nils-herrmann/clean_scirepeval huggingface dataset:
Design and Implementation of an Anthropomorphic Hand for Replicating Human Grasping Functions

How to design an anthropomorphic hand with a few actuators to replicate the grasping functions of the human hand is still a challenging problem. This paper aims to develop a general theory for designing the anthropomorphic hand and endowing the designed hand with natural grasping functions. A grasping experimental paradigm was set up for analyzing the grasping mechanism of the human hand in daily living. The movement relationship among joints in a digit, among digits in the human hand, and the postural synergic characteristic of the fingers were studied during the grasping. The design principle of the anthropomorphic mechanical digit that can reproduce the digit grasping movement of the human hand was developed. The design theory of the kinematic transmission mechanism that can be embedded into the palm of the anthropomorphic hand to reproduce the postural synergic characteristic of the fingers by using a limited number of actuators is proposed. The design method of the anthropomorphic hand for replicating human grasping functions was formulated. Grasping experiments are given to verify the effectiveness of the proposed design method of the anthropomorphic hand.
The Design with Intent Method: a design tool for influencing user behaviour.

Using product and system design to influence user behaviour offers potential for improving performance and reducing user error, yet little guidance is available at the concept generation stage for design teams briefed with influencing user behaviour. This article presents the Design with Intent Method, an innovation tool for designers working in this area, illustrated via application to an everyday human-technology interaction problem: reducing the likelihood of a customer leaving his or her card in an automatic teller machine. The example application results in a range of feasible design concepts which are comparable to existing developments in ATM design, demonstrating that the method has potential for development and application as part of a user-centred design process.
Torque-Controlled Lightweight Arms and Articulated Hands: Do We Reach Technological Limits Now?

In this paper we briefly address DLR’s (German Aerospace Center) background in space robotics by hand of corresponding milestone projects including systems on the International Space Station. We then discuss the key technologies needed for the development of an artificial “robonaut” generation with mechatronic ultra-lightweight arms and multifingered hands. The third arm generation is nearly finished now, approaching the limits of what is technologically achievable today with respect to light weight and power losses. In a similar way DLR’s second generation of artificial four-fingered hands was a big step towards higher reliability, manipulability and overall
mirracle: An augmented reality magic mirror system for anatomy education

We present an augmented reality magic mirror for teaching anatomy. The system uses a depth camera to track the pose of a user standing in front of a large display. A volume visualization of a CT dataset is augmented onto the user, creating the illusion that the user can look into his body. Using gestures, different slices from the CT and a photographic dataset can be selected for visualization. In addition, the system can show 3D models of organs, text information and images about anatomy. For interaction with this data we present a new interaction metaphor that makes use of the depth camera. The visibility of hands and body is modified based on the distance to a virtual interaction plane. This helps the user to understand the spatial relations between his body and the virtual interaction plane.
Neither Global Nor Local: Regularized Patch-Based Representation for Single Sample Per Person Face Recognition

This paper presents a regularized patch-based representation for single sample per person face recognition. We represent each image by a collection of patches and seek their sparse representations under the gallery images patches and intra-class variance dictionaries at the same time. For the reconstruction coefficients of all the patches from the same image, by imposing a group sparsity constraint on the reconstruction coefficients corresponding to the patches from the gallery images, and by imposing a sparsity constraint on the reconstruction coefficients corresponding to the intra-class variance dictionaries, our formulation harvests the advantages of both patch-based image representation and global image representation, i.e. our method overcomes the side effect of those patches which are severely corrupted by the variances in face recognition, while enforcing those less discriminative patches to be constructed by the gallery patches from the right person. Moreover, instead of using the manually designed intra-class variance dictionaries, we propose to learn the intra-class variance dictionaries which not only greatly accelerate the prediction of the probe images but also improve the face recognition accuracy in the single sample per person scenario. Experimental results on the AR, Extended Yale B, CMU-PIE, and LFW datasets show that our method outperforms sparse coding related face recognition methods as well as some other specially designed single sample per person face representation methods, and achieves the best performance. These encouraging results demonstrate the effectiveness of regularized patch-based face representation for single sample per person face recognition.
Ordering adverbs by their scaling effect on adjective intensity

In recent years, theoretical and computational linguistics has paid much attention to linguistic items that form scales. In NLP, much research has focused on ordering adjectives by intensity (tiny < small). Here, we address the task of automatically ordering English adverbs by their intensifying or diminishing effect on adjectives (e.g. extremely small < very small). We experiment with 4 different methods: 1) using the association strength between adverbs and adjectives; 2) exploiting scalar patterns (such as not only X but Y); 3) using the metadata of product reviews; 4) clustering. The method that performs best is based on the use of metadata and ranks adverbs by their scaling factor relative to unmodified adjectives.
A High-Performance Photovoltaic Module-Integrated Converter (MIC) Based on Cascaded Quasi-Z-Source Inverters (qZSI) Using eGaN FETs

