Reviewerly/exHarmony huggingface dataset:
Visible-Infrared Person Re-Identification Using Privileged Intermediate Information

Visible-infrared person re-identification (ReID) aims to recognize a same person of interest across a network of RGB and IR cameras. Some deep learning (DL) models have directly incorporated both modalities to discriminate persons in a joint representation space. However, this cross-modal ReID problem remains challenging due to the large domain shift in data distributions between RGB and IR modalities. This paper introduces a novel approach for a creating intermediate virtual domain that acts as bridges between the two main domains (i.e., RGB and IR modalities) during training. This intermediate domain is considered as privileged information (PI) that is unavailable at test time, and allows formulating this cross-modal matching task as a problem in learning under privileged information (LUPI). We devised a new method to generate images between visible and infrared domains that provide additional information to train a deep ReID model through an intermediate domain adaptation. In particular, by employing color-free and multi-step triplet loss objectives during training, our method provides common feature representation spaces that are robust to large visible-infrared domain shifts. Experimental results on challenging visible-infrared ReID datasets indicate that our proposed approach consistently improves matching accuracy, without any computational overhead at test time. The code is available at: https://github.com/alehdaghi/Cross-Modal-Re-ID-via-LUPI .
mfeeU-Net: A multi-scale feature extraction and enhancement U-Net for automatic liver segmentation from CT Images

&lt;abstract&gt; &lt;p&gt;Medical image segmentation of the liver is an important prerequisite for clinical diagnosis and evaluation of liver cancer. For automatic liver segmentation from Computed Tomography (CT) images, we proposed a Multi-scale Feature Extraction and Enhancement U-Net (mfeeU-Net), incorporating Res2Net blocks, Squeeze-and-Excitation (SE) blocks, and Edge Attention (EA) blocks. The Res2Net blocks which are conducive to extracting multi-scale features of the liver were used as the backbone of the encoder, while the SE blocks were also added to the encoder to enhance channel information. The EA blocks were introduced to skip connections between the encoder and the decoder, to facilitate the detection of blurred liver edges where the intensities of nearby organs are close to the liver. The proposed mfeeU-Net was trained and evaluated using a publicly available CT dataset of LiTS2017. The average dice similarity coefficient, intersection-over-union ratio, and sensitivity of the mfeeU-Net for liver segmentation were 95.32%, 91.67%, and 95.53%, respectively, and all these metrics were better than those of U-Net, Res-U-Net, and Attention U-Net. The experimental results demonstrate that the mfeeU-Net can compete with and even outperform recently proposed convolutional neural networks and effectively overcome challenges, such as discontinuous liver regions and fuzzy liver boundaries.&lt;/p&gt; &lt;/abstract&gt;
Self-Supervised Graph Structure Refinement for Graph Neural Networks

Graph structure learning (GSL), which aims to learn the adjacency matrix for graph neural networks (GNNs), has shown great potential in boosting the performance of GNNs. Most existing GSL works apply a joint learning framework where the estimated adjacency matrix and GNN parameters are optimized for downstream tasks. However, as GSL is essentially a link prediction task, whose goal may largely differ from the goal of the downstream task. The inconsistency of these two goals limits the GSL methods to learn the potential optimal graph structure. Moreover, the joint learning framework suffers from scalability issues in terms of time and space during the process of estimation and optimization of the adjacency matrix. To mitigate these issues, we propose a graph structure refinement (GSR) framework with a pretrain-finetune pipeline. Specifically, The pre-training phase aims to comprehensively estimate the underlying graph structure by a multi-view contrastive learning framework with both intra- and inter-view link prediction tasks. Then, the graph structure is refined by adding and removing edges according to the edge probabilities estimated by the pre-trained model. Finally, the fine-tuning GNN is initialized by the pre-trained model and optimized toward downstream tasks. With the refined graph structure remaining static in the fine-tuning space, GSR avoids estimating and optimizing graph structure in the fine-tuning phase which enjoys great scalability and efficiency. Moreover, the fine-tuning GNN is boosted by both migrating knowledge and refining graphs. Extensive experiments are conducted to evaluate the effectiveness (best performance on six benchmark datasets), efficiency, and scalability (13.8 times faster using 32.8% GPU memory compared to the best GSL baseline on Cora) of the proposed model.
Single-photon switching in a Floquet waveguide-QED system with time-modulated coupling constants

