Reviewerly/exHarmony huggingface dataset:
Using Artificial Neural Networks to Compensate Negative Effects of Latency in Commercial Real-Time Strategy Games

Cloud-based game streaming allows gamers to play Triple-A games on any device, anywhere, almost instantly. However, they entail one major disadvantage - latency. Latency, the time between input and output, worsens the players’ experience and performances. Reducing the latency of game streaming is crucial to provide gamers the same game experience as in local gaming. Previous work demonstrates that deep learning-based techniques can compensate for a game’s latency if the artificial neural network has access to the game’s internal state during inference. However, it is unclear if deep learning can be used to compensate for the latency of unmodified commercial video games. Hence, this work investigates the use of deep learning-based latency compensation in commercial video games. In a first study, we collected data from 21 participants playing real-time strategy games. We used the data to train two artificial neural networks. In a second study with 12 participants, we compared three different scenarios: (1) playing without latency, (2) playing with 50 ms of controlled latency, and (3) playing with 50 ms latency fully compensated by our system. Our results show that players associated the gaming session with less negative feelings and were less tired when supported by our system. We conclude that deep learning-based latency compensation can compensate the latency of commercial video games without accessing the internal state of the game. Ultimately, our work enables cloud-based game streaming providers to offer gamers a better and more responsive gaming experience.
Revisiting Aspect-Sentiment-Opinion Triplet Extraction: Detailed Analyses Towards a Simple and Effective Span-Based Model

Aspect-based sentiment information extraction has attracted increasing attention in the research community of natural language processing. Various methods, such as sequence tagging, sequence-to-sequence generation and span-based extraction, have been proposed, which own different advantages and disadvantages. In this article, we revisit the span-based method for aspect-sentiment-opinion triplet extraction, by designing and analyzing a simple yet effective Span-based Model, called SMTFASTE. Our model leverages a tidily three-layer architecture, including a BERT-based encoding layer, a span representation layer and an aspect-sentiment-opinion prediction layer. In the experiments of two widely-used benchmarks (ASTE-V2 and ASOTE-V2), we find that our model outperforms a number of complicated state-of-the-art models in most evaluation metrics. Therefore, we conduct detailed analyses for our model, such as ablation studies of the core components of our model and the benefit of explicitly using context information, and obtain some insightful findings and conclusion. Through this study, we show that a simple span-based model is able to achieve competitive results without much feature and architecture engineering. Our model is easy to follow and we have opened our code to facilitate related research.
PBidm: Privacy-Preserving Blockchain-Based Identity Management System for Industrial Internet of Things

Industrial Internet of Things (IIoT) is revolutionizing plenty of industrial applications by utilizing large-scale smart devices in manufacturing and industrial processes. However, IIoT is facing the disclosure of identity privacy. The identity information is precious and critical, thereby inspiring a line of follow-up privacy-preserving studies, i.e., anonymous credential protocols, or privacy-preserving identity management schemes. However, they are either too anonymous to be used in the IIoT environment, or the system is highly centralized, which implies the risk of a single point of failure. In this article, we propose <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PBidm</small> , a privacy-preserving blockchain-based identity management scheme for IIoT. Specifically, by leveraging blockchain and diversified cryptographic tools, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PBidm</small> can fully support the desirable properties, i.e., unforgeability, blindness, unlikability, traceability, revocability, and public verifiability. Then, we provide security analysis to ensure reasonable security assurance. Finally, we present a performance evaluation of the proposed scheme to demonstrate the practicability in IIoT applications.
LifeLonger: A Benchmark for Continual Disease Classification

Deep learning models have shown a great effectiveness in recognition of findings in medical images. However, they cannot handle the ever-changing clinical environment, bringing newly annotated medical data from different sources. To exploit the incoming streams of data, these models would benefit largely from sequentially learning from new samples, without forgetting the previously obtained knowledge. In this paper we introduce LifeLonger, a benchmark for continual disease classification on the MedMNIST collection, by applying existing state-of-the-art continual learning methods. In particular, we consider three continual learning scenarios, namely, task and class incremental learning and the newly defined cross-domain incremental learning. Task and class incremental learning of diseases address the issue of classifying new samples without re-training the models from scratch, while cross-domain incremental learning addresses the issue of dealing with datasets originating from different institutions while retaining the previously obtained knowledge. We perform a thorough analysis of the performance and examine how the well-known challenges of continual learning, such as the catastrophic forgetting exhibit themselves in this setting. The encouraging results demonstrate that continual learning has a major potential to advance disease classification and to produce a more robust and efficient learning framework for clinical settings. The code repository, data partitions and baseline results for the complete benchmark are publicly available $$^{1}$$ ( https://github.com/mmderakhshani/LifeLonger ).
Robust Segmentation of Brain MRI in the Wild with Hierarchical CNNs and No Retraining

