Reviewerly/exHarmony huggingface dataset:
V-Net Light - Parameter-Efficient 3-D Convolutional Neural Network for Prostate MRI Segmentation

Prostate MRI segmentation has become an important tool for quantitative estimation of the gland volume during diagnostic imaging. It is also a critical step in the fusion between MRI and transrectal ultrasound (TRUS) for fusion guided biopsy or therapy. 3-D neural networks have demonstrated strong potential for this task, but require substantial computational resources due to their large number of parameters. In this work, we focus on the efficiency of the segmentation network in terms of speed and memory requirements. Specifically, we aim at reaching state-of-the-art results with smaller networks, involving significantly fewer parameters, thus making the network easier to train and operate. A novel 3-D network architecture, called V-net Light (VnL) is proposed, based on an efficient 3-D Module called 3-D Light, that minimizes the number of network parameters while maintaining state-of-the-art segmentation results. The proposed method is validated on the PROMISE12 challenge data [1]. The proposed VnL has only 9.1% of V-net's parameters, 3.2% of its floating point operations (FLOPs) and uses only 9.1% of hard-disk storage compared to V-net, yet V-net and VnL has comparable accuracy.
An approach to reduce energy consumption and performance losses on heterogeneous servers using power capping

Abstract Rapid growth of demand for remote computational power, along with high energy costs and infrastructure limits, has led to treating power usage as a primary constraint in data centers. Especially, recent challenges related to development of exascale systems or autonomous edge systems require tools that will limit power usage and energy consumption. This paper presents a power capping method that allows operators to quickly adjust the power usage to external conditions and, at the same time, to reduce energy consumption and negative impact on performance of applications. We propose an optimization model and both heuristic and exact methods to solve this problem. We present an evaluation of power capping approaches supported by results of application benchmarks and experiments performed on new heterogeneous servers.
An Extended Hybrid Image Compression Based on Soft-to-Hard Quantification

Recently, the deep learning methods have been widely used in lossy compression schemes, greatly improving image compression performance. In this paper, we propose an extended hybrid image compression scheme based on soft-to-hard quantification, which has only two layers. The compact representation of the input image is encoded by the FLIF codec as the base layer. The residual of the input image and the reconstructed image is encoded by the BPG codec as the enhancement layer. The results using the Kodak and Tecnick datasets show that the performance of our proposed methods exceeds some image compression schemes based on deep learning methods and some traditional coding standards including BPG in SSIM metric across a wide range of bit rates, when the images are coded in the RGB444 domain. We explore the issue of bit rates allocation of the base layer and enhancement layer and the impact of enhancement layer codecs. Also, we analyze the limitations of the hybrid coding scheme.
A Parallel Cerebrovascular Segmentation Algorithm Based on Focused Multi-Gaussians Model and Heterogeneous Markov Random Field

A complete and detailed cerebrovascular image segmented from time-of-flight magnetic resonance angiography (TOF-MRA) data is essential for the diagnosis and therapy of the cerebrovascular diseases. In recent years, three-dimensional cerebrovascular segmentation algorithms based on statistical models have been widely used, but the existed methods always perform poorly on stenotic vessels and are not robust enough. In this paper, we propose a parallel cerebrovascular segmentation algorithm based on focused multi-Gaussians model and heterogeneous Markov random field. Specifically, we present a focused multi-Gaussians (FMG) model with local fitting region to model the vascular tissue more accurately and introduce the chaotic oscillation particle swarm optimization (CO-PSO) algorithm to improve the global optimization capability in the parameter estimation. Furthermore, we design a heterogeneous Markov Random Field (MRF) in the three-dimensional neighborhood system to incorporate precise local character of image. Finally, the algorithm has been performed parallel optimization based on GPUs and obtain about 60 times speedup compared to serial execution. The experiments show that the proposed algorithm can produce more detailed segmentation result in shorter time and performs well on the stenotic vessels robustly.
How Can a Robot Calculate the Level of Visual Focus of Human’s Attention

Attention is the behavioral and cognitive process of selectively concentrating on a discrete aspect of information. It is the taking possession by the mind in clear and colorful form of one out of what seem several simultaneous objects or trains of thought. The purpose of this work is to establish a human–robot interaction system to detect the visual focus of attention (VFOA) based on human attention (in case of both reading and browsing purposes). The system detects the person’s current task (attention) and estimates the level by detecting the head and estimating eye region specially detected iris center within eye area (gaze pattern calculation). The system also determines the interest or willingness of the target person to interact with it based upon a certain level of VFOA. Then, depending on the level of interest of the target person, the system/robot generates awareness and establishes a communication channel with her/him.
Characterizing Public Cloud Resource Contention to Support Virtual Machine Co-residency Prediction

