nils-herrmann/clean_scirepeval huggingface dataset:
Multimodal visualization of the optomechanical response of silicon cantilevers with ultrafast electron microscopy

The manner in which structure at the mesoscale affects emergent collective dynamics has become the focus of much attention owing, in part, to new insights into how morphology on these spatial scales can be exploited for enhancement and optimization of macroscopic properties. Key to advancements in this area is development of multimodal characterization tools, wherein access to a large parameter space (energy, space, and time) is achieved (ideally) with a single instrument. Here, we describe the study of optomechanical responses of single-crystal Si cantilevers with an ultrafast electron microscope. By conducting structural-dynamics studies in both real and reciprocal space, we are able to visualize MHz vibrational responses from atomicto micrometerscale dimensions. With nanosecond selected-area and convergent-beam diffraction, we demonstrate the effects of spatial signal averaging on the isolation and identification of eigenmodes of the cantilever. We find that the reciprocal-space methods reveal eigenmodes mainly below 5 MHz, indicative of the first five vibrational eigenvalues for the cantilever geometry studied here. With nanosecond real-space imaging, however, we are able to visualize local vibrational frequencies exceeding 30 MHz. The heterogeneously-distributed vibrational response is mapped via generation of pixel-by-pixel time-dependent Fourier spectra, which reveal the localized highfrequency modes, whose presence is not detected with parallel-beam diffraction. By correlating the transient response of the three modalities, the oscillation, and dissipation of the optomechanical response can be compared to a linear-elastic model to isolate and identify the spatial threedimensional dynamics.
Attribute-Based Encryption with Multi-keyword Search

Public key encryption with keyword search applies only to the certain circumstances that keyword ciphertext can only be retrieved by a specific user and only supports single-keyword matching. In the existing searchable encryption schemes, either the communication mode is one-to-one, or only single-keyword search is supported. This paper proposes a searchable encryption that is based on attributes and supports multi-keyword search. The proposed scheme allows keyword ciphertext to be inquired correctly by multiple users if and only if the users defined by a series of attributes satisfy the access structure. This can realize the multi-user information query. In particular, the keyword of the user query does not need to match the keyword ciphertext exactly. The keywords in the ciphertext simply determine whether the keyword belongs to the set of keywords which are used for encryption. Extensive analysis indicates that the proposed scheme is secure and efficient.
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?

There are two major types of uncertainty one can model. Aleatoric uncertainty captures noise inherent in the observations. On the other hand, epistemic uncertainty accounts for uncertainty in the model – uncertainty which can be explained away given enough data. Traditionally it has been difficult to model epistemic uncertainty in computer vision, but with new Bayesian deep learning tools this is now possible. We study the benefits of modeling epistemic vs. aleatoric uncertainty in Bayesian deep learning models for vision tasks. For this we present a Bayesian deep learning framework combining input-dependent aleatoric uncertainty together with epistemic uncertainty. We study models under the framework with per-pixel semantic segmentation and depth regression tasks. Further, our explicit uncertainty formulation leads to new loss functions for these tasks, which can be interpreted as learned attenuation. This makes the loss more robust to noisy data, also giving new state-of-the-art results on segmentation and depth regression benchmarks.
Virtual fitting: real-time garment simulation for online shopping

Without physically trying on a garment, online clothes shoppers are unable to decide the best size or color, and therefore are more likely to purchase clothes that do not fit. A solution to this online fit problem is virtual fitting. Using 3D modeling to help customers visualize how garments look on them requires garment simulation in real time. This paper proposes an innovative approach that utilizes machine learning to meet this real-time requirement and can be applied to virtual fitting systems.
Comparison Between Geometry-Based and Gabor-Wavelets-Based Facial Expression Recognition Using Multi-Layer Perceptron

In this paper, we investigate the use of two types of features extracted from face images for recognizing facial expressions. The first type is the geometric positions of a set of fiducial points on a face. The second type is a set of multi-scale and multi-orientation Gabor wavelet coefficients extracted from the face image at the fiducial points. They can be used either independently or jointly. The architecture we developed is based on a two-layer perceptron. The recognition performance with different types of features has been compared, which shows that Gabor wavelet coefficients are much more powerful than geometric positions. Furthermore, since the first layer of the perceptron actually performs a nonlinear reduction of the dimensionality of the feature space, we have also studied the desired number of hidden units, i.e., the appropriate dimension to represent a facial expression in order to achieve a good recognition rate. It turns out that five to seven hidden units are probably enough to represent the space of feature expressions.
Design and optimization of a novel six-axis force/torque sensor with good isotropy and high sensitivity

