Reviewerly/exHarmony huggingface dataset:
Metal remobilization and ore-fluid perturbation during episodic replacement of auriferous pyrite from an epizonal orogenic gold deposit

Mineral-scale episodic replacement of auriferous pyrite by texturally-complex pyrite, marcasite and minor arsenopyrite occurred in breccia ores from the Daqiao epizonal orogenic gold deposit, West Qinling Orogen, China. This study uses a novel combination of laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS), Nanoscale secondary ion mass spectrometry (NanoSIMS), and secondary ion mass spectrometry (SIMS) to investigate the remobilization and re-concentration of gold and other trace elements during this complex replacement process and the probable mechanism. Several lines of evidence including some degree of preservation of external morphology, sharp contacts and compositional differences between the parent pyrite and product pyrite and marcasite, and reaction-induced porosity suggest that the replacement of parent pyrite proceeds via a two-step replacement via a dissolution and reprecipitation mechanism, plus an additional marcasite overgrowth. During the replacement of euhedral pyrite, depletion of gold and other trace elements (Te, Se, Zn, Co, Tl, Ni, W, and As) in porous product pyrite relative to its precursor indicate exsolution and remobilization of these metals from crystal lattice of the original pyrite. In the subsequent replacement of porous pyrite by two types of marcasite and minor arsenopyrite, euhedral product marcasite contains low contents of trace elements, possibly due to high metal solubility in the acidic fluids favorable for marcasite precipitation. The complex-zoned marcasite significantly enriched in gold and other metals relative to porous pyrite (W, Tl, As, Sb, Ag, Se, and Zn) is thought to have formed via precipitation triggered by further oxidation and/or immediate reduction in threshold supersaturation. Dissolution of the impurity-rich pyrite and precipitation of new pyrite and marcasite generations could have occurred at low pH plus high concentrations of dissolved Fe2+ condition caused by partial oxidation of aqueous H2S and/or S2− in ore fluids. The fluid oxidation is evidenced by a general decreasing trend of δ34S values from the parent euhedral pyrite, to product porous pyrite, euhedral marcasite, and complex-zoned marcasite. The isotopic results are consistent with ore fluid oxidation controlled by pressure fluctuations during multistage hydraulic fracturing in a fault-valve regime at Daqiao deposit. This quantitative study emphasizes that the pressure-driven hydrothermal process plays a key role in the micron- to nano-scale redistribution and re-enrichment of gold and other trace metals during episodic replacement of auriferous pyrite in brittle rheological zones from epizonal orogenic gold systems.
The Ordinal Nature of Emotions: An Emerging Approach

Computational representation of everyday emotional states is a challenging task and, arguably, one of the most fundamental for affective computing. Standard practice in emotion annotation is to ask people to assign a value of intensity or a class value to each emotional behavior they observe. Psychological theories and evidence from multiple disciplines including neuroscience, economics and artificial intelligence, however, suggest that the task of assigning reference-based values to subjective notions is better aligned with the underlying representations. This paper draws together the theoretical reasons to favor ordinal labels for representing and annotating emotion, reviewing the literature across several disciplines. We go on to discuss good and bad practices of treating ordinal and other forms of annotation data and make the case for preference learning methods as the appropriate approach for treating ordinal labels. We finally discuss the advantages of ordinal annotation with respect to both reliability and validity through a number of case studies in affective computing, and address common objections to the use of ordinal data. More broadly, the thesis that emotions are by nature ordinal is supported by both theoretical arguments and evidence, and opens new horizons for the way emotions are viewed, represented and analyzed computationally.
Scene graph captioner: Image captioning based on structural visual representation

While deep neural networks have recently achieved promising results on the image captioning task, they do not explicitly use the structural visual and textual knowledge within an image. In this work, we propose the Scene Graph Captioner (SGC) framework for the image captioning task, which captures the comprehensive structural semantic of visual scene by explicitly modeling objects, attributes of objects, and relationships between objects. Firstly, we develop an approach to generate the scene graph by learning individual modules on the large object, attribute and relationship datasets. Then, SGC incorporates high-level graph information and visual attention information into a deep captioning framework. Specifically, we propose a novel framework to embed a scene graph into the structural representation, which captures the semantic concepts and the graph topology. Further, we develop the scene-graph-driven method to generate the attention graph by exploiting high internal homogeneity and external inhomogeneity among the nodes in the scene graph. Finally, a LSTM-based framework translates these information into text. We evaluate the proposed framework on a held-out MSCOCO dataset.
&lt;italic&gt;pRide&lt;/italic&gt;: Privacy-Preserving Ride Matching Over Road Networks for Online Ride-Hailing Service

