Reviewerly/exHarmony huggingface dataset:
Cervical Cell Image Classification-Based Knowledge Distillation

Current deep-learning-based cervical cell classification methods suffer from parameter redundancy and poor model generalization performance, which creates challenges for the intelligent classification of cervical cytology smear images. In this paper, we establish a method for such classification that combines transfer learning and knowledge distillation. This new method not only transfers common features between different source domain data, but also realizes model-to-model knowledge transfer using the unnormalized probability output between models as knowledge. A multi-exit classification network is then introduced as the student network, where a global context module is embedded in each exit branch. A self-distillation method is then proposed to fuse contextual information; deep classifiers in the student network guide shallow classifiers to learn, and multiple classifier outputs are fused using an average integration strategy to form a classifier with strong generalization performance. The experimental results show that the developed method achieves good results using the SIPaKMeD dataset. The accuracy, sensitivity, specificity, and F-measure of the five classifications are 98.52%, 98.53%, 98.68%, 98.59%, respectively. The effectiveness of the method is further verified on a natural image dataset.
Joint optimization of scoring and thresholding models for online multi-label classification

Existing online multi-label classification works cannot well handle the online label thresholding problem and lack the regret analysis for their online algorithms. This paper proposes a novel framework of adaptive label thresholding algorithms for online multi-label classification, with the aim to overcome the drawbacks of existing methods. The key feature of our framework is that both scoring and thresholding models are included as important components of the online multi-label classifier and are incorporated into one online optimization problem. Further, in order to establish the relationship between scoring and thresholding models, a novel multi-label classification loss function is derived, which measures to what an extent the multi-label classifier can distinguish between relevant labels and irrelevant ones for an incoming instance. Based on this new framework and loss function, we present a first-order linear algorithm and a second-order one, which both enjoy closed form update, but rely on different techniques for updating the multi-label classifier. Both algorithms are proved to achieve a sub-linear regret. Using Mercer kernels, our first-order algorithm has been extended to deal with nonlinear multi-label prediction tasks. Experiments show the advantage of our linear and nonlinear algorithms, in terms of various multi-label performance metrics.
Integrating Digital Transformation and Servitization into Digital Servitization: A Case Study on Domino's

Purpose: The purpose of this study is to suggest a digital servitization model which can be implemented by the restaurant industry and to explore the opportunities and challenges of digital servitization.&#x0D; Design/methodology/approach: The case study analysis method was applied for this research.&#x0D; Findings: This study reveals that pizza firms can gain competitive advantages and create value by implementing the digital servitization business model. In addition, firms can understand the opportunities and challenges of digital servitization and prepare themselves before implementing the business model.&#x0D; Research limitations/implications: Digital servitization model was proposed to analyze a single pizza company but a comparative study with other firms can be conducted to identify differences and create a common strategy.&#x0D; Originality/value: Firms commonly use an integrated digital transformation and servitization model to create customer value, but few case studies on integrated case models are available. This study investigates this research gap using a case study. The findings of this study have meaningful implications for restaurant managers to implement a digital servitization strategy.
How can we enhance reputation in Blockchain consensus for Industry 4.0–A proposed approach by extending the PageRank algorithm

This research presents a cost-effective approach toward an enhancement of Blockchain consensus targeting enhanced data security and privacy in the Industry 4.0 scenario by incorporating an enhanced reputation score at a cost-effective approach. Cellular Automata (CAs)-based PageRank validation (i.e., an extended PageRank) algorithm is incorporated towards consensus design in Blockchain Trilemma to facilitate cost-effectiveness in Industry 4.0 scenario. A detailed investigation is carried out to explore the true potential for CAs-based PageRank validation towards computation of Reputation score in Blockchain Trilemma. Further investigations and experimental analysis for the proposed approach towards computation of Reputation scores explore the efficiency of the proposed CAs-based PageRank validation-based approach over several existing approaches e.g., PageRank, NCDawareRank, and HodgeRank-based approaches. Additionally, energy efficiency in the proposed approach is ensured as low-energy consumption may be achieved with CAs-based physical modeling.
BKGNN-TI: A Bilinear Knowledge-Aware Graph Neural Network Fusing Text Information for Recommendation

