Reviewerly/exHarmony huggingface dataset:
Mitigating motion sickness in automated vehicles with vibration cue system

Motion sickness is very common in road transport. To guarantee ride comfort and user experience, there is an urgent need for effective solutions to motion sickness mitigation in semi- and fully-automated vehicles. Considering both effectiveness and user-friendliness, a vibration cue system is proposed to inform passengers of the upcoming vehicle movement through tactile stimulation. By integrating the motion planning results from automated driving algorithms, the vibration cueing timing and patterns are optimised with the theory of motion anticipation. Using a cushion-based prototype of a vibration cue system, 20 participants were invited to evaluate this solution in two conditions of driving simulator experiments. Results show that the proposed vibration cue system could also help participants to comprehend the cues and to generate motion anticipation. The participants' motion sickness degrees were significantly lowered. This research may serve as one foundation for detailed system development in practical applications. Practitioner Summary: In automated vehicles, passengers engaging in non-driving tasks are apt to severe motion sickness. A vibration cue system and cueing strategy are proposed and optimised to inform passengers of the upcoming vehicle movement. Simulator experiments of 20 participants proved its effectiveness in promoting motion anticipation and reducing motion sickness.
High-Dynamic Dance Motion Recognition Method Based on Video Visual Analysis

In the field of computer vision, high-dynamic dance motion recognition is a difficult problem to solve. Its goal is to recognize human motion by analyzing video data using image processing and classification recognition technology. Video multifeature fusion has sparked a surge in research in a variety of fields. Several pixel points that can be distinguished and displayed in several adjacent images that can reflect their characteristics are referred to as multifeature fusion. It is responsible for a significant portion of the similarity results between the two video segments. Motion recognition relies heavily on video multifeature fusion, which has a direct impact on the robustness and accuracy of recognition results. The directional gradient histogram features, optical flow direction histogram features, and audio features extracted from dance video are used to characterize dance movements after all of the characteristics of dance movements have been considered. This paper focuses on the high-dynamic dance action recognition method based on video multifeature fusion, which aims to combine high-dynamic dance action recognition and video multifeature fusion.
Exploiting gated graph neural network for detecting and explaining self-admitted technical debts

Self-admitted technical debt (SATD) refers to a specific type of technical debt that is introduced intentionally in the software development and maintenance processes. SATD enables practitioners to take some temporary solutions instead of making comprehensive decisions, which will lead to the high complexity of the software. However, most existing studies relied on manual methods for detecting SATDs. A recent study proposed a method HATD that used a hybrid attention-based method to automatically detect SATDs and it achieved the state-of-the-art performance. However, HATD mainly focused on the locality of the comment instances and lacked of the relationship between long-distance and discontinuous comment instances. To address such an issue, in this work, we propose a novel approach named GGSATD. Specifically, GGSATD first builds the graph for comment instances and then employs the gated graph neural network to iteratively update node representation. The global representation can be obtained by the soft attention mechanism and pooling operation. Experiments on 10 projects show that our GGSATD method obtains promising performance against five baseline methods in both within-project and cross-project scenarios. Extended experiments on seven real-world projects illustrate the effectiveness of our GGSATD method.
Generating Subpopulation-Specific Biventricular Anatomy Models Using Conditional Point Cloud Variational Autoencoders

Generative statistical models have a wide variety of applications in modelling of cardiac anatomy and function, including disease diagnosis and prediction, personalized shape analysis, and generation of population cohorts for electrophysiological and mechanical computer simulations. In this work, we propose a novel geometric deep learning method based on the variational autoencoder (VAE) framework capable of accurately encoding, reconstructing, and synthesizing 3D surface models of the biventricular anatomy. Our non-linear approach works directly with memory-efficient point clouds and is able to process multiple substructures of the cardiac anatomy at the same time in a multi-class setting. Furthermore, we introduce subpopulation-specific characteristics as additional conditional inputs to allow the generation of new personalized anatomies. Our method achieves high reconstruction quality on a dataset derived from the UK Biobank study with average Chamfer distances between reconstructed and gold standard point clouds below the underlying image pixel resolution, for all anatomical substructures and combinations of conditional inputs. We investigate our method’s generative capabilities and show that it is able to synthesize virtual populations of realistic hearts with volumetric measurements in line with established clinical precedent. We also analyse the effects of variations in the latent space of the autoencoder on the generated anatomies and find interpretable changes in cardiac shapes and sizes.
A Novel Neural Multi-Store Memory Network for Autonomous Visual Navigation in Unknown Environment

