Reviewerly/exHarmony huggingface dataset:
Critical Affects

Techlash encapsulates a breaking point reached with the critique of technology companies. To investigate how this whirlwind of rage, inquiry, and accountability affects the lives of tech workers, we conducted interviews with 19 tech workers. Our methodological approach and contribution adopts a style of writing and analysis associated with anthropologist Kathleen Stewart, where we focus on the affective textures of everyday life in an attempt to redirect the temptation to representational thinking to a slowed ethnographic practice. This paper dwells on the affects of tech workers facing critique and scrutiny. Through this approach, we find that emotional habitus conditions the possibilities of personal and political action and inaction in response to critique. By emotional habitus, we refer to the emotional dispositions honed among tech workers by tech culture's rationality and optimism. This habitus must shift if people are to access new ways of relating and acting. We argue for more fruitful attitudes and practices in relation to critique.
Datamations: Animated Explanations of Data Analysis Pipelines

Plots and tables are commonplace in today's data-driven world, and much research has been done on how to make these figures easy to read and understand. Often times, however, the information they contain conveys only the end result of a complex and subtle data analysis pipeline. This can leave the reader struggling to understand what steps were taken to arrive at a figure, and what implications this has for the underlying results. In this paper, we introduce datamations, which are animations designed to explain the steps that led to a given plot or table. We present the motivation and concept behind datamations, discuss how to programmatically generate them, and provide the results of two large-scale randomized experiments investigating how datamations affect people's abilities to understand potentially puzzling results compared to seeing only final plots and tables containing those results.
Generative Adversarial Network with Multi-branch Discriminator for imbalanced cross-species image-to-image translation

There has been an increased interest in high-level image-to-image translation to achieve semantic matching. Through a powerful translation model, we can efficiently synthesize high-quality images with diverse appearances while retaining semantic matching. In this paper, we address an imbalanced learning problem using a cross-species image-to-image translation. We aim to perform the data augmentation through the image translation to boost the recognition performance of imbalanced learning. It requires a strong ability of the model to perform a biomorphic transformation on a semantic level. To tackle this problem, we propose a novel, simple, and effective structure of Multi-Branch Discriminator (termed as MBD) based on Generative Adversarial Networks (GANs). We demonstrate the effectiveness of the proposed MBD through theoretical analysis as well as empirical evaluation. We provide theoretical proof of why the proposed MBD is an effective and optimal case to achieve remarkable performance. Comprehensive experiments on various cross-species image translation tasks illustrate that our MBD can dramatically promote the performance of popular GANs with state-of-the-art results in terms of both objective and subjective assessments. Extensive downstream image recognition evaluations at a few-shot setting have also been conducted to demonstrate that the proposed method can effectively boost the performance of imbalanced learning.
ConvMath: A Convolutional Sequence Network for Mathematical Expression Recognition

Despite the recent advances in optical character recognition (OCR), mathematical expressions still face a great challenge to recognize due to their two-dimensional graphical layout. In this paper, we propose a convolutional sequence modeling network, ConvMath, which converts the mathematical expression description in an image into a LaTeX sequence in an end-to-end way. The network combines an image encoder for feature extraction and a convolutional decoder for sequence generation. Compared with other Long Short Term Memory(LSTM) based encoder-decoder models, ConvMath is entirely based on convolution, thus it is easy to perform parallel computation. Besides, the network adopts multi-layer attention mechanism in the decoder, which allows the model to align output symbols with source feature vectors automatically, and alleviates the problem of lacking coverage while training the model. The performance of ConvMath is evaluated on an open dataset named IM2LATEX-100K, including 103556 samples. The experimental results demonstrate that the proposed network achieves state-of-the-art accuracy and much better efficiency than previous methods.
Research on the application of university teaching management evaluation system based on Apriori algorithm

Abstract University teaching quality evaluation is supported by big data, which can collect rich and comprehensive data materials as the basis of evaluation. However, it is difficult to find accurate and effective information from the huge data materials. This paper gradually establish “the whole process of implementation, multi stakeholder participation in the teaching quality evaluation system”. This paper take “the collection of data to achieve standardization, the application of data to achieve diversification and the management of data to achieve institutionalization” management measures. This paper make full use of the advantages of big data to improve the teaching quality of undergraduate teaching in local colleges and universities. This paper mainly expounds the Apriori algorithm in association rules of data mining, and uses it to further analyze the teaching evaluation results, trying to dig out the potential valuable information. These information can be used as a reference for further decision-making of teaching management department, so as to effectively improve the quality of education and teaching and accelerate the realization of education modernization.
RECAST

