Reviewerly/exHarmony huggingface dataset:
Towards End-to-End Image Compression and Analysis with Transformers

We propose an end-to-end image compression and analysis model with Transformers, targeting to the cloud-based image classification application. Instead of placing an existing Transformer-based image classification model directly after an image codec, we aim to redesign the Vision Transformer (ViT) model to perform image classification from the compressed features and facilitate image compression with the long-term information from the Transformer. Specifically, we first replace the patchify stem (i.e., image splitting and embedding) of the ViT model with a lightweight image encoder modelled by a convolutional neural network. The compressed features generated by the image encoder are injected convolutional inductive bias and are fed to the Transformer for image classification bypassing image reconstruction. Meanwhile, we propose a feature aggregation module to fuse the compressed features with the selected intermediate features of the Transformer, and feed the aggregated features to a deconvolutional neural network for image reconstruction. The aggregated features can obtain the long-term information from the self-attention mechanism of the Transformer and improve the compression performance. The rate-distortion-accuracy optimization problem is finally solved by a two-step training strategy. Experimental results demonstrate the effectiveness of the proposed model in both the image compression and the classification tasks.
Fault diagnosis of electric transformers based on infrared image processing and semi-supervised learning

It is crucial to maintain the safe and stable operation of distribution transformers, which constitute a key part of power systems. In the event of transformer failure, the fault type must be diagnosed in a timely and accurate manner. To this end, a transformer fault diagnosis method based on infrared image processing and semi-supervised learning is proposed herein. First, we perform feature extraction on the collected infrared-image data to extract temperature, texture, and shape features as the model reference vectors. Then, a generative adversarial network (GAN) is constructed to generate synthetic samples for the minority subset of labelled samples. The proposed method can learn information from unlabeled sample data, unlike conventional supervised learning methods. Subsequently, a semi-supervised graph model is trained on the entire dataset, i.e., both labeled and unlabeled data. Finally, we test the proposed model on an actual dataset collected from a Chinese electricity provider. The experimental results show that the use of feature extraction, sample generation, and semi-supervised learning model can improve the accuracy of transformer fault classification. This verifies the effectiveness of the proposed method.
Task-Attribute-Based Access Control Scheme for IoT via Blockchain

As a new form of network, the Internet of things (IoT) is becoming more widely used in people’s lives. In this paper, related theoretical research and practical applications of the IoT are explored. The security of the IoT has become a hot research topic. Access controls are methods that control reasonable allocations of data and resources and ensure the security of the IoT. However, most access control systems do not dynamically assign users’ rights. Additionally, with some access control systems, there is a risk of overstepping other user’s authority, and there may exist a central authority that is a single point of failure. Therefore, to solve these problems, this paper proposes a Task-Attribute-Based Access Control scheme for the IoT via blockchain that combines the access control technologies of both the IoT and blockchain. This model, which merges the advantages of task-based access controls and attribute-based access controls, is perfectly integrated with blockchain technology. This model uses hash functions and digital signature algorithms to ensure the authenticity and integrity of the data, and it can dynamically allocate users’ minimum privileges and thus perfectly solves the single point of failure problem. The model is implemented using a Geth client and solidity code, and the simulation results demonstrate the effectiveness of the model.
A quick synopsis of blockchain technology

Blockchain evolved as a core technology of Bitcoin, and earned a significant attraction for entrepreneurs and researchers nowadays. The blockchain is an open, immutable distributed public ledger that allows transactions to take place in a decentralised manner without a need for a trusted third party. Bitcoin, the first successful cryptocurrency, is a peer-to-peer electronic payment system. The thought for Bitcoin started as a means for making a secure currency that had no centralised control. Blockchain application is not only restricted to Bitcoin, but it also ignited the idea of using it for a wide range of fields such as ehealth, governance, arts, culture, education, electricity trading, etc. To apply this technology to many areas, one should have a complete understanding of what it is, this paper is meant to give a quick synopsis of blockchain technology (BT).
Super-resolution reconstruction of flow field of hydrogen-fueled scramjet under self-ignition conditions

