Reviewerly/exHarmony huggingface dataset:
A Novel Adaptive Kalman Filter With Unknown Probability of Measurement Loss

A novel variational Bayesian (VB)-based adaptive Kalman filter (AKF) is proposed to solve the filtering problem of a linear system with unknown probability of measurement loss. The sum of two likelihood functions is transformed into an exponential multiplication form, and the state vector, the Bernoulli random variable and the probability of measurement loss are jointly inferred based on the VB approach. Simulation results demonstrate the superiority of the proposed AKF as compared with the existing filtering algorithms with unknown probability of measurement loss.
Show, Tell, and Polish: Ruminant Decoding for Image Captioning

The encoder-decoder framework has been the base of popular image captioning models, which typically predicts the target sentence based on the encoded source image one word at a time in sequence. However, such a single-pass decoding framework encounters two problems. First, mistakes in the predicted words cannot be corrected and may propagate to the entire sentence. Second, because the single-pass decoder cannot access the following un-generated words, it can only perform local planning to choose every single word according to the preceding words, while lacks the global planning ability as for maintaining the semantic consistency and fluency of the whole sentence. In order to address the above two problems, in this work, we design a ruminant captioning framework which contains an image encoder, a base decoder, and a ruminant decoder. Specifically, the outputs of the former/base decoder are utilized as the global information to guide the words prediction of the latter/ruminant decoder, in an attempt to mimic human polishing process. We enable jointly training of the whole framework and overcome the non-differential problem of discrete words by designing a novel reinforcement learning based optimization algorithm. Experiments on two datasets (MS COCO and Flickr30 k) demonstrate that our ruminant decoding method can bring significant improvements over traditional single-pass decoding based models and achieves state-of-the-art performance.
Multiview Semi-Supervised Learning Model for Image Classification

Semi-supervised learning models for multiview data are important in image classification tasks, since heterogeneous features are easy to obtain and semi-supervised schemes are economical and effective. To model the view importance, conventional graph-based multiview learning models learn a linear combination of views while assuming a priori weights distribution. In this paper, we present a novel structural regularized semi-supervised model for multiview data, termed Adaptive MUltiview SEmi-supervised model (AMUSE). Our new model learns weights from a priori graph structure, which is more reasonable than weight regularization. Theoretical analysis reveals the significant difference between AMUSE and the prior arts. An efficient optimization algorithm is provided to solve the new model. Experimental results on six real-world data sets demonstrate the effectiveness of the structural regularized weights learning scheme.
Reliable Data Aggregation in Internet of ViSAR Vehicles Using Chained Dual-Phase Adaptive Interpolation and Data Embedding

Recently, the use of industrial synthetic aperture radar (SAR) sensors for unmanned aerial vehicles (UAVs) has received more attention. Despite the recent growth in the use of SAR, the connection bandwidth and the low data rate communications among networked UAVs in an Internet of Things (IoT) environment is a challenge, specifically, for exchanging big data such as remote sensing videos. To this end, we propose a lossless data aggregation technique to reduce redundant information of industrial sensing and embed managerial and control data. The proposed method uses reversible watermarking through a dual-phase interpolation-based embedding with a greedy network of weights, which can be updated under an unsupervised statistical procedure. Our method is reversible with respect to enhanced embedding capacity for reliable wireless payload communications. The simulation results on the benchmarks of video synthetic aperture radar confirm the theoretical analysis and demonstrate that the proposed approach outperforms the past works.
ShuffleFaceNet: A Lightweight Face Architecture for Efficient and Highly-Accurate Face Recognition

The recent success of convolutional neural networks has led to the development of a variety of new effective and efficient architectures. However, few of them have been designed for the specific case of face recognition. Inspired on the state-of-the-art ShuffleNetV2 model, a lightweight face architecture is presented in this paper. The proposal, named ShuffleFaceNet, introduces significant modifications in order to improve face recognition accuracy. First, the Global Average Pooling layer is replaced by a Global Depth-wise Convolution layer, and Parametric Rectified Linear Unit is used as a non-linear activation function. Under the same experimental conditions, ShuffleFaceNet achieves significantly superior accuracy than the original ShuffleNetV2, maintaining the same speed and compact storage. In addition, extensive experiments conducted on three challenging benchmark face datasets, show that our proposal improves not only state-of-the-art lightweight models but also very deep face recognition models.
Microsimulation model calibration using incremental mixture approximate Bayesian computation

