Reviewerly/exHarmony huggingface dataset:
A Bayesian Neo-Normal Mixture Model (Nenomimo) for MRI-Based Brain Tumor Segmentation

The detection of a brain tumor through magnetic resonance imaging (MRI) is still challenging when the image is in low quality. Image segmentation could be done to provide a clear brain tumor area as the region of interest. In this study, we propose an improved model-based clustering approach for MRI-based image segmentation. The main contribution is the use of the adaptive neo-normal distributions in the form of a finite mixture model that could handle both symmetrical and asymmetrical patterns in an MRI image. The neo-normal mixture model (Nenomimo) also resolves the limitation of the Gaussian mixture model (GMM) and the generalized GMM (GGMM), which are limited by the short-tailed form of their distributions and their sensitivity against noise. Model estimation is done through an optimization process using the Bayesian method coupled with a Markov chain Monte Carlo (MCMC) approach, and it employs a silhouette coefficient to find the optimum number of clusters. The performance of the Nenomimo was evaluated against the GMM and the GGMM using the misclassification ratio (MCR). Finally, this study discovered that the Nenomimo provides better segmentation results for both simulated and real data sets, with an average MCR for MRI brain tumor image segmentation of less than 3%.
Enhanced Feature Pyramid Networks by Feature Aggregation Module and Refinement Module

Feature pyramids executing refinements on the raw feature maps produced by the backbone (e.g., ResNet, VGG) are universally employed in object detection tasks (e.g., Faster R-CNN, Mask R-CNN, YOLO, SSD, RetinaNet) to mitigate scale variation problem. Although these object detections with feature pyramids accomplish a boost in accuracy without compromising speed, they have some limitations since that they only naturally design the feature pyramid with consecutive scales, the pyramidal architecture of the backbone, which are initially constructed for the classification task. This problem leads to the feature imbalance between high-level features and low-level features in object detection. In this work, the proposed method introduces Feature Aggregation Module (FAM) and Refinement Module (RM) to obtain more powerful feature pyramids for predicting objects of different scales. First, the multi-level feature maps (i.e., multiple layers) extracted by the backbone network are aggregated as the basic feature. Second, the basic feature is enhanced by a refinement module exploiting long-range dependency. Three, to construct a feature pyramid for object detection, the proposed FAM is used by converting the basic feature (after utilizing a refinement module) into multi-level features. Finally, refined multi-level features and raw features generated by the backbone could be enhanced through shortcut connections to capture more representative. To perform the efficiency, the proposed method integrates the FAM and the RAM into the architecture of Faster R-CNN called EFPN Faster R-CNN. Especially on the MS-COCO dataset, EFPN Faster R-CNN achieves 2.2 points higher Average Precision (AP) than FPN Faster R-CNN without bells and whistles.
A Gentle Introduction to Recommendation as Counterfactual Policy Learning

The objective of this tutorial is to give a structured overview of the conceptual frameworks behind current state-of-the-art recommender systems, explain their underlying assumptions, the resulting methods and their shortcomings, and to introduce an exciting new class of approaches that frames the task of recommendation as a counterfactual policy learning problem. The tutorial can be divided into two modules. In module 1, participants learn about current approaches for building real-world recommender systems that comprise mainly of two frameworks, namely: recommendation as optimal auto-completion of user behaviour and recommendation as reward modelling. In module 2, we present the framework of recommendation as a counterfactual policy learning problem and go over the theoretical guarantees that address the shortcomings of the previous frameworks. We then proceed to go over the associated algorithms and test them against classical methods in RecoGym, an open-source recommendation simulation environment.
Assistive Hand Gesture Glove for Hearing and Speech Impaired

Hand based gestures are words of communication for people impaired of speech and hearing. This results in communication mismatch between such impaired people and a normal one. A deaf and dumb person many times finds difficulty in informing about basic phrases or words representing certain actions like “How are you?”, “Yes”, etc. to a normal human. To tackle this issue, one hand gesture recognition system is presented which uses a sensor and a microcontroller to capture a gesture movement in the form of a signal. This hand gesture recognition system uses 1D Convolutional Neural Network (1D-CNN) which can extract feature directly from the raw temporal signals captured. The resulted word or phrase is communicated to a normal person through the Mobile phone of a disabled person in terms of audio voice and text-based notification. Furthermore, to reduce the latency in predicting output, the trained 1D-CNN model is deployed in Android phone itself rather than running the model on the server. The trained model achieves recognition accuracy of 97.96% on test data consisting of many samples of 10 different patterns.
Unexplored and familiar: experiencing interactive spatio-temporal visualization

