Reviewerly/exHarmony huggingface dataset:
Segmentation and detection of sickle cell red blood image

The most common and dangerous hereditary disease that affect red blood cells (RBC) is sickle cell anaemia due to its morphological characteristics of the cells and caused episodes of pains to the affected individual. This work proposed algorithms in two phase, firstly is to compare segmentation systems such as watershed, edge detection, laplacian of Gaussian and Otsu thresholding on sickle cell anaemia blood smear images and secondly is to detect the presence of cell abnormalities in blood smear images using labelling method by considering eccentricity and form factor features. The RBCs of sickle cell anaemia patient have several abnormalities apart from the sickle shape that will guide medical practitioners on the severity level. The major requirement of the system is to get accurate thresholding level in order to detect the abnormalities of sickle cell anaemia patients for excellent management of the affected individuals to reduce episodes of crises. The phase one proved Otsu thresholding with the highest accuracy, sensitivity and specificity of 93%,94% and 80% respectively by considering 30 blood smear images while the classification gives accuracy, sensitivity and specificity of 88%,93% and 50% respectively.
Can Emoji’s assess patients’ mood and emotion in the emergency department? An emoji based study

Food products targeted at children are usually marketed using persuasive elements aimed at creating positive hedonic and emotional associations. For this reason, changes in children's emotional associations with unhealthy food products can discourage their consumption. In this context, the aim of the present study was to compare children's emotional associations with food products featuring different front-of-pack (FOP) nutrition labelling schemes. A total of 492 children (6–12 years old) were randomly divided into three groups, each of which evaluated a series of packages featuring different FOP nutrition labelling schemes: guidelines daily amount (GDA), traffic light system (TLS) and nutritional warnings. For each of the six packages and three unpackaged products, children were asked to select all the emoji from a list that described how they would feel eating the product. Data were analyzed using generalized linear models. FOP nutrition labelling significantly influenced the frequency of use five of the 16 emoji. In general, children who evaluated packages with directive and semi-directive schemes used emoji associated with positive emotions less frequently than those who evaluated packages with the GDA system. The effect of FOP nutrition labelling scheme on emotional associations was moderated by age and type of school. The effect of directive and semi-directive FOP nutritional schemes, in special nutritional warnings, tended to be higher for younger children and children from public schools. These results suggest that FOP nutrition labelling may contribute to discouraging consumption of products with high content of nutrients associated with non-communicable diseases among children.
Text Entry Method for Immersive Virtual Environments Using Curved Keyboard

In this paper, we introduce a curved QWERTY keyboard, bent spherically in front of the user, to facilitate 3D word-gesture text entry in immersive virtual environments. Using the curved keyboard, the number of candidate words in the 3D word-gesture text entry is reduced compared with that using a planar keyboard. In the pilot study, the text entry performance of the first author was 21.0 WPM (SD = 5.06), with a total error rate of 26.0% (SD = 15.2).
Physically Tightly Coupled, Logically Loosely Coupled, Near-Memory BNN Accelerator (PTLL-BNN)

In this paper, a physically tightly coupled, logically loosely coupled, near-memory binary neural network accelerator (PTLL-BNN) is designed and fabricated. Both architecture-level and circuit-level optimizations are presented. From the perspective of processor architecture, the PTLL-BNN includes two new design choices. First, the proposed BNN accelerator is placed close to the SRAM of the embedded processors (i.e., physically tightly coupled and near-memory); thus, the extra SRAM cost that is incurred by the accelerator is as low as 0.5 KB. Second, the accelerator is a memory-mapped IO (MMIO) device (i.e., logically loosely coupled), so all embedded processors can be equipped with the proposed accelerator without the burden of changing their compilers and pipelines. From the circuit perspective, this work employs four techniques to optimize the power and costs of the accelerator. First, this design adopts a unified input-kernel-output memory instead of separate ones, which many previous works adopt. Second, the data layout that this work chooses increases the sequentiality of the SRAM accesses and reduces the buffer size of storing the intermediate values. Third, this work innovatively proposes to fuse the max-pooling, batch-normalization, and binarization layers of the BNNs to significantly reduce the hardware complexity. Finally, a novel methodology of generating the scheduler hardware of the accelerator is included. We fabricate the accelerator using the TSMC 180 nm technology. The chip measurement results reach 91 GOP/s on average (307 GOP/s at peak) at 200 MHz. The achieved GOP/s per million logic gates and GOP/s per KB SRAM are 2.6 to 237 times greater than that of previous works, respectively. We also realize an FPGA system to demonstrate the recognition of CIFAR-10/100 images using the fabricated accelerator.
Improving Collaborative Filtering’s Rating Prediction Accuracy by Introducing the Common Item Rating Past Criterion

