Reviewerly/exHarmony huggingface dataset:
An Eye-Tracking Study of Sketch Processing: Evidence From Russian

This study investigates the online process of reading and analyzing of sketchnotes (visual notes containing a handwritten text and drawings) on Russian language material. Using the eye-tracking method, we compared the processing of different types of sketchnotes (‘path’ (trajectory), linear, and radial) and the processing of a verbal text. Biographies of Russian writers were used as the material. In a preliminary experiment, we asked 89 college students to read the biographies and to evaluate each text or sketch using five scales (from -2 to +2). The best example for each of three formats of sketchnotes and a verbal text were chosen. In the main experiment, twenty-one secondary school students examined four different biographies in four different formats (three sketchnotes and a verbal text), answered to the factual and analytical questions to these texts and estimated the difficulty of each text. We measured the total dwell time, the total fixation count, the average fixation duration for each stimulus as well as for separate zones inside the sketches including verbal and non-verbal information. Our results show that readers process the information better and faster while reading sketchnotes than a verbal text. In the trajectory sketchnotes, the readers followed the order of elements aimed by the author of the sketchnotes better than in the radial and linear sketchnotes. The analysis of participants’ eye movements while processing the stimuli made it possible to propose several recommendations for creating effective sketchnotes.
Monte Carlo science

This paper explores how far the scientific discovery process can be automated. Using the identification of causally significant flow structures in two-dimensional turbulence as an example, it probes how far the usual procedure of planning experiments to test hypotheses can be substituted by ‘blind’ randomised experiments and notes that the increased efficiency of computers is beginning to make such a ‘Monte-Carlo’ approach practical in fluid mechanics. The process of data generation, classification and model creation is described in some detail, stressing the importance of validation and verification. Although the purpose of the paper is to explore the procedure, rather than to model two-dimensional turbulence, it is encouraging that the Monte Carlo process naturally leads to the consideration of vortex dipoles as building blocks of the flow, on par with the more conventional individual vortex cores. Although not completely novel, this ‘spontaneous’ discovery supports the claim that an important advantage of randomised experiments is to bypass researcher prejudice and alleviate paradigm lock. It is finally noted that the method can be extended to three-dimensional flows in practical times.
A diffeomorphic unsupervised method for deformable soft tissue image registration

Background and Objectives: The image registration methods for deformable soft tissues utilize nonlinear transformations to align a pair of images precisely. In some situations, when there is huge gray scale difference or large deformation between the images to be registered, the deformation field tends to fold at some local voxels, which will result in the breakdown of the one-to-one mapping between images and the reduction of invertibility of the deformation field. In order to address this issue, a novel registration approach based on unsupervised learning is presented for deformable soft tissue image registration. Methods: A novel unsupervised learning based registration approach, which consists of a registration network, a velocity field integration module and a grid sampling module, is presented for deformable soft tissue image registration. The main contributions are: (1) A novel encoder–decoder network is presented for the evaluation of stationary velocity field. (2) A Jacobian determinant based penalty term (Jacobian loss) is developed to reduce the folding voxels and to improve the invertibility of the deformation field. Results and Conclusions: The experimental results show that a new pair of images can be accurately registered using the trained registration model. In comparison with the conventional state-of-the-art method, SyN, the invertibility of the deformation field, accuracy and speed are all improved. Compared with the deep learning based method, VoxelMorph, the proposed method improves the invertibility of the deformation field.
Deep Learning Based Robust Text Classification Method via Virtual Adversarial Training

The existing methods of generating adversarial texts usually change the original meanings of texts significantly and even generate the unreadable texts. These less readable adversarial texts can misclassify the machine classifier successfully, but they cannot deceive the human observers very well. In this paper, we propose a novel method that generates readable adversarial texts with some perturbations that can also confuse human observers successfully. Based on the continuous bag-of-words (CBOW) model, the proposed method looks for the appropriate perturbations to generate the adversarial texts through controlling the perturbation direction vectors. Meanwhile, we apply adversarial training to regularize the classification model and extend it to semi-supervised tasks with virtual adversarial training. Experiments are conducted to show that the generated adversaries are interpretable and confused to humans and the virtual adversarial training effectively improves the robustness of the model.
Quantum Search on Encrypted Data Based on Quantum Homomorphic Encryption

Abstract We propose a homomorphic search protocol based on quantum homomorphic encryption, in which a client Alice with limited quantum ability can give her encrypted data to a powerful but untrusted quantum server and let the server search for her without decryption. By outsourcing the interactive key-update process to a trusted key center, Alice only needs to prepare and encrypt her original data and to decrypt the ciphered search result in linear time. Besides, we also present a compact and perfectly secure quantum homomorphic evaluation protocol for Clifford circuits, where the decryption key can be calculated by Alice with polynomial overhead with respect to the key length.
Mobile Robot Path Planning Based on a Generalized Wavefront Algorithm