Spatio-temporal (ST) visualization has the power to convey complex information, allowing the user to observe, identify, and interpret occurrences and the evolution of events. A comparative case study was designed to assess the predominantly privileged strategies to represent the articulation between temporal variation and geographic data. The systematic analysis of 25 cases of ST visualization has led to the identification and acknowledgment of shared characteristics between them, and the distinction of disparities and exceptions. The application of 50 criteria, assorted in 11 categories, resulted in nine general findings and two startling remarks. This study provides an understanding of the most prevalent author's decisions when designing interactive ST visualization.
Towards Chatbot-based Interactive What- and How-Question Answering Systems: the Adobot Approach

An interactive question answering (QA) system is defined as a QA system that supports a series of exchanges between users and the system to clarify user intent and to enable follow-ups. In the domains of applications related to technical support and training, there is an urgent need for an interactive QA system that supports complicated questions in a coherent manner. This paper presents the approach for designing and implementing chatbot-based interactive What- and How-QA systems based on an integrated ontology-based knowledge base.
Securing Transparency and Governance of Organ Supply Chain Through Blockchain

The governance and supply chain of organs is a complicated process throughout the life cycle; from the outset of pre-assessment of organ placement, it’s supply chain journey and important post donor analysis. Healthcare organisations face a huge challenge in the diverse collation of data that are held in systems which are mostly in silo operation and little scope for interoperability or accessibility of medical data. Lack of data access or trust in its accuracy makes the task more challenging and problematic for healthcare institutions whose preference undoubtably would be to focus their energies on the decision making side of a patient’s health in assessing organ donor suitability and urgency to organ match due to the receiving patient criticalities, rather than time and resources spent on validating data authenticity, etc. There are further complications that can occur in potential mix-ups of organs, contamination of DNA during organ transplant, non-ethical organ supply and audit trail transparency related to these activities. There is a serious question on how to create a single source of the truth and blockchain may provide the best possibilities. Blockchain is becoming a more sought after technology being used in the healthcare space due to its attributes of immutability, traceability and security whilst providing that assurance of transparency and audit trail. Blockchain looks to be a good fit to manage the supply chain of organ procurement/placement and an audit control method to analyse data in any pre or post operation event. Combined with the right processes, in the form of a cyber security framework/maturity model for the healthcare industry, would ensure that all those who signed up to the blockchain deployed for the supply chain logistics would respect the ethics and requirements and expect transparency for those authorised to access. However, some challenges exist in GDPR compliancy of data that would exist on a certain proposed blockchain models and needs further exploring with regards to benefits in data held off-chain.
Resolving Conflicting Insights in Asynchronous Collaborative Visual Analysis

Abstract Analyzing large and complex datasets for critical decision making can benefit from a collective effort involving a team of analysts. However, insights and findings from different analysts are often incomplete, disconnected, or even conflicting. Most existing analysis tools lack proper support for examining and resolving the conflicts among the findings in order to consolidate the results of collaborative data analysis. In this paper, we present CoVA, a visual analytics system incorporating conflict detection and resolution for supporting asynchronous collaborative data analysis. By using a declarative visualization language and graph representation for managing insights and insight provenance, CoVA effectively leverages distributed revision control workflow from software engineering to automatically detect and properly resolve conflicts in collaborative analysis results. In addition, CoVA provides an effective visual interface for resolving conflicts as well as combining the analysis results. We conduct a user study to evaluate CoVA for collaborative data analysis. The results show that CoVA allows better understanding and use of the findings from different analysts.
Detection of Nuclei Cell in Histopathological Images of Uterine Cancer: Adenocarcinoma of Endometrium

Pathologist visual inspection of Histopathological uterine tissue sample is still considered as confirmatory test for uterine cancer finding. Manual assessment depends upon the knowledge and experience of pathologist. Hence subject of concern is objective analysis. An effective way of diagnosis, grading and classification of endometrium adenocarcinoma is structural analysis of the tissue sample. The effectiveness of computer assisted diagnosis, grading and classification depends on glandular structure and detection of individual nuclei cell. Hematoxylin channel from H&E stained image is extracted using color deconvolution algorithm. Morphological operation and thresholding are carried out in this work for structural analysis of glandular region and for detection of nuclei cell. To rise the effectiveness of the system elimination of cell nuclei from stroma is done before feature extraction is also described.
Road Scene Segmentation Based on Deep Learning

