nils-herrmann/clean_scirepeval huggingface dataset:
Layer Depth Denoising and Completion for Structured-Light RGB-D Cameras

The recent popularity of structured-light depth sensors has enabled many new applications from gesture-based user interface to 3D reconstructions. The quality of the depth measurements of these systems, however, is far from perfect. Some depth values can have significant errors, while others can be missing altogether. The uncertainty in depth measurements among these sensors can significantly degrade the performance of any subsequent vision processing. In this paper, we propose a novel probabilistic model to capture various types of uncertainties in the depth measurement process among structured-light systems. The key to our model is the use of depth layers to account for the differences between foreground objects and background scene, the missing depth value phenomenon, and the correlation between color and depth channels. The depth layer labeling is solved as a maximum a-posteriori estimation problem, and a Markov Random Field attuned to the uncertainty in measurements is used to spatially smooth the labeling process. Using the depth-layer labels, we propose a depth correction and completion algorithm that outperforms other techniques in the literature.
Copy-Move Forgery Detection using DCT and SIFT

Digital images are the most prevalent way to spread a message. So the authenticity of images is very essential. But due to advancement of the technology the editing of images has become very effortless. Copy-move forgery is most basic technique to alter an image. In this one part of image is copied, called as snippet, and pasted within same image and most likely post-processing it. Considerable number of algorithms is proposed to detect different post-processing on snippet of image. In this paper novel approach is proposed to detect combination of different post-processing operations by single method. It is analyzed that block-based features method DCT is robust to Gaussian noise and JPEG compression, secondly the keypoint-based feature method SIFT is robust to rotation and scaling. Thus by combining SIFT and DCT we are able to detect forgery under post-processing operations of rotation, scaling, Gaussian noise, and JPEG compression and thus the efficiency to detect forgery improves.
Joint 3D Object and Layout Inference from a Single RGB-D Image

Inferring 3D objects and the layout of indoor scenes from a single RGB-D image captured with a Kinect camera is a challenging task. Towards this goal, we propose a high-order graphical model and jointly reason about the layout, objects and superpixels in the image. In contrast to existing holistic approaches, our model leverages detailed 3D geometry using inverse graphics and explicitly enforces occlusion and visibility constraints for respecting scene properties and projective geometry. We cast the task as MAP inference in a factor graph and solve it efficiently using message passing. We evaluate our method with respect to several baselines on the challenging NYUv2 indoor dataset using 21 object categories. Our experiments demonstrate that the proposed method is able to infer scenes with a large degree of clutter and occlusions.
Nonverbal behavior and the vertical dimension of social relations: a meta-analysis.

The vertical dimension of interpersonal relations (relating to dominance, power, and status) was examined in association with nonverbal behaviors that included facial behavior, gaze, interpersonal distance, body movement, touch, vocal behaviors, posed encoding skill, and others. Results were separately summarized for people's beliefs (perceptions) about the relation of verticality to nonverbal behavior and for actual relations between verticality and nonverbal behavior. Beliefs/perceptions were stronger and much more prevalent than were actual verticality effects. Perceived and actual relations were positively correlated across behaviors. Heterogeneity was great, suggesting that verticality is not a psychologically uniform construct in regard to nonverbal behavior. Finally, comparison of the verticality effects to those that have been documented for gender in relation to nonverbal behavior revealed only a limited degree of parallelism.
Contextual Text Understanding in Distributional Semantic Space

Representing discrete words in a continuous vector space turns out to be useful for natural language applications related to text understanding. Meanwhile, it poses extensive challenges, one of which is due to the polysemous nature of human language. A common solution (a.k.a word sense induction) is to separate each word into multiple senses and create a representation for each sense respectively. However, this approach is usually computationally expensive and prone to data sparsity, since each sense needs to be managed discriminatively. In this work, we propose a new framework for generating context-aware text representations without diving into the sense space. We model the concept space shared among senses, resulting in a framework that is efficient in both computation and storage. Specifically, the framework we propose is one that: i) projects both words and concepts into the same vector space; ii) obtains unambiguous word representations that not only preserve the uniqueness among words, but also reflect their context-appropriate meanings. We demonstrate the effectiveness of the framework in a number of tasks on text understanding, including word/phrase similarity measurements, paraphrase identification and question-answer relatedness classification.
Monte Carlo Sampling Methods for Approximating Interactive POMDPs

