Reviewerly/exHarmony huggingface dataset:
Task-dependence in scene perception: Head unrestrained viewing using mobile eye-tracking

Real-world scene perception is typically studied in the laboratory using static picture viewing with restrained head position. Consequently, the transfer of results obtained in this paradigm to real-word scenarios has been questioned. The advancement of mobile eye-trackers and the progress in image processing, however, permit a more natural experimental setup that, at the same time, maintains the high experimental control from the standard laboratory setting. We investigated eye movements while participants were standing in front of a projector screen and explored images under four specific task instructions. Eye movements were recorded with a mobile eye-tracking device and raw gaze data were transformed from head-centered into image-centered coordinates. We observed differences between tasks in temporal and spatial eye-movement parameters and found that the bias to fixate images near the center differed between tasks. Our results demonstrate that current mobile eye-tracking technology and a highly controlled design support the study of fine-scaled task dependencies in an experimental setting that permits more natural viewing behavior than the static picture viewing paradigm.
Mining Interpretable AOG Representations From Convolutional Networks via Active Question Answering

In this paper, we present a method to mine object-part patterns from conv-layers of a pre-trained convolutional neural network (CNN). The mined object-part patterns are organized by an And-Or graph (AOG). This interpretable AOG representation consists of a four-layer semantic hierarchy, i.e., semantic parts, part templates, latent patterns, and neural units. The AOG associates each object part with certain neural units in feature maps of conv-layers. The AOG is constructed with very few annotations (e.g., 3-20) of object parts. We develop a question-answering (QA) method that uses active human-computer communications to mine patterns from a pre-trained CNN, in order to explain features in conv-layers incrementally. During the learning process, our QA method uses the current AOG for part localization. The QA method actively identifies objects, whose feature maps cannot be explained by the AOG. Then, our method asks people to annotate parts on the unexplained objects, and uses answers to discover CNN patterns corresponding to newly labeled parts. In this way, our method gradually grows new branches and refines existing branches on the AOG to semanticize CNN representations. In experiments, our method exhibited a high learning efficiency. Our method used about 1/6- 1/3 of the part annotations for training, but achieved similar or better part-localization performance than fast-RCNN methods.
The controlling of paleo-riverbed migration on Arsenic mobilization in groundwater in the Red River Delta, Vietnam

In the Red River Delta, the concentrations of Arsenic in groundwater of alluvial dominated systems are very high, exceeding the WHO’s permissible. The correlation between the Arsenic concentrations in groundwater and the age of Holocene sediment as a key controlling groundwater Arsenic concentration in the Red River delta has been investigated. The evolution of sediments in the Holocene is closely related to paleo-riverbed migration in the past. A combination of methods is implemented including remote sensing, multi-electrode profiling (MEP), gamma-logging, drilling, soil sample and groundwater modeling. The resul has identified the shape, sediment compositions and location of the six paleo-riverbed periods. The age of the paleo-riverbed is determined by drilling, soil sampling and optically stimulated luminescence (OSL) in the laboratory. The oldest sediments is 5.9±0.4 ka BP in Phung Thuong near the mountain, the youngest one is from 0.4÷0.6 ka BP in H-transect near the Red River and the rest of the other is around 3.5 ka BP. The modeling results by using MODFLOW and MT3D show that the dynamics of paleo-riverbeds controlling Arsenic mobilization in groundwater in the Red River Delta. When the river moved to another position, the current river position at that time was filled with younger sediments and became paleo-riverbed formation with reducing conditions, Arsenic content which was adsorbed in the previous stage then released into groundwater. Therefore, Arsenic concentration in groundwater of young Holocene sediments is higher than in older ones which elucidates that paleo-riverbed migration controls on Arsenic mobilization in groundwater in the study area.
Optimized calibration method for ultra-field dual bands cameras based on thermal radiation checkerboard

Camera calibration is a vital and important work for machine vision. In the paper, an optimized calibration method for ultra-field dual bands cameras is proposed, which is based on the designed thermal radiation checkerboard. The optimizations are achieved in subpixel location, center location and mapping relationship, respectively. Specifically, the subpixel corner is located by performing the designed variable quadrilateral template on edge gradients and calculating their minimum orthogonal value. Then, the center location is accomplished through Gaussian fitting, in which the bottom point is taken as the potential center in iterations. Moreover, the RBF network is employed to replace the polynomial fitting and fit the mapping relationship between the camera coordinates and sensor coordinates. Thus, the accuracy of the mapping is increased, which contributes to the optimization of the intrinsic and extrinsic parameters. The experiments results show that the average root mean square error of the reprojection is reduced by nearly 50%. The calibrated parameters are accurate and have physical significance in the proposed calibration scheme.
Mixture distribution modeling for scalable graph-based semi-supervised learning

