Reviewerly/exHarmony huggingface dataset:
New Approaches to Federated XGBoost Learning for Privacy-Preserving Data Analysis

In this paper, we propose a new privacy-preserving machine learning algorithm called Federated-Learning XGBoost (FL-XGBoost), in which a federated learning scheme is introduced into XGBoost, a state-of-the-art gradient boosting decision tree model. The proposed FL-XGBoost can train a sensitive task to be solved among different entities without revealing their own data. The proposed FL-XGBoost can achieve significant reduction in the number of communications between entities by exchanging decision tree models. In our experiments, we carry out the performance comparison between FL-XGBoost and a different federated learning approach to XGBoost called FATE. The experimental results show that the proposed method can achieve high prediction accuracy with less communication even if the number of entities is increase.
IMAGE ENCRYPTION TECHNOLOGY BASED ON FRACTIONAL TWO-DIMENSIONAL DISCRETE CHAOTIC MAP ACCOMPANIED WITH MENEZES-VANSTONE ELLIPTIC CURVE CRYPTOSYSTEM

A new fractional two-dimensional triangle function combination discrete chaotic map (2DTFCDM) with the discrete fractional difference is proposed in this paper. The chaos behaviors are observed through the bifurcation diagrams, the largest Lyapunov exponent plot and the phase portraits. The proposed map is applied in color image encryption with the secret keys generated by Menezes–Vanstone Elliptic Curve Cryptosystem. The image encryption system is analyzed using six aspects indicating the superiority of the proposed algorithm compared to the other algorithms.
Pose Augmentation: Class-Agnostic Object Pose Transformation for Object Recognition

Object pose increases intraclass object variance which makes object recognition from 2D images harder. To render a classifier robust to pose variations, most deep neural networks try to eliminate the influence of pose by using large datasets with many poses for each class. Here, we propose a different approach: a class-agnostic object pose transformation network (OPT-Net) can transform an image along 3D yaw and pitch axes to synthesize additional poses continuously. Synthesized images lead to better training of an object classifier. We design a novel eliminate-add structure to explicitly disentangle pose from object identity: first ‘eliminate’ pose information of the input image and then ‘add’ target pose information (regularized as continuous variables) to synthesize any target pose. We trained OPT-Net on images of toy vehicles shot on a turntable from the iLab-20M dataset. After training on unbalanced discrete poses (5 classes with 6 poses per object instance, plus 5 classes with only 2 poses), we show that OPT-Net can synthesize balanced continuous new poses along yaw and pitch axes with high quality. Training a ResNet-18 classifier with original plus synthesized poses improves mAP accuracy by 9% over training on original poses only. Further, the pre-trained OPT-Net can generalize to new object classes, which we demonstrate on both iLab-20M and RGB-D. We also show that the learned features can generalize to ImageNet. (The code is released at this github url ).
Communication: a post-discipline

The field of communication is characterized in large part by a barely-contained eclecticism that has often been thought to undermine the field’s claims to legitimacy. Communication scholars wrestle...
Few-Shot Object Detection by Second-Order Pooling

In this paper, we tackle a challenging problem of Few-shot Object Detection rather than recognition. We propose Power Normalizing Second-order Detector consisting of the Encoding Network (EN), the Multi-scale Feature Fusion (MFF), Second-order Pooling (SOP) with Power Normalization (PN), the Hyper Attention Region Proposal Network (HARPN) and Similarity Network (SN). EN takes support image crops and a query image per episode to produce covolutional feature maps across several layers while MFF combines them into multi-scale feature maps. SOP aggregates them per support image while PN detects the presence of visual feature instead of counting its frequency of occurrence. HARPN cross-correlates the PN pooled support features against the query feature map to match regions and produce query region proposals that are then aggregated with SOP/PN. Finally, support and query second-order descriptors are passed to SN.
Neural data-to-text generation with dynamic content planning

Neural data-to-text generation models have achieved significant advancement in recent years. However, these models have two shortcomings: the generated texts tend to miss some vital information, and they often generate descriptions that are not consistent with the structured input data. To alleviate these problems, we propose a Neural data-to-text generation model with Dynamic content Planning, named NDP 2for abbreviation. The NDP can utilize the previously generated text to dynamically select the appropriate entry from the given structured data. We further design a reconstruction mechanism with a novel objective function that can reconstruct the whole entry of the used data sequentially from the hidden states of the decoder, which aids the accuracy of the generated text. Empirical results show that the NDP achieves superior performance over the state-of-the-art on ROTOWIRE and NBAZHN datasets, in terms of relation generation (RG), content selection (CS), content ordering (CO) and BLEU metrics. The human evaluation result shows that the texts generated by the proposed NDP are better than the corresponding ones generated by NCP in most of time. And using the proposed reconstruction mechanism, the fidelity of the generated text can be further improved significantly.
Estimation of Domain of Attraction for Discrete-Time Positive Interval Type-2 Polynomial Fuzzy Systems With Input Saturation