Six-axis force/torque sensor plays an important role in many fields. A novel six-axis force/torque sensor based on strain gauge was designed and optimized with design of experiments (DOE) approach and response surface methodology. In order to reduce the errors bonding strain gauges, we adopted strain distribution analysis to acquire the accurately positions. The strain compliance matrix was attained by the signals from strain gauges bonded on the elastic body of the sensor. According to strain compliance matrix of the sensor we can obtain a sensor with rather good measurement isotropy, high sensitivity and natural frequency which is nearly as bandwidth of commercial six-axis sensor.
Digital Control Strategy for Four Quadrant Operation of Three Phase BLDC Motor With Load Variations

Brushless DC (BLDC) motor drives are becoming more popular in industrial, traction applications. This makes the control of BLDC motor in all the four quadrants very vital. This paper deals with the digital control of three phase BLDC motor. The motor is controlled in all the four quadrants without any loss of power; in fact energy is conserved during the regenerative period. The digital controller dsPIC30F4011, which is very advantageous over other controllers, as it combines the calculation capability of Digital Signal Processor and controlling capability of PIC microcontroller, to achieve precise control.
Interactions between working memory, attention and eye movements.

This paper reviews the recent findings on working memory, attention and eye movements. We discuss the research that shows that many phenomena related to visual attention taking place when selecting relevant information from the environment are similar to processes needed to keep information active in working memory. We discuss new data that show that when retrieving information from working memory, people may allocate visual spatial attention to the empty location in space that used to contain the information that has to be retrieved. Moreover, we show that maintaining a location in working memory not only may involve attention rehearsal, but might also recruit the oculomotor system. Recent findings seem to suggest that remembering a location may involve attention-based rehearsal in higher brain areas, while at the same time there is inhibition of specific motor programs at lower brain areas. We discuss the possibility that working memory functions do not reside at a special area in the brain, but emerge from the selective recruitment of brain areas that are typically involved in spatial attention and motor control.
Broadband millimetre-wave passive spatial combiner based on coaxial waveguide

A broadband millimetre-wave passive spatial combiner using a microstrip probe array and an oversized coaxial waveguide is successfully designed and tested. The equivalent circuit approach of transverse electromagnetic (TEM) lines is adopted to synthesise the coaxial stepped impedance transformer from a K connector with the oversized coaxial waveguide and to analyse the microstrip probe array. The equivalent susceptances of the step discontinuities for the power divider are calculated. A four-way coaxial waveguide passive power combining circuit operating at the entire Ka-band is designed, fabricated and measured. Experiments on the four-way passive combiner show that a minimum insertion loss of 0.7 dB has been achieved at about 29.5 GHz. The combiner has shown a bandwidth of 26.5–40 GHz with 10 dB return loss and less than 2.5 dB insertion loss.
Passive in-home measurement of stride-to-stride gait variability comparing vision and Kinect sensing

We present an analysis of measuring stride-to-stride gait variability passively, in a home setting using two vision based monitoring techniques: anonymized video data from a system of two web-cameras, and depth imagery from a single Microsoft Kinect. Millions of older adults fall every year. The ability to assess the fall risk of elderly individuals is essential to allowing them to continue living safely in independent settings as they age. Studies have shown that measures of stride-to-stride gait variability are predictive of falls in older adults. For this analysis, a set of participants were asked to perform a number of short walks while being monitored by the two vision based systems, along with a marker based Vicon motion capture system for ground truth. Measures of stride-to-stride gait variability were computed using each of the systems and compared against those obtained from the Vicon.
1 An End-to-End Framework for Evaluating Surface Reconstruction