Graph-based semi-supervised learning (SSL) has been widely investigated in recent works considering its powerful ability to naturally incorporate the diverse types of information and measurements. However, traditional graph-based SSL methods have cubic complexities and leading to low scalability. In this paper, we propose to perform graph-based SSL on mixture distribution components, named Mixture-distribution based Graph Smoothing (MGS), to address this challenge. Specifically, the intrinsic distributions of data are captured by a mixture density estimation model. A novel mixture-distribution based objective energy function is further proposed to incorporate few available annotations, which ensures the model complexity is irrelevant to the number of raw instances. The energy function can be simplified and effectively solved by viewing the instances and mixture components as the point clouds. Experiments on large datasets demonstrate the remarkable performance improvements and scalability of the proposed model, which proves the superiority of the MGS model.
Russia and post-Soviet countries compared: coverage of papers by Scopus and Web of Science, languages, and productivity of researchers

Objective: To analyse the productivity of post-Soviet countries, adjusted by population, in terms of research papers published and the proportions of those papers indexed by Scopus and the Web of Science.Methods: Relevant data on the journals indexed in Scopus and the Web of Science were analysed. Where required, data were also extracted from Russian Science Citation Index databases and websites of journals.Results: On average, the post-Soviet countries had 31 researchers per 10,000 people. The average numbers of publications per researcher in journals indexed by Scopus was 1.04 and the corresponding figure for the Web of Science was 0.87. In terms of the number of journals indexed in Scopus and the Web of Science, the leading countries were Estonia, Latvia and Lithuania.Conclusion: Although the post-Soviet countries differed considerably in terms of bibliometric indices, the overall values were low. Main features of the journals were as follows: articles published in national languages &amp;ndash; in Russian in many cases &amp;ndash; and in English, articles mostly by authors within the region, and only a minority of foreigners as members of editorial boards. Thus most of the journals cannot be considered international. All the journals examined have websites in a national language and/or in English and invariably carry information on ethical practices, although such information is not given in a uniform format and varies from country to country.
Unextendible product bases, bound entangled states, and the range criterion

An unextendible product basis (UPB) is a set of orthogonal product states which span a subspace of a given Hilbert space while the complementary subspace contains no product state. These product bases are useful to produce bound entangled (BE) states. In this work we consider reducible and irreducible UPBs of maximum size, which can produce BE states of minimum rank. From a reducible UPB, it is possible to eliminate one or more states locally, keeping the post-measurement states orthogonal. On the other hand, for an irreducible UPB, the above is not possible. Particularly, the UPBs of the present size are important as they might be useful to produce BE states, having ranks of the widest variety, which satisfy the range criterion. Here we talk about such BE states. We also provide other types of BE states and analyze certain properties of the states. Some of the present BE states are associated with the tile structures. Furthermore, we provide different UPBs corresponding to the present BE states of minimum rank and discuss important properties of the UPBs.
Mitigation of Decoherence-Induced Quantum-Bit Errors and Quantum-Gate Errors Using Steane’s Code

Quantum processors require Quantum Error Correction Codes (QECC's) for improving the fidelity of quantum logic gates. Fault tolerant QECC's are capable of providing error rate improvements in quantum processors as long as the components are operating below a certain gate error probability. In this contribution, we quantify the depolarization probability bound, below which transversal QECC's would give a better error probability than an uncoded gate. Both a low-complexity repetition code and Steane's 7-bit QECC are characterized.
Geolocation of covert communication entity on the Internet for post-steganalysis

Abstract Geolocation of covert communication entity is significantly important for the forensics of the crime but has significant challenges when the steganalyst locks the guilty actor IP and wants to know the physical location of the actor. This kind of post-steganalysis involves not only the stegos transmitted on the Internet but the IP package head and content. This paper presents a geolocation method for the location of the covert communication entity based on hop-hot path coding. The method estimates the location of the covert communication entity by combining the path and delay between probes and the covert communication entity IP, which improves the deficiency that similar delays do not necessarily mean close geographical locations of the IPs. Moreover, the similarity between the IPs’ paths can be measured by coding the paths between IPs and probes. The results of a series of experiments show that the median error of the proposed method is within 6.16 km using different thresholds.
Bayesian model inversion using stochastic spectral embedding

