Reviewerly/exHarmony huggingface dataset:
Variational Bayesian Blind Color Deconvolution of Histopathological Images

Most whole-slide histological images are stained with two or more chemical dyes. Slide stain separation or color deconvolution is a crucial step within the digital pathology workflow. In this paper, the blind color deconvolution problem is formulated within the Bayesian framework. Starting from a multi-stained histological image, our model takes into account both spatial relations among the concentration image pixels and similarity between a given reference color-vector matrix and the estimated one. Using Variational Bayes inference, three efficient new blind color deconvolution methods are proposed which provide automated procedures to estimate all the model parameters in the problem. A comparison with classical and current state-of-the-art color deconvolution algorithms using real images has been carried out demonstrating the superiority of the proposed approach.
Identification of Hydrothermal Alteration Minerals for Exploring Gold Deposits Based on SVM and PCA Using ASTER Data: A Case Study of Gulong

Dayaoshan, as an important metal ore-producing area in China, is faced with the dilemma of resource depletion due to long-term exploitation. In this paper, remote sensing methods are used to circle the favorable metallogenic areas and find new ore points for Gulong. Firstly, vegetation interference was removed by using mixed pixel decomposition method with hyperplane and genetic algorithm (GA) optimization; then, altered mineral distribution information was extracted based on principal component analysis (PCA) and support vector machine (SVM) methods; thirdly, the favorable areas of gold mining in Gulong was delineated by using the ant colony algorithm (ACA) optimization SVM model to remove false altered minerals; and lastly, field surveys verified that the extracted alteration mineralization information is correct and effective. The results show that the mineral alteration extraction method proposed in this paper has certain guiding significance for metallogenic prediction by remote sensing.
Promises of Meta-Learning for Device-Free Human Sensing

In this study, we explore potential opportunities for leveraging meta-learning algorithms to enable device-free sensing. We refer to this solution as "meta-sensing", which is mainly learning to sense by discovering the available information rather than deploying additional sensors. We specifically are interested in application of zero-shot learning, a specific algorithm that required no a priori information to learn. This class of methods aim at learning to learn, i.e., meta-sensing does not only learn from the available data, but also learns how to learn over time without requiring extra sensing inputs. Meta-sensing learns and predicts through data transformation with respect to the test data. It executes the process of mapping data and updates information; learns how to transform input data to new feature vector space; and generates a new data cluster. Meta-learner, an agent who updates the learning model, plays a pivotal role in meta-sensing. Once new data are received, meta-learner updates its bias information using transformed data. The meta-learner learns based on ontology first rather than a priori information or training data set.
Adversarial Preference Learning with Pairwise Comparisons

When facing rich multimedia content and making a decision, users tend to be overwhelmed with redundant options. Recommendation system can improve the users' experience by predicting the possible preference of a given user. The vast majority of the literature adopts the collaborative framework, which relies on a static and fixed formulation of the rating score prediction function (in most cases an inner product function). However, such a static learning paradigm is not consistent with the dynamic feature of human intelligence. Motivated by this, we present a novel adversarial framework for collaborative ranking. On one hand, we leverage a deep generator to approximate an arbitrary continuous score function in terms of pairwise comparison. On the other hand, a discriminator provides personalized supervision signals with increasing difficulty. Different from the traditional static learning framework, our proposed approach enjoys a dynamic nature and unifies both the generative and the discriminative model for collaborative ranking. Comprehensive empirical studies on three real-world datasets show significant improvements of the adversarial framework over the state-of-the-art methods.
On-Device Image Classification with Proxyless Neural Architecture Search and Quantization-Aware Fine-Tuning

It is challenging to efficiently deploy deep learning models on resource-constrained hardware devices (e.g., mobile and IoT devices) with strict efficiency constraints (e.g., latency, energy consumption). We employ Proxyless Neural Architecture Search (ProxylessNAS) to auto design compact and specialized neural network architectures for the target hardware platform. ProxylessNAS makes latency differentiable, so we can optimize not only accuracy but also latency by gradient descent. Such direct optimization saves the search cost by 200× compared to conventional neural architecture search methods. Our work is followed by quantization-aware fine-tuning to further boost efficiency. In the Low Power Image Recognition Competition at CVPR'19, our solution won the 3rd place on the task of Real-Time Image Classification (online track).
The Retrieval of the Beautiful

