Reviewerly/exHarmony huggingface dataset:
On the Potential of Algorithm Fusion for Demographic Bias Mitigation in Face Recognition

With the rise of deep neural networks, the performance of biometric systems has increased tremendously. Biometric systems for face recognition are now used in everyday life, e.g., border control, crime prevention, or personal device access control. Although the accuracy of face recognition systems is generally high, they are not without flaws. Many biometric systems have been found to exhibit demographic bias, resulting in different demographic groups being not recognized with the same accuracy. This is especially true for facial recognition due to demographic factors, e.g., gender and skin color. While many previous works already reported demographic bias, this work aims to reduce demographic bias for biometric face recognition applications. In this regard, 12 face recognition systems are benchmarked regarding biometric recognition performance as well as demographic differentials, i.e., fairness. Subsequently, multiple fusion techniques are applied with the goal to improve the fairness in contrast to single systems. The experimental results show that it is possible to improve the fairness regarding single demographics, e.g., skin color or gender, while improving fairness for demographic subgroups turns out to be more challenging.
Assessing the Rapid Expansion of Technology in Africa

The chapter assesses the state of digital technology in Africa as a catalyst for economic growth and development. The chapter also appraises the state of higher education on the continent in order to determine its preparedness to produce professionals who can function in a digital economy. A number of technological diffusion models which include diffusion of innovation (DIO), theory of reasoned action, and technology adoption model (TAM) were used to explore the diffusion of innovation to Africa from developed countries. Although there is a rapid growth in technological penetration in Africa impacting sectors such as agriculture, education, and finance, more investment in technology is still needed to power strides towards optimum levels of economic development. Africa, with its rapidly growing population and ever expanding market, is perceived as the next economic frontier after Asia. Higher education through curriculum models such as Education 5.0 can lead the way to make Africa the next economic frontier.
A topic relevance-aware click model for web search

To better understand users’ behavior patterns in web search, numerous click models are proposed to extract the implicit interaction feedback. Most existing click models are heavily based on the implicit information to model user behaviors, ignoring the impact of explicit information between queries and documents in search sessions. In this paper, we fully consider the topic relevance between queries and documents in search sessions and propose a novel topic relevance-aware click model (TRA-CM) for web search. TRA-CM consists of a relevance estimator and an examination predictor. The relevance estimator consists of a topic relevance predictor and a click context encoder. In the topic relevance predictor, we utilize the pre-trained BERT model to model the content information of queries and documents in search sessions. Meanwhile, we use transformer to encode users’ click behaviors in the click context encoder. We further apply a two-stage fusion strategy to obtain the final relevance scores. The examination predictor estimates the examination probability of each document. We further utilize learnable filters to attenuate log noise and obtain purer input features in both relevance estimator and examination predictor, and investigate different combination functions to integrate relevance scores and examination probabilities into click prediction. Extensive experiment results on two real-world session datasets prove that TRA-CM outperforms existing click models in both click prediction and relevance estimation tasks.
Diffusion models in text generation: a survey

Diffusion models are a kind of math-based model that were first applied to image generation. Recently, they have drawn wide interest in natural language generation (NLG), a sub-field of natural language processing (NLP), due to their capability to generate varied and high-quality text outputs. In this article, we conduct a comprehensive survey on the application of diffusion models in text generation. We divide text generation into three parts (conditional, unconstrained, and multi-mode text generation, respectively) and provide a detailed introduction. In addition, considering that autoregressive-based pre-training models (PLMs) have recently dominated text generation, we conduct a detailed comparison between diffusion models and PLMs in multiple dimensions, highlighting their respective advantages and limitations. We believe that integrating PLMs into diffusion is a valuable research avenue. We also discuss current challenges faced by diffusion models in text generation and propose potential future research directions, such as improving sampling speed to address scalability issues and exploring multi-modal text generation. By providing a comprehensive analysis and outlook, this survey will serve as a valuable reference for researchers and practitioners interested in utilizing diffusion models for text generation tasks.
Study of real-time parameter measurement of ring rolling pieces based on machine vision

Real time parameter measurement cannot be carried out to dynamic ring parts during automation ring rolling processes so that rolling process parameters cannot be adjusted in time. Considering effects of shaping of ring rolling parts, a visual measurement platform was set up and a machine vision-based non-contact real -time measurement method was put forward. This article improves the subpixel level edge extraction algorithm to extract edge data information of circular rolling pieces. Based on the characteristics of circular rolling pieces, an RG-Hough transform method is proposed to fit the detected edge data information. The conversion relationship between pixel and actual sizes were determined in combination with the camera calibration to gain parameters of ring rolling parts. Measurements of ring parts (OD: 462.12mm; and ID: 315.53mm) were applied to verify the effectiveness of our method. Our measurement error is ±0.25mm and our average speed can be up to 104ms/frame. Our study can provide powerful technical supports for intelligent control of ring rolling pieces.
Botox Optimization Algorithm: A New Human-Based Metaheuristic Algorithm for Solving Optimization Problems

