Reviewerly/exHarmony huggingface dataset:
Time Optimal Trajectory Planing Based on Improved Sparrow Search Algorithm

Complete trajectory planning includes path planning, inverse solution solving and trajectory optimization. In this paper, a highly smooth and time-saving approach to trajectory planning is obtained by improving the kinematic and optimization algorithms for the time-optimal trajectory planning problem. By partitioning the joint space, the paper obtains an inverse solution calculation based on the partitioning of the joint space, saving 40% of the inverse kinematics solution time. This means that a large number of computational resources can be saved in trajectory planning. In addition, an improved sparrow search algorithm (SSA) is proposed to complete the solution of the time-optimal trajectory. A Tent chaotic mapping was used to optimize the way of generating initial populations. The algorithm was further improved by combining it with an adaptive step factor. The experiments demonstrated the performance of the improved SSA. The robot's trajectory is further optimized in time by an improved sparrow search algorithm. Experimental results show that the method can improve convergence speed and global search capability and ensure smooth trajectories.
SynthMorph: Learning Contrast-Invariant Registration Without Acquired Images

We introduce a strategy for learning image registration without acquired imaging data, producing powerful networks agnostic to contrast introduced by magnetic resonance imaging (MRI). While classical registration methods accurately estimate the spatial correspondence between images, they solve an optimization problem for every new image pair. Learning-based techniques are fast at test time but limited to registering images with contrasts and geometric content similar to those seen during training. We propose to remove this dependency on training data by leveraging a generative strategy for diverse synthetic label maps and images that exposes networks to a wide range of variability, forcing them to learn more invariant features. This approach results in powerful networks that accurately generalize to a broad array of MRI contrasts. We present extensive experiments with a focus on 3D neuroimaging, showing that this strategy enables robust and accurate registration of arbitrary MRI contrasts even if the target contrast is not seen by the networks during training. We demonstrate registration accuracy surpassing the state of the art both within and across contrasts, using a single model. Critically, training on arbitrary shapes synthesized from noise distributions results in competitive performance, removing the dependency on acquired data of any kind. Additionally, since anatomical label maps are often available for the anatomy of interest, we show that synthesizing images from these dramatically boosts performance, while still avoiding the need for real intensity images. Our code is available at doic https://w3id.org/synthmorph.
Joint Training for Pivot-Based Neural Machine Translation

While recent neural machine translation approaches have delivered state-of-the-art performance for resource-rich language pairs, they suffer from the data scarcity problem for resource-scarce language pairs. Although this problem can be alleviated by exploiting a pivot language to bridge the source and target languages, the source-to-pivot and pivot-to-target translation models are usually independently trained. In this work, we introduce a joint training algorithm for pivot-based neural machine translation. We propose three methods to connect the two models and enable them to interact with each other during training. Experiments on Europarl and WMT corpora show that joint training of source-to-pivot and pivot-to-target models leads to significant improvements over independent training across various languages.
Dealing with difficult minority labels in imbalanced mutilabel data sets

Multilabel classification is an emergent data mining task with a broad range of real world applications. Learning from imbalanced multilabel data is being deeply studied latterly, and several resampling methods have been proposed in the literature. The unequal label distribution in most multilabel datasets, with disparate imbalance levels, could be a handicap while learning new classifiers. In addition, this characteristic challenges many of the existent preprocessing algorithms. Furthermore, the concurrence between imbalanced labels can make harder the learning from certain labels. These are what we call \textit{difficult} labels. In this work, the problem of difficult labels is deeply analyzed, its influence in multilabel classifiers is studied, and a novel way to solve this problem is proposed. Specific metrics to assess this trait in multilabel datasets, called \textit{SCUMBLE} (\textit{Score of ConcUrrence among iMBalanced LabEls}) and \textit{SCUMBLELbl}, are presented along with REMEDIAL (\textit{REsampling MultilabEl datasets by Decoupling highly ImbAlanced Labels}), a new algorithm aimed to relax label concurrence. How to deal with this problem using the R mldr package is also outlined.
CryptCloud+: Secure and Expressive Data Access Control for Cloud Storage

Secure cloud storage, which is an emerging cloud service, is designed to protect the confidentiality of outsourced data but also to provide flexible data access for cloud users whose data is out of physical control. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is regarded as one of the most promising techniques that may be leveraged to secure the guarantee of the service. However, the use of CP-ABE may yield an inevitable security breach which is known as the misuse of access credential (i.e., decryption rights), due to the intrinsic “all-or-nothing” decryption feature of CP-ABE. In this paper, we investigate the two main cases of access credential misuse: one is on the semi-trusted authority side, and the other is on the side of cloud user. To mitigate the misuse, we propose the first accountable authority and revocable CP-ABE based cloud storage system with white-box traceability and auditing, referred to as CryptCloud±. We also present the security analysis and further demonstrate the utility of our system via experiments.
Privacy-Preserving Outsourced Calculation Toolkit in the Cloud