Learning to achieve a user-specified objective from a random position in unseen environments is challenging for image-guided navigation agents. The abilities of long-horizon reasoning and semantic understanding are still lacking. Inspired by the human memory mechanism, we introduce a neural multi-store memory network to the reinforcement learning framework for target-driven visual navigation. The proposed memory network utilizes three temporal stages of memory to build time dependency for better scene understanding. Sensory memory encodes observations and embeds transient information into working memory, which is short-term and realized by a gated recurrent neural network (RNN). Then, the long-term memory stores the latent state from each step of the RNN into a single slot. Finally, a self-attention reading mechanism is designed to retrieve goal-related information from long-term memory. In addition, to improve the scene generalization capability of the agent, we facilitate training of the visual representation with a self-supervised auxiliary task and image augmentation. This method can navigate agents in unknown visual-realistic environments using only egocentric observations, without the need for any position sensors or pretrained models. The evaluation results on the Matterport3D dataset through the Habitat simulator demonstrate that our method outperforms the state-of-the-art approaches.
Bayesian Networks modeling of diarrhetic shellfish poisoning in Mytilus edulis harvested in Bantry Bay, Ireland

Diarrhetic Shellfish Poisoning (DSP) results from the human consumption of contaminated shellfish with marine biotoxins, which are produced by some species of marine dinoflagellates, mainly belonging to the genus Dinophysis. Shellfish contamination with marine biotoxins not only pose a threat to human health, but also lead to financial loss to aquaculture operations from the temporary closure of production areas when toxin concentrations exceed regulatory levels. In this study, we developed a Bayesian Network (BN) model for forecasting the short-term variations of DSP toxins in blue mussels (Mytilus edulis) from Bantry Bay, Southwest Ireland. Data inputs to a BN model from 10 production sites in Bantry Bay included plankton cell densities in sea water, DSP toxin concentration in mussels and sea surface temperature. The model was trained with data from 2014 to 2018, and validated with data of 2019. Validation consisted of predicting the DSP toxin concentration at one production site using the model parameters from the other locations as input values. Model validation showed that the prediction accuracy was higher than 86%. Sensitivity analysis indicated that in general, DSP toxin concentration was more relevant than plankton abundance. This initial work has demonstrated the usefulness of BN modeling as an approach to short term forecasting. Further work is ongoing to use the model for scenario testing and to increase the number of environmental parameters used as inputs to the model.
<i>POWER-SUPPLaY:</i> Leaking Sensitive Data From Air-Gapped, Audio-Gapped Systems by Turning the Power Supplies into Speakers

In this article, we introduce a new covert channel that enables attackers to leak data acoustically from highly secured, air-gapped and audio-gapped systems. The proposed malware can exploit the computer power supply unit (PSU) to play sounds and use it as an out-of-band, secondary speaker with limited capabilities. Our method enables the production of audio streams in a frequency band of 0-24 kHz and playing audio files (e.g., WAV) from a computer power supply without the need for audio hardware or speakers. Furthermore, we developed techniques that exploit the multiple CPU cores and their corresponding switching frequencies. It enables the production of multi-channel soundtracks and controlling the bit-depth and volume of sound played through the power supply. Sensitive information (documents, biometric data, encryption keys, etc.) can be covertly transmitted to a nearby receiver (e.g., smartphone). We show that our technique works with various types of systems: PC workstations and servers, as well as embedded systems and IoT devices that have no audio hardware at all. We also show that the attack can operate from an ordinary user mode process, run on virtual machines monitors (VMMs), and doesn’t need special privileges.
Precision Medicine for Hypertension Patients with Type 2 Diabetes via Reinforcement Learning

Precision medicine is a new approach to understanding health and disease based on patient-specific data such as medical diagnoses; clinical phenotype; biologic investigations such as laboratory studies and imaging; and environmental, demographic, and lifestyle factors. The importance of machine learning techniques in healthcare has expanded quickly in the last decade owing to the rising availability of vast multi-modality data and developed computational models and algorithms. Reinforcement learning is an appealing method for developing efficient policies in various healthcare areas where the decision-making process is typically defined by a long period or a sequential process. In our research, we leverage the power of reinforcement learning and electronic health records of South Koreans to dynamically recommend treatment prescriptions, which are personalized based on patient information of hypertension. Our proposed reinforcement learning-based treatment recommendation system decides whether to use mono, dual, or triple therapy according to the state of the hypertension patients. We evaluated the performance of our personalized treatment recommendation model by lowering the occurrence of hypertension-related complications and blood pressure levels of patients who followed our model's recommendation. With our findings, we believe that our proposed hypertension treatment recommendation model could assist doctors in prescribing appropriate antihypertensive medications.
Dynamic Asynchronous Anti Poisoning Federated Deep Learning with Blockchain-Based Reputation-Aware Solutions