The dynamical control of single-photon scattering in a one-dimensional waveguide coupled to a Floquet atom-cavity system with time-modulated coupling constants is investigated. The analytical expressions for determining the scattering properties are obtained by using an effective Floquet Hamiltonian in real space. The photon transport is extremely sensitive to the time-modulated atom-cavity coupling strengths. Two cases, i.e., the effective Floquet Hamiltonian either includes or excludes a static coupling term, are discussed in detail. The results show that, with such Floquet atom-cavity configuration, an active photonic switch with nearly ideal switching contrast could be implemented. The transmission of the waveguide photons with different frequencies can dynamically be switched on or off via adjusting the modulated amplitude or relative modulated phase no matter whether the static coupling between the atom and the two cavity modes is considered or not. The application of the dynamic modulated atom-cavity coupling strengths instead of a purely static one makes our photonic switch more tunable. These results are expected to be applicable in quantum information processing and in active quantum device design involving dynamical modulation.
Implementation of Generative Adversarial Networks in Mobile Applications for Image Data Enhancement

This article aims to explore and research GANs as a tool for mobile devices that can generate high-resolution images from low-resolution samples and reduce blurring. In addition, the authors also analyse the specifics of GAN, SRGAN, and ESRGAN loss functions and their features. GANs are widely used for a vast range of applied tasks for image manipulations. They’re able to synthesize, combine, and restore graphical samples of high quality that are almost indistinguishable from real data. The main scope of the research is to study the possibility to use GANs for the said tasks, and their potential implementation in mobile applications.
EDUCATION 3.0: BLOCKCHAIN-BACKED MOOCS

MOOCs ability to provide valuable skills and competencies at a fraction of the cost of other educational offers, in a fraction of the time, and from the comfort of one's own home, has proven to be a promising business reference to digital transformation, as applied in higher education. This paper overviews the impact of blockchain features, such as the trendy self-sovereign identity adding precious disruptive value along MOOCs value chains, especially around content acquisition, digital marketing and monetization, as well as facilitating acquisition and transfer of credentials in a digital trust-focused environment. A sound, trackable and protective digital identity has been long considered a needed asset, whereas the owner of the credentials, either the university, individual or employer has full control over the data that is shared across the educational ecosystem, on a case-by-case basis. The article also aspires at critically observing whether using blockchain tech for courseware aggregation and delivery constitutes a sound business case, as well as a worthy exploration for intrinsic learning motivation and an enhancement to higher education value chain. Secondly, blockchain and the self-sovereign identity offer benefits for the education authoring providers in particular and token-based monetization models for OER education. Blockchain has the potential to disrupt significantly current business models, by facilitating legally binding smart-contracts, helping streamline the credentials' processes, protecting the viability and legitimacy of education records as well as increasing the effectiveness of educational agregators. Reliably easy and quick to transfer credentials are essential for the labor force to be able to swiftly introduce newly acquired skills and competencies to the real business. In the same time, education providers are incentivized by an education governance model that enables better management and sustainability.
PROMOTING CYBERSECURITY CULTURE THROUGH EDUCATION

Generally, for the effective protection of information systems and computer networks, including the protection of information flowing over them and the protection of Internet users, we have to notice the importance of promoting cybersecurity culture. Taking into account the considerable amount of Internet users, along with digital products development and evolution and the history of cyber-attacks, promoting a cybersecurity culture is imperative. The main means of promoting cybersecurity culture is education, which by its forms: formal, non-formal and informal, becomes a beneficial tool meant to complements failure of technical solutions to meet all the specific cyberspace vulnerabilities. Thus, by acquiring a global dimension, cybersecurity culture can be promoted on many levels through education, implying numerous investments of international non-state entities and organizations. Cybersecurity culture is directed to evolution and development, adapting to the new information technologies and the newest ways of promotion. Once people regardless of age, have access to the Internet, they could also benefit from other security solutions other than those that came along with the product or application provided by the manufacturer or supplier. This situation highlights the promotion of cybersecurity culture through education, for people of almost any age, which gives it a central position at the individual level of promotion. Also, through the promotion of the cybersecurity culture, there have to be considered several issues relating to people who do not use Internet services. Going beyond the individual level, cybersecurity culture in organizations amounts to the promotion of safe and solid cybersecurity practices that integrate very well with the work of employees. It is making employees aware of cyberspace threats which implies behavioral changes.
RINet: Relative Importance-Aware Network for Fixation Prediction

