Reviewerly/exHarmony huggingface dataset:
Deep Network with Pixel-Level Rectification and Robust Training for Handwriting Recognition

Offline handwriting recognition is a well-known challenging task in the optical character recognition (OCR) field due to the difficulty caused by various unconstraint handwriting styles. In order to learn invariant feature representations for handwriting, we propose a novel method to incorporate pixel-level rectification into a CNN and RNN based model. We also propose an adjacent output mixup method for RNN layer's training to improve the generalization ability of the model, i.e., the previous output of an RNN layer is added to the current output with random weights. We additionally adopt a series of techniques including pre-training, data augmentation and language model, and further analyze their contributions to the improvement of the model performance. The proposed method performs well on three public benchmarks, including the IAM, Rimes and IFN/ENIT datasets.
EGuide

Mid-air arm movements are important for various activities. However, common resources for their self-directed practice require practitioners to divide their focus between an external source (e.g., a video screen) and moving. Past research found benefits for egocentric guidance visualizations compared to common resources. However, there is limited evidence about how such visualizations should look and behave. EGuide supports the investigation of different egocentric visualizations for the guidance of mid-air arm movements. We compared two visual appearances for egocentric guidance visualizations that differ in their shape (look), and three guidance techniques that differ by how they guide a user (behavior). For visualizations with a continuously moving guidance technique, our results suggest a higher movement accuracy for a realistic than an abstract shape. For user experience and preference, our results suggest that visualizations with an abstract shape and a guidance technique that visualizes important postures should not pause at important postures.
Towards Making Deep Transfer Learning Never Hurt

Transfer learning have been frequently used to improve deep neural network training through incorporating weights of pre-trained networks as the starting-point of optimization for regularization. While deep transfer learning can usually boost the performance with better accuracy and faster convergence, transferring weights from inappropriate networks hurts training procedure and may lead to even lower accuracy. In this paper, we consider deep transfer learning as minimizing a linear combination of empirical loss and regularizer based on pre-trained weights, where the regularizer would restrict the training procedure from lowering the empirical loss, with conflicted descent directions (e.g., derivatives). Following the view, we propose a novel strategy making regularization-based Deep Transfer learning Never Hurt (DTNH) that, for each iteration of training procedure, computes the derivatives of the two terms separately, then re-estimates a new descent direction that does not hurt the empirical loss minimization while preserving the regularization affects from the pre-trained weights. Extensive experiments have been done using common transfer learning regularizers, such as L2-SP and knowledge distillation, on top of a wide range of deep transfer learning benchmarks including Caltech, MIT indoor 67, CIFAR-10 and ImageNet. The empirical results show that the proposed descent direction estimation strategy DTNH can always improve the performance of deep transfer learning tasks based on all above regularizers, even when transferring pre-trained weights from inappropriate networks. All in all, DTNH strategy can improve state-of-the-art regularizers in all cases with 0.1%-7% higher accuracy in all experiments.
The Role of Information and Interaction Processes in the Adoption of Agriculture Inputs in Uganda

Agriculture is an essential component of food security, sustainable livelihoods, and economic development in sub-Saharan Africa (SSA). Smallholder farmers, however, are restricted in the number of crops they can grow due to small plot sizes. Agriculture inputs, such as fertilizers, herbicides or pesticides, and improved seed varieties, could prove to be useful resources to improve yield. Despite the potential of these agriculture technologies, input use throughout much of SSA remains low. This paper aims to better understand the process of innovation diffusion through information and interaction processes at the individual, social network, and community levels. A total of 203 participants were surveyed using a semi-structured interview method in four rural communities located in the Mbale, Lira, Kabale, and Masaka districts of Uganda. Participants were asked about their access to information technologies, information sources via social network ties, level of engagement in the local community, and agricultural input use. Results indicate households with higher levels of information access through cell phone use and weak-tie information sources are more likely to use inputs. Significant findings also include the interactional effect of cell phones and weak ties on fertilizer adoption. This research could inform policy makers of cost-effective methods of disseminating agriculture information and encouraging innovation diffusion.
TMDA: Task-Specific Multi-source Domain Adaptation via Clustering Embedded Adversarial Training

