Reviewerly/exHarmony huggingface dataset:
BezelGlide: Interacting with Graphs on Smartwatches with Minimal Screen Occlusion

We present BezelGlide, a novel suite of bezel interaction techniques, designed to minimize screen occlusion and ‘fat finger’ effects, when interacting with common graphs on smartwatches. To explore the design of BezelGlide, we conducted two user studies. First, we quantified the amount of screen occlusion experienced when interacting with the smartwatch bezel. Next, we designed two techniques that involve gliding the finger along the smartwatch bezel for graph interaction. Full BezelGlide (FBG) and Partial BezelGlide (PBG), use the full or a portion of the bezel, respectively, to reduce screen occlusion while scanning a line chart for data. In the common value detection task, we find that PBG outperforms FBG and Shift, a touchscreen occlusion-free technique, both quantitatively and subjectively, also while mobile. We finally illustrate the generzability potential of PBG to interact with common graph types making it a valuable interaction technique for smartwatch users.
Head-Worn Displays for Emergency Medical Services Staff

Head-worn displays (HWDs) offer their users high mobility, hands-free operation, and “see-what-I-see” features. In the prehospital environment, emergency medical services (EMS) staff could benefit from the unique characteristics of HWDs. We conducted a field study to analyze work practices of EMS staff and the potential of HWDs to support their activities. Based on our observations and the comments of EMS staff, we propose three use cases for HWDs in the prehospital environment. They are (1) enhanced communication between different care providers, (2) hands-free access to clinical monitoring and imaging, (3) and improved realism of training scenarios. We conclude with a set of design considerations and suggest that for the successful implementation of HWDs in EMS environments, researchers, designers, and clinical stakeholders should consider the harsh outdoor environment in which HWDs will be used, the extensive workload of staff, the complex collaboration performed, privacy requirements, and the high variability of work.
Cross-Silo Federated Training in the Cloud with Diversity Scaling and Semi-Supervised Learning

Federated learning is a machine learning approach that allows a loose federation of trainers to collaboratively improve a shared model, while making minimum assumptions on central availability of data. In cross-siloed federated learning, data is partitioned into silos, each with an associated trainer. This work presents results from training an end-to-end ASR model with cross-silo federated learning system. We propose a novel aggregation algorithm that takes update diversity into account and significantly outperforms Federated Averaging (FedAvg). The system design used in this paper allows joint training with human transcribed and semi-supervised (SSL) data, yielding 7.6% relative word error rate reduction on head test set and 13.9% on tail test set, when using 20kHr of SSL data. Gains further improve to 13.8% and 20.5% respectively when SSL data is increased from 20kHr to 200kHr.
Computation of Solution Spaces for Optimization-Based Trajectory Planning

The nonlinear vehicle dynamics and the non-convexity of collision avoidance constraints pose major challenges for optimization-based trajectory planning of automated vehicles. Current solutions are either tailored to specific traffic scenarios, simplify the vehicle dynamics, are computationally demanding, or may get stuck in local minima. This work presents a novel approach to address the aforementioned shortcomings by identifying collision-free driving corridors that represent spatio-temporal constraints for motion planning using set-based reachability analysis. We derive a suitable formulation of collision avoidance constraints from driving corridors that can be integrated into arbitrary nonlinear programs as well as (successive) convexification procedures. When combining our approach with existing motion planning methods based on continuous optimization, trajectories can be planned in arbitrary traffic situations in a computationally efficient way. We demonstrate the efficacy of our approach using scenarios from the CommonRoad benchmark suite.
VOILA: Visual-Observation-Only Imitation Learning for Autonomous Navigation

While imitation learning for vision-based au-tonomous mobile robot navigation has recently received a great deal of attention in the research community, existing approaches typically require state-action demonstrations that were gathered using the deployment platform. However, what if one cannot easily outfit their platform to record these demonstration signals or-worse yet-the demonstrator does not have access to the platform at all? Is imitation learning for vision-based autonomous navigation even possible in such scenarios? In this work, we hypothesize that the answer is yes and that recent ideas from the Imitation from Observation (IfO) literature can be brought to bear such that a robot can learn to navigate using only ego-centric video collected by a demonstrator, even in the presence of viewpoint mismatch. To this end, we introduce a new algorithm, Visual-Observation-only Imitation Learning for Autonomous navigation (VOILA), that can successfully learn navigation policies from a single video demonstration collected from a physically different agent. We evaluate VOILA in the AirSim simulator and show that VOILA not only successfully imitates the expert, but that it also learns navigation policies that can generalize to novel environments. Further, we demonstrate the effectiveness of VOILA in a real-world setting by showing that it allows a wheeled Jackal robot to successfully imitate a human walking in an environment while recording video with a handheld mobile phone camera.
Collaborative learning in VR for cross-disciplinary distributed student teams

