Reviewerly/exHarmony huggingface dataset:
Statistical learning of motor preparation.

Statistical learning, the process of extracting regularities from the environment, is one of the most fundamental abilities playing an essential role in almost all aspects of human cognition. Previous studies have shown that attentional selection is biased toward locations that are likely to contain a target and away from locations that are likely to contain a distractor. The current study investigated whether participants can also learn to extract that a specific motor response is more likely when the target is presented at specific locations within the visual field. To that end, the additional singleton paradigm was adapted such that when the singleton target was presented at one specific location, one response (e.g., right index finger) was more likely than the other (e.g., right middle finger) and the reverse was true for another location. The results show that participants learned to extract that a particular motor response is more likely when the singleton target (which was unrelated to the response) was presented at a specific location within the visual field. The results also suggest that it is the location of the target and not its shape that is associated with the biased response. This learning cannot be considered as being top-down or conscious as participants showed little, if any, awareness of the response biases present. The results are discussed in terms of the event coding theory. The study increases the scope of statistical learning and shows how individuals adapt automatically, without much awareness, to the regularities present in the environment. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
A decentralized and security-enhanced professional title evaluation system in universities under mobile Internet of Things

<h2>Abstract</h2> Digital online application is a commonly used method for professional title evaluation in colleges and universities. With the rapid development of the mobile Internet of Things, the fastest way for users to access the evaluation system is through mobile devices. However, this also poses security challenges to the traditional professional title evaluation system. Therefore, this paper proposes a decentralized and security-enhanced digital title evaluation system in mobile IoT. Since the system uses blockchain to implement a decentralized review process, the transparency and fairness of the review process are guaranteed. At the same time, the review data and reviewee information in the system are encrypted and stored in the distributed network to ensure the security and non-tamperability of the data. All review records are recorded on the blockchain, and anyone can view and verify the legitimacy of the review results. Additionally, the system incorporates security enhancement mechanisms such as identity verification, smart contracts, and auditing functions to improve the credibility of the evaluation process and prevent cheating under mobile IoT. Experiments have shown that this system has important application value in the field of professional title review in universities, which can improve review efficiency, reduce human intervention, and provide strong support for the credibility of review results.
Artificial intelligence’s impact on breast cancer pathology: a literature review

Abstract This review discusses the profound impact of artificial intelligence (AI) on breast cancer (BC) diagnosis and management within the field of pathology. It examines the various applications of AI across diverse aspects of BC pathology, highlighting key findings from multiple studies. Integrating AI into routine pathology practice stands to improve diagnostic accuracy, thereby contributing to reducing avoidable errors. Additionally, AI has excelled in identifying invasive breast tumors and lymph node metastasis through its capacity to process large whole-slide images adeptly. Adaptive sampling techniques and powerful convolutional neural networks mark these achievements. The evaluation of hormonal status, which is imperative for BC treatment choices, has also been enhanced by AI quantitative analysis, aiding interobserver concordance and reliability. Breast cancer grading and mitotic count evaluation also benefit from AI intervention. AI-based frameworks effectively classify breast carcinomas, even for moderately graded cases that traditional methods struggle with. Moreover, AI-assisted mitotic figures quantification surpasses manual counting in precision and sensitivity, fostering improved prognosis. The assessment of tumor-infiltrating lymphocytes in triple-negative breast cancer using AI yields insights into patient survival prognosis. Furthermore, AI-powered predictions of neoadjuvant chemotherapy response demonstrate potential for streamlining treatment strategies. Addressing limitations, such as preanalytical variables, annotation demands, and differentiation challenges, is pivotal for realizing AI’s full potential in BC pathology. Despite the existing hurdles, AI’s multifaceted contributions to BC pathology hold great promise, providing enhanced accuracy, efficiency, and standardization. Continued research and innovation are crucial for overcoming obstacles and fully harnessing AI’s transformative capabilities in breast cancer diagnosis and assessment. Graphical Abstract
Multi-Label Contrastive Learning for Abstract Visual Reasoning