Microsimulation models (MSMs) are used to inform policy by predicting population-level outcomes under different scenarios. MSMs simulate individual-level event histories that mark the disease process (such as the development of cancer) and the effect of policy actions (such as screening) on these events. MSMs often have many unknown parameters; calibration is the process of searching the parameter space to select parameters that result in accurate MSM prediction of a wide range of targets. We develop Incremental Mixture Approximate Bayesian Computation (IMABC) for MSM calibration, which results in a simulated sample from the posterior distribution of model parameters given calibration targets. IMABC begins with a rejection-based ABC step, drawing a sample of points from the prior distribution of model parameters and accepting points that result in simulated targets that are near observed targets. Next, the sample is iteratively updated by drawing additional points from a mixture of multivariate normal distributions and accepting points that result in accurate predictions. Posterior estimates are obtained by weighting the final set of accepted points to account for the adaptive sampling scheme. We demonstrate IMABC by calibrating CRC-SPIN 2.0, an updated version of a MSM for colorectal cancer (CRC) that has been used to inform national CRC screening guidelines.
How low should we go when warning for earthquakes?

Social responses to alerts are critical but understudied
The Capacity of Private Information Retrieval With Private Side Information Under Storage Constraints

We consider the problem of private information retrieval (PIR) of a single message out of K messages from N replicated and non-colluding databases where a cache-enabled user (retriever) of cache-size S possesses side information in the form of uncoded portions of the messages where the message identities are unknown to the databases. The identities of these side information messages need to be kept private from the databases, i.e., we consider PIR with private side information (PSI). We characterize the optimal normalized download cost for this PIR-PSI problem under the storage constraint S as D* = 1+ 1/N + 1/N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> +· · ·+ 1/N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K-1-M</sup> + 1-r <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</sub> /N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K-M</sup> + 1-r <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M-1</sub> /N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K-M+1</sup> + · · · + 1-r <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> /N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K-1</sup> , where M is the number of side information messages and ri is the portion of the ith side information message that is cached with Σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i=1</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</sup> r <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> = S. Based on this capacity result, we prove two facts: First, for a fixed memory size S and a fixed number of accessible messages M, uniform caching achieves the lowest normalized download cost, i.e., r <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> = S/M, for i = 1, . . . , M, is optimum. Second, for a fixed memory size S, among all possible K - ⌈S⌉ + 1 uniform caching schemes, the uniform caching scheme which caches M = K messages achieves the lowest normalized download cost.
Blockchain-Based Anti-Key-Leakage Key Aggregation Searchable Encryption for IoT

The Internet of Things (IoT) makes our life more intelligent. Its combination with the cloud server can solve big data processing problems to meet users' needs and bring us great convenience. However, there are two challenges we need to face: data sharing and key leakage. To solve the above challenges, attribute-based encryption (ABE) is used to achieve data sharing combined with searchable encryption (SE). Most of the existing attribute-based searchable encryption (SE) schemes are inefficient and not suitable for IoT devices because of the large amount of attributes and keys. The key-leakage problem is serious in practice which very little literature focused on it. In order to address both problems, in this article, we propose a key aggregation searchable encryption (KASE) scheme based on the blockchain with auxiliary input (AI), which is capable of achieving secure data sharing on the encrypted data. Our scheme is presented through a novel chosen plaintext attack (CPA) secure scheme. We prove our scheme is chosen ciphertext attack (CCA) secure against key leakage under the decisional Diffie-Hellman assumption and Goldreich-Leivin theorem. Moreover, we adopt the proposed scheme to establish a data-sharing system based on blockchain, which improves search efficiency and connects the global ecology. In addition, extensive performance evaluations are conducted, and the results indicate our scheme is really efficient in cloud-computing-enhanced IoT.
Interval Error Correction Auxiliary Model Based Gradient Iterative Algorithms for Multirate ARX Models

To improve the identification efficiency of the auxiliary model based gradient iterative algorithm, this article derives a couple of interval error correction auxiliary model based gradient iterative (IEC-AM-GI) algorithms for multirate AutoRegressive eXogenous (ARX) models. The unmeasurable outputs between two measured outputs are estimated by two interval error correction auxiliary models. Unlike the AM-GI algorithm, the IEC-AM-GI algorithms can estimate the missing outputs by the auxiliary model and further fine tune through the measurable outputs, which thus are more efficient and accurate than those by only using the auxiliary model. In addition, this article makes a comparison between these two interval error correction auxiliary model methods and through which the optimal interval error correction auxiliary model method for the unmeasurable outputs can be chosen. The effectiveness of the proposed algorithms is demonstrated by the simulation results.
Noise-robust dictionary learning with slack block-Diagonal structure for face recognition

