Reviewerly/exHarmony huggingface dataset:
Adaptive Convolutional Kernels

The quest for increased computer vision recognition performance has led to the development of high complexity neural network architectures, each time with evolving deeper topologies. To avoid high computing resource requirements of such complex networks, and to enable operation on devices with limited resources, this work introduces the concept of adaptive kernels applied to convolutional layers. Motivated by the non-linear perception response in human visual cells, the input image is used to define the weights of a dynamically changing kernel, named adaptive kernel. This novel adaptive kernel is used to perform a second convolution operation over the input image in order to generate the output features. Adaptive kernels enable accurate recognition with significant lower memory requirements; this is accomplished by reducing the number of kernels and the number of layers needed as compared to typical CNN configurations. Additionally, the use of adaptive kernels allow the decrease by 2X the number of epochs required for training, and the number of activation function computations. Our experimental results show a reduction of 66X of the parameters needed of a CNN compared to LeNet when evaluated with the MNIST dataset, maintaining >99% of accuracy. Additionally, when using adaptive kernels implemented in a ResNet18, we observed a higher performance when compared to a known ResNet100 reported in the literature for CIFAR10 and it also gets better accuracy for ImageNet database.
Directed disease networks to facilitate multiple-disease risk assessment modeling

We investigate multiple disease risk prediction modeling, aimed at assessing future disease risks for an individual who is ready for discharge after hospitalization. We propose a novel framework that combines directed disease network and recommendation system techniques to substantially enhance multiple disease risk predictive modeling. Firstly, a directed disease network considering temporal information is developed. Then based on this directed disease network, we look into different disease risk score computing approaches. We validate the proposed approaches with two real-world datasets from two independent hospitals. The predicted results can be promisingly utilized as a reference for medical experts to offer effective healthcare guidance for both inpatients and outpatients. The proposed framework can also be utilized for developing an innovative tool that helps individuals create and maintain a better healthcare plan over time.
Patchwork: A Patch-Wise Attention Network for Efficient Object Detection and Segmentation in Video Streams

Recent advances in single-frame object detection and segmentation techniques have motivated a wide range of works to extend these methods to process video streams. In this paper, we explore the idea of hard attention aimed for latency-sensitive applications. Instead of reasoning about every frame separately, our method selects and only processes a small sub-window of the frame. Our technique then makes predictions for the full frame based on the sub-windows from previous frames and the update from the current sub-window. The latency reduction by this hard attention mechanism comes at the cost of degraded accuracy. We made two contributions to address this. First, we propose a specialized memory cell that recovers lost context when processing sub-windows. Secondly, we adopt a Q-learning-based policy training strategy that enables our approach to intelligently select the sub-windows such that the staleness in the memory hurts the performance the least. Our experiments suggest that our approach reduces the latency by approximately four times without significantly sacrificing the accuracy on the ImageNet VID video object detection dataset and the DAVIS video object segmentation dataset. We further demonstrate that we can reinvest the saved computation into other parts of the network, and thus resulting in an accuracy increase at a comparable computational cost as the original system and beating other recently proposed state-of-the-art methods in the low latency range.
Accurate and robust segmentation of neuroanatomy in T1‐weighted MRI by combining spatial priors with deep convolutional neural networks

Abstract Neuroanatomical segmentation in magnetic resonance imaging (MRI) of the brain is a prerequisite for quantitative volume, thickness, and shape measurements, as well as an important intermediate step in many preprocessing pipelines. This work introduces a new highly accurate and versatile method based on 3D convolutional neural networks for the automatic segmentation of neuroanatomy in T1‐weighted MRI. In combination with a deep 3D fully convolutional architecture, efficient linear registration‐derived spatial priors are used to incorporate additional spatial context into the network. An aggressive data augmentation scheme using random elastic deformations is also used to regularize the networks, allowing for excellent performance even in cases where only limited labeled training data are available. Applied to hippocampus segmentation in an elderly population (mean Dice coefficient = 92.1%) and subcortical segmentation in a healthy adult population (mean Dice coefficient = 89.5%), we demonstrate new state‐of‐the‐art accuracies and a high robustness to outliers. Further validation on a multistructure segmentation task in a scan–rescan dataset demonstrates accuracy (mean Dice coefficient = 86.6%) similar to the scan–rescan reliability of expert manual segmentations (mean Dice coefficient = 86.9%), and improved reliability compared to both expert manual segmentations and automated segmentations using FIRST. Furthermore, our method maintains a highly competitive runtime performance (e.g., requiring only 10 s for left/right hippocampal segmentation in 1 × 1 × 1 mm 3 MNI stereotaxic space), orders of magnitude faster than conventional multiatlas segmentation methods.
Core-Sets: Updated Survey