With the widespread use of toxic language online, platforms are increasingly using automated systems that leverage advances in natural language processing to automatically flag and remove toxic comments. However, most automated systems -- when detecting and moderating toxic language -- do not provide feedback to their users, let alone provide an avenue of recourse for these users to make actionable changes. We present our work, RECAST, an interactive, open-sourced web tool for visualizing these models' toxic predictions, while providing alternative suggestions for flagged toxic language. Our work also provides users with a new path of recourse when using these automated moderation tools. RECAST highlights text responsible for classifying toxicity, and allows users to interactively substitute potentially toxic phrases with neutral alternatives. We examined the effect of RECAST via two large-scale user evaluations, and found that RECAST was highly effective at helping users reduce toxicity as detected through the model. Users also gained a stronger understanding of the underlying toxicity criterion used by black-box models, enabling transparency and recourse. In addition, we found that when users focus on optimizing language for these models instead of their own judgement (which is the implied incentive and goal of deploying automated models), these models cease to be effective classifiers of toxicity compared to human annotations. This opens a discussion for how toxicity detection models work and should work, and their effect on the future of online discourse.
A lithological sequence classification method with well log via SVM-assisted bi-directional GRU-CRF neural network

When available core samples are limited, logging data becomes important in the lithology classification. For different lithologies, the distribution of logging data usually overlap each other, which increases the solution multiplicity of a single spatial point. The modeling of lithologic sequences depending on the vertical spatial relationship can reduce this multiplicity. To improve the modeling ability of sequences, we propose a lithological sequence classification algorithm modeled by bi-directional Gated Recurrent Units and Conditional Random Field layer (Bi-GRU-CRF) referring the proposed Artificial Neural Networks and Hidden Markov Models (ANN-HMM) hybrid framework. However, due to the limited training data and the difference of lithologic sequences, unlike the point-based algorithms, the generalization performance of the sequence-based algorithms can be significantly reduced. For this problem, we concatenate the probability output vector of Support Vector Machine (SVM) with original data as input to Bi-GRU-CRF, and the overall structure is named SVM + Bi-GRU-CRF. In the cross-validation with field data, whenever the sequences of training and test dataset are similar or dissimilar, SVM + Bi-GRU-CRF can generally achieve the best results comparing with all point-based and other sequence-based algorithms. Furthermore, the applicable conditions of this algorithm are discussed in three aspects: the relationship between the algorithm and data, the function of each module, and the influence of step size parameters. This work is progressed in three designed experiments with two groups of comparative synthetic datasets, which are generated with Gaussian Mixture Models (GMMs). Finally, a convictive and comprehensive evaluation of SVM + Bi-GRU-CRF is given out.
Deep Learning for the Detection of Breast Cancers on Chest Computed Tomography

Incidental breast cancers can be detected on chest computed tomography (CT) scans. With the use of deep learning, the sensitivity of incidental breast cancer detection on chest CT would improve. This study aimed to evaluate the performance of a deep learning algorithm to detect breast cancers on chest CT and to validate the results in the internal and external datasets.This retrospective study collected 1170 preoperative chest CT scans after the diagnosis of breast cancer for algorithm development (n = 1070), internal test (n = 100), and external test (n = 100). A deep learning algorithm based on RetinaNet was developed and tested to detect breast cancer on chest CT.In the internal test set, the algorithm detected 96.5% of breast cancers with 13.5 false positives per case (FPs/case). In the external test set, the algorithm detected 96.1% of breast cancers with 15.6 FPs/case. When the candidate probability of 0.3 was used as the cutoff value, the sensitivities were 92.0% with 7.36 FPs/case for the internal test set and 93.0% with 8.85 FPs/case for the external test set. When the candidate probability of 0.4 was used as the cutoff value, the sensitivities were 88.5% with 5.24 FPs/case in the internal test set and 90.7% with 6.3 FPs/case in the external test set.The deep learning algorithm could sensitively detect breast cancer on chest CT in both the internal and external test sets.
Blockchain Based Smart Healthcare System for Chronic –Illness Patient Monitoring