Retrospective analysis of brain MRI scans acquired in the clinic has the potential to enable neuroimaging studies with sample sizes much larger than those found in research datasets. However, analysing such clinical images "in the wild" is challenging, since subjects are scanned with highly variable protocols (MR contrast, resolution, orientation, etc.). Nevertheless, recent advances in convolutional neural networks (CNNs) and domain randomisation for image segmentation, best represented by the publicly available method SynthSeg, may enable morphometry of clinical MRI at scale. In this work, we first evaluate SynthSeg on an uncurated, heterogeneous dataset of more than 10,000 scans acquired at Massachusetts General Hospital. We show that SynthSeg is generally robust, but frequently falters on scans with low signal-to-noise ratio or poor tissue contrast. Next, we propose SynthSeg $$^{+}$$ , a novel method that greatly mitigates these problems using a hierarchy of conditional segmentation and denoising CNNs. We show that this method is considerably more robust than SynthSeg, while also outperforming cascaded networks and state-of-the-art segmentation denoising methods. Finally, we apply our approach to a proof-of-concept volumetric study of ageing, where it closely replicates atrophy patterns observed in research studies conducted on high-quality, 1 mm, T1-weighted scans. The code and trained model are publicly available at https://github.com/BBillot/SynthSeg .
Few-shot Medical Image Segmentation Regularized with Self-reference and Contrastive Learning

Despite the great progress made by deep convolutional neural networks (CNN) in medical image segmentation, they typically require a large amount of expert-level accurate, densely-annotated images for training and are difficult to generalize to unseen object categories. Few-shot learning has thus been proposed to address the challenges by learning to transfer knowledge from a few annotated support examples. In this paper, we propose a new prototype-based few-shot segmentation method. Unlike previous works, where query features are compared with the learned support prototypes to generate segmentation over the query images, we propose a self-reference regularization where we further compare support features with the learned support prototypes to generate segmentation over the support images. By this, we argue for that the learned support prototypes should be representative for each semantic class and meanwhile discriminative for different classes, not only for query images but also for support images. We additionally introduce contrastive learning to impose intra-class cohesion and inter-class separation between support and query features. Results from experiments conducted on two publicly available datasets demonstrated the superior performance of the proposed method over the state-of-the-art (SOTA).
Attentive Symmetric Autoencoder for Brain MRI Segmentation

Self-supervised learning methods based on image patch reconstruction have witnessed great success in training auto-encoders, whose pre-trained weights can be transferred to fine-tune other downstream tasks of image understanding. However, existing methods seldom study the various importance of reconstructed patches and the symmetry of anatomical structures, when they are applied to 3D medical images. In this paper we propose a novel Attentive Symmetric Auto-encoder (ASA) based on Vision Transformer (ViT) for 3D brain MRI segmentation tasks. We conjecture that forcing the auto-encoder to recover informative image regions can harvest more discriminative representations, than to recover smooth image patches. Then we adopt a gradient based metric to estimate the importance of each image patch. In the pre-training stage, the proposed auto-encoder pays more attention to reconstruct the informative patches according to the gradient metrics. Moreover, we resort to the prior of brain structures and develop a Symmetric Position Encoding (SPE) method to better exploit the correlations between long-range but spatially symmetric regions to obtain effective features. Experimental results show that our proposed attentive symmetric auto-encoder outperforms the state-of-the-art self-supervised learning methods and medical image segmentation models on three brain MRI segmentation benchmarks.
Reverse engineering of one-qubit filter functions with dynamical invariants

We derive an integral expression for the filter-transfer function of an arbitrary one-qubit gate through the use of dynamical invariant theory and Hamiltonian reverse engineering. We use this result to define a cost function which can be efficiently optimized to produce one-qubit control pulses that are robust against specified frequency bands of the noise power spectral density. We demonstrate the utility of our result by generating optimal control pulses that are designed to suppress broadband detuning and pulse amplitude noise. We report an order of magnitude improvement in gate fidelity in comparison with known composite pulse sequences. More broadly, we also use the same theoretical framework to prove the robustness of nonadiabatic geometric quantum gates under specific error models and control constraints.
Does Chatbot Language Formality Affect Users’ Self-Disclosure?