For a long time, the ability to solve abstract reasoning tasks was considered one of the hallmarks of human intelligence. Recent advances in the application of deep learning (DL) methods led to surpassing human abstract reasoning performance, specifically in the most popular type of such problems-Raven's progressive matrices (RPMs). While the efficacy of DL systems is indeed impressive, the way they approach the RPMs is very different from that of humans. State-of-the-art systems solving RPMs rely on massive pattern-based training and sometimes on exploiting biases in the dataset, whereas humans concentrate on the identification of the rules/concepts underlying the RPM to be solved. Motivated by this cognitive difference, this work aims at combining DL with the human way of solving RPMs. Specifically, we cast the problem of solving RPMs into a multilabel classification framework where each RPM is viewed as a multilabel data point, with labels determined by the set of abstract rules underlying the RPM. For efficient training of the system, we present a generalization of the noise contrastive estimation algorithm to the case of multilabel samples and a new sparse rule encoding scheme for RPMs. The proposed approach is evaluated on the two most popular benchmark datasets [I-RAVEN and procedurally generated matrices (PGM)] and on both of them demonstrate an advantage over the state-of-the-art results.
Unsupervised Feature Selection With Flexible Optimal Graph

In the unsupervised feature selection method based on spectral analysis, constructing a similarity matrix is a very important part. In existing methods, the linear low-dimensional projection used in the process of constructing the similarity matrix is too hard, it is very challenging to construct a reliable similarity matrix. To this end, we propose a method to construct a flexible optimal graph. Based on this, we propose an unsupervised feature selection method named unsupervised feature selection with flexible optimal graph and l2,1 -norm regularization (FOG-R). Unlike other methods that use linear projection to approximate the low-dimensional manifold of the original data when constructing a similarity matrix, FOG-R can learn a flexible optimal graph, and by combining flexible optimal graph learning and feature selection into a unified framework to get an adaptive similarity matrix. In addition, an iterative algorithm with a strict convergence proof is proposed to solve FOG-R. l2,1 -norm regularization will introduce an additional regularization parameter, which will cause parameter-tuning trouble. Therefore, we propose another unsupervised feature selection method, that is, unsupervised feature selection with a flexible optimal graph and l2,0 -norm constraint (FOG-C), which can avoid tuning additional parameters and obtain a more sparse projection matrix. Most critically, we propose an effective iterative algorithm that can solve FOG-C globally with strict convergence proof. Comparative experiments conducted on 12 public datasets show that FOG-R and FOG-C perform better than the other nine state-of-the-art unsupervised feature selection algorithms.
Refining Graph Structure for Incomplete Multi-View Clustering

As a challenging problem, incomplete multi-view clustering (MVC) has drawn much attention in recent years. Most of the existing methods contain the feature recovering step inevitably to obtain the clustering result of incomplete multi-view datasets. The extra target of recovering the missing feature in the original data space or common subspace is difficult for unsupervised clustering tasks and could accumulate mistakes during the optimization. Moreover, the biased error is not taken into consideration in the previous graph-based methods. The biased error represents the unexpected change of incomplete graph structure, such as the increase in the intra-class relation density and the missing local graph structure of boundary instances. It would mislead those graph-based methods and degrade their final performance. In order to overcome these drawbacks, we propose a new graph-based method named Graph Structure Refining for Incomplete MVC (GSRIMC). GSRIMC avoids recovering feature steps and just fully explores the existing subgraphs of each view to produce superior clustering results. To handle the biased error, the biased error separation is the core step of GSRIMC. In detail, GSRIMC first extracts basic information from the precomputed subgraph of each view and then separates refined graph structure from biased error with the help of tensor nuclear norm. Besides, cross-view graph learning is proposed to capture the missing local graph structure and complete the refined graph structure based on the complementary principle. Extensive experiments show that our method achieves better performance than other state-of-the-art baselines.
An Effective Video Transformer With Synchronized Spatiotemporal and Spatial Self-Attention for Action Recognition

Convolutional neural networks (CNNs) have come to dominate vision-based deep neural network structures in both image and video models over the past decade. However, convolution-free vision Transformers (ViTs) have recently outperformed CNN-based models in image recognition. Despite this progress, building and designing video Transformers have not yet obtained the same attention in research as image-based Transformers. While there have been attempts to build video Transformers by adapting image-based Transformers for video understanding, these Transformers still lack efficiency due to the large gap between CNN-based models and Transformers regarding the number of parameters and the training settings. In this work, we propose three techniques to improve video understanding with video Transformers. First, to derive better spatiotemporal feature representation, we propose a new spatiotemporal attention scheme, termed synchronized spatiotemporal and spatial attention (SSTSA), which derives the spatiotemporal features with temporal and spatial multiheaded self-attention (MSA) modules. It also preserves the best spatial attention by another spatial self-attention module in parallel, thereby resulting in an effective Transformer encoder. Second, a motion spotlighting module is proposed to embed the short-term motion of the consecutive input frames to the regular RGB input, which is then processed with a single-stream video Transformer. Third, a simple intraclass frame interlacing method of the input clips is proposed that serves as an effective video augmentation method. Finally, our proposed techniques have been evaluated and validated with a set of extensive experiments in this study. Our video Transformer outperforms its previous counterparts on two well-known datasets, Kinetics400 and Something-Something-v2.
Toward Zero-Trust Security for the Metaverse

