Reviewerly/exHarmony huggingface dataset:
Burrows–Wheeler Transform Based Lossless Text Compression Using Keys and Huffman Coding

Text compression is one of the most significant research fields, and various algorithms for text compression have already been developed. This is a significant issue, as the use of internet bandwidth is considerably increasing. This article proposes a Burrows–Wheeler transform and pattern matching-based lossless text compression algorithm that uses Huffman coding in order to achieve an excellent compression ratio. In this article, we introduce an algorithm with two keys that are used in order to reduce more frequently repeated characters after the Burrows–Wheeler transform. We then find patterns of a certain length from the reduced text and apply Huffman encoding. We compare our proposed technique with state-of-the-art text compression algorithms. Finally, we conclude that the proposed technique demonstrates a gain in compression ratio when compared to other compression techniques. A small problem with our proposed method is that it does not work very well for symmetric communications like Brotli.
Bayesian-Assisted Inference from Visualized Data

A Bayesian view of data interpretation suggests that a visualization user should update their existing beliefs about a parameter's value in accordance with the amount of information about the parameter value captured by the new observations. Extending recent work applying Bayesian models to understand and evaluate belief updating from visualizations, we show how the predictions of Bayesian inference can be used to guide more rational belief updating. We design a Bayesian inference-assisted uncertainty analogy that numerically relates uncertainty in observed data to the user's subjective uncertainty, and a posterior visualization that prescribes how a user should update their beliefs given their prior beliefs and the observed data. In a pre-registered experiment on 4,800 people, we find that when a newly observed data sample is relatively small (N=158), both techniques reliably improve people's Bayesian updating on average compared to the current best practice of visualizing uncertainty in the observed data. For large data samples (N=5208), where people's updated beliefs tend to deviate more strongly from the prescriptions of a Bayesian model, we find evidence that the effectiveness of the two forms of Bayesian assistance may depend on people's proclivity toward trusting the source of the data. We discuss how our results provide insight into individual processes of belief updating and subjective uncertainty, and how understanding these aspects of interpretation paves the way for more sophisticated interactive visualizations for analysis and communication.
Improving Spoofed Website Detection Using Machine Learning

Fake webpages/websites are created by cyber attackers who either try to advertise their products, attempt to transmit malware to the target device, or steal victims’ login credentials. An illegitimate action of endeavoring to solicit sensitive and valuable information from user by masquerading as a truthful agent is called phishing. It involves the use of website and e-mail spoofing. A spoofed e-mail can be used to redirect a user to spoofed website which in turn can trick the user to reveal his valuable personal information. The traditional solutions for detecting spoofed or phishing websites are based on signature based methods. These methods are not able to detect the newly created spoofed websites or web pages. In order to solve this problem, researchers are coming up with machine learning methods. This paper brings out a diverse set of robust features categorized into the three categories, i.e., webpage, URL and HTML based features. The features under these categories are firstly used individually to classify webpages. Thereafter, a technique is proposed where the integration of all the features is used for classification purpose. The experimental results demonstrate that the features under URL based category are most effective in classifying the webpages. Further, there occurs a significant improvement in classification accuracy using proposed approach and random forest turns out to be the best classifier offering the accuracy of 99.5% with FPR and FNR as 0.006 and 0.005 respectively.
Multimarginal Wasserstein Barycenter for Stain Normalization and Augmentation

Variations in hematoxylin and eosin (H&E) stained images (due to clinical lab protocols, scanners, etc) directly impact the quality and accuracy of clinical diagnosis, and hence it is important to control for these variations for a reliable diagnosis. In this work, we present a new approach based on the multimarginal Wasserstein barycenter to normalize and augment H&E stained images given one or more references. Specifically, we provide a mathematically robust way of naturally incorporating additional images as intermediate references to drive stain normalization and augmentation simultaneously. The presented approach showed superior results quantitatively and qualitatively as compared to state-of-the-art methods for stain normalization. We further validated our stain normalization and augmentations in the nuclei segmentation task on a publicly available dataset, achieving state-of-the-art results against competing approaches.
Online Doctor Recommendation with Convolutional Neural Network and Sparse Inputs