Partially observable Markov decision processes (POMDPs) provide a principled framework for sequential planning in uncertain single agent settings. An extension of POMDPs to multiagent settings, called interactive POMDPs (I-POMDPs), replaces POMDP belief spaces with interactive hierarchical belief systems which represent an agent’s belief about the physical world, about beliefs of other agents, and about their beliefs about others’ beliefs. This modification makes the difficulties of obtaining solutions due to complexity of the belief and policy spaces even more acute. We describe a general method for obtaining approximate solutions of I-POMDPs based on particle filtering (PF). We introduce the interactive PF, which descends the levels of the interactive belief hierarchies and samples and propagates beliefs at each level. The interactive PF is able to mitigate the belief space complexity, but it does not address the policy space complexity. To mitigate the policy space complexity – sometimes also called the curse of history – we utilize a complementary method based on sampling likely observations while building the look ahead reachability tree. While this approach does not completely address the curse of history, it beats back the curse’s impact substantially. We provide experimental results and chart future work.
Deep Belief Networks using discriminative features for phone recognition

Deep Belief Networks (DBNs) are multi-layer generative models. They can be trained to model windows of coefficients extracted from speech and they discover multiple layers of features that capture the higher-order statistical structure of the data. These features can be used to initialize the hidden units of a feed-forward neural network that is then trained to predict the HMM state for the central frame of the window. Initializing with features that are good at generating speech makes the neural network perform much better than initializing with random weights. DBNs have already been used successfully for phone recognition with input coefficients that are MFCCs or filterbank outputs [1, 2]. In this paper, we demonstrate that they work even better when their inputs are speaker adaptive, discriminative features. On the standard TIMIT corpus, they give phone error rates of 19.6% using monophone HMMs and a bigram language model and 19.4% using monophone HMMs and a trigram language model.
Inverse Reinforcement Learning in Swarm Systems

Inverse reinforcement learning (IRL) has become a useful tool for learning behavioral models from demonstration data. However, IRL remains mostly unexplored for multi-agent systems. In this paper, we show how the principle of IRL can be extended to homogeneous large-scale problems, inspired by the collective swarming behavior of natural systems. In particular, we make the following contributions to the field: 1) We introduce the swarMDP framework, a subclass of decentralized partially observable Markov decision processes endowed with a swarm characterization. 2) Exploiting the inherent homogeneity of this framework, we reduce the resulting multi-agent IRL problem to a single-agent one by proving that the agent-specific value functions in this model coincide. 3) To solve the corresponding control problem, we propose a novel heterogeneous learning scheme that is particularly tailored to the swarm setting. Results on two example systems demonstrate that our framework is able to produce meaningful local reward models from which we can replicate the observed global system dynamics.
Noncontact vital-sign radar sensor using metamaterial-based scanning leaky-wave antenna

In this paper, a metamaterial-based scanning leaky-wave antenna is developed, and then applied to the Doppler radar system for the noncontact vital-sign detection. With the benefit of the antenna beam scanning, the radar system can not only sense human subject, but also detect the vital signs within the specific scanning region. The Doppler radar module is designed at 5.8 GHz, and implemented by commercial integrated circuits and coplanar waveguide (CPW) passive components. Two scanning antennas are then connected with the transmitting and receiving ports of the module, respectively. In addition, since the main beam of the developed scanning antenna is controlled by the frequency, one can easily tune the frequency of the radar source from 5.1 to 6.5 GHz to perform the 59° spatial scanning. The measured respiration and heartbeat rates are in good agreement with the results acquired from the medical finger pulse sensor.
Cross-Project Defect Prediction Using a Connectivity-Based Unsupervised Classifier

Defect prediction on projects with limited historical data has attracted great interest from both researchers and practitioners. Cross-project defect prediction has been the main area of progress by reusing classifiers from other projects. However, existing approaches require some degree of homogeneity (e.g., a similar distribution of metric values) between the training projects and the target project. Satisfying the homogeneity requirement often requires significant effort (currently a very active area of research).
 An unsupervised classifier does not require any training data, therefore the heterogeneity challenge is no longer an issue. In this paper, we examine two types of unsupervised classifiers: a) distance-based classifiers (e.g., k-means); and b) connectivity-based classifiers. While distance-based unsupervised classifiers have been previously used in the defect prediction literature with disappointing performance, connectivity-based classifiers have never been explored before in our community.
 We compare the performance of unsupervised classifiers versus supervised classifiers using data from 26 projects from three publicly available datasets (i.e., AEEEM, NASA, and PROMISE). In the cross-project setting, our proposed connectivity-based classifier (via spectral clustering) ranks as one of the top classifiers among five widely-used supervised classifiers (i.e., random forest, naive Bayes, logistic regression, decision tree, and logistic model tree) and five unsupervised classifiers (i.e., k-means, partition around medoids, fuzzy C-means, neural-gas, and spectral clustering). In the within-project setting (i.e., models are built and applied on the same project), our spectral classifier ranks in the second tier, while only random forest ranks in the first tier. Hence, connectivity-based unsupervised classifiers offer a viable solution for cross and within project defect predictions.
Learning to Control a Brain–Machine Interface for Reaching and Grasping by Primates