Chatbots are increasingly used to replace human interviewers and survey forms for soliciting information from users. This paper presents two studies that investigate how the formality of a chatbot's conversational style can affect the likelihood of users engaging with and disclosing sensitive information to a chatbot. In our first study, we show that the domain and sensitivity of the information being requested impact users' preferred conversational style. Specifically, when users were asked to disclose sensitive health information, they perceived a formal style as more competent and appropriate. In our second study, we investigate the health domain further by analysing the quality of user utterances as users talk to a chatbot about their dental flossing. We found that users who do not floss every day gave higher quality responses when talking to a formal chatbot. These findings can help designers choose a chatbot's language formality for their given use case.
Comparing Command Construction in Native and Non-Native Speaker IPA Interaction through Conversation Analysis

Intelligent Personal Assistants (IPAs) are limited in the languages they support, meaning many people are left to interact using a non-native language. Yet, we know little about how people interact with IPAs in this way. Through a conversation analysis (CA) perspective, we examine native (L1) and non-native (L2) English speaker interactions with Google Assistant, comparing how both user groups produce IPA commands. Our work shows that L1 and L2 speakers similarly used pauses, partial or complete repetition, and hyper-articulation when constructing commands. However, L2 speakers tended to experience issues in lexical access, syntactic construction and pronunciation, resulting in the use of code-mixing, increased pause lengths and off-task rehearsal to help generate commands. We consider reasons for such effects, whilst exploring ways to design IPA interaction to ensure it is sensitive to L2 challenges in command production.
SAR-PeGA: A Generation Method of Adversarial Examples for SAR Image Target Recognition Network

Deep learning (DL) is widely used in automatic target recognition (ATR) of synthetic aperture radar (SAR) images. Related researches show that DL models for SAR ATR are vulnerable to adversarial examples attack in the digital domain. However, how to generate adversarial examples in practical scenarios is critical and challenging. In this paper, we propose a systematic SAR perturbation generation algorithm (SAR-PeGA) for target recognition network. Firstly, assuming that some reflection phase tuning samples are located in the fixed area of SAR target, we adjust the phase characteristics of reflected signal with variable phase sequences. Secondly, we take the imperceptible perturbations from universal adversarial perturbations (UAP) as reference. Then, we construct the unconstrained minimum optimization model to find the specific phase sequences of tuning samples, and optimize the model with the adaptive moment estimation (Adam) optimizer. Finally, SAR adversarial examples can be flexibly generated through the proposed deceptive jamming model. Experimental results demonstrate that the proposed method can generate imperceptible jamming and effectively attack three classical recognition models.
Pyramid Spatial-Temporal Graph Transformer for Skeleton-Based Action Recognition

Although graph convolutional networks (GCNs) have shown their demonstrated ability in skeleton-based action recognition, both the spatial and the temporal connections rely too much on the predefined skeleton graph, which imposes a fixed prior knowledge for the aggregation of high-level semantic information via the graph-based convolution. Some previous GCN-based works introduced dynamic topology (vertex connection relationships) to capture flexible spatial correlations from different actions. Then, the local relationships from both the spatial and temporal domains can be captured by diverse GCNs. This paper introduces a more straightforward and more effective backbone to obtain the spatial-temporal correlation between skeleton joints with a local-global alternation pyramid architecture for skeleton-based action recognition, namely the pyramid spatial-temporal graph transformer (PGT). The PGT consists of four stages with similar architecture but different scales: graph embedding and transformer blocks. We introduce two kinds of transformer blocks in our work: the spatial-temporal transformer block and joint transformer block. In the former, spatial-temporal separated attention (STSA) is proposed to calculate the connection of the global nodes of the graph. Due to the spatial-temporal transformer block, self-attention can be performed on skeleton graphs with long-range temporal and large-scale spatial aggregation. The joint transformer block flattens the tokens in both the spatial and temporal domains to jointly capture the overall spatial-temporal correlations. The PGT is evaluated on three public skeleton datasets: the NTU RGBD 60, NTU RGBD 120 and NW-UCLA datasets. Better or comparable performance with the state of the art (SOTA) shows the effectiveness of our work.
Mammogram classification based on a novel convolutional neural network with efficient channel attention

Early accurate mammography screening and diagnosis can reduce the mortality of breast cancer. Although CNN-based breast cancer computer-aided diagnosis (CAD) systems have achieved significant results in recent years, precise diagnosis of lesions in mammogram remains a challenge due to low signal-to-noise ratio (SNR) and physiological characteristics. Many researchers achieved excellent performance in detecting mammographic images by inputting region of interest (ROI) annotations while ROI annotations require a great quantity of manual labor, time and resources. We propose a two-stage method that combines images preprocessing and model optimization to address the aforementioned challenges. Firstly, we propose the breast database preprocess (BDP) method to preprocess INbreast then we get INbreast†. The only label we need is benign or malignant label of one mammogram, not manual labeling such as ROI annotations. Secondly, we apply focal loss to ECA-Net50 which is an improved model based on ResNet50 with efficient channel attention (ECA) module. Our method can adaptively extract the key features of mammograms, meanwhile solving the problem of hard-to-classify samples and unbalanced categories. The AUC value of our method on INbreast† is 0.960, accuracy is 0.929, Recall is 0.928. The precision of our method on INbreast† is 0.883 which improved by 0.254 compared to ResNet50. In addition, we use Grad-CAM to visualize the effect of our model. The visualized heatmaps extracted by our method can focus more on lesion regions. Both numerical and visualized experiments demonstrate that our method achieves satisfactory performance.
Local to Global Feature Learning for Salient Object Detection