We present a benchmark for the evaluation and comparison of algorithms which reconstruct a surface from point cloud data. Although a substantial amount of effort has been dedicated to the problem of surface reconstruction, a comprehensive means of evaluating this class of algorithms is noticeably absent. We propose a simple pipeline for measuring surface reconstruction algorithms, consisting of three main phases: surface modeling, sampling, and evaluation.We employ implicit surfaces for modeling shapes which are expressive enough to contain details of varying size, in addition to preserving sharp features. From these implicit surfaces, we produce point clouds by synthetically generating range scans which resemble realistic scan data. We validate our synthetic sampling scheme by comparing against scan data produced via a commercial optical laser scanner, wherein we scan a 3D-printed version of the original implicit surface. Last, we perform evaluation by comparing the output reconstructed surface to a dense uniformly-distributed sampling of the implicit surface. We decompose our benchmark into two distinct sets of experiments. The first set of experiments measures reconstruction against point clouds of complex shapes sampled under a wide variety of conditions. Although these experiments are quite useful for the comparison of surface reconstruction algorithms, they lack a fine-grain analysis. Hence to complement this, the second set of experiments are designed to measure specific properties of surface reconstruction, both from a sampling and surface modeling viewpoint. Together, these experiments depict a detailed examination of the state of surface reconstruction algorithms. An End-to-End Framework for Evaluating Surface Reconstruction
Determining Gains Acquired from Word Embedding Quantitatively Using Discrete Distribution Clustering

Word embeddings have become widelyused in document analysis. While a large number of models for mapping words to vector spaces have been developed, it remains undetermined how much net gain can be achieved over traditional approaches based on bag-of-words. In this paper, we propose a new document clustering approach by combining any word embedding with a state-of-the-art algorithm for clustering empirical distributions. By using the Wasserstein distance between distributions, the word-to-word semantic relationship is taken into account in a principled way. The new clustering method is easy to use and consistently outperforms other methods on a variety of data sets. More importantly, the method provides an effective framework for determining when and how much word embeddings contribute to document analysis. Experimental results with multiple embedding models are reported.
Using heuristics to estimate an appropriate number of latent topics in source code analysis

Latent Dirichlet Allocation (LDA) is a data clustering algorithm that performs especially well for text documents. In natural-language applications it automatically finds groups of related words (called “latent topics”) and clusters the documents into sets that are about the same “topic”. LDA has also been applied to source code, where the documents are natural source code units such as methods or classes, and the words are the keywords, operators, and programmer-defined names in the code. The problem of determining a topic count that most appropriately describes a set of source code documents is an open problem. We address this empirically by constructing clusterings with different numbers of topics for a large number of software systems, and then use a pair of measures based on source code locality and topic model similarity to assess how well the topic structure identifies related source code units. Results suggest that the topic count required can be closely approximated using the number of software code fragments in the system. We extend these results to recommend appropriate topic counts for arbitrary software systems based on an analysis of a set of open source systems.
Participatory Simulations: Building Collaborative Understanding Through Immersive Dynamic Modeling

The Participatory Simulations Project explores how a new kind of collaborative learning environment, which is supported by small, wearable computers, can facilitate collaborative theory-building and lead to a richer understanding of scientific experimentation. In a Participatory Simulation, participants become "agents" in a full-scale simulation. Unlike previous work, Participatory Simulations combines the notion of a microworld with the affordances of real world experience. By involving a large number of people (typically between 15 and 30) in a "life-sized" experience, the project brings a microworld off of the computer screen and into the physical world. In particular, this study explored the use of a series of Participatory Simulations in a high school Biology class. The students became "agents" in a simulation of a disease infecting their community. The results of this project show that Participatory Simulations fully engage participants in the simulation, facilitate collaborative problem solving, provide a substrate for collaboratively designing and running experiments, and support the definition of new vocabulary to discuss the underlying rules of the simulation.  This research was sponsored by the National Science Foundation (grant # CDA-9616444), the Lego Group, and the Things That Think and Digital Life consortia.  Building Collaborative Understanding through Immersive Dynamic Modeling
Transfer Learning : From a Translation Model to a Dense Sentence Representation with Application to Paraphrase Detection