This paper introduces the Botox Optimization Algorithm (BOA), a novel metaheuristic inspired by the Botox operation mechanism. The algorithm is designed to address optimization problems, utilizing a human-based approach. Taking cues from Botox procedures, where defects are targeted and treated to enhance beauty, the BOA is formulated and mathematically modeled. Evaluation on the CEC 2017 test suite showcases the BOA's ability to balance exploration and exploitation, delivering competitive solutions. Comparative analysis against twelve well-known metaheuristic algorithms demonstrates the BOA's superior performance across various benchmark functions, with statistically significant advantages. Moreover, application to constrained optimization problems from the CEC 2011 test suite highlights the BOA's effectiveness in real-world optimization tasks.
Quantitative assessment of H&amp;E staining for pathology: development and clinical evaluation of a novel system

Staining tissue samples to visualise cellular detail and tissue structure is at the core of pathology diagnosis, but variations in staining can result in significantly different appearances of the tissue sample. While the human visual system is adept at compensating for stain variation, with the growth of digital imaging in pathology, the impact of this variation can be more profound. Despite the ubiquity of haematoxylin and eosin staining in clinical practice worldwide, objective quantification is not yet available. We propose a method for quantitative haematoxylin and eosin stain assessment to facilitate quality assurance of histopathology staining, enabling truly quantitative quality control and improved standardisation.The stain quantification method comprises conventional microscope slides with a stain-responsive biopolymer film affixed to one side, called stain assessment slides. The stain assessment slides were characterised with haematoxylin and eosin, and implemented in one clinical laboratory to quantify variation levels.Stain assessment slide stain uptake increased linearly with duration of haematoxylin and eosin staining (r = 0.99), and demonstrated linearly comparable staining to samples of human liver tissue (r values 0.98-0.99). Laboratory implementation of this technique quantified intra- and inter-instrument variation of staining instruments at one point in time and across a five-day period.The proposed method has been shown to reliably quantify stain uptake, providing an effective laboratory quality control method for stain variation. This is especially important for whole slide imaging and the future development of artificial intelligence in digital pathology.
Multi-sensor fusion federated learning method of human posture recognition for dual-arm nursing robots

Human posture estimation is a significant pre-imperative for intelligent nursing robot. However, most existing studies suffer from the driven data deficiencies, because of the concerns about poor information safety in the private healthcare environment. Federated Learning is able to solve the problem, as it can improve the learning ability of the models as well as protecting the data privacy. In this paper, we proposed a Federated Learning Human Posture Recognition (FL-HPR) framework according to image and point cloud fusion. FL-HPR benefits the information flow in the global model while ensuring the data privacy of local models. Furthermore, FL-HPR optimizes the local dynamic graph edge convolution network of robot, which improves the recognition accuracy of each body limb and enhance robustness. Experiments on Non-IID datasets illustrate that the presented FL-HPR remarkably outperforms non-federated learning methods. The developed frame improves the accuracy of human joint estimation, which indicates the proposed FL-HPR can be integrated into the intelligent nursing robot in practical. The open-source code and videos are available at https://github.com/Hebut-LEVO/.
Melanoma Prevention using an Augmented Reality-based Serious Game

The purpose of this study was to field-test a recently developed AR-based serious game designed to promote SSE self-efficacy, called Spot. Thirty participants played the game and answered 3 questionnaires: a baseline questionnaire, a second questionnaire immediately after playing the game, and a third questionnaire 1 week later (follow-up). The majority of participants considered that the objective quality of the game was high, and considered that the game could have a real impact in SSE promotion. Participants showed statistically significant increases in SSE self-efficacy and intention at follow-up. Of the 24 participants that had never performed a SSE or had done one more than 3 months ago, 12 (50.0%) reported doing a SSE at follow-up. This study provides supporting evidence to the use of serious games in combination with AR to educate and motivate users to perform SSE. Spot seems to be an inconspicuous but effective strategy to promote SSE, a cancer prevention behavior, among healthy individuals. Patient education is essential to tackle skin cancer, particularly melanoma. Serious games, such as Spot, have the ability to effectively educate and motivate patients to perform a cancer prevention behavior.
High invisibility image steganography with wavelet transform and generative adversarial network