Collaborative filtering formulates personalized recommendations by considering ratings submitted by users. Collaborative filtering algorithms initially find people having similar likings, by inspecting the similarity of ratings already present in the ratings database. Users exhibiting high similarity regarding their likings are classified as “near neighbors” (NNs) and the ratings entered by each user’s near neighbors drive the formulation of recommendations for that user. To quantify the similarity between users, in order to determine a user’s NNs, a similarity metric is used. Insofar, similarity metrics proposed in the literature either consider all user ratings equally or take into account temporal variations within the users’ or items’ ratings history. However users’ ratings are co-shaped according to the experiences that they had in the past; therefore if two users enter similar (or dissimilar) ratings for an item while having experienced to a large extent the same items in the past, this constitutes stronger evidence about user similarity (or dissimilarity). Insofar however, no similarity metric takes into account this aspect. In this work, we propose and evaluate an algorithm that considers the common item rating past when computing rating predictions, in order to increase rating prediction accuracy.
DynaPro: Dynamic Wireless Sensor Network Data Protection Algorithm in IoT via Differential Privacy

With the advent of the era of intelligent Internet of Things (IoT), more and more personal information are collected in the process of deploying IoT in various domains.This brings the problem of data protection in IoT.To address this problem, we propose a data protection algorithm, called DynaPro, based on differential privacy for dynamic wireless sensor networks (WSN), which is the sensor layer of IoT.DynaPro has addressed two issues in data protection of wireless sensor networks.The first issue is the dynamic topology of the network.In order to solve this problem, DynaPro has adopted methods like time window, hierarchical sampling, and graph similarity to process the snapshots of the dynamic network topology.The second issue is addition of noise in differential privacy.To address this issue, DynaPro uses Hierarchical Random Graph (HRG) as middleware to apply differential privacy protection.Instead of adding noise to network data and topology directly, DynaPro adds noise to HRG.In this way, DynaPro can hide the network topology but retain the necessary structural information to support data analysis.Theoretical analysis and experimental results show that DynaPro can preserve the important network features of the original network topology under the premise of the differential privacy protection model.
Effects of dental students’ training using immersive virtual reality technology for home dental practice

Observation training of home dental practice is easily influenced by the site and time, and differences in learning content are likely to occur among students. The aim of the present study is to prototype virtual reality (VR) teaching material for home dental practice to provide dental students with the chance to observe patients receiving home dental care. The VR teaching material was created before the study, using a patient’s home as the medical treatment scene, and practical training was then conducted using this VR teaching material with dental students. To examine its educational effectiveness, 101 dental students, who attended practical training between June 2018 and January 2019, responded to survey questions before and after watching the VR teaching material. They took a comparative examination, based on the questionnaire results, using text mining and hierarchical cluster analysis. Two variables were tested: changes in self-confidence regarding knowledge of home dental practice (knowledge confidence) and changes in self-confidence regarding treatment assistance (assistance confidence). There was a significant increase in student’s knowledge confidence and assistance confidence scores (p < .001). Moreover, hierarchical cluster analysis indicated a change in the cluster before and after they watched the VR teaching material. Given that VR teaching material makes it possible to conduct home observation training for a variety of reasons, such as lack of available facilities or patient reservation issues, the findings indicate that VR teaching material can supplement dental students’ training in home dental practice.
Data mining and machine learning approaches for prediction modelling of schistosomiasis disease vectors

This research presents viable solutions for prediction modelling of schistosomiasis disease based on vector density. Novel training models proposed in this work aim to address various aspects of interest in the artificial intelligence applications domain. Topics discussed include data imputation, semi-supervised labelling and synthetic instance simulation when using sparse training data. Innovative semi-supervised ensemble learning paradigms are proposed focusing on labelling threshold selection and stringency of classification confidence levels. A regression-correlation combination (RCC) data imputation method is also introduced for handling of partially complete training data. Results presented in this work show data imputation precision improvement over benchmark value replacement using proposed RCC on 70% of test cases. Proposed novel incremental transductive models such as ITSVM have provided interesting findings based on threshold constraints outperforming standard SVM application on 21% of test cases and can be applied with alternative environment-based epidemic disease domains. The proposed incremental transductive ensemble approach model enables the combination of complimentary algorithms to provide labelling for unlabelled vector density instances. Liberal (LTA) and strict training approaches provided varied results with LTA outperforming Stacking ensemble on 29.1% of test cases. Proposed novel synthetic minority over-sampling technique (SMOTE) equilibrium approach has yielded subtle classification performance increases which can be further interrogated to assess classification performance and efficiency relationships with synthetic instance generation.
Towers of Saliency