This article focuses on expanding the estimation of the domain of attraction (DOA) for discrete-time positive nonlinear systems subject to input saturation and parameter uncertainties. To facilitate analysis and design, the interval type-2 (IT2) polynomial fuzzy model is used to represent the nonlinear plant and capture uncertainties. Combining with the IT2 polynomial fuzzy controller, the discrete-time positive IT2 polynomial fuzzy-model-based (PIT2PFMB) control system is formed to facilitate analysis. To enlarge the estimation of DOA of the discrete-time PIT2PFMB system, polyhedron is used to characterize the DOA with the help of linear copositive Lyapunov function (LCLF). Referring to the nonconvex conditions derived by LCLF, an effective convexification method is proposed in this article. For comparison purposes, the saturation-dependent-Lyapunov-function-based method is extended to the PIT2PFMB control system by adding the corresponding positivity conditions. In addition, this article attempts to enlarge the estimation of the DOA by improving the IT2 membership-function-dependent (IT2MFD) method and extending it to all conditions, including the stability conditions and the DOA estimation conditions. Finally, an example with simulation results is given to verify the effectiveness of all the methods proposed in this article for expanding the estimation of the DOA.
Trustzone-based secure lightweight wallet for hyperledger fabric

With the development of blockchain-based digital currencies, the security of digital wallets becomes more and more important. As far as we know, there is no safe lightweight wallet in hyperledger fabric. To solve the problem, we proposed a Trustzone-based Secure Lightweight Wallet for Hyperledger Fabric (hereafter referred to as TSLWHF). Firstly, we designed an Unspent Transaction Output (UTXO) set of transactions under blockchain and a signature verification mechanism for transactions, which made it possible to implement the lightweight wallet in hyperledger fabric. Then, we implemented a reliable protection mechanism for private keys and wallet’s address, which solved the problem that users’ information might be stolen or replaced. Meanwhile, the transaction verification results are guaranteed not to be tampered by hackers through verifying transactions in Trusted Execution Environment (TEE) and encrypting local block headers. Finally, to demonstrate utility, we deployed the system in hyperledger fabric and trustzone. Experiments showed that the wallet reduces the size of locally stored data while protecting the security of user’s assets. The time spent on TSLWHF to execute a transaction is 0.589s, which improves transaction’s performance compared to Bitcoin wallet.
PR-NN: RNN-Based Detection for Coded Partial-Response Channels

In this paper, we investigate the use of recurrent neural network (RNN)-based detection of magnetic recording channels with inter-symbol interference (ISI). We refer to the proposed detection method, which is intended for recording channels with partial-response equalization, as Partial-Response Neural Network (PR-NN). We train bi-directional gated recurrent units (bi-GRUs) to recover the ISI channel inputs from noisy channel output sequences and evaluate the network performance when applied to continuous, streaming data. The computational complexity of PR-NN during the evaluation process is comparable to that of a Viterbi detector. The recording system on which the experiments were conducted uses a rate-2/3, (1,7) runlength-limited (RLL) code with an E <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> PR4 partial-response channel target. Experimental results with ideal PR signals show that the performance of PR-NN detection approaches that of Viterbi detection in additive white gaussian noise (AWGN). Moreover, the PR-NN detector outperforms Viterbi detection and achieves the performance of Noise-Predictive Maximum Likelihood (NPML) detection in additive colored noise (ACN) at different channel densities. A PR-NN detector trained with both AWGN and ACN maintains the performance observed under separate training. Similarly, when trained with ACN corresponding to two different channel densities, PR-NN maintains its performance at both densities. Experiments confirm that this robustness is consistent over a wide range of signal-to-noise ratios (SNRs). Finally, PR-NN displays robust performance when applied to a more realistic magnetic recording channel with MMSE-equalized Lorentzian signals.
A lightweight, low-cost and self-diagnosing mechatronic jaw gripper for the aerial picking with unmanned aerial vehicles

