Reviewerly/exHarmony huggingface dataset:
GeCo: Classification Restricted Boltzmann Machine Hardware for On-Chip Semisupervised Learning and Bayesian Inference

The probabilistic Bayesian inference of real-time input data is becoming more popular, and the importance of semisupervised learning is growing. We present a classification restricted Boltzmann machine (ClassRBM)-based hardware accelerator with on-chip semisupervised learning and Bayesian inference capability. ClassRBM is a specific type of Markov network that can perform classification tasks and reconstruct its input data. ClassRBM has several advantages in terms of hardware implementation compared to other backpropagation-based neural networks. However, its accuracy is relatively low compared to backpropagation-based learning. To improve the accuracy of ClassRBM, we propose the multi-neuron-per-class (multi-NPC) voting scheme. We also reveal that the contrastive divergence (CD) algorithm, which is commonly used to train RBM, shows poor performance in this multi-NPC ClassRBM. As an alternative, we propose an asymmetric contrastive divergence (ACD) training algorithm that improves the accuracy of multi-NPC ClassRBM. With the ACD learning algorithm, ClassRBM operates in the form of a combination of Markov Chain training and Bayesian inference. The experimental results on a field-programmable gate array (FPGA) board for a Modified National Institute of Standards and Technology data set confirm that the inference accuracy of the proposed ACD algorithm is 5.82% higher for a supervised learning case and 12.78% higher for a 1% labeled semisupervised learning case than the conventional CD algorithm. Also, the GeCo ver.2 hardware implemented on a Xilinx ZCU102 FPGA board was 349.04 times faster than the C simulation on CPU.
Electrical Data Matrix Decomposition in Smart Grid

As the development of smart grid and energy internet, this leads to a significant increase in the amount of data transmitted in real time. Due to the mismatch with communication networks that were not designed to carry high-speed and real time data, data losses and data quality degradation may happen constantly. For this problem, according to the strong spatial and temporal correlation of electricity data which is generated by human’s actions and feelings, we build a low-rank electricity data matrix where the row is time and the column is user. Inspired by matrix decomposition, we divide the low-rank electricity data matrix into the multiply of two small matrices and use the known data to approximate the low-rank electricity data matrix and recover the missed electrical data. Based on the real electricity data, we analyze the low-rankness of the electricity data matrix and perform the Matrix Decomposition-based method on the real data. The experimental results verify the efficiency and efficiency of the proposed scheme.
Image Tweet Popularity Prediction with Convolutional Neural Network

Predicting popularity of a post in microblogging services such as Twitter is an important task beneficial for both publishers and regulators. Traditionally, the prediction is done through various manually designed features extracted from post and user contexts. In recent years, deep learning models such as convolutional neural networks (CNN) have shown significant effectiveness in image processing. In this paper, we make a novel investigation of the effectiveness of deep learning models in predicting image post popularity, with the raw image as the input. In contrast to previous works that use existing model trained for object detection, we trained a CNN model targeting directly at predicting popularity. We show that a dedicated CNN is more effective than networks trained for other purposes and is comparable to text-based predictors.
Efficient box particle implementation of the multi-sensor GLMB filter in the presence of triple measurement uncertainty

The multi-sensor generalized labeled multi-Bernoulli (MSGLMB) filter is a tractable solution to multi-sensor multi-target tracking problems. The particle MSGLMB filter, which is time-consuming, can be used for the situation where some sensors return point measurements, while the others return box measurements. The box particle MSGLMB filter, which is potentially time-saving, can also be used for the situation if the point measurements are first transformed into boxes by their three-sigma supports. However, such box particle MSGLMB filter can be inefficient due to the rapidly multiplying contracted versions of box particles during multi-sensor update especially when the number of sensors or measurements therefrom is large. To reduce the inefficiency, we seek to reduce the number of contracted versions by processing point measurements directly with a new likelihood, namely the likelihood of a point measurement given a box particle, which does not require point measurements to contract box particles. Although an exact form of the said likelihood is unobtainable, two crude approximations of it are derived and studied, based on which an efficient box particle implementation for the situation is contributed. Simulation results prove that the proposed approach is effective.
Multi-Exponential Relaxometry Using $\ell_{{1}}$ -Regularized Iterative NNLS (MERLIN) With Application to Myelin Water Fraction Imaging