Beauty product retrieval is a challenging task due to the severe image variation issue in real-world scenes. In this work, to mitigate the data variation problem, we contribute a background-agnostic feature extractor, which is trained by a self-supervised salient object detection method. In particular, we first propose a foreground augmentation technique to acquire the augmentation image with its foreground mask. Next, a feature extractor with an attention pooling layer is proposed to learn background-agnostic representations by performing the salient object detection in a self-supervised manner. Finally, we ensemble the background-agnostic features of multiple models to perform the beauty product retrieval. Extensive experimental results have demonstrated the superiority of our proposed framework.
Visual Object Tracking via Guessing and Matching

Visual object tracking is a fundamental and time-critical vision task. However, most trackers such as SiamFC and CFNet missed the object movement and simply defined the searching region centered at the location of the target in the previous frame. So they tend to fail in the cases with severe occlusion or a large displacement of the target. In this paper, we consider the object tracking as a dual-task problem of guessing and matching. A guess module is to estimate the motion trend of the target by reinforcement learning based on the observations on appearance changes and motion history. Rather than using the previous location of the target, we may have a more accurate center to locate the searching region. Benefited from such improved searching region, the match module becomes less prone to the object drift problem, and can easily identify the target from the potential distractors in the background. Extensive experimental results on benchmark datasets such as RGBT, OTB-2013, OTB-50 and OTB-100, show that the proposed method achieves leading performance compared to state-of-the-art trackers. Moreover, the proposed tracker could maintain real-time speed, giving itself the potential in practical applications.
Learning-Based User Clustering and Link Allocation for Content Recommendation Based on D2D Multicast Communications

Content recommendation based on device-to-device (D2D) multicast communications is expected to become a promising approach to improve local area services. Importantly, two main challenges should be considered: i) user clustering-in order to be tailored to recommend contents, the members in the same cluster should have great similarity in multiple characteristics, and ii) link allocation-we should use as little resource consumption and information exchange as possible while keeping the recommendation accuracy. In this paper, we firstly quantify the degree of the similarity between two target users with regard to multiple characteristics. Guided by such similarity, we define a clustering validity index in terms of between-within proportion (BWP) to characterize the clustering performance. Then, the issue of user clustering is modeled as a sum BWP maximum problem, and a user clustering algorithm based on modified K-means algorithm is designed to solve it in a fast-operating and low-complexity way. After user clustering, we model the issue of link allocation as a weighted aggregate interference minimization problem, and then transform it to an exact potential game. As such, a link allocation algorithm based on stochastic learning algorithm is proposed which helps to obtain the result of this game in a distributed way without complete information. Also, we analyze its convergence and optimality performance. Simulation results demonstrate the effectiveness of our proposed algorithms.
Fewer-Shots and Lower-Resolutions

Is it possible to train an effective face recognition model with fewer shots that works efficiently on low-resolution faces in the wild? To answer this question, this paper proposes a few-shot knowledge distillation approach to learn an ultrafast face recognizer via two steps. In the first step, we initialize a simple yet effective face recognition model on synthetic low-resolution faces by distilling knowledge from an existing complex model. By removing the redundancies in both face images and the model structure, the initial model can provide an ultrafast speed with impressive recognition accuracy. To further adapt this model into the wild scenarios with fewer faces per person, the second step refines the model via few-shot learning by incorporating a relation module that compares low-resolution query faces with faces in the support set. In this manner, the performance of the model can be further enhanced with only fewer low-resolution faces in the wild. Experimental results show that the proposed approach performs favorably against state-of-the-arts in recognizing low-resolution faces with an extremely low memory of 30KB and runs at an ultrafast speed of 1,460 faces per second on CPU or 21,598 faces per second on GPU.
Interaction-Based Behavioral Analysis of Twitter Social Network Accounts

This article considers methodological approaches to determine and prevent social media manipulation specific to Twitter. Behavioral analyses of Twitter users were performed by using their profile structures and interaction types, and Twitter users were classified according to their effect size values by determining their asset values. User profiles were classified into three different categories, namely popular-active, observer-passive, and spam-bot-malicious by using k-nearest neighbor (K-NN), support vector machine (SVM), and artificial neural network (ANN) algorithms. For classification, the study used the basic characteristics of users, such as density, centralization, and diameter, as well as suggested time series such as the simple moving average and cumulative moving average. The highest accuracy was obtained by the K-NN algorithm. The results obtained with K-NN for all classes were higher than the F1-Score values obtained for the other algorithms. According to the results obtained, classification accuracy values were found to reach a maximum of 96.81% and a minimum of 92.33%. Our classification results showed that the proposed method was satisfactory for popular-active, observer-passive, and spam-bot-malicious account separation.
Blind Reader: An Object Identification Mobile- based Application for the Blind using Augmented Reality Detection

