Reviewerly/exHarmony huggingface dataset:
PTQ4ViT: Post-training Quantization for Vision Transformers with Twin Uniform Quantization

Quantization is one of the most effective methods to compress neural networks, which has achieved great success on convolutional neural networks (CNNs). Recently, vision transformers have demonstrated great potential in computer vision. However, previous post-training quantization methods performed not well on vision transformer, resulting in more than 1% accuracy drop even in 8-bit quantization. Therefore, we analyze the problems of quantization on vision transformers. We observe the distributions of activation values after softmax and GELU functions are quite different from the Gaussian distribution. We also observe that common quantization metrics, such as MSE and cosine distance, are inaccurate to determine the optimal scaling factor. In this paper, we propose the twin uniform quantization method to reduce the quantization error on these activation values. And we propose to use a Hessian guided metric to evaluate different scaling factors, which improves the accuracy of calibration at a small cost. To enable the fast quantization of vision transformers, we develop an efficient framework, PTQ4ViT. Experiments show the quantized vision transformers achieve near-lossless prediction accuracy (less than 0.5% drop at 8-bit quantization) on the ImageNet classification task.
Two Novel SMOTE Methods for Solving Imbalanced Classification Problems

The imbalanced classification problem has always been one of the important challenges in neural network and machine learning. As an effective method to deal with imbalanced classification problems, the synthetic minority oversampling technique (SMOTE) has its disadvantage: Some noise samples may participate in the process of synthesizing new samples; As a result, the new synthetic sample lacks its rationality, which will reduce the classification performances of the network. To remedy this shortcoming, two novel improved SMOTE method are proposed in this paper: Center point SMOTE (CP-SMOTE) method and Inner and outer SMOTE (IO-SMOTE) method. The CP-SMOTE method generates new samples based on finding several center points, then linearly combining the minority samples with their corresponding center points. The IO-SMOTE method divides minority samples into inner and outer samples, and then uses inner samples as much as possible in the subsequent process of generating new samples. Numerical experiments are conducted to prove that compared with no-sampling and conventional SMOTE methods, the CP-SMOTE and IO-SMOTE methods can achieve better classification performances.
How Augmented Reality Increases Engagement Through Its Impact on Risk and the Decision Process

Augmented reality (AR) overcomes one of the main limitations of e-commerce, such as the prepurchase trial. The consumer can virtually see how a product is integrated into the real world through this technology. Therefore, AR may reduce the perceived risk of buying a product online. Despite existing research, the impact of AR on the consumer decision process needs to be further explored. Consequently, this research aims to understand the impact of AR on the perception of risk and the purchase decision process, considering decision comfort and decision confidence. Through a between-subjects experiment, the results show that AR reduces the risk of online shopping. However, it has no direct effect on the decision process. Reducing risk and the comfort it brings generates decision confidence and satisfaction with the shopping experience. This satisfaction will generate engagement toward the online shop platform. The research highlights the process through which AR impacts the decision-making process. The implications for AR marketing theory and managerial implications in the age of the metaverse are discussed.
Interpretability of Clinical Decision Support Systems Based on Artificial Intelligence from Technological and Medical Perspective: A Systematic Review

Background. Artificial intelligence (AI) has developed rapidly, and its application extends to clinical decision support system (CDSS) for improving healthcare quality. However, the interpretability of AI-driven CDSS poses significant challenges to widespread application. Objective. This study is a review of the knowledge-based and data-based CDSS literature regarding interpretability in health care. It highlights the relevance of interpretability for CDSS and the area for improvement from technological and medical perspectives. Methods. A systematic search was conducted on the interpretability-related literature published from 2011 to 2020 and indexed in the five databases: Web of Science, PubMed, ScienceDirect, Cochrane, and Scopus. Journal articles that focus on the interpretability of CDSS were included for analysis. Experienced researchers also participated in manually reviewing the selected articles for inclusion/exclusion and categorization. Results. Based on the inclusion and exclusion criteria, 20 articles from 16 journals were finally selected for this review. Interpretability, which means a transparent structure of the model, a clear relationship between input and output, and explainability of artificial intelligence algorithms, is essential for CDSS application in the healthcare setting. Methods for improving the interpretability of CDSS include ante-hoc methods such as fuzzy logic, decision rules, logistic regression, decision trees for knowledge-based AI, and white box models, post hoc methods such as feature importance, sensitivity analysis, visualization, and activation maximization for black box models. A number of factors, such as data type, biomarkers, human-AI interaction, needs of clinicians, and patients, can affect the interpretability of CDSS. Conclusions. The review explores the meaning of the interpretability of CDSS and summarizes the current methods for improving interpretability from technological and medical perspectives. The results contribute to the understanding of the interpretability of CDSS based on AI in health care. Future studies should focus on establishing formalism for defining interpretability, identifying the properties of interpretability, and developing an appropriate and objective metric for interpretability; in addition, the user's demand for interpretability and how to express and provide explanations are also the directions for future research.
A Medical Image Segmentation Method Based on Improved UNet 3+ Network