This paper presents a single-phase grid-connected photovoltaic (PV) module-integrated converter (MIC) based on cascaded quasi-Z-source inverters (qZSI). In this system, each qZSI module serves as an MIC and is connected to one PV panel. Due to the cascaded structure and qZSI topology, the proposed MIC features low-voltage gain requirement, single-stage energy conversion, enhanced reliability, and good output power quality. Furthermore, the enhancement mode gallium nitride field-effect transistors (eGaN FETs) are employed in the qZSI module for efficiency improvement at higher switching frequency. It is found that the qZSI is very suitable for the application of eGaN FETs because of the shoot-through capability. Optimized module design is developed based on the derived qZSI ac equivalent model and power loss analytical model to achieve high efficiency and high power density. A design example of qZSI module is presented for a 250-W PV panel with 25-50-V output voltage. The simulation and experimental results prove the validity of the analytical models. The final module prototype design achieves up to 98.06% efficiency with 100-kHz switching frequency.
Exploiting N-Best Hypotheses to Improve an SMT Approach to Grammatical Error Correction

Grammatical error correction (GEC) is the task of detecting and correcting grammatical errors in texts written by second language learners. The statistical machine translation (SMT) approach to GEC, in which sentences written by second language learners are translated to grammatically correct sentences, has achieved state-of-the-art accuracy. However, the SMT approach is unable to utilize global context. In this paper, we propose a novel approach to improve the accuracy of GEC, by exploiting the n-best hypotheses generated by an SMT approach. Specifically, we build a classifier to score the edits in the n-best hypotheses. The classifier can be used to select appropriate edits or re-rank the n-best hypotheses. We apply these methods to a state-of-the-art GEC system that uses the SMT approach. Our experiments show that our methods achieve statistically significant improvements in accuracy over the best published results on a benchmark test dataset on GEC.
A critical evaluation of the emotional intelligence construct

This study critically evaluated the Emotional Intelligence (EI) construct (the ability to perceive, understand, and manage emotions), as measured by the Multi-factor Emotional Intelligence Scale (MEIS in press). We administered the MEIS to Australian undergraduates along with a battery of IQ, personality, and other theoretically relevant criterion measures, including life satisfaction and relationship quality. We also induced moods in the students and examined whether people high in EI were better than others at managing their moods and preventing their moods from biasing their social judgments. Analyses revealed that EI was not related to IQ but was related, as expected, to speci®c personality measures (e.g., empathy) and to other criterion measures (e.g., life satisfaction) even after controlling for IQ and personality traits. EI was also related to people's ability to manage their moods, but not to their ability to prevent moods from biasing their judgments. IQ was surprisingly related to both these mood processes. The results suggest that the EI construct is distinctive and useful, but that traditional IQ may also be important in understanding emotional processes.
Leaf disease detection and grading using computer vision technology & fuzzy logic

In Agriculture, leaf diseases have grown to be a dilemma as it can cause significant diminution in both quality and quantity of agricultural yields. Thus, automated recognition of diseases on leaves plays a crucial role in agriculture sector. This paper imparts a simple and computationally proficient method used for leaf disease identification and grading using digital image processing and machine vision technology. The proposed system is divided into two phases, in first phase the plant is recognized on the basis of the features of leaf, it includes pre-processing of leaf images, and feature extraction followed by Artificial Neural Network based training and classification for recognition of leaf. In second phase the disease present in the leaf is classified, this process includes K-Means based segmentation of defected area, feature extraction of defected portion and the ANN based classification of disease. Then the disease grading is done on the basis of the amount of disease present in the leaf.
Habits in everyday life: thought, emotion, and action.

To illustrate the differing thoughts and emotions involved in guiding habitual and nonhabitual behavior, 2 diary studies were conducted in which participants provided hourly reports of their ongoing experiences. When participants were engaged in habitual behavior, defined as behavior that had been performed almost daily in stable contexts, they were likely to think about issues unrelated to their behavior, presumably because they did not have to consciously guide their actions. When engaged in nonhabitual behavior, or actions performed less often or in shifting contexts, participants' thoughts tended to correspond to their behavior, suggesting that thought was necessary to guide action. Furthermore, the self-regulatory benefits of habits were apparent in the lesser feelings of stress associated with habitual than nonhabitual behavior.
An artificial immune model for network intrusion detection