A new parameter estimation algorithm, MERLIN, is presented for accurate and robust multi-exponential relaxometry using magnetic resonance imaging, a tool that can provide valuable insight into the tissue microstructure of the brain. Multi-exponential relaxometry is used to analyze the myelin water fraction and can help to detect related diseases. However, the underlying problem is ill-conditioned, and as such, is extremely sensitive to noise and measurement imperfections, which can lead to less precise and more biased parameter estimates. MERLIN is a fully automated, multi-voxel approach that incorporates state-of-the-art l1 -regularization to enforce sparsity and spatial consistency of the estimated distributions. The proposed method is validated in simulations and in vivo experiments, using a multi-echo gradient-echo (MEGE) sequence at 3 T. MERLIN is compared to the conventional single-voxel l2 -regularized NNLS (rNNLS) and a multi-voxel extension with spatial priors (rNNLS + SP), where it consistently showed lower root mean squared errors of up to 70 percent for all parameters of interest in these simulations.
Optimal Trajectory Planning for Spherical Robot Using Evolutionary Algorithms

Abstract The trajectory planning problem of robot is addressed in this paper, which is consist of finding an optimal trajectory for a given path from an initial and final positions in the operating space. We aim to determine the optimal trajectory for three degrees of freedom spherical wrist with concurrent axis by using genetic algorithm. The optimized trajectory must have a minimum trajectory time and minimum joint traveling distance with free path collision in the workspace. In order to move from the initial point to the final point, we propose the use of polynomial interpolation, the optimization technique allow to determine the coefficients of the optimal trajectory. The simulation results for a three degrees of freedom robots are presented and discussed.
Matrix factorization based heuristics for constraint-based recommenders

The main challenges for recommender systems are: producing high quality recommendations and performing many real-time recommendations per second for millions of customers and products. This paper addresses both challenges in the context of constraint-based recommenders where users specify their requirements and the system recommends a solution. We propose a novel approach to determine value ordering heuristics on the basis of matrix factorization. As far as we are aware, no researches exist in constraint-based recommendation domain which exploit matrix factorization techniques. The main idea of our approach consists in the prediction of value ordering heuristics based on historical transactions which can either represent past customer purchases or requirements. Thereby, value ordering heuristics are computed which are specific to each user's requirements. A series of experiments on real-world datasets for calculating constraint-based recommendations has shown that our approach outperforms compared methods in terms of runtime efficiency and prediction quality.
Camera calibration method based on Pascal’s theorem

As one of the classical theories of projective geometry, Pascal’s theorem is closely related to the conic, which is the image of a circle. A circle is the geometric element typically used as a template for camera calibration methods. In this work, we propose a calibration method based on Pascal’s theorem and its inference, in which a circle is used as the calibration template. The proposed calibration method can be applied to solve the image of the circular points via the Newton iteration method. The intrinsic parameters of the camera can then be determined based on the constraints of the images of the circular points and the image of the absolute conic. The results of simulations and experiments conducted verify the validity and feasibility of the proposed calibration method.
Evaluating Evolving Structure in Streaming Data With Modified Dunn's Indices

Dunn's internal cluster validity index is used to assess partition quality and identify a “best” crisp c-partition of n objects built from static data sets. This index is quite sensitive to inliers and outliers in the input data, so a subsequent study developed a family of 17 generalized Dunn's indices that extend and improve the original measure in various ways. This paper presents online versions of two modified generalized Dunn's indices that can be used for the dynamic evaluation of an evolving (cluster) structure in streaming data. We argue that this method is a good way to monitor the ongoing performance of streaming clustering algorithms, and we illustrate several types of inferences that can be drawn from such indices. Streaming clustering algorithms are incremental, process incoming data points only once and then discard them, adapt as the data stream evolves, flag outliers, and most importantly, spawn new emerging structures. We compare the two new indices to the incremental Xie-Beni and Davies-Boudin indices, which to our knowledge offer the only comparable approach, with numerical examples on a variety of synthetic and real datasets.
Fast rates for a kNN classifier robust to unknown asymmetric label noise

We consider classification in the presence of class-dependent asymmetric label noise with unknown noise probabilities. In this setting, identifiability conditions are known, but additional assumptions were shown to be required for finite sample rates, and so far only the parametric rate has been obtained. Assuming these identifiability conditions, together with a measure-smoothness condition on the regression function and Tsybakov's margin condition, we show that the Robust kNN classifier of Gao et al. attains, the minimax optimal rates of the noise-free setting, up to a log factor, even when trained on data with unknown asymmetric label noise. Hence, our results provide a solid theoretical backing for this empirically successful algorithm. By contrast the standard kNN is not even consistent in the setting of asymmetric label noise. A key idea in our analysis is a simple kNN based method for estimating the maximum of a function that requires far less assumptions than existing mode estimators do, and which may be of independent interest for noise proportion estimation and randomised optimisation problems.
Combined Kalman Filter and Multifeature Fusion Siamese Network for Real-Time Visual Tracking