In optimization or machine learning problems we are given a set of items, usually points in some metric space, and the goal is to minimize or maximize an objective function over some space of candidate solutions. For example, in clustering problems, the input is a set of points in some metric space, and a common goal is to compute a set of centers in some other space (points, lines) that will minimize the sum of distances to these points. In database queries, we may need to compute such a sum for a specific query set of k centers. However, traditional algorithms cannot handle modern systems that require parallel real-time computations of infinite distributed streams from sensors such as GPS, audio or video that arrive to a cloud, or networks of weaker devices such as smartphones or robots. Core-set is a “small data” summarization of the input “big data,” where every possible query has approximately the same answer on both data sets. Generic techniques enable efficient coreset maintainance of streaming, distributed, and dynamic data. Traditional algorithms can then be applied on these coresets to maintain the approximated optimal solutions. The challenge is to design coresets with provable trade-off between their size and approximation error. This survey summarizes such constructions in a retrospective way that aims to unify and simplify the state of the art.
Deep multiple instance learning for digital histopathology

Multiple instance learning (MIL) is a variation of supervised learning where a single class label is assigned to a set of instances, e.g., image patches. After providing a comprehensive introduction, we give a probabilistic definition of MIL. We move on introducing three different MIL approaches and show how they dictate the design of deep neural networks for MIL. Consequently a variety of MIL pooling functions is presented. We compare those pooling functions regarding their interpretability and flexibility. Finally, we evaluate the different MIL approaches and pooling functions on two histopathology datasets. Here, we put great emphasis on the details of the experiment design, including histopathology-specific augmentation techniques and MIL-specific evaluation metrics.
Astaire

Despite the growth of Virtual Reality (VR), the design space of collocated social play in VR remains narrow. Here we present Astaire, a collaborative hybrid VR dance game for two players sharing an HTC Vive VR system. The game resulted from a design research process using embodied design methods, and drawing upon concepts in HCI and Play Design, including social affordances, and asymmetric and interdependent play. Here we present insights from a study playtesting Astaire alongside two VR games that inspired ours: Keep Talking and Nobody Explodes (KTNE), and Audioshield. We examined players' and spectators' enjoyment, and interpersonal relationships, which were self-reported higher for Astaire. Using data from semi-structured interviews, we foreground design elements that impacted our participants' play experience, grouped under the themes of balance of players' roles, the physicality afforded by the game, and the social experience enabled. Our work contributes to opening the design space of hybrid collocated VR--through our game, we surface inspirational design concepts in HCI, and share design knowledge gained during our design process.
MultiSeg: Semantically Meaningful, Scale-Diverse Segmentations From Minimal User Input

Existing deep learning-based interactive image segmentation approaches typically assume the target-of-interest is always a single object and fail to account for the potential diversity in user expectations, thus requiring excessive user input when it comes to segmenting an object part or a group of objects instead. Motivated by the observation that the object part, full object, and a collection of objects essentially differ in size, we propose a new concept called scale-diversity, which characterizes the spectrum of segmentations w.r.t. different scales. To address this, we present MultiSeg, a scale-diverse interactive image segmentation network that incorporates a set of two-dimensional scale priors into the model to generate a set of scale-varying proposals that conform to the user input. We explicitly encourage segmentation diversity during training by synthesizing diverse training samples for a given image. As a result, our method allows the user to quickly locate the closest segmentation target for further refinement if necessary. Despite its simplicity, experimental results demonstrate that our proposed model is capable of quickly producing diverse yet plausible segmentation outputs, reducing the user interaction required, especially in cases where many types of segmentations (object parts or groups) are expected.
Vision-Language Recommendation via Attribute Augmented Multimodal Reinforcement Learning

