Reviewerly/exHarmony huggingface dataset:
Fast Model Update for IoT Traffic Anomaly Detection With Machine Unlearning

It is often needed to update deep learning-based detection models in traffic anomaly detection systems for the Internet of Things (IoT) because of mislabeled samples or device firmware upgrades. Machine unlearning, a technique that quickly updates the anomaly detection model without retraining the model from scratch, has recently attracted much research attention. We propose a novel machine unlearning method, called virtual federated learning approach (ViFLa), which groups training data based on estimated unlearning probability and treats each group as a virtual client in the federated learning framework. Since the virtual clients are physically in the same machine, ViFLa only leverages the concept of data/local models isolation in federated learning without incurring any network communication. ViFLa adopts an attention-based aggregation method called enhanced class distribution weighted sum (ECDWS) to tackle the nonindependent and identically distributed (non-iid) data problem caused by the data grouping strategy. It also introduces a new state transition ring mechanism into the statistical query (SQ) learning framework to update the local model of each virtual client quickly. Using real-world IoT traffic data, we showcase the benefit of ViFLa regarding its efficiency and completeness for model updates in the context of IoT traffic anomaly detection.
Development of a small form factor tactile image sensor for mobile manipulator

We are developing a mobile manipulator with a small footprint that works in confined spaces such as a home kitchen or a small factory. To perform various tasks, the gripper of the mobile manipulator is equipped with a tactile image sensor using a camera. In this study, we developed a compact and lightweight tactile image sensor that can be mounted on a gripper. Through experiments, we have shown that this sensor is capable of estimating the shear force and determining the contact position. Also, we proposed a method to estimate the direction of door opening based on the direction of the shear force applied to the two fingers.
Low-Overhead Early-Stopping Policies for Efficient Random Forests Inference on Microcontrollers

Random Forests (RFs) are popular Machine Learning models for edge computing, due to their lightweight nature and high accuracy on several common tasks. Large RFs however, still have significant energy costs, a serious concern for battery-operated ultra-low-power devices. Following the adaptive (or dynamic) inference paradigm, we introduce a hardware-friendly early stopping policy for RF-based classifiers, halting the execution as soon as a sufficient prediction confidence is achieved. We benchmark our approach on three state-of-the-art datasets relative to different embedded classification tasks, and deploy our models on a single core RISC-V microcontroller. We achieve an energy reduction ranging from 18% to more than 91%, with an accuracy drop lower than 0.5%. Additionally, we compare our approach with other early-stopping policies, showing that we outperform them.
Enhancing Robustness in Federated Learning by Supervised Anomaly Detection

Recent years have seen the increasing attention and popularity of federated learning (FL), a distributed learning framework for privacy and data security. However, by its fundamental design, federated learning is inherently vulnerable to model poisoning attacks: a malicious client may submit the local updates to influence the weights of the global model. Therefore, detecting malicious clients against model poisoning attacks in federated learning is useful in safety-critical tasks.However, existing methods either fail to analyze potential malicious data or are computationally restrictive. To overcome these weaknesses, we propose a robust federated learning method where the central server learns a supervised anomaly detector using adversarial data generated from a variety of state-of-the-art poisoning attacks. The key idea of this powerful anomaly detector lies in a comprehensive understanding of the benign update through distinguishing it from the diverse malicious ones. The anomaly detector would then be leveraged in the process of federated learning to automate the removal of malicious updates (even from unforeseen attacks).Through extensive experiments, we demonstrate its effectiveness against backdoor attacks, where the attackers inject adversarial triggers such that the global model will make incorrect predictions on the poisoned samples. We have verified that our method can achieve 99.0% detection AUC scores while enjoying longevity as the model converges. Our method has also shown significant advantages over existing robust federated learning methods in all settings. Furthermore, our method can be easily generalized to incorporate newly-developed poisoning attacks, thus accommodating ever-changing adversarial learning environments.
A Multi-view Matching Method Based on PatchmatchNet with Sparse Point Information

