Reviewerly/exHarmony huggingface dataset:
Multiple Distance-Based Coding: Toward Scalable Feature Matching for Large-Scale Web Image Search

For scalable feature matching in large-scale web image search, the bag-of-visual-words-based (BOW) approaches generally code local features as visual words to construct an inverted index file to match features efficiently. Both the popular feature coding techniques, i.e., K-means-based vector quantization and scalar quantization, directly quantize features to generate visual words. K-means-based vector quantization requires expensive visual codebook training, whereas scalar quantization leads to the miss of many matches due to the low stability of individual components of feature vectors. To address the above issues, we demonstrate that the corresponding sub-vectors of similar features generally have similar distances to multiple reference points in feature subspace and propose a multiple distance-based feature coding scheme for scalable feature matching. Specifically, based on the distances between the sub-vectors and multiple distinct reference points, we transform each feature to a set of feature codes, where one code is treated as a visual word required to construct the inverted index file whereas the others are embedded into the index file to further verify the feature matching based on the visual words. The proposed coding scheme does not need visual codebook training and shows desirable stability and discriminability. Moreover, in the matching verification, a feature-distance estimation method is proposed to estimate the Euclidean distances between features for an accurate matching verification. Extensive experimental results demonstrate the superiority of the proposed approach in comparison to the other approaches using recent feature quantization methods for large-scale web image search.
Robust Subspace Clustering With Independent and Piecewise Identically Distributed Noise Modeling

Most of the existing subspace clustering (SC) frameworks assume that the noise contaminating the data is generated by an independent and identically distributed (i.i.d.) source, where the Gaussianity is often imposed. Though these assumptions greatly simplify the underlying problems, they do not hold in many real-world applications. For instance, in face clustering, the noise is usually caused by random occlusions, local variations and unconstrained illuminations, which is essentially structural and hence satisfies neither the i.i.d. property nor the Gaussianity. In this work, we propose an independent and piecewise identically distributed (i.p.i.d.) noise model, where the i.i.d. property only holds locally. We demonstrate that the i.p.i.d. model better characterizes the noise encountered in practical scenarios, and accommodates the traditional i.i.d. model as a special case. Assisted by this generalized noise model, we design an information theoretic learning (ITL) framework for robust SC through a novel minimum weighted error entropy (MWEE) criterion. Extensive experimental results show that our proposed SC scheme significantly outperforms the state-of-the-art competing algorithms.
Image Encryption and Analysis using Dynamic AES

AES (Advanced Encryption Algorithm) is a block cipher, which is world wide implemented for encryption of data. It has been accepted as a standard for data security since 2001. AES is a substitution and permutation cipher, which provides confusion by using substitution box (S-Box) in the algorithm. The main drawback of AES is that it uses static S-Box throughout algorithm, which compromise the security of AES and may be exposed to different algebraic attacks. So to overcome this problem new Dynamic AES algorithm developed by key dependent dynamic S-Box using dynamic irreducible polynomial and affine constant. The analysis is done on gray scale and colour images. Both the images are encrypted and decrypted by using standard AES and Dynamic AES. Quality of algorithm and the level of security is analyzed based on the parameters like Image Histogram Analysis, Adjacent Pixel Correlation Analysis, Image Entropy, Number of Pixels Change Rate (NPCR), Unified Average Changing Intensity (UACI), Encryption Quality.
Data Augmentation for Speaker Identification under Stress Conditions to Combat Gender-Based Violence

A Speaker Identification system for a personalized wearable device to combat gender-based violence is presented in this paper. Speaker recognition systems exhibit a decrease in performance when the user is under emotional or stress conditions, thus the objective of this paper is to measure the effects of stress in speech to ultimately try to mitigate their consequences on a speaker identification task, by using data augmentation techniques specifically tailored for this purpose given the lack of data resources for this condition. An extensive experimentation has been carried out for assessing the effectiveness of the proposed techniques. First, we conclude that the best performance is always obtained when naturally stressed samples are included in the training set, and second, when these are not available, their substitution and augmentation with synthetically generated stress-like samples improves the performance of the system.
Direct observation and measurement of Au and Ag in epithermal mineralizing fluids

