nils-herrmann/clean_scirepeval huggingface dataset:
Comparative analysis of blockchain technologies and TOR network: Two faces of the same reality?

Blockchain technology is being considered as one of the ultimate revolutions that will be able to disrupt several pillars of our society. It is a public and distributed ledger built for security and interoperability. Blockchain provides all parties a secure and synchronized record of immutable transactions assembled together and permanently stored with a fingerprint, creating therefore an irreversible chain. In order to operate, this technology does not rely on any central authority. All transactions are sent over the network and the consensus is achieved by the mutual calculation and agreement. In this paper, we evaluate the blockchain technology and its evolution. Then, we characterize some essential features of the distributed ledger technologies (DLT) focusing on the three main blockchains actors: Bitcoin, Ethereum and Hyperledger. Besides, we present their security challenges and explore their drawbacks that can lead to use the blockchain network in order to conduct several attack scenarios. Finally, we describe their relationship with the onion router network (Tor) that beyond the malicious uses of the blockchain via Tor, these two networks share many common points.
Overview of Stator-Permanent Magnet Brushless Machines

Permanent magnet (PM) brushless machines having magnets and windings in stator (the so-called stator-PM machines) have attracted more and more attention in the past decade due to its definite advantages of robust structure, high power density, high efficiency, etc. In this paper, an overview of the stator-PM machine is presented, with particular emphasis on concepts, operation principles, machine topologies, electromagnetic performance, and control strategies. Both brushless ac and dc operation modes are described. The key features of the machines, including the merits and drawbacks of the machines, are summarized. Moreover, the latest development of the machines is also discussed.
Dual Inverter Strategy for High Speed Operation of HEV Permanent Magnet Synchronous Motor

Bi-directional buck-boost DC/DC converter is normally utilized for high speed operation of motors for hybrid electric vehicles (HEV). In this work, a dual inverter strategy is proposed for HEV motor with the purpose of accommodating high speed operation without increasing battery voltage. By setting the secondary inverter to take care of only reactive voltage component, the secondary inverter do not carry voltage source other than capacitor bank. DC link voltage regulation is achieved through the control of active power flow
SPARSE REPRESENTATION ON GRAPHS BY TIGHT WAVELET FRAMES AND APPLICATIONS

In this paper, we introduce a unified theory of tight wavelet frames on non-flat domains in both continuum setting, i.e. on manifolds, and discrete setting, i.e. on graphs; discuss how fast tight wavelet frame transforms can be computed and how they can be effectively used to process graph data. We start from defining multiresolution analysis (MRA) generated by a single generator on manifolds, and discuss the conditions needed for a generator to form an MRA. With a given MRA, we show how MRA-based tight frames can be constructed and the conditions needed for them to generate a tight wavelet frame on manifolds. In particular, we show that under suitable conditions, framelets on R constructed from the unitary extension principle [1] can also generate tight frame systems on manifolds. We also discuss how the transition from the continuum to the discrete setting can be naturally defined, which leads to multi-level discrete tight wavelet frame transforms (decomposition and reconstruction) on graphs. In order for the proposed discrete tight wavelet frame transforms to be useful in applications, we show how the transforms can be computed efficiently and accurately. More importantly, numerical simulations show that the proposed discrete tight wavelet frame transform maps piecewise smooth data to a set of sparse coefficients. This indicates that the proposed tight wavelet frames indeed provide sparse representation on graphs. Finally, we consider two specific applications: graph data denoising and semi-supervised clustering. Utilizing the proposed sparse representation, we introduce l1-norm based optimization models for denoising and semi-supervised clustering, which are inspired by the models used in image restoration and image segmentation.
Snap ML: A Hierarchical Framework for Machine Learning

We describe a new software framework for fast training of generalized linear models. The framework, named Snap Machine Learning (Snap ML), combines recent advances in machine learning systems and algorithms in a nested manner to reflect the hierarchical architecture of modern computing systems. We prove theoretically that such a hierarchical system can accelerate training in distributed environments where intra-node communication is cheaper than inter-node communication. Additionally, we provide a review of the implementation of Snap ML in terms of GPU acceleration, pipelining, communication patterns and software architecture, highlighting aspects that were critical for achieving high performance. We evaluate the performance of Snap ML in both single-node and multi-node environments, quantifying the benefit of the hierarchical scheme and the data streaming functionality, and comparing with other widely-used machine learning software frameworks. Finally, we present a logistic regression benchmark on the Criteo Terabyte Click Logs dataset and show that Snap ML achieves the same test loss an order of magnitude faster than any of the previously reported results, including those obtained using TensorFlow and scikit-learn.
Learning Aligned Cross-Modal Representations from Weakly Aligned Data