Chronic disease exists over a period of time and it is very crucial because it can affect the health of disability people more. Normally people think that they are not suffering or affected by the disease because they do not have symptoms. But it does not necessarily mean a person with no symptom has no chronic disease. This paper presents design and prototype implementation of a blockchain system which remotely monitors patient health records. The patient monitoring system is a big challenge and other security threats arises as the health care data transferred from one to another and authorization to create and read transactions, is a major concern related to data loss. The data loss of the patient protected health information which are generated from the devices should be handled carefully. So in this paper we proposed a solidity based smart contract run over a Ethereum virtual machine to facilitate the secure analysis and storing of transactions from and to the sensors. Using the Ethereum protocol, based on a private blockchain and writing the smart contracts to keep track of the patient health information. We have created an automatic system through which the sensors collects the data, then communicates with the decentralized application via the smart contract. The method inside the smart contract collects data when they are called, also reads and writes transactions of all events on the Ethereum virtual network. This solidity based smart contract system along with injected web3, metamask, and truffle environment would support alert transaction to the patient and medical professionals.
Face Mask Detection Using Deep Learning

In order to prevent the spread of CORONA virus, everyone must wear a mask during the pandemic. In these tough times of COVID-19 it is necessary to build a model that detects people with and without mask in real-time as it works as a simple precautionary measure to prevent the spread of virus. If deployed correctly, this machine learning technique helps in simplifying the work of frontline warriors and saving their lives. A basic Convolutional Neural Network (CNN) model is built using TensorFlow, Keras, Scikit-learn and OpenCV to make the algorithm as accurate as possible. Javascript API helps in accessing webcam for real-time face mask detection. Since Google Colab runs on web browser it can't access local hardware like a camera without APIs. The proposed work contains three stages: (i) pre-processing, (ii) Training a CNN and (iii) Real-time classification. The first part is the Pre-processing section, which can be divided into “Grayscale Conversion” of RGB image, “image resizing and normalization” to avoid false predictions. Then the proposed CNN, classifies faces with and without masks as the output layer of proposed CNN architecture contains two neurons with Softmax activation to classify the same. Categorical cross-entropy is employed as loss function. The proposed model has Validation accuracy of 96%. If anyone in the video stream is not wearing a protective mask a Red coloured rectangle is drawn around the face with a dialog entitled as NO MASK and a Green coloured rectangle is drawn around the face of a person wearing MASK.
Actionable UI Design Guidelines for Smartphone Applications Inclusive of Low-Literate Users

With easy access to affordable internet-powered smartphones, developing countries are adopting smartphone applications to provide enabling services to its citizens, through eHealth, eGovernance, and digital payments. The challenge is to ensure equitable access to these services by everyone, including people with semi-literacy or low-literacy who form a large part of the population in developing countries. However, extensive HCI literature has identified literacy as one of the barriers to designing user interfaces. In this work, we propose a framework of actionable guidelines for designing smartphone UIs that would be usable by low-literate users. We reviewed the last two decades of HCI literature engaging people with low literacy, to synthesize our framework-designing SARAL. To evaluate the framework, we conducted a preliminary study with a group of 20 practitioners and researchers working in the field of UI/UX/HCI. We also analyzed six publicly available industry reports on designing UIs for people with low-literacy. The proposed guidelines intend to support researchers, practitioners, designers, and implementers in the design and evaluation of UIs of smartphone applications for people with low literacy. We present the evolutionary nature of the proposed framework while highlighting the importance of adopting a translational approach when building such frameworks.
Differentially private and utility-aware publication of trajectory data

Trajectory data is valuable for various applications, especially for intelligent transportation systems, which hunger for plenty of trajectories. However, publishing trajectory data while respecting users’ privacy has been a long-standing challenge. Currently, the prevailing releasing solutions usually merge trajectory locations based on k-means and add unbounded noise with Laplace distribution to the count of trajectory to achieve differential privacy protection. The trajectory merging methods based on k-means have a low efficiency and unbounded noise with Laplace distribution will leak user’ privacy and suffer from serious utility loss. To solve the above two problems, we devise two differentially private and utility-aware publication methods of trajectory data. More specifically, we first propose two trajectory merging schemes based on k-means || clustering. The first one is to use k-means || clustering algorithm to cluster the location area, and all the points in the cluster are replaced by the center of cluster. The other is to utilize Staircase mechanism to perturb the cluster centers in order to improve the level of privacy protection. Afterwards, we propose a bounded Staircase noise generation algorithm to perturb the true count of generalized trajectories. We prove our proposed methods preserve differential privacy theoretically. Experimental comparison show that our proposed publication methods significantly outperform existing approaches in terms of data utility and efficiency.
Towards commissioning, resilience and added value of Augmented Reality in robotics: Overcoming technical obstacles to industrial applicability