Deep reinforcement learning (DRL) has had many successes on complex tasks, but is typically considered a black box. Opening this black box would enable better understanding and trust of the model which can be helpful for researchers and end users to better interact with the learner. In this paper, we propose a new visualization to better analyze DRL agents and present a case study using the Pommerman benchmark domain. This visualization combines two previously proven methods for improving human understanding of systems: saliency mapping and immersive visualization.
Heterogeneous Image Matching via a Novel Feature Describing Model

Computer vision has been developed greatly in the past several years, and many useful and interesting technologies have been presented and widely applied. Image matching is an important technology based on similarity measurement. In this paper, we propose a novel feature describing model based on scale space and local principle component analysis for heterogeneous image matching. The traditional uniform eight-direction statistics is updated by a task-related k-direction statistics based on prior information of the keypoints. In addition, the k directions are determined by an approximately solution of a Min-Max problem. The principle component analysis is introduced to compute the main directions of local patches based on the gradient field. In addition, the describing vector is formed by then implementing PCA on each sub-patch of a 4 × 4 mesh. Experimental results show the accuracy and efficiency of proposed method.
A privacy-preserving model to control social interaction behaviors in social network sites

Social Network Sites (SNSs) served as an invaluable platform to transfer information across a large number of users. SNSs also disseminate users data to third-parties to provide more interesting services for users as well as gaining profits. Users grant access to third-parties to use their services, although they do not necessarily protect users’ data privacy. Controlling social network data diffusion among users and third-parties is difficult due to the vast amount of data. Hence, undesirable users’ data diffusion to unauthorized parties in SNSs may endanger users’ privacy. This paper highlights the privacy breaches on SNSs and emphasizes the most significant privacy issues to users. The goals of this paper are to i) propose a privacy-preserving model for social interactions among users and third-parties; ii) enhance users’ privacy by providing access to the data for appropriate third-parties. These advocate to not compromising the advantages of SNSs information sharing functionalities.
Multimodal Web Based Video Annotator with Real-Time Human Pose Estimation

This paper presents a multi-platform Web-based video annotator to support multimodal annotation that can be applied to several working areas, such as dance rehearsals, among others. The CultureMoves’ “Motion-Notes” Annotator was designed to assist the creative and exploratory processes of both professional and amateur users, working with a digital device for personal annotations. This prototype is being developed for any device capable of running in a modern Web browser. It is a real-time multimodal video annotator based on keyboard, touch and voice inputs. Five different ways of adding annotations have been already implemented: voice, draw, text, web URL, and mark annotations. Pose estimation functionality uses machine learning techniques to identify a person skeleton in the video frames, which gives the user another resource to identify possible annotations.
Generalizing Graph Convolutional Neural Networks with Edge-Variant Recursions on Graphs

This paper reviews graph convolutional neural networks (GCNNs) through the lens of edge-variant graph filters. The edge-variant graph filter is a finite order, linear, and local recursion that allows each node, in each iteration, to weigh differently the information of its neighbors. By exploiting this recursion, we put forth a general framework for GCNNs which considers state-of-the-art solutions as particular cases. This framework results useful to i) understand the tradeoff between local detail and the number of parameters of each solution and ii) provide guidelines for developing a myriad of novel approaches that can be implemented locally in the vertex domain. One of such approaches is presented here showing superior performance w.r.t. current alternatives in graph signal classification problems.
Constrained Co-embedding Model for User Profiling in Question Answering Communities

In this paper, we study the problem of user profiling in question answering communities. We address the problem by proposing a constrained co-embedding model (CCEM). CCEM jointly infers the embeddings of both users and words in question answering communities such that the similarities between users and words can be semantically measured. Our CCEM works with constraints which enforce the inferred embeddings of users and words subject to this criteria: given a question in the community, embeddings of users whose answers receive more votes are closer to the embeddings of the words occurring in these answers, compared to the embeddings of those whose answers receive less votes. Experiments on a Chinese dataset, Zhihu dataset, demonstrate that our proposed co-embedding algorithm outperforms state-of-the-art methods in the task of user profiling.
Anomaly detection method using center offset measurement based on leverage principle

