Reviewerly/exHarmony huggingface dataset:
Bit-Level Quantization for Efficient Layout Hotspot Detection

Hotspot detection is an essential step in the physical verification flow to identify layout patterns that are sensitive to process variations. Recent advances in machine learning have enabled deep neural networks (DNNs) to achieve good performance for hotspot detection; however, these models require a high computational complexity and memory footprint. To reduce such costs, quantization provides a promising solution by compressing DNNs into low-bit inference schemes. In this paper, we propose several quantization algorithms specifically designed for a classic neural network based hotspot detector while taking into account the feature distribution of the dataset used. Our experiments show that our compressed model can achieve competitive results compared with full-precision models while significantly reducing inference runtime at the same time.
Intelligent Tutoring Systems in Healthcare for Low Literacy Population: A Concise Review

Intelligent tutoring systems (ITSs) can offer a scalable alternative to instruction for underserved populations which can help in healthcare related fields, such as breast cancer, where education can greatly bridge inequity gaps. Breast cancer is one of the leading causes of cancer-related deaths in the Hispanic population. Breast cancer survivors often experience ongoing diagnosis and treatment-related symptoms that negatively impact their health-related quality of life. In this paper we review existing ITSs in healthcare that cater to low-literacy population education with the goal of building a prototype that improves the quality of ITSs in healthcare for Hispanic breast cancer survivors.
Accuracy Analysis of Supervised and Unsupervised Techniques on Breast Cancer Datasets

Modern medicine benefits extensively from technology as a valuable assistant to diagnose diseases at early stages and cure them with less complication and more success. In the health sector, cancer diseases undeniably, possess the most variety and complexity of all diseases. Therefore, this paper focuses on breast cancer and applies supervised, unsupervised, and deep learning as one candidate from each major machine learning field to evaluate different models and analyze their outcomes. We aim to answer the following questions: Which dataset produces the best result and improves its metrics and performance using machine learning optimization techniques? Will there be any definite decision on which machine learning models or algorithms have superiority over the others? The original and diagnostic datasets are trained by Logistic Regression, K-means clustering, and multi-layer perceptron or artificial neural network model (ANN). Then, unsupervised techniques, heatmaps, and Principal Component Analysis (PCA) are used to reduce dimensionality and concise the dataset for any probable improvements. The original dataset produced better results for the machine learning models, and ANN obtained the best accuracy score. The comprehensive and systematic calculation of the metrics and indexes of the breast cancer datasets and the thorough optimization by unsupervised technics is the novelty of this research. The comparison between these two datasets has not been approached before. The clustering by K-means creates novel visualization of the datasets, which could give the experts in the field ideas of the cancerous mass's characteristics.
Rethinking Self-Supervision for Few-Shot Class-Incremental Learning

Few-Shot Class-Incremental Learning (FSCIL) focuses on progressively absorbing new concepts given only limited training data. For tackling this challenge, several recent FS-CIL works resort to pre-training models with Self-Supervised Learning (SSL) to obtain features that can generalize well to new classes. However, to avoid overfitting and catastrophic forgetting, previous works only leverage SSL in the base session and keep all or most parameters fixed in incremental sessions, resulting in inadequate adaptation to novel classes. Thus, in this paper, we explore the setting where more parameters can be updated for adapting to novel concepts, and discover that the model pre-trained with SSL leads to degraded performance even compared to that without SSL. It can be attributed to the severer forgetting of base class knowledge. To address this issue, we propose an imprinting-based distillation module for effectively regularizing the adaption process, and a mathematically provable routing strategy for further improved results. The effectiveness of our approach is verified on 3 popular FSCIL benchmarks by significantly outperforming previous methods.
LSAS: Lightweight Sub-attention Strategy for Alleviating Attention Bias Problem

In computer vision, the performance of deep neural networks (DNNs) is highly related to the feature extraction ability, i.e., the ability to recognize and focus on key pixel regions in an image. However, in this paper, we quantitatively and statistically illustrate that DNNs have a serious attention bias problem on many samples from some popular datasets: (1) Position bias: DNNs fully focus on label-independent regions; (2) Range bias: The focused regions from DNN are not completely contained in the ideal region. Moreover, we find that the existing self-attention modules can alleviate these biases to a certain extent, but the biases are still non-negligible. To further mitigate them, we propose a lightweight sub-attention strategy (LSAS), which utilizes high-order sub-attention modules to improve the original self-attention modules. The effectiveness of LSAS is demonstrated by extensive experiments on widely-used benchmark datasets and popular attention networks. We release our code to help other researchers to reproduce the results of LSAS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Meta Perturbed Re-Id Defense