This paper reports experiments on a hydrogen-fueled scramjet performed at different equivalence ratios in a ground pulse combustion wind tunnel with a Mach-2.5 incoming flow. In the non-chemical reaction flow before the fuel was ignited, the flow field was oscillatory, and from the pressure monitor, the oscillation period was 0.07 s and the oscillation amplitude was 0.035 MPa. Schlieren and flame self-luminescence images of the combustor flow were obtained synchronously, and the flow-field structure was stable with the flame concentrated in the shear layer. Deep learning was used to subject the low-resolution combustion flow field to super-resolution analysis to reconstruct a high-resolution flow field. To improve the spatial resolution of the flow field during self-ignition of the hydrogen-fueled scramjet and study the flow mechanism and coupling rule of turbulent fluctuations in the ignition process, a multipath asymmetric residual network (MARN) is proposed based on a single-path super-resolution convolutional neural network (SRCNN) and a residual network model (ResNet_16). The experimental results show that compared with SRCNN and ResNet_16, MARN has the best precision and performance regarding the super-resolution flow field in the self-ignition of hydrogen fuel in terms of the mean peak signal-to-noise ratio, mean structural similarity, and average correlation coefficient as well as being the least complicated. The proposed method offers the possibility of developing lightweight super-resolution models for the flow fields in supersonic combustors; it shows enormous potential for revealing the physical flow of the fuel and air mixture, and it offers accurate forecasts of self-ignition times.
Conscious Empathic AI in Service

Recent advances in artificial intelligence (AI) have achieved human-scale speed and accuracy for classification tasks. Current systems do not need to be conscious to recognize patterns and classify them. However, for AI to advance to the next level, it needs to develop capabilities such as metathinking, creativity, and empathy. We contend that such a paradigm shift is possible through a fundamental change in the state of artificial intelligence toward consciousness, similar to what took place for humans through the process of natural selection and evolution. To that end, we propose that consciousness in AI is an emergent phenomenon that primordially appears when two machines cocreate their own language through which they can recall and communicate their internal state of time-varying symbol manipulation. Because, in our view, consciousness arises from the communication of inner states, it leads to empathy. We then provide a link between the empathic quality of machines and better service outcomes associated with empathic human agents that can also lead to accountability in AI services.
Critical Quantum Metrology in the Non-Linear Quantum Rabi Model

The quantum Rabi model (QRM) with linear coupling between light mode and qubit exhibits the analog of a second-order phase transition for vanishing mode frequency which allows for criticality-enhanced quantum metrology in a few-body system. We show that the QRM including a nonlinear coupling term exhibits much higher measurement precisions due to its first-order-like phase transition at finite frequency, avoiding the detrimental slowing-down effect close to the critical point of the linear QRM. When a bias term is added to the Hamiltonian, the system can be used as a fluxmeter or magnetometer if implemented in circuit QED platforms.
Adoption challenges of blockchain technology for reverse logistics in the food processing industry

Streamlining the product movement through blockchain technology helps organisations manage logistics functions more efficiently and gain advantages such as speedy delivery, better traceability, decentralised data, transparency, etc. At the same time, blockchain has many implementation challenges too. This paper empirically examines the factors affecting blockchain technology adoption for reverse logistics in the food processing industry. This research has used the exploratory factor analysis method to derive factors perceived as challenges to adopting blockchain for reverse logistics. The study findings revealed six factors, i.e., lack of resources, cooperation issues between partners, fear of data breach, technology asynchronization, intensive capital requirement, and mishandling of bulk orders, which emerged as significant challenges. Managing identified challenges can help address issues related to adopting blockchain technology for reverse logistics functions and make operations more efficient.
HAKG

Knowledge graph (KG) plays an increasingly important role to improve the recommendation performance and interpretability. A recent technical trend is to design end-to-end models based on the information propagation schemes. However, existing propagation-based methods fail to (1) model the underlying hierarchical structures and relations, and (2) capture the high-order collaborative signals of items for learning high-quality user and item representations.
Noisy propagation of Gaussian states in optical media with finite bandwidth

Abstract We address propagation and entanglement of Gaussian states in optical media characterised by nontrivial spectral densities. In particular, we consider environments with a finite bandwidth $$J(\omega ) = J_0 \left[ \theta (\omega -\Omega ) - \theta (\omega - \Omega - \delta )\right] $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>J</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>ω</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> <mml:mo>=</mml:mo> <mml:msub> <mml:mi>J</mml:mi> <mml:mn>0</mml:mn> </mml:msub> <mml:mfenced> <mml:mi>θ</mml:mi> <mml:mo>(</mml:mo> <mml:mi>ω</mml:mi> <mml:mo>-</mml:mo> <mml:mi>Ω</mml:mi> <mml:mo>)</mml:mo> <mml:mo>-</mml:mo> <mml:mi>θ</mml:mi> <mml:mo>(</mml:mo> <mml:mi>ω</mml:mi> <mml:mo>-</mml:mo> <mml:mi>Ω</mml:mi> <mml:mo>-</mml:mo> <mml:mi>δ</mml:mi> <mml:mo>)</mml:mo> </mml:mfenced> </mml:mrow> </mml:math> , and show that in the low temperature regime $$T\ll \Omega ^{-1}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>T</mml:mi> <mml:mo>≪</mml:mo> <mml:msup> <mml:mi>Ω</mml:mi> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:math> : (i) secular terms in the master equation may be neglected; (ii) attenuation (damping) is strongly suppressed; (iii) the overall diffusion process may be described as a Gaussian noise channel with variance depending only on the bandwidth. We find several regimes where propagation is not much detrimental and entanglement may be protected form decoherence.
A Novel Chaos-Based Cryptography Algorithm and Its Performance Analysis