By focusing on immersive interaction among users, the burgeoning Metaverse can be viewed as a natural extension of existing social media. Similar to traditional online social networks, there are numerous security and privacy issues in the Metaverse (e.g., attacks on user authentication and impersonation). In this paper, we develop a holistic research agenda for zero-trust user authentication in social virtual reality (VR), an early prototype of the Metaverse. Our proposed research includes four concrete steps: investigating biometrics-based authentication that is suitable for continuously authenticating VR users, leveraging federated learning (FL) for protecting user privacy in biometric data, improving the accuracy of continuous VR authentication with multimodal data, and boosting the usability of zero-trust security with adaptive VR authentication. Our preliminary study demonstrates that conventional FL algorithms are not well suited for biometrics-based authentication of VR users, leading to an accuracy of less than 10%. We discuss the root cause of this problem, the associated open challenges, and several future directions for realizing our research vision.
DRBM-ClustNet: A Deep Restricted Boltzmann–Kohonen Architecture for Data Clustering

A Bayesian deep restricted Boltzmann-Kohonen architecture for data clustering termed deep restricted Boltzmann machine (DRBM)-ClustNet is proposed. This core-clustering engine consists of a DRBM for processing unlabeled data by creating new features that are uncorrelated and have large variance with each other. Next, the number of clusters is predicted using the Bayesian information criterion (BIC), followed by a Kohonen network (KN)-based clustering layer. The processing of unlabeled data is done in three stages for efficient clustering of the nonlinearly separable datasets. In the first stage, DRBM performs nonlinear feature extraction by capturing the highly complex data representation by projecting the feature vectors of d dimensions into n dimensions. Most clustering algorithms require the number of clusters to be decided a priori; hence, here, to automate the number of clusters in the second stage, we use BIC. In the third stage, the number of clusters derived from BIC forms the input for the KN, which performs clustering of the feature-extracted data obtained from the DRBM. This method overcomes the general disadvantages of clustering algorithms, such as the prior specification of the number of clusters, convergence to local optima, and poor clustering accuracy on nonlinear datasets. In this research, we use two synthetic datasets, 15 benchmark datasets from the UCI Machine Learning repository, and four image datasets to analyze the DRBM-ClustNet. The proposed framework is evaluated based on clustering accuracy and ranked against other state-of-the-art clustering methods. The obtained results demonstrate that the DRBM-ClustNet outperforms state-of-the-art clustering algorithms.
Guidance Through Surrogate: Toward a Generic Diagnostic Attack

Adversarial training (AT) is an effective approach to making deep neural networks robust against adversarial attacks. Recently, different AT defenses are proposed that not only maintain a high clean accuracy but also show significant robustness against popular and well-studied adversarial attacks, such as projected gradient descent (PGD). High adversarial robustness can also arise if an attack fails to find adversarial gradient directions, a phenomenon known as "gradient masking." In this work, we analyze the effect of label smoothing on AT as one of the potential causes of gradient masking. We then develop a guided mechanism to avoid local minima during attack optimization, leading to a novel attack dubbed guided projected gradient attack (G-PGA). Our attack approach is based on a "match and deceive" loss that finds optimal adversarial directions through guidance from a surrogate model. Our modified attack does not require random restarts a large number of attack iterations or a search for optimal step size. Furthermore, our proposed G-PGA is generic, thus it can be combined with an ensemble attack strategy as we demonstrate in the case of auto-attack, leading to efficiency and convergence speed improvements. More than an effective attack, G-PGA can be used as a diagnostic tool to reveal elusive robustness due to gradient masking in adversarial defenses.
Efficient <i>N:M</i> Sparse DNN Training Using Algorithm, Architecture, and Dataflow Co-Design