Fixation prediction aims to simulate human visual selection mechanism and estimate the visual saliency degree of regions in a scene. In semantically rich scenes, there are generally multiple salient regions. This condition requires a fixation prediction model to understand the relative importance relationship of multiple salient regions, that is, to identify which region is more important. In practice, existing fixation prediction models implicitly explore the relative importance relationship in the end-to-end training process while they do not work well. In this article, we propose a novel Relative Importance-aware Network (RINet) to explicitly explore the modeling of relative importance in fixation prediction. RINet perceives multi-scale local and global relative importance through the Hierarchical Relative Importance Enhancement (HRIE) module. Within a single scale subspace, on the one hand, HRIE module regards the similarity matrix as the local relative importance map to weight the input feature. On the other hand, HRIE module integrates a set of local relative importance maps into one map, defined as the global relative importance map, to grasp global relative importance. Moreover, we propose a Complexity-Relevant Focal (CRF) loss for network training. As such, we can progressively emphasize learning difficult samples for better handling the complicated scenarios, further improving the performance. The ablation studies confirm the contributions of key components of our RINet, and extensive experiments on five datasets demonstrate our RINet is superior to 28 relevant state-of-the-art models. Our code and results are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Mango321321/RINet</uri>
How consumers react differently toward humanoid vs. nonhumanoid robots after service failures: a moderated chain mediation model

Purpose With the increasing use of robots in service scenarios in hospitality industries, service failure frequently occurs during the service process, and consumers may react differently toward humanoid vs. nonhumanoid robots due to different performance expectancies. This study focuses on consumers' reactions to service failures by humanoid vs. nonhumanoid robots and the different impacts on brand forgiveness and revisit intentions through performance expectancy for different genders. Design/methodology/approach The study used a sample of 280 participants to test the moderated chain mediation model. The participants were instructed to report their performance expectancies for humanoid/nonhumanoid robots and imagine a hotel check-in scenario in which a service failure occurs. Brand forgiveness, brand revisit intention and other demographic information were assessed. Findings The results show that consumers have higher performance expectancy for nonhumanoid robots. This performance expectancy generates brand forgiveness and revisit intentions for male consumers but does not affect female consumers' forgiveness and revisit behaviors. Originality/value This study contributes to the literature by taking a long-term perspective to investigate the outcomes after service failure, providing evidence for pending questions in previous studies and enriching studies of gender differences. Additionally, this study provides practical implications to consider the use of anthropomorphism in robots, advocate for functional confidence in robots and target consumers across genders.
Virtual reality in human-robot interaction: Challenges and benefits

&lt;abstract&gt; &lt;p&gt;Virtual reality (VR) technology has been increasingly employed in human-robot interaction (HRI) research to enhance the immersion and realism of the interaction. However, the integration of VR into HRI also introduces new challenges, such as latency, mismatch between virtual and real environments and potential adverse effects on human users. Despite these challenges, the use of VR in HRI has the potential to provide numerous benefits, including improved communication, increased safety and enhanced training and education. Yet, little research has been done by scholars to review the state of the art of VR applications in human-robot interaction. To bridge the gap, this paper provides an overview of the challenges and benefits of using VR in HRI, as well as current research in the field and future directions for development. It has been found that robots are getting more personalized, interactive and engaging than ever; and with the popularization of virtual reality innovations, we might be able to foresee the wide adoption of VR in controlling robots to fulfill various tasks of hospitals, schools and factories. Still, there are several challenges, such as the need for more advanced VR technologies to provide more realistic and immersive experiences, the development of more human-like robot models to improve social interactions and the need for better methods of evaluating the effectiveness of VR in human-robot interaction.&lt;/p&gt; &lt;/abstract&gt;
Shuffle Split-Edited Nearest Neighbor: A Novel Intelligent Control Model Compression for Smart Lighting in Edge Computing Environment

One of the IoT case studies is to apply intelligent control to smart lighting using k-nearest neighbor (KNN). However, migrating computing from the cloud to the edge becomes more challenging due to the limited capacity of the microcontroller’s flash memory and the large size of the KNN model. This paper proposes shuffle split-edited nearest neighbor (SSENN), a novel method to compress intelligent control models for smart lighting while maintaining its performance. The method adopts the high compression ratio (CR) of random under-sampling (RU) and the high accuracy of edited nearest neighbor (ENN). The methodology trains basic KNN and observes its performance and characteristics as the number of datasets increases. Then we synthesize the SSENN on the KNN and further evaluate and benchmark the model with RU and ENN. The test results show that KNN + SSENN has higher $$\text {Average}\pm \text {Std. Dev.}$$ accuracy than KNN + RU, which is $$0.915\pm 0.01$$ . In addition, the CR of KNN + SSENN is higher than KNN + ENN, which is 2.60. With a model size of 14.2 kB, the KNN + SSENN model can fit in the microcontroller’s flash memory.
Physical Reservoir Computing Using Nonlinear MEMS Resonator Having High Memory Capacity at "Edge of Chaos"