Existing works mainly focus on how to aggregate multi-level features for salient object detection, which may generate sub-optimal results due to interference with redundant details. To handle this problem, we aim to learn a local to global feature representation, so as to segment the detailed structures in a local perspective and locate the salient objects in a global perspective. In particular, we design a novel L2GF network which mainly consists of three modules, i.e., L-Net, G-Net, and F-Net. L-Net employs our enhanced auto-encoder structure to extract local contexts that provide rich boundary information of objects, which is able to learn rich local features in a certain receptive field. G-Net feeds the tokenized feature patches as input sequence, and leverages the well-known Transformer structure to extract global contexts which are helpful to derive the relationship between multiple salient regions and produce more complete salient results. F-Net is a coarse-to-fine process, which takes the features and maps of both local and global branches as inputs and calculate the final high-quality salient map. Extensive experiments on five benchmark datasets demonstrate that our L2GF network performs favorably against the state-of-the-art approaches.
Subsidy policies of a fresh supply chain considering the inputs of blockchain traceability service system

Abstract To stimulate the development and application of blockchain technology, Chinese government put forward subsidy strategy. To explore the subsidy policies under the new background, we chose a fresh supply chain with one producer, one blockchain-based traceability service provider, and one retailer as the research object, and government subsidy strategies were divided into a fixed strategy and a varying strategy. Afterward, considering the trust level of blockchain-based traceability information and consumers’ preference to the blockchain-based traceability information, we revised the demand function, and three subsidy models were proposed and analyzed. Findings: (1) the varying subsidy will help the retailer, the producer, and the traceability service provider set lower prices. (2) Meanwhile, the varying subsidies offered to the blockchain-based traceability service provider and the producer will help the whole supply chain members obtain more revenues.
Security Enhancement in Smart Logistics with Blockchain Technology: A Home Delivery Use Case

Home delivery (B2C) experienced rapid growth during the COVID-19 pandemic, especially food delivery. Nonrepudiation is a problem in security and affects delivery. Blockchain technology is a new technology that addresses this issue. This paper proposes preventing nonrepudiation in home delivery through blockchain technology. We also design a data structure and smart contract for distributed application. In addition, we evaluate the performance of the proposed framework in terms of transaction fees and response times. We experimented on the blockchain emulator, stored data in RDBMS, and implemented a client with a mobile application. The data in the smart contract were directly impacted by the gas used and the response time. Primarily, the function processes the large data size and involves high transaction fees and long response times. The gas used accounts for 9061 times of data length, while the response time accounts for 2.84 times of data length. Finally, we propose a security policy for the proposed framework.
FPFS: Filter-level pruning via distance weight measuring filter similarity

Deep Neural Networks (DNNs) enjoy the welfare of convolution, while also bearing huge computational pressure. Therefore, model compression techniques are used to alleviate this problem, where filter-based neural network has received extensive attention as the research object of this paper. Common approaches treat filters as independent individuals and choose retrained filters by evaluating their performance, while more complex macro methods consider relationship between filters. Therefore, we propose a facile distance-based filter selection method, called FPFS, to visualize the similarity between filters from a global perspective. We calculate and sum the distance between filters to get filters' "Distance Weight" which is applied as a metric to assess filters. We use four common and appropriate distances for filters evaluation. To verify the performance of our algorithm, we introduce FPFS to classical DCNNs and test it on general classification datasets CIFAR-10, CIFAR-100 and mageNet. For example, FPFS reduces Parameters and FLOPs of the lightweight model DenseNet-40 to about half of the original while maintain accuracy on CIFAR-10 by 94.40% (the original model is 94.80%). To ResNet-56 on CIFAR-100, FPFS compresses FLOPs to less than half of the original, while model accuracy reaches 71.46% (the original model is 71.44%). About ResNet-50 on ImageNet, FPFS achieves 60.3% FLOPs pruning rate accompanied by 0.96% top-1 accuracy loss. We also compare the experimental results with state-of-the-art filter pruning algorithms to highlight the effectiveness of FPFS.