SiamFC has a simple network structure and can be pretrained offline on a large data set, so it has attracted the attention of many researchers. It has no online learning process at all. Hence, there are no good solutions for some complex tracking scenarios such as occlusion and large target deformation. For this problem, we propose a method using the Kalman filter method and fusion multiresolution features and get multiple response scores. The Kalman filter acquires the target's trajectory information, which is used to process complex tracking scenes and to change the selection method of the search area. This also enables our tracker to stably track fast moving targets.The introduction of the Kalman filter compensates for the shortcomings that SiamFC can only track offline, and the tracking network has an online learning process. The fusion of multiresolution features to obtain multiple response scores map helps the tracker to obtain robust features that can be adapted to a variety of tracking targets. Our proposed method has reached the state-of-the-art in testing on five data sets and can be run in real time (40 fps), including OTB2013, OTB2015, OTB50, VOT2015 and VOT 2016.
Exact Solutions to the Quantum Rabi-Stark Model Within Tunable Coherent States*

Abstract The quantum Rabi-Stark model, where the linear dipole coupling and the nonlinear Stark-like coupling is present on an equal footing, is studied within the tunable extended coherent states. The eigenvalues and eigenstates are therefore obtained exactly. Surprisingly, the entanglement entropy in the ground-state is found to jump suddenly with the coupling strength. The first-order quantum phase transition can be detected by level crossing of the ground state and the first excited state, which is however lacking in the original linear quantum Rabi model. Performing the first-order approximation in the present theory, we can derive closed-form analytical results for the ground-state. Interestingly, it agrees well with the exact solutions up to the ultra-strong coupling regime in a wide range of model parameters. The spectral collapses when the absolute value of the nonlinear coupling strength approaches to twice the cavity frequency is observed with the help of new solutions in the limits .
A Fuzzy Based Hybrid Firefly Optimization Technique for Load Balancing in Cloud Datacenters

Cloud computing technology has massive inferences with the use of virtualization technologies. Most of the organizations have incorporated to practice the virtualization strategies to create and operate an effective dynamic data center. The growing maturity of the technologies and utilities of the cloud make the users hasten the adoption of the cloud. The dynamic demanding nature of cloud resources leads to an imbalance in virtual machine utilization and radically increases the energy consumption and operating cost of the data center. In this paper, we propose a fuzzy based hybrid load balancing algorithm for the optimal utilization of virtual machines. The proposed algorithm aim is to reduce the makespan, response time and cost with minimal energy usage and resource wastage. The fuzzy based hybrid optimization approach unveils better performance than existing metaheuristic load balancing algorithms.
Mobile Edge Assisted Live Streaming System for Omnidirectional Video

As a popular form of virtual reality (VR) media, omnidirectional video (OV) has been continuously developed in recent years. OV contains the view of the scene in every direction, which will ask for around 120 Mbps with 8k resolution and 25 fps (frames per second). Although there has been a lot of work to optimize the transmission for on-demand of OV, the research on the live streaming of OV is still very lacking. Another big challenge for the OV live streaming system is the huge demand for computing resources. The existing terminal devices are difficult to completely carry tasks such as stitching, encoding, and rendering. This paper proposes a mobile edge assisted live streaming system for omnidirectional video (MELiveOV); the MELiveOV can intelligently offload the processing tasks to the edge computing enabled 5G base stations. The MELiveOV consists of an omnidirectional video generation module, a streaming module, and a viewpoint prediction module. A prototype system of MELiveOV is implemented to prove its complete end-to-end OV live streaming service. Evaluation result demonstrates that compared with the traditional solution, MELiveOV can reduce the network bandwidth requirement by about 50% and the transmission delay of more than 70% while ensuring the quality of the user’s experience.
Resource-Aware Data Stream Mining Using the Restricted Boltzmann Machine

In this paper, we consider the problem of data stream mining with an application of the Restricted Boltzmann Machine (RBM). If the data incoming rate is very fast, an appropriate algorithm should be resource-aware and work as fast as possible. Two RBM learning algorithms are investigated, i.e. the Contrastive Divergence and the Persistent Contrastive Divergence. We test three strategies for dealing with a buffer overflow in the case of high-speed data streams: load shedding, minibatch resizing, and controlling the number of Gibbs steps in the learning algorithm. Considered approaches are verified on the real MNIST dataset which is treated as a part of a data stream.
A Survey on Measuring Anonymity in Anonymous Communication Systems