In this paper, we propose a privacy-preserving outsourced calculation toolkit, Pockit, designed to allow data owners to securely outsource their data to the cloud for storage. The outsourced encrypted data can be processed by the cloud server to achieve commonly-used plaintext arithmetic operations without involving additional servers. Specifically, we design both signed and unsigned integer circuits using a fully homomorphic encryption (FHE) scheme, construct a new packing technique (hereafter referred to as integer packing), and extend the secure circuits to its packed version. This achieves significant improvements in performance compared with the original secure signed/unsigned integer circuit. The secure integer circuits can be used to construct a new data mining application, which we refer to as secure k-nearest neighbours classifier, without compromising the privacy of original data. Finally, we prove that the proposed Pockit achieves the goal of secure computation without privacy leakage to unauthorized parties, and demonstrate the utility and efficiency of Pockit.
Patchwork-Based Audio Watermarking Robust Against De-Synchronization and Recapturing Attacks

Watermarking is a solution for copyright protection and forensics tracking, but recapturing and de-synchronization attacks may be used to effectively remove audio watermarks. Although much effort has been made in recent years, the robustness of audio watermarking against recapturing and de-synchronization attacks is still a challenging issue. Specifically, we first construct the frequency-domain coefficients logarithmic mean (FDLM) feature of digital audio. By theoretical analysis, we conclude that the residual of the two groups' FDLM feature is robust against recapturing attack. We then propose a robust audio watermarking method based on this feature using the patchwork framework. Compared with the method having the best robustness performance against recapturing attack, the BER value of our method is decreased by 7%. Besides that, the proposed method outperforms the state-of-the-art patchwork-based watermarking methods notably, under recapturing and post-processed with signal processing operations and de-synchronization attacks.
Block-Based Optical Color Image Encryption Based on Double Random Phase Encoding

This paper investigates and presents a block-based opto-color cipher using double random phase encoding (DRPE) with different block sizes.The color plainimage is divided into equal-sized blocks and then converted to an optical signal by an optical emitter.The obtained optical signal is encrypted by employing the DRPE technique, which applies two types of phase modulation, time, and Fourier domains.Finally, the optical color cipherimage is, upon detection, converted to digital format by a charge-coupled device digital camera.Experiments and security analysis show that the proposed block-based optical color image cipher using DRPE with increased block size is secure, effective, and including a good immunity to channel noise.
Learning Resource Allocation and Pricing for Cloud Profit Maximization

Cloud computing has been widely adopted to support various computation services. A fundamental problem faced by cloud providers is how to efficiently allocate resources upon user requests and price the resource usage, in order to maximize resource efficiency and hence provider profit. Existing studies establish detailed performance models of cloud resource usage, and propose offline or online algorithms to decide allocation and pricing. Differently, we adopt a blackbox approach, and leverage model-free Deep Reinforcement Learning (DRL) to capture dynamics of cloud users and better characterize inherent connections between an optimal allocation/pricing policy and the states of the dynamic cloud system. The goal is to learn a policy that maximizes net profit of the cloud provider through trial and error, which is better than decisions made on explicit performance models. We combine long short-term memory (LSTM) units with fully-connected neural networks in our DRL to deal with online user arrivals, and adjust the output and update methods of basic DRL algorithms to address both resource allocation and pricing. Evaluation based on real-world datasets shows that our DRL approach outperforms basic DRL algorithms and state-of-theart white-box online cloud resource allocation/pricing algorithms significantly, in terms of both profit and the number of accepted users.
A hierarchical attention model for rating prediction by leveraging user and product reviews

Recently, recommendations accompanied by review text have become increasingly common. Some studies have incorporated user and product information to generate review representations; however, the corresponding methods generally suffer from high model complexity and only consider textual review information at the word level rather than the semantic level. To address the above issues, we present a hierarchical attention model called HAUP based on user and product reviews. The model jointly learns user and product information from ratings and user review text in recommendation. First, a hierarchical bidirectional gated recurrent unit (Bi-GRU) is constructed that includes word and sentence-level review information. The Bi-GRU structure can handle long-range dependency in the review text. Then, attention mechanisms are applied at the word and sentence levels to identify the most important content when constructing the review document representation. Finally, the generated latent user and product representations are merged into the same vector space for estimating ratings. Experiments conducted based on two real-world data sets demonstrate that the proposed architecture outperforms existing state-of-the-art models. A visualization of the different attention layers demonstrates that the proposed model selects important words and sentences in review documents.
A Knowledge-Based Approach for Multiagent Collaboration in Smart Home: From Activity Recognition to Guidance Service