Adversarial attacks have gained significant attention in object re-identification (Re-Id). However, very few works target defense, and they primarily adopt adversarial training. While adversarial training has been shown to be a major line of defense, we observe that vanilla adversarial training alone does not provide a robust defense against adversarial attacks. Towards this, we propose a novel meta perturbed defense algorithm for Re-Id task. Our contributions are, (i) we introduce anisotropic and isotropic perturbations to design a stochastic neural network, and train it with a novel meta-learning strategy with tasks as vanilla, perturbed, and perturbed-adversarial training; (ii) we show the generalizability of our model against various unseen attacks; and (iii) we derive a novel feature covariance alignment (FCA) loss which gives us a high clean performance while providing robustness against different attacks. Extensive experiments on Market-1501, MSMT17 and VeRi-776 reveal SOTA performance. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Performance Analysis of a Micromodel-Based Multinomial Classifier

Supervised learning methods demand large scores of labelled training data to achieve high predictive accuracy for classification problems. In contrast, the less accurate unsupervised learning models can work on raw data but fail to provide any useful insight. Micromodel-based multinomial classifier is a one-stop solution for increased performance with less data and less time. This paper investigates the effectiveness of micromodels for multiclass classification as against the existing supervised and unsupervised machine learning models and artificial neural networks. The model built using One-class Neural Network (OC-NN) to classify students' resumes into four different streams for admission into postgraduate programmes in top-ranked universities, outperforms some of the most powerful machine learning models.
Microimage-based Two-step Search For Plenoptic 2.0 Video Coding

The plenoptic 2.0 video can record a time-varying dense light field, which benefits many immersive visual applications such as AR/VR. However, traditional inter motion estimation methods perform inefficiently in such kinds of video sequences due to the distinctive temporal characteristics caused by the imaging principle. In this paper, a microimage-based two- step search (MTSS) is proposed to achieve a better trade-off between coding performance and coding complexity. Based on microimage focus variation analysis in imaging dynamic scenes, a microlens-diameter and matching-distance spatial search with local refinement is proposed to exploit the image correlations among the microimage and to compensate the defocused inaccuracy. Implementing the proposed motion estimation in H.266 platform VTM-11.0 and comparing with the state-of-the-art methods, obvious compression efficiency improvements are achieved with limited complexity increment, which benefits the standardization of plenoptic video coding.
SIRL: Self-Imitation Reinforcement Learning for Single-step Hitting Tasks

Reinforcement learning (RL) has demonstrated significant success in various sequential decision-making tasks. However, standard RL frameworks suffer from low efficiency in single-step robotic hitting tasks, which require accurate control and single-step decision-making under delayed reward. To address this challenge of single-step tasks, a Self-Imitation Reinforcement Learning (SIRL) algorithm is proposed to better utilize each interaction sample. With SIRL, the agent can obtain optimal successful samples during itself interactions without human demonstrations, even if the actual interaction fails. The proposed SIRL uses the self-imitation learning of these optimal samples to accelerate the learning of RL policy. In this paper, we create two challenging hitting tasks in MuJoCo simulation, Slide, and TableTennis, to evaluate our approach. Experimental results demonstrate that the proposed SIRL algorithm outperforms the standard RL methods and supervised learning methods in terms of both sample efficiency and performance. Especially, in sparse reward settings, SIRL stands out as the only RL-based method that can learn these tasks, as self-imitation learning provides the agent with more gradient information for policy optimization.
A Modeling Strategy Using Bayesian Optimization with POMDP for Exploration and Informative Path Planning of UAVs in Monitoring of Forest

Unmanned Aerial Vehicles (UAVs) have been used for several applications in monitoring complex and unknown environments. The challenge is to plan missions for the UAV in situations where the vehicle needs to visit and explore an area and analyze it in real-time to define the route to be followed. This visit occurs with the search area's maximization from the definition of the trajectories, making it possible to collect information to acquire knowledge about the environment and provide a map. This type of problem is known as Informative Path Planning (IPP) and Autonomous Exploration (AE). In this context, Bayesian Optimization (BO) has been adopted. Moreover, there is a need to define the planning for decision-making based on information about the environment considering specific restrictions. Partially Observable Markov Decision Processes (POMDP) can be used to define the planner responsible for the decision-making. Therefore, considering these methods and the strategy of exploring the environment in a continuous 3D space, this paper proposes a development of a modeling strategy that is exploration and informative path planning using Sequential Bayesian Optimization with POMDP in forest monitoring with Canopy gap. A sequential decision-maker was developed under uncertainty based on the Sequential Bayesian Optimization approach with Partially Observable Markov Decision Process (BO-POMDP).
Study of DNN Network Architecture Search for Robot Vision