This study develops a generalized wavefront algorithm for conducting mobile robot path planning. The algorithm combines multiple target point sets, multilevel grid costs, logarithmic expansion around obstacles, and subsequent path optimization. The planning performances obtained with the proposed algorithm, the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:msup><mml:mrow><mml:mi>A</mml:mi></mml:mrow><mml:mrow><mml:mi>∗</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:math> algorithm, and the rapidly exploring random tree (RRT) algorithm optimized using a Bézier curve are compared using simulations with different grid map environments comprising different numbers of obstacles with varying shapes. The results demonstrate that the generalized wavefront algorithm generates smooth and safe paths around obstacles that meet the required kinematic conditions associated with the actual maneuverability of mobile robots and significantly reduces the planned path length compared with the results obtained with the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:msup><mml:mrow><mml:mi>A</mml:mi></mml:mrow><mml:mrow><mml:mi>∗</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:math> algorithm and the optimized RRT algorithm with a computation time acceptable for real-time applications. Therefore, the generated path is not only smooth and effective but also conforms to actual robot maneuverability in practical applications.
Improving the Security of UML Sequence Diagram Using Genetic Algorithm

A sequence diagram is a modeling approach for visualizing the behavioral execution of a system.The objective of this research is to investigate the problem of security in a behavioral model (sequence diagram) through the application of model refactoring.We propose detection and correction techniques, empirical evaluation of the proposed techniques and assessment of security improvement in sequence diagrams.The detection of security bad smells is achieved through the adaptation of a genetic algorithm, while correction is accomplished by the model transformation approach.The results show significant detection recall and correction efficacy of the proposed detection and correction approaches, respectively.Our results show that the proposed approach is effective in detecting and correcting bad smells and can improve the security of UML Sequence Diagram.
Ensemble Learning in CNN Augmented with Fully Connected Subnetworks

Convolutional Neural Networks (CNNs) have shown remarkable performance in image recognition tasks. In this letter, we propose a new CNN model called the EnsNet which is composed of one base CNN and multiple Fully Connected SubNetworks (FCSNs). In this model, the set of feature maps generated by the last convolutional layer in the base CNN is divided along channels into disjoint subsets, and these subsets are assigned to the FCSNs. Each of the FCSNs is trained independent of others so that it can predict the class label of each feature map in the subset assigned to it. The output of the overall model is determined by majority vote of the base CNN and the FCSNs. Experimental results using the MNIST, Fashion-MNIST and CIFAR-10 datasets show that the proposed approach further improves the performance of CNNs. In particular, an EnsNet achieves a state-of-the-art error rate of 0.16% on MNIST.
Visual Structural Assessment and Anomaly Detection for High-Velocity Data Streams

The widespread use of Internet-of-Things (IoT) technologies, smartphones, and social media services generates huge amounts of data streaming at high velocity. Automatic interpretation of these rapidly arriving data streams is required for the timely detection of interesting events that usually emerge in the form of clusters. This article proposes a new relative of the visual assessment of the cluster tendency (VAT) model, which produces a record of structural evolution in the data stream by building a cluster heat map of the entire processing history in the stream. The existing VAT-based algorithms for streaming data, called inc-VAT/inc-iVAT and dec-VAT/dec-iVAT, are not suitable for high-velocity and high-volume streaming data because of high memory requirements and slower processing speed as the accumulated data increases. The scalable iVAT (siVAT) algorithm can handle big batch data, but for streaming data, it needs to be (re)applied everytime a new datapoint arrives, which is not feasible due to the associated computation complexities. To address this problem, we propose an incremental siVAT algorithm, called inc-siVAT, which deals with the streaming data in chunks. It first extracts a small size smart sample using an intelligent sampling scheme, called maximin random sampling (MMRS), then incrementally updates the smart sample points on the fly, using our novel incremental MMRS (inc-MMRS) algorithm, to reflect changes in the data stream after each chunk is processed, and finally, produces an incrementally built iVAT image of the updated smart sample, using the inc-VAT/inc-iVAT and dec-VAT/dec-iVAT algorithms. These images can be used to visualize the evolving cluster structure and for anomaly detection in streaming data. Our method is illustrated with one synthetic and four real datasets, two of which evolve significantly over time. Our numerical experiments demonstrate the algorithm's ability to successfully identify anomalies and visualize changing cluster structure in streaming data.
Deep quantization generative networks