As promising privacy-preserving machine learning technology, federated learning enables multiple clients to train the joint global model via sharing model parameters. However, inefficiency and vulnerability to poisoning attacks significantly reduce federated learning performance. To solve the aforementioned issues, we propose a dynamic asynchronous anti poisoning federated deep learning framework to pursue both efficiency and security. This paper proposes a lightweight dynamic asynchronous algorithm considering the averaging frequency control and parameter selection for federated learning to speed up model averaging and improve efficiency, which enables federated learning to adaptively remove the stragglers with low computing power, bad channel conditions, or anomalous parameters. In addition, a novel local reliability mutual evaluation mechanism is presented to enhance the security of poisoning attacks, which enables federated learning to detect the anomalous parameter of poisoning attacks and adjust the weight proportion of in model aggregation based on evaluation score. The experiment results on three datasets illustrate that our design can reduce the training time by 30% and is robust to the representative poisoning attacks significantly, confirming the applicability of our scheme.
Road to Salvation: Streaming Clients and Content Delivery Networks Working Together

Streaming media has truly become one of the most popular applications on the Internet. Viewers are spoiled for choice, and content providers compete to provide the best viewer experience. Traditionally, it has been challenging for content providers to get deep insights into the performance of their large-scale streaming operations. A new standard, Common Media Client Data (CMCD), aims to fundamentally change this. After providing an overview of this standard and describing some possible application scenarios, we present an open source sample implementation for readers to explore this topic further in their own practical environments.
Fuzzy Clustered Federated Learning Algorithm for Solar Power Generation Forecasting

Federated learning (FL) is a promising technique to construct a solar power generation forecasting model based on data collected from local generators. However, a set of local generators (i.e., cluster) for FL should be carefully defined to construct a high-accuracy forecasting model. Herein, we propose a fuzzy clustered FL algorithm (FCFLA) where each local generator can be included in more than one cluster. In FCFLA, a local generator has its own membership degree representing its sense of belonging to a specific cluster. Based on this membership degree, FCFLA can generate the high-accuracy forecasting model by catching different characteristics of the data of local generators while addressing the training data shortage problem. Evaluation results demonstrate that FCFLA has the fastest convergence time in achieving the desired accuracy.
Leveraging asynchronous federated learning to predict customers financial distress

In recent years, as economic stability is shaking, and the unemployment rate is growing high due to the COVID-19 effect, assigning credit scoring by predicting consumers' financial conditions has become more crucial. The conventional machine learning (ML) and deep learning approaches need to share customer's sensitive information with an external credit bureau to generate a prediction model that opens up the door of privacy leakage. A recently invented privacy-preserving distributed ML scheme referred to as Federated learning (FL) enables generating a target model without sharing local information through on-device model training on edge resources. In this paper, we propose an FL-based application to predict customers' financial issues by constructing a global learning model that is evolved based on the local models of the distributed agents. The local models are generated by the network agents using their on-device data and local resources. We used the FL concept because the learning strategy does not require sharing any data with the server or any other agent that ensures the preservation of customers' sensitive data. To that end, we enable partial works from the weak agents that eliminate the issue if the model convergence is retarded due to straggler agents. We also leverage asynchronous FL that cut off the extra waiting time during global model generation. We simulated the performance of our FL model considering a popular dataset, Give me Some Credit (Freshcorn, 2017). We evaluated our proposed method considering a a different number of stragglers and setting up various computational tasks (e.g., local epoch, batch size), and simulated the training loss and testing accuracy of the prediction model. Finally, we compared the F1-score of our proposed model with the existing centralized and decentralized approaches. Our results show that our proposed model achieves an almost identical F1-score as like centralized model even when we set up a skew-level of more than 80% and outperforms the state-of-the-art FL models by obtaining an average of 5∼6% higher accuracy when we have resource-constrained agents within a learning environment.
Partial Alignment for Object Detection in the Wild