Hypervisors used to implement virtual machines (VMs) for infrastructure-as-a-service (IaaS) cloud platforms have undergone continued improvements for the past decade. VM components including CPU, memory, network, and storage I/O have evolved from full software emulation, to paravirtualization, to hardware virtualization. While these innovations have helped reduce performance overhead when simulating a computer, considerable performance loss is still possible in the public cloud from resource contention of co-located VMs. In this paper, we investigate the extent of performance degradation from resource contention by leveraging well-known benchmarks run in parallel across three generations of virtualization hypervisors. Using a Python-based test harness we orchestrate execution of CPU, disk, and network I/O bound benchmarks across up to 48 VMs sharing the same Amazon Web Services dedicated host server. We found that executing benchmarks on hosts with many idle Linux VMs produced unexpected performance degradation. As public cloud users are interested in avoiding resource contention from co-located VMs, we next leveraged our dedicated host performance measurements as independent variables to train models to predict the number of co-resident VMs. We evaluated multiple linear regression and random forest models using test data from independent benchmark runs across 96 vCPU dedicated hosts running up to 48 x 2 vCPU VMs where we controlled VM placements. Multiple linear regression over normalized data achieved R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> =.942, with mean absolute error of VM co-residency predictions of ±1.61 VMs. We then leveraged our models to infer VM co-residency among a set of 50 VMs on the public cloud, where co-location data is unavailable. Here models cannot be independently verified, but results suggest the relative occupancy level of public cloud hosts enabling users to infer when their VMs reside on busy hosts. Our results characterize how recent hypervisor and hardware advancements are addressing resource contention, while demonstrating the potential to leverage co-located benchmarks for VM co-residency prediction in a public cloud.
Learning a Loss Function for Segmentation: A Feasibility Study

When training neural networks for segmentation, the Dice loss is typically used. Alternative loss functions could help the networks achieve results with higher user acceptance and lower correction effort, but they cannot be used directly if they are not differentiable. As a solution, we propose to train a regression network to approximate the loss function and combine it with a U-Net to compute the loss during segmentation training. As an example, we introduce the contour Dice coefficient (CDC) that estimates the fraction of contour length that needs correction. Applied to CT bladder segmentation, we show that a weighted combination of Dice and CDC loss improves segmentations compared to using only Dice loss, with regard to both CDC and other metrics.
Deblurring Cataract Surgery Videos Using a Multi-Scale Deconvolutional Neural Network

A common quality impairment observed in surgery videos is blur, caused by object motion or a defocused camera. Degraded image quality hampers the progress of machine-learning-based approaches in learning and recognizing semantic information in surgical video frames like instruments, phases, and surgical actions. This problem can be mitigated by automatically deblurring video frames as a preprocessing method for any subsequent video analysis task. In this paper, we propose and evaluate a multi-scale deconvolutional neural network to deblur cataract surgery videos. Experimental results confirm the effectiveness of the proposed approach in terms of the visual quality of frames as well as PSNR improvement.
FIN

Multi-layer detection is a widely used method in the field of object detection. It extracts multiple feature maps with different resolutions from the backbone network to detect objects of different scales, which can effectively cope with the problem of object scale change in object detection. Although the multi-layer detection utilizes multiple detection layers to alleviate the burden of one single detection layer and can improve the detection accuracy to some extent, this method has two limitations. First, manually assigning anchor boxes of different sizes to different feature maps is too dependent on the human experience. Second, there is a semantic gap between each detection layer in multi-layer detection. The same detector needs to simultaneously process the detection layers with inconsistent semantic strength, which increases the optimization difficulty of the detector. In this article, we propose a feature integrated network (FIN) based on single layer detection to deal with the problems mentioned above. Different from the existing methods, we design a series of verification experiments based on the multi-layer detection model, which shows that the shallow high-resolution feature map has the potential to simultaneously and effectively detect objects of various scales. Considering that the semantic information of the shallow feature map is weak, we propose two modules to enhance the representation ability of the single detection layer. First, we propose a detection adaptation network (DANet) to extract powerful feature maps that are useful for object detection tasks. Second, we combine global context information and local detail information with a verified hourglass module (VHM) to generate a single feature map with high resolution and rich semantic information so that we can assign all anchor boxes to this detection layer. In our model, all the detection operations are concentrated on a high-resolution feature map whose semantic information and detailed information are enhanced as much as possible. Therefore, the proposed model can solve the problem of anchor assignment and inconsistent semantic strength between multiple detection layers mentioned above. A large number of experiments on the Pattern Analysis, Statistical Modelling and Computational Learning Visual Object Classes (PASCAL VOC) and Microsoft Common Objects in Context (MS COCO) datasets show that our model has good detection performance for objects of various sizes. The proposed model can achieve&lt;?brk?&gt; 81.9 mAP when the size of the input image is 300 × 300.
HCI and mHealth Wearable Tech: A Multidisciplinary Research Challenge