Anomaly detection is an important branch of data mining and has been researched within diverse research areas and application domains. Many existing unsupervised anomaly detection methods have high computational complexity and more adjustable parameters. In addition, the proportion of anomalies needs to be estimated by long-term experience in most methods to set the threshold that distinguishes normal instances and anomalies, which makes the algorithms more subjective. This paper presents an anomaly detection algorithm based on the leverage principle. When detecting a testing instance, we copy it in large quantities and take these replicated data into the training dataset. The anomaly degree of the testing instance can be assessed by measuring the offset of the dataset center. Meanwhile, an adaptive threshold setting method using the golden ratio is proposed to solve the subjectivity in distinguishing normal instances and anomalies. In the experiments, we compare the anomaly detection algorithm proposed in this paper with other eight detection methods and report the experimental results in terms of AUC, the F1 of the anomaly classes and the running time. The results show that our algorithm can achieve high detection performance with high efficiency, and the proposed threshold setting method also has strong practicality in unsupervised anomaly detection.
Immersive applications: what if users are in the autism spectrum?

Domain experts generally agree on the potential of VR-based treatment aimed at improving emotional competencies and social skills in individuals with Autism Spectrum Disorder (ASD). The need arises for Immersive Virtual Environments evaluation frameworks appropriate for this specific user population. In this paper we sketch results of a study focused on engagement, with the multifold objective of (1) evaluating two headsets differing in the degree to which the physical world is cutout from the view (Oculus Rift and HoloLens) to study their general appropriateness with respect to possible VR-based ASD people treatment, (2) reasoning about the interpretation of selected measured engagement aspects in the cases of Typically Development people and ASD people, and (3) outlining the nature of VR-based possible ASD-oriented applications based on the two headsets.
Machine Learning Lifecycle for Earth Science Application: A Practical Insight into Production Deployment

Enterprises are making machine learning for production as an integral part of their future roadmaps and Earth science domain is no exception. However, there is common problem in transitioning machine learning from science to production due to a major difference in constructing a model versus deploying it for people to use to make decisions. Phases of machine learning lifecycle that includes model transition to production using a successful application is discussed.
Renewal Monte Carlo: Renewal Theory-Based Reinforcement Learning

An online reinforcement learning algorithm called renewal Monte Carlo (RMC) is presented. RMC works for infinite horizon Markov decision processes with a designated start state. RMC is a Monte Carlo algorithm that retains the key advantages of Monte Carlo-viz., simplicity, ease of implementation, and low bias-while circumventing the main drawbacks of Monte Carlo- viz., high variance and delayed updates. Given a parameterized policy <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">πθ</sub> , the algorithm consists of three parts: estimating the expected discounted reward R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">θ</sub> and the expected discounted time T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">θ</sub> over a regenerative cycle; estimating the derivatives Δ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">θ</sub> R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">θ</sub> and Δ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">θ</sub> T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">θ</sub> ; and updating the policy parameters using stochastic approximation to find the roots of R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">θ</sub> Δ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">θ</sub> T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">θ</sub> - T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">θ</sub> Δ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">θ</sub> R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">θ</sub> . It is shown that under mild technical conditions, RMC converges to a locally optimal policy. It is also shown that RMC works for postdecision state models as well. An approximate version of RMC is proposed where a regenerative cycle is defined as successive visits to a prespecified “renewal set”. It is shown that if the value function of the system is locally Lipschitz on the renewal set, then RMC converges to an approximate locally optimal policy. Three numerical experiments are presented to illustrate RMC and compare it with other state-of-the-art reinforcement learning algorithms.
Learning to Trace: Expressive Line Drawing Generation from Photographs

Abstract In this paper, we present a new computational method for automatically tracing high‐resolution photographs to create expressive line drawings. We define expressive lines as those that convey important edges, shape contours, and large‐scale texture lines that are necessary to accurately depict the overall structure of objects (similar to those found in technical drawings) while still being sparse and artistically pleasing. Given a photograph, our algorithm extracts expressive edges and creates a clean line drawing using a convolutional neural network (CNN). We employ an end‐to‐end trainable fully‐convolutional CNN to learn the model in a data‐driven manner. The model consists of two networks to cope with two sub‐tasks; extracting coarse lines and refining them to be more clean and expressive. To build a model that is optimal for each domain, we construct two new datasets for face/body and manga background. The experimental results qualitatively and quantitatively demonstrate the effectiveness of our model. We further illustrate two practical applications.