switched tunable filters are switched on and off using the tuning elements that tune their center frequencies and/or bandwidths, without requiring an increase in the tuning range of the tuning elements. Because external RF switches are not needed, substantial improvements in insertion loss, linearity, dc power consumption, control complexity, size, and weight are possible compared to conventional approaches. An intrinsically switched varactor-tuned bandstop filter and bandpass filter bank are demonstrated here for the first time. The intrinsically switched bandstop filter prototype has a second-order notch response with more than 50 dB of rejection continuously tunable from 665 to 1000 MHz (50%) with negligible passband ripple in the intrinsic off state. The intrinsically switched tunable bandpass filter bank prototype, comprised of three third-order bandpass filters, has a constant 50-MHz bandwidth response continuously tunable from 740 to 1644 MHz (122%) with less than 5 dB of passband insertion loss and more than 40 dB of isolation between bands.
A Survey on Energy Internet Communications for Sustainability

Energy Internet (EI) is proposed as the evolution of smart grid, aiming to integrate various forms of energy into a highly flexible and efficient grid that provides energy packing and routing functions, similar to the Internet. As an essential part in EI system, a scalable and interoperable communication infrastructure is critical in system construction and operation. In this article, we survey the recent research efforts on EI communications. The motivation and key concepts of EI are first introduced, followed by the key technologies and standardizations enabling the EI communications as well as security issues. Open challenges in system complexity, efficiency, reliability are explored and recent achievements in these research topics are summarized as well.
Fourier fringe analysis: the two-dimensional phase unwrapping problem.

A new phase unwrapping algorithm is described that uses local phase information to mask out those parts of the field that cause inconsistencies in the unwrapping. Unlike earlier techniques, which produce only a consistent unwrapping of the phase in the presence of discontinuities, this technique can produce an approximately correct unwrapping. The technique is tolerant of discontinuities and noise in the phase and is fast, efficient, and simple to implement. In the absence of discontinuities an rms signal-to-noise ratio in the wrapped phase of <2:1 can be tolerated.
Sapienz: multi-objective automated testing for Android applications

We introduce Sapienz, an approach to Android testing that uses multi-objective search-based testing to automatically explore and optimise test sequences, minimising length, while simultaneously maximising coverage and fault revelation. Sapienz combines random fuzzing, systematic and search-based exploration, exploiting seeding and multi-level instrumentation. Sapienz significantly outperforms (with large effect size) both the state-of-the-art technique Dynodroid and the widely-used tool, Android Monkey, in 7/10 experiments for coverage, 7/10 for fault detection and 10/10 for fault-revealing sequence length. When applied to the top 1,000 Google Play apps, Sapienz found 558 unique, previously unknown crashes. So far we have managed to make contact with the developers of 27 crashing apps. Of these, 14 have confirmed that the crashes are caused by real faults. Of those 14, six already have developer-confirmed fixes.
Earthquake Prediction based on Spatio-Temporal Data Mining : An LSTM Network Approach

Earthquake prediction is a very important problem in seismology, the success of which can potentially save many human lives. Various kinds of technologies have been proposed to address this problem, such as mathematical analysis, machine learning algorithms like decision trees and support vector machines, and precursors signal study. Unfortunately, they usually do not have very good results due to the seemingly dynamic and unpredictable nature of earthquakes. In contrast, we notice that earthquakes are spatially and temporally correlated because of the crust movement. Therefore, earthquake prediction for a particular location should not be conducted only based on the history data in that location, but according to the history data in a larger area. In this paper, we employ a deep learning technique called long short-term memory (LSTM) networks to learn the spatio-temporal relationship among earthquakes in different locations and make predictions by taking advantage of that relationship. Simulation results show that the LSTM network with two-dimensional input developed in this paper is able to discover and exploit the spatio-temporal correlations among earthquakes to make better predictions than before.
Autonomous Dual Wheel Self Balancing Robot Based on Microcontroller

An autonomous dual wheel self balancing robot is developed that is capable of balancing its position around predetermined position. Initially the system was nonlinear and unstable. It is observed that the system becomes stable after redesigning the physical structure of the system using PID controller and analyzing its dynamic behavior using mathematical modeling. The position of self balancing robot is controlled by PID controller. Simulation results using PROTEOUS, MATLAB, and VM lab are observed and verified vital responses of different components. Balancing is claimed and shown the verification for this nonlinear and unstable system. Some fluctuations in forward or backward around its mean position is observed, afterwards it acquires its balanced position in reasonable settling time. The research is applicable in gardening, hospitals, shopping malls and defense systems etc.
State of the art on vision-based structured light systems for 3D measurements