Data security represents an essential task in the present day, in which chaotic models have an excellent role in designing modern cryptosystems. Here, a novel oscillator with chaotic dynamics is presented and its dynamical properties are investigated. Various properties of the oscillator, like equilibria, bifurcations, and Lyapunov exponents (LEs), are discussed. The designed system has a center point equilibrium and an interesting chaotic attractor. The existence of chaotic dynamics is proved by calculating Lyapunov exponents. The region of attraction for the chaotic attractor is investigated by plotting the basin of attraction. The oscillator has a chaotic attractor in which its basin is entangled with the center point. The complexity of the chaotic dynamic and its entangled basin of attraction make it a proper choice for image encryption. Using the effective properties of the chaotic oscillator, a method to construct pseudo-random numbers (PRNGs) is proposed, then utilizing the generated PRNG sequence for designing secure substitution boxes (S-boxes). Finally, a new image cryptosystem is presented using the proposed PRNG mechanism and the suggested S-box approach. The effectiveness of the suggested mechanisms is evaluated using several assessments, in which the outcomes show the characteristics of the presented mechanisms for reliable cryptographic applications.
The use of cloud technologies when studying geography by higher school students

The article is devoted to the topical issue of the cloud technologies implementation in educational process in general and when studying geography, in particular. The authors offer a selection of online services which can contribute to the effective acquisition of geographical knowledge in higher school. The publication describes such cloud technologies as Gapminder, DESA, Datawrapper.de, Time.Graphics, HP Reveal, MOZAIK education, Settera Online, Click-that-hood, Canva, Paint Instant. It is also made some theoretical generalization of their economic, technical, technological, didactic advantages and disadvantages. Visual examples of application are provided in the article. The authors make notice that in the long run the technologies under study should become a valuable educational tool of creation virtual information and education environments connected into common national, and then global, educational space.
SVASeg: Sparse Voxel-Based Attention for 3D LiDAR Point Cloud Semantic Segmentation

3D LiDAR has become an indispensable sensor in autonomous driving vehicles. In LiDAR-based 3D point cloud semantic segmentation, most voxel-based 3D segmentors cannot efficiently capture large amounts of context information, resulting in limited receptive fields and limiting their performance. To address this problem, a sparse voxel-based attention network is introduced for 3D LiDAR point cloud semantic segmentation, termed SVASeg, which captures large amounts of context information between voxels through sparse voxel-based multi-head attention (SMHA). The traditional multi-head attention cannot directly be applied to the non-empty sparse voxels. To this end, a hash table is built according to the incrementation of voxel coordinates to lookup the non-empty neighboring voxels of each sparse voxel. Then, the sparse voxels are grouped into different groups, and each group corresponds to a local region. Afterwards, position embedding, multi-head attention and feature fusion are performed for each group to capture and aggregate the context information. Based on the SMHA module, the SVASeg can directly operate on the non-empty voxels, maintaining a comparable computational overhead to the convolutional method. Extensive experimental results on the SemanticKITTI and nuScenes datasets show the superiority of SVASeg.
Uncoding library chatbots: deploying a new virtual reference tool at the San Jose State University library

Purpose This paper aims to detail how a university library developed an AI chatbot to meet a growing need for virtual reference services. This chatbot was developed using Google's free Dialogflow bot platform and embedded in the library's website. With the onset of COVID-19 and a greater reliance on virtual services, chatbots have become of increasing interest to libraries as a tool to provide enhanced services during non-staffed hours and to perform basic information triage when virtual chat transactions reach an overwhelming number of available staff. Design/methodology/approach Using in-depth research into current practices and readily available tools, a small non-technical team at a university library designed and piloted an AI chatbot that employs natural language processing and AI training. This article describes the chatbot development and implementation process. Results of chatbot interactions after one academic year of usage are also reviewed. Findings This study reveals that a university library chatbot may be developed and deployed with minimal coding knowledge using existing tools. Chatbot content can be populated through current library information sources and trained to address typical information inquiries. However, additional development and testing is needed to increase user engagement. Originality/value This study indicates that libraries can develop and deploy chatbots to meet user information inquiries without onerous technical training or IT resources. It describes best practices for chatbots and the steps necessary to deploy a chatbot on a library website.
Amodal Panoptic Segmentation