Reaching and grasping in primates depend on the coordination of neural activity in large frontoparietal ensembles. Here we demonstrate that primates can learn to reach and grasp virtual objects by controlling a robot arm through a closed-loop brain-machine interface (BMIc) that uses multiple mathematical models to extract several motor parameters (i.e., hand position, velocity, gripping force, and the EMGs of multiple arm muscles) from the electrical activity of frontoparietal neuronal ensembles. As single neurons typically contribute to the encoding of several motor parameters, we observed that high BMIc accuracy required recording from large neuronal ensembles. Continuous BMIc operation by monkeys led to significant improvements in both model predictions and behavioral performance. Using visual feedback, monkeys succeeded in producing robot reach-and-grasp movements even when their arms did not move. Learning to operate the BMIc was paralleled by functional reorganization in multiple cortical areas, suggesting that the dynamic properties of the BMIc were incorporated into motor and sensory cortical representations.
ReNoun: Fact Extraction for Nominal Attributes

Search engines are increasingly relying on large knowledge bases of facts to provide direct answers to users’ queries. However, the construction of these knowledge bases is largely manual and does not scale to the long and heavy tail of facts. Open information extraction tries to address this challenge, but typically assumes that facts are expressed with verb phrases, and therefore has had difficulty extracting facts for noun-based relations. We describe ReNoun, an open information extraction system that complements previous efforts by focusing on nominal attributes and on the long tail. ReNoun’s approach is based on leveraging a large ontology of noun attributes mined from a text corpus and from user queries. ReNoun creates a seed set of training data by using specialized patterns and requiring that the facts mention an attribute in the ontology. ReNoun then generalizes from this seed set to produce a much larger set of extractions that are then scored. We describe experiments that show that we extract facts with high precision and for attributes that cannot be extracted with verb-based techniques.
Monkey Says, Monkey Does: Security and Privacy on Voice Assistants

The introduction of smart mobile devices has radically redesigned user interaction, as these devices are equipped with numerous sensors, making applications context-aware. To further improve user experience, most mobile operating systems and service providers are gradually shipping smart devices with voice controlled intelligent personal assistants, reaching a new level of human and technology convergence. While these systems facilitate user interaction, it has been recently shown that there is a potential risk regarding devices, which have such functionality. Our independent research indicates that this threat is not merely potential, but very real and more dangerous than initially perceived, as it is augmented by the inherent mechanisms of the underlying operating systems, the increasing capabilities of these assistants, and the proximity with other devices in the Internet of Things (IoT) era. In this paper, we discuss and demonstrate how these attacks can be launched, analysing their impact in real world scenarios.
MRAS based estimation of speed in sensorless PMSM drive

To increase the mechanical robustness of drive system and to make the drive cheaper elimination of the position sensor is highly encouraged. Sensors are not reliable in explosive environment like in chemical industries and may cause the EMI problem. This has made the speed and position sensorless drive very attractive. A speed and position estimator for PMSM drive has been investigated. In the algorithm the PMSM used as reference model. The adaptation mechanism uses a PI controller to process the error between reference model and adjustable model. The estimation algorithm used is independent of stator resistance, computationally less complex, free from integrator problem because back-emf estimation is not required and provides stable operation of drive system. So the performance at low and zero speed is also good. The estimation algorithm is implemented in MATLAB. Simulations results show the validity of MRAS estimator presented here.
The ICB-2015 Competition on Finger Vein Recognition

Finger vein recognition is a newly developed and promising biometrics technology. To facilitate evaluation in this area and study state-of-the-art performance of the finger vein recognition algorithms, we organized The ICB-2015 Competition on Finger Vein Recognition (ICFVR2015). This competition is held on a general recognition algorithm evaluation platform called RATE, with 3 data sets collected from volunteers and actual usage. 7 algorithms were finally submitted, with the best EER achieving 0.375%. This paper will first introduce the organization of the competition and RATE, then describe data sets and test protocols, and finally present results of the competition.
Three-dimensional bipedal walking control using Divergent Component of Motion

In this paper, we extend the Divergent Component of Motion (DCM, also called `Capture Point') to 3D. We introduce the “Enhanced Centroidal Moment Pivot point” (eCMP) and the “Virtual Repellent Point” (VRP), which allow for the encoding of both direction and magnitude of the external (e.g. leg) forces and the total force (i.e. external forces plus gravity) acting on the robot. Based on eCMP, VRP and DCM, we present a method for real-time planning and control of DCM trajectories in 3D. We address the problem of underactuation and propose methods to guarantee feasibility of the finally commanded forces. The capabilities of the proposed control framework are verified in simulations.
An Affordable Open-Source Turbidimeter