Smart home (SH) as an emerging paradigm for alleviating the overstretched healthcare resources, and enhancing the quality of life has received increasing attention. While the remarkable progress has been made for the development of SH, it still suffers from a number of issues (e.g., device heterogeneity, composite activities recognition, and providing appropriate services). To address these issues, this paper proposes a knowledge-based approach for multiagent (MA) collaboration. Specifically, a layered architecture of SH that combines ontology and MA technologies is designed to automatically acquire semantic knowledge, and support heterogeneity and interoperability services. In such architecture, a generic inference algorithm is presented based on unordered actions and temporal property of activity for inferring both continuous composite activity and personalized service in real time. Then a novel idea is introduced for agent to learn the knowledge of human activity (HA) autonomously and translate into itself knowledge, the purpose of which is to guide agent for performing services in a way that is compatible with HA. The proposed schemes have been implemented in an SH, and evaluated through extensive experiments. The results are provided to show the feasibility, effectiveness, and robustness of our proposal.
Vehicle Driving Behavior Recognition Based on Multi-View Convolutional Neural Network With Joint Data Augmentation

This paper proposes a method for vehicle driving behavior recognition based on a six-axis motion processor. This method uses deep-learning technology to learn the sample data collected by the on-board sensor. To solve the problem of small sample size and easy overfitting, we propose a joint data augmentation (JDA) scheme, and design a new multi-view convolutional neural network model (MV-CNN). The JDA includes the multi-axis weighted fusion algorithm, background noise fusion algorithm, and random cropping algorithm to construct a sample dataset that is more in line with a complex real driving environment. With the CNN model, the direction of information propagation improved, and a new MV-CNN model was developed for the training, learning, and recognition of driving behavior. The performance of MV-CNN is experimentally compared with CNN, recurrent neural networks (RNN), LSTM, CNN+LSTM, and three-dimensional CNN. The results show that MV-CNN can obtain the best recall, precision, and F1-score. At the same time, MV-CNN and JDA have better generalization ability, reduce the training variance and deviation, and increase the stability of the model training process.
Depth-aware saliency detection using convolutional neural networks

This paper proposes a new end-to-end depth-aware saliency model using three convolutional neural networks including color saliency network, depth saliency network and saliency fusion network, for saliency detection in RGBD images and stereoscopic images. Firstly, the color image is fed to the color saliency network to generate the color saliency map. Then, by sharing the weights of some layers in the color saliency network, the depth saliency network exploits the weight initialization and multi-layer pyramid feature fusion to learn effective depth features from the three-channel depth image, which is converted from the original depth map, and generates the depth saliency map. Finally, the saliency fusion network integrates the color saliency map with the depth saliency map into the final saliency map, which can highlight salient object regions and suppress background regions more effectively. Experimental results on five public datasets demonstrate that our model achieves the better performance compared with the state-of-the-art depth-aware saliency models.
Intelligent fall detection method based on accelerometer data from a wrist-worn smart watch

Accurate and reliable automatic fall detection based on wearable devices enables elderly people to receive instant treatment and can alleviate the severe consequences of falls. Falls are abnormal activities that occur rarely compared with normal daily activities; therefore, fall detection can be considered a one-class classification problem. However, it is difficult and dangerous to collect sufficient fall data in practice, making it difficult to use supervised learning methods to detect falls automatically. Among wearable devices, wrist-worn devices for fall detection are more likely to be accepted because of comfort; however, high accuracy cannot typically be realized due to sensitivity to interference from the diverse activities of the hand and wrist. Combining ensemble stacked autoencoders (ESAEs) with one-class classification based on the convex hull (OCCCH), this paper proposes a novel intelligent fall detection method, namely, ESAEs-OCCCH, which is based on accelerometer data from a wrist-worn smart watch. In the proposed method, ESAEs are first adopted for unsupervised feature extraction to overcome the disadvantages of artificial feature extraction, namely, the requirements in terms of experience and time. Then, OCCCH is used for pattern recognition. Finally, the majority voting strategy and weight adaptive adjustment strategy are combined to improve the performance and stability of fall detection. According to the behavioral characteristics of the elderly, the uncertainty of activities of daily living (ADLs) and fall activities (FAs), and the influence of the intense activities of the hand on the accelerometer signal, thirteen FAs and sixteen ADLs, including intense hand and wrist activities, are simulated by young volunteers of various genders, ages, heights and weights in two experiments. The experimental results demonstrate the performance and stability of the proposed method.
Human trajectory prediction in crowded scene using social-affinity Long Short-Term Memory

