Reviewerly/exHarmony huggingface dataset:
A Novel Bayesian Functional Spatial Partitioning Method with Application to Prostate Cancer Lesion Detection Using MRI

Spatial partitioning methods correct for nonstationarity in spatially related data by partitioning the space into regions of local stationarity. Existing spatial partitioning methods can only estimate linear partitioning boundaries. This is inadequate for detecting an arbitrarily shaped anomalous spatial region within a larger area. We propose a novel Bayesian functional spatial partitioning (BFSP) algorithm, which estimates closed curves that act as partitioning boundaries around anomalous regions of data with a distinct distribution or spatial process. Our method utilizes transitions between a fixed Cartesian and moving polar coordinate system to model the smooth boundary curves using functional estimation tools. Using adaptive Metropolis-Hastings, the BFSP algorithm simultaneously estimates the partitioning boundary and the parameters of the spatial distributions within each region. Through simulation we show that our method is robust to shape of the target zone and region-specific spatial processes. We illustrate our method through the detection of prostate cancer lesions using magnetic resonance imaging.
Image quality enhancement using hybrid attention networks

Image quality enhancement aims to recover rich details from degraded images, which is applied into many fields, such as medical imaging, filming production and autonomous driving. Deep convolutional neural networks (CNNs) have enabled rapid development of image quality enhancement. However, most existing CNN-based methods lack versatility when targeting different subtasks in terms of the design of networks. Besides, they often fail to balance precise spatial representations and necessary contextual information. To deal with these problems, this paper proposes a novel unified framework for low-light image enhancement, image denoising and image super-resolution. The core of this architecture is a residual hybrid attention block (RHAB), which consists of several dynamic down-sampling modules (DDM) and hybrid attention up-sampling modules (HAUM). Specifically, multi-scale feature maps are fully interacted with each other with the help of nested subnetworks so that both high-resolution spatial details and high-level contextual information can be combined to improve the representation ability of the network. Further, a hybrid attention network (HAN) is proposed and evaluations on three separate subtasks demonstrate its good performance. Extensive experiments on the authors' synthetic dataset, a more complex version, show that the authors' method achieve better quantitative and visual results compared to other state-of-the-art methods.
Facial Emotions over Static Facial Images Using Deep Learning Techniques with Hysterical Interpretation

Abstract Recognition of facial expression has many potential applications that have attracted the researcher’s attention during the last decade. Taking out of features, is an important step in the analysis of expression that contributes to a quick and accurate recognition of expression, i.e., happiness, surprise and disgust, sadness, anger, and fear are expressions of the faces. Facial expressions are most frequently used to interpret human emotions. Two categories contain a range of different emotions: positive emotions and non-positive emotions. Face Detection, Extraction, Classification, and Recognition are major steps used in the proposed system. The proposed segmentation techniques are applied and compared to determine which method is appropriate for splitting the mouth region, and then the mouth region can be extracted using techniques for stretching contrasts and segmenting the image. After the extraction of the mouth area, the facial emotions are graded in the face picture region of the extracted mouth based on white pixel values. The Supervisory Learning Approach is widely used for face identification algorithms and it takes more computation time and effort. It may also give incorrect class labels in the classification process. For this reason, supervised learning and reinforcement learning is being used. In general, it will be like a trial-and-error method that is, in the training process it tries to learn and produce expected results. It was specified accordingly. Reinforcement learning always tries to enhance the results.
Iterative Translation-Based Data Augmentation Method for Text Classification Tasks

A well-known limitation of existing rule-based text augmentation is that it cannot be applied to other languages because it depends on grammatical and structural characteristics. Moreover, most text Generative Adversarial Networks (GAN) are unstable in training due to inefficient generator optimization and rely on maximum likelihood pre-training. This paper addresses the above problems by proposing a novel augmentation method with a Sentence Generator (SG) and Sentence Discriminator (SD) for Iterative Translation-based Data Augmentation (ITDA). This paper makes three original contributions. First, the ITDA SG is designed to provide universal multiple-language support by generating comprehensive augmented sentences through serial and parallel iterations of an existing translator, such as Google Translate. Second, given that the quality of the generated sentences varies depending on the translation combination or the type of sentence, the ITDA addresses this issue using a discriminator to achieve sentence augmentation, which can select high-quality augmented data using a text classifier. Third, the ITDA can perform sentence augmentation for 109 different languages using discriminators based on text classifiers trained for a specific language or type of data set. Extensive experiments are conducted to evaluate the efficacy of the ITDA using a Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), CNN-BiLSTM, and self-attention. The results demonstrate that when the ITDA is applied to 480 sentence classification tasks, the average accuracy increases by 4.24%.
Impacts of Blockchains on International Maritime Trade