The recommendation system in the online medical consultation website is a system to assist patients to find appropriate doctors. Based on the analysis of the current situation of the development of an online medical community (Haodf.com) in China, this paper puts forward recommendation suggestions of finding the right hospital and doctor to promote the rapid integration of Internet technology and traditional medical services. A new recommendation model called Probabilistic Matrix Factorization integrated with Convolutional Neural Network (PMF-CNN) is proposed in the paper. Doctors’ data in Haodf.com were used to evaluate the performance of our system. The model improves the performance of medical consultation recommendations by fusing review text and doctor information based on CNN (Convolutional Neural Network). Specifically, CNN is used to learn the feature representation of the review text and the doctors’ information. Furthermore, the extended matrix factorization model is exploited to fuse the review information feature and the initial value of the doctors’ information for recommendation. As is shown in the experimental results on Haodf.com datasets, the proposed PMF-CNN achieves better recommendation performances than the other state-of-the-art recommendation algorithms. And the recommendation system in an online medical website improves the utilization efficiency of doctors and the balance of public health resources allocation.
An Egocentric Action Anticipation Framework via Fusing Intuition and Analysis

In this paper, we focus on egocentric action anticipation from videos, which enables various applications, such as helping intelligent wearable assistants understand users' needs and enhance their capabilities in the interaction process. It requires intelligent systems to observe from the perspective of the first person and predict an action before it occurs. Owing to the uncertainty of future, it is insufficient to perform action anticipation relying on visual information especially when there exists salient visual difference between past and future. In order to alleviate this problem, which we call visual gap in this paper, we propose one novel Intuition-Analysis Integrated (IAI) framework inspired by psychological research, which mainly consists of three parts: Intuition-based Prediction Network (IPN), Analysis-based Prediction Network (APN) and Adaptive Fusion Network (AFN). To imitate the implicit intuitive thinking process, we model IPN as an encoder-decoder structure and introduce one procedural instruction learning strategy implemented by textual pre-training. On the other hand, we allow APN to process information under designed rules to imitate the explicit analytical thinking, which is divided into three steps: recognition, transitions and combination. Both the procedural instruction learning strategy in IPN and the transition step of APN are crucial to improving the anticipation performance via mitigating the visual gap problem. Considering the complementarity of intuition and analysis, AFN adopts attention fusion to adaptively integrate predictions from IPN and APN to produce the final anticipation results. We conduct experiments on the largest egocentric video dataset. Qualitative and quantitative evaluation results validate the effectiveness of our IAI framework, and demonstrate the advantage of bridging visual gap by utilizing multi-modal information, including both visual features of observed segments and sequential instructions of actions.
Generating Handwriting via Decoupled Style Descriptors

Representing a space of handwriting stroke styles includes the challenge of representing both the style of each character and the overall style of the human writer. Existing VRNN approaches to representing handwriting often do not distinguish between these different style components, which can reduce model capability. Instead, we introduce the Decoupled Style Descriptor (DSD) model for handwriting, which factors both character- and writer-level styles and allows our model to represent an overall greater space of styles. This approach also increases flexibility: given a few examples, we can generate handwriting in new writer styles, and also now generate handwriting of new characters across writer styles. In experiments, our generated results were preferred over a state of the art baseline method 88% of the time, and in a writer identification task on 20 held-out writers, our DSDs achieved 89.38% accuracy from a single sample word. Overall, DSDs allows us to improve both the quality and flexibility over existing handwriting stroke generation approaches.
Learning From Science News via Interactive and Animated Data Visualizations: An Investigation Combining Eye Tracking, Online Survey, and Cued Retrospective Reporting

Relying on a multimethod approach with eye tracking, cued retrospective reporting, and a memory test, this experimental study ( N = 45) shows how individuals engage with static, interactive, and interactive-animated data visualizations embedded in online science news. The results suggest that interactivity and animation engage participants most strongly: The second part of the news article is fixated the longest by participants exposed to the interactive-animated visualization, which translates into higher learning outcomes. However, the dynamic process of news reception requires a nuanced understanding of how users attend to visual and textual parts of a message to make informed statements about their effectiveness.
Generative adversarial network-based sinogram super-resolution for computed tomography imaging