This paper explores the challenges of HCI work within multidisciplinary research projects across health sciences, social sciences, and engineering, through discussion of a specific case study research project focused on supporting under-resourced pregnant women. Capturing the perspectives of community-based health care workers (N=14) using wearable technology for servicing pregnant women provided insight into considerations for technology for this specific population. Methods of inquiry included design and development of a prototype wearable and mobile system as well as self-report via a survey detailing their experience with the system and how it can possibly benefit their duties of monitoring their pregnant patients. The process outcomes of this work, however, provide broader insight into the challenges of conducting this kind of interdisciplinary work that remain despite decades of effort and financial investment to support interdisciplinary research, particularly in health informatics and interactive technologies for health.
Clash of Times: Respectful Technology Space for Integrating Community Stories in Intangible Exhibits

Emerging research in Human Computer Interaction (HCI) has considered the use of technology to preserve Intangible Cultural Heritage (ICH) while wrestling with the dilemma of local participation in the face of post-colonialism. There remains a need to understand how ICH is portrayed by museums and texts, how communities regard these representations, and how technology would affect preservation. We conducted a study in the North Rift region of Kenya to understand how ICH is preserved and disseminated by the museum in comparison with the community. The findings describe a respectful technology space where community needs and museum needs can co-exist. We also articulate social challenges that should be considered by designers when recommending or designing technological solutions. This paper concludes by recommending ways for researchers to smoothly integrate technology with ICH through community participation and an awareness of the respectful space.
Impact of Key Shape and Dimension on Text Entry in Virtual Reality

Virtual Qwerty is the most popular method of text entry in virtual reality. Since virtual keyboards are not constrained by the physical limitations of actual keyboards, designers are taking the liberty of designing novelty keys for these keyboards. However, it is unknown whether key design affects text entry performance or user experience. This work presents results of a user study that investigated the effects of different key shapes and dimensions on text entry performance and user experience. Results revealed that key shape affects text entry speed, dimension affects accuracy, while both affect user experience. Overall, square-shaped 3D keys yielded the best actual and perceived performance, also was the most preferred by the users.
Learning Aligned-Spatial Graph Convolutional Networks for Graph Classification

In this paper, we develop a novel Aligned-Spatial Graph Convolutional Network (ASGCN) model to learn effective features for graph classification. Our idea is to transform arbitrary-sized graphs into fixed-sized aligned grid structures, and define a new spatial graph convolution operation associated with the grid structures. We show that the proposed ASGCN model not only reduces the problems of information loss and imprecise information representation arising in existing spatially-based Graph Convolutional Network (GCN) models, but also bridges the theoretical gap between traditional Convolutional Neural Network (CNN) models and spatially-based GCN models. Moreover, the proposed ASGCN model can adaptively discriminate the importance between specified vertices during the process of spatial graph convolution, explaining the effectiveness of the proposed model. Experiments on standard graph datasets demonstrate the effectiveness of the proposed model.
Effect of Fixed and Infinite Ray Length on Distal 3D Pointing in Virtual Reality

Ray casting is frequently used to point and select distant targets in Virtual Reality (VR) systems. In this work, we evaluate user performance in 3D pointing with two different ray casting versions: infinite ray casting, where the cursor is positioned on the surface of the first object along the ray that said ray points at, and finite ray-casting, where the cursor is attached to the ray at a fixed distance from the controller. Twelve subjects performed a Fitts' law experiment where the targets were placed 1, 2, or 3 meters away from the user. According to the results, subjects were faster and made fewer errors with the infinite ray length. Interestingly, their (effective) pointing throughput was higher when the ray length was constrained. We illustrate the advantages of both methods in immersive VR applications and provide information for practitioners and developers to choose the most appropriate ray-casting-based selection method for VR.
Accessibility of Open Educational Resources: how well are they suited for English learners?

