Reviewerly/exHarmony huggingface dataset:
Few-shot traffic sign recognition with clustering inductive bias and random neural network

Reliable and fast traffic sign recognition (TSR) that locates the traffic sign from an image and then estimates its category is a crucial perception function for Advanced Driver Assistance Systems (ADAS) of autonomous vehicles. Most of the popular deep convolutional neural networks (DCNNs) based TSR techniques advocate discriminative feature learning for traffic signs against their appearance variability. However, such feature learning scheme may suffer from the diversity of traffic signs categories, especially when samples within each category are limited for model training (i.e., few-shot learning). Here, we present a generative feature learning based TSR network with well generalization capacity and high computational efficiency. Instead of relying on large amounts of supervision to learn discriminative features, our method devotes to learn common but unique properties of class-specific traffic signs with few training samples. Specifically, we combine clustering inductive bias with a random neural network, and then exploit computational advantages offered by a fast random projection algorithm. Experiments on two TSR benchmarks illustrate that our method achieves comparable or higher recognition accuracy than state-of-the-art DCNN-based methods with less training data and inference time consumption.
Tackling Overfitting in Boosting for Noisy Healthcare Data

Analyzing healthcare data poses several challenges including the limited number of samples, missing measurements, noisy labels, and heterogeneous data types. Tree-based boosting is well-suited for modeling such data as it is insensitive to data types and missingness. Moreover, Stochastic Gradient TreeBoost is often found in many winning solutions in public data science challenges. Unfortunately, the best performance requires extensive hyperparameter tuning and can be prone to overfitting. We propose PaloBoost, a Stochastic Gradient TreeBoost model that uses novel regularization techniques to guard against overfitting and is robust to hyperparameter settings. PaloBoost uses the out-of-bag samples to perform gradient-aware pruning and estimate adaptive learning rates. Unlike other Stochastic Gradient TreeBoost models that use the out-of-bag samples to estimate test errors, PaloBoost treats the samples as a second batch of training samples to prune the trees and adjust the learning rates. As a result, PaloBoost can dynamically adjust tree depths and learning rates to achieve faster learning at the start and slower learning as the algorithm converges. Experimental results on four datasets demonstrate that PaloBoost is robust to overfitting and is less sensitive to the hyperparameters.
New temperature-based predicting model for global solar radiation using support vector regression

This paper proposes a new approach for daily horizontal global solar radiation prediction using only air temperature as an input. The proposed approach is based on the support vector machine (SVM) by fusing the outcomes of two SVM models. The first one is used to estimate the horizontal global solar radiation, whereas the second one is used to estimate the error produced by the first model. Finally, a weighted sum is applied to predict the global solar radiation. The proposed approach (Corrected-SVM) has been evaluated for Ghardaïa site (Algeria) which is characterised by semi-arid climate conditions, using the measurement of three years (2013–2015). It was found that the proposed Corrected-SVM model improves the performance of the conventional SVM-model. The obtained statistical parameters (mean absolute bias error (MABE), root mean square error (RMSE), relative square error and correlation coefficient (r)) of the C-SVM and conventional SVM model are 1.623, 2.286 MJ/m2, 11.35, 94.20% and 1.713, 2.575 MJ/m2, 12.61, and 93.06%, respectively.
An inland Wireless Sensor Network system for monitoring seismic activity

Efficient early-warning systems for earthquakes should be able to provide accurate information on the seismic event allowing ample time for precautionary measures. In this paper, an assessment of the feasibility of using a Wireless Sensor Networks (WSNs) for monitoring seismic activity on the island of Mauritius using primary waves (P-waves) is carried out. Mauritius is one of the countries which are highly at risk to natural calamities. A simulation platform is designed and implemented to test the efficiency of a WSN in monitoring seismic activity. The system estimates the location of the hypocenter and the local velocity using time delays in P-wave arrival at each sensing node. Deviations between the estimated and actual quantities are used to study the efficiency of the system. Cross-correlation analysis of the recorded P-wave signals allowed accurate propagation delay estimation up to signal-to-noise ratios (SNRs) as low as -15dB. Time synchronization between the sensing nodes using timing-sync protocol for sensor networks improved the performance of the relocation algorithm by reducing the nodes’ clock offsets to within 0.50ms. The proposed system was tested through numerical simulations with various SNRs, sampling frequencies and number of sensing nodes.
Non-local weighted regularization for optical flow estimation