It is becoming increasingly difficult to judge the originality of digital content, especially with the continuously growing corpus of digital text. Traditional methods such as n-gram overlap are susceptible to simple obfuscation techniques. In this paper, we investigate several methods of measuring sentence similarity, with particular emphasis on paraphrase detection. We show that a bag-of-vectors approach provides a simple yet effective baseline. We then demonstrate how the encoder from a neural machine translation model can be used to build a powerful paraphrase detection classifier. Duplication of digital text, whether accidentally or deliberately copied, is a major concern for the academic and business communities, alike. Traditional plagiarism detection methods, such as ngram overlap, are susceptible to simple obfuscation techniques (Potthast et al., 2010). A more robust method for comparing the semantic content of sentences is needed. Recently, researchers have shown that using dense representations of words and sentences, along with deep learning techniques, can provide impressive results on a number of natural language processing (NLP) tasks (Socher et al., 2011; Wu et al., 2016). A major roadblock in the development of paraphrase classification models is the lack of large, highquality datasets. Most of the large paraphrase datasets have less than 20,000 sentence pairs (Dolan and Brockett, 2005; Xu et al., 2014). While sufficient for traditional machine learning techniques, the publicly available paraphrase datasets are not sufficiently large to train complex deep learning models. The training difficulty arises because accurate paraphrase detection requires a certain level of semantic understanding. One solution is to apply transfer learning, where a natural language model is pretrained on a separate task, before being applied to the target task (Pan and Yang, 2010). Transfer learning has seen many applications in image processing, where the lower layers of a pretrained model are often used to initialize a new model (Oquab et al., 2014). In some cases the weights of the lower layers can be fixed while training the second model. A similar approach is applied in (Pan et al., 2010) where a denoising autoencoder is trained on a large dataset and then used for a sentiment classification task. In this work, we demonstrate how the encoder from a neural machine translation (NMT) model can be used to build a powerful paraphrase detection classifier. We start by constructing a simple bagof-words baseline for the paraphrase detection task. We then demonstrate that the context vector from a translation vector can be used to classify paraphrases with greater accuracy than the baseline. Finally, we combine the baseline model with the NMT model, to generate an ensemble model which approaches state-of-the-art performance.
From the Test Benches to the First Prototype of the muFly Micro Helicopter

The goal of the European project muFly is to build a fully autonomous micro helicopter, which is comparable to a small bird in size and mass. The rigorous size and mass constraints infer various problems related to energy efficiency, flight stability and overall system design. In this research, aerodynamics and flight dynamics are investigated experimentally to gather information for the design of the helicopter’s propulsion group and steering system. Several test benches are designed and built for these investigations. A coaxial rotor test bench is used to measure the thrust and drag torque of different rotor blade designs. The effects of cyclic pitching of the swash plate and the passive stabilizer bar are studied on a test bench measuring rotor forces and moments with a 6–axis force sensor. The gathered knowledge is used to design a first prototype of the muFly helicopter. The prototype is described in terms of rotor configuration, structure, actuator and sensor selection according to the project demands, and a first version of the helicopter is shown. As a safety measure for the flight tests and to analyze the helicopter dynamics, a 6DoF vehicle test bench for tethered helicopter flight is used.
Amazigh Isolated-Word speech recognition system using Hidden Markov Model toolkit (HTK)

This paper aims to build a speaker-independent automatic Amazigh Isolated-Word speech recognition system. Hidden Markov Model toolkit (HTK) that uses hidden Markov Models has been used to develop the system. The recognition vocabulary consists on the Amazigh Letters and Digits. The system has been trained to recognize the Amazigh 10 first digits and 33 alphabets. Mel frequency spectral coefficients (MFCCs) have been used to extract the feature. The training data has been collected from 60 speakers including both males and females. The test-data used for evaluating the system-performance has been collected from 20 speakers. The experimental results show that the presented system provides the overall word-accuracy 80%. The initial results obtained are very satisfactory in comparison with the training database's size, this encourages us to increase system performance to achieve a higher recognition rate.
Robust Keyframe-based Dense SLAM with an RGB-D Camera

In this paper, we present RKD-SLAM, a robust keyframe-based dense SLAM approach for an RGB-D camera that can robustly handle fast motion and dense loop closure, and run without time limitation in a moderate size scene. It not only can be used to scan high-quality 3D models, but also can satisfy the demand of VR and AR applications. First, we combine color and depth information to construct a very fast keyframe-based tracking method on a CPU, which can work robustly in challenging cases (e.g. fast camera motion and complex loops). For reducing accumulation error, we also introduce a very efficient incremental bundle adjustment (BA) algorithm, which can greatly save unnecessary computation and perform local and global BA in a unified optimization framework. An efficient keyframe-based depth representation and fusion method is proposed to generate and timely update the dense 3D surface with online correction according to the refined camera poses of keyframes through BA. The experimental results and comparisons on a variety of challenging datasets and TUM RGB-D benchmark demonstrate the effectiveness of the proposed system.