Reviewerly/exHarmony huggingface dataset:
Queer in HCI

As Queer Human-Computer Interaction (HCI) becomes an established part of the larger field, both in terms of research on and with queer populations and in terms of employing queering theories and methods, the role of queer researchers has become a timely topic of discussion. However, these discussions have largely centered around member-researcher status and positionality when working with queer populations. Based on insights gathered at multiple ACM events over the past year, we identified two pressing issues: (1) we need to better support queer people doing HCI research not specific to queer populations, and (2) we need to identify how to best support member-researchers in leading Queer HCI while including collaborators beyond the queer community. This Special Interest Group (SIG) aims to directly address these challenges by convening a broad community of queer researchers and allies, working not only on explicitly-queer topics but across a broad range of HCI topics.
A Generalized Asymmetric Dual-Front Model for Active Contours and Image Segmentation

The Voronoi diagram-based dual-front active contour models are known as a powerful and efficient way for addressing the image segmentation and domain partitioning problems. In the basic formulation of the dual-front models, the evolving contours can be considered as the interfaces of adjacent Voronoi regions. Among these dual-front models, a crucial ingredient is regarded as the geodesic metrics by which the geodesic distances and the corresponding Voronoi diagram can be estimated. In this paper, we introduce a type of asymmetric quadratic metrics dual-front model. The metrics considered are built by the integration of the image features and a vector field derived from the evolving contours. The use of the asymmetry enhancement can reduce the risk of contour shortcut or leakage problems especially when the initial contours are far away from the target boundaries or the images have complicated intensity distributions. Moreover, the proposed dual-front model can be applied for image segmentation in conjunction with various region-based homogeneity terms. The numerical experiments on both synthetic and real images show that the proposed dual-front model indeed achieves encouraging results.
Slot-consistent NLG for Task-oriented Dialogue Systems with Iterative Rectification Network

Data-driven approaches using neural networks have achieved promising performances in natural language generation (NLG).However, neural generators are prone to make mistakes, e.g., neglecting an input slot value and generating a redundant slot value.Prior works refer this to hallucination phenomenon.In this paper, we study slot consistency for building reliable NLG systems with all slot values of input dialogue act (DA) properly generated in output sentences.We propose Iterative Rectification Network (IRN) for improving general NLG systems to produce both correct and fluent responses.It applies a bootstrapping algorithm to sample training candidates and uses reinforcement learning to incorporate discrete reward related to slot inconsistency into training.Comprehensive studies have been conducted on multiple benchmark datasets, showing that the proposed methods have significantly reduced the slot error rate (ERR) for all strong baselines.Human evaluations also have confirmed its effectiveness.
Bayesian Hierarchical Words Representation Learning

This paper presents the Bayesian Hierarchical Words Representation (BHWR) learning algorithm. BHWR facilitates Variational Bayes word representation learning combined with semantic taxonomy modeling via hierarchical priors. By propagating relevant information between related words, BHWR utilizes the taxonomy to improve the quality of such representations. Evaluation of several linguistic datasets demonstrates the advantages of BHWR over suitable alternatives that facilitate Bayesian modeling with or without semantic priors. Finally, we further show that BHWR produces better representations for rare words.
Parallel Sentence Mining by Constrained Decoding

We present a novel method to extract parallel sentences from two monolingual corpora, using neural machine translation.Our method relies on translating sentences in one corpus, but constraining the decoding by a prefix tree built on the other corpus.We argue that a neural machine translation system by itself can be a sentence similarity scorer and it efficiently approximates pairwise comparison with a modified beam search.When benchmarked on the BUCC shared task, our method achieves results comparable to other submissions.
Too Many Cooks: Coordinating Multi-agent Collaboration Through Inverse Planning

Humans collaborate in dynamic and flexible ways. Collaboration requires agents to coordinate their behavior on the fly, sometimes jointly solving a single task together and other times dividing it up into sub-tasks to work on in parallel. We develop Bayesian Delegation, a learning mechanism for decentralized multi-agent coordination that enables agents to rapidly infer the sub-tasks that other agents are working on by inverse planning. These inferences enable agents to determine, in the absence of communication, whether to plan jointly with others or work on complementary sub-tasks. We test this model in a suite of decentralized multi-agent environments inspired by cooking problems. To succeed, agents must coordinate both their high-level plans (sub-task) and their low-level actions (avoiding collisions). Including joint sub-tasks in the prior of Bayesian delegation enables agents to carry out sub-tasks that neither agent can finish independently. The full system outperforms lesioned systems that are missing one or more of these capabilities.
Sentiment Analysis in Social Media using Machine Learning Techniques