Compared with the conventional 1×1 acquisition mode of projection in computed tomography (CT) image reconstruction, the 2×2 acquisition mode improves the collection efficiency of the projection and reduces the x-ray exposure time. However, the collected projection based on the 2×2 acquisition mode has low resolution (LR) and the reconstructed image quality is poor, thus limiting the use of this mode in CT imaging systems. In this study, a novel sinogram-super-resolution (SR) generative adversarial network model is proposed to obtain high-resolution (HR) sinograms from LR sinograms, thereby improving the reconstruction image quality under the 2×2 acquisition mode. The proposed generator is based on the residual network for LR sinogram feature extraction and SR sinogram generation. A relativistic discriminator is designed to render the network capable of obtaining more realistic SR sinograms. Moreover, we combine the cycle consistency loss, sinogram domain loss, and reconstruction image domain loss in the total loss function to supervise SR sinogram generation. Then, a trained model can be obtained by inputting the paired LR/HR sinograms into the network. Finally, the classic filtered-back-projection reconstruction algorithm is used for CT image reconstruction based on the generated SR sinogram. The qualitative and quantitative results of evaluations on digital and real data illustrate that the proposed model not only obtains clean SR sinograms from noisy LR sinograms but also outperforms its counterparts.
Development of augmented reality serious games with a vibrotactile feedback jacket

In the past few years, augmented reality (AR) has rapidly advanced and has been applied in different fields. One of the successful AR applications is the immersive and interactive serious games, which can be used for education and learning purposes. In this project, a prototype of an AR serious game is developed and demonstrated. Gamers utilize a head-mounted device and a vibrotactile feedback jacket to explore and interact with the AR serious game. Fourteen vibration actuators are embedded in the vibrotactile feedback jacket to generate immersive AR experience. These vibration actuators are triggered in accordance with the designed game scripts. Various vibration patterns and intensity levels are synthesized in different game scenes. This article presents the details of the entire software development of the AR serious game, including game scripts, game scenes with AR effects design, signal processing flow, behavior design, and communication configuration. Graphics computations are processed using the graphics processing unit in the system. The performance of the AR serious game prototype is evaluated and analyzed. The computation loads and resource utilization of normal game scenes and heavy computation scenes are compared. With 14 vibration actuators placed at different body positions, various vibration patterns and intensity levels can be generated by the vibrotactile feedback jacket, providing different real-world feedback. The prototype of this AR serious game can be valuable in building large-scale AR or virtual reality educational and entertainment games. Possible future improvements of the proposed prototype are also discussed in this article.
Immersive Virtual Reality for Clinical Pain: Considerations for Effective Therapy

Immersive virtual reality is transforming medical and psychological research and treatment, including the treatment of clinical pain. In this short perspective paper, we present some of the methodological difficulties that are rarely discussed in the literature of pain research when using virtual reality. These often-unmentioned problems can confound research investigations or interfere with the therapeutic efficacy in clinical trials. We propose practical solutions based on our research experience. We first outline the mechanisms of, and challenges to, the sensations of embodiment and presence, which are critical to creating effective virtual reality illusions, before discussing the particular considerations that need to be contemplated when working with patients with clinical pain. Finally, we discuss some upcoming technological advances that may influence significantly this rapidly expanding field in the near future.
Antecedents for enhanced level of cyber-security in organisations

Purpose The present study aims to identify and investigate the antecedents of enhanced level of cyber-security at the organisational level from both the technical and the human resource perspective using human–organisation–technology (HOT) theory. Design/methodology/approach The study has been conducted on 151 professionals who have expertise in dealing with cyber-security in organisations in sectors such as retail, education, healthcare, etc. in India. The analysis of the data is carried out using partial least squares based structural equation modelling technique (PLS-SEM). Findings The results from the study suggest that “legal consequences” and “technical measures” adopted for securing cyber-security in organisations are the most important antecedents for enhanced cyber-security levels in the organisations. The other significant antecedents for enhanced cyber-security in organisations include “role of senior management” and “proactive information security”. Research limitations/implications This empirical study has significant implications for organisations as they can take pre-emptive measures by focussing on important antecedents and work towards enhancing the level of cyber-security. Originality/value The originality of this research is combining both technical and human resource perspective in identifying the determinants of enhanced level of cyber-security in the organisations.
Attention-Based LSTM with Filter Mechanism for Entity Relation Classification