Colloidal gold particles have been observed in c. 300 °C low salinity fluids from the Arapucandere intermediate sulphidation epithermal base-metal-Au deposit in NW Turkey. This is the first time colloidal gold has been recorded in an ancient mineralizing fluid. Growth, in veins, of large euhedral quartz crystals, after the deposition of sulphides, occurred in a number of stages from the introduction of fresh pulses of fluid. The quartz overgrowths have a fibrous texture which facilitated trapping of large elongate fluid inclusions between the quartz fibres which grew perpendicular to the crystal faces of the pre-existing quartz. Episodic periods of intense trapping of fluid inclusions occurred throughout the growth of quartz. Trapped within primary fluid inclusions are numerous particles of gold, the largest observed is c. 1 μm but most are smaller. BSE element mapping shows these to contain Au, Ag, Cu ± Hg. LA-ICP-MS profiles of the fluid inclusions confirm Au and Ag is not present in solution, being present as numerous particles. We have quantified the concentration of gold in fluid inclusions which are orders of magnitude greater than has been previously measured or thought likely in crustal fluids. The average Ag concentration is c. 32 ppm and Au is c. 41 ppm but the maximum concentrations of both may reach several 100's to 1000 ppm. Calcite forms a coating on the inner surface of the inclusions and barite, pyrite, galena, sphalerite and unidentified minerals are also present with the Au-Ag particles. It is clear that the Au-Ag particles could not have precipitated in the fluid inclusions, therefore they must have precipitated elsewhere and been carried with the hydrothermal fluid. The high concentrations and their colloidal nature have implications for the enrichment of gold in mineral deposits.
Data Capture and Analysis of Darknet Markets

Since the advent of darknet markets, or illicit cryptomarkets, there has been a sustained interest in studying their operations: the actors, products, payment methods, and so on. However, this research has been limited by a variety of obstacles, including the difficulty in obtaining reliable and representative data, which present challenges to undertaking a complete and systematic study. The Australian National University’s Cybercrime Observatory has developed tools that can be used to collect and analyse data obtained from darknet markets. This paper describes these tools in detail. While the proposed methods are not error-free, they provide a further step in providing a transparent and comprehensive solution for observing darknet markets tailored for data scientists, social scientists, criminologists and others interested in analysing trends from darknet markets.
Mapping and modelling human exposure to uraniferous mine waste using a GIS-supported virtual geographic environment

The exposure of residents to large volumes of uraniferous mine waste deposits scattered across densely populated areas in the South African goldfields has long been identified as a potential health threat. To quantify this threat for selected locations numerous studies determined concentration levels of uranium and other radionuclides in different environmental media of the Witwatersrand basin including streams, soils, tailings deposits, mine water effluents, fluvial sediments, aquatic organisms as well as in the food chain (fish, cattle, crops, vegetables etc.). This paper describes how the secondary data were extracted from various sources in order to map the spatial extent and intensity of uranium pollution in selected goldfields and to quantify associated exposure of residents. Steps included geo-referencing, normalisation against selected benchmark values, and superimposition of complementary data sets in GIS. For easing access to the data base for non-GIS experts a virtual geographic environment (VGE) was used, offering updated satellite imagery and additional functionality that aided in modelling exposure of target populations. Exposure pathways considered here include ingestion of polluted water, food and soil (geophagia) as well as the inhalation of wind-blown tailings dust. In addition, anecdotal evidence and media-based reports on mining-related pollution and impacts were captured. Maps derived from the VGE were used to identify hot spots of radioactive pollution aiding the selection of suitable sampling sites for a screening survey on U-uptake by exposed residents using hair as a bio-indicator.
Osprey

Supervised methods are commonly used for machine-learning based applications but require expensive labeled dataset creation and maintenance. Increasingly, practitioners employ weak supervision approaches, where training labels are pro-grammatically generated in higher-level but noisier ways. However, these approaches require domain experts with programming skills. Additionally, highly imbalanced data is often a significant practical challenge for these approaches. In this work, we propose Osprey, a weak-supervision system suited for highly imbalanced data, built on top of the Snorkel framework. In order to support non-coders, the programmatic labeling is decoupled into a code layer and a configuration one. This decoupling enables a rapid development of end-to-end systems by encoding the business logic into the configuration layer. We apply the resulting system on highly imbalanced (0.05% positive) social-media data using a synthetic data rebalancing and augmentation approach, and a novel technique of ensembling a generative model over the legacy rules with a learned discriminative model. We demonstrate how an existing rule-based model can be transformed easily into a weakly-supervised one. For 3 relation extraction applications based on real-world deployments at Intel, we show that with a fraction of the cost, we achieve gains of 18.5 precision points and 28.5 coverage points over prior traditionally supervised and rule-based approaches.
Double Nuclear Norm Based Low Rank Representation on Grassmann Manifolds for Clustering