Over the last period, social media achieved a widespread use worldwide where the statistics indicate that more than three billion people are on social media, leading to large quantities of data online. To analyze these large quantities of data, a special classification method known as sentiment analysis, is used. This paper presents a new sentiment analysis system based on machine learning techniques, which aims to create a process to extract the polarity from social media texts. By using machine learning techniques, sentiment analysis achieved a great success around the world. This paper investigates this topic and proposes a sentiment analysis system built on Bayesian Rough Decision Tree (BRDT) algorithm. The experimental results show the success of this system where the accuracy of the system is more than 95% on social media data.
Accuracy of Six Interpolation Methods Applied on Pupil Diameter Data

Eye-tracking is a method of recording the location of gaze as well as pupil diameter (dilation) during active visual behavior. Due to blinking and noise in the recording system, these signals are often briefly “lost”, leading to missing data. Here, we aim to analyze the accuracy of six interpolation methods to complete missing values from pupil diameter data. Data interpolation is a type of estimation that constructs new data from existing, neighboring values. Having the possibility to choose from different types of methods, the question is which interpolation method is the most suitable for pupil diameter data. Here, we applied the linear method, the previous neighbors method and four cubic interpolations. Using real data recorded during a visual eye-tracking experiment, we compared the maximum deviations of each interpolation method. Results indicate that for pupil diameter data, the most accurate reconstruction of pupil loss is obtained by Akima, Makima, and the Piecewise Cubic Hermite Interpolating Polynomial.
GoonAR: A Bilingual Children Storybook through Augmented Reality Technology Using Unity with Vuforia Framework

This paper aims to apply the augmented reality (AR) technology in storytelling to enhance and give a more motivating and fun reading experience to children.The primary objective of this study is to develop a bilingual AR children storybook mobile application called GoonAR.GoonAR is an android smartphone application that uses augmented reality technology to display 3D characters and environments in the story.The app offers auditory, kinesthetic, and visual learners a fun experience as they are immersed in learning activities.An original story was developed, and a pseudo-book is created.The pseudo-book incorporates the AR technology that displays 3D assets.Grade 1 students under 6 -7 years of age are the target users for this application, as children at this age start to learn reading.The users are able to understand the story by (1) the aid of a narrator that speaks both English and Filipino language or (2) by reading it themselves on the phone and view displayed 3D objects that were made possible through the use of Unity with Vuforia framework.
Dumbo, Jumbo, and Delirium: Parallel Authenticated Encryption for the Lightweight Circus

With the trend to connect more and more devices to the Internet, authenticated encryption has become a major backbone in securing the communication, not only between these devices and servers, but also the direct communication among these devices. Most authenticated encryption algorithms used in practice are developed to perform well on modern high-end devices, but are not necessarily suited for usage on resource-constrained devices. We present a lightweight authenticated encryption scheme, called Elephant. Elephant retains the advantages of GCM such as parallelism, but is tailored to the needs of resource-constrained devices. The two smallest instances of Elephant, Dumbo and Jumbo, are based on the 160-bit and 176-bit Spongent permutation, respectively, and are particularly suited for hardware; the largest instance of Elephant, Delirium, is based on 200-bit Keccak and is developed towards software use. All three instances are parallelizable, have a small state size while achieving a high level of security, and are constant time by design.
Backward Compatible Object Detection Using HDR Image Content

Convolution Neural Network (CNN)-based object detection models have achieved unprecedented accuracy in challenging detection tasks. However, existing detection models (detection heads) trained on 8-bits/pixel/channel low dynamic range (LDR) images are unable to detect relevant objects under lighting conditions where a portion of the image is either under-exposed or over-exposed. Although this issue can be addressed by introducing High Dynamic Range (HDR) content and training existing detection heads on HDR content, there are several major challenges, such as the lack of real-life annotated HDR dataset(s) and extensive computational resources required for training and the hyper-parameter search. In this paper, we introduce an alternative backwards-compatible methodology to detect objects in challenging lighting conditions using existing CNN-based detection heads. This approach facilitates the use of HDR imaging without the immediate need for creating annotated HDR datasets and the associated expensive retraining procedure. The proposed approach uses HDR imaging to capture relevant details in high contrast scenarios. Subsequently, the scene dynamic range and wider colour gamut are compressed using HDR to LDR mapping techniques such that the salient highlight, shadow, and chroma details are preserved. The mapped LDR image can then be used by existing pre-trained models to extract relevant features required to detect objects in both the under-exposed and over-exposed regions of a scene. In addition, we also conduct an evaluation to study the feasibility of using existing HDR to LDR mapping techniques with existing detection heads trained on standard detection datasets such as PASCAL VOC and MSCOCO. Results show that the images obtained from the mapping techniques are suitable for object detection, and some of them can significantly outperform traditional LDR images.
NPALOSS: NEIGHBORING PIXEL AFFINITY LOSS FOR SEMANTIC SEGMENTATION IN HIGH-RESOLUTION AERIAL IMAGERY