An online ride-hailing (ORH) service, such as Uber and Didi Chuxing, can provide on-demand transportation service to users via mobile phones, which brings great convenience to people's daily lives. Along with the convenience, high privacy concerns are also raised when using an ORH service since users and drivers must share their real-time locations with the ORH server, which results in the leakage of the mobility patterns and additional privacy of users and drivers. In this paper, we propose a privacy-preserving ride-matching scheme, called pRide, for ORH service. pRide allows an ORH server to efficiently match rider and drivers based on their distances in the road network without revealing the location privacy of riders and drivers. Specifically, we make use of the road network embedding technique together with cryptographic primitives and design a scheme to securely and efficiently estimate the shortest distances between riders and drivers in road networks approximately. Moreover, by incorporating garbled circuits, the proposed scheme is able to output the nearest driver around a rider. We implement the scheme and evaluate it on the representative real-world datasets. The theoretical analysis and experimental results demonstrate that pRide achieves an efficient, secure, and yet accurate ride matching for ORH service.
Circuit-Based Quantum Random Access Memory for Classical Data

A prerequisite for many quantum information processing tasks to truly surpass classical approaches is an efficient procedure to encode classical data in quantum superposition states. In this work, we present a circuit-based flip-flop quantum random access memory to construct a quantum database of classical information in a systematic and flexible way. For registering or updating classical data consisting of M entries, each represented by n bits, the method requires O(n) qubits and O(Mn) steps. With post-selection at an additional cost, our method can also store continuous data as probability amplitudes. As an example, we present a procedure to convert classical training data for a quantum supervised learning algorithm to a quantum state. Further improvements can be achieved by reducing the number of state preparation queries with the introduction of quantum forking.
Self-adaptive differential artificial bee colony algorithm for global optimization problems

Artificial bee colony algorithm (ABC) has attracted wide attention in the recent decade. Although ABC algorithms can achieve good performance on separable problems by optimizing each variable independently, their performances on complex non-separable problems are still unsatisfactory. In this paper, through incorporating multiple differential search strategies and a self-adaptive mechanism into the framework of ABC, we propose a new ABC algorithm, called self-adaptive differential artificial bee colony (sdABC) algorithm. By means of differential search strategies, more variables will be updated each time based on the combination of mutation and crossover. Thus, sdABC has much enhanced ability for solving complex non-separable problems. Our proposed sdABC algorithm is evaluated on 28 benchmarks functions, including both common separable problems and complex non-separable CEC2015 functions. The experimental results show that sdABC can achieve much more desirable performances than the previous ABC algorithms on both separable and non-separable functions, and is also very competitive compared with well-established differential evolution and other meta-heuristic algorithms.
Lip Reading-Based User Authentication Through Acoustic Sensing on Smartphones

To prevent users privacy from leakage, more and more mobile devices employ biometric-based authentication approaches, such as fingerprint, face recognition, voiceprint authentications, and so on, to enhance the privacy protection. However, these approaches are vulnerable to replay attacks. Although the state-of-art solutions utilize liveness verification to combat the attacks, existing approaches are sensitive to ambient environments, such as ambient lights and surrounding audible noises. Toward this end, we explore liveness verification of user authentication leveraging users mouth movements, which are robust to noisy environments. In this paper, we propose a lip reading-based user authentication system, LipPass, which extracts unique behavioral characteristics of users speaking mouths through acoustic sensing on smartphones for user authentication. We first investigate Doppler profiles of acoustic signals caused by users' speaking mouths and find that there are unique mouth movement patterns for different individuals. To characterize the mouth movements, we propose a deep learning-based method to extract efficient features from Doppler profiles and employ softmax function, support vector machine and support vector domain description to construct multi-class identifier, binary classifiers and spoofer detectors for mouth state identification, user identification and spoofer detection, respectively. Afterward, we develop a balanced binary tree-based authentication approach to accurately identify each individual leveraging these binary classifiers and spoofer detectors with respect to registered users. Through extensive experiments involving 48 volunteers in four real environments, LipPass can achieve 90.2% accuracy in user identification and 93.1% accuracy in spoofer detection.
Recognition of American Sign Language Gestures in&#x0D; a Virtual Reality Using Leap Motion

We perform gesture recognition in a Virtual Reality (VR) environment using dataproduced by the Leap Motion device. Leap Motion generates a virtual three-dimensional (3D) handmodel by recognizing and tracking user‘s hands. From this model, the Leap Motion applicationprogramming interface (API) provides hand and finger locations in the 3D space. We present asystem that is capable of learning gestures by using the data from the Leap Motion device and theHidden Markov classification (HMC) algorithm. We have achieved the gesture recognitionaccuracy (mean ± SD) is 86.1 ± 8.2% and gesture typing speed is 3.09 ± 0.53 words per minute(WPM), when recognizing the gestures of the American Sign Language (ASL).
A New Method to Identify Incomplete Frame of Discernment in Evidence Theory

