Reviewerly/exHarmony huggingface dataset:
EKFNet: Learning System Noise Statistics from Measurement Data

In this paper, to reduce the time and manpower spent on fine-tuning an extended Kalman filter (EKF), we propose a new learning framework, EKFNet, for automatically estimating the best process and measurement noise covariance pair from the real measurement data. The EKFNet is trained by using backpropagation through time (BPTT). The proposed method can choose among several optimization criteria, such as maximizing the likelihood, minimizing the measurement residual error, or minimizing the posterior state estimation error. We illustrate the proposed method's performance using real GPS data, which outperforms existing methods and a manually tuned EKF.
What Can CHI Do About Dark Patterns?

Imagine buying flowers for a loved one. After selecting a bouquet, at checkout you discover that the site sneaked a paid greeting card into your shopping cart. This is an example of a dark pattern, an interface designed to manipulate a user into behavior that goes against their best interests. The notion of dark patterns has fostered a growing critical discussion about which interfaces go too far in exploiting the user. The first aim of this workshop at CHI 2021 is to bring together a transdisciplinary group of design practitioners and researchers to discuss dark patterns across domains. The second aim is to identify actions to address dark patterns from within the design community, which might include e.g., setting industry norms, articulating values during the design process, or incorporating dark patterns into design education curricula. The third aim is to look beyond the design community and consider what changes designers might advocate for via interactions with e.g., consumers, media, and policymakers.
Improving clinical outcome predictions using convolution over medical entities with multimodal learning

Early prediction of mortality and length of stay (LOS) of a patient is vital for saving a patient's life and management of hospital resources. Availability of Electronic Health Records (EHR) makes a huge impact on the healthcare domain and there are several works on predicting clinical problems. However, many studies did not benefit from the clinical notes because of the sparse, and high dimensional nature. In this work, we extract medical entities from clinical notes and use them as additional features besides time-series features to improve proposed model predictions. The proposed convolution based multimodal architecture, which not only learns effectively combining medical entities and time-series Intensive Care Unit (ICU) signals of patients but also allows to compare the effect of different embedding techniques such as Word2vec and FastText on medical entities. Results show that the proposed deep multimodal method outperforms all other baseline models including multimodal architectures and improves the mortality prediction performance for Area Under the Receiver Operating Characteristics (AUROC) and Area Under Precision-Recall Curve (AUPRC) by around 3%. For LOS predictions, there is an improvement of around 2.5% over the time-series baseline. The code for the proposed method is available at https://github.com/tanlab/ConvolutionMedicalNer.
Latent Gaussian Model Boosting

Latent Gaussian models and boosting are widely used techniques in statistics and machine learning. Tree-boosting shows excellent prediction accuracy on many data sets, but potential drawbacks are that it assumes conditional independence of samples, produces discontinuous predictions for, e.g., spatial data, and it can have difficulty with high-cardinality categorical variables. Latent Gaussian models, such as Gaussian process and grouped random effects models, are flexible prior models which explicitly model dependence among samples and which allow for efficient learning of predictor functions and for making probabilistic predictions. However, existing latent Gaussian models usually assume either a zero or a linear prior mean function which can be an unrealistic assumption. This article introduces a novel approach that combines boosting and latent Gaussian models to remedy the above-mentioned drawbacks and to leverage the advantages of both techniques. We obtain increased prediction accuracy compared to existing approaches in both simulated and real-world data experiments.
Field of View Effect on Distance Perception in Virtual Reality

Recent state-of-the-art Virtual Reality (VR) Head-Mounted Displays (HMD) provide wide Field of Views (FoV) which were not possible in the past. Due to this development, HMD FoVs are now approaching a level that parallels natural human eyesight. Previous efforts have shown that reduced FoVs affect user perception of distance in a given environment, but none have investigated VR HMDs with wide FoVs. Therefore, in this paper we directly investigate the effect of HMD FoV on distance estimation in virtual environments. We performed a user study with 14 participants who performed a blind throwing task wearing a Pimax 5K Plus HMD, in which we virtually restricted the FoV to 200°, 110°, and 60°. We found a significant difference in perceived distance between the 200° and 60° FoVs, as well as between the 110° and 60° FoVs. However, no significant difference was observed between 200° and 110°. Our results indicate that users tend to underestimate distance with the narrower FoV.
Blockchain‐based framework for secured storage, sharing, and querying of electronic healthcare records