Unsupervised clustering for high-dimension data (such as imageset or video) is a hard issue in data processing and data mining area since these data always lie on a manifold (such as Grassmann manifold). Inspired of Low Rank representation theory, researchers proposed a series of effective clustering methods for high-dimension data with non-linear metric. However, most of these methods adopt the traditional single nuclear norm as the relaxation of the rank function, which would lead to suboptimal solution deviated from the original one. In this paper, we propose a new low rank model for high-dimension data clustering task on Grassmann manifold based on the Double Nuclear norm which is used to better approximate the rank minimization of matrix. Further, to consider the inner geometry or structure of data space, we integrated the adaptive Laplacian regularization to construct the local relationship of data samples. The proposed models have been assessed on several public datasets for imageset clustering. The experimental results show that the proposed models outperform the state-of-the-art clustering ones.
Automatic Tool for Fast Generation of Custom Convolutional Neural Networks Accelerators for FPGA

This paper presents a platform that automatically generates custom hardware accelerators for convolutional neural networks (CNNs) implemented in field-programmable gate array (FPGA) devices. It includes a user interface for configuring and managing these accelerators. The herein-presented platform can perform all the processes necessary to design and test CNN accelerators from the CNN architecture description at both layer and internal parameter levels, training the desired architecture with any dataset and generating the configuration files required by the platform. With these files, it can synthesize the register-transfer level (RTL) and program the customized CNN accelerator into the FPGA device for testing, making it possible to generate custom CNN accelerators quickly and easily. All processes save the CNN architecture description are fully automatized and carried out by the platform, which manages third-party software to train the CNN and synthesize and program the generated RTL. The platform has been tested with the implementation of some of the CNN architectures found in the state-of-the-art for freely available datasets such as MNIST, CIFAR-10, and STL-10.
Workload-Adaptive Configuration Tuning for Hierarchical Cloud Schedulers

Cluster schedulers provide flexible resource sharing mechanism for best-effort cloud jobs, which occupy a majority in modern datacenters. Properly tuning a scheduler's configurations is the key to these jobs' performance because it decides how to allocate resources among them. Today's cloud scheduling systems usually rely on cluster operators to set the configuration and thus overlook the potential performance improvement through optimally configuring the scheduler according to the heterogeneous and dynamic cloud workloads. In this paper, we introduce AdaptiveConfig, a run-time configurator for cluster schedulers that automatically adapts to the changing workload and resource status in two steps. First, a comparison approach estimates jobs' performances under different configurations and diverse scheduling scenarios. The key idea here is to transform a scheduler's resource allocation mechanism and their variable influence factors (configurations, scheduling constraints, available resources, and workload status) into business rules and facts in a rule engine, thereby reasoning about these correlated factors in job performance comparison. Second, a workload-adaptive optimizer transforms the cluster-level searching of huge configuration space into an equivalent dynamic programming problem that can be efficiently solved at scale. We implement AdaptiveConfig on the popular YARN Capacity and Fair schedulers and demonstrate its effectiveness using real-world Facebook and Google workloads, i.e., successfully finding best configurations for most of scheduling scenarios and considerably reducing latencies by a factor of two with low optimization time.
Sign-Consistency Based Variable Importance for Machine Learning in Brain Imaging

An important problem that hinders the use of supervised classification algorithms for brain imaging is that the number of variables per single subject far exceeds the number of training subjects available. Deriving multivariate measures of variable importance becomes a challenge in such scenarios. This paper proposes a new measure of variable importance termed sign-consistency bagging (SCB). The SCB captures variable importance by analyzing the sign consistency of the corresponding weights in an ensemble of linear support vector machine (SVM) classifiers. Further, the SCB variable importances are enhanced by means of transductive conformal analysis. This extra step is important when the data can be assumed to be heterogeneous. Finally, the proposal of these SCB variable importance measures is completed with the derivation of a parametric hypothesis test of variable importance. The new importance measures were compared with a t-test based univariate and an SVM-based multivariate variable importances using anatomical and functional magnetic resonance imaging data. The obtained results demonstrated that the new SCB based importance measures were superior to the compared methods in terms of reproducibility and classification accuracy.
Designing and Prototyping from the Perspective of AI in the Wild

This paper describes ObjectResponder --- a tool that allows designers to use Artificial Intelligence (AI) to rapidly prototype concepts for context-aware intelligent interaction in the wild. To our knowledge, there are currently no available tools for designing and prototyping with AI within the actual context of use. Our application uses Google Cloud Vision to allow designers assigning chat bot-like responses to objects recognized by the smart-phone camera. This enables designers to use object recognition labels as a means to diverge on possible interpretations of the context and start generating ideas that can then be immediately tested and iterated. Initial results suggest that looking at the world from the perspective of the AI may enable designers to balance human and nonhuman biases, enrich a designer's understanding of the context, and open up unexpected directions for idea generation.
Long-Reach Aerial Manipulation Employing Wire-Suspended Hand With Swing-Suppression Device