In recent years, segmentation details and computing efficiency have become more important in medical image segmentation for clinical applications. In deep learning, UNet based on a convolutional neural network is one of the most commonly used models. UNet 3+ was designed as a modified UNet by adopting the architecture of full-scale skip connections. However, full-scale feature fusion can result in excessively redundant computations. This study aimed to reduce the network parameters of UNet 3+ while further improving the feature extraction capability. First, to eliminate redundancy and improve computational efficiency, we prune the full-scale skip connections of UNet 3+. In addition, we use the attention module called Convolutional Block Attention Module (CBAM) to capture more essential features and thus improve the feature expression capabilities. The performance of the proposed model was validated by three different types of datasets: skin cancer segmentation, breast cancer segmentation, and lung segmentation. The parameters are reduced by about 36% and 18% compared to UNet and UNet 3+, respectively. The results show that the proposed method not only outperformed the comparison models in a variety of evaluation metrics but also achieved more accurate segmentation results. The proposed models have lower network parameters that enhance feature extraction and improve segmentation performance efficiently. Furthermore, the models have great potential for application in medical imaging computer-aided diagnosis.
Adaptive infinite impulse response system identification using an enhanced golden jackal optimization

Golden jackal optimization (GJO) is inspired by the cooperative attacking behavior of golden jackals and mainly simulates searching for prey, stalking and enclosing prey, and pouncing on prey to solve complicated optimization problems. However, the basic GJO has the disadvantages of premature convergence, a slow convergence rate and low computation precision. To enhance the overall search and optimization abilities, an enhanced golden jackal optimization (EGJO) method with the elite opposition-based learning technique and the simplex technique is proposed to address adaptive infinite impulse response system identification. The intention is to minimize the error fitness value and obtain the appropriate control parameters. The elite opposition-based learning technique boosts population diversity, enhances the exploration ability, extends the search range and avoids search stagnation. The simplex technique accelerates the search process, enhances the exploitation ability, improves the computational precision and increases the optimization depth. EGJO can not only achieve complementary advantages to avoid search stagnation but also balance exploration and exploitation to arrive at the best value. Three sets of experiments are used to verify the effectiveness and feasibility of EGJO. The experimental results clearly demonstrate that the optimization efficiency and recognition accuracy of EGJO are superior to those of AOA, GTO, HHO, MDWA, RSO, WOA, TSA and GJO. EGJO has a faster convergence rate, higher computation precision, better control parameters and better fitness value, and it is stable and resilient in solving the IIR system identification problem.
When Object Detection Meets Knowledge Distillation: A Survey

Object detection (OD) is a crucial computer vision task that has seen the development of many algorithms and models over the years. While the performance of current OD models has improved, they have also become more complex, making them impractical for industry applications due to their large parameter size. To tackle this problem, knowledge distillation (KD) technology was proposed in 2015 for image classification and subsequently extended to other visual tasks due to its ability to transfer knowledge learned by complex teacher models to lightweight student models. This paper presents a comprehensive survey of KD-based OD models developed in recent years, with the aim of providing researchers with an overview of recent progress in the field. We conduct an in-depth analysis of existing works, highlighting their advantages and limitations, and explore future research directions to inspire the design of models for related tasks. We summarize the basic principles of designing KD-based OD models, describe related KD-based OD tasks, including performance improvements for lightweight models, catastrophic forgetting in incremental OD, small object detection, and weakly/semi-supervised OD. We also analyze novel distillation techniques, i.e. different types of distillation loss, feature interaction between teacher and student models, etc. Additionally, we provide an overview of the extended applications of KD-based OD models on specific datasets, such as remote sensing images and 3D point cloud datasets. We compare and analyze the performance of different models on several common datasets and discuss promising directions for solving specific OD problems.
Quasi-reflection learning arithmetic optimization algorithm firefly search for feature selection