Within recent years the gripping and manipulation of individual objects using unmanned aerial vehicles (UAVs) has become an area of broad scientific research as UAVs allow a fast and three-dimensional transportation of the grasped objects. However, currently there are neither standards for aerial gripping devices nor are there grippers available that are both lightweight and robust enough for the usage with drones and can provide the required feedback on whether the gripping process was successful. In this paper, we therefore present a lightweight, low-cost and self-diagnosing mechatronic jaw gripper for the application with autonomous UAVs. The gripper offers an energy-saving design and an integrated measurement system to detect whether an object was successfully grasped. To evaluate the performance of the gripper it is attached to a quadrotor and tested within different scenarios. The tests include the detection of successfully grasped or dropped out objects and the autonomous pickup and deposit.
Feature selection for high‐dimensional regression via sparse LSSVR based on Lp‐norm

When solving many regression problems, there exist a large number of input features. However, not all features are relevant for current regression, and sometimes, including irrelevant features may deteriorate the learning performance. Therefore, it is essential to select the most relevant features, especially for high-dimensional regression. Feature selection is an effective way to solve this problem. It tries to represent original data by extracting relevant features that contain useful information. In this paper, aiming to effectively select useful features in least squares support vector regression (LSSVR), we propose a novel sparse LSSVR based on L p -norm (SLSSVR), 0 < p ≤ 1 . Different from the existing L 1 -norm LSSVR ( L 1 -LSSVR) and L p -norm LSSVR ( L p -LSSVR), SLSSVR uses a smooth approximation of the nonsmooth nonconvex L p -norm term along with an effective solving algorithm. The proposed algorithm avoids the singularity issue that may encounter in L p -LSSVR, and its convergency is also guaranteed. Experimental results support the effectiveness of SLSSVR on both feature selection ability and regression performance.
Advances toward validating examiner writership opinion based on handwriting kinematics

A National Research Council report on strengthening forensic science raised concern over the lack of scientific studies supporting the validity of examining and interpreting forensic evidence. However, establishing the foundational validity of subjective methods can be challenging. The present study aimed to establish the scientific validity of expert writership opinions and the two-stage approach to evidence interpretation using measures derived from research on handwriting motor control. Regression-based procedures were used to address two experimental questions: 1) what are the relative contributions of kinematic and pressure features in predicting examiner support for alternate writership propositions when examining pairs of questioned handwriting samples; and 2) to what extent does information about the rarity of the kinematic feature dissimilarity scores improve the accuracy of a predictive model based on dissimilarity alone. Regarding the first question, we identified a multifactor model consisting of feature dissimilarity scores and their population distributions having correlation coefficients (R2) of 0.84 and 0.88 for the same-writer and different-writers propositions, respectively. Temporal features contributed up to 21% to the predictive value of the model, whereas spatial features contributed only 9% and pen pressure contributed up to 17%. When we compared models reflecting a single-stage process (based on feature dissimilarities) of forming opinions with models reflecting a two-stage process (based on feature dissimilarities and rarity) we found that the two-stage models had an average of 15.25% greater predictive value than single-stage models. These findings support the scientific validity of FDE writership determinations and underscore the importance of the two-stage approach for evidence interpretation.
Extreme Learning Machine Based on Firefly Adaptive Flower Pollination Algorithm Optimization

Extreme learning machine (ELM) has aroused a lot of concern and discussion for its fast training speed and good generalization performance, and it has been used diffusely in both regression and classification problems. However, on account of the randomness of input parameters, it requires more hidden nodes to obtain the desired accuracy. In this paper, we come up with a firefly-based adaptive flower pollination algorithm (FA-FPA) to optimize the input weights and thresholds of the ELM algorithm. Nonlinear function fitting, iris classification and personal credit rating experiments show that the ELM with FA-FPA (FA-FPA-ELM) can obtain significantly better generalization performance (such as root mean square error, classification accuracy) than traditional ELM, ELM with firefly algorithm (FA-ELM), ELM with flower pollination algorithm (FPA-ELM), ELM with genetic algorithm (GA-ELM) and ELM with particle swarm optimization (PSO-ELM) algorithms.
The Impact of Data Filtration on the Accuracy of Multiple Time-Domain Forecasting for Photovoltaic Power Plants Generation

The paper reports the forecasting model for multiple time-domain photovoltaic power plants, developed in response to the necessity of bad weather days’ accurate and robust power generation forecasting. We provide a brief description of the piloted short-term forecasting system and place under close scrutiny the main sources of photovoltaic power plants’ generation forecasting errors. The effectiveness of the empirical approach versus unsupervised learning was investigated in application to source data filtration in order to improve the power generation forecasting accuracy for unstable weather conditions. The k-nearest neighbors’ methodology was justified to be optimal for initial data filtration, based on the clusterization results, associated with peculiar weather and seasonal conditions. The photovoltaic power plants’ forecasting accuracy improvement was further investigated for a one hour-ahead time-domain. It was proved that operational forecasting could be implemented based on the results of short-term day-ahead forecast mismatches predictions, which form the basis for multiple time-domain integrated forecasting tools. After a comparison of multiple time series forecasting approaches, operational forecasting was realized based on the second-order autoregression function and applied to short-term forecasting errors with the resulting accuracy of 87%. In the concluding part of the article the authors from the points of view of computational efficiency and scalability proposed the hardware system composition.
A Survey of Educational Efforts to Accelerate a Growing Quantum Workforce