Object tracking in crowded spaces is a challenging but very important task in computer vision applications. However, due to interactions among large-scale pedestrians and common social rules, predicting the complex human mobility in a crowded scene becomes difficult. This paper proposes a novel human trajectory prediction model in a crowded scene called the social-affinity LSTM model. Our model can learn general human mobility patterns and predict individual’ s trajectories based on their past positions, in particular, with the influence of their neighbors in the Social Affinity Map (SAM). The SAM clusters the relative positions of surrounding individuals, and represents the distribution of the relative positions by different bins with semantic descriptions. We formulate the problem of trajectory prediction together with interactions among people as a sequence generation task with social affinity. The proposed model utilizes the LSTM to learn general human moving patterns as well as the Social Affinity Map to connect neighbors with a weight matrix corresponding to SAM bins for learning the social dependencies between correlated pedestrians. By capturing the object’ s past positions and connecting the hidden states of it’ s neighbors in different SAM bins with different elements of the weight matrix, the social-affinity LSTM is able to predict the trajectory of each pedestrian with its own features and neighbors’ influence. We compare the performance of our method with the Social LSTM model on several public datasets. Our model outperforms state-of-the-art methods on these datasets with the best results, especially the datasets with more social affinity phenomena.
A new secure transmission scheme between senders and receivers using HVCHC without any loss

This paper presents a novel secure medical image transmission scheme using hybrid visual cryptography and Hill cipher (HVCHC) between sender and receiver. The gray scale medical images have been considered as a secret image and split into different shares by visual cryptography (VC) encryption process. The split shares are once again encoded by Hill cipher (HC) encode process for improving the efficiency of the proposed method. In this process, the encrypted medical image (shares) pixels are converted as characters based on the character determination (CD) and lookup tables. In result, a secret image is converted into characters. These characters are sent to the receiver/authenticated person for the reconstruction process. In receiver side, the ciphertext has been decoded by HC decode process for reconstructing the shares. The reconstructed shares are decrypted by the VC decryption process for retaining the original secret medical image. The proposed algorithm has provided better CC, less execution time, higher confidentiality, integrity, and authentication (CIA). Therefore, using this proposed method, cent percent of the original secret medical image can be obtained and the secret image can be prevented from the interception of intruders/third parties.
DD-CycleGAN: Unpaired image dehazing via Double-Discriminator Cycle-Consistent Generative Adversarial Network

Despite the recent progress in image dehazing, the task remains tremendous challenging. To improve the performance of haze removal, we propose a scheme for haze removal based on Double-Discriminator Cycle-Consistent Generative Adversarial Network (DD-CycleGAN), which leverages CycleGAN to translate a hazy image to the corresponding haze-free image. Unlike other methods, it does not need pairs of haze and their corresponding haze-free images for training. Extensive experiments demonstrate that the proposed method achieves significant improvements over the existing methods, both quantitatively as well as qualitatively. And our method can also achieve good effects qualitatively when applied to the real scenes too.
Lightweight Searchable Public-Key Encryption with Forward Privacy over IIoT Outsourced Data

As more data from Industrial Internet of Things (IIoT) devices are been outsourced to the cloud, the need to ensure/achieve data privacy will be increasingly pressing. Searchable public-key encryption is a promising tool to achieve data privacy without sacrificing data usability. However, most existing searchable public-key encryption schemes are either inefficient (e.g., expensive in terms of encryption and searching keywords) or lack the required (security) features such as forward privacy. To address these limitations, we propose a lightweight searchable public-key encryption with forward privacy. Specifically, the scheme allows us to achieve forward privacy in a public-key cryptography (i.e., asymmetric) setting, and has the same search performance as a number of practical searchable symmetric encryption schemes. The formal security analysis shows that the proposed scheme is chosen-keyword attack resilient and achieves forward privacy. Finally, the experimental results on a real-world dataset demonstrate that the proposed scheme is highly efficient and scalable in IIoT applications.
Multilingual Unsupervised NMT using Shared Encoder and Language-Specific Decoders

In this paper, we propose a multilingual unsupervised NMT scheme which jointly trains multiple languages with a shared encoder and multiple decoders.Our approach is based on denoising autoencoding of each language and back-translating between English and multiple non-English languages.This results in a universal encoder which can encode any language participating in training into an interlingual representation, and language-specific decoders.Our experiments using only monolingual corpora show that multilingual unsupervised model performs better than the separately trained bilingual models achieving improvement of up to 1.48 BLEU points on WMT test sets.We also observe that even if we do not train the network for all possible translation directions, the network is still able to translate in a many-to-many fashion leveraging encoder's ability to generate interlingual representation.