This paper discusses the use of virtual reality collaborative learning spaces for students working in cross-disciplinary groups across two geographically distant campuses. As part of the course, students worked in groups implementing educational VR tools, including the one used for collaboration. Each group had regular meetings in VR to collaborate with another group located in a different campus. Each group had to choose a topic, determine their team structure, define their communication methods and collaborate to implement VR-based tools both with their local group and their remote teammates in another campus. This paper reflects on the experience of collaborating in VR as part of the course, over a period of 2 years. The paper’s contribution is presenting the benefits and challenges for collaborative learning using the presented VR-based tools, including challenges with audio communication, socializing, VR interactions, and efficiency.
Unsupervised Domain Adaptation with Multiple Domain Discriminators and Adaptive Self-Training

Unsupervised Domain Adaptation (UDA) aims at improving the generalization capability of a model trained on a source domain to perform well on a target domain for which no labeled data is available. In this paper, we consider the semantic segmentation of urban scenes and we propose an approach to adapt a deep neural network trained on synthetic data to real scenes addressing the domain shift between the two different data distributions. We introduce a novel UDA framework where a standard supervised loss on labeled synthetic data is supported by an adversarial module and a self-training strategy aiming at aligning the two domain distributions. The adversarial module is driven by a couple of fully convolutional discriminators dealing with different domains: the first discriminates between ground truth and generated maps, while the second between segmentation maps coming from synthetic or real world data. The self-training module exploits the confidence estimated by the discriminators on unlabeled data to select the regions used to reinforce the learning process. Furthermore, the confidence is thresholded with an adaptive mechanism based on the per-class overall confidence. Experimental results prove the effectiveness of the proposed strategy in adapting a segmentation network trained on synthetic datasets like GTA5 and SYNTHIA, to real world datasets like Cityscapes and Mapillary.
Histopathological image and gene expression pattern analysis for predicting molecular features and prognosis of head and neck squamous cell carcinoma

Histopathological image features offer a quantitative measurement of cellular morphology, and probably help for better diagnosis and prognosis in head and neck squamous cell carcinoma (HNSCC).We first used histopathological image features and machine-learning algorithms to predict molecular features of 212 HNSCC patients from The Cancer Genome Atlas (TCGA). Next, we divided TCGA-HNSCC cohort into training set (n = 149) and test set (n = 63), and obtained tissue microarrays as an external validation set (n = 126). We identified the gene expression profile correlated to image features by bioinformatics analysis.Histopathological image features combined with random forest may predict five somatic mutations, transcriptional subtypes, and methylation subtypes, with area under curve (AUC) ranging from 0.828 to 0.968. The prediction model based on image features could predict overall survival, with 5-year AUC of 0.831, 0.782, and 0.751 in training, test, and validation sets. We next established an integrative prognostic model of image features and gene expressions, which obtained better performance in training set (5-year AUC = 0.860) and test set (5-year AUC = 0.826). According to histopathological transcriptomics risk score (HTRS) generated by the model, high-risk and low-risk patients had different survival in training set (HR = 4.09, p < 0.001) and test set (HR=3.08, p = 0.019). Multivariate analysis suggested that HTRS was an independent predictor in training set (HR = 5.17, p < 0.001). The nomogram combining HTRS and clinical factors had higher net benefit than conventional clinical evaluation.Histopathological image features provided a promising approach to predict mutations, molecular subtypes, and prognosis of HNSCC. The integration of image features and gene expression data had potential for improving prognosis prediction in HNSCC.
A hierarchical model for quantifying software security based on static analysis alerts and software metrics

Despite the acknowledged importance of quantitative security assessment in secure software development, current literature still lacks an efficient model for measuring internal software security risk. To this end, in this paper, we introduce a hierarchical security assessment model (SAM), able to assess the internal security level of software products based on low-level indicators, i.e., security-relevant static analysis alerts and software metrics. The model, following the guidelines of ISO/IEC 25010, and based on a set of thresholds and weights, systematically aggregates these low-level indicators in order to produce a high-level security score that reflects the internal security level of the analyzed software. The proposed model is practical, since it is fully automated and operationalized in the form of a standalone tool and as part of a broader Computer-Aided Software Engineering (CASE) platform. In order to enhance its reliability, the thresholds of the model were calibrated based on a repository of 100 popular software applications retrieved from Maven Repository. Furthermore, its weights were elicited in a way to chiefly reflect the knowledge expressed by the Common Weakness Enumeration (CWE), through a novel weights elicitation approach grounded on popular decision-making techniques. The proposed model was evaluated on a large repository of 150 open-source software applications retrieved from GitHub and 1200 classes retrieved from the OWASP Benchmark. The results of the experiments revealed the capacity of the proposed model to reliably assess internal security at both product level and class level of granularity, with sufficient discretion power. They also provide preliminary evidence for the ability of the model to be used as the basis for vulnerability prediction. To the best of our knowledge, this is the first fully automated, operationalized and sufficiently evaluated security assessment model in the modern literature.
An Empirical Study for Common Language Features Used in Python Projects