Recently, extensive research is showing that deep learning has enormous potential in image steganography. However, most steganography methods based on deep learning are not sufficiently invisible. In this paper, a novel end-to-end deep neural network for image steganography based on generative adversarial network (GAN) and discrete wavelet transformation (DWT) is proposed, which can greatly improve the invisibility of the method. Firstly, the cover image is transformed from the RGB domain to the DWT domain to embed information in frequency. Furthermore, a multi-scale attention convolution to fuse different scales of feature information is proposed, which can help the model to find a better location to embed the information. Finally, the fusion module is used to embed the information into the cover image at different modification intensities. In addition, a new Discriminator that compares cover images and stego images in a patch-to-patch way to improve the ability of the Discriminator. We analyze our algorithm from multiple aspects (e.g., security and invisibility) to verify the superior performance of the proposed method. To demonstrate generalization ability, experiments are conducted on three widely used datasets and show the superior performance of our method compared to other steganography methods based on deep learning.
Oscillating photonic Bell state from a semiconductor quantum dot for quantum key distribution

Abstract An on-demand source of bright entangled photon pairs is desirable for quantum key distribution (QKD) and quantum repeaters. The leading candidate to generate such pairs is based on spontaneous parametric down-conversion (SPDC) in non-linear crystals. However, its pair extraction efficiency is limited to 0.1% when operating at near-unity fidelity due to multiphoton emission at high brightness. Quantum dots in photonic nanostructures can in principle overcome this limit, but the devices with high entanglement fidelity (99%) have low pair extraction efficiency (0.01%). Here, we show a measured peak entanglement fidelity of 97.5% ± 0.8% and pair extraction efficiency of 0.65% from an InAsP quantum dot in an InP photonic nanowire waveguide. We show that the generated oscillating two-photon Bell state can establish a secure key for peer-to-peer QKD. Using our time-resolved QKD scheme alleviates the need to remove the quantum dot energy splitting of the intermediate exciton states in the biexciton-exciton cascade.
Blockchain-Based Electronic Voting System: Significance and Requirements

In a democratic regime, voting is crucial to making collective decisions. Unfortunately, although this activity has great significance and value, little effort has been made to improve the way we vote. Paper ballots are still the most used method, although this method is relatively simple, brings many inconveniences, and represents a contradiction to the modern world and its advances. This paper mostly focuses on a review study of blockchain-based voting systems. It aims at identifying the strategies and the guidelines as well as provides a comprehensive end-to-end electronic voting system based on blockchain, with the help of cryptographic techniques such as zero-knowledge proofs to improve privacy. The novelty of this paper is that we tackle the limitations of electronic voting systems found in the literature, including cost, identity management, and scalability problems. Our purpose is to provide key elements for organizations on how to design their proper electronic voting system based on blockchain technology.
Spatio-temporal neural network with handcrafted features for skeleton-based action recognition

Abstract The task of human action recognition (HAR) can be found in many computer vision practical applications. Various data modalities have been considered for solving this task, including joint-based skeletal representations which are suitable for real-time applications on platforms with limited computational resources. We propose a spatio-temporal neural network that uses handcrafted geometric features to classify human actions from video data. The proposed deep neural network architecture combines graph convolutional and temporal convolutional layers. The experiments performed on public HAR datasets show that our model obtains results similar to other state-of-the-art methods but has a lower inference time while offering the possibility to obtain an explanation for the classified action.
Convolution smoothing and non-convex regularization for support vector machine in high dimensions

The support vector machine (SVM) is a well-known statistical learning tool for binary classification. One serious drawback of SVM is that it can be adversely affected by redundant variables, and research has shown that variable selection is crucial and necessary for achieving good classification accuracy. Hence some SVM variable selection studies have been devoted, and they have an unified “empirical hinge loss plus sparse penalty” formulation. However, a noteworthy issue is the computational complexity of existing methods is high especially for large-scale problems, due to the non-smoothness of the hinge loss. To solve this issue, we first propose a convolution smoothing approach, which turns the non-smooth hinge loss into a smooth surrogate one, and they are asymptotically equivalent. Moreover, we construct computationally more efficient SVM variable selection procedure by implementing non-convex penalized convolution smooth hinge loss. In theory, we prove that the resulting variable selection possesses the oracle property when the number of predictors is diverging. Numerical experiments also confirm the good performance of the new method.
Dual-perspective semantic-aware representation blending for multi-label image recognition with partial labels