Augmented Reality (AR) has the potential for facilitating the interaction with robots by enhancing the operator’s spatial understanding as well as providing further cognitive support, e.g. in order to make manual programming processes more efficient and provide on-site simulation. However, we consider major issues that prevent a widespread use of AR towards robotics in industries. Initially, the commissioning of AR devices for robotic applications demands spatial referencing of robots and AR devices. Fiducial markers are a popular artificial aid, but hard to implement in industrial use cases because of additional efforts and a lack of robustness. A further bottleneck for developing AR applications in robotics is the restricted technical and ergonomic maturity of AR devices, e.g. limited local computation and short product life-cycles. Furthermore, benefit through AR in comparison to classic online and offline programming techniques is still unclear for most applicators. In this paper, we present approaches towards a distributed, hardware-agnostic microservice architecture with standard interfaces for an interoperable usage of AR in robotics. Furthermore, we present marker-less pose estimation methods for articulated robot arms as well as mobile robots based on RGB and depth images that serve automatic referencing. Finally, we reveal further application potential of AR in robotics in terms of combining advantages from online and offline programming.
A KCF-Based Incremental Target Tracking Method With Constant Update Speed

With a good balance between tracking accuracy and speed, kernelized correlation filter (KCF)based trackers have now become dominant approaches in the field of online object tracking.However, a KCF-based tracker updates templates by linearly combining historical objects, and its performance inevitably degrades when the accumulated error causes the model to look more similar to the background than the foreground.To address this model drifting problem, we propose an incremental kernel principal component analysis (IKPCA)-based KCF (IKPCA-KCF) tracking scheme.In IKPCA-KCF, IKPCA technology is used to incrementally learn the target features to alleviate the model drifting problem.Moreover, reduced-set (RS) expansions are adopted to compress the historical target samples to maintain a constant template update rate.Additionally, KCF-based trackers use the response map generated by the correlation filter to determine the target location.However, when the target leaves the field of view, using the maximum response value to reflect the target's position information will certainly induce tracking errors.To further solve the tracking stability problem, a simple and effective nearest neighbor classifier is used to confirm the tracking target during the tracking process.Extensive experimental results demonstrate that the proposed IKPCA-KCF tracker is competitive with state-of-the-art trackers on eight challenging video datasets.
Firefox Voice: An Open and Extensible Voice Assistant Built Upon the Web

Voice assistants are fundamentally changing the way we access information. However, voice assistants still leverage little about the web beyond simple search results. We introduce Firefox Voice, a novel voice assistant built on the open web ecosystem with an aim to expand access to information available via voice. Firefox Voice is a browser extension that enables users to use their voice to perform actions such as setting timers, navigating the web, and reading a webpage’s content aloud. Through an iterative development process and use by over 12,000 active users, we find that users see voice as a way to accomplish certain browsing tasks efficiently, but struggle with discovering functionality and frequently discontinue use. We conclude by describing how Firefox Voice enables the development of novel, open web-powered voice-driven experiences.
FingerText: Exploring and Optimizing Performance for Wearable, Mobile and One-Handed Typing

Typing on wearables while situationally impaired, such as while walking, is challenging. However, while HCI research on wearable typing is diverse, existing work focuses on stationary scenarios and fine-grained input that will likely perform poorly when users are on-the-go. To address this issue we explore single-handed wearable typing using intra-hand touches between the thumb and fingers, a modality we argue will be robust to the physical disturbances inherent to input while mobile. We first examine the impact of walking on performance of these touches, noting no significant differences in accuracy or speed, then feed our study data into a multi-objective optimization process in order to design keyboard layouts (for both five and ten keys) capable of supporting rapid, accurate, comfortable, and unambiguous typing. A final study tests these layouts against QWERTY baselines and reports performance improvements of up to 10.45% WPM and 39.44% WER when users type while walking.
Respecting Domain Relations: Hypothesis Invariance for Domain Generalization

In domain generalization, multiple labeled nonindependent and non-identically distributed source domains are available during training while neither the data nor the labels of target domains are. Currently, learning so-called domain invariant representations (DIRs) is the prevalent approach to domain generalization. In this work, we define DIRs employed by existing works in probabilistic terms and show that by learning DIRs, overly strict requirements are imposed concerning the invariance. Particularly, DIRs aim to perfectly align representations of different domains, i.e. their input distributions. This is, however, not necessary for good generalization to a target domain and may even dispose of valuable classification information. We propose to learn so-called hypothesis invariant representations (HIRs), which relax the invariance assumptions by merely aligning posteriors, instead of aligning representations. We report experimental results on public domain generalization datasets to show that learning HIRs is more effective than learning DIRs. In fact, our approach can even compete with approaches using prior knowledge about domains.