Abstract. The performance of semantic segmentation in high-resolution aerial imagery has been improved rapidly through the introduction of deep fully convolutional neural network (FCN). However, due to the complexity of object shapes and sizes, the labeling accuracy of small-sized objects and object boundaries still need to be improved. In this paper, we propose a neighboring pixel affinity loss (NPALoss) to improve the segmentation performance of these hard pixels. Specifically, we address the issues of how to determine the classifying difficulty of one pixel and how to get the suitable weight margin between well-classified pixels and hard pixels. Firstly, we convert the first problem into a problem that the pixel categories in the neighborhood are the same or different. Based on this idea, we build a neighboring pixel affinity map by counting the pixel-pair relationships for each pixel in the search region. Secondly, we investigate different weight transformation strategies for the affinity map to explore the suitable weight margin and avoid gradient overflow. The logarithm compression strategy is better than the normalization strategy, especially the common logarithm. Finally, combining the affinity map and logarithm compression strategy, we build NPALoss to adaptively assign different weights for each pixel. Comparative experiments are conducted on the ISPRS Vaihingen dataset and several commonly-used state-of-the-art networks. We demonstrate that our proposed approach can achieve promising results.
EMIP: The eye movements in programming dataset

A large dataset that contains the eye movements of N=216 programmers of different experience levels captured during two code comprehension tasks is presented. Data are grouped in terms of programming expertise (from none to high) and other demographic descriptors. Data were collected through an international collaborative effort that involved eleven research teams across eight countries on four continents. The same eye tracking apparatus and software was used for the data collection. The Eye Movements in Programming (EMIP) dataset is freely available for download. The varied metadata in the EMIP dataset provides fertile ground for the analysis of gaze behavior and may be used to make novel insights about code comprehension.
AR-Child: Analysis, Evaluation, and Effect of Using Augmented Reality as a Learning Media for Preschool Children

The use of technology to improve the experience and teaching and learning process in the classroom has been promoted now. One of these technologies is Augmented Reality (AR). Users are in a virtual space while still having nuances in real life called AR. AR has been proven to offer several solutions in the context of education, namely increasing learning involvement and increasing understanding of some materials. This study proposes AR-assisted learning media with the method of 3D Based Tracking and audio Marker, which is used by preschool children to recognize animals and fruits in English alphabetically, then analyze, evaluate, and know the effects on their use. The Extreme Programming (XP) method is used as an application development method. Application testing is done by black box testing, testing variations between distance, angle, lighting, background, and font size of the marker, and AR-Child response time to the detection of markers by the camera for the appearance of virtual objects. Thirty preschool children aged 4-6 years were involved in using the AR-Child application and analyzing application testing on the attractiveness and fun elements of students with fun testing. The effect of using AR-Child is seen from the learning outcomes at the end of the second meeting.
Analysis of Face Recognition with Fuzzy C-Means Clustering Image Segmentation and Learning Vector Quantization

Digital image processing is used to analyze images consisting of visual perceptions whose purpose is to manipulate, modify the image, improve the quality of the image and prioritize the special features contained in the image by using the special attributes of the image characteristics that can be grouped based on the similarity of the image characteristics with each other. One method used in image recognition from artificial neural networks is Learning Vector Quantization (LVQ). The application of the LVQ method for face recognition requires a feature segmentation process so that the feature extraction values obtained only cover the face, this stage is carried out using the Fuzzy C-Means and K-Means Clustering methods. The results of face recognition testing using the LVQ method and K-Means Clustering segmentation obtained an accuracy of 92.22% (training) and 72.5% (testing). Face recognition with Fuzzy C-Means Clustering segmentation obtained face recognition results of 95.78% (training) and 76% (testing). The LVQ-FCM method is better than the LVQ with KMeans, obtained training accuracy of 3.56% and 3.5% for network testing.
Symmetric Encryption Algorithms: Review and Evaluation Study