With the whirlwind evolution of technology, the quantity of stored data within datasets is rapidly expanding. As a result, extracting crucial and relevant information from said datasets is a gruelling task. Feature selection is a critical preprocessing task for machine learning to reduce the excess data in a set. This research presents a novel quasi-reflection learning arithmetic optimization algorithm - firefly search, an enhanced version of the original arithmetic optimization algorithm. Quasi-reflection learning mechanism was implemented for enhancement of population diversity, while firefly algorithm metaheuristics were used to improve the exploitation abilities of the original arithmetic optimization algorithm. The aim of this wrapper-based method is to tackle a specific classification problem by selecting an optimal feature subset. The proposed algorithm is tested and compared with various well-known methods on ten unconstrained benchmark functions, then on twenty-one standard datasets gathered from the University of California, Irvine Repository and Arizona State University. Additionally, the proposed approach is applied to the Corona disease dataset. The experimental results verify the improvements of the presented method and their statistical significance.
Impact of Artificial Intelligence System and Volumetric Density on Risk Prediction of Interval, Screen-Detected, and Advanced Breast Cancer

PURPOSE Artificial intelligence (AI) algorithms improve breast cancer detection on mammography, but their contribution to long-term risk prediction for advanced and interval cancers is unknown. METHODS We identified 2,412 women with invasive breast cancer and 4,995 controls matched on age, race, and date of mammogram, from two US mammography cohorts, who had two-dimensional full-field digital mammograms performed 2-5.5 years before cancer diagnosis. We assessed Breast Imaging Reporting and Data System density, an AI malignancy score (1-10), and volumetric density measures. We used conditional logistic regression to estimate odds ratios (ORs), 95% CIs, adjusted for age and BMI, and C-statistics (AUC) to describe the association of AI score with invasive cancer and its contribution to models with breast density measures. Likelihood ratio tests (LRTs) and bootstrapping methods were used to compare model performance. RESULTS On mammograms between 2-5.5 years prior to cancer, a one unit increase in AI score was associated with 20% greater odds of invasive breast cancer (OR, 1.20; 95% CI, 1.17 to 1.22; AUC, 0.63; 95% CI, 0.62 to 0.64) and was similarly predictive of interval (OR, 1.20; 95% CI, 1.13 to 1.27; AUC, 0.63) and advanced cancers (OR, 1.23; 95% CI, 1.16 to 1.31; AUC, 0.64) and in dense (OR, 1.18; 95% CI, 1.15 to 1.22; AUC, 0.66) breasts. AI score improved prediction of all cancer types in models with density measures ( P LRT values &lt; .001); discrimination improved for advanced cancer (ie, AUC for dense volume increased from 0.624 to 0.679, Δ AUC 0.065, P = .01) but did not reach statistical significance for interval cancer. CONCLUSION AI imaging algorithms coupled with breast density independently contribute to long-term risk prediction of invasive breast cancers, in particular, advanced cancer.
An Attention-Aware Long Short-Term Memory-Like Spiking Neural Model for Sentiment Analysis

LSTM-SNP model is a recently developed long short-term memory (LSTM) network, which is inspired from the mechanisms of spiking neural P (SNP) systems. In this paper, LSTM-SNP is utilized to propose a novel model for aspect-level sentiment analysis, termed as ALS model. The LSTM-SNP model has three gates: reset gate, consumption gate and generation gate. Moreover, attention mechanism is integrated with LSTM-SNP model. The ALS model can better capture the sentiment features in the text to compute the correlation between context and aspect words. To validate the effectiveness of the ALS model for aspect-level sentiment analysis, comparison experiments with 17 baseline models are conducted on three real-life data sets. The experimental results demonstrate that the ALS model has a simpler structure and can achieve better performance compared to these baseline models.
Role of Teachers To impart quality education for equitable learning.

It is widely acknowledged that quality education as one of the strong and powerful sources that guarantee sustainable development, it has a wide range of formal and informal educational applications, and which can provide numerous positive effects for the broader population. Because of its significance, this article presents a role of teachers to ensure quality education for equitable learning. As we all know Sustainable Development Goal (SDG) 4 that attempts to “Ensure inclusive and quality education for all and promote lifelong learning” as one of the primary objectives of the 2030 Agenda. Education is the ultimate domain of the Humans; it is a process designed to socialize and humanizing citizens throughout their lives from conception to dying off. It is institutionalized and formal for a set amount of time, yet it is ongoing and practiced outside the boundaries of the institution. Education is a journey of enlightenment and personal empowerment of the individuals for improved quality of life. The Education commission (1964-66) has firmly said that “The quality and competence and character of teachers to be the most significant factor influencing the quality of education and its contribution to National development”. Teachers have a crucial role in directing and shaping how students use digital tools and maximize the educational advantages of their digital experiences. They also promote inclusive, equitable education and diversity acceptance as a positive aspect of our cultures. The current paper only uses qualitative analysis of secondary data sources. Information is gathered from books, journals, government reports, official websites, and other media. The paper will discuss about the role of teachers in providing high quality education for equitable learning. A crucial factor in the success of the educational system is “Service Quality.” It is crucial for putting into practice educational and institutional norms. This calls out for understanding, what is the quality of service that the teachers must contribute and what is the role of teacher in equitable learning? And this article briefly discusses it thematically.
First Person Sketch-based Terrain Editing