Summary Medical records contain vital, critical, and confidential information of any person. Paper‐based medical records are being used by many hospitals that suffer from various issues like large physical storage requirements, limited security, and life. Electronic health records (EHRs) can transform this entire system but faces various security threats like unintentional disclosure, tampering, data leaks, lack of security mechanisms, and data access policies. Thus, there arises a need for a decentralized, tamper‐proof, and transparent healthcare network. In this article, a permissioned blockchain‐based framework using Hyperledger Fabric (HLF) is proposed, which provides secure storage, sharing, and querying of EHRs. For better data accessibility and security, algorithms have been designed to define the functionality of patients, doctors, and third‐party administrator using smart‐contracts. In the existing approaches, LevelDB (key store database) is used as a state database that is not suitable for handling complex queries. To overcome this issue, we have used CouchDB (document store database) as a state database where the search is based on keys and data values both rather than only keys. To measure the various performance indicators for this framework, a blockchain benchmark tool “Hyperledger Caliper” is used. The experimental results show the effectiveness and efficiency of the proposed framework.
Sejong face database: A multi-modal disguise face database

Commercial application of facial recognition demands robustness to a variety of challenges such as illumination, occlusion, spoofing, disguise, etc. Disguised face recognition is one of the emerging issues for access control systems, such as security checkpoints at the borders. However, the lack of availability of face databases with a variety of disguise add-ons limits the development of academic research in the area. In this paper, we present a multi-modal disguised face dataset to facilitate the disguised face recognition research. The presented database contains 8 facial add-ons and 7 additional combinations of these add-ons to create a variety of disguised face images. Each facial image is captured in visible, visible plus infrared, infrared, and thermal spectra. Specifically, the database contains 100 subjects divided into Subset-A (30 subjects, 1 image per modality) and Subset-B (70 subjects, 5 plus images per modality). We also present baseline face detection results performed on the proposed database to provide reference results and compare the performance in different modalities. Qualitative and quantitative analysis is performed to evaluate the challenging nature of disguise add-ons. The dataset will be publicly available with the acceptance of the research article.
Application of Natural Neighbor-based Algorithm on Oversampling SMOTE Algorithms

Classification performance depends highly on data distribution. In real life, data often come imbalanced where one class is found more often than others. SMOTE-based algorithms are usually used to handle the class imbalance problem. One key parameter that algorithms in SMOTE family require is k-the number of nearest neighbors with respect to a certain data point. K that fits the dataset the most gives the optimum performance. This paper proposes an approach to suggest a value of the parameter k using Natural Neighbor algorithm. Datasets are made balanced by four SMOTE-based algorithms-standard SMOTE, Safe-Level-SMOTE, ModifiedSMOTE and Weighted-SMOTE. The F-measure and Recall matrices are used to evaluate classification performance of a Support Vector Machine classifier running against six datasets with different imbalance ratios. The results show that, the average classification performance achieved by the proposed k's is closer to the optimum when compared with the performance given by the default value of k.
Co-designing Resources for Ethics Education in HCI

Due to the evolving nature of technology and its impact on individuals, communities and society, practitioners and designers in Human-Computer Interaction (HCI) are expected to consider ethics in their work. This role has inspired the development of a number of resources for practice, such as tools, frameworks and methods to tackle ethical issues in HCI. But these suffer from low adoption rate potentially because they are not yet part of the standard body of knowledge. To mitigate the issue, we argue that there is an urgent need for ethics education in HCI. Beyond defining ethics, an ethics curriculum must enable practitioners to reflect and allow consideration of intended and unintended consequences of the technologies they create from the ground up, rather than as a fix or an afterthought. In this co-design workshop, we aim to build upon existing practices and knowledge of ethics in HCI and work with the CHI community to enrich ethics curriculum. We will scaffold our collective understandings of the existing resources and create guidelines that support interactive educational experiences to support HCI ethics curriculum.
Gesture Knitter: A Hand Gesture Design Tool for Head-Mounted Mixed Reality Applications

Hand gestures are a natural and expressive input method enabled by modern mixed reality headsets. However, it remains challenging for developers to create custom gestures for their applications. Conventional strategies to bespoke gesture recognition involve either hand-crafting or data-intensive deep-learning. Neither approach is well suited for rapid prototyping of new interactions. This paper introduces a flexible and efficient alternative approach for constructing hand gestures. We present Gesture Knitter: a design tool for creating custom gesture recognizers with minimal training data. Gesture Knitter allows the specification of gesture primitives that can then be combined to create more complex gestures using a visual declarative script. Designers can build custom recognizers by declaring them from scratch or by providing a demonstration that is automatically decoded into its primitive components. Our developer study shows that Gesture Knitter achieves high recognition accuracy despite minimal training data and delivers an expressive and creative design experience.
Learning Autocompletion from Real-World Datasets