Recently, autonomous driving becomes a hot topic in research and industry area. Autonomous driving technology needs to perceive the semantic information of road scenes in the all-day and open environment. In this article, the semantic recognition of traffic scenes is studied using a deep learning network model, and a semantic representation model of road scenes is established. Besides, a semantic recognition algorithm of road scenes based on image data is proposed. Finally, a self-built data set is used to train the proposed model, and verified in the field of the test vehicle. It is found that the proposed method can quickly capture the perceptual road scene and over-performs better than traditional methods and demonstrated great potential to be used in road scene applications.
Sand fraction is not suitable for forensic investigations in subtropical soils

Most of the forensic comparison of soils has focused on the clay and silt fractions at the expense of the coarser particles.This study aimed to test the potential of elemental and physical analyses in the sand fraction from subtropical soils to discriminate samples collected in areas under different parent material (claystone and marble) and in areas with the same parent material at a simulated crime scene.Scanning electron microscopy coupled to an energy dispersive X-ray spectrometer (SEM-EDS) analysis was used on the finer sized sand particles (0.05-0.25 mm).X-ray diffraction (XRD) and particle size distribution (PSD) analyses were performed on the whole sand fraction.These methods did not provide clear discrimination of the sand of the soils sampled in the subtropical environment.This can be explained by the large homogenization observed in the sand fraction related to its chemical (EDS), physical (particle size distribution), morphological (SEM), and mineralogical (XRD).Under tropical and subtropical conditions, the chemical weathering processes dissolve most of the primary minerals, such as the feldspars, biotite, and Fe-bearing particles, and concentrates quartz in the sand fraction.In these environments, we recommend the prioritization of the finer soil fractions for forensic studies, both inorganic and organic.
An Active Learning Method for Empirical Modeling in Performance Tuning

Tuning performance of scientific applications is a challenging problem since performance can be a complicated nonlinear function with respect to application parameters. Empirical performance modeling is a useful approach to approximate the function and enable efficient heuristic methods to find sub-optimal parameter configurations. However, empirical performance modeling requires a large number of samples from the parameter space, which is resource and time-consuming. To address this issue, existing work based on active learning techniques proposed PBU Sampling method considering performance before uncertainty, which iteratively performs performance biased sampling to model the high-performance subspace instead of the entire space before evaluating the most uncertain samples to reduce redundancy. Compared with uniformly random sampling, this approach can reduce the number of samples, but it still involves redundant sampling that potentially can be improved.We propose a novel active learning based method to exploit the information of evaluated samples and explore possible high-performance parameter configurations. Specifically, we adopt a Performance Weighted Uncertainty (PWU) sampling strategy to identify the configurations with either high performance or high uncertainty and determine which ones are selected for evaluation. To evaluate the effectiveness of our proposed method, we construct random forest to predict the execution time of kernels from SPAPT suite and two typical scientific parallel applications kripke, hypre. Experimental results show that compared with existing methods, our proposed method can reduce the cost of modeling by up to 21x and 3x on average meanwhile hold the same prediction accuracy.
Perceived Factors Influencing Blockchain Adoption in the Asset and Wealth Management Industry in the Western Cape, South Africa

Blockchain is an archive of data records known as blocks with key functionalities that include validation, security and preservation. Blockchain has been applied in a wide range of industries, including the financial sector. However, little is known about its adoption and usefulness in industries operating within developing countries. Moreover, while some studies have focused on the financial sector, the Asset Wealth Management (AWM) industry remains relatively unexplored. This preliminary study has identified factors influencing the adoption of blockchain in the AWM industry in the Western Cape province of South Africa. Through a qualitative exploratory study, perceptions of AWM experts were gathered on how blockchain adoption is influenced by the technology’s compatibility, complexity and relative advantage. The influence of the South African context was also explored. A preliminary framework is being proposed that will inform an in-depth, longitudinal study on blockchain adoption and use within AWM organizations in South Africa.
Spam Filter Based on Naive Bayesian Classifier

Abstract Spam now accounts for over 70% of all emails and the harm to users is increasing, such as waste a lot of network bandwidth to transfer and space to store, has large quantity, repetitive, fraud, and unhealthy content, etc. As spam is usually embedded in normal e-mails, it is difficult to identify them. This paper analyzed the main technologies to identify and block spam, such as information or content filtering technology, blacklist and white list technology, intention and behaviour analysis technology, etc. A model to determine an e-mail is a spam or not based on naive Bayesian classifier is presented in the paper. The test result shows that the model is effective.
Anchor-free object detection with mask attention