This paper aims at giving an overview on the state of the art of vision-based, non-contact 3D measurement systems based on structured light. Such systems are equipped with a camera and a light projector, and are referred to in the literature as active stereo systems. This report is structured into structured light, calibration methods and 3D measurement techniques. Moreover, several selected research works are described in order to illustrate these different topics
Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection

To assure that an autonomous car is driving safely on public roads, its object detection module should not only work correctly, but show its prediction confidence as well. Previous object detectors driven by deep learning do not explicitly model uncertainties in the neural network. We tackle with this problem by presenting practical methods to capture uncertainties in a 3D vehicle detector for Lidar point clouds. The proposed probabilistic detector represents reliable epistemic uncertainty and aleatoric uncertainty in classification and localization tasks. Experimental results show that the epistemic uncertainty is related to the detection accuracy, whereas the aleatoric uncertainty is influenced by vehicle distance and occlusion. The results also show that we can improve the detection performance by 1%–5% by modeling the aleatoric uncertainty.
Violent and nonviolent video games produce opposing effects on aggressive and prosocial outcomes

a r t i c l e i n f o Experimental studies routinely show that participants who play a violent game are more aggressive immediately following game play than participants who play a nonviolent game. The underlying assumption is that nonviolent games have no effect on aggression, whereas violent games increase it. The current studies demonstrate that, although violent game exposure increases aggression, nonviolent video game exposure decreases aggressive thoughts and feelings (Exp 1) and aggressive behavior (Exp 2). When participants assessed after a delay were compared to those measured immediately following game play, violent game players showed decreased aggressive thoughts, feelings and behavior, whereas nonviolent game players showed increases in these outcomes. Experiment 3 extended these findings by showing that exposure to nonviolent puzzle-solving games with no expressly prosocial content increases prosocial thoughts, relative to both violent game exposure and, on some measures, a no-game control condition. Implications of these findings for models of media effects are discussed. A major development in mass media over the last 25 years has been the advent and rapid growth of the video game industry. From the earliest arcade-based console games, video games have been immediately and immensely popular, particularly among young people and their subsequent introduction to the home market only served to further elevate their prevalence (Gentile, 2009). Given their popularity, social scientists have been concerned with the potential effects of video games on those who play them, focusing particularly on games with violent content. While a large percentage of games have always involved the destruction of enemies, recent advances in technology have enabled games to become steadily more realistic. Coupled with an increase in the number of adult players, these advances have enabled the development of games involving more and more graphic violence. Over the past several years, the majority of best-selling games have involved frequent and explicit acts of violence as a central gameplay theme (Smith, Lachlan, & Tamborini, 2003). A video game is essentially a simulated experience. Virtually every major theory of human aggression, including social learning theory, predicts that repeated simulation of antisocial behavior will produce an increase in antisocial behavior (e.g., aggression) and a decrease in prosocial behavior (e.g., helping) outside the simulated environment (i.e., in " real life "). In addition, an increase in the perceived realism of the simulation is posited to increase the strength of negative effects (Gentile & Anderson, 2003). Meta-analyses …
Homophily, Structure, and Content Augmented Network Representation Learning

Advances in social networking and communication technologies have witnessed an increasing number of applications where data is not only characterized by rich content information, but also connected with complex relationships representing social roles and dependencies between individuals. To enable knowledge discovery from such networked data, network representation learning (NRL) aims to learn vector representations for network nodes, such that off-the-shelf machine learning algorithms can be directly applied. To date, existing NRL methods either primarily focus on network structure or simply combine node content and topology for learning. We argue that in information networks, information is mainly originated from three sources: (1) homophily, (2) topology structure, and (3) node content. Homophily states social phenomenon where individuals sharing similar attributes (content) tend to be directly connected through local relational ties, while topology structure emphasizes more on global connections. To ensure effective network representation learning, we propose to augment three information sources into one learning objective function, so that the interplay roles between three parties are enforced by requiring the learned network representations (1) being consistent with node content and topology structure, and also (2) following the social homophily constraints in the learned space. Experiments on multi-class node classification demonstrate that the representations learned by the proposed method consistently outperform state-of-the-art NRL methods, especially for very sparsely labeled networks.