We present a new method for first person sketch-based editing of terrain models. As in usual artistic pictures, the input sketch depicts complex silhouettes with cusps and T-junctions, which typically correspond to non-planar curves in 3D. After analysing depth constraints in the sketch based on perceptual cues, our method best matches the sketched silhouettes with silhouettes or ridges of the input terrain. A specific deformation algorithm is then applied to the terrain, enabling it to exactly match the sketch from the given perspective view, while insuring that none of the user-defined silhouettes is hidden by another part of the terrain. As our results show, this method enables users to easily personalize an existing terrain, while preserving its plausibility and style.
Customized Network Security for Cloud Service

Modern cloud computing platforms based on virtual machine monitors (VMMs) host a variety of complex businesses which present many network security vulnerabilities. In order to protect network security for these businesses in cloud computing, nowadays, a number of middleboxes are deployed at front-end of cloud computing or parts of middleboxes are deployed in cloud computing. However, the former is leading to high cost and management complexity, and also lacking of network security protection between virtual machines while the latter does not effectively prevent network attacks from external traffic. To address the above-mentioned challenges, we introduce a novel customized network security for cloud service (CNS), which not only prevents attacks from external and internal traffic to ensure network security of services in cloud computing, but also affords customized network security service for cloud users. CNS is implemented by modifying the Xen hypervisor and proved by various experiments which showing the proposed solution can be directly applied to the extensive practical promotion in cloud computing.
&lt;inline-formula&gt; &lt;tex-math notation="LaTeX"&gt;$\mathcal{DBSDA}$ &lt;/tex-math&gt; &lt;/inline-formula&gt;: Lowering the Bound of Misclassification Rate for Sparse Linear Discriminant Analysis via Model Debiasing

Linear discriminant analysis (LDA) is a well-known technique for linear classification, feature extraction, and dimension reduction. To improve the accuracy of LDA under the high dimension low sample size (HDLSS) settings, shrunken estimators, such as Graphical Lasso, can be used to strike a balance between biases and variances. Although the estimator with induced sparsity obtains a faster convergence rate, however, the introduced bias may also degrade the performance. In this paper, we theoretically analyze how the sparsity and the convergence rate of the precision matrix (also known as inverse covariance matrix) estimator would affect the classification accuracy by proposing an analytic model on the upper bound of an LDA misclassification rate. Guided by the model, we propose a novel classifier, DBSDA, which improves classification accuracy through debiasing. Theoretical analysis shows that DBSDA possesses a reduced upper bound of misclassification rate and better asymptotic properties than sparse LDA (SDA). We conduct experiments on both synthetic datasets and real application datasets to confirm the correctness of our theoretical analysis and demonstrate the superiority of DBSDA over LDA, SDA and other downstream competitors under HDLSS settings.
PKP-Based Signature Scheme

In this document, we introduce $$\textsf {PKP}\hbox {-}\textsf {DSS}$$ : a Digital Signature Scheme based on the Permuted Kernel Problem (PKP) [23]. PKP is a simple NP-hard [10] combinatorial problem that consists of finding a kernel for a publicly known matrix, such that the kernel vector is a permutation of a publicly known vector. This problem was used to develop an Identification Scheme (IDS) which has a very efficient implementation on low-cost smart cards. From this zero-knowledge identification scheme, we derive $$\textsf {PKP}\hbox {-}\textsf {DSS}$$ with the traditional Fiat-Shamir transform [9]. Thus, $$\textsf {PKP}\hbox {-}\textsf {DSS}$$ has a security that can be provably reduced, in the (classical) random oracle model, to the hardness of random instances of PKP (or, if wanted, to any specific family of $$\text {PKP}$$ instances). We propose parameter sets following the thorough analysis of the State-of-the-art attacks on PKP presented in [17]. We show that $$\textsf {PKP}\hbox {-}\textsf {DSS}$$ is competitive with other signatures derived from Zero-Knowledge identification schemes. In particular, PKP-DSS-128 gives a signature size of approximately 20 KBytes for 128 bits of classical security, which is approximately $$30\%$$ smaller than MQDSS. Moreover, our proof-of-concept implementation shows that PKP-DSS-128 is an order of magnitude faster than MQDSS which in its turn is faster than Picnic2, SPHINCS, ... Since the $$\text {PKP}$$ is NP-hard and since there are no known quantum attacks for solving PKP significantly better than classical attacks, we believe that our scheme is post-quantum secure.