Abstract Knowledge graph (KG)-based recommendation methods effectively alleviate the data sparsity and cold-start problems in collaborative filtering. Among these methods, neighborhood-based methods are the mainstream methods. However, these methods ignore some meta-information about the items, specifically, the diversity of item information (e.g., texts) and feature interaction between neighboring nodes. In this paper, we propose a Bilinear Knowledge-aware Graph Neural Network Fusing Text Information (BKGNN-TI), which can model both knowledge graph information and text information. In particular, the information in knowledge graph contains not only the existing high-order connectivity but also feature interactions between neighboring nodes at the same level in KG. First, we construct the information propagation layer using the bilinear collector and linear collector. Feature interactions between neighboring nodes and the high-order connectivity are collected in the information propagation layer to generate the item knowledge representations. The bilinear collector emphasizes the importance of second-order feature interaction between neighboring nodes in the KG. Then, texts are also introduced when computing the item representations, which can help further infer user interests. We choose objective program titles and introductions as text information to avoid the influence of subjective factors. BKGNN-TI designs an ALBERT-based sequence encoder to encode texts by the structure of ALBERT+Bi-LSTM+Attention, thus enriching the feature representations of the items. In the experiments, we utilize two language datasets, i.e., the English public dataset Movielens-20M and the Chinese dataset IPTV constructed by ourselves. The results demonstrate that our BKGNN-TI outperforms baselines, indicating that our BKGNN-TI is a generalization for both Chinese and English datasets.
Deep Reinforcement Learning-Based End-to-End Control for UAV Dynamic Target Tracking

Uncertainty of target motion, limited perception ability of onboard cameras, and constrained control have brought new challenges to unmanned aerial vehicle (UAV) dynamic target tracking control. In virtue of the powerful fitting ability and learning ability of the neural network, this paper proposes a new deep reinforcement learning (DRL)-based end-to-end control method for UAV dynamic target tracking. Firstly, a DRL-based framework using onboard camera image is established, which simplifies the traditional modularization paradigm. Secondly, neural network architecture, reward functions, and soft actor-critic (SAC)-based speed command perception algorithm are designed to train the policy network. The output of the policy network is denormalized and directly used as speed control command, which realizes the UAV dynamic target tracking. Finally, the feasibility of the proposed end-to-end control method is demonstrated by numerical simulation. The results show that the proposed DRL-based framework is feasible to simplify the traditional modularization paradigm. The UAV can track the dynamic target with rapidly changing of speed and direction.
State-of-the-art analysis of the integration of augmented reality with construction technologies to improve construction safety

Purpose This paper aims to explore augmented reality (AR) applications in construction safety academic literature and propose possible improvements for future scholarly works. The paper explicitly focuses on AR integration with Construction 4.0 technologies as an effective solution to safety concerns in the construction industry. Design/methodology/approach This study applied a systematic review approach. In total, 387 potentially relevant articles from databases were identified. Once filtering criteria were applied, 29 eligible papers where selected. The inclusion criteria were being directly associated with construction safety focused on an AR application and AR interactions associated with the Construction 4.0 technologies. Findings This study investigated the structure of AR applications in construction safety. To this end, the authors studied the safety purposes of AR applications in construction safety: pre-event (intelligent operation, training, safety inspection and hazard alerting), during-event (pinpointing hazard) and post-event (safety estimation) applications. Then, the integration of AR with Construction 4.0 technologies was elaborated. The systematic review also revealed that the AR integration has contributed to developing several technical aspects of AR technology: display, tracking and human–computer interaction. The study results indicate that AR integration with construction is effective in mitigating safety concerns; however, further research studies are required to support this statement. Originality/value This study contributes to exploring applications and integrations of AR into construction safety in order to facilitate the leverage of this technology. This review can help encourage practitioners and researchers to conduct further academic investigations into AR application in construction safety.
Point-by-point visual enhancement with spatially and spectrally tunable laser illumination

Vision is responsible for most of the information that humans perceive of the surrounding world. Many studies attempt to enhance the visualization of the entire scene by optimizing and tuning the overall illumination spectrum. However, by using a spatially uniform illumination spectrum for the entire scene, only certain global color shifts with respect to a reference illumination spectrum can be realized, resulting in moderate visual enhancement. In this paper, a new visual enhancement method is presented that relies on a spatially variable illumination spectrum. Such an approach can target much more dedicated visual enhancements by optimizing the incident illumination spectrum to the surface reflectance at each position. First, a geometric calibration of the projector-camera system is carried out for determining the spatial mapping from the projected pixel grid to the imaged pixel grid. Secondly, the scene is segmented for implementing the visual enhancement approach. And finally, one of three visual enhancement scenarios is applied by projecting the required color image onto the considered segmented scene. The experimental results show that the visual salience of the scene or region of interest can be efficiently enhanced when our proposed method is applied to achieve colorfulness enhancement, hue tuning, and background lightness reduction.
Nonreciprocal Directional Dichroism in Magnetoelectric Spin Glass