Interactive recommenders have demonstrated the advantage over traditional recommenders with dynamic change of items. However, the traditional user feedback in the format of clicks or ratings, provides limited user preference information and limited history tracking capabilities. As a result, it takes a user many interactions to find a desired item. Data of other modalities, such as item visual appearance and user comments in natural language, may enable richer user feedback. However, there are several critical challenges to be addressed when utilizing these multimodal data: multimodal matching, user preference tracking, and adaptation to dynamic unseen items. Without properly handling these challenges, the recommendations can easily violate the users' preference from their past natural language feedback. In this paper, we introduce a novel approach, called vision-language recommendation, that enables users to provide natural language feedback on visual products to have more natural and effective interactions. To model more explicit and accurate multimodal matching, we propose a novel visual attribute augmented reinforcement learning approach that enhances the grounding of natural language to visual items. Furthermore, to effectively track the users' preference and overcome the performance deficiency on dynamic unseen items after deployment, we propose a novel history multimodal matching reward to continuously adapt the model on-the-fly. Empirical results show that, our system augmented by visual attribute and history multimodal matching can significantly increase the success rate, reduce the number of recommendations that violate the user's previous feedback, and need less number of user interactions to find the desired items.
A Catalogue of Inter-parameter Dependencies in RESTful Web APIs

Web services often impose dependency constraints that restrict the way in which two or more input parameters can be combined to form valid calls to the service. Unfortunately, current specification languages for web services like the OpenAPI Specification provide no support for the formal description of such dependencies, which makes it hardly possible to automatically discover and interact with services without human intervention. Researchers and practitioners are openly requesting support for modelling and validating dependencies among input parameters in web APIs, but this is not possible unless we share a deep understanding of how dependencies emerge in practice—the aim of this work. In this paper, we present a thorough study on the presence of dependency constraints among input parameters in web APIs in industry. The study is based on a review of more than 2.5K operations from 40 real-world RESTful APIs from multiple application domains. Overall, our findings show that input dependencies are the norm, rather than the exception, with 85% of the reviewed APIs having some kind of dependency among their input parameters. As the main outcome of our study, we present a catalogue of seven types of dependencies consistently found in RESTful web APIs.
Multifactor Authentication Protocol in a Mobile Environment

The implementation of services that process confidential data in a mobile environment requires an adequate level of security with the strictest possible mechanisms of information protection.The dominance of mobile devices as client applications of distributed systems has led to the development of new techniques that combine traditional methods of protection with protocols leveraging the potential of numerous interfaces available from a smartphone.For this reason, an upward trend in the use of biometricsbased methods and dynamically generated OTP secrets can be observed.Mobile devices are increasingly used in complex business processes that require strong user authentication methods, which, according to the European Commission (Regulation), must use at least two authentication factors belonging to different categories.Therefore, on the basis of the analysis of the solutions presented so far, a distributed protocol has been proposed.It enables user authentication using three authentication factors: possession, knowledge, and inherence.The described authentication scheme refers to the possibility of carrying out the process in the mobile environment of the Android platform with guaranteed authentication support.
Virtual Thin Slice: 3D Conditional GAN-based Super-Resolution for CT Slice Interval

Many CT slice images are stored with large slice intervals to reduce storage size in clinical practice. This leads to low resolution perpendicular to the slice images (i.e., z-axis), which is insufficient for 3D visualization or image analysis. In this paper, we present a novel architecture based on conditional Generative Adversarial Networks (cGANs) with the goal of generating high resolution images of main body parts including head, chest, abdomen and legs. However, GANs are known to have a difficulty with generating a diversity of patterns due to a phenomena known as mode collapse. To overcome the lack of generated pattern variety, we propose to condition the discriminator on the different body parts. Furthermore, our generator networks are extended to be three dimensional fully convolutional neural networks, allowing for the generation of high resolution images from arbitrary fields of view. In our verification tests, we show that the proposed method obtains the best scores by PSNR/SSIM metrics and Visual Turing Test, allowing for accurate reproduction of the principle anatomy in high resolution. We expect that the proposed method contribute to effective utilization of the existing vast amounts of thick CT images stored in hospitals.
Johnny's Journey Toward Usable Secure Email

Since the publication of "Why Johnny Can't Encrypt,"<sup>10</sup> there has been interest in creating usable, secure email that is adoptable by the general public. In this article, we summarize research from the usable-security community on this topic, identify open problems, and call for more research on usable key management.
Learning to Train with Synthetic Humans