Code completion is a popular software development tool integrated into all major IDEs. Many neural language models have achieved promising results in completion suggestion prediction on synthetic benchmarks. However, a recent study When Code Completion Fails: a Case Study on Real-World Completions demonstrates that these results may not translate to improvements in real-world performance. To combat this effect, we train models on real-world code completion examples and find that these models outperform models trained on committed source code and working version snapshots by 12.8% and 13.8% accuracy respectively. We observe this improvement across modeling technologies and show through A/B testing that it corresponds to a 6.2% increase in programmers' actual autocompletion usage. Furthermore, our study characterizes a large corpus of logged autocompletion usages to investigate why training on real-world examples leads to stronger models.
Multitask GANs for Semantic Segmentation and Depth Completion With Cycle Consistency

Semantic segmentation and depth completion are two challenging tasks in scene understanding, and they are widely used in robotics and autonomous driving. Although several studies have been proposed to jointly train these two tasks using some small modifications, such as changing the last layer, the result of one task is not utilized to improve the performance of the other one despite that there are some similarities between these two tasks. In this article, we propose multitask generative adversarial networks (Multitask GANs), which are not only competent in semantic segmentation and depth completion but also improve the accuracy of depth completion through generated semantic images. In addition, we improve the details of generated semantic images based on CycleGAN by introducing multiscale spatial pooling blocks and the structural similarity reconstruction loss. Furthermore, considering the inner consistency between semantic and geometric structures, we develop a semantic-guided smoothness loss to improve depth completion results. Extensive experiments on the Cityscapes data set and the KITTI depth completion benchmark show that the Multitask GANs are capable of achieving competitive performance for both semantic segmentation and depth completion tasks.
Language Model is all You Need: Natural Language Understanding as Question Answering

Different flavors of transfer learning have shown tremendous impact in advancing research and applications of machine learning. In this work we study the use of a certain family of transfer learning, where the target domain is mapped to the source domain. Specifically we map Natural Language Understanding (NLU) problems to Question Answering (QA) problems and we show that in low data regimes this approach offers significant improvements compared to other approaches to NLU. Moreover, we show that these gains could be increased through sequential transfer learning across NLU problems from different domains. We show that our approach could reduce the amount of required data for the same performance by up to a factor of 10.
A Serial-Parallel Self-Attention Network Joint With Multi-Scale Dilated Convolution

Semantic segmentation is a high-level task in the field of computer vision, which paves the way for the realization of a complete understanding of the scene, and has been widely used in automatic driving, human-computer interaction, virtual reality, and other aspects. Recently, the semantic segmentation method of convolutional neural networks with deep structure has been more accurate and efficient than other methods. However, there are some problems in these methods, such as the loss of information caused by the down-sampling operation, the lack of usage of image context information, and the neglect of the relationship between spatial features and channel features. To solve these problems, a novel self-attention network based on the series-parallel structure is proposed in the paper. Firstly, a multi-scale dilated convolution backbone network is constructed by combining the dilated convolution and the residual network, which makes up for the information loss caused by the restriction of the receptor field in the ordinary network and improves the richness of extracted features. Secondly, the self-attention modules are stacked with serial and parallel structures, which can effectively extract the contextual information of space, channel, and space-channel and fully integrate them. Finally, the proposed algorithm is tested extensively and compared with the existing classical algorithms. The experimental results show that the proposed algorithm achieves state-of-the-art performance on the public dataset.
Attentive Visual Semantic Specialized Network for Video Captioning

As an essential high-level task of video understanding topic, automatically describing a video with natural language has recently gained attention as a fundamental challenge in computer vision. Previous models for video captioning have several limitations, such as the existence of gaps in current semantic representations and the inexpressibility of the generated captions. To deal with these limitations, in this paper, we present a new architecture that we call Attentive Visual Semantic Specialized Network (AVSSN), which is an encoder-decoder model based on our Adaptive Attention Gate and Specialized LSTM layers. This architecture can selectively decide when to use visual or semantic information into the text generation process. The adaptive gate makes the decoder to automatically select the relevant information for providing a better temporal state representation than the existing decoders. Besides, the model is capable of learning to improve the expressiveness of generated captions attending to their length, using a sentence-length-related loss function. We evaluate the effectiveness of the proposed approach on the Microsoft Video Description (MSVD) and the Microsoft Research Video-to-Text (MSR-VTT) datasets, achieving state-of-the-art performance with several popular evaluation metrics: BLEU-4, METEOR, CIDEr, and ROUGE <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</sub> .
Multi-Label Classification Using Binary Tree of Classifiers