This paper investigates the subject of intrusi on detection over networks. Existing network-based IDS’s are categorised into three groups and the overall architectu re of each group is summarised and assessed. A new methodology to this problem is then presented, which is inspired by the human immune system and based on a novel artificial immune model. The architecture of the model is presented and its characteristics are compared with the requirements of network-based IDS’s. The paper concludes tha t this new approach shows considerable promise for future network-based IDS’s.
All-frequency precomputed radiance transfer using spherical radial basis functions and clustered tensor approximation

This paper introduces a new data representation and compression technique for precomputed radiance transfer (PRT). The light transfer functions and light sources are modeled with spherical radial basis functions (SRBFs). A SRBF is a rotation-invariant function that depends on the geodesic distance between two points on the unit sphere. Rotating functions in SRBF representation is as straightforward as rotating the centers of SRBFs. Moreover, high-frequency signals are handled by adjusting the bandwidth parameters of SRBFs. To exploit inter-vertex coherence, the light transfer functions are further classified iteratively into disjoint clusters, and tensor approximation is applied within each cluster. Compared with previous methods, the proposed approach enables real-time rendering with comparable quality under high-frequency lighting environments. The data storage is also more compact than previous all-frequency PRT algorithms.
Observing the unexpected enhances infants’ learning and exploration

Given the overwhelming quantity of information available from the environment, how do young learners know what to learn about and what to ignore? We found that 11-month-old infants (N = 110) used violations of prior expectations as special opportunities for learning. The infants were shown events that violated expectations about object behavior or events that were nearly identical but did not violate expectations. The sight of an object that violated expectations enhanced learning and promoted information-seeking behaviors; specifically, infants learned more effectively about objects that committed violations, explored those objects more, and engaged in hypothesis-testing behaviors that reflected the particular kind of violation seen. Thus, early in life, expectancy violations offer a wedge into the problem of what to learn.
Neural Networks for Real-Time Traffic Signal Control

Real-time traffic signal control is an integral part of the urban traffic control system, and providing effective real-time traffic signal control for a large complex traffic network is an extremely challenging distributed control problem. This paper adopts the multiagent system approach to develop distributed unsupervised traffic responsive signal control models, where each agent in the system is a local traffic signal controller for one intersection in the traffic network. The first multiagent system is developed using hybrid computational intelligent techniques. Each agent employs a multistage online learning process to update and adapt its knowledge base and decision-making mechanism. The second multiagent system is developed by integrating the simultaneous perturbation stochastic approximation theorem in fuzzy neural networks (NN). The problem of real-time traffic signal control is especially challenging if the agents are used for an infinite horizon problem, where online learning has to take place continuously once the agent-based traffic signal controllers are implemented into the traffic network. A comprehensive simulation model of a section of the Central Business District of Singapore has been developed using PARAMICS microscopic simulation program. Simulation results show that the hybrid multiagent system provides significant improvement in traffic conditions when evaluated against an existing traffic signal control algorithm as well as the SPSA-NN-based multiagent system as the complexity of the simulation scenario increases. Using the hybrid NN-based multiagent system, the mean delay of each vehicle was reduced by 78% and the mean stoppage time, by 85% compared to the existing traffic signal control algorithm. The promising results demonstrate the efficacy of the hybrid NN-based multiagent system in solving large-scale traffic signal control problems in a distributed manner
Learning a Neural-network-based Representation for Open Set Recognition

Open set recognition problems exist in many domains. For example in security, new malware classes emerge regularly; therefore malware classication systems need to identify instances from unknown classes in addition to discriminating between known classes. In this paper we present a neural network based representation for addressing the open set recognition problem. In this representation instances from the same class are close to each other while instances from dierent classes are further apart, resulting in statistically signicant improvement when compared to other approaches on three datasets from two dierent domains.
Design of single-stage folded-cascode gain boost amplifier for 14bit 12.5Ms/S pipelined analog-to digital converter

This paper describes the design and simulation of a fully-differential, high gain, high speed CMOS Operational Transconductance Amplifier (OTA). The op-amp is designed for unity gain sampler stage of 14bit 12.5Ms/s pipeline analog-to digital converter. The design is implemented using a folding cascode topology with the addition of gain boosting amplifiers for increased gain. Common-mode feedback (CMFB) is used to stable the designed OTA against temperature and other process variations. This design has been implemented in 0.13μm IBM RF mixed signal CMOS Technology. The Spectre simulation shows the DC gain of 91.5 dB and a unity-gain frequency of 714.5MHz with phase margin of 62° (double 7.5-pF load) while consuming 9 mW power. For the normal corner, the settling time to 1/2 LSB of 14bit A/D converter accuracy is 40 ns.
Circular Data Matrix Fiducial System and Robust Image Processing for a Wearable Vision-Inertial Self-Tracker