The learning-based multi-view stereo (MVS) method has become a research hotspot in 3D reconstruction. Deep learning can extract more robust semantic features of images and better adapt to scenes of soft texture and non-diffuse reflection. However, current deep learning methods focus more on improving the quality of reconstruction, and we believe that reducing depth estimation time and GPU memory consumption is equally important. Therefore, this paper proposes S-PatchmatchNet with higher accuracy and faster efficiency. Firstly, in the initial stage of depth estimation, we use Colmap to obtain sparse points and generate initial depth information through triangulation and interpolation to replace the random initialization of PatchmatchNet, which reduces the time consumed in random depth and improves computational efficiency. Secondly, we design an effective data enhancement mechanism. Specifically, a mask 1/3 of the size of the image is used to randomly erase data on the image. By minimizing the error between the prediction result of enhanced data and the ground reality, the sample prediction is standardized, the robustness of the model is enhanced, and the accuracy of depth prediction is improved. To test the validity of our method, we conducted tests on the DTU dataset. Compared to PatchmatchNet, the efficiency and accuracy of our approach are improved to varying degrees. Meanwhile, we get competitive results on challenging Tanks and Temples datasets.
Hybrid Discrete-Continuous Path Planning for Lattice Traversal

Lattice structures allow robotic systems to operate in complex and hazardous environments, e.g. construction, mining and nuclear plants, reliably and effectively. However, current navigation systems for these structures are neither realistic, as they assume simplistic motion primitives and obstacle-free workspaces, nor efficient as they rely solely on global discrete search in an attempt to leverage the modularity of lattices. This paper tackles this gap and studies how robots can navigate lattice structures efficiently. We present a realistic application environment where robots have to avoid obstacles and the structure itself to reach target locations. Our solution couples discrete optimal search, using a domain-dependent heuristic, and sampling-based motion planning to find feasible trajectories in the discrete search space and in the continuous joint space at the same time. We provide two search graph formulations and a path planning approach. Simulation experiments, based on structures and robots created for the Innovate UK Connect-R project, examine scalability to large grid spaces while maintaining performances close to optimal.
Adversarial Attacks on Deep Local Feature Matching Models of 3D Point Clouds

Adversarial attacks on deep local feature matching models of 3D point clouds play a critical role to evaluate and improve their adversarial robustness. Three adversarial attack methods are proposed based on adversarial points, i.e., adversarial point perturbation by changing the coordinates of all points in the partial point cloud to be matched; adversarial point addition by adding points to the positions of key points in a pre-calculated saliency map and then applying perturbation; adversarial point deletion by moving key points of the saliency map to the center of the shapes to simulate deletion. Extensive experimental results on the 3DMatch dataset show that all three adversarial attack methods can fool the DIP and SpinNet models. Besides, it is observed that the attack performance is positively related to the perturbation size. Under the requirement of maintaining imperceptibility, with the increase of disturbance, the attack performance improves, e.g., the feature matching recall of the DIP model can be reduced from 100% to 2% after the attack.
Enhanced e-learning experience using simulation technologies – An experimental evaluation of intelligent simulation systems in a virtual classroom

Augmented and Virtual Reality simulation technologies are among the fast-growing consumer-level intelligent simulation technologies and have been great solutions to enhance the user experience (UX) and interactivity. Many studies have shown that these technologies do help in virtual educational applications, but a discussion and comparison of their effects on students’ eLearning is quite limited. This experimental study focuses on evaluating the potential of AR and VR applications specially designed for eLearning purposes and tests the effects of intelligent simulations used in an e-learning virtual environment. This was done by presenting an online lecture both in VR and AR to two different groups of learners. The study was conducted among the students of multimedia in a reputed private university in India. The simulation technologies were compared in terms of participants’ test scores, engagement, excitement, and level of distraction. Scores of both the groups were compared and reported along with the participant's perception of each simulation technology. Quantitative and Qualitative data acquired from both groups have been analyzed and the results indicate that AR works best when incorporated into e-learning. This is one of the first conceptual studies of comparison between VR and AR for interactive online teaching. Further, this paper discusses the inevitability of making new tools to enable instructors to become creators of AR/VR experiences in education.
Analyzability of SOA using Soft Computing Technique

Maintainability is needed to add new features and upgrade software without introducing bugs in service oriented software. Analyzability is a factor of maintainability and maintainability is diagnosed through analyzability. It helps to find out which part of the software needs to be maintained. Through fuzzy logic, analyzability can be easily fixed during the maintenance of any software. In this research paper, accurate analyzability of service-oriented architecture is estimated using fuzzy logic.
An Ensemble-based Machine Learning Model for Accurate Predictions using Multiple Categorical Datasets