Eyes are our window to this world, however not for everyone. Visual impairments can highly disrupt human's normal activities as simple as recognize all the things around them properly. Technological development of augmented reality is one of the fastest growing technology. Augmented reality, which combines the real world and the virtual world, allows interactivity between both including as an assistive tool such for those who are visually impaired. In this research, we propose a mobile application that identifies objects using smartphone camera by utilizing markerless detection. We used Unity 3D and Vuforia SDK as the place to make the dataset of the marker and running on the Android-based smartphone. The result of this research is an android application that can identify objects and produce a sound output of the product name.
Autonomous Climbing Robot for Tank Inspection

A detailed design of mechanisms is presented, along with the required electromagnetic system to attach the robot to the tank's wall. This is a novel mechanical design in which the robotic wheels rotate changing the instantaneous center of rotation. The robot mechanics is designed and simulated using Solidworks. The proposed electromagnetic system has to provide the necessary currents to control magnetic forces to be strong enough to attach the robot to the wall. However, because of the electromagnetic forces the control positioning method has to consider the strong Coulomb friction that appears against wheels movement. The sensor and acquisition system for navigation and control are also detailed. An inspection system has been proposed based on client/server architecture. Autonomous tracking and a mapping system powered by IMU sensor fusion is proposed. This design is suitable to be used along with the robot motion positioning strategy and, especially, the system must be robust to the disturbances and noise. The robot runs a server application and a remote PC is the client. The robot configuration has been developed with the capability of climbing up steel walls and navigating welding lines. The remote inspection system has been illustrated. A sensorial system and data fusion strategy to estimate the absolute robot position have been proposed. In order to track the desired trajectories, the robot's kinematics is described. Some simulations of the position control of the wheels and the complete control for the tracking performance were simulated using Matlab/Simulink. This robotic system has demonstrated the capability to track an accurate trajectory given by the defined kinematic equations.
Identity-Based Linkable Ring Signature Scheme

A ring signature is an anonymous signature that implements both the authentication of the message and the anonymity of the signer.In a ''normal'' ring signature scheme, the same signer can generate multiple ring signatures, but the verifier cannot find this fact.Linkable ring signature (LRS) solves the problem.In the setting, the identity of the signer is still anonymous, and if the same signer generates multiple ring signatures, the verifier can confirm the fact.Linkable ring signatures are applied to some actual scenarios, such as e-cash, e-voting and ad-hoc network authentication.In this paper, we presented a new identity-based linkable ring signature scheme that avoids certificate management.We then gave the security proofs in the random oracle model (ROM) and compared the efficiency of it with the previous schemes.The new scheme requires only 7 pairing operations in signing and verifying.It is the most efficient linkable ring signature in the identity-based setting.
Talking Video Heads

Over-the-top (OTT) streaming services like YouTube and Netflix induce massive amounts of video traffic. To combat the resulting network load, this article empirically explores the use of the object-based video (OBV) methodology that allows for the quality-variant HTTP Adaptive Streaming of respectively the background and foreground object(s) of a video scene. In particular, we study two alternative video object representation methods where the first meticulously follows the object contour, while the second uses axis-aligned bounding box enclosures. We subjectively compare both techniques to traditional, frame-based video compression in the context of live action content featuring talking persons. The resulting mixed methods data shows that (i) OBV-informed users tolerate substantial background quality degradations, and (ii) at an average bitrate reduction of 14 percent, perceptual differences between respectively contour-based OBV and traditional encoding are small or even non-existing for the non-movie content in our corpus. Although our evaluation focuses on interview-like footage, our qualitative data hints that the presented results might be extrapolatable to other video genres. As such, our findings inform content owners and network operators about video bitrate saving opportunities with marginal perceptual impact.
Making Nonvisually

The Maker movement promises access to activities from crafting to digital fabrication for anyone to invent and customize technology. But people with disabilities, who could benefit from Making, still encounter significant barriers to do so. In response, we share our personal experiences Making nonvisually and supporting its instruction. Specifically, we draw on examples from a series of workshops where we introduced Arduino to blind hobbyists and guided assembly of an accessible voltmeter prototype [24]. In so doing, we offer future directions for accessible Making research and application.
Comparison of matrix-effect corrections for ordinary and uncertainty weighted linear regressions and determination of major element mean concentrations and total uncertainties of sixty-two international geochemical reference materials from wavelength-dispersive X-ray fluorescence spectrometry