The impact of blockchains on maritime trade has been studied from economic and managerial angles. But their combined study from the legal, economic, and managerial angles has rarely been studied. If the economic stakes are beginning to be identified, the way in which the law defines blockchains is more delicate to understand; the international dimension of maritime transport associated with the various national regulations complicates the analysis. After studying the economic benefits of blockchains on maritime trade we analyzed the legal benefits and the outstanding legal issues. These analyses led to a study of the relationship between the maritime supply chain and blockchains. This study shows that the adoption of this innovative technology by maritime trade actors will have many economic, logistical, and legal consequences that cannot be dissociated and are nevertheless systematically dissociated; but that it is difficult to clearly identify or confirm due to the small number of case studies. JEL Codes: O33, R40
Visualization of Data Changes in 2.5D Treemaps usingProcedural Textures and Animated Transitions

This work investigates the extent to which animated procedural texture patterns can be used to support the representation of changes in 2.5D treemaps. Changes in height, color, and area of individual nodes can easily be visualized using animated transitions. Especially for changes in the color attribute, plain animated transitions are not able to directly communicate the direction of change itself. We show how procedural texture patterns can be superimposed to the color mapping and support transitions. To this end, we discuss qualitative properties of each pattern, demonstrate their ability to communicate change direction both with and without animation, and conclude which of the patterns are more likely to increase effectiveness and correctness of the change mapping in 2.5D treemaps.
Malaria Parasite Diagnosis Using Computational Techniques: A Comprehensive Review

Abstract Malaria is a very serious disease that caused by the transmitted of parasites through the bites of infected Anopheles mosquito. Malaria death cases can be reduced and prevented through early diagnosis and prompt treatment. A fast and easy-to-use method, with high performance is required to differentiate malaria from non-malarial fevers. Manual examination of blood smears is currently the gold standard, but it is time-consuming, labour-intensive, requires skilled microscopists and the sensitivity of the method depends heavily on the skills of the microscopist. Currently, microscopy-based diagnosis remains the most widely used approach for malaria diagnosis. The development of automated malaria detection techniques is still a field of interest. Automated detection is faster and high accuracy compared to the traditional technique using microscopy. This paper presents an exhaustive review of these studies and suggests a direction for future developments of the malaria detection techniques. This paper analysis of three popular computational approaches which is k-mean clustering, neural network, and morphological approach was presented. Based on overall performance, many research proposed based on the morphological approach in order to detect malaria.
Blind Quality Evaluator of Tone-Mapped HDR and Multi-Exposure Fused Images for Electronic Display

The problem of reproducing high dynamic range (HDR) images on electronic display and photography with restricted dynamic range has gained a lot of interest in the consumer electronics community. There exist various approaches to this issue, e.g., tone mapping operators (TMOs) and multi-exposure fusion algorithms (MEFs). Many existing image quality assessment (IQA) methods have been proposed to compare images of quality degradation generated by TMOs/MEFs. Although promising performances have been achieved, they seldom consider local specific artifacts difference (i.e., abnormal exposure and color cast) related with the TMOs/MEFs. To address this limitation, this paper proposes a Blind Quality Evaluator of Tone-Mapped HDR and Multi-Exposure Fused Images (BQE-TM/MEFI). First, two purpose-designed segment models are utilized to distinguish well-exposedness dense patches (WEDPes) and non-WEDPes, color cast patches (CCPes) and non-CCPes respectively. Second, multiple quality-perception features are extracted to measure local artifacts: 1) structure and sharpness features from WEDPes, 2) saturation features from non-CCPes, and 3) edge structure features. Then, three new low-complexity regional features (over-exposure ratio, entropy and color confidence index) are calculated based on over-exposure segmentation model. Finally, all extracted features are aggregated into a machine-learning regression model to pool a quality score. The simplicity and good performance of the proposed method makes it suitable for electronic displays and other consumer electronics.
Design of Intelligent Recognition English Translation Model Based on Feature Extraction Algorithm