In this paper we propose a new sampling-free approach to solve Bayesian model inversion problems that is an extension of the previously proposed spectral likelihood expansions (SLE) method. Our approach, called stochastic spectral likelihood embedding (SSLE), uses the recently presented stochastic spectral embedding (SSE) method for local spectral expansion refinement to approximate the likelihood function at the core of Bayesian inversion problems. We show that, similar to SLE, this approach results in analytical expressions for key statistics of the Bayesian posterior distribution, such as evidence, posterior moments and posterior marginals, by direct post-processing of the expansion coefficients. Because SSLE and SSE rely on the direct approximation of the likelihood function, they are in a way independent of the computational/mathematical complexity of the forward model. We further enhance the efficiency of SSLE by introducing a likelihood specific adaptive sample enrichment scheme. To showcase the performance of the proposed SSLE, we solve three problems that exhibit different kinds of complexity in the likelihood function: multimodality, high posterior concentration and high nominal dimensionality. We demonstrate how SSLE significantly improves on SLE, and present it as a promising alternative to existing inversion frameworks.
Virtual Tour: An Immersive Low Cost Telepresence System

In this work we present Virtual Tour, a low cost telepresence system that provides the possibility of visiting a remote place almost reaching the feeling of being physically there. This is accomplished by a fully mobile, consumer setup composed of a VR Headset, 360° video camera and a full-duplex audio system. On one hand, the remote environment is captured, stitched and encoded by the 360° camera. Resulting video and audio are streamed using a custom low latency streaming solution. On the other hand, user wears an untethered VR HMD to experiment a full immersion and close interaction with the remote space. In addition, conference capabilities of the proposed system boosts the telepresence feeling since the user can have a real-time conversation with people on the remote scenario.
Learning clinical networks from medical records based on information estimates in mixed-type data

The precise diagnostics of complex diseases require to integrate a large amount of information from heterogeneous clinical and biomedical data, whose direct and indirect interdependences are notoriously difficult to assess. To this end, we propose an efficient computational approach to simultaneously compute and assess the significance of multivariate information between any combination of mixed-type (continuous/categorical) variables. The method is then used to uncover direct, indirect and possibly causal relationships between mixed-type data from medical records, by extending a recent machine learning method to reconstruct graphical models beyond simple categorical datasets. The method is shown to outperform existing tools on benchmark mixed-type datasets, before being applied to analyze the medical records of eldery patients with cognitive disorders from La Pitié-Salpêtrière Hospital, Paris. The resulting clinical network visually captures the global interdependences in these medical records and some facets of clinical diagnosis practice, without specific hypothesis nor prior knowledge on any clinically relevant information. In particular, it provides some physiological insights linking the consequence of cerebrovascular accidents to the atrophy of important brain structures associated to cognitive impairment.
An Adaptive Target Tracking Algorithm Based on EKF for AUV with Unknown Non-Gaussian Process Noise

An adaptive target tracking method based on extended Kalman filter (TT-EKF) is proposed to simultaneously estimate the state of an Autonomous Underwater Vehicle (AUV) and an mobile recovery system (MRS) with unknown non-Gaussian process noise in homing process. In the application scenario of this article, the process noise includes the measurement noise of AUV heading and forward speed and the estimation error of MRS heading and forward speed. The accuracy of process noise covariance matrix (PNCM) can affect the state estimation performance of the TT-EKF. The variational Bayesian based algorithm is applied to estimate the process noise statistics. We use a Gaussian mixture distribution to model the non-Gaussian noisy forward speed of AUV and MRS. We use a von-Mises distribution to model the noisy heading of AUV and MRS. The variational Bayesian algorithm is applied to estimate the parameters of these distributions, and then the PNCM can be calculated. The prediction error of TT-EKF is online compensated by using a multilayer neural network, and the neural network is online trained during the target tracking process. Matlab simulation and experimental data analysis results verify the effectiveness of the proposed method.
Comparisons of extreme learning machine and backpropagation-based i-vector approach for speaker identification