Optical absorption spectra in the visible and near-infrared light were measured for magnetoelectric spin glass Ni_{0.4}Mn_{0.6}TiO_{3} under various field-cooled conditions. Despite the absence of long-range magnetic-dipole order, this spin-glass system exhibits nonreciprocal directional dichroism (NDD) at zero external field after a magnetoelectric field-cooled procedure. This result is distinct from previous studies on NDD in systems with magnetic toroidal moments induced either by long-range magnetic-dipole order or by applying crossed electric and magnetic fields. The present Letter conclusively demonstrates that the observed NDD originates from magnetoelectrically induced ferroic order of magnetic toroidal moments without conventional magnetic-dipole order.
A Reinforcement-Learning-Based Model for Resilient Load Balancing in Hyperledger Fabric

Blockchain with its numerous advantages is often considered a foundational technology with the potential to revolutionize a wide range of application domains, including enterprise applications. These enterprise applications must meet several important criteria, including scalability, performance, and privacy. Enterprise blockchain applications are frequently constructed on private blockchain platforms to satisfy these criteria. Hyperledger Fabric is one of the most popular platforms within this domain. In any privacy blockchain system, including Fabric, every organisation needs to utilise a peer node (or peer nodes) to connect to the blockchain platform. Due to the ever-increasing size of blockchain and the need to support a large user base, the monitoring and the management of different resources of such peer nodes can be crucial for a successful deployment of such blockchain platforms. Unfortunately, little attention has been paid to this issue. In this work, we propose the first-ever solution to this significant problem by proposing an intelligent control system based on reinforcement learning for distributing the resources of Hyperledger Fabric. We present the architecture, discuss the protocol flows, outline the data collection methods, analyse the results and consider the potential applications of the proposed approach.
WebtoonMe: A Data-Centric Approach for Full-Body Portrait Stylization

Full-body portrait stylization has drawn attention recently. However, most methods have focused only on converting face regions. A recently proposed two-stage method expands to full bodies, but the outputs are less plausible and fail to achieve quality robustness of non-face regions. Furthermore, they cannot reflect diverse skin tones. In this study, we propose a data-centric solution to build a production-level full-body portrait stylization system. We construct a novel and advanced dataset preparation paradigm that can effectively resolve the aforementioned problems.
Dynamic load identification based on piecewise fitting trend term and smooth curve

Abstract wilson – θ back analysis method is a good load identification method. However, there are some problems, such as unsatisfactory load identification results caused by acceleration measurement errors. By deriving the recurrence formula of cumulative acceleration error of velocity and displacement, the different situations of displacement and velocity error are summarized. This paper introduces an algorithm for identifying the extreme points of trend items. The correction speed and displacement are modified by subsection fitting. The midpoint smoothing fitting of the load data after the initial identification is proposed which can better improve the accuracy of the load identification wilson – θ back analysis method. Finally, a simulation example shows that the displacement, velocity and the identified load error level are well controlled.
SkyBridge

Multi-cloud is a new paradigm that deploys cloud applications and stores data on multiple different cloud services from different providers to prevent vendor lock-in, decrease cost, and improve reliability. However, managing such a system is complicated because of the manufactured barriers among different clouds, such as different APIs for services with similar features. Sky Computing attempts to solve this problem by introducing a compatibility layer on top of the clouds. In this article, I propose a cross-cloud storage system, SkyBridge, that acts as the compatibility layer for cloud storage. With SkyBridge, users can effortlessly manage and access data across many clouds using the same set of APIs, and it can be integrated into a more sophisticated Sky Computing platform as the data manager.
Enhancing Drones for Law Enforcement and Capacity Monitoring at Open Large Events