In recent years, with the deepening of globalization, international cooperation is becoming more and more extensive, and the importance of English is increasing. Aiming at the problems that the semantic context of English is not obvious in the process of English translation in traditional English translation system, the optimal translation solution is not reached in the process of selecting the optimal feature semantics, and the translation accuracy is low, an intelligent recognition English translation model based on feature extraction algorithm is designed. Search module is used to complete the search of basic meaning and subject content of vocabulary to be proofread, grasp the user's behavior data through behavior log and optimize the system; In the method based on the maximum entropy principle, the whole task of clause recognition is divided into three parts: sentence head recognition, sentence tail recognition and complete clause recognition. Experimental results show that the proposed algorithm has higher recognition rate.
Multi-level Stacking of Long Short Term Memory Recurrent Models for Time Series Forecasting of Solar Radiation

The ability of predicting solar radiation strength is particularly important for the best administration of power grids involving photovoltaic solar energy. The production-consumption balance of energy is one of the most important characteristics that should be maintained in energy grids. A qualified solar energy short-term can help in mitigating such type of problems, and therefore helps in improving the reliability of the overall energy system. The contribution of the present research consists of proposing and validating multi-stacking model that consists of a committee of recurrent long short-term memory (LSTM) models to predict the strength of solar radiation one day ahead. The individual models of the committee have been generated by modifying the internal structure and parameters of a base recurrent LSTM model. A multi-level structure that consists of two layers of stacking meta-models have been examined and validated through a cross validation strategy. The proposed models have been trained on time series dataset of solar radiation records, and then validated and tested with on subset of data records over one year using correlation and statistical metrics.
Impostor-based Rendering Acceleration for Virtual, Augmented, and Mixed Reality

This paper presents an image-based rendering approach to accelerate rendering time of virtual scenes containing a large number of complex high poly count objects. Our approach replaces complex objects by impostors, light-weight image-based representations leveraging geometry and shading related processing costs. In contrast to their classical implementation, our impostors are specifically designed to work in Virtual-, Augmented- and Mixed Reality scenarios (XR for short), as they support stereoscopic rendering to provide correct depth perception. Motion parallax of typical head movements is compensated by using a ray marched parallax correction step. Our approach provides a dynamic run-time recreation of impostors as necessary for larger changes in view position. The dynamic run-time recreation is decoupled from the actual rendering process. Hence, its associated processing cost is therefore distributed over multiple frames. This avoids any unwanted frame drops or latency spikes even for impostors of objects with complex geometry and many polygons. In addition to the significant performance benefit, our impostors compare favorably against the original mesh representation, as geometric and textural temporal aliasing artifacts are heavily suppressed.
Double Fractional-order Masks Image Enhancement

Image enhancement is better achieved when fractional-order masks are used rather than integer-order ones, this is due to the flexibility of fractional-order parameters control. This paper proposes a combination of fractional-order masks to be used in parallel as double filters system structure to improve image enhancement rather than using a single-stage filter. Various performance metrics are used in this work to evaluate the proposed system, such as Information Entropy (IE), Average Gradient (AG), Structural Similarity Index Metric (SSIM) and Peak Signal to Noise Ratio (PSNR). Based on visual as well as numerical results, it is found that the combination of two double masks is superior to the single fractional-order system in terms of enhancing texture and edges.
Using Word2Vec-LDA-Cosine Similarity for Discovering News Dissemination Pattern to Support Government–Citizen Engagement

Evidence of ineffective government–citizen engagement was observed when the Malaysian government decided to make face masks mandatory in public spaces. It is especially critical during a COVID-19 pandemic, where public compliance depends on the speed and clarity at which regulations are announced. Hundreds of arrested cases due to violation were met with confusion and demanded greater clarification. This evidence signifies the need to identify if government-disseminated information is communicated effectively to the citizens through news coverage. Despite this need, current literature has limitations in effectively analysing huge numbers of articles as they mainly employ manual intervention for data news content analysis. Furthermore, there has been no usage of systematic text analytics approaches in government–citizen engagement studies through newspapers. As such, we researched and implemented a modelling framework for discovering how news coverage pattern aligns with government-disseminated information through a case study of COVID-19 in Malaysia during the pandemic. A Word2Vec-LDA-cosine similarity technique was employed in our framework to determine topic similarities as the indication of alignment between news content and government-disseminated information. Our results show that this framework succeeds in capturing the semantics of the corpus to describe news coverage at the same time identified the challenges in general topic comparison tasks.
Segmentation Driven Peeling for Visual Analysis of Electronic Transitions