Abstract The anchor-free method based on key point detection has made great progress. However, the anchor-free method is too dependent on using a convolutional network to generate a rough heatmap. This is difficult to detect for objects with a large size variation and dense and overlapping objects. To solve this problem, first, we propose a mask attention mechanism for object detection methods and make full use of the advantages of the attention mechanism to improve the accuracy of network detection heatmap generation. Then, we designed an optimized fire model to reduce the size of the model. The fire model is an extension of grouped convolution. The fire model allows each group of convolutional network features to learn the same feature through purposeful grouping. In this paper, the mask attention mechanism uses object segmentation images to guide the generation of corner heatmaps. Our approach achieved an accuracy of 91.84% and a recall of 89.83% in the Tencent-100 K dataset. Compared with the popular object detection methods, the proposed method has advantages in model size and accuracy.
A Noise-Robust Modulation Signal Classification Method Based on Continuous Wavelet Transform

The feature extraction of Automatic Modulation Classification (AMC) is difficult, and the classification performance is poor, particularly for low SNRs and fading channels. To solve these problems, a combinatorial model based on Convolutional Neural Network (CNN) and Long-Short-Term Memory (LSTM) is proposed in this paper. This method transforms the time-domain signals to the two-dimensional time-frequency domain samples by Continuous Wavelet Transform (CWT). It then extracts in-depth features through the CNN-LSTM model to conduct classification on modulation signals. Analyses show that the proposed model yields an classification accuracy higher than 90% at varying SNR conditions ranging from -6dB to 12dB. Compared with traditional algorithms, this method effectively improves the problem of poor classification performance for low SNRs and possesses better robustness.
Aadhar Card Verification Base Online Polling

Currently, bogus voting in elections is remaining as a serious issue. The question of casting a ballot as far as health and protection are concerned is still important. The undertaking is primarily planned for giving a most verified and easy to understand Online Election System. An online political decision system is a voting system. In Traditional political race condition casting a ballot procedure now and again become very badly designed because of the reluctances of specific voters to visit a surveying stall to cast casts a ballot other than including tremendous social and HR. This paper manages the development and progress of an electronic voting system using distributed computing and aadhar card to provide the voting system with a superior with high security. Additionally, web innovation is used to make the voting system reasonable. The Internet surveying system is the system that empowers the client to cast a ballot on the web. In particular, the Voting system comprised of server administrations which are each connected with a database for putting away persevering information. Voters can likewise utilize the administrations to sign into the electronic voting system site. The field official will send the voter data and their voting's and that data will be kept up by creator card check. Administrators will keep up all data concerning voter and tallies consequently their deciding in favor offs their chosen gatherings.
Towards a Social and Semantic Web Platform for Sharing Endogenous Knowledge to Adapt to Climate Change

In this article, we focus on farmers' perceptions of climate change and their strategies for adapting cultures, which constitute endogenous knowledge. This leads us to the three major challenges of opening, interconnecting and sharing endogenous knowledge through the use of different tools and methods of the semantic web (or linked open data).
Adaptive Blending Units: Trainable Activation Functions for Deep Neural Networks

The most widely used activation functions in current deep feed-forward neural networks are rectified linear units (ReLU), and many alternatives have been successfully applied, as well. However, none of the alternatives have managed to consistently outperform the rest and there is no unified theory connecting properties of the task and network with properties of activation functions for most efficient training. A possible solution is to have the network learn its preferred activation functions. In this work, we introduce Adaptive Blending Units (ABUs), a trainable linear combination of a set of activation functions. Since ABUs learn the shape, as well as the overall scaling of the activation function, we also analyze the effects of adaptive scaling in common activation functions. We experimentally demonstrate advantages of both adaptive scaling and ABUs over common activation functions across a set of systematically varied network specifications. We further show that adaptive scaling works by mitigating covariate shifts during training, and that the observed advantages in performance of ABUs likewise rely largely on the activation function's ability to adapt over the course of training.
Developing an Interactive Tabletop Mediated Activity to Induce Collaboration by Implementing Design Considerations Based on Cooperative Learning Principles

Constructive collaboration can be a difficult matter. For this reason, we are implementing and studying an interactive-tabletop-mediated activity that aims at inducing collaboration among participants. The resulting activity ‘Orbitia’ is designed as a serious game. Participants are asked to act as a space-mining crew, which has to collect minerals with a rover and rely on a camera-drone for reconnaissance, while keeping the rover out of harm and managing limited resources. In this paper we provide an account of how we designed Orbitia’s pedagogical structuring by relying on the Johnsons’ cooperative learning approach whose fundamental concept is “positive interdependence”. More particularly, we show how we worked on resource, role and task interdependence to design three collaboration-inducing ‘flagship’ devices: the rover-steering-device (RSD), the item-locating-device (ILD) and the responsibility-activating-device (RAD).