Reviewerly/exHarmony huggingface dataset:
Exergaming With Beat Saber: An Investigation of Virtual Reality Aftereffects

Background Virtual reality (VR) exergaming has the potential to target sedentary behavior. Immersive environments can distract users from the physical exertion of exercise and can motivate them to continue exergaming. Despite the recent surge in VR popularity, numerous users still experience VR sickness from using head-mounted displays (HMDs). Apart from the commonly assessed self-reported symptoms, depth perception and cognition may also be affected. Considering the potential benefits of VR exergaming, it is crucial to identify the adverse effects limiting its potential and continued uptake. Objective This study aims to investigate the consequences of playing one of the most popular VR exergames for 10 and 50 min on aspects of vision, cognition, and self-reported VR sickness. Methods A total of 36 participants played an exergame, called Beat Saber, using an HMD. A repeated measures within-subject design was conducted to assess changes in vision, cognition, and well-being after short (10 min) and long (50 min) durations of VR exposure. We measured accommodation, convergence, decision speed, movement speed, and self-reported sickness at 3 test periods—before VR, immediately after VR, and 40 min after VR (late). Results Beat Saber was well tolerated, as there were no dropouts due to sickness. For most participants, any immediate aftereffects were short-lived and returned to baseline levels after 40 min of exiting VR. For both short and long exposures, there were changes in accommodation (F1,35=8.424; P=.006) and convergence (F1,35=7.826; P=.008); however, in the late test period, participants returned to baseline levels. Measures on cognition revealed no concern. The total simulator sickness questionnaire (SSQ) scores increased immediately after VR (F1,35=26.515; P&lt;.001) and were significantly higher for long compared with short exposures (t35=2.807; P=.03), but there were no differences in exposure duration in the late test period, with scores returning to baseline levels. Although at a group level, participants’ sickness levels returned to baseline 40 min after VR exposure, approximately 14% of the participants still reported high levels of sickness in the late test period after playing 50 min of Beat Saber. We also showed that the participants who experienced a high level of sickness after a short exposure were almost certain to experience a high level of symptoms after a longer exposure. Conclusions Irrespective of the duration of exposure, this study found no strong evidence for adverse symptoms 40 min after exiting VR; however, some individuals still reported high levels of VR sickness at this stage. We recommend that users commit to a waiting period after exiting VR to ensure that any aftereffects have deteriorated. Exergames in HMDs have the potential to encourage people to exercise but are understudied, and the aftereffects of exergaming need to be closely monitored to ensure that VR exergames can reach their full potential.
Learning interaction dynamics with an interactive LSTM for conversational sentiment analysis

Conversational sentiment analysis is an emerging, yet challenging subtask of the sentiment analysis problem. It aims to discover the affective state and sentimental change in each person in a conversation based on their opinions. There exists a wealth of interaction information that affects speaker sentiment in conversations. However, existing sentiment analysis approaches are insufficient in dealing with this subtask due to two primary reasons: the lack of benchmark conversational sentiment datasets and the inability to model interactions between individuals. To address these issues, in this paper, we first present a new conversational dataset that we created and made publicly available, named ScenarioSA, to support the development of conversational sentiment analysis models. Then, we investigate how interaction dynamics are associated with conversations and study the multidimensional nature of interactions, which is understandability, credibility and influence. Finally, we propose an interactive long short-term memory (LSTM) network for conversational sentiment analysis to model interactions between speakers in a conversation by (1) adding a confidence gate before each LSTM hidden unit to estimate the credibility of the previous speakers and (2) combining the output gate with the learned influence scores to incorporate the influences of the previous speakers. Extensive experiments are conducted on ScenarioSA and IEMOCAP, and the results show that our model outperforms a wide range of strong baselines and achieves competitive results with the state-of-art approaches.
A new way of teaching business ethics: The evaluation of virtual reality-based learning media

Business schools have challenges in internalizing ethical values in business because of the gap between ethical theories and their application in the real world. Learning activities using real situations involve high costs, lack infrastructure, and are too risky. Therefore, the virtual environment will help students understand the actual context. This study aims to evaluate learning media based on virtual reality (VR) for teaching business ethics. VR-based learning media is implemented for accounting students who take business ethics course at the Faculty of Economics, Yogyakarta State University, Indonesia. The results show that virtual-reality based media make the learning process motivating, interesting, and increases perceived learning effectiveness. Furthermore, VR-based learning media can increase the level of ethical efficiency of individuals by increasing their self-efficacy. This research contributes to the learning of business ethics by developing VR-based learning media to improve students' abilities in dealing with ethical dilemmas.
A view-free image stitching network based on global homography