Open Educational Resources aim to offer learning to all, yet the language level used in resources could be a barrier to many potential learners. This paper examines the readability of 200 OER courses in English from two major OER course platforms. We compared the means of readability metrics between these OER courses at different educational levels and subject categories that the platforms offer using inferential statistics as well as cluster analyses. Results prove that there is a progression of difficulty between lower and higher educational levels with introductory courses being easier to read. However, the analysis also highlighted that more than 86% of the courses require an advanced level of English language proficiency. On the other hand, subject matter does not appear to be linked with the readability of the courses. This study contributes further to the current discussion of the inclusiveness of OER and the factors that hinder its universal use. The study addresses a gap in the literature as, to our knowledge, no other studies have analysed the linguistic accessibility of OER English learners and consideration of the meaning of the educational levels assigned to OER courses has been limited.
HomoPAI: A Secure Collaborative Machine Learning Platform based on Homomorphic Encryption

Homomorphic Encryption (HE) allows encrypted data to be processed without decryption, which could maximize the protection of user privacy without affecting the data utility. Thanks to strides made by cryptographers in the past few years, the efficiency of HE has been drastically improved, and machine learning on homomorphically encrypted data has become possible. Several works have explored machine learning based on HE, but most of them are restricted to the outsourced scenario, where all the data comes from a single data owner. We propose HomoPAI, an HE-based secure collaborative machine learning system, enabling a more promising scenario, where data from multiple data owners could be securely processed. Moreover, we integrate our system with the popular MPI framework to achieve parallel HE computations. Experiments show that our system can train a logistic regression model on millions of homomorphically encrypted data in less than two minutes.
ARchitect: Building Interactive Virtual Experiences from Physical Affordances by Bringing Human-in-the-Loop

Automatic generation of Virtual Reality (VR) worlds which adapt to physical environments have been proposed to enable safe walking in VR. However, such techniques mainly focus on the avoidance of physical objects as obstacles and overlook their interaction affordances as passive haptics. Current VR experiences involving interaction with physical objects in surroundings still require verbal instruction from an assisting partner. We present ARchitect, a proof-of-concept prototype that allows flexible customization of a VR experience with human-in-the-loop. ARchitect brings in an assistant to map physical objects to virtual proxies of matching affordances using Augmented Reality (AR). In a within-subjects study (9 user pairs) comparing ARchitect to a baseline condition, assistants and players experienced decreased workload and players showed increased VR presence and trust in the assistant. Finally, we defined design guidelines of ARchitect for future designers and implemented three demonstrative experiences.
Designing Distributed Ledger Technologies for Social Change

Distributed ledger technologies (DLTs) have been celebrated for promoting transparency, trust, and efficiency in several domains. However, recent research has also pointed out the potential of these technologies to increase power asymmetries and deepen social inequality. In this paper, we contribute to this discussion by reporting on a collective effort of academics, development partners, local authorities, businesses, and farming groups to look at the potential of DLTs, particularly Blockchains, to support socio-economic development in rural communities in the Caribbean. We present a series of design concepts resulting from this effort and reflect on a method to facilitate stakeholders' experience of possible implementations and enable them to voice concerns, preferences, and expectations. Results from workshops with different groups of stakeholders contribute insights into opportunities and limitations of these applications to enable social development and to level the playing field in agricultural exchanges in developing countries.
HUMAINE: Human Multi-Agent Immersive Negotiation Competition

Competitions that directly pit software agents against one another have proven to be an effective and entertaining way to advance the state of the art in a multitude of AI domains. Less frequently, human-agent competitions have been held to gauge the relative competence of humans vs. agents, or agents vs. agents as measured indirectly by their performance against humans. We are developing a platform that supports a new type of AI competition that involves both agent-agent and human-agent interactions situated in an immersive environment. In this competition, human buyers haggle (in English) with two life-size AI agents that attempt to sell them various goods. We describe several research challenges that arise in this context, present the platform architecture and accompanying technologies, and report on early experiments with simple agents that establish feasibility and suggest that human participants enjoy the experience.
About the project developing “MIPRA” – the intelligent planner in the state space for vehicles, tractors, and robots based on the architectural solutions of the Mivar systems for traffic enforcement

Abstract The task of creating a logical “strong” artificial intelligence (AI) to be used as new decision-making systems for autonomous vehicles, tractors, and robotic systems draws increasing attention of scientists around the world. Essentially, it is the creation of “brains” for vehicles and any other transport systems, including cyber-physical systems. The mivar technologies of logical AI allowed solving many problems at a qualitatively new level and reducing the decision-making time from billions of years to hundredths of seconds, which enables real-time vehicle control. This paper shows how the application of mivar products (mivar expert systems) allows solving another class of problems – state-space planning (STRIPS planning). In turn, the solutions of these problems enable the development of driver assistance systems at a new level and creation of a fully autonomous vehicle.