This article proposes a binary tree of classifiers for multi-label classification that preserves label dependencies and handles class imbalance. At each node, the input data is strategically split into two subsets for its subsequent child nodes, keeping the label correlations intact. A novel approach of partitioning the data based on label-set proximity has also been proposed. Various data appropriate classifiers are trained to learn the binary partition at every internal node. The tree of classifiers grows iteratively depending on two parameters – multi-label entropy and sample cardinality, computed on the data at the current node. During training, the decision at any node is based on these parameters and the branching out is restricted, if deemed unnecessary. Specific classifiers at the leaf nodes perform the final classification task and assign appropriate label-sets to the unlabelled data. The proposed system aims to appropriately split the data and build the hierarchical structure such that the training and classification tasks become simpler. Also, the problem of class imbalance leads to the irregular splitting of data and excessive branching out of the tree which is handled through the novel use of suitable classifiers and parameters at the intermediate and leaf nodes. The proposed method has shown significant performance improvement on fourteen datasets against fourteen existing multi-label classifiers. Two-tailed Wilcoxon signed rank test statistics show that for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$T_{Wilcoxon}(14,0.2)=31$</tex-math></inline-formula> the proposed method outperforms all the other comparison models.
Blockchain Technologies and Their Applications in Data Science and Cyber Security

Blockchain technologies have been very effective in processing distributed transactions securely. They have many applications including in handling bitcoin cryptocurrencies and smart contracts. More recently the use of blockchain has been explored for data science applications. This paper examines blockchain technologies and discusses their applications in data science and cyber security.
Field-of-View Restriction to Reduce VR Sickness Does Not Impede Spatial Learning in Women

Women are more likely to experience virtual reality (VR) sickness than men, which could pose a major challenge to the mass market success of VR. Because VR sickness often results from a visual-vestibular conflict, an effective strategy to mitigate conflict is to restrict the user’s field-of-view (FOV) during locomotion. Sex differences in spatial cognition have been well researched, with several studies reporting that men exhibit better spatial navigation performance in desktop three-dimensional environments than women. However, additional research suggests that this sex difference can be mitigated by providing a larger FOV as this increases the availability of landmarks, which women tend to rely on more than men. Though FOV restriction is already a widely used strategy for VR headsets to minimize VR sickness, it is currently not well understood if it impedes spatial learning in women due to decreased availability of landmarks. Our study (n=28, 14 men and 14 women) found that a dynamic FOV restrictor was equally effective in reducing VR sickness in both sexes, and no sex differences in VR sickness incidence were found. Our study did find a sex difference in spatial learning ability, but an FOV restrictor did not impede spatial learning in either sex.
Towards Robust Speaker Verification with Target Speaker Enhancement

This paper proposes the target speaker enhancement based speaker verification network (TASE-SVNet), an all neural model that couples target speaker enhancement and speaker embedding extraction for robust speaker verification (SV). Specifically, an enrollment speaker conditioned speech enhancement module is employed as the front-end for extracting target speaker from its mixture with interfering speakers and environmental noises. Compared with the conventional target speaker enhancement models, nontarget speaker/interference suppression should draw additional attention for SV. Therefore, an effective nontarget speaker sampling strategy is explored. To improve speaker embedding extraction with a light-weighted model, a teacher-student (T/S) training is proposed to distill speaker discriminative information from large models to small models. Iterative inference is investigated to address the noisy speaker enrollment problem. We evaluate the proposed method on two SV tasks, i.e., one heavily overlapped speech and the other one with comprehensive noise types in vehicle environments. Experiments show significant and consistent improvements in Equal Error Rate (EER) over the state-of-the-art baselines.
A Conceptual Model for Cybersecurity Governance

Cybersecurity is a growing problem associated with everything an individual or an organization does that is facilitated by the Internet. It is a multi-facetted program that can be addressed by cybersecurity governance. However, research has shown that many organizations face at least five basic challenges of cybersecurity. In this study, we developed a model for an effective cybersecurity governance that hopes to address these challenges, conceptualized as factors that must continuously be measured and evaluated. They are: (1) Cybersecurity strategy; (2) Standardized processes, (3) Compliance, (4) Senior leadership oversight, and (5) Resources.