As a dynamic programming language, Python is widely used in many fields. For developers, various language features affect programming experience. For researchers, they affect the difficulty of developing tasks such as bug finding and compilation optimization. Former research has shown that programs with Python dynamic features are more change-prone. However, we know little about the use and impact of Python language features in real-world Python projects. To resolve these issues, we systematically analyze Python language features and propose a tool named PYSCAN to automatically identify the use of 22 kinds of common Python language features in 6 categories in Python source code. We conduct an empirical study on 35 popular Python projects from eight application domains, covering over 4.3 million lines of code, to investigate the the usage of these language features in the project. We find that single inheritance, decorator, keyword argument, for loops and nested classes are top 5 used language features. Meanwhile different domains of projects may prefer some certain language features. For example, projects in DevOps use exception handling frequently. We also conduct in-depth manual analysis to dig extensive using patterns of frequently but differently used language features: exceptions, decorators and nested classes/functions. We find that developers care most about ImportError when handling exceptions. With the empirical results and in-depth analysis, we conclude with some suggestions and a discussion of implications for three groups of persons in Python community: Python designers, Python compiler designers and Python developers.
A combination of clustering-based under-sampling with ensemble methods for solving imbalanced class problem in intelligent systems

Nowadays, most real-world datasets suffer from the problem of imbalanced distribution of data samples in classes, especially when the number of data representing the larger class (majority) is much greater than that of the smaller class (minority). In order to solve this problem, various types of undersampling or oversampling techniques have been proposed to create a dataset with equal number of samples in each class by reducing or increasing the number of samples in majority or minority classes, respectively. Ensemble classifiers use multiple learning algorithms to enhance the accuracy of classification. Based on the results, combining undersampling or oversampling methods with ensemble classifiers can result in models with better performance. By using both clustering and new undersampling methods, the present study aimed to propose a novel clustering-based undersampling method to create a balanced dataset. This method uses k-means clustering algorithm for clustering the data, Mahalanobis distance to analyze samples distance in each cluster to centroid, and a selection method that preserves the pattern of data distribution in each cluster. Regarding the experimental results obtained by 44 benchmark datasets from KEEL repository, the proposed approach performed better than that of seven state-of-the-art approaches.
Salient feature extractor for adversarial defense on deep neural networks

Recent years have witnessed unprecedented success achieved by deep learning models in the field of computer vision. However, their vulnerability towards carefully-crafted adversarial examples has also attracted the increasing attention of researchers. Motivated by the observation that adversarial examples are due to the non-robust feature learned from the original dataset by models, we propose the concepts of salient feature (SF) and trivial feature (TF). The former represents the class-related feature, while the latter is usually adopted to mislead the model. We extract these two features with coupled generative adversarial network model and put forward a novel detection and defense method named salient feature extractor (SFE) to defend against adversarial attacks. Concretely, detection is realized by separating and comparing the difference between SF and TF of the input. At the same time, correct labels are obtained by re-identifying SF. Extensive experiments are carried out on MNIST, CIFAR-10, and ImageNet datasets where SFE shows superior results in effectiveness and efficiency compared with state-of-the-art baselines. Furthermore, we provide an interpretable understanding of the defense and detection process. The code of SFE could be downloaded from ( https://github.com/haibinzheng/SFE).
A Few-shot Learning Approach for Historical Ciphered Manuscript Recognition

Encoded (or ciphered) manuscripts are a special type of historical documents that contain encrypted text. The automatic recognition of this kind of documents is challenging because: 1) the cipher alphabet changes from one document to another, 2) there is a lack of annotated corpus for training and 3) touching symbols make the symbol segmentation difficult and complex. To overcome these difficulties, we propose a novel method for handwritten ciphers recognition based on few-shot object detection. Our method first detects all symbols of a given alphabet in a line image, and then a decoding step maps the symbol similarity scores to the final sequence of transcribed symbols. By training on synthetic data, we show that the proposed architecture is able to recognize handwritten ciphers with unseen alphabets. In addition, if few labeled pages with the same alphabet are used for fine tuning, our method surpasses existing unsupervised and supervised HTR methods for ciphers recognition.
PinpointFly: An Egocentric Position-control Drone Interface using Mobile AR