In X-ray fluorescence (XRF) spectrometry, matrix-effect corrections for two types of linear regressions (ordinary and uncertainty-based weighted) have been performed for the first time and compared for the determination of major elements in rocks and minerals. The analytical data from different laboratories for 62 international geochemical reference materials (GRMs) were first processed to obtain better estimates of the mean values, along with lower uncertainties (narrower confidence limits of the mean), which were used in the XRF calibrations. The blank intensities were subtracted from the response for each GRM and the respective uncertainties were estimated. The uncertainty-based weighted least-squares linear regression (UWLR) model, being statistically more appropriate than the ordinary least-squares linear regression (OLR) model, provided more reliable regression equations than the OLR. All calculations and matrix-effect corrections were achieved through a newly developed online computer program. Similarly, matrix-effect corrections involving 11 α and 26 α (alphas) are presented and compared. The best UWLR method involving 26 α was successfully applied to the 62 GRMs and 4 similarly complex rock matrices as the calibrators. Both evaluations confirmed the usefulness of the UWLR model. This UWLR model was also compared with the OLR 26 α model for 62 GRMs treated as unknowns and shown to perform better. Thus, the UWLR model, along with the proposed matrix-effect correction method of 26 α, can be recommended as the most appropriate procedure for the calibration of XRF instruments, instead of the commonly used OLR models. This is the first time when 26 α are advantageously in a better estimate of influence coefficients used for major element determinations in rock and mineral samples. Because the UWLR calibration is based on a large number of GRMs of many rock and mineral types, it should be useful for all kinds of geological materials.
Assistive Sign Language Converter for Deaf and Dumb

Deaf and dumb people are humans at the deepest psychological level. Many of these people are not even exposed to sign languages and it is observed that it gives a great relief on a psychological level, when they find out about signing to connect themselves with others by expressing their love or emotions. About 5% population in world are suffering from hearing loss. Deaf and dumb people use sign language as their primary means to express their thoughts and ideas to the people around them with different hand and body gestures. There are only about 250 certified sign language interpreters in India for a deaf population of around 7 million. In this work, the design of prototype of an assistive device for Deaf-mute people is presented so as to reduce this communication gap with the normal people. This device is portable and can hang over the neck. This device allows the person to communicate with sign hand postures in order to recognize different gestures based signs. The controller of this assistive device is developed for processing the images of gestures by employing various imageprocessing techniques and deep learning models to recognize the sign. This sign is converted into speech in real-time using text-to-speech module.
X-Section: Cross-Section Prediction for Enhanced RGB-D Fusion

Detailed 3D reconstruction is an important challenge with application to robotics, augmented and virtual reality, which has seen impressive progress throughout the past years. Advancements were driven by the availability of depth cameras (RGB-D), as well as increased compute power, e.g.\ in the form of GPUs -- but also thanks to inclusion of machine learning in the process. Here, we propose X-Section, an RGB-D 3D reconstruction approach that leverages deep learning to make object-level predictions about thicknesses that can be readily integrated into a volumetric multi-view fusion process, where we propose an extension to the popular KinectFusion approach. In essence, our method allows to complete shape in general indoor scenes behind what is sensed by the RGB-D camera, which may be crucial e.g.\ for robotic manipulation tasks or efficient scene exploration. Predicting object thicknesses rather than volumes allows us to work with comparably high spatial resolution without exploding memory and training data requirements on the employed Convolutional Neural Networks. In a series of qualitative and quantitative evaluations, we demonstrate how we accurately predict object thickness and reconstruct general 3D scenes containing multiple objects.
Computing partition functions in the one-clean-qubit model

We present a method to approximate partition functions of quantum systems using mixed-state quantum computation. For positive-semidefinite Hamiltonians, our method has an expected running-time that is almost linear in ${[M/({\ensuremath{\epsilon}}_{\mathrm{rel}}\mathcal{Z})]}^{2}$, where $M$ is the dimension of the quantum system, $\mathcal{Z}$ is the partition function, and ${\ensuremath{\epsilon}}_{\mathrm{rel}}$ is the relative precision. It is based on approximations of the exponential operator as linear combinations of certain operators related to block-encoding of Hamiltonians or Hamiltonian evolutions. The trace of each operator is estimated using a standard algorithm in the one-clean-qubit model. For large values of $\mathcal{Z}$, our method may run faster than exact classical methods, whose complexities are polynomial in $M$. We also prove that a version of the partition function estimation problem within additive error is complete for the so-called DQC1 complexity class, suggesting that our method provides a superpolynomial speedup for certain parameter values. To attain a desired relative precision, we develop a classical procedure based on a sequence of approximations within predetermined additive errors that may be of independent interest.