Equipped with powerful convolutional neural networks (CNNs), generative models have achieved tremendous success in various vision applications. However, deep generative networks suffer from high computational and memory costs in both model training and deployment. While many efforts have been devoted to accelerate discriminative models by quantization, effectively reducing the costs for deep generative models is more challenging and remains unexplored. In this work, we investigate applying quantization technology to deep generative models. We find that keeping as much information as possible for quantized activations is key to obtain high-quality generative models. With this in mind, we propose Deep Quantization Generative Networks (DQGNs) to effectively accelerate and compress deep generative networks. By expanding the dimensions of the quantization basis space, DQGNs can achieve lower quantization error and are highly adaptive to complex data distributions. Various experiments on two powerful frameworks (i.e., variational auto-encoders, and generative adversarial networks) and two practical applications (i.e., style transfer, and super-resolution) demonstrate our findings and the effectiveness of our proposed approach.
Extracting Knowledge from Web Text with Monte Carlo Tree Search

To extract knowledge from general web text, it requires to build a domain-independent extractor that scales to the entire web corpus. This task is known as Open Information Extraction (OIE). This paper proposes to apply Monte-Carlo Tree Search (MCTS) to accomplish OIE. To achieve this goal, we define a Markov Decision Process for OIE and build a simulator to learn the reward signals, which provides a complete reinforcement learning framework for MCTS. Using this framework, MCTS explores candidate words (and symbols) under the guidance of a pre-trained Sequence-to-Sequence (Seq2Seq) predictor and generates abundant exploration samples during training. We apply the exploration samples to update the reward simulator and the predictor, based on which we implement another MCTS to search the optimal predictions during inference. Empirical evaluation demonstrates that the MCTS inference substantially improves the accuracy of prediction (more than 10%) and achieves a leading performance over other state-of-the-art comparison models.
Variance analysis of linear canonical Wigner distribution

Exact expression for the linear canonical Wigner distribution's variance of deterministic signals added with stationary white circular Gaussian noise is derived. It is shown that only one of the linear canonical transform free parameters, the parameter b, affects the value of variance.
InterBERT: An Effective Multi-Modal Pretraining Approach via Vision-and-Language Interaction

We propose a novel method for multi-modal pretraining, namely InterBERT (BERT for Interaction). The proposed architecture owns a strong capability of modeling interaction between the information flows of different modalities. The single-stream interaction module is capable of effectively processing information of multiple modalities, and the two-stream extraction module on top preserves the independence of each modality to avoid significant performance downgrade in single-modal tasks. The proposed pretraining task called masked group modeling (MGM) includes masked segment modeling and masked region modeling. It encourages the model to model a span or region instead of a single word or object, and it requires the model to learn from the general context. We pretrain the model with MGM and the conventional image-text matching, and finetune it on a series of vision-and-language downstream tasks, including caption-based image retrieval, zero-shot image retrieval, and visual commonsense reasoning. Experimental results demonstrate that InterBERT outperforms a series of strong baselines, including the most recent multi-modal pretraining methods. The analysis shows that the proposed MGM is effective for pretraining, and our method for multi-modal pretraining can adapt to single-modal tasks without significant performance decrease in comparison with the BERT-base model.
Rare Disease Prediction by Generating Quality-Assured Electronic Health Records

Predicting diseases for patients is an important and practical task in healthcare informatics. Existing disease prediction models focus on common diseases, i.e., there are enough available EHR data and prior medical knowledge for analyzing them. However, those models may not work for rare disease prediction as it is extremely hard to collect enough EHR data with such diseases. To tackle these issues, in this paper, we design a novel rare disease prediction system, which not only generates EHR data but also automatically selects high-quality generated data to further improve the predictive performance. Three components are designed in the system: data generation, data selection, and prediction. In particular, we propose MaskEHR to generate diverse EHR data based on the data from patients suffering from the given diseases. To remove noise information in the generated EHR data, we further design a reinforcement learning-based data selector, called RL-Selector, which can automatically choose the high-quality generated EHR data. Finally, the prediction component is used to identify patients who will potentially suffer the given diseases. These three components work together and enhance each other. Experiments on three real healthcare datasets show that the proposed system outperforms existing approaches on rare disease prediction task.
Lane Determination of Vehicles Based on a Novel Clustering Algorithm for Intelligent Traffic Monitoring