One assumption of Dempster-Shafer evidence theory (D-S theory) is the closed world.However, how to determine whether the frame of discernment is incomplete or not is still an open issue.In this paper, a new method is proposed based on a minimum spanning tree.For each new sample collected in the system, the mass function of the empty set is determined based on the proposed method.The value of the mass function of the empty set is used to determine whether the frame of discernment is incomplete or not.The experimental results on several benchmark datasets illustrate that the proposed method has a high accuracy in identifying the incomplete frame of discernment while the error rate is low.
Automatic Visual Features for Writer Identification: A Deep Learning Approach

Identification of a person from his writing is one of the challenging problems; however, it is not new.No one can repudiate its applications in a number of domains, such as forensic analysis, historical documents, and ancient manuscripts.Deep learning-based approaches have proved as the best feature extractors from massive amounts of heterogeneous data and provide promising and surprising predictions of patterns as compared with traditional approaches.We apply a deep transfer convolutional neural network (CNN) to identify a writer using handwriting text line images in English and Arabic languages.We evaluate different freeze layers of CNN (Conv3, Conv4, Conv5, Fc6, Fc7, and fusion of Fc6 and Fc7) affecting the identification rate of the writer.In this paper, transfer learning is applied as a pioneer study using ImageNet (base data-set) and QUWI data-set (target data-set).To decrease the chance of over-fitting, data augmentation techniques are applied like contours, negatives, and sharpness using textline images of target data-set.The sliding window approach is used to make patches as an input unit to the CNN model.The AlexNet architecture is employed to extract discriminating visual features from multiple representations of image patches generated by enhanced pre-processing techniques.The extracted features from patches are then fed to a support vector machine classifier.We realized the highest accuracy using freeze Conv5 layer up to 92.78% on English, 92.20% on Arabic, and 88.11% on the combination of Arabic and English, respectively.
DLFace: Deep local descriptor for cross-modality face recognition

Cross-modality face recognition aims to identify faces across different modalities, such as matching sketches with photos, low resolution face images with high resolution images, and near infrared images with visual lighting images, which is challenging because of the modality gap caused by texture, resolution, and illumination variations. Existing approaches either utilized hand-crafted approaches which ignore inherent data distribution characteristic, or applied deep learning-based algorithms on holistic face images with facial local information ignored. In this paper, we propose a deep local descriptor learning framework for cross-modality face recognition, which aims to learn discriminant and compact local information directly from raw facial patches. A novel cross-modality enumeration loss is proposed to eliminate the modality gap on local patch level, which is then integrated into a convolutional neural networks for deep local descriptor extraction. The proposed deep local descriptor can be easily applied to any traditional face recognition systems, and we use Fisherface as an example in the paper. Extensive experiments on six widely used cross-modality face recognition datasets demonstrate the superiority of proposed method over state-of-the-art methods.
Dilated Residual Networks with Symmetric Skip Connection for image denoising

Due to the fast inference and good performance, convolutional neural network (CNN) has been widely applied in image denoising. Some new approaches, such as residual learning and batch normalization are quite effective at accelerating the training process as well as improving accuracy. The batch normalization has been proved to handle Gaussian denoising effectively and efficiently, and can further boost the denoising performance of networks. However, it still leaves much space to improve. In this paper, we attempt to introduce a novel network structure without batch normalization, namely Dilated Residual Networks with Symmetric Skip Connection (DSNet), which depends on a combination of symmetric skip connection and dilated convolution. The advantage of this novel approach is computationally efficient in training, because the layers and the parameters of our networks are less than the previous network structure. Our network structure is more feasible for the task of image denoising, especially Gaussian noise. Our extensive experiments demonstrate that the proposed method can not only outperform the state-of-the-art methods in terms of both accuracy and speed, but also be efficiently implemented by benefiting from GPU computing.
REQ-YOLO