Beyond classical domain-specific adversarial training, a recently proposed task-specific framework has achieved a great success in single source domain adaptation by utilizing task-specific decision boundaries. However, compared to single-source-single-target setting, multi-source domain adaptation (MDA) shows more powerful capability to handle with most real-life cases. To align target domain with diverse multi-source domains using task-specific decision boundaries, we provide a deep insight of task-specific framework on MDA for the first time. Accordingly, we propose a novel task-specific multi-source domain adaptation method (TMDA) with a clustering embedded adversarial training process. Specifically, the proposed TMDA detects and refines less discriminative target representations through a max-min optimization over two adversarial task-specific classifiers. Moreover, our analysis implies that scattered multi-source representations disturb the adversarial training under the task-specific framework. To tight up the dispersed source representations, we embeds a relationship-based domain clustering into TMDA. Empirical results demonstrate that our TMDA outperforms state-of-the-art methods on toy dataset, sentiment analysis and digit classification.
Better Compression With Deep Pre-Editing

Could we compress images via standard codecs while avoiding visible artifacts? The answer is obvious -- this is doable as long as the bit budget is generous enough. What if the allocated bit-rate for compression is insufficient? Then unfortunately, artifacts are a fact of life. Many attempts were made over the years to fight this phenomenon, with various degrees of success. In this work we aim to break the unholy connection between bit-rate and image quality, and propose a way to circumvent compression artifacts by pre-editing the incoming image and modifying its content to fit the given bits. We design this editing operation as a learned convolutional neural network, and formulate an optimization problem for its training. Our loss takes into account a proximity between the original image and the edited one, a bit-budget penalty over the proposed image, and a no-reference image quality measure for forcing the outcome to be visually pleasing. The proposed approach is demonstrated on the popular JPEG compression, showing savings in bits and/or improvements in visual quality, obtained with intricate editing effects.
Blockchain-Based Secure and Searchable EHR Sharing Scheme

Electronic Health Record (EHR) not only contains great value, but also implies a large amount of personal privacy. Aiming at the security problems such as data forgery and tampering in EHR data sharing process, this paper proposes a blockchain-based EHR data searchable scheme to realize the calculation and application of EHR data in the decentralized network environment. This paper stores the EHR data ciphertext on the cloud, and stores the keyword index of the EHR data on the blockchain. The data visitor's search result on the blockchain is only the blind value of the data identifier, and is generated after blinding. The token for downloading ciphertext data in the cloud not only improves storage efficiency of the system, but also ensures the integrity, immutability and traceability of the EHR data index. Based on the attribute encryption mechanism, the fine-grained access control of cloud data is realized, and the attribute signature technology is used to verify the authenticity of the EHR data source. Finally, experiments show that the encryption and search time of this scheme is constant, regardless of the number of attributes.
Sampling informative patterns from large single networks

The set of all frequent patterns that are extracted from a single network can be huge. A technique recently proposed for obtaining a compact, informative and useful set of patterns is output sampling, where a small set of frequent patterns is randomly chosen. However, existing output sampling algorithms work only in the transactional setting, where the database consists of a collection of relatively small graphs. In this paper, first we extend the output sampling framework to the single network setting where the database is a large single graph, counting supports of patterns is more complicated, and frequent patterns might be sampled based on any arbitrary target distribution. Then, we propose sampling techniques that are based on more interesting/informative measures or those that are specific to large single networks, such as product of the pattern size with its support, network compressibility, and pattern density. Finally, we study the empirical behavior of our algorithm in a real-world case study.
Selective Style Transfer for Text

This paper explores the possibilities of image style transfer applied to text maintaining the original transcriptions. Results on different text domains (scene text, machine printed text and handwritten text) and cross-modal results demonstrate that this is feasible, and open different research lines. Furthermore, two architectures for selective style transfer, which means transferring style to only desired image pixels, are proposed. Finally, scene text selective style transfer is evaluated as a data augmentation technique to expand scene text detection datasets, resulting in a boost of text detectors performance. Our implementation of the described models is publicly available.
ICDAR2019 Competition on Recognition of Early Indian Printed Documents – REID2019

This paper presents an objective comparative evaluation of page analysis and recognition methods for historical documents with text mainly in Bengali language and script. It describes the competition rules, dataset, and evaluation methodology. Results are presented for five methods - three submit-ted, one re-run, and one open source state-of-the-art system. The focus is on optical character recognition (OCR) performance. Different evaluation metrics were used to gain an in-sight into the algorithms, including new character accuracy metrics to better reflect the difficult circumstances presented by the documents. The results indicate that deep learning approaches are promising, but there are still significant challenges for historic material of this nature.
Symbolic Time Series Analysis for Anomaly Detection in Measure-Invariant Ergodic Systems