Relation classification is an important research area in the field of natural language processing (NLP), which aims to recognize the relationship between two tagged entities in a sentence. The noise caused by irrelevant words and the word distance between the tagged entities may affect the relation classification accuracy. In this paper, we present a novel model multi-head attention long short term memory (LSTM) network with filter mechanism (MALNet) to extract the text features and classify the relation of two entities in a sentence. In particular, we combine LSTM with attention mechanism to obtain the shallow local information and introduce a filter layer based on attention mechanism to strength the available information. Besides, we design a semantic rule for marking the key word between the target words and construct a key word layer to extract its semantic information. We evaluated the performance of our model on SemEval-2010 Task8 dataset and KBP-37 dataset. We achieved an F1-score of 86.3% on SemEval-2010 Task8 dataset and F1-score of 61.4% on KBP-37 dataset, which shows that our method is superior to the previous state-of-the-art methods.
Blockchain in Intelligent Transportation Systems

Blockchain is an emerging technology that has shaken the financial sector, and which is already perceived as having an impact. A blockchain is a network of many interconnected nodes, both trustworthy and malicious, which can reach a consensus and generate valid data. The resulting information is packed into a block and permanently saved on the network in a tamper-proof way. In this paper, we propose an adaptation of blockchain for securely storing data in a vehicular-based network. Our approach can work for storing data such as traffic events and user reputation. The proposed solution has two interconnected components: the Intelligent Transportation System (ITS) blockchain and the reputation system. The paper presents synthetic tests which validate the use cases of the solution: users reporting speeds and alerts behind which we see a fair reputation system penalising the (wrong/false) users.
Optimal probes for continuous-variable quantum illumination

Quantum illumination is the task of determining the presence of an object in a noisy environment. We determine the optimal continuous variable states for quantum illumination in the limit of zero object reflectivity. We prove that the optimal single mode state is a coherent state, while the optimal two mode state is the two-mode squeezed-vacuum state. We find that these probes are not optimal at non-zero reflectivity, but remain near optimal. This demonstrates the viability of the continuous variable platform for an experimentally accessible, near optimal quantum illumination implementation.
MRI image synthesis with dual discriminator adversarial learning and difficulty-aware attention mechanism for hippocampal subfields segmentation

Hippocampal subfields (HS) segmentation accuracy on high resolution (HR) MRI images is higher than that on low resolution (LR) MRI images. However, HR MRI data collection is more expensive and time-consuming. Thus, we intend to generate HR MRI images from the corresponding LR MRI images for HS segmentation.To generate high-quality HR MRI hippocampus region images, we use a dual discriminator adversarial learning model with difficulty-aware attention mechanism in hippocampus regions (da-GAN). A local discriminator is applied in da-GAN to evaluate the visual quality of hippocampus region voxels of the synthetic images. And the difficulty-aware attention mechanism based on the local discriminator can better model the generation of hard-to-synthesis voxels in hippocampus regions. Additionally, we design a SemiDenseNet model with 3D Dense CRF postprocessing and an Unet-based model to perform HS segmentation. The experiments are implemented on Kulaga-Yoskovitz dataset. Compared with conditional generative adversarial network (c-GAN), the PSNR of generated HR T2w images acquired by our da-GAN achieves 0.406 and 0.347 improvement in left and right hippocampus regions. When using two segmentation models to segment HS, the DSC values achieved on the generated HR T1w and T2w images are both improved than that on LR T1w images.Experimental results show that da-GAN model can generate higher-quality MRI images, especially in hippocampus regions, and the generated MRI images can improve HS segmentation accuracy.
Building a Smart and Sustainable Environment using Internet of Things

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Trajectory Prediction in Heterogeneous Environment via Attended Ecology Embedding

Trajectory prediction is a highly desirable feature for safe navigation or autonomous vehicle in complex traffic. In this paper, we consider the practical environment of predicting trajectory in the heterogeneous traffic ecology. The proposed method has various applications in trajectory prediction problems and also in applied fields beyond tracking. One challenge stands out of the trajectory prediction-heterogeneous environment. Particularly, many factors should be considered in the environments, i.e., multiple types of road-agents, social interactions and terrains. The information is complicated and large that may result in inaccurate trajectory prediction. We propose two social and visual enforced attention modules to circumvent the problem and a variant of an Info-GAN structure to predict the trajectory with multi-modal behaviors. Experimental results show that the proposed method significantly outperforms state-of-the-art methods in both heterogeneous and homogeneous real environments.