This paper identifies several education efforts for individuals entering the quantum computing workforce. This survey report identifies current approaches to educate students from varied learning groups, including professionals looking to increase their skillsets, high school students exploring their interests, and individuals seeking a formal master's degree in the field. This survey provides a view into the rapidly evolving efforts of the quantum workforce-development ecosystem.
Secured Inter-Healthcare Patient Health Records Exchange Architecture

The increase in cyberattacks against the healthcare system, notably Electronic Health Records (EHRs) breaches, has cost the healthcare providers more in recent years. This situation is estimated to increase in the coming years as the healthcare systems are proposing a consortium EHRs repository. In an attempt to secure these health records, researchers propose firewalls, cryptography, cloud antivirus. Although these approaches proved useful and practical in protecting EHRs; however, the shared data security cannot be guaranteed. More specifically, maintaining the integrity and consistency of shared EHRs becomes a significant concern. In this on-going research, we propose a blockchain-based solution that facilitates a scalable and secured inter-healthcare EHRs exchange. These healthcare providers maintain their records on separate blockchain networks and are independent of each other. The proposed architecture can detect and prevent malicious activities on both EHRs at rest and in transit. It can also verify the integrity and consistency of EHR requests and replies from other healthcare systems and presents them in a format that can be easily understood by different healthcare nodes. We evaluate the security analysis against frequently encounter outsider and insider threats within a healthcare system in the preliminary result. The result shows that the architecture detects and prevents insider threats from uploading compromising EHRs into the blockchain and prevents unauthorized retrieval of patient information.
Feature Calibration Network for Occluded Pedestrian Detection

Pedestrian detection in the wild remains a challenging problem especially for scenes containing serious occlusion. In this paper, we propose a novel feature learning method in the deep learning framework, referred to as Feature Calibration Network (FC-Net), to adaptively detect pedestrians under various occlusions. FC-Net is based on the observation that the visible parts of pedestrians are selective and decisive for detection, and is implemented as a self-paced feature learning framework with a self-activation (SA) module and a feature calibration (FC) module. In a new self-activated manner, FC-Net learns features which highlight the visible parts and suppress the occluded parts of pedestrians. The SA module estimates pedestrian activation maps by reusing classifier weights, without any additional parameter involved, therefore resulting in an extremely parsimony model to reinforce the semantics of features, while the FC module calibrates the convolutional features for adaptive pedestrian representation in both pixel-wise and region-based ways. Experiments on CityPersons and Caltech datasets demonstrate that FC-Net improves detection performance on occluded pedestrians up to 10% while maintaining excellent performance on non-occluded instances.
Color Image Restoration Exploiting Inter-Channel Correlation With a 3-Stage CNN

Image restoration is a critical component of image processing pipelines and for low-level computer vision tasks. Conventional image restoration approaches are mostly based on hand-crafted image priors. The inter-channel correlation of color images is not fully exploited. Motivated by the special characteristics of the inter-channel correlation (higher correlation for red/green and green/blue channels than for red/blue) in color images and general characteristics (green channel always shows the best image quality among the three color components) of distorted color images, in this paper, a three-stage convolutional neural network (CNN) structure is proposed for color image restoration tasks. Since the green channel is found to have the best quality among all three channels, in the first stage, the network is designed to reconstruct the green component. Then, with the guidance of the reconstructed green channel from the first stage, the red and blue channels are reconstructed in the second stage with two parallel networks. Finally, the intermediate reconstructions from the previous stages are concatenated and further refined jointly. We demonstrate the capabilities of the proposed three-stage structure with three typical color image restoration tasks: color image demosaicking, color compression artifacts reduction, and real-world color image denoising. In addition, we integrate pixel-shuffle convolution into our scheme to improve the efficiency, and also introduce a quality-blind training strategy to simplify the training process for the compression artifacts reduction task. Extensive experimental results and analyses show that the proposed structure successfully exploits the spatial and inter-channel correlation of color images and outperforms the state-of-the-art image reconstruction approaches.