Neural networks need big annotated datasets for training. However, manual annotation can be too expensive or even unfeasible for certain tasks, like multi-person 2D pose estimation with severe occlusions. A remedy for this is synthetic data with perfect ground truth. Here we explore two variations of synthetic data for this challenging problem; a dataset with purely synthetic humans and a real dataset augmented with synthetic humans. We then study which approach better generalizes to real data, as well as the influence of virtual humans in the training loss. Using the augmented dataset, without considering synthetic humans in the loss, leads to the best results. We observe that not all synthetic samples are equally informative for training, while the informative samples are different for each training stage. To exploit this observation, we employ an adversarial student-teacher framework; the teacher improves the student by providing the hardest samples for its current state as a challenge. Experiments show that the student-teacher framework outperforms normal training on the purely synthetic dataset.
Security and Trust Issues on Digital Supply Chain

This exploratory investigation aims to discuss current status and challenges, especially in aspect of security and trust problems, of digital supply chain management system with applying some advanced information technologies, such as Internet of Things, cloud computing and blockchain, for improving various system performance and properties, i.e. transparency, visibility, accountability, traceability and reliability. This paper introduces the general histories and definitions, in terms of information science, of the supply chain and relevant technologies which have been applied or are potential to be applied on supply chain with purpose of lowering cost, facilitating its security and convenience. It provides a comprehensive review of current relative research work and industrial cases from several famous companies. It also illustrates requirements or performance of digital supply chain system, security management and trust issues. Finally, this paper concludes several potential or existing security issues and challenges which supply chain management is facing.
Blockchain-Enabled Smart Contracts for Enhancing Distributor-to-Consumer Transactions

This article examines options to improve efficiency and prevent counterfeits related to distributor-to-consumer (D2C) transaction processing in the consumer electronics (CE) industry. Particularly, we propose a smart contract enabled D2C model that provides a blueprint for the smart integration of CE transactions. To the best of our knowledge, the proposed CE-specific D2C model is among the very few attempts of leveraging smart contracts to enhance the efficiency and efficacy of CE applications.
FastBuild: Accelerating Docker Image Building for Efficient Development and Deployment of Container

Docker containers have been increasingly adopted on various computing platforms to provide a lightweight virtualized execution environment. Compared to virtual machines, this technology can often reduce the launch time from a few minutes to less than 10 seconds, assuming the Docker image has been locally available. However, Docker images are highly customizable, and are mostly built at runtime from a remote base image by running instructions in a script (the Dockerfile). During the instruction execution, a large number of input files may have to be retrieved via the Internet. The image building may be an iterative process as one may need to repeatedly modify the Dockerfile until a desired image composition is received. In the process, every input file required by an instruction has to be remotely retrieved, even if it has been recently downloaded. This can make the process of building of an image and launching of a container unexpectedly slow. To address the issue, we propose a technique, named FastBuild, that maintains a local file cache to minimize the expensive file downloading. By non-intrusively intercepting remote file requests, and supplying files locally, FastBuild enables file caching in a manner transparent to image building. To further accelerate the image building, FastBuild overlaps operations of instructions' execution and writing intermediate image layers to the disk. We have implemented FastBuild. And experiments with images and Dockerfiles obtained from Docker Hub show that the system can improve building speed by up to 10 times, and reduce downloaded data by 72%.
Single image super resolution using dictionary learning and sparse coding with multi-scale and multi-directional Gabor feature representation

Recently, sparse representation theory has shown the state-of-art performance in image super resolution (SR) field. The sparse representation based single image SR methods learn dictionaries to discover the co-occurence relationship between low-resolution (LR) and high-resolution (HR) image feature spaces to generate satisfactory SR images. However, most existing learning based methods focus on Gradient and Laplacian filters to achieve accurate feature representation in SR task. In this paper, a novel multi-scale and multi-directional feature descriptor approach is proposed to improve SR quality. In the proposed approach, Gabor filter is first used as feature representation in learning based super resolution to extract image features at different scales and orientations. Therefore, the difficulty of capturing the complex local structures in all scale and directions using traditional 1-D filters is resolved by the proposed Gabor filter approach. Then the efficient mapping between LR and HR images is achieved by searching the sparse representation coefficients over the LR and HR dictionaries in dictionary learning phase. Finally, the learned relationship is applied to validation LR input in order to achieve accurate SR image in reconstruction phase. The experimental results show that the proposed method outperforms other state-of-art methods in terms of both quantity and quality.