Conventional object detectors often encounter remarkable performance drops due to the domain shift caused by environmental changes. However, labeling sufficient training data drawn from various domains is cost-ineffective and labor-intensive. To this end, unsupervised domain adaptive object detection (DAOD) has attracted much attention, in which the detector is transferred from the label-rich source domain to a label-agnostic target domain. Most of the existing cross-domain object detectors are designed with a parameter-shared network architecture, which, however, has an inherent flaw to accumulate the source errors caused by the inaccurate target distribution. As a result, the target data instead of decisive source data may dominate the learning towards model collapse. To overcome the risk, we propose an Asymmetric Tri-way Faster-RCNN (ATF) for DAOD tasks, in which a novel ancillary net is deployed with the chief net and it brings two merits. 1) The ancillary net enables the source distribution to be preserved without accumulating source error/distortion, such that the target distribution is dominated by source data and the model collapse is alleviated. 2) Ancillary target features can be generated by the ancillary net, which further enhances the discrimination of the target domain object detector. Furthermore, in order to remove the useless source-specific knowledge and exploit the informative domain-invariant knowledge, we propose a Partial Alignment based ATF (PA-ATF) model, in which an adversarial shuffling based mutual-information minimization strategy for domain disentanglement is provided. Intuitively, transferring only the source-domain invariant information to the target domain is more conscionable. Extensive experiments on benchmark datasets, including the Cityscapes, Foggy Cityscapes, Pascal VOC, Clipart, Watercolor, SIM10K, and KITTI demonstrate the remarkable performance of our models over other state-of-the-art approaches.
Towards Universal GAN Image Detection

The ever higher quality and wide diffusion of fake images have spawn a quest for reliable forensic tools. Many GAN image detectors have been proposed, recently. In real world scenarios, however, most of them show limited robustness and generalization ability. Moreover, they often rely on side information not available at test time, that is, they are not universal. We investigate these problems and propose a new GAN image detector based on a limited sub-sampling architecture and a suitable contrastive learning paradigm. Experiments carried out in challenging conditions prove the proposed method to be a first step towards universal GAN image detection, ensuring also good robustness to common image impairments, and good generalization to unseen architectures.
Adaptive Video Configuration and Bitrate Allocation for Teleoperated Vehicles

Vehicles with autonomous driving capabilities are present on public streets. However, edge cases remain that still require a human in-vehicle driver. Assuming the vehicle manages to come to a safe state in an automated fashion, teleoperated driving technology enables a human to resolve the situation remotely by a control interface connected via a mobile network. While this is a promising solution, it also introduces technical challenges, one of them being the necessity to transmit video data of multiple cameras from the vehicle to the human operator. In this paper, an adaptive video streaming framework specifically designed for teleoperated vehicles is proposed and demonstrated. The framework enables automatic reconfiguration of the video streams of the multi-camera system at runtime. Predictions of variable transmission service quality are taken into account. With the objective to improve visual quality, the framework uses so-called rate-quality models to dynamically allocate bitrates and select resolution scaling factors. Results from deploying the proposed framework on an actual teleoperated driving system are presented.
Dynamic Graph Reasoning for Conversational Open-Domain Question Answering

In recent years, conversational agents have provided a natural and convenient access to useful information in people’s daily life, along with a broad and new research topic, conversational question answering (QA). On the shoulders of conversational QA, we study the conversational open-domain QA problem, where users’ information needs are presented in a conversation and exact answers are required to extract from the Web. Despite its significance and value, building an effective conversational open-domain QA system is non-trivial due to the following challenges: (1) precisely understand conversational questions based on the conversation context; (2) extract exact answers by capturing the answer dependency and transition flow in a conversation; and (3) deeply integrate question understanding and answer extraction. To address the aforementioned issues, we propose an end-to-end Dynamic Graph Reasoning approach to Conversational open-domain QA (DGRCoQA for short). DGRCoQA comprises three components, i.e., a dynamic question interpreter (DQI), a graph reasoning enhanced retriever (GRR), and a typical Reader, where the first one is developed to understand and formulate conversational questions while the other two are responsible to extract an exact answer from the Web. In particular, DQI understands conversational questions by utilizing the QA context, sourcing from predicted answers returned by the Reader, to dynamically attend to the most relevant information in the conversation context. Afterwards, GRR attempts to capture the answer flow and select the most possible passage that contains the answer by reasoning answer paths over a dynamically constructed context graph . Finally, the Reader, a reading comprehension model, predicts a text span from the selected passage as the answer. DGRCoQA demonstrates its strength in the extensive experiments conducted on a benchmark dataset. It significantly outperforms the existing methods and achieves the state-of-the-art performance.