Image stitching is a traditional but challenging computer vision task, aiming to obtain a seamless panoramic image. Recently, researchers begin to study the image stitching task using deep learning. However, the existing learning methods assume a relatively fixed view during the image capturing, thus show a poor generalization ability to flexible view cases. To address the above problem, we present a cascaded view-free image stitching network based on a global homography. This novel image stitching network does not have any restriction on the view of images and it can be implemented in three stages. In particular, we first estimate a global homography between two input images from different views. And then we propose a structure stitching layer to obtain the coarse stitching result using the global homography. In the last stage, we design a content revision network to eliminate ghosting effects and refine the content of the stitching result. To enable efficient learning on various views, we also present a method to generate synthetic datasets for network training. Experimental results demonstrate that our method can achieve almost 100% elimination of artifacts in overlapping areas at the cost of acceptable slight distortions in non-overlapping areas, compared with traditional methods. In addition, the proposed method is view-free and more robust especially in a scene where feature points are difficult to detect.
Sequoia

Serverless computing is a rapidly growing paradigm that easily harnesses the power of the cloud. With serverless computing, developers simply provide an event-driven function to cloud providers, and the provider seamlessly scales function invocations to meet demands as event-triggers occur. As current and future serverless offerings support a wide variety of serverless applications, effective techniques to manage serverless workloads becomes an important issue. This work examines current management and scheduling practices in cloud providers, uncovering many issues including inflated application run times, function drops, inefficient allocations, and other undocumented and unexpected behavior. To fix these issues, a new quality-of-service function scheduling and allocation framework, called Sequoia, is designed. Sequoia allows developers or administrators to easily def ne how serverless functions and applications should be deployed, capped, prioritized, or altered based on easily configured, flexible policies. Results with controlled and realistic workloads show Sequoia seamlessly adapts to policies, eliminates mid-chain drops, reduces queuing times by up to 6.4X, enforces tight chain-level fairness, and improves run-time performance up to 25X.
A high-performance CNN method for offline handwritten Chinese character recognition and visualization

Recent researches introduced fast, compact and efficient convolutional neural networks (CNNs) for offline handwritten Chinese character recognition (HCCR). However, many of them did not address the problem of network interpretability. We propose a new architecture of a deep CNN with high recognition performance which is capable of learning deep features for visualization. A special characteristic of our model is the bottleneck layers which enable us to retain its expressiveness while reducing the number of multiply-accumulate operations and the required storage. We introduce a modification of global weighted average pooling (GWAP)—global weighted output average pooling (GWOAP). This paper demonstrates how they allow us to calculate class activation maps (CAMs) in order to indicate the most relevant input character image regions used by our CNN to identify a certain class. Evaluating on the ICDAR-2013 offline HCCR competition dataset, we show that our model enables a relative 0.83% error reduction while having 49% fewer parameters and the same computational cost compared to the current state-of-the-art single-network method trained only on handwritten data. Our solution outperforms even recent residual learning approaches.
Volumetric Attention for 3D Medical Image Segmentation and Detection

A volumetric attention (VA) module for 3D medical image segmentation and detection is proposed. VA attention is inspired by recent advances in video processing, enables 2.5D networks to leverage context information along the z direction, and allows the use of pretrained 2D detection models when training data is limited, as is often the case for medical applications. Its integration in the Mask R-CNN is shown to enable state-of-the-art performance on the Liver Tumor Segmentation (LiTS) Challenge, outperforming the previous challenge winner by 3.9 points and achieving top performance on the LiTS leader board at the time of paper submission. Detection experiments on the DeepLesion dataset also show that the addition of VA to existing object detectors enables a 69.1 sensitivity at 0.5 false positive per image, outperforming the best published results by 6.6 points.
solc-verify: A Modular Verifier for Solidity Smart Contracts

We present solc-verify, a source-level verification tool for Ethereum smart contracts. solc-verify takes smart contracts written in Solidity and discharges verification conditions using modular program analysis and SMT solvers. Built on top of the Solidity compiler, solc-verify reasons at the level of the contract source code, as opposed to the more common approaches that operate at the level of Ethereum bytecode. This enables solc-verify to effectively reason about high-level contract properties while modeling low-level language semantics precisely. The properties, such as contract invariants, loop invariants, and function pre- and post-conditions, can be provided as annotations in the code by the developer. This enables automated, yet user-friendly formal verification for smart contracts. We demonstrate solc-verify by examining real-world examples where our tool can effectively find bugs and prove correctness of non-trivial properties with minimal user effort.
Resource theory of asymmetric distinguishability for quantum channels