Global optical flow characterizes the overall motion while local optical flow characterizes the individual's movement. Therefore, both global and local optical flow is important to analyze the motion of each object in an image and of the camera. In order to robustly and accurately estimate the optical flow which includes both global and local motion information, this paper proposes a novel variational framework with non-local weighted regularization for optical flow estimation (NLWOF). The proposed NLWOF strategy includes the following two key parts: Firstly, non-local prior is used a regularization to robustly estimate the local optical flow due to that the similar structure patches using in non-local weight can suppress noise interference. Secondly, the solution of the NLWOF model can be approximated by the employment of the Lagrange–Euler formula and the approximation of the Laplace operator. Experimental results in both quantitation and qualitation verify the effectiveness of the NLWOF scheme, and even better than those of the state-of-the-arts.
A Kernelized Unified Framework for Domain Adaptation

The performance of the supervised learning algorithms such as k-nearest neighbor (k-NN) depends on the labeled data. For some applications (Target Domain), obtaining such labeled data is very expensive and labor-intensive. In a real-world scenario, the possibility of some other related application (Source Domain) is always accompanied by sufficiently labeled data. However, there is a distribution discrepancy between the source domain and the target domain application data as the background of collecting both the domains data is different. Therefore, source domain application with sufficient labeled data cannot be directly utilized for training the target domain classifier. Domain Adaptation (DA) or Transfer learning (TL) provides a way to transfer knowledge from source domain application to target domain application. Existing DA methods may not perform well when there is a much discrepancy between the source and the target domain data, and the data is non-linear separable. Therefore, in this paper, we provide a Kernelized Unified Framework for Domain Adaptation (KUFDA) that minimizes the discrepancy between both the domains on linear or non-linear data-sets and aligns them both geometrically and statistically. The substantial experiments verify that the proposed framework outperforms state-of-the-art Domain Adaptation and the primitive methods (Non- Domain Adaptation) on real-world Office-Caltech and PIE Face data-sets. Our proposed approach (KUFDA) achieved mean accuracies of 86.83% and 74.42% for all possible tasks of Office-Caltech with VGG-Net features and PIE Face data-sets.
E^2 C-Chain: A Two-Stage Incentive Education Employment and Skill Certification Blockchain

Employment and workforce industry become more important because the human capital value is linked to the company's profitability. Company becomes employee-centric in the 21st century. To hire an appropriate employee, and reduce hiring liability workplace violence, background screening marketing growth quickly. However, in the human resource industry, background check still is a pain point, especially for employment, education, and skill verification. The traditional background check is prolonged and inaccurate. For overseas recruitment, background check will be more troublesome. This paper comprehensively addresses the challenge of the education, employment and skill verification problem. We propose an innovation E^2 C-Chain which is a two-stage blockchain based education, employment and skill certification system. In the first stage, the new blocks are created when a trust organization verifies the education and employment information of the employee. In the second stage, to encourage the verifiers to participate in the skill verification process, we employ a Vickrey-Clarke-Groves (VCG) game based incentive mechanism to find the Nash Equilibrium and ensure social cost minimization. We also present the theoretical proofs and extensive simulations to demonstrate beneficial properties and efficiency of our proposed system.
An Image Steganography Algorithm using LSB Replacement through XOR Substitution

Least Significant Bit replacement, a spatial domain algorithm, is the most popular and widely used technique in image steganography due to its simplicity and effectiveness. Different methods of data hiding in spatial domain have been proposed and continue to be improved upon. Among them, the LSB replacement method is quite simple and the most popular. However, because the LSB replacement method is quite simple, compared to the other methods, some of its security issues must be improved upon. This paper proposes a highly secured data hiding technique in the spatial domain of image steganography. The proposed scheme takes the message bit and performs XOR operation with the 7th bit of every RGB component and, after then, the produced output is embedded within the 8th bit of each component of RGB. The embedding procedure is done in a way that there will be no sign of original message inside the cover object and, obviously, without using any outside key. A detailed study of the proposed LSB replacement algorithm including PSNR- and MSE-based investigations has been made. Experimental results shows a very good peak signal-to-noise ratio (PSNR) (55.90 dB for 65,536 bits of message within a 256x256 pixel cover image) and mean square error (MSE) value which indicates to less imperceptibility and more security. The comparative results prove that the proposed technique provides more security to secret information sharing, compared to other related techniques.
Variability in Direct and Diffuse Solar Radiation Across China From 1958 to 2017