Strict '0-1' block-diagonal structure has been widely used for learning structured representation in face recognition problems. However, it is questionable and unreasonable to assume the within-class representations are the same. To circumvent this problem, in this paper, we propose a slack block-diagonal (SBD) structure for representation where the target structure matrix is dynamically updated, yet its blockdiagonal nature is preserved. Furthermore, in order to depict the noise in face images more precisely, we propose a robust dictionary learning algorithm based on mixed-noise model by utilizing the above SBD structure (SBD2L). SBD2L considers that there exists two forms of noise in data which are drawn from Laplacian and Gaussion distribution, respectively. Moreover, SBD2L introduces a low-rank constraint on the representation matrix to enhance the dictionary's robustness to noise. Extensive experiments on four benchmark databases show that the proposed SBD2L can achieve better classification results than several state-of-the-art dictionary learning methods.
A Survey of Vehicle Re-Identification Based on Deep Learning

Vehicle re-identification is one of the core technologies of intelligent transportation systems, and it is crucial for the construction of smart cities. With the rapid development of deep learning, vehicle re-identification technologies have made significant progress in recent years. Therefore, making a comprehensive survey about the vehicle re-identification methods based on deep learning is quite indispensable. There are mainly five types of deep learning-based methods designed for vehicle re-identification, i.e. methods based on local features, methods based on representation learning, methods based on metric learning, methods based on unsupervised learning, and methods based on attention mechanism. The major contributions of our survey come from three aspects. First, we give a comprehensive review of the current five types of deep learning-based methods for vehicle re-identification, and we further compare them from characteristics, advantages, and disadvantages. Second, we sort out vehicle public datasets and compare them from multiple dimensions. Third, we further discuss the challenges and possible research directions of vehicle re-identification in the future based on our survey.
Robust Stability and Stabilization Conditions for Nonlinear Networked Control Systems With Network-Induced Delay via T–S Fuzzy Model

Robust stability and stabilization problems for a class of discrete-time nonlinear systems are studied in this article. The control signal is transmitted to the nonlinear systems through a lossy communication channel in which time-varying network-induced delay occurs. A Takagi-Sugeno (T-S) fuzzy model is used to represent the nonlinear systems. Using membership function boundary information, a novel membership-function-dependent (MFD) analysis approach is presented. Unlike most previous results, the proposed MFD analysis approach does not need to increase the number of slack variables to reduce conservatism. Combining auxiliary matrices and this approach, we propose some new fuzzy Lyapunov-Krasovskii functionals (FLKFs) that do not require the restrictions of positive definiteness imposed on Lyapunov matrices. Using the FLKF and MFD analysis approach, a sufficient condition in the form of linear matrix inequalities (LMIs) is developed to guarantee the stability of T-S fuzzy time-varying delay systems. Based on the results for stability, less conservative stabilization conditions are also derived. Finally, numerical examples show that less conservative results can be obtained by the proposed approach. The inverted pendulum system with state delay or network-induced delay is also studied to verify the effectiveness of the suggested approach.
Mining weighted subgraphs in a single large graph

Weighted single large graphs are often used to simulate complex systems, and thus mining frequent subgraphs in a weighted large graph is an important issue that has attracted the attention of many researchers. Within the studies on the mining of frequent subgraphs, the Graph Mining (GraMi) algorithm is considered as a state-of-the-art algorithm. However, this algorithm can only be implemented on graph datasets which have node labels and edge labels, without weights, and this means that the importance of all objects in a large graph is the same. In this paper, we propose a frequent subgraph algorithm on a weighted large graph, called Weighted Graph Mining (WeGraMi), which is based on two effective strategies to mining weighted subgraphs. Firstly, we apply a new strategy to calculate the weight of all mined subgraphs, which is based on the weights of the nodes in that subgraph. Secondly, we apply a search space pruning strategy based on the existing weights; if a frequent subgraph cannot satisfy the given weighting threshold, that subgraph will be pruned, which can reduce the processing time and storage space needed. With both directed and undirected graph datasets, our experimental results show that the runtime as well as the memory requirements of our algorithm are significantly better than those of GraMi.
A Proposal of Yoga Pose Assessment Method Using Pose Detection for Self-Learning

Nowadays, Yoga is popular around the world. A lot of people are participating in it by themselves through watching TV/videos or teaching each other. However, it is not easy for novice people to find the incorrect parts of their Yoga poses by themselves. In this paper, we propose a Yoga pose assessment method using pose detection to help the self-learning of Yoga. The system first detects a Yoga pose using multi parts detection only with PC camera. Then, it calculates the difference of the specified body angles between the pose of an instructor and that of a user. Then, it calculates the difference of the specified body angles between the pose of an instructor and that of a user, and suggests the correction if larger than the given threshold. The total angle difference values are calculated averagely and defined as performance class level in Table 1. For evaluations, we applied the proposal to three persons with three Yoga poses of basic and easy Yoga poses for beginners and confirmed that it found the incorrect parts of each pose.
Towards random-honest miners selection and multi-blocks creation: Proof-of-negotiation consensus mechanism in blockchain networks