Abstract This paper presents a novel framework of symbolic time series analysis (STSA) for anomaly detection in dynamical systems. The core concept is built upon a property of measure-preserving transformation (MPT) sequence, acting on a probability space with ergodic measure, that the eigenfunctions of these transformations would be time-invariant. As a result, unlike a standard STSA that is required to generate time-homogeneous Markov chains, the proposed MPT-based STSA is allowed to have time-inhomogeneous Markov chains, where the (possibly time-varying) state transition probability matrices have time-invariant eigenvectors. Such a time-invariance facilitates analysis of the dynamical system by using short-length time series of measurements. This is particularly important in applications, where the underlying dynamics and process anomalies need fast monitoring and control actions in order to mitigate any potential structural damage and/or to avoid catastrophic failures. The MPT-based STSA has been applied for low-delay detection of fatigue damage, which is a common source of failures in mechanical structures and which is known to have uncertain dynamical characteristics. The underlying algorithm has been validated with experimental data generated from a laboratory apparatus that uses ultrasonic sensors to detect fatigue damage in polycrystalline–alloy specimens. The performance of the proposed MPT-based STSA is evaluated by comparison with those of a standard STSA and a hidden Markov model (HMM) on the same experimental data. The results consistently show superior performance of the MPT-based STSA.
Adaptive Adversarial Videos on Roadside Billboards: Dynamically Modifying Trajectories of Autonomous Vehicles

Deep neural networks (DNNs) are being incorporated into various autonomous systems like self-driving cars and robots. However, there is a rising concern about the robustness of these systems because of their susceptibility to adversarial attacks on DNNs. Past research has established that DNNs used for classification and object detection are prone to attacks causing targeted misclassification. In this paper, we show the effectiveness of an adversarial dynamic attack on an end-to-end trained DNN controlling an autonomous vehicle. We launch the attack by installing a billboard on the roadside and displaying videos to approaching vehicles to cause the DNN controller in the vehicle to generate steering commands that cause, for example, unintended lane changes or motion off the road causing accidents. The billboard has an integrated camera estimating the pose of the on-coming vehicle. The approach enables dynamic adversarial perturbation that adapts to the relative pose of the vehicle and uses the dynamics of the vehicle to steer it along adversary-chosen trajectories while being robust to variations in view, lighting, and weather. We demonstrate the effectiveness of the attack on a recently published off-the-shelf end-to-end learning-based autonomous navigation system in a high-fidelity simulator, CARLA (CAR Learning to Act). The proposed approach may also be applied to other systems driven by an end-to-end trained network.
E-DBPN: Enhanced Deep Back-Projection Networks for Remote Sensing Scene Image Superresolution

Generative adversarial networks (GANs) have been widely used to single image superresolution (SR) (SISR), and these GAN-based methods have achieved significant performance on natural images due to their abilities to generate realistic textures. However, previous GAN-based methods are of poor performance when applied to remote sensing scene image SR. In order to further enhance the visual quality, in this article, we present a GAN-based SISR method by proposing a novel generator, which is capable of generating perceptually pleasing remote sensing scene images. First, we design the enhanced deep back-projection network (E-DBPN) generators based on the architecture of the original DBPN and mainly make two modifications. The first one is to add the proposed enhanced residual channel attention module (ERCAM) into the original DBPN, which can keep good properties of the original input features but also has the ability to emphasize more important features and suppressing less useful features. The other is to replace the concatenation operation with the proposed sequential feature fusion module (SFFM) for dealing with the feature maps generated by different up-projection units discriminatorily. As for the training process, the E-DBPN generator is first trained using the mean squared error (MSE) loss. Next, in order to improve the perceptual quality of the recovered images, we employ the content loss and the adversarial loss to train our initialized generator network. Experiments show that our method achieves state-of-the-art performance compared to other SISR methods.
Classification Performance-Based Feature Selection Algorithm for Machine Learning: P-Score

Feature selection algorithms are the cornerstone of machine learning. By increasing the properties of the samples and samples, the feature selection algorithm selects the significant features. The general name of the methods that perform this function is the feature selection algorithm. The general purpose of feature selection algorithms is to select the most relevant properties of data classes and to increase the classification performance. Thus, we can select features based on their classification performance. In this study, we have developed a feature selection algorithm based on decision support vectors classification performance. The method can work according to two different selection criteria. We tested the classification performances of the features selected with P-Score with three different classifiers. Besides, we assessed P-Score performance with 13 feature selection algorithms in the literature. According to the results of the study, the P-Score feature selection algorithm has been determined as a method which can be used in the field of machine learning.
Context-Dependent Search for Generating Paths for Redundant Manipulators in Cluttered Environments