The increased exchange of data over the Internet in the past two decades has brought data security and confidentiality to the fore front. Information security can be achieved by implementing encryption and decryption algorithms to ensure data remains secure and confidential, especially when transmitted over an insecure communication channel. Encryption is the method of coding information to prevent unauthorized access and ensure data integrity and confidentiality, whereas the reverse process is known as decryption. All encryption algorithms aim to secure data, however, their performance varies according to several factors such as file size, type, complexity, and platform used. Furthermore, while some encryption algorithms outperform others, they have been proven to be vulnerable against certain attacks. In this paper, we present a general overview of common encryption algorithms   and explain their inner workings. Additionally, we select ten different symmetric encryption algorithms and conduct a simulation in Java to test their performance. The algorithms we compare are: AES, BLOWFISH, RC2, RC4, RC6, DES, DESede, SEED, XTEA, and IDEA. We present the results of our simulation in terms of encryption speed, throughput, and CPU utilization rate for various file sizes ranging from 1MB to 1GB. We further analyze our results for all measures that have been tested, taking into account the level of security they provide.
MACD R-CNN: An Abnormal Cell Nucleus Detection Method

The detection of abnormal cell nuclei is a key technique of the cytopathic automatic screening system, which directly determines the performance of the system.Although the Mask R-CNN which combines target detection and semantic segmentation has achieved good performance in general target detection tasks, the performance in abnormal cell detection is still unsatisfactory.To solve this problem, we design a new deep neural network for abnormal cell detection based on the Mask R-CNN, named mask abnormal cell detection R-CNN (MACD R-CNN).First, in the classification branch of Mask R-CNN, it generates the same size of feature maps from different size of RoIs as the input.The nuclei in this part of the feature maps will be deformed to varying degrees.We design a fixed proposal module to generate fixed-sized feature maps of nuclei, which allows the new information of nucleus is used for classification.Then we use the attention mechanism to merge the original RoI and Fixed RoI features.Finally, we increase the depth of the convolution layer to further improve the accuracy of cell classification.Experiments show that the MACD R-CNN can effectively improve the performance of abnormal cell detection.
Brain Tumor Segmentation Using 3D Generative Adversarial Networks

Brain tumors have high morbidity and may lead to highly lethal cancer. In clinics, accurate segmentation of tumors is the means for diagnosis and determination of subsequent treatment options. Due to the irregularity and blurring of tumor boundaries, accurately segmenting the tumor lesions has received extensive attention in medical image analysis. In view of this situation, this paper proposed a brain tumor segmentation method based on generative adversarial networks (GANs). The GAN architecture consists of a densely connected three-dimensional (3D) U-Net used for segmentation and a classification network for discrimination, both of which use 3D convolutions to fuse multi-dimensional context information. The densely connected 3D U-Net model introduces a dense connection to accelerate network convergence, extracting more detailed information. The adversarial training makes the distribution of segmentation results closer to that of labeled data, which enables the network to segment some unexpected small tumor subregions. Alternately, train two networks and finally achieve a highly accurate classification of each voxel. The experiments conducted on BraTS2017 brain tumor MRI dataset show that the proposed method has higher accuracy in brain tumor segmentation.
A Deep Graph Structured Clustering Network

Graph clustering is a fundamental task in data analysis and has attracted considerable attention in recommendation systems, mapping knowledge domain, and biological science. Because graph convolution is very effective in combining the feature information and topology information of graph data, some graph clustering methods based on graph convolution have achieved superior performance. However, current methods lack the consideration of structured information and the process of graph convolution. Specifically, most of existing methods ignore the implicit interaction between topology information and feature information, and the stacking of a small number of graph convolutional layers leads to insufficient learning of complex information. Inspired by graph convolutional network and auto-encoder, we propose a deep graph structured clustering network that applies a deep clustering method to graph structured data processing. Deep graph convolution is employed in the backbone network, and evaluates the result of each iteration with node feature and topology information. In order to optimize the network without supervision, a triple self-supervised module is designed to help update parameters for overall network. In our model, we exploit all information of the graph structured data and perform self-supervised learning. Furthermore, improved graph convolution layers significantly alleviate the problem of clustering performance degradation caused by over-smoothing. Our model is designed to perform on representative and indirect graph datasets, and experimental results demonstrate that our model achieves superior performance over state-of-the-art models.