Robot vision, which integrates measurement and perception, plays an important role in robot manipulation applications. Although the current deep learning-based visual neural network models have high perception capabilities, their network structures are usually too large to be implemented on embedded devices for robot vision. In this paper, we propose neural network architecture search (NAS) that combines pre-training and pruning operations to simplify deep neural network architectures. It not only solves this problem without losing network accuracy, but also significantly alleviates the difficulties of long network computation time and redundant search space in traditional NAS methods. Finally, the experimental results show that the neural network generated by the proposed algorithm outperforms the artificially designed neural network, which demonstrates the effectiveness of the method. At the end of the paper, the rationality of the method is proved by experiments and comparisons. The performance of the new algorithm and the generated neural network is better than that of the artificially designed neural network.
A $k-\text{Way}$ Partitioning Framework for Compression on Social Networks

Social networks have been studied in the context of matrices or graph theory for efficient representation, and manipulation. Knowledge gleaned from these graphs are useful to better coordinate events, advertise, and in recommendation of friends or games. Many social networks represented as graphs or matrices are large containing 100's of millions or even billions of entries. Hence it has become important to store them efficiently using data compression. The authors have used different kinds of compressed data structures such as quad-tree, array of binary trees (ABT), and differential, compressed binary trees (DCBT) in previous research for representing massive social networks and performing various queries [15]–[17]. In this paper, we provide a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k-\mathbf{way}$</tex> partitioning framework that generalizes the array of binary trees structure by providing a combinatorial basis that is shown to lead to substantial improvements in compressing social network graphs.
Deep Learning-Based Multimodal Image Retrieval Combining Image and Text

Multimodal learning is omnipresent in our lives. Human absorbs features in different ways, whether through pictures or text. Combining these features in computational science, especially in Image retrieval problems, poses two significant challenges: how and when to fuse them. Most image retrieval systems use images or text data associated with the image. In this paper, we study the image retrieval task, where the input query is an image plus text sentence that describes the image. The system starts a query triggered by input image and text while taking the help of the Transformer model, which puts attention on both modalities and combines embedded features through the feature fusion technique. We proposed a feature fusion layer using modified Text Image Residual Gating in our work. We have used two methods based on the features extracted from the fusion layer. First, we trained K Nearest Neighbor (KNN) algorithm on the training data, and later we used test data to find a similar image. Second, we used the clustering technique and a support vector machine to compute the nearest neighbor points and cluster the center to see a similar image. We found that SVM (Support vector Machine) is more effective from the results, giving an overall accuracy of 92%.
A Multisensory Neural Network System for Cross-modal Integration

In the paper, we proposed a multi-sensory neural network with cross-modal integration. By distributing multiple sensors and integrating a hierarchical biomimetic architecture, our system can sense, process, and memorize multi-modal information and fuse multi-sensory data at software levels. We develop the LCMER (Learning Cross-Modality Encoder Representations) framework to build multi-modal information connections, where a multi-modal fusion model uses a single-stream model fusion. We build the LCMER model using a software system to realize hand gesture recognition. The experimental results demonstrate that the proposed LCMER framework can achieve superior gesture recognition performance in multiple scenarios.
Promoting adversarial transferability with enhanced loss flatness

Carefully crafted small perturbations, when added to an image, can mislead the deep neural networks to give wrong outputs. Such mischievous images are called adversarial examples. Transfer-based black-box attacks use a surrogate white-box model to generate adversarial examples which can be transferred and attack black-box models with little known information. We propose to increase the transferability of adversarial examples by smoothing the geometric surface of loss function at the adversarial example point. By looking ahead the optimization path for a few steps, we define a future geometric vicinity using the integration of neighbourhood of those predicted data points. By sampling in this area and using the summation of gradients at those sampled data points for optimization, our method avoids local fluctuation of loss function. Experiments on ImageNet validation dataset show that our method outperforms state-of-the-art attacks by a large margin.
Cyber-Criminals Metrics Engine for Attack Determination on Web Technologies Platforms

Web technologies have created a worldwide web of problems and cyber risks for individuals and organizations. In this paper, we evaluate web technologies and present the different technologies and their positive impacts on individuals and business sectors. Also, we present a cyber-criminals metrics engine for attack determination on web technologies platforms' weaknesses. Finally, this paper offers a cautionary note to protect Small and Medium Businesses (SMBs) and make recommendations to help minimize cyber risks and save individuals and organizations from cyberattack distress.
PQMPV: Parallel and Quantified Dense Stereo Disparity Estimation Based on Multi-Path Viterbi