We present a context-dependent bi-directional tree-search framework for point-to-point path planning for manipulators. Conceptually, our framework is composed of six modules: tree selection, focus selection, node selection, target selection, extend selection and connection type selection. Each module consists of a set of interchangeable strategies. By exploiting synergistic interaction between these strategies and selecting appropriate strategies based the contextual cues from the search state, we show an instance of our framework that computes high-quality solutions in a variety of complex scenarios with a low failure rate. We also show that some popular path planning methods in the literature can be easily represented in our framework. We compare our approach with these popular methods in a diverse set of test scenarios. We report a 15-fold reduction in failure rate coupled with at least a 26% drop in solution suboptimality when compared to the best of the alternative methods.
Solar PV Power Prediction Using A New Approach Based on Hybrid Deep Neural Network

This paper contributes to the field of deep learning application to power system forecasting problems and presents a novel approach to forecast solar photovoltaic (PV) power output using a Hybrid Deep Neural Network (HDNN) model. The proposed HDNN model, which is the combination of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network, considers the historical PV power and other weather parameters as inputs. Several PV power forecasting models are available in the literature with mixed forecasting accuracies. However, the major advantage of the proposed HDNN model is that it extracts the salient features from local raw data to predict the solar PV power output for next several hours and days. The prediction capability of the proposed HDNN model is validated by comparing its performance with other soft computing models. Simulation results demonstrate the suitability of the proposed model to produce a higher degree of short-term PV power forecast accuracy for multiple seasons of the year.
Hybrid Force/Motion Control and Implementation of an Aerial Manipulator towards Sustained Contact Operations

Contact-based operation in moving process is a challenging problem for aerial manipulators. It requires the whole system to maintain steady contact with external environment, to track some predefined trajectories on surfaces, and simultaneously to present some fixed contact force. Aiming at this problem, a hybrid force/motion control framework is proposed in this paper. In this framework, contact force control and position control are performed separately in two orthogonal subspaces: constrained space and free-flight space. To control the contact force, the closed-loop unmanned aerial vehicle is first theoretically shown to behave dynamically as a spring-mass-damper system. Further, an inverse-dynamics-based controller is proposed. To control the moving along the contact surface, trajectory planning and position controller are combined to achieve the steady behavior in a free-flight subspace. In the end, an aerial manipulator system with a roller-type end-effector was designed, and practical flight experiments was performed. The results indicate that the proposed framework is effective and validity.
Scalable Open-Vote Network on Ethereum

McCorry et al. (Financial Cryptography 2017) presented the first implementation of a decentralized self-tallying voting protocol on Ethereum. However, their implementation did not scale beyond 40 voters since all the computations were performed on the smart contract. In this paper, we tackle this problem by delegating the bulk computations to an off-chain untrusted administrator in a verifiable manner. Specifically, the administrator tallies the votes off-chain and publishes a Merkle tree that encodes the tallying computation trace. Then, the administrator submits the Merkle tree root and the tally result to the smart contract. Subsequently, the smart contract transits to an intermediate phase where at least a single honest voter can contend the administrator’s claimed result if it was not computed correctly. Then, in the worst case, the smart contract verifies the dispute at the cost of an elliptic curve point addition and scalar multiplication, and two Merkle proofs of membership which are logarithmic in the number of voters. This allows our protocol to achieve higher scalability without sacrificing the public verifiability or voters’ privacy. To assess our protocol, we implemented an open-source prototype on Ethereum and carried out multiple experiments for different numbers of voters. The results of our implementation confirm the scalability and efficiency of our proposed solution which does not exceed the current block gas limit for any practical number of voters.
STNN: A Novel TLS/SSL Encrypted Traffic Classification System Based on Stereo Transform Neural Network

Nowadays, encrypted traffic classification has become a challenge for network monitoring and cyberspace security. However, the existing methods cannot meet the requirements of encrypted traffic classification because of the encryption protocol in communication. Therefore, we design a novel neural network named Stereo Transform Neural Network (STNN) to classify encrypted network traffic. In STNN, we combine Long Short Term Memory (LSTM) and Convolution Neural Network (CNN) based on statistical features. STNN gains average precision about 95%, average recall about 95%, average F1-measure about 95% and average accuracy about 99.5% in multi-classification. Besides, the experiment shows that STNN obviously accelerates the convergence rate and improves the classification accuracy.