A wearable low-power hybrid vision-inertial tracker has been demonstrated based on a flexible sensor fusion core architecture, which allows easy reconfiguration by plugging-in different kinds of sensors. A particular prototype implementation consists of one inertial measurement unit and one outward-looking wide-angle Smart Camera, with a built-in DSP to run all required image-processing tasks. The Smart Camera operates on newly designed 2-D bar-coded fiducials printed on a standard black-and-white printer. The fiducial design allows having thousands of different codes, thus enabling uninterrupted tracking throughout a large building or even a campus at very reasonable cost. The system operates in various real-world lighting conditions without any user intervention due to homomorphic image processing algorithms for extracting fiducials in the presence of very non-uniform lighting .
Hand gesture recognition based on shape parameters

Pattern recognition and Gesture recognition are the growing fields of research. Being a significant part in non verbal communication hand gestures are playing vital role in our daily life. Hand Gesture recognition system provides us an innovative, natural, user friendly way of interaction with the computer which is more familiar to the human beings. Gesture Recognition has a wide area of application including human machine interaction, sign language, immersive game technology etc. By keeping in mind the similarities of human hand shape with four fingers and one thumb, this paper aims to present a real time system for hand gesture recognition on the basis of detection of some meaningful shape based features like orientation, centre of mass (centroid), status of fingers, thumb in terms of raised or folded fingers of hand and their respective location in image. The approach introduced in this paper is totally depending on the shape parameters of the hand gesture. It does not consider any other mean of hand gesture recognition like skin color, texture because these image based features are extremely variant to different light conditions and other influences. To implement this approach we have utilized a simple web cam which is working on 20 fps with 7 mega pixel intensity. On having the input sequence of images through web cam it uses some pre-processing steps for removal of background noise and employs K-means clustering for segmenting the hand object from rest of the background, so that only segmented significant cluster or hand object is to be processed in order to calculate shape based features. This simple shape based approach to hand gesture recognition can identify around 45 different gestures on the bases of 5 bit binary string resulted as the output of this algorithm. This proposed implemented algorithm has been tested over 450 images and it gives approximate recognition rate of 94%.
Accelerated quadratic proxy for geometric optimization

We present the Accelerated Quadratic Proxy (AQP) - a simple first-order algorithm for the optimization of geometric energies defined over triangular and tetrahedral meshes.
 The main stumbling block of current optimization techniques used to minimize geometric energies over meshes is slow convergence due to ill-conditioning of the energies at their minima. We observe that this ill-conditioning is in large part due to a Laplacian-like term existing in these energies. Consequently, we suggest to locally use a quadratic polynomial proxy, whose Hessian is taken to be the Laplacian, in order to achieve a preconditioning effect. This already improves stability and convergence, but more importantly allows incorporating acceleration in an almost universal way, that is independent of mesh size and of the specific energy considered.
 Experiments with AQP show it is rather insensitive to mesh resolution and requires a nearly constant number of iterations to converge; this is in strong contrast to other popular optimization techniques used today such as Accelerated Gradient Descent and Quasi-Newton methods, e.g., L-BFGS. We have tested AQP for mesh deformation in 2D and 3D as well as for surface parameterization, and found it to provide a considerable speedup over common baseline techniques.
Substrate-Integrated-Waveguide-Fed Array Antenna Covering 57–71 GHz Band for 5G Applications

A wideband linearly polarized antenna element with complementary sources is proposed and exploited for array antennas. The element covers a bandwidth of 38.7% from 50 to 74 GHz with an average gain of 8.7 dBi. The four-way broad wall coupler is applied for the 2 <inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 2 subarray, which suppresses the cross-polarization of a single element. Based on the designed 2 <inline-formula> <tex-math notation="LaTeX">$ \times $ </tex-math></inline-formula> 2 subarray, two larger arrays have been designed and measured. The <inline-formula> <tex-math notation="LaTeX">$4 \times 4$ </tex-math></inline-formula> array exhibits 26.7% bandwidth, fully covering the 57–71 GHz unlicensed band. The <inline-formula> <tex-math notation="LaTeX">$8 \times 8$ </tex-math></inline-formula> array antenna covers a bandwidth of 14.5 GHz (22.9%) from 56.1 to 70.6 GHz with a peak gain of 26.7 dBi, and the radiation efficiency is around 80% within the matching band. It is demonstrated that the proposed antenna element and arrays can be used for future 5G applications to cover the 22% bandwidth of the unlicensed band with high gain and low loss.