We herein propose a long-reach aerial manipulator to allow the manipulation of objects several meters away from an unmanned aerial vehicle (UAV). The system consists of a multirotor platform with winch mechanism and wire-suspended gripper module equipped with a camera and swing-suppression device. The winch controls the distance of the wire-suspended device from the UAV. To stabilize the device suspended from the UAV by two wires, we developed a swing-suppression device using an IMU and ducted fans. Through experiments, we verified effectiveness of the swing-suppression system; that is, it successfully stabilized the wire-suspended device within a distance of 3.5 m from the UAV. Furthermore, we performed a pick-and-place task using a gripper that was lowered from the UAV by 3 m.
Supervised learning classifiers for Arabic gestures recognition using Kinect V2

Human–Computer Interaction (HCI) refers to the interaction between the computers and human. One of the most important applications of (HCI) is sign language recognition. Several research works aimed to interpret and translate the sign language to a spoken language to help the hear impaired persons in integrating with communities. Sign language is the main way of communication for the deaf persons and hearing impaired which enable them to communicate with their societies and between each other’s. According to the World Health Organization, there are 466 million hearing loss people (i.e. 5% of the world population), 432 million or 93% of them are adults and 34 million or 17% of them are children. The hearing-impaired persons often need to the same level of mental capability as the normal persons. The main problem is the most of the hearing persons who cannot understand the sign language and most of hearing impaired cannot read or write our spoken language, this represents a barrier between the deaf persons and their societies so developing an automatic sign language recognition system is very necessary. This research introduces dynamic Arabic Sign Language recognition system using Microsoft Kinect. The recognition depends on using two machine learning algorithms (a) Decision Tree and (b) Bayesian Network then applied Ada-Boosting technique to enhance the recognition of the system, we compared the results with two direct matching techniques: (a) Dynamic Time Wrapping and Hidden Markov Model the system was applied on 42 Arabic gestures related to medical field. The experimental results showed that the proposed system recognition rate reached 91.18% for Decision Tree classifier, 92.50% for Bayesian classifier and 93.7% after applying Ada-Boosting.
Impact of different immersive techniques on the perceived sense of presence measured via subjective scales

Virtual reality has once again gained recognition and attention with the recent hardware-driven technological advances and there is also a diversity of applications from videos produced using 360° camera systems to animated immersive 3D animations and interactive experiences or games. While the variety of the VR hardware, VR applications and their appeal are staggering, the user experience of modern day VR is still associated with the concept of presence. Assuming that there should be a difference in terms of presence between different immersive techniques, such as watching a 360° video and an interactive 3D game, this study attempts to answer whether the presence questionnaires developed before the new VR era are still capable of measuring it or not. For this purpose, three questionnaires, ITC-SOPI, TPI and MEC-SPQ, were selected with an emphasis on a questionnaires' ability to discriminate between different media conditions. Three different VR environments were chosen according to their content domain and production method: respectively, a 360° video, a computer generated 3D animation and an interactive 3D game. 22 participants were engaged with these three different virtual reality environments in a within-subjects experimental design, using an Oculus Rift head-mounted display system and touch controllers. Results indicated that users' sense of presence measured in terms of ITC-SOPI subscales are not sensitive to the different immersive techniques. TPI subscales are also not sensitive to the differences in immersive techniques, with the exception of the Social Realism dimension. Results provided evidence that among the subscales of MEC-SPQ, only the four item version of "Possible Actions" dimension was sensitive to immersive techniques. However, it should be noted that the Possible Actions dimension provides different results for 4-item, 6-item and 8 item versions.
A 3D reconstruction method based on grid laser and gray scale photo for visual inspection of welds

Three-dimensional reconstruction of the weld surface is critical for online visual inspection of welds. A new three-dimensional reconstruction algorithm is proposed by fusion analysis of the images of weld under grid laser illumination and without additional light sources. A grid laser was used to form a 9 × 9 orthogonal beam grid on the weld surface and a single camera was used to make sure the images were captured in the same coordinate system. For every single grid, the height value at the points with grid laser beam was calculated. After that, the height value was combined with the gray value of the image to reconstruct the three-dimensional contour of the weld by nonlinear fitting algorithm. The developed laser-photo hybrid method acquires the 3D information of weld surface by a shot of picture rather than the continuous collecting method used in the line-laser sensor. The designed laser-photo based system can measure welds with satisfied accuracy and reliability, which eliminates errors caused by deflection between measured system and the workpiece.