This paper develops the resource theory of asymmetric distinguishability for quantum channels, generalizing the related resource theory for states [arXiv:1010.1030; arXiv:1905.11629]. The key constituents of the channel resource theory are quantum channel boxes, consisting of a pair of quantum channels, which can be manipulated for free by means of an arbitrary quantum superchannel (the most general physical transformation of a quantum channel). One main question of the resource theory is the approximate channel box transformation problem, in which the goal is to transform an initial channel box (or boxes) to a final channel box (or boxes), while allowing for an asymmetric error in the transformation. The channel resource theory is richer than its counterpart for states because there is a wider variety of ways in which this question can be framed, either in the one-shot or $n$-shot regimes, with the latter having parallel and sequential variants. As in our prior work [arXiv:1905.11629], we consider two special cases of the general channel box transformation problem, known as distinguishability distillation and dilution. For the one-shot case, we find that the optimal values of the various tasks are equal to the non-smooth or smooth channel min- or max-relative entropies, thus endowing all of these quantities with operational interpretations. In the asymptotic sequential setting, we prove that the exact distinguishability cost is equal to the channel max-relative entropy and the distillable distinguishability is equal to the amortized channel relative entropy of [arXiv:1808.01498]. This latter result can also be understood as a solution to Stein's lemma for quantum channels in the sequential setting. Finally, the theory simplifies significantly for environment-seizable and classical--quantum channel boxes.
Weakly Supervised 3D Hand Pose Estimation via Biomechanical Constraints

Estimating 3D hand pose from 2D images is a difficult, inverse problem due to the inherent scale and depth ambiguities. Current state-of-the-art methods train fully supervised deep neural networks with 3D ground-truth data. However, acquiring 3D annotations is expensive, typically requiring calibrated multi-view setups or labour intensive manual annotations. While annotations of 2D keypoints are much easier to obtain, how to efficiently leverage such weakly-supervised data to improve the task of 3D hand pose prediction remains an important open question. The key difficulty stems from the fact that direct application of additional 2D supervision mostly benefits the 2D proxy objective but does little to alleviate the depth and scale ambiguities. Embracing this challenge we propose a set of novel losses that constrain the prediction of a neural network to lie within the range of biomechanically feasible 3D hand configurations. We show by extensive experiments that our proposed constraints significantly reduce the depth ambiguity and allow the network to more effectively leverage additional 2D annotated images. For example, on the challenging freiHAND dataset, using additional 2D annotation without our proposed biomechanical constraints reduces the depth error by only $$15\%$$ , whereas the error is reduced significantly by $$50\%$$ when the proposed biomechanical constraints are used.
Conditional Sequential Modulation for Efficient Global Image Retouching

Photo retouching aims at enhancing the aesthetic visual quality of images that suffer from photographic defects such as over/under exposure, poor contrast, inharmonious saturation. Practically, photo retouching can be accomplished by a series of image processing operations. In this paper, we investigate some commonly-used retouching operations and mathematically find that these pixel-independent operations can be approximated or formulated by multi-layer perceptrons (MLPs). Based on this analysis, we propose an extremely light-weight framework - Conditional Sequential Retouching Network (CSRNet) - for efficient global image retouching. CSRNet consists of a base network and a condition network. The base network acts like an MLP that processes each pixel independently and the condition network extracts the global features of the input image to generate a condition vector. To realize retouching operations, we modulate the intermediate features using Global Feature Modulation (GFM), of which the parameters are transformed by condition vector. Benefiting from the utilization of $$1\times 1$$ convolution, CSRNet only contains less than 37 k trainable parameters, which is orders of magnitude smaller than existing learning-based methods. Extensive experiments show that our method achieves state-of-the-art performance on the benchmark MIT-Adobe FiveK dataset quantitively and qualitatively. Code is available at https://github.com/hejingwenhejingwen/CSRNet .
ETH Relay: A Cost-efficient Relay for Ethereum-based Blockchains

Current blockchain relay schemes require the immediate validation of each relayed block header by the destination blockchain. This leads to high operating cost when deploying these relays between Ethereum-based blockchains where validating block headers on-chain is computationally expensive.To overcome these limitations, we introduce a novel relay scheme that employs a validation-on-demand pattern combined with economic incentives to reduce the cost of operating a relay between Ethereum-based blockchains by up to 92%. With this relay scheme, decentralized interoperability between blockchains like Ethereum and Ethereum Classic becomes feasible.
Blockchain Technology and Its Role in Enhancing Supply Chain Integration Capability and Reducing Carbon Emission: A Conceptual Framework