In intelligent traffic monitoring, speed measuring millimeter waves (MMW) radar is one of the most commonly used tools for traffic enforcement. In traffic enforcement field, the radar must provide the evidence of each vehicle belongs to which lane. In this paper, we propose a novel kernel line segment adaptive possibilistic c-means clustering algorithm (KLSAPCM) for lane determination of vehicles. Firstly, the raw measurement data is preprocessed using the extracting method of data adjacent lane centerlines. Secondly, according to the improved minimum radius data search method, outliers are removed and the proposed KLSAPCM algorithm is initialized. Finally, the accuracy of lane determination has been improved by the proposed KLSAPCM clustering algorithm based on adaptive kernel line segment that conforms to the shape features of the measurement data in the actual scene. The experiment results for multiple scenes were: the KLSAPCM algorithm is compared with the DBSCAN, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -means, the FCM, the PCM, the AMPCM, and the APCM algorithms on real measurement datasets, and the results highlight the classification rate of the proposed algorithm. Meanwhile, the proposed algorithm gets a good real-time performance and strong robustness for some sparse moving vehicle scene applications.
Continuous quantum error correction for evolution under time-dependent Hamiltonians

We develop a protocol for continuous operation of a quantum error correcting code for protection of coherent evolution due to an encoded Hamiltonian against environmental errors, using the three-qubit bit-flip code and bit-flip errors as a canonical example. To detect errors in real time, we filter the output signals from continuous measurement of the error syndrome operators and use a double thresholding protocol for error diagnosis, while correction of errors is done as in the conventional operation. We optimize our continuous operation protocol for evolution under quantum memory and under quantum annealing, by maximizing the fidelity between the target and actual logical states at a specified final time. In the case of quantum memory, we show that our continuous operation protocol yields a logical error rate that is slightly larger than the one obtained from using the optimal Wonham filter for error diagnosis while being simpler to implement. For quantum annealing, we show that our continuous quantum error correction protocol can significantly reduce the final logical state infidelity when the continuous measurements are sufficiently strong relative to the strength of the time-dependent Hamiltonian. We also show that this continuous quantum error correction protocol can reduce the time-to-solution relative to the value obtained from a classical parallelization scheme. These results suggest that a continuous implementation is suitable for quantum error correction in the presence of encoded time-dependent Hamiltonians, opening the possibility of many applications in quantum simulation and quantum annealing.
Efficient and Private Computations with Code-Based Masking

Code-based masking is a very general type of masking scheme that covers Boolean masking, inner product masking, direct sum masking, and so on. The merits of the generalization are twofold. Firstly, the higher algebraic complexity of the sharing function decreases the information leakage in “low noise conditions” and may increase the “statistical security order” of an implementation (with linear leakages). Secondly, the underlying error-correction codes can offer improved fault resistance for the encoded variables. Nevertheless, this higher algebraic complexity also implies additional challenges. On the one hand, a generic multiplication algorithm applicable to any linear code is still unknown. On the other hand, masking schemes with higher algebraic complexity usually come with implementation overheads, as for example witnessed by inner-product masking. In this paper, we contribute to these challenges in two directions. Firstly, we propose a generic algorithm that allows us (to the best of our knowledge for the first time) to compute on data shared with linear codes. Secondly, we introduce a new amortization technique that can significantly mitigate the implementation overheads of code-based masking, and illustrate this claim with a case study. Precisely, we show that, although performing every single code-based masked operation is relatively complex, processing multiple secrets in parallel leads to much better performances. This property enables code-based masked implementations of the AES to compete with the state-of-the-art in randomness complexity. Since our masked operations can be instantiated with various linear codes, we hope that these investigations open new avenues for the study of code-based masking schemes, by specializing the codes for improved performances, better side-channel security or improved fault tolerance.
Extended Rationale-Based Model for Tacit Knowledge Elicitation in Requirements Elicitation Context

The Requirements elicitation involves knowledge intensive and collaborative activities.Requirements engineering research has proposed a range of knowledge elicitation and requirements gathering techniques, few of which apply specific strategies for eliciting tacit knowledge from stakeholders.The difficulty of taking the deliberate advantage of important tacit knowledge of stakeholders is one of the eminent problems in requirements elicitation.This paper proposes a model to define and elicit the tacit knowledge that has been generated during the requirements elicitation process.The model is based on adopting and extending the rationale model for requirements rationale knowledge elicitation in the requirements elicitation process context.Moreover, this paper presents a representation code to express the tacit knowledge in this context.Finally, to evaluate the feasibility of the model, a survey instrument was applied on domain experts to gather their opinion regarding the ability of the proposed model in facilitating tacit knowledge elicitation.In addition, controlled experiment used to evaluate the proposed model.To explore the model, a post-questionnaire was used to identify participants' feedback.The findings of the evaluation methodologies that were used showed that the proposed model can facilitate tacit knowledge elicitation in the requirements elicitation context.Also, the proposed model has a significant effect on improving the requirements elicitation.