The extreme learning machine (ELM) is one of the machine learning applications used for regression and classification systems. In this paper, an extended comparison between an ELM and the backpropagation neural network (BPNN)-based i-vector is given in terms of a closed-set speaker identification task using 120 speakers from the TIMIT database. The system is composed of the mel frequency cepstal coefficient (MFCC) and power normalized cepstal coefficient (PNCC) approaches to form the feature extraction stage, while the cepstral mean variance normalization (CMVN) and feature warping are applied in order to mitigate the linear channel effect. The system is utilized with equal numbers of speakers of both genders with 120 speakers with eight dialects from the TIMIT database. The results demonstrate that the combination of the i-vector with the ELM for different features has the highest speaker identification accuracy (SIA) compared with the combination of the BPNN with the i-vector. The results also show that the i-vector with ELM approach is faster than the BPNN-based i-vector and it has the highest SIA.
Mining Sequential Patterns with VC-Dimension and Rademacher Complexity

Sequential pattern mining is a fundamental data mining task with application in several domains. We study two variants of this task—the first is the extraction of frequent sequential patterns, whose frequency in a dataset of sequential transactions is higher than a user-provided threshold; the second is the mining of true frequent sequential patterns, which appear with probability above a user-defined threshold in transactions drawn from the generative process underlying the data. We present the first sampling-based algorithm to mine, with high confidence, a rigorous approximation of the frequent sequential patterns from massive datasets. We also present the first algorithms to mine approximations of the true frequent sequential patterns with rigorous guarantees on the quality of the output. Our algorithms are based on novel applications of Vapnik-Chervonenkis dimension and Rademacher complexity, advanced tools from statistical learning theory, to sequential pattern mining. Our extensive experimental evaluation shows that our algorithms provide high-quality approximations for both problems we consider.
Intertarget Occlusion Handling in Multiextended Target Tracking Based on Labeled Multi-Bernoulli Filter Using Laser Range Finder

Multiextended target tracking (METT) has important applications in many fields, including autonomous vehicles, traffic flow monitoring, etc. However, as an acknowledged METT algorithm, the labeled multi-Bernoulli (LMB) filter cannot handle the intertarget occlusion problem, which frequently observed among METT, leading to an estimated trajectory break and even target loss. To tackle this problem, this article proposes an improved LMB filter with intertarget occlusion handling ability for METT. First, an intertarget occlusion probability (IOP) model based on a labeled random finite set using a laser range finder is developed. The IOP model theoretically describes the probability that each tracked target being occluded, considering both partial and full occlusion. Second, a true detection probability (TDP) model is presented to describe the probability that a target will generate corresponding measurement of a sensor in the presence of occlusion. The TDPs of tracked targets provide a way to integrate their estimated occlusion probabilities into the LMB filter to improve its capability of handling occluded tracks. We carried out multiple vehicle tracking simulation and field test using a laser range finder. Both simulation and experiment results illustrate that our improved LMB filter with occlusion handling ability can successfully tackle the intertarget occlusion problem and outperform conventional algorithms.
CrossVis: A visual analytics system for exploring heterogeneous multivariate data with applications to materials and climate sciences

We present a new visual analytics system, called CrossVis, that allows flexible exploration of multivariate data with heterogeneous data types. After presenting the design requirements, which were derived from prior collaborations with domain experts, we introduce key features of CrossVis beginning with a tabular data model that coordinates multiple linked views and performance enhancements that enable scalable exploration of complex data. Next, we introduce extensions to the parallel coordinates plot, which include new axis representations for numerical, temporal, categorical, and image data, an embedded bivariate axis option, dynamic selections, focus+context axis scaling, and graphical indicators of key statistical values. We demonstrate the practical effectiveness of CrossVis through two scientific use cases; one focused on understanding neural network image classifications from a genetic engineering project and another involving general exploration of a large and complex data set of historical hurricane observations. We conclude with discussions regarding domain expert feedback, future enhancements to address limitations, and the interdisciplinary process used to design CrossVis.
An Experimental Performance Comparison of Widely Used Face Detection Tools

Face detection is the task of detecting faces on photos, videos as well as the streaming data such as a webcam. Face detection, which is a specific type of general-purpose object detection, is a key prerequisite for many other artificial intelligence tasks such as face verification, face tagging and retrieval, and face tracking. In addition to that, nowadays, face detection is commonly used in daily routines such as social media, and camera software of smartphones. As a result of this necessity, several face detection tools have been proposed. In this study, an experimental performance comparison of well-known face detection tools in terms of (1) accuracy, and (2) elapsed time of detection, which has become even more critical criteria especially when the face detection mechanism is utilized for a real-time system, is proposed. As a result of this experimental study, it is aimed that shed light on the much-concerned query “which face detection tool provides the best performance?”. In addition to that, this study succeeds in showing that convolutional neural networks achieve great accuracy for face detection.