Abstract Long‐term variability of direct and diffuse solar radiation ( R dir and R dif ) is essential for climate change study. However, R dir and R dif observations suffer from low spatiotemporal coverage and inhomogeneity. This study improved hybrid models to calculate R dir and R dif from sunshine duration and meteorological data at ~2,000 stations from 1958 to 2017 over China and demonstrated their reliability. We identified that R dir observations show a spurious steep downtrend before 1990 due to the sensitivity drift of the measuring instruments, implying an overestimation of global dimming. Long‐term trends and spatiotemporal details in R dir and R dif were also revealed. From 1958 to 1989, our results show that R dir displays a significant downtrend (−3.52 W m −2 per decade), whereas R dif shows a significant increasing trend (0.84 W m −2 per decade), especially over the North China Plain. From 1990 to 2017, R dir decreases nonsignificantly by −0.47 W m −2 per decade but R dif shows a slight decline of −0.28 W m −2 per decade.
Ethics of quantification or quantification of ethics?

Something can be gained by looking at common ethical features of different instances of quantification. While ethics of algorithms is perceived at present as an urgent issue, similar concerns can easily be associated to the use of metrics, of statistical inference, and of mathematical modelling. By reviewing these common features, strategies of resistance can be imagined to cope with computational dystopias, metrics fixation and numerical abuse.
Transfer Learning Based Classification of Cervical Cancer Immunohistochemistry Images

Cervical cancer is the fourth leading cause of cancer-related deaths. It is very important to make the precise diagnosis for the early stage of cervical cancer. In recent years, transfer Learning makes a great breakthrough in the field of machine learning, and the use of transfer learning technology in cervical histopathology image classification becomes a new research domain. In this paper, we propose a transfer learning framework of Inception-V3 network to classify well, moderately and poorly differentiated cervical histopathology images, which are stained using immunohistochemistry methods. In this framework, an Inception-V3 based transfer learning structure is first built up. Then, a fine-tuning approach is applied to extract effective deep learning features from the structure. Finally, the extracted features are designed for the final classification. In the experiment, a practical images stained by AQP, HIF and VEGF approaches are applied to test the proposed transfer learning network, and an average accuracy of 77.3% is finally achieved.
Research on the visual elements of augmented reality assembly processes

To solve the problem of visualization in augmented reality (AR), for assembly process information, we report here on our study into the composition of AR assembly process information. Our work led us to classify the visual elements of assembly processes into six categories, and after looking further into visual element expression characteristics used in assembly process information in the AR environment, standard assembly process elements have been identified and visual element layout principles studied. Typical visualization elements have been presented, using an AR-based assembly instruction system.
Consensus Algorithms in Blockchain Technology: A Survey

Blockchain is an immutable, transparent, public ledger that is distributed among the nodes in the network. It is a decentralized system in which transactions run on untrusted devices. To ensure equality and fairness, these transactions need to agree on some protocols. They are known as consensus algorithms. They are the core of blockchain and decide how blockchain works. Applying these algorithms, it is almost impossible for unauthorized users to crack the confidential information in the blocks. However, there are some security and performance issues that are required to be improved. In this paper, we have an overlook on various consensus algorithms, their working and where they are applied. In addition, we have reviewed blockchain technology, its application areas, advantages, and issues.
The use of eye tracking technology to explore learning and performance within virtual reality and mixed reality settings: a scoping review

This scoping review examines studies using eye tracking technology to monitor learning and performance in virtual or mixed reality settings. The aim of this review is to describe the various ways in which eye tracking devices have been deployed in relation to key aspects of virtual reality and mixed reality environments, list the eye tracking measures most salient to such environments and identify emergent patterns in the findings that the eye tracking data in the studies reviewed have brought to light. Drawing on these findings, an analytical framework for attending to and analysing eye tracking data is proposed and recommendations for future research using eye tracking to optimise learning and performance within virtual reality and mixed reality environments are discussed.
Research on Recommendation of Insurance Products Based on Random Forest

With the rapid development of recommendation system, how to predict user's behavior accurately become more and more important. In this paper, random forest is applied to recommend insurance products and compared with ID3, C4.5, Nave-Bayes and Nearest-neighbor. Experiment results show that the prediction error of random forest is 2.02% lower than ID3, 1.09% lower than C4.5, 1.67% lower than Nave-Bayes and 5.97% lower than Nearest-neighbor. Therefore, it is highly feasible to recommend insurance products with random forests.
Combining Pairwise Feature Matches from Device Trajectories for Biometric Authentication in Virtual Reality Environments