Turbidity is an internationally recognized criterion for assessing drinking water quality, because the colloidal particles in turbid water may harbor pathogens, chemically reduce oxidizing disinfectants, and hinder attempts to disinfect water with ultraviolet radiation. A turbidimeter is an electronic/optical instrument that assesses turbidity by measuring the scattering of light passing through a water sample containing such colloidal particles. Commercial turbidimeters cost hundreds or thousands of dollars, putting them beyond the reach of low-resource communities around the world. An affordable open-source turbidimeter based on a single light-to-frequency sensor was designed and constructed, and evaluated against a portable commercial turbidimeter. The final product, which builds on extensive published research, is intended to catalyze further developments in affordable water and sanitation monitoring.
Quranic-based concepts: Verse relations extraction using Manchester OWL syntax

In recent years, there is global demand for Islamic knowledge by both Muslims and non-Muslims. This has brought about number of automated applications that ease the retrieval of knowledge from the holy books. However current retrieval methods lack semantic information they are mostly base on keywords matching approach. In this paper we have proposed a Model that will make use of semantic Web technologies (ontology) to model Quran domain knowledge. The system will enhance Quran knowledge by enabling queries in natural language.
Deep domain adaptation for describing people based on fine-grained clothing attributes

We address the problem of describing people based on fine-grained clothing attributes. This is an important problem for many practical applications, such as identifying target suspects or finding missing people based on detailed clothing descriptions in surveillance videos or consumer photos. We approach this problem by first mining clothing images with fine-grained attribute labels from online shopping stores. A large-scale dataset is built with about one million images and fine-detailed attribute sub-categories, such as various shades of color (e.g., watermelon red, rosy red, purplish red), clothing types (e.g., down jacket, denim jacket), and patterns (e.g., thin horizontal stripes, houndstooth). As these images are taken in ideal pose/lighting/background conditions, it is unreliable to directly use them as training data for attribute prediction in the domain of unconstrained images captured, for example, by mobile phones or surveillance cameras. In order to bridge this gap, we propose a novel double-path deep domain adaptation network to model the data from the two domains jointly. Several alignment cost layers placed inbetween the two columns ensure the consistency of the two domain features and the feasibility to predict unseen attribute categories in one of the domains. Finally, to achieve a working system with automatic human body alignment, we trained an enhanced RCNN-based detector to localize human bodies in images. Our extensive experimental evaluation demonstrates the effectiveness of the proposed approach for describing people based on fine-grained clothing attributes.
HeartToGo: A Personalized medicine technology for cardiovascular disease prevention and detection

To date, cardiovascular disease (CVD) is the first leading cause of global death. The Electrocardiogram (ECG) is the most widely adopted clinical tool that measures and records the electrical activity of the heart from the body surface. The mainstream resting ECG machines for CVD diagnosis and supervision can be ineffective in detecting abnormal transient heart activities, which may not occur during an individual's hospital visit. Common Holter-based portable solutions offer 24-hour ECG recording, containing hundreds of thousands of heart beats that not only are tedious and time-consuming to analyze manually but also miss the capability to provide any real-time feedback. In this study, we seek to establish a cell phone-based personalized medicine technology for CVD, capable of performing continuous monitoring and recording of ECG in real time, generating individualized cardiac health summary report in layman's language, automatically detecting abnormal CVD conditions and classifying them at any place and anytime. Specifically, we propose to develop an artificial neural network (ANN)-based machine learning technique, combining both individualized medical information and clinical ECG database data, to train the cell phone to learn to adapt to its user's physiological conditions to achieve better ECG feature extraction and more accurate CVD classification results.
Echo state networks with filter neurons and a delay&sum readout

Echo state networks (ESNs) are a novel approach to recurrent neural network training with the advantage of a very simple and linear learning algorithm. It has been demonstrated that ESNs outperform other methods on a number of benchmark tasks. Although the approach is appealing, there are still some inherent limitations in the original formulation. Here we suggest two enhancements of this network model. First, the previously proposed idea of filters in neurons is extended to arbitrary infinite impulse response (IIR) filter neurons. This enables such networks to learn multiple attractors and signals at different timescales, which is especially important for modeling real-world time series. Second, a delay&sum readout is introduced, which adds trainable delays in the synaptic connections of output neurons and therefore vastly improves the memory capacity of echo state networks. It is shown in commonly used benchmark tasks and real-world examples, that this new structure is able to significantly outperform standard ESNs and other state-of-the-art models for nonlinear dynamical system modeling.