The popularity of Internet applications has made communication privacy an increasingly important security requirement. As an important aspect of privacy, anonymity ensures that a subject may use resources or services without disclosing user identity or corresponding relationships. Since the seminal work by Chaum for anonymous communication, many different anonymous communication systems, and anonymous protocols have been developed and investigated extensively. In recent years, anonymous communication systems have evolved from academic tools used by specialists to mass-market software used by millions of ordinary people. How to evaluate and quantify the anonymity that different anonymous communication systems can offer has been a new focus. Though some efforts have been made on anonymity metrics and measuring techniques, systematic research on measuring anonymity of anonymous communication systems is still needed. In this paper, we give a comprehensive overview of state-of-the-art research on measuring anonymity. We first summarize the anonymous mechanisms applied by different anonymous systems. We then introduce the formalization of the notion of anonymity for measuring the anonymity degree. Further, metrics based on various theories and approaches are reviewed. We particularly elaborate on the metrics based on information theory as a separate section because of its popularity and multiple branches. The metrics based on information entropy is probably of the greatest interest.
A critical analysis of the advantages brought by blockchain technology to the global economy

The blockchain distributed ledger technology offers a secure, transparent, verifiable, democratic, decentralised, efficient, and tamper-resistant way to record and transfer data. Now much of the discussion revolves around the attributes that determine its many potential applications as this technology is expected to help transform sectors that are hampered by inefficiencies as well as facilitate the appearance of new business models based on distributed markets and technology. Given this technological revolution has already started, the core objectives of this article are to acquaint the reader with the technology and with the current and latent applications of this innovation while recognising its potential worldwide economic impact. To accomplish these goals we review the literature encompassing both academic and professional works that refer to the characteristics and properties of the blockchain, its many existent and plausible applications, and the reasons for its anticipated global economic repercussions.
High-dimensional image descriptor matching using highly parallel KD-tree construction and approximate nearest neighbor search

To overcome the high computational cost associated with the high-dimensional digital image descriptor matching, this paper presents a set of integrated parallel algorithms for the construction of K-dimensional tree (KD-tree) and P approximate nearest neighbor search (P-ANNS) on the modern massively parallel architectures (MPA). To improve the runtime performance of the P-ANNS, we propose an efficient sliding window for a parallel buffered P-ANNS on KD-tree to mitigate the high cost of global memory accesses. When applied to high dimensional real-world image descriptor datasets, the proposed KD-tree construction and the buffered P-ANNS algorithms are of comparable matching quality to the traditional sequential counterparts on CPU, while outperforming their serial CPU counterparts by speedup factors of up to 17and 163, respectively. The algorithms are also studied for the performance impact factors to obtain the optimal runtime configurations for various datasets. Moreover, we verify the features of the parallel algorithms on typical 3D image matching scenarios. With the classical local image descriptor signature of histograms of orientations (SHOT) datasets, the parallel KD-tree construction and image descriptor matching can achieve up to 11 and 138-fold speedups, respectively.
Part-Based Background-Aware Tracking for UAV With Convolutional Features

In recent years, visual tracking is a challenging task in UAV applications. The standard correlation filter (CF) has been extensively applied for UAV object tracking. However, the CF-based tracker severely suffers from boundary effects and cannot effectively cope with object occlusion, which results in suboptimal performance. Besides, it is still a tough task to obtain an appearance model precisely with hand-crafted features. In this paper, a novel part-based tracker is proposed for the UAV. With successive cropping operations, the tracking object is separated into several parts. More specially, the background-aware correlation filters with different cropping matrices are applied. To estimate the translation and scale variation of the tracking object, a structured comparison, and a Bayesian inference approach are proposed, which jointly achieve a coarse-to-fine strategy. Moreover, an adaptive mechanism is used to update the local appearance model of each part with a Gaussian process regression method. To construct a better appearance model, features extracted from the convolutional neural network are utilized instead of hand-crafted features. Through extensive experiments, the proposed tracker reaches competitive performance on 123 challenging UAV image sequences and outperforms other 20 popular state-of-the-art visual trackers in terms of overall performance and different challenging attributes.
Current Trends in Collaborative Filtering Recommendation Systems

Many different approaches for designing recommendation systems exist, including collaborative filtering, content-based, and hybrid approaches. Following an overview of different collaborative filtering recommendation system design methodologies, this paper reviews 71 journals articles and conference papers to provide a detailed literature review of model-based collaborative filtering. The articles selected for this review were published within the last decade between 2008–2018. They are classified by database, application field, methodology, and publication year. Papers using Clustering, Bayesian, Association Rule, Neural Networks, Regression, and Ensemble methodologies are surveyed. Application areas include books, music, movies, social networks, and business. This survey also analyzes the type of the data that was used for application field. This literature review identifies trends for model-based collaborative filtering and through empirical results gives insight into future research trajectories in this field.