Deep neural networks (DNNs), as the basis of object detection, will play a key role in the development of future autonomous systems with full autonomy. The autonomous systems have special requirements of real-time, energy-e cient implementations of DNNs on a power-budgeted system. Two research thrusts are dedicated to per- formance and energy e ciency enhancement of the inference phase of DNNs. The first one is model compression techniques while the second is e cient hardware implementations. Recent researches on extremely-low-bit CNNs such as binary neural network (BNN) and XNOR-Net replace the traditional oating point operations with bi- nary bit operations, signi cantly reducing memory bandwidth and storage requirement, whereas suffering non-negligible accuracy loss and waste of digital signal processing (DSP) blocks on FPGAs. To overcome these limitations, this paper proposes REQ-YOLO, a resource aware, systematic weight quantization framework for object detection, considering both algorithm and hardware resource aspects in object detection. We adopt the block-circulant matrix method and propose a heterogeneous weight quantization using Alternative Direction Method of Multipliers (ADMM), an e ective optimization technique for general, non-convex optimization problems. To achieve real-time, highly efficient implementations on FPGA, we present the detailed hardware implementation of block circulant matrices on CONV layers and de- velop an e cient processing element (PE) structure supporting the heterogeneous weight quantization, CONV data ow and pipelining techniques, design optimization, and a template-based automatic synthesis framework to optimally exploit hardware resource. Experimental results show that our proposed REQ-YOLO framework can signi cantly compress the YOLO model while introducing very small accuracy degradation. The related codes are here: https://github.com/Anonymous788/heterogeneous_ADMM_YOLO.
A grey wolf optimizer using Gaussian estimation of distribution and its application in the multi-UAV multi-target urban tracking problem

To overcome premature convergence in the grey wolf optimizer (GWO), in this study, a modified GWO integrating the basic GWO with the Gaussian estimation of distribution (GED) strategy, called GEDGWO, is proposed. GEDGWO employs the Gauss probability model to estimate the distribution of the selected superior individuals and shifts the weighted mean to adjust the search directions. Additionally, a Gaussian distribution based inferior solutions repair (ISR) method is introduced to modify the ill-shaped distribution of the population. A disturbed Gaussian random walk method is utilized to strengthen the local exploration ability. The performance of GEDGWO is compared with those of other promising GWO variants and state-of-the-art algorithms on a benchmarking CEC 2014 test suite. Non-parametric Wilcoxon and Friedman tests as well as the post hoc Iman–Davenport test are performed to further verify the efficacy of GEDGWO. Moreover, GEDGWO is applied to solve multi-UAV multi-target urban tracking path planning problem. To overcome the shortcomings of the previous solution model, a new model is described to address this complex real-time engineering optimization problem. The validity and practicability of the problem models as well as the accuracy and efficiency of GEDGWO are demonstrated by the experimental results.
Effective Diagnosis and Treatment through Content-Based Medical Image Retrieval (CBMIR) by Using Artificial Intelligence

Medical-image-based diagnosis is a tedious task' and small lesions in various medical images can be overlooked by medical experts due to the limited attention span of the human visual system, which can adversely affect medical treatment. However, this problem can be resolved by exploring similar cases in the previous medical database through an efficient content-based medical image retrieval (CBMIR) system. In the past few years, heterogeneous medical imaging databases have been growing rapidly with the advent of different types of medical imaging modalities. Recently, a medical doctor usually refers to various types of imaging modalities all together such as computed tomography (CT), magnetic resonance imaging (MRI), X-ray, and ultrasound, etc of various organs in order for the diagnosis and treatment of specific disease. Accurate classification and retrieval of multimodal medical imaging data is the key challenge for the CBMIR system. Most previous attempts use handcrafted features for medical image classification and retrieval, which show low performance for a massive collection of multimodal databases. Although there are a few previous studies on the use of deep features for classification, the number of classes is very small. To solve this problem, we propose the classification-based retrieval system of the multimodal medical images from various types of imaging modalities by using the technique of artificial intelligence, named as an enhanced residual network (ResNet). Experimental results with 12 databases including 50 classes demonstrate that the accuracy and F1.score by our method are respectively 81.51% and 82.42% which are higher than those by the previous method of CBMIR (the accuracy of 69.71% and F1.score of 69.63%).
Robot Teleoperation with Augmented Reality Virtual Surrogates

Teleoperation remains a dominant control paradigm for human interaction with robotic systems. However, teleoperation can be quite challenging, especially for novice users. Even experienced users may face difficulties or inefficiencies when operating a robot with unfamiliar and/or complex dynamics, such as industrial manipulators or aerial robots, as teleoperation forces users to focus on low-level aspects of robot control, rather than higher level goals regarding task completion, data analysis, and problem solving. We explore how advances in augmented reality (AR) may enable the design of novel teleoperation interfaces that increase operation effectiveness, support the user in conducting concurrent work, and decrease stress. Our key insight is that AR may be used in conjunction with prior work on predictive graphical interfaces such that a teleoperator controls a virtual robot surrogate, rather than directly operating the robot itself, providing the user with foresight regarding where the physical robot will end up and how it will get there. We present the design of two AR interfaces using such a surrogate: one focused on real-time control and one inspired by waypoint delegation. We compare these designs against a baseline teleoperation system in a laboratory experiment in which novice and expert users piloted an aerial robot to inspect an environment and analyze data. Our results revealed that the augmented reality prototypes provided several objective and subjective improvements, demonstrating the promise of leveraging AR to improve human-robot interactions.