Most current blockchain and carbon emission studies are from engineering and sciences disciplines. By incorporating blockchain technology into supply chain integration capabilities, the firms are be able to work collaboratively with each other to enhance the supply chain integration and simultaneously reduce the carbon emission in a supply chain. This paper presents a conceptual framework to understand the role of blockchain in a low carbon supply chain management. Applying the Socio-Technical Theory and Resource-Based View, the research propositions between blockchain, supply chain integration capability and carbon emission are proposed in the research framework. The results indicate that the blockchain technology may be viewed as a strategic management approach to enhance supply chain integration and reduce the carbon emissions. In addition, it may be adopted as an operational tool to track carbon footprint, streamline processes and improve efficiency of carbon management to minimize the overall emissions in supply chains. The paper contributes to the blockchain literature and its applications in low carbon supply chain management and provides recommendation for future research.
DeepEM: Deep Neural Networks Model Recovery through EM Side-Channel Information Leakage

Neural Network (NN) accelerators are currently widely deployed in various security-crucial scenarios, including image recognition, natural language processing and autonomous vehicles. Due to economic and privacy concerns, the hardware implementations of structures and designs inside NN accelerators are usually inaccessible to the public. However, these accelerators still tend to leak crucial information through Electromagnetic (EM) side channels in addition to timing and power information. In this paper, we propose an effective and efficient model stealing attack against current popular large-scale NN accelerators deployed on hardware platforms through side-channel information. Specifically, the proposed attack approach contains two stages: 1) Inferring the underlying network architecture through EM sidechannel information; 2) Estimating the parameters, especially the weights, through a margin-based, adversarial active learning method. The experimental results show that the proposed attack approach can accurately recover the large-scale NN through EM side-channel information leakages. Overall, our attack highlights the importance of masking EM traces for large-scale NN accelerators in real-world applications.
Quantization Friendly MobileNet (QF-MobileNet) Architecture for Vision Based Applications on Embedded Platforms

Deep Neural Networks (DNNs) have become popular for various applications in the domain of image and computer vision due to their well-established performance attributes. DNN algorithms involve powerful multilevel feature extractions resulting in an extensive range of parameters and memory footprints. However, memory bandwidth requirements, memory footprint and the associated power consumption of models are issues to be addressed to deploy DNN models on embedded platforms for real time vision-based applications. We present an optimized DNN model for memory and accuracy for vision-based applications on embedded platforms. In this paper we propose Quantization Friendly MobileNet (QF-MobileNet) architecture. The architecture is optimized for inference accuracy and reduced resource utilization. The optimization is obtained by addressing the redundancy and quantization loss of the existing baseline MobileNet architectures. We verify and validate the performance of the QF-MobileNet architecture for image classification task on the ImageNet dataset. The proposed model is tested for inference accuracy and resource utilization and compared to the baseline MobileNet architecture. The inference accuracy of the proposed QF-MobileNetV2 float model attained 73.36% and the quantized model has 69.51%. The MobileNetV3 float model attained an inference accuracy of 68.75% and the quantized model has 67.5% respectively. The proposed model saves 33% of time complexity for QF-MobileNetV2 and QF-MobileNetV3 models against the baseline models. The QF-MobileNet also showed optimized resource utilization with 32% fewer tunable parameters, 30% fewer MAC’s operations per image and reduced inference quantization loss by approximately 5% compared to the baseline models. The model is ported onto the android application using TensorFlow API. The android application performs inference on the native devices viz. smartphones, tablets and handheld devices. Future work is focused on introducing channel-wise and layer-wise quantization schemes to the proposed model. We intend to explore quantization aware training of DNN algorithms to achieve optimized resource utilization and inference accuracy.
Data engineering for fraud detection

Financial institutions increasingly rely upon data-driven methods for developing fraud detection systems, which are able to automatically detect and block fraudulent transactions. From a machine learning perspective, the task of detecting suspicious transactions is a binary classification problem and therefore many techniques can be applied. Interpretability is however of utmost importance for the management to have confidence in the model and for designing fraud prevention strategies. Moreover, models that enable the fraud experts to understand the underlying reasons why a case is flagged as suspicious will greatly facilitate their job of investigating the suspicious transactions. Therefore, we propose several data engineering techniques to improve the performance of an analytical model while retaining the interpretability property. Our data engineering process is decomposed into several feature and instance engineering steps. We illustrate the improvement in performance of these data engineering steps for popular analytical models on a real payment transactions data set.