People can recognize scenes across many different modalities beyond natural images. In this paper, we investigate how to learn cross-modal scene representations that transfer across modalities. To study this problem, we introduce a new cross-modal scene dataset. While convolutional neural networks can categorize cross-modal scenes well, they also learn an intermediate representation not aligned across modalities, which is undesirable for crossmodal transfer applications. We present methods to regularize cross-modal convolutional neural networks so that they have a shared representation that is agnostic of the modality. Our experiments suggest that our scene representation can help transfer representations across modalities for retrieval. Moreover, our visualizations suggest that units emerge in the shared representation that tend to activate on consistent concepts independently of the modality.
VoxNet: A 3D Convolutional Neural Network for real-time object recognition

Robust object recognition is a crucial skill for robots operating autonomously in real world environments. Range sensors such as LiDAR and RGBD cameras are increasingly found in modern robotic systems, providing a rich source of 3D information that can aid in this task. However, many current systems do not fully utilize this information and have trouble efficiently dealing with large amounts of point cloud data. In this paper, we propose VoxNet, an architecture to tackle this problem by integrating a volumetric Occupancy Grid representation with a supervised 3D Convolutional Neural Network (3D CNN). We evaluate our approach on publicly available benchmarks using LiDAR, RGBD, and CAD data. VoxNet achieves accuracy beyond the state of the art while labeling hundreds of instances per second.
Mobile Device Profiling and Intrusion Detection Using Smart Batteries

This paper introduces capabilities developed for a battery-sensing intrusion protection system (B-SIPS) for mobile computers, which alerts when abnormal current changes are detected. The intrusion detection system's (IDS's) IEEE 802.15.1 (Bluetooth) and 802.11 (Wi-Fi) capabilities are enhanced with iterative safe process checking, wireless connection determination, and an automated intrusion protection disconnect ability. The correlation intrusion detection engine (CIDE) provides power profiling for mobile devices and a correlated view of B-SIPS and snort alerts. An examination of smart battery drain times was conducted to ascertain the optimal transmission rate for the B-SIPS client. A 10 second reporting rate was used to assess 9 device types, which were then compared with their corresponding baseline battery lifetime. Lastly, an extensive usability study was conducted to improve the B-SIPS client and CIDE features. The 31 expert participants provided feedback and data useful for validating the system's viability as a complementary IDS for mobile devices.
A simple linear time (1 + /spl epsiv/)-approximation algorithm for k-means clustering in any dimensions

We present the first linear time (1 + /spl epsiv/)-approximation algorithm for the k-means problem for fixed k and /spl epsiv/. Our algorithm runs in O(nd) time, which is linear in the size of the input. Another feature of our algorithm is its simplicity - the only technique involved is random sampling.
Active learning for biomedical citation screening

Active learning (AL) is an increasingly popular strategy for mitigating the amount of labeled data required to train classifiers, thereby reducing annotator effort. We describe a real-world, deployed application of AL to the problem of biomedical citation screening for systematic reviews at the Tufts Medical Center's Evidence-based Practice Center. We propose a novel active learning strategy that exploits a priori domain knowledge provided by the expert (specifically, labeled features)and extend this model via a Linear Programming algorithm for situations where the expert can provide ranked labeled features. Our methods outperform existing AL strategies on three real-world systematic review datasets. We argue that evaluation must be specific to the scenario under consideration. To this end, we propose a new evaluation framework for finite-pool scenarios, wherein the primary aim is to label a fixed set of examples rather than to simply induce a good predictive model. We use a method from medical decision theory for eliciting the relative costs of false positives and false negatives from the domain expert, constructing a utility measure of classification performance that integrates the expert preferences. Our findings suggest that the expert can, and should, provide more information than instance labels alone. In addition to achieving strong empirical results on the citation screening problem, this work outlines many important steps for moving away from simulated active learning and toward deploying AL for real-world applications.
Neural Architectures for Fine-grained Entity Type Classification