This paper reports physical reservoir computing (PRC) using a single nonlinear electrostatic resonator and demonstrates its high memory capacity at "edge of chaos." The resonator is a simple doubly supported resonator fabricated from a silicon-on-insulator wafer. We proposed a PRC system without feedback loop, in which its memory capacity relies on the decay time of the high-Q resonator. The benchmark task results indicate that the system shows good linear and nonlinear memory capacities at the resonance and the maximum capacity was obtained at the vicinity of the instability edge of the frequency response.
A Clinician’s Guide to Running Custom Machine-Learning Models in an Electronic Health Record Environment

We recently brought an internally developed machine-learning model for predicting which patients in the emergency department would require hospital admission into the live electronic health record environment. Doing so involved navigating several engineering challenges that required the expertise of multiple parties across our institution. Our team of physician data scientists developed, validated, and implemented the model. We recognize a broad interest and need to adopt machine-learning models into clinical practice and seek to share our experience to enable other clinician-led initiatives. This Brief Report covers the entire model deployment process, starting once a team has trained and validated a model they wish to deploy in live clinical operations.
An Improvised Algorithm for a Dynamic Key Generation Model

Data may be kept private, secure, and authentic via cryptography. Computational resources and communication channel performance impede the development of an efficient key generation model in IoT devices for encryption and decryption applications. Most IoT networks encode information with session keys, which are less secure than dynamic key encryption used in communication channels. Furthermore, because of the resource limitations of most IoT networks, it is impractical to use the current dynamic key generation approaches. To solve these problems, a dynamic key generator model has been designed that continuously generates unique keys in the range of 1000–10,000,000. We also discuss our proposed model’s security and performance analysis to validate the feasibility of its operation in such a resource-constrained IoT environment.
Multi-label Classification of Cervix Types with Image Size Optimization for Cervical Cancer Prescreening by Deep Learning

Cervical cancer is one of the most pressing and embarrassing problems in the medical, psychological, and social aspects of women’s lives. The multi-class classification problem and multi-label classification problem with image size optimization (on the basis of the updated 6-class labeling) are considered on the basis of the dataset published during Intel and MobileODT Cervical Cancer Screening competition on Kaggle platform. In the multi-class classification problem, the better metrics were obtained after training the standard DNNs with the initial weights obtained after pretraining on ImageNet dataset with the conclusion as to possible useful usage of the smaller versions of DNNs (like MobileNetV2, NASNetMobile). In the multi-label classification problem with image size optimization, EfficientNetB0 model was used as an example for investigation of the effective method of metric improvement by image size optimization. It allowed us to improve the mean AUC values by 2.7–2.8% (in comparison with the standard image sizes of 224 * 224 pixels used for pretraining the standard models on ImageNet dataset) in the limits of the standard deviation 0.3–1.8% by the proper selection of the optimal input image size. In general, this approach based on averaging values of metrics obtained by various regimes of DNN training (with and without DA) and verification (validation and testing) for the pairs of smallest and biggest available image sizes with further extrapolation of metric tendencies could be the useful strategy for image size optimization in other use cases.
A New Authentication Protocol for Hardware-Based Authentication Systems in an IoT Environment

Many companies have recently introduced smart IoT and wearable devices. Most of these devices have sensors that interact with them as well as features that enable them to connect to the Internet. However, if proper security and safety are not considered, the extent of damage will be greatly increased. This research work proposes a novel authentication scheme for two devices that can mutually authenticate and use dynamic keys for encryption and decryption based on the AES-GCM authenticated encryption algorithm. The security framework of the proposed scheme demonstrates that it is resistant to a variety of common attacks and enhances security in an IoT environment.
Spatial-Temporal Adaptive Graph Convolutional Network for Skeleton-Based Action Recognition

Skeleton-based action recognition approaches usually construct the skeleton sequence as spatial-temporal graphs and perform graph convolution on these graphs to extract discriminative features. However, due to the fixed topology shared among different poses and the lack of direct long-range temporal dependencies, it is not trivial to learn the robust spatial-temporal feature. Therefore, we present a spatial-temporal adaptive graph convolutional network (STA-GCN) to learn adaptive spatial and temporal topologies and effectively aggregate features for skeleton-based action recognition. The proposed network is composed of spatial adaptive graph convolution (SA-GC) and temporal adaptive graph convolution (TA-GC) with an adaptive topology encoder. The SA-GC can extract the spatial feature for each pose with the spatial adaptive topology, while the TA-GC can learn the temporal feature by modeling the direct long-range temporal dependencies adaptively. On three large-scale skeleton action recognition datasets: NTU RGB+D 60, NTU RGB+D 120, and Kinetics Skeleton, the STA-GCN outperforms the existing state-of-the-art methods. The code is available at https://github.com/hang-rui/STA-GCN .