Sparse training is one of the promising techniques to reduce the computational cost of DNNs while retaining high accuracy. In particular, N:M fine-grained structured sparsity, where only N out of consecutive M elements can be nonzero, has attracted attention due to its hardware-friendly pattern and capability of achieving a high sparse ratio. However, the potential to accelerate N:M sparse DNN training has not been fully exploited, and there is a lack of efficient hardware supporting N:M sparse training. To tackle these challenges, this paper presents a computation-efficient training scheme for N:M sparse DNNs using algorithm, architecture, and dataflow co-design. At the algorithm level, a bidirectional weight pruning method, dubbed BDWP, is proposed to leverage the N:M sparsity of weights during both forward and backward passes of DNN training, which can significantly reduce the computational cost while maintaining model accuracy. At the architecture level, a sparse accelerator for DNN training, namely SAT, is developed to neatly support both the regular dense operations and the computation-efficient N:M sparse operations. At the dataflow level, multiple optimization methods ranging from interleave mapping, pre-generation of N:M sparse weights, and offline scheduling, are proposed to boost the computational efficiency of SAT. Finally, the effectiveness of our training scheme is evaluated on a Xilinx VCU1525 FPGA card using various DNN models and datasets. Experimental results show the SAT accelerator with the BDWP sparse training method under 2:8 sparse ratio achieves an average speedup of 1.75x over that with the dense training, accompanied by a negligible accuracy loss of 0.56% on average. Furthermore, our proposed training scheme significantly improves the training throughput by 2.97~25.22x and the energy efficiency by 1.36~3.58x over prior FPGA-based accelerators.
Latent variable modeling with applications to education assessment and NSF-REU projects for engineering students

Abstract The latent variable modeling technique in education assessment is illustrated using problem solving examples in first semester physics for engineering and technology students. The regression modeling of data in the Newton second law laboratory class has equivalence to an analysis with the internal force serving as the latent variable. A path diagram would contain the internal force as a latent variable which manifest its influence on the observed datasets, namely, acceleration and force. Similarly, the pre-score and post-score data collected for education assessment can be studied with latent variable modeling to broaden the scope beyond regression which requires an independent variable. The numerical and flexibility advantages of latent variable modeling versus regression modeling in education assessment are presented. Free LISREL student version working details and free confirmatory factor analysis Microsoft Excel software from university faculty are presented. The adaptation of Queensborough Community College Physics faculty education research into Queensborough NSF-REU projects for engineering students is discussed.
Interpolation-Based Contrastive Learning for Few-Label Semi-Supervised Learning

Semi-supervised learning (SSL) has long been proved to be an effective technique to construct powerful models with limited labels. In the existing literature, consistency regularization-based methods, which force the perturbed samples to have similar predictions with the original ones have attracted much attention for their promising accuracy. However, we observe that the performance of such methods decreases drastically when the labels get extremely limited, e.g., 2 or 3 labels for each category. Our empirical study finds that the main problem lies with the drift of semantic information in the procedure of data augmentation. The problem can be alleviated when enough supervision is provided. However, when little guidance is available, the incorrect regularization would mislead the network and undermine the performance of the algorithm. To tackle the problem, we: 1) propose an interpolation-based method to construct more reliable positive sample pairs and 2) design a novel contrastive loss to guide the embedding of the learned network to change linearly between samples so as to improve the discriminative capability of the network by enlarging the margin decision boundaries. Since no destructive regularization is introduced, the performance of our proposed algorithm is largely improved. Specifically, the proposed algorithm outperforms the second best algorithm (Comatch) with 5.3% by achieving 88.73% classification accuracy when only two labels are available for each class on the CIFAR-10 dataset. Moreover, we further prove the generality of the proposed method by improving the performance of the existing state-of-the-art algorithms considerably with our proposed strategy. The corresponding code is available at https://github.com/xihongyang1999/ICL_SSL.
Maneuvering Target State Estimation in Range-Squared Coordinate

In tracking applications without angle measurements, multiple sensors are usually applied. Accurate range-Doppler (R-D) estimates with a single sensor play an important role in clutter eliminating and date fusion in multi-sensor target tracking. In this paper, maneuvering models are first established in the range-squared (RS) coordinate, then the R-D estimation of a maneuvering target is investigated. According to the Cartesian nearly constant acceleration and constant turn models, pseudostate vectors are defined in the RS coordinate and linear time-evolution equations of pseudostate are derived to describe the corresponding maneuvers. The motion models are incorporated into the converted measurement Kalman filter to extract R-D estimates from converted measurements. Corresponding to the models, the filter initialization approach, which utilizes the prior information on motion model and kinematic parameters of target, is developed. Based on the model set in the RS coordinate and the interacting multiple model (IMM) method, a recursive estimator is developed to produce pseudostate estimates of a maneuvering target. Final R-D estimates are provided through the conversion adopting the scaled unscented transformation outside the estimation recursions to avoid the propagation of nonlinearity approximation errors. Numerical experiments in different scenarios demonstrate that the maneuvering models accurately describe the time-evolution of pseudostate and the IMM estimator achieves superior estimation accuracy and consistency thanks to the exploitation of linear motion models.