In this work, we investigate several neural network architectures for fine-grained entity type classification and make three key contributions. Despite being a natural comparison and addition, previous work on attentive neural architectures have not considered hand-crafted features and we combine these with learnt features and establish that they complement each other. Additionally, through quantitative analysis we establish that the attention mechanism learns to attend over syntactic heads and the phrase containing the mention, both of which are known to be strong hand-crafted features for our task. We introduce parameter sharing between labels through a hierarchical encoding method, that in lowdimensional projections show clear clusters for each type hierarchy. Lastly, despite using the same evaluation dataset, the literature frequently compare models trained using different data. We demonstrate that the choice of training data has a drastic impact on performance, which decreases by as much as 9.85% loose micro F1 score for a previously proposed method. Despite this discrepancy, our best model achieves state-of-the-art results with 75.36% loose micro F1 score on the well-established FIGER (GOLD) dataset and we report the best results for models trained using publicly available data for the OntoNotes dataset with 64.93% loose micro F1 score. a	  	  	  match	  	  	  series	  	  	  	  against	  	  	  New	  	  	  	  Zealand	  	  	  	  	  	  is	  	  	  	  	  	  	  	  held	  	  	  	  	  on	  	  	  	  	  Monday Output Word Embeddings LSTM Layers A9en:ons Context	  Representa:on Men:on	  Representa:on /organiza)on,	  	  /organiza)on/sports_team  An illustration of the attentive encoder neural model predicting fine-grained semantic types for the mention “New Zealand” in the expression “a match series against New Zealand is held on Monday”.
Proxemics with multiple dynamic characters in an immersive virtual environment

An experiment was carried out to examine the impact on electrodermal activity of people when approached by groups of one or four virtual characters at varying distances. It was premised on the basis of proxemics theory that the closer the approach of the virtual characters to the participant, the greater the level of physiological arousal. Physiological arousal was measured by the number of skin conductance responses within a short time period after the approach, and the maximum change in skin conductance level 5 s after the approach. The virtual characters were each either female or a cylinder of human size, and one or four characters approached each subject a total of 12 times. Twelve male subjects were recruited for the experiment. The results suggest that the number of skin conductance responses after the approach and the change in skin conductance level increased the closer the virtual characters approached toward the participants. Moreover, these response variables were inversely correlated with the number of visits, showing a typical adaptation effect. There was some evidence to suggest that the number of characters who simultaneously approached (one or four) was positively associated with the responses. Surprisingly there was no evidence of a difference in response between the humanoid characters and cylinders on the basis of this physiological data. It is suggested that the similarity in this quantitative arousal response to virtual characters and virtual objects might mask a profound difference in qualitative response, an interpretation supported by questionnaire and interview results. Overall the experiment supported the premise that people exhibit heightened physiological arousal the closer they are approached by virtual characters.
Ka-Band Wilkinson Power Divider Based on Chip Resistor

In this paper, a design method of Ka-band Wilkinson power divider based on the detail model of real lumped chip resistor is presented, and this model is got from its geometric size and electric property. The structure of this power divider and the formulas used to determine the design parameters have been given. Experimental results show that the insertion loss is about 4.7 dB with return loss below -13 dB, and the isolation between two output ports is better than -25 dB over 28.3 GHz~33.8 GHz.
NAVIG: augmented reality guidance system for the visually impaired

Navigating complex routes and finding objects of interest are challenging tasks for the visually impaired. The project NAVIG (Navigation Assisted by artificial VIsion and GNSS) is directed toward increasing personal autonomy via a virtual augmented reality system. The system integrates an adapted geographic information system with different classes of objects useful for improving route selection and guidance. The database also includes models of important geolocated objects that may be detected by real-time embedded vision algorithms. Object localization (relative to the user) may serve both global positioning and sensorimotor actions such as heading, grasping, or piloting. The user is guided to his desired destination through spatialized semantic audio rendering, always maintained in the head-centered reference frame. This paper presents the overall project design and architecture of the NAVIG system. In addition, details of a new type of detection and localization device are presented. This approach combines a bio-inspired vision system that can recognize and locate objects very quickly and a 3D sound rendering system that is able to perceptually position a sound at the location of the recognized object. This system was developed in relation to guidance directives developed through participative design with potential users and educators for the visually impaired.
Adapting deep RankNet for personalized search

RankNet is one of the widely adopted ranking models for web search tasks. However, adapting a generic RankNet for personalized search is little studied. In this paper, we first continue-trained a variety of RankNets with different number of hidden layers and network structures over a previously trained global RankNet model, and observed that a deep neural network with five hidden layers gives the best performance. To further improve the performance of adaptation, we propose a set of novel methods categorized into two groups. In the first group, three methods are proposed to properly assess the usefulness of each adaptation instance and only leverage the most informative instances to adapt a user-specific RankNet model. These assessments are based on KL-divergence, click entropy or a heuristic to ignore top clicks in adaptation queries. In the second group, two methods are proposed to regularize the training of the neural network in RankNet: one of these methods regularize the error back-propagation via a truncated gradient approach, while the other method limits the depth of the back propagation when adapting the neural network. We empirically evaluate our approaches using a large-scale real-world data set. Experimental results exhibit that our methods all give significant improvements over a strong baseline ranking system, and the truncated gradient approach gives the best performance, significantly better than all others.
Reconfigurable Reflectarrays and Array Lenses for Dynamic Antenna Beam Control: A Review