Humans have the remarkable ability to perceive objects as a whole, even when parts of them are occluded. This ability of amodal perception forms the basis of our perceptual and cognitive understanding of our world. To enable robots to reason with this capability, we formulate and propose a novel task that we name amodal panoptic segmentation. The goal of this task is to simultaneously predict the pixel-wise semantic segmentation labels of the visible regions of stuff classes and the instance segmentation labels of both the visible and occluded regions of thing classes. To facilitate research on this new task, we extend two established benchmark datasets with pixel-level amodal panoptic segmentation labels that we make publicly available as KITTI-360-APS and BDD100K-APS. We present several strong baselines, along with the amodal panoptic quality (APQ) and amodal parsing coverage (APC) metrics to quantify the performance in an interpretable manner. Furthermore, we propose the novel amodal panoptic segmentation network (APSNet), as a first step towards addressing this task by explicitly modeling the complex relationships between the occluders and occludes. Extensive experimental evaluations demonstrate that APSNet achieves state-of-the-art performance on both benchmarks and more importantly exemplifies the utility of amodal recognition. The datasets are available at http://amodal-panoptic.cs.uni-freiburg.de.
Exploring Attention-Aware Network Resource Allocation for Customized Metaverse Services

Emerging with the support of computing and communications technologies, Metaverse is expected to bring users unprecedented service experiences. However, the increase in the number of Metaverse users places a heavy demand on network resources, especially for Metaverse services that are based on graphical extended reality and require rendering a plethora of virtual objects. To make efficient use of network resources and improve the Quality-of-Experience (QoE), we design an attentionaware network resource allocation scheme to achieve customized Metaverse services. The aim is to allocate more network resources to virtual objects in which users are more interested. We first discuss several key techniques related to Metaverse services, including QoE analysis, eye-tracking, and remote rendering. We then review existing datasets and propose the user-objectattention level (UOAL) dataset that contains the ground truth attention of 30 users to 96 objects in 1; 000 images. With the help of UOAL, we propose an attention-aware network resource allocation algorithm that has two steps, i.e., attention prediction and QoE maximization. Specially, we provide an overview of the designs of two types of attention prediction methods, i.e., interestaware and time-aware prediction. By using the predicted userobject- attention values, network resources such as the rendering capacity of edge devices can be allocated optimally to maximize the QoE. Finally, we propose promising research directions related to Metaverse services.
Domain Generalization by Mutual-Information Regularization with Pre-trained Models

Domain generalization (DG) aims to learn a generalized model to an unseen target domain using only limited source domains. Previous attempts to DG fail to learn domain-invariant representations only from the source domains due to the significant domain shifts between training and test domains. Instead, we re-formulate the DG objective using mutual information with the oracle model, a model generalized to any possible domain. We derive a tractable variational lower bound via approximating the oracle model by a pre-trained model, called Mutual Information Regularization with Oracle (MIRO). Our extensive experiments show that MIRO significantly improves the out-of-distribution performance. Furthermore, our scaling experiments show that the larger the scale of the pre-trained model, the greater the performance improvement of MIRO. Code is available at https://github.com/kakaobrain/miro .
Few-Shot Classification with Contrastive Learning

A two-stage training paradigm consisting of sequential pre-training and meta-training stages has been widely used in current few-shot learning (FSL) research. Many of these methods use self-supervised learning and contrastive learning to achieve new state-of-the-art results. However, the potential of contrastive learning in both stages of FSL training paradigm is still not fully exploited. In this paper, we propose a novel contrastive learning-based framework that seamlessly integrates contrastive learning into both stages to improve the performance of few-shot classification. In the pre-training stage, we propose a self-supervised contrastive loss in the forms of feature vector vs. feature map and feature map vs. feature map, which uses global and local information to learn good initial representations. In the meta-training stage, we propose a cross-view episodic training mechanism to perform the nearest centroid classification on two different views of the same episode and adopt a distance-scaled contrastive loss based on them. These two strategies force the model to overcome the bias between views and promote the transferability of representations. Extensive experiments on three benchmark datasets demonstrate that our method achieves competitive results.