Accurate drone positioning is challenging because pilots only have a limited position and direction perception of a flying drone from their perspective. This makes conventional joystick-based speed control inaccurate and more complicated and significantly degrades piloting performance. We propose PinpointFly, an egocentric drone interface that allows pilots to arbitrarily position and rotate a drone using position-control direct interactions on a see-through mobile AR where the drone position and direction are visualized with a virtual cast shadow (i.e., the drone's orthogonal projection onto the floor). Pilots can point to the next position or draw the drone's flight trajectory by manipulating the virtual cast shadow and the direction/height slider bar on the touchscreen. We design and implement a prototype of PinpointFly for indoor and visual line of sight scenarios, which are comprised of real-time and predefined motion-control techniques. We conduct two user studies with simple positioning and inspection tasks. Our results demonstrate that PinpointFly makes the drone positioning and inspection operations faster, more accurate, simpler and fewer workload than a conventional joystick interface with a speed-control method.
An Efficient Alternating Direction Method for Graph Learning from Smooth Signals

We consider the problem of identifying the graph topology from a set of smooth graph signals. A well-known approach to this problem is minimizing the Dirichlet energy accompanied with some Frobenius norm regularization. Recent works have incorporated the logarithmic barrier on the node degrees to improve the overall graph connectivity without compromising graph sparsity, which is shown to be quite effective in enhancing the quality of the learned graphs. Although a primal-dual algorithm has been proposed in the literature to solve this type of graph learning formulations, it lacks a rigorous convergence analysis and appears to have a slow empirical performance. In this paper, we cast the graph learning formulation as a nonsmooth, strictly convex optimization problem and develop an efficient alternating direction method of multipliers to solve it. We show that our algorithm converges to the global minimum with arbitrary initialization. We conduct extensive experiments on various synthetic and real-world graphs, the results of which show that our method exhibits sharp linear convergence and is substantially faster than the commonly adopted primal-dual method.
Decouple the High-Frequency and Low-Frequency Information of Images for Semantic Segmentation

As a special kind of signal processing technology, image processing has been developed rapidly after the appearance of convolutional neural network (CNN). At present, the semantic segmentation methods are all based on CNN and ignore the advantages of traditional image processing technology. We combine the two and make them promote each other. The high frequency component of the image represents the edge part and the low frequency represents the body part. Based on this assumption, we use Fourier transform to obtain the high and low frequency component from images. Then, a multi-branch parallel network structure is designed, and the high and low frequency components are sent into two branches respectively to obtain the body and edge features. Finally, the two features are fused together through one deep feature fusion to obtain the final output. In this way, the edge information and body information are decoupled on original images and extracted separately, which not only ensures the consistency of the internal information within objects, but also strengthens the supervision of the edge part which is the most error prone area in semantic segmentation. The results on Cityscapes and KITTI fully demonstrate the effectiveness of our work.
Reversible data hiding for encrypted image based on adaptive prediction error coding

Reversible data hiding (RDH) is a useful technique of data security. Embedding capacity is one of the most important performance of RDH for encrypted image. Many existing RDH algorithms for encrypted image do not reach desirable embedding capacity yet. To address this problem, a new RDH algorithm is proposed for encrypted image based on adaptive prediction error coding. The proposed RDH algorithm uses a block-based encryption scheme to preserve spatial correlation of original image in the encrypted domain and exploits a novel technique called adaptive prediction error coding to vacate room for data embedding. A key contribution of the proposed RDH algorithm is the adaptive prediction error coding. It can efficiently vacate room from encrypted image block by adaptively coding prediction errors according to block content and thus contributes to a large embedding capacity. Many experiments on benchmark image databases are done to validate performance of the proposed RDH algorithm. The results show that the average embedding rates on the open databases of UCID, BOSSBase and BOWS-2 are 1.7081, 2.4437 and 2.3083 bpp, respectively. Comparison results illustrate that the proposed RDH algorithm outperforms some state-of-the-art RDH algorithms in embedding capacity.
Parallel GPQUICK

We modified GPQuick to use SIMD parallel floating point AVX 512 bit instructions and 48 threads to give up to 139 billion GP operations per second, 139 giga GPops, on a single Intel Xeon Gold 6126 2.60 GHz server. The multi-threaded single instruction multiple data genetic programming GP interpreter has evolved binary trees of more than 396 million instructions using subtree crossover and run populations for a million generations.