Recently, multi-label image recognition with partial labels (MLR-PL) has attracted increasing attention, in which only some labels are known while others are unknown for each image. However, current algorithms rely on pre-trained image similarity models or iteratively updating the image classification models to generate pseudo labels for unknown labels. Thus, they depend on a certain amount of annotations and inevitably suffer from obvious performance drops. To address this dilemma, we propose a dual-perspective semantic-aware representation blending (DSRB) framework that blends multi-granularity category-specific semantic representation across different images, from an instance and prototype perspective, respectively, to transfer information of known labels to complement unknown labels. Specifically, an instance-perspective representation blending (IPRB) module is designed to blend the representations of the known labels in an image with the representations of the corresponding unknown labels in another image to complement these unknown labels. Meanwhile, a prototype-perspective representation blending (PPRB) module is introduced to learn more stable representation prototypes for each category and blends the representation of unknown labels with the prototypes of corresponding labels in a location-sensitive manner to complement these unknown labels. Extensive experiments on various datasets show that the proposed DSRB consistently outperforms current state-of-the-art algorithms on all known label proportion settings.
AdaMO: Adaptive Meta-Optimization for cold-start recommendation

Meta-learning has been proven effective in tackling the cold-start problem of recommendation systems. Most work in this line adopts the meta-optimization idea that learns global knowledge to initialize the base recommender and adapts it for unseen recommendation tasks. Typically, the process is non-adaptive in the sense that both the initialization and model fitting are not task-specific. Such non-adaptive approaches do not work well for the strict cold-start problem, where historical interaction data of the new users is scarce or not available at all. In this paper, we propose a novel adaptive meta-optimization approach, called Adaptive Meta-Optimization (AdaMO), to address the problem. AdaMO is fully adaptive, namely, it simultaneously customizes the initialization and model fitting according to task-specific information. During the cold-start phase, it aggregates task-specific information with transferred knowledge from all historical tasks to tune the initialization of the base model. In the subsequent warm-up phase, AdaMO mines the adaptive transition information from the behavior patterns and parameter status to effectively fit the model using transitional learning. By making a sophisticated balance between shared knowledge and task-specific information, AdaMO outperforms the state-of-the-art methods in various cold-start scenarios.
A contrastive learning based unsupervised multi-view stereo with multi-stage self-training strategy

Recent years, unsupervised multi-view stereo (MVS) methods have achieved excellent success that can produce comparable results to earlier supervised work. However, as unsupervised MVS uses image reconstruction as pretext task, it faces two vital drawbacks: RGB value, which is the measurement of image, is not robust enough across views due to complicated environment like lighting conditions and reconstruction itself cannot reflect quality of depth estimation linearly. These problems cause the actual optimization goal to diverge from the expected optimization goal, thus could impair the training process. To enhance robustness of pretext task, we propose a contrastive learning based constraint. The constraint adds featuremetric consistency across views by forcing the features between matching points similar and the features between unmatched points opposite. To add linear properties to overall training procedure, we propose a multi-stage training strategy that uses pseudo label as supervision after unsupervised training at the beginning. On the other hand, we adopt an iterative optimizer that proven to be quite effective in supervised MVS to accelerate training. Finally, we conduct a series of experiments on the DTU dataset and Tanks and Temples dataset that demonstrate the efficiency and robustness of our method compared with the state-of-the-art methods in terms of accuracy, completeness and speed.
Coal and gas protrusion risk evaluation based on cloud model and improved combination of assignment

The proposed study presents an enhanced combination weighting cloud model for accurate assessment of coal and gas outburst risks. Firstly, a comprehensive evaluation index system for coal and gas outburst risks is established, consisting of primary indicators such as coal rock properties and secondary indicators including 13 factors. Secondly, the improved Analytic Hierarchy Process (IAHP) based on the 3-scale method and the improved CRITIC based on indicator correlation weight determination method are employed to determine subjective and objective weights of evaluation indicators respectively. Additionally, the Lagrange multiplier method is introduced to fuse these weights in order to obtain optimal weights. Subsequently, a prominent danger assessment model is developed based on cloud theory. Finally, using a mine in Hebei Province as an example, the results obtained from IAHP combined with improved CRITIC weighting method are compared with those from traditional AHP method and AHP-CRITIC combination weighting method. The findings demonstrate that among all methods considered, IAHP combined with improved CRITIC exhibits superior performance in terms of distribution expectation Ex, entropy value En, and super entropy He within cloud digital features; thus indicating that the risk level of coal and gas outbursts in this particular mine can be classified as general risk. These evaluation results align well with actual observations thereby validating the effectiveness of this approach. Consequently, this constructed model enables rapid yet accurate determination of coal and gas outburst risks within mines.