In this paper we provide an approach to perform seamless continual biometric authentication of users in virtual reality (VR) environments by combining position and orientation features from the headset, right hand controller, and left hand controller of a VR system. The rapid growth of VR in mission critical applications in military training, flight simulation, therapy, manufacturing, and education necessitates authentication of users based on their actions within the VR space as opposed to traditional PIN and password based approaches. To mimic goal-oriented interactions as they may occur in VR environments, we capture a VR dataset of trajectories from 33 users throwing a ball at a virtual target with 10 samples per user captured on a training day, and 10 samples on a test day. Due to the sparseness in the number of training samples per user, typical of realistic interactions, we perform authentication by using pairwise relationships between trajectories. Our approach uses a perceptron classifier to learn weights on the matches between position and orientation features on two trajectories from the headset and the hand controllers, such that a low classifier score is obtained for trajectories belonging to the same user, and a high score is obtained otherwise. We also perform extensive evaluation on the choice of position and orientation features, combination of devices, and choice of match metrics and trajectory alignment method on the accuracy, and demonstrate a maximum accuracy of 93.03% for matching 10 test actions per user by using orientation from the right hand controller and headset.
Fast Item Ranking under Neural Network based Measures

Recently, plenty of neural network based recommendation models have demonstrated their strength in modeling complicated relationships between heterogeneous objects (i.e., users and items). However, the applications of these fine trained recommendation models are limited to the off-line manner or the re-ranking procedure (on a pre-filtered small subset of items), due to their time-consuming computations. Fast item ranking under learned neural network based ranking measures is largely still an open question.
Non-inertial opposition-based particle swarm optimization and its theoretical analysis for deep learning applications

Particle Swarm Optimization (PSO) and its variants are often used to train and optimize the structure and parameters of deep learning models to improve accuracy of learning results in the reasonable time-consuming. The performance of PSO is completed determined by its kinetic equation of particles. To accelerate convergence rate, a novel kinetic equation without inertial term is devised and applied to PSO, and then a non-inertial opposition-based particle swarm optimization (NOPSO) is generated combined with a adaptive elite mutation strategy and generalized opposition-based learning strategy. Simulation Experimental results show that the new kinetic equation has effectively accelerated convergence rate of PSO. Meanwhile, Theoretical analysis of the new kinetic equation is carried out by order-2 difference recurrence equation, the inference conclusions of which are consistent with the results of simulation experiments. NOPSO algorithm with a new kinetic equation is a highly competitive algorithm compared with some state-of-art PSOs and is suitable for deep learning applications.
Specific Emitter Identification Techniques for the Internet of Things

Specific Emitter Identification (SEI) detects the individual emitter according its varied signal characteristics. The method operates in the physical layer of the internet and can effectively improve the security of the Internet of Things (IoT). Generally, SEI identifies the uniqueness of the transmitting platform by using the unintentional modulation information of the emitter such as radar, which has “fingerprint” characteristics. Existing SEI methods are based on hand-crafted features to distinguish different emitters. In this paper, traditional feature extraction methods are studied and a new recognition method is proposed. To determine the effectiveness of the method, the output signals of eight amplifiers are collected as the research object. The power spectrum characteristics and adjacent channel power ratio (ACPR) of the signal are then extracted and eight amplifiers are distinguished. Finally, the quadrature-phase signals are converted into pictures, and convolutional neural networks are used to automatically extract features for classification and recognition. The results show that the recognition rate of converting signals into pictures can reach 95%, when SNR is 20dB.
Time to Shop for Valentine's Day

Currently, most sequence-based recommendation models aim to predict a user's next actions (e.g. next purchase) based on their past actions. These models either capture users' intrinsic preference (e.g. a comedy lover, or a fan of fantasy) from their long-term behavior patterns or infer their current needs by emphasizing recent actions. However, in e-commerce, intrinsic user behavior may be shifted by occasions such as birthdays, anniversaries, or gifting celebrations (Valentine's Day or Mother's Day), leading to purchases that deviate from long-term preferences and are not related to recent actions. In this work, we propose a novel next-item recommendation system which models a user's default, intrinsic preference, as well as two different kinds of occasion-based signals that may cause users to deviate from their normal behavior. More specifically, this model is novel in that it: (1) captures a personal occasion signal using an attention layer that models reoccurring occasions specific to that user (e.g. a birthday); (2) captures a global occasion signal using an attention layer that models seasonal or reoccurring occasions for many users (e.g. Christmas); (3) balances the user's intrinsic preferences with the personal and global occasion signals for different users at different timestamps with a gating layer. We explore two real-world e-commerce datasets (Amazon and Etsy) and show that the proposed model outperforms state-of-the-art models by 7.62% and 6.06% in predicting users' next purchase.