Police tasks related with law enforcement and citizen protection have gained a very useful asset in drones. Crowded demonstrations, large sporting events, or summer festivals are typical situations when aerial surveillance is necessary. The eyes in the sky are moving from the use of manned helicopters to drones due to costs, environmental impact, and discretion, resulting in local, regional, and national police forces possessing specific units equipped with drones. In this paper, we describe an artificial intelligence solution developed for the Castelldefels local police (Barcelona, Spain) to enhance the capabilities of drones used for the surveillance of large events. In particular, we propose a novel methodology for the efficient integration of deep learning algorithms in drone avionics. This integration improves the capabilities of the drone for tasks related with capacity control. These tasks have been very relevant during the pandemic and beyond. Controlling the number of persons in an open area is crucial when the expected crowd might exceed the capacity of the area and put humans in danger. The new methodology proposes an efficient and accurate execution of deep learning algorithms, which are usually highly demanding for computation resources. Results show that the state-of-the-art artificial intelligence models are too slow when utilised in the drone standard equipment. These models lose accuracy when images are taken at altitudes above 30 m. With our new methodology, these two drawbacks can be overcome and results with good accuracy (96% correct segmentation and between 20% and 35% mean average proportional error) can be obtained in less than 20 s.
Investigating the Perceived Realism of the Other User’s Look-Alike Avatars

There are outstanding questions regarding the fidelity of realistic look-alike avatars that show that there is still substantial development to be done, especially as the virtual world plays a more vital role in our education, work and recreation. The use of look-alike avatars could completely change how we interact virtually. This paper investigates which features of other people’s look-alike avatars influence our perceived realism. Four levels of avatar representations were assessed in this pilot study: a static avatar, a static avatar with lip sync corresponding to an audio recording, full face animation with audio and a full body animation. Results show that full-face and body animations are very important in increasing the perceived realism of avatars. More importantly, participants found the lip sync animation more unsettling (uncanny valley effect) than any of the other animations. The results have implications for the perception of other people’s look-alike avatars in collaborative virtual environments.
What Drives Students to Feel the Impact of Online Learning in Using a Cloud Accounting Integrated System?

cloud accounting is an accounting information system based on cloud technology that can make transactions online. This study focused on determining the elements that influence the acceptance of cloud accounting in education. These research targets are students who use cloud accounting in their learning classes. By using the census method, a total of 240 students were used as respondents to answer questionnaires after completing learning through cloud accounting. Then, the data were analysed using a partial least squares approach. The results show that Cloud accounting quality (CAQ), Instructor characteristics (IC), confirmation (CN), and Perceived ease of use (PEOU) influence perceived usefulness (PU). Additionally, PU and PEOU improve Intention to use (INCA) and Satisfaction (SS). The findings of this research provide input to lecturers to improve teaching methods and instructions in using learning media through technology so that students understand the learning is carried out.
A study on skeleton-based action recognition and its application to physical exercise recognition

In recent years, human action recognition (HAR) has been an attractive research topic in computer vision since HAR has been widely applied in various fields, such as gaming, healthcare, surveillance, and human-machine interaction. In this paper, we present a feasible solution to develop a real-time HAR application that helps one monitor physical exercises. As an end-to-end solution, the proposed framework consists of techniques that recognize the physical exercises, spot each in real-time and assess the quality of the practicing users. Firstly, we define nine common physical exercises, such as arm circles, squats, jumping jacks, etc. We then construct a dataset named COMVIS-FITNESS consisting of the physical exercises dataset of nine subjects. We argue the advantages of two different deep neural networks for HAR to recognize the exercises. One is a compact HAR neuronal network named DD-Net, and another is a high-performance graph convolutional neural network called FF-AAGCN. For spotting the exercises from an image sequence, a sliding window method is combined with these two networks to make a real-time classification. A technique to evaluate the workout of the practicing user is proposed. Experimental results on COMVIS-FITNESS show that deep neural networks can recognize all exercises in the datasets with high accuracy. The accuracies and F1-scores of DD-Net are 99.24% and 99.23%, while those obtained by FF-AAGCN are 98.48% and 98.32%, respectively. The completed pipeline of the proposed method is integrated in an application. The application works in real-time on edge device as Jetson Xavier AGX. The proposed techniques and dataset are made publicly available and can be downloaded from https://bit.ly/3F9J1qb.
Entanglement entropy scaling of noisy random quantum circuits in two dimensions

Whether noisy quantum devices without error correction can provide quantum advantage over classical computers is a critical issue of current quantum computation. In this work, the random quantum circuits, which are used as the paradigm model to demonstrate quantum advantage, are simulated with depolarizing noise on experiment-relevant two-dimensional architecture. With comprehensive numerical simulation and theoretical analysis, we find that the maximum achievable operator entanglement entropy, which indicates maximal simulation cost, has area law scaling with the system size for constant noise rate. On the other hand, we also find that the maximum achievable operator entanglement entropy has power-law scaling with the noise rate for fixed system size, and the volume law scaling can be obtained only if the noise rate decreases when system size increase.