Electronic transitions in molecules due to absorption or emission of light is a complex quantum mechanical process. Their study plays an important role in the design of novel materials. A common yet challenging task in the study is to determine the nature of those electronic transitions, i.e. which subgroups of the molecule are involved in the transition by donating or accepting electrons, followed by an investigation of the variation in the donor-acceptor behavior for different transitions or conformations of the molecules. In this paper, we present a novel approach towards the study of electronic transitions based on the visual analysis of a bivariate field, namely the electron density in the hole and particle Natural Transition Orbital (NTO). The visual analysis focuses on the continuous scatter plots (CSPs) of the bivariate field linked to their spatial domain. The method supports selections in the CSP visualized as fiber surfaces in the spatial domain, the grouping of atoms, and segmentation of the density fields to peel the CSP. This peeling operator is central to the visual analysis process and helps identify donors and acceptors. We study different molecular systems, identifying local excitation and charge transfer excitations to demonstrate the utility of the method.
A Novel Hybrid Level Set Model for Non-Rigid Object Contour Tracking

Most existing trackers use bounding boxes for object tracking. However, the background contained in the bounding box inevitably decreases the accuracy of the target model, which affects the performance of the tracker and is particularly pronounced for non-rigid objects. To address the above issue, this paper proposes a novel hybrid level set model, which can robustly address the issue of topology changing, occlusions and abrupt motion in non-rigid object tracking by accurately tracking the object contour. In particular, an appearance model is first obtained by repeatedly training and relabeling the initial labeled frame using competing one-class SVMs. Then, by integrating the trained appearance model, an edge detector and image spatial information into the level set model, a new hybrid level set model is presented, which accurately locates the object contour and feeds back to the competing one-class SVMs to update the appearance model of the next frame. In addition, a motion model is defined to predict the accurate location of the object when occlusion and abrupt motion occur in the next frame. Finally, the experimental results on state-of-the-art benchmarks demonstrate the feasibility and effectiveness of the proposed model and the superiority of the proposed method over existing trackers in terms of accuracy and robustness.
Migrating Software from x86 to ARM Architecture: An Instruction Prediction Approach

For decades, the x86 architecture supported by Intel and AMD has been the dominate target for software development. Recently, ARM has solidified itself as a highly competitive and promising CPU architecture by exhibiting both high performance and low power consumption simultaneously. In the foreseeable future, a copious amount of software will be fully migrated to the ARM architecture or support both x86 and ARM simultaneously. Nevertheless, software ports from x86 to ARM are not trivial for a number of reasons. First, it is time consuming to write code that resolves all compatibility issues for a new architecture. Second, specific hardware (e.g. ARM chips) and supporting toolkits (e.g. libraries and compilers) may not be readily available for developers, which will delay the porting process. Third, it is hard to predict the performance of software before testing it on production chips. In this paper, we strive to tackle these challenges by proposing an instruction prediction method that can automatically generate AARCH64 code from existing x86-64 executables. Although the generated code might not be directly executable, it provides a cheap and efficient solution for developers to estimate certain runtime metrics before actually building, deploying and testing code on an ARM-based CPU. Our experimental results show that AARCH64 instructions derived using prediction can achieve a high Bilingual Evaluation Understudy (BLEU) Score. This indicates a quality match between generated executables and natively ported AARCH64 software.
Operation Digital Ant

Insiders pose severe threats to the supply chain, the security of infrastructures, and the safety of products and services. "Operation Digital Ant" is a tabletop game that explores insider threats in the food supply chain. Three to four teams compete against each other in developing malicious insider roles and attacks. The game can produce plausible and consistent insider threat roles and attacks as a basis for further analyses. “Operation Digital Ant” also raises awareness for insider threats. This article describes the serious game “Operation Digital Ant” with game material, the game development process – following the Design Science paradigm – and the validation methods and results. We released the game Operation Digital Ant with game boards, game cards, and guidelines under a Creative Commons license.
Ellipses Ring Marker for High-speed Finger Tracking

High-speed finger tracking is necessary for augmented reality and operation in human-machine cooperation without latency discomfort, but conventional markerless finger tracking methods are not fast enough and the marker-based methods have low wearability. In this paper, we propose an ellipses ring marker (ERM), a finger-ring marker consisting of multiple ellipses and its high-speed image recognition algorithm. The finger-ring shape has highly wearing continuity, and the surface shape is suitable for various viewing angle observation. The invariance of the ellipse in the perspective projection enables accurate and low-latency posture estimation. We have experimentally investigated the advantage in normal distribution, validated the sufficient accuracy and computational cost in the marker tracking, and showed a demonstration of dynamic projection mapping on a palm.