Stereo depth estimation plays a crucial role in enabling robots, autonomous driving and other applications. As an ill-posed problem, both deep learning based and traditional methods have been extensively studied in recent years. Traditional methods mainly focus on feature matching between multiple images to estimate depth, but often result in blank pixels with zero disparity and also require significant computation time. In this paper, we propose a new stereo disparity estimation algorithm based on the dynamic programming method known as Viterbi. Our approach optimize the range of disparity calculation through image pyramid, combined with Viterbi algorithm for cost aggregation, and accelerates several calculation steps through parallel computing techniques. Additionally, we improve data transmission and processing speed by quantifying data from float to integer. Experiments on the KITTI dataset and MiddEval benchmark demonstrate that our algorithm achieves significant improvements in average error rate and processing speed of disparity map, making it a promising solution for stereo depth estimation in practical applications.
Privacy-Enhanced Zero-Shot Learning via Data-Free Knowledge Transfer

Considering the increasing concerns about data copyright and sensitivity issues, we present a novel Privacy-Enhanced Zero-Shot Learning (PE-ZSL) paradigm. The key innovation is to involve a teacher model as the data safeguard to guide the PE-ZSL model training without data sharing. The PE-ZSL model consists of a generator and student network, which can achieve data-free knowledge transfer while maintaining the performance of teacher model. We investigate ‘black-’ and ‘white-box’ scenarios in PE-ZSL task as different levels of framework privacy. Besides, we provide the discussion of teacher model in both omniscient and quasi-omniscient settings according to the knowledge space. Despite simple implementations and data-missing disadvantages, our PE-ZSL framework can retain state-of-the-art ZSL and GZSL performance under the ‘white-box’ scenario. Extensive qualitative and quantitative analysis also demonstrates promising results when deploying the model under ‘black-box’ scenario.
Pose-Motion Video Anomaly Detection via Memory-Augmented Reconstruction and Conditional Variational Prediction

Video anomaly detection (VAD) is a challenging computer vision problem. Due to the scarcity of anomalous events in training, the models learned by existing methods would mistakenly fit the ubiquitous non-causal or even spurious correlations, leading to failure in inference. In this paper, we propose a new two-phase Pose-Motion Video Anomaly Detection (PoMo) approach by jointly exploiting the informative features including the poses and optical flows that have rich causal correlations with abnormality. PoMo can effectively prevent the non-causal features from leaking in by either encoding only the essential information, i.e., the poses and optical flows, with our normalized autoencoder (phase one), or separately modeling the knowledge learned in phase one using our causal-conditioned autoencoder (phase two). The difference between normal and abnormal events can be amplified through these two phases. Thus the generalization ability can be reinforced. Extensive experimental results demonstrate the superiority of our approach over the existing methods and the improvements in AUC-ROC can be up to 1.5%.
Weight-based Regularization for Improving Robustness in Image Classification

Deep Neural Networks (DNNs) are known to be vulnerable to adversarial attacks. Recently, Stochastic Neural Networks (SNNs) have been proposed to enhance adversarial robustness by injecting uncertainty into the models. However, existing SNNs often inspired by intuition and rely on adversarial training, which is computationally costly. To address this issue, we propose a novel SNN called the Weight-based Stochastic Neural Network (WB-SNN), which is based on optimizing an error upper bound of adversarial robustness from the perspective of weight distribution. To the best of our knowledge, we are the first to propose a theoretically guaranteed weight-based stochastic neural network without relying on adversarial training. In comparison to normal adversarial training, our method saves about three times the computation cost. Extensive experiments on various datasets, networks, and adversarial attacks have demonstrated the effectiveness of the proposed method.
Transform Decomposition Switching for Efficient Attribute Compression of 3D Point Clouds Using Neural Networks

An adaptive technique to switch between RAHT and Dyadic RAHT using 3D Sobel filter has been found to improve the compression in 3D point clouds by offering substantial cumulative compression gains. However, the drawback of this switching scheme is its need for tuned thresholds. To this end, we propose to use neural networks to resolve the threshold dependency issue so that the switching becomes truly adaptive. Two publicly available point cloud datasets were used to test the effectiveness of the proposed method. We achieved significant gains on MVUB and minor gains on 8iVFB dataset over all Dyadic approach.
Feature Bias Correction: A Feature Augmentation Method for Long-tailed Recognition

The features extracted by the network trained on the long-tailed dataset have significant bias, and the existing methods exhibit poor performance. To alleviate this bias, we propose a Feature Bias Correction (FBC) method, which solves this problem by migrating the biased features back to their correct locations. FBC consists of two core components: Feature Saliency Rebalancing and Similarity Feature Distinguishing. Specifically, Feature Saliency Rebalancing encourages features to be more significant by giving less weight to the feature map, which allows the feature map to represent the sample better. The Similarity Feature Distinguishing module guides the model training by giving more accurate labels to the samples. Finally, our method can be easily combined with the existing long-tailed recognition methods. Experiments on multiple datasets show that our FBC achieves state-of-the-art performance on long-tailed recognition tasks.