As one of the fundamental characteristics of blockchain networks, consensus mechanism has attracted tremendous interests from both the academics and industries. Recently, more attention has been paid to the consensus mechanism with fair multi-miners participation. However, most of them are fixed-miners based and one-by-one block creation, and thus offering opportunities for attackers to launch DoS attack against the fixed miners or manipulate the blocks creation by bribing the fixed miners. Additionally, one-by-one block creation may result in vast transactions waiting to be confirmed, especially in a large scale of blockchain network. In this paper, we propose a novel consensus mechanism called proof-of-negotiation (PoN). With negotiation rules, trust management is introduced to evaluate the trustworthiness of miners, and thus achieving the random-honest miners selection for a round of block creation. To further improve the efficiency of PoN in creating blocks, parallel multi-blocks creation can be implemented synchronously or asynchronously by dividing the miner team as k groups. Meanwhile, a distributed blocks storage scheme is designed with a map function to alleviate the storage volume caused by massive blocks creation. Simulation results show that PoN is more effective than traditional consensus mechanism in one-by-one block creation and more efficient in creating blocks.
Detecting Trees in Street Images via Deep Learning With Attention Module

Although object detection techniques have been widely employed in various practical applications, automatic tree detection is still a difficult challenge, especially for street-view images. In this article, we propose a unified end-to-end trainable network for automatic street tree detection based on a state-of-the-art deep learning-based object detector. We tackle low illumination and heavy occlusion conditions in tree detection, which have not been extensively studied until now, due to clear challenges. Existing generic object detectors cannot be directly applied to this task due to aforementioned challenges. To address these issues, we first present a simple, yet effective image brightness adjustment method to handle low illuminance cases. Moreover, we propose a novel loss and a tree part-attention module to reduce false detections caused by heavy occlusion, inspired by the previously proposed occlusion-aware region-convolutional neural network (R-CNN) work. We train and evaluate several versions of the proposed network and validate the importance of each component. It is demonstrated that the resulting framework, part attention network for tree detection (PANTD), can efficiently detect trees in street-view images. The experimental results show that our approach achieves high accuracy and robustness under various conditions.
Chinese Micro-Blog Sentiment Analysis Based on Multiple Sentiment Dictionaries and Semantic Rule Sets

Sentiment analysis of Chinese micro-blog based on sentiment dictionary has become a challenging research subject in the field of artificial intelligence.However, due to insufficient sentiment words, Chinese micro-blog sentiment analysis is difficult to process high accuracy.Aimed at this issue, we propose a method for constructing multiple sentiment dictionaries, which mainly constructs original sentiment dictionary, emoji dictionary, and other related dictionaries.Among them, we have innovatively constructed a Chinese micro-blog new word sentiment dictionary.Multiple sentiment dictionaries increase the coverage of sentiment words.At the same time, we further analyze semantic rule sets between Chinese micro-blog texts and take the inter-sentence analysis rules and sentence pattern analysis rules into the sentiment analysis of Chinese micro-blog, which further improves the accuracy of Chinese micro-blog sentiment analysis.Finally, based on the method of multiple sentiment dictionaries and semantic rule sets, we propose an algorithm for Chinese micro-blog sentiment calculation from complex sentences to clauses and then from clauses to words, and finally combined with the emoji.This algorithm can accurately classify Chinese micro-blog into positive Chinese micro-blog, negative Chinese micro-blog, and neutral Chinese micro-blog.The experimental results show that this method has greatly improved the sentiment analysis of Chinese micro-blog.
Fuzzy c-means clustering using Jeffreys-divergence based similarity measure

In clustering, similarity measure has been one of the major factors for discovering the natural grouping of a given dataset by identifying hidden patterns. To determine a suitable similarity measure is an open problem in clustering analysis for several years. The purpose of this study is to make known a divergence based similarity measure. The notion of the proposed similarity measure is derived from Jeffrey-divergence. Various features of the proposed similarity measure are explained. Afterwards we develop fuzzy c-means (FCM) by making use of the proposed similarity measure, which guarantees to converge to local minima. The various characteristics of the modified FCM algorithm are also addressed. Some well known real-world and synthetic datasets are considered for the experiments. In addition to that two remote sensing image datasets are also adopted in this work to illustrate the effectiveness of the proposed FCM over some existing methods. All the obtained results demonstrate that FCM with divergence based proposed similarity measure outperforms three latest FCM algorithms.