Advances in reflectarrays and array lenses with electronic beam-forming capabilities are enabling a host of new possibilities for these high-performance, low-cost antenna architectures. This paper reviews enabling technologies and topologies of reconfigurable reflectarray and array lens designs, and surveys a range of experimental implementations and achievements that have been made in this area in recent years. The paper describes the fundamental design approaches employed in realizing reconfigurable designs, and explores advanced capabilities of these nascent architectures, such as multi-band operation, polarization manipulation, frequency agility, and amplification. Finally, the paper concludes by discussing future challenges and possibilities for these antennas.
The mystique of numbers: belief in quantitative approaches to segmentation and persona development

Quantitative market research and qualitative user-centered design research have long had an uneasy and complex relationship. A trend toward increasingly complex statistical segmentations and associated personas will once again increase the urgency of addressing paradigm differences to allow the two disciplines to collaborate effectively.
 We present an instructive case in which qualitative field research helped contribute to abandoning a "state of the art" quantitative user segmentation that was used in an attempt to unify both marketing and user experience planning around a shared model of users. This case exposes risks in quantitative segmentation research, common fallacies in the evolving practice of segmentation and use of personas, and the dangers of excessive deference to quantitative research generally.
Learning to predict indoor illumination from a single image

We propose an automatic method to infer high dynamic range illumination from a single, limited field-of-view, low dynamic range photograph of an indoor scene. In contrast to previous work that relies on specialized image capture, user input, and/or simple scene models, we train an end-to-end deep neural network that directly regresses a limited field-of-view photo to HDR illumination, without strong assumptions on scene geometry, material properties, or lighting. We show that this can be accomplished in a three step process: 1) we train a robust lighting classifier to automatically annotate the location of light sources in a large dataset of LDR environment maps, 2) we use these annotations to train a deep neural network that predicts the location of lights in a scene from a single limited field-of-view photo, and 3) we fine-tune this network using a small dataset of HDR environment maps to predict light intensities. This allows us to automatically recover high-quality HDR illumination estimates that significantly outperform previous state-of-the-art methods. Consequently, using our illumination estimates for applications like 3D object insertion, produces photo-realistic results that we validate via a perceptual user study.
Dual-polarized log.-periodic antenna on a conical MID substrate

This paper presents the design of a dual-polarized log.-periodic four arm antenna bent on a conical MID substrate. The bending of a planar structure in free space is highlighted and the resulting effects on the input impedance and radiation characteristic are analyzed. The subsequent design of the UWB compliant prototype is introduced. An adequate agreement between simulated and measured performance can be observed. The antenna provides an input matching of better than −8 dB over a frequency range from 3GHz to 9GHz. The antenna pattern is characterized by a radiation with two linear, orthogonal polarizations and a front-to-back ratio of 6 dB. A maximum gain of 5.6 dBi is achieved at 5.5GHz. The pattern correlation coefficients confirm the suitability of this structure for diversity and MIMO applications. The overall antenna diameter and height are 50mm and 24mm respectively. It could therefore be used as a surface mounted or ceiling antenna in buildings, vehicles or aircrafts for communication systems.
A Wearable Context-Aware ECG Monitoring System Integrated with Built-in Kinematic Sensors of the Smartphone

Continuously monitoring the ECG signals over hours combined with activity status is very important for preventing cardiovascular diseases. A traditional ECG holter is often inconvenient to carry because it has many electrodes attached to the chest and because it is heavy. This work proposes a wearable, low power context-aware ECG monitoring system integrated built-in kinetic sensors of the smartphone with a self-designed ECG sensor. The wearable ECG sensor is comprised of a fully integrated analog front-end (AFE), a commercial micro control unit (MCU), a secure digital (SD) card, and a Bluetooth module. The whole sensor is very small with a size of only 58 × 50 × 10 mm for wearable monitoring application due to the AFE design, and the total power dissipation in a full round of ECG acquisition is only 12.5 mW. With the help of built-in kinetic sensors of the smartphone, the proposed system can compute and recognize user's physical activity, and thus provide context-aware information for the continuous ECG monitoring. The experimental results demonstrated the performance of proposed system in improving diagnosis accuracy for arrhythmias and identifying the most common abnormal ECG patterns in different activities. In conclusion, we provide a wearable, accurate and energy-efficient system for long-term and context-aware ECG monitoring without any extra cost on kinetic sensor design but with the help of the widespread smartphone.