In the consumer sector, electronic reviews are more common and comprehensive. Manufacturers, retailers, and customers are all aware of it. All require this knowledge to benefit from considering and guiding the massive and energetic data spaces that follow. Many social media outlets give polarized feedback. A fundamental problem with the internet's destructive content is that it makes it impossible for people to read important information. We'll look at all of the traditional machine learning approaches to catch the true sentiment. The accuracy using decision tree, naïve bayes and KNN applied on nursery dataset. All these three techniques achieved precisons of 72 to 90%. Decision tree performed well and accurate result as compared to knn and naïve bayes.The decision tree faced overfitting issues and KNN faced issues in deciding the value of K. On a large dataset, complexity increase when we apply a decision tree. Zero probability issues are a major challenge in naïve Bayes. To solve all those issues, an ensemble machine learning model (NKD) for accurate predictions is proposed to check the performance of the model. The proposed methodology applied on nursery dataset and IRIS dataset. The accuracy achieved using iris dataset is 100%.
Generating Adversarial Examples on Sar Images by Optimizing Flow Field Directly in Frequency Domain

Deep neural networks (DNNs) have become significant methods for SAR image analysis. However, there is a non-negligible security problem with deep learning. DNNs are particularly vulnerable to adversarial perturbations added to the input images. The study of adversarial examples generation can undoubtedly facilitate the study of defense algorithms and effectively improve the security and robustness of deep learning systems. We propose a novel method for generating adversarial examples on SAR images by optimizing flow field directly in frequency domain. We first transform the SAR images from spatial domain to frequency domain via discrete cosine transform. Then we optimize the flow field directly in frequency domain to generate the adversarial examples. Experiment of classification task on MSTAR dataset shows that our method can fool the DNN with 100% success rate, and the average number of attacks is greatly reduced from 36.58 to 5.58, which significantly improves the attack efficiency.
Generative Adversarial Network for Colorization of Mammograms

Breast cancer is one of the most frequently diagnosed cancers among women worldwide. This paper proposes an automated system that will aid the already existing methods for accurate diagnosis in the early stages of breast cancer. Our proposed approach colorizes the breast mammogram images using a generative adversarial network (GAN) model to increase the mammogram images’ features. We have used the CWAS-S dataset, which provided pixel-level annotations curated by experts for all the mammogram images. For preparing the target-colored mammogram, we colored the training images with the help of the pixel labels by assigning unique colors for each label. For auto-colorization of breast mammograms, we trained a GAN model with pairs of grayscale mammograms and target-colored mammograms. Four evaluation metrics, namely RMSE, PSNR, SSIM, and FSIM, are used to assess the estimated and target color image objectively. The colored results obtained from this approach can be used for better visualization of mammogram features. It can also help achieve better results in different automated diagnosis methods like—segmentation and classification. We performed segmentation on grayscale and colored mammograms and evaluated their performance using metrics like precision, recall, DSC, and IoU. Compared to a grayscale mammogram, a colored mammogram (generated using our method) achieved an increment of 4.16% in DSC and 15.1% in IoU metric values.
Multi-Label Learning based on Label-Specific Features and Single-Annulus Clustering

The label-specific features learning that extracts specific features from different labels for classification has attracted wide attention in recent years. The strategy of label-specific features is to exploit customized features in place of the original feature representation in their discrimination processes. However, there is a massive bias between the number of positive and negative instances in most multi-label data. In addition, the cross-distribution of instances and unbalanced label density further increase the difficulty of multi-label classification simultaneously. In this paper, we present a novel strategy called single-annulus clustering to form hierarchical label-specific features that prevent instances of unstable aggregation in the case of significant bias and improve classification efficiency. The principal objective of this strategy is to bisect instances for each label into annuluses with different sizes and then extract underlying features within each layer of annuluses. Experiments on benchmark multi-label data sets and contrastive research against other well-known algorithms show the effectiveness of the proposed strategy.
Implementation of Online Learning on the Understanding of Elementary Schools Students

This study aims to find out what the implementation of online learning is like at SDI Ma'arif Bandulan and to find out the benchmarks for understanding grade VI students at SDI Ma'arif Bandulan. In this study using descriptive qualitative research. The findings of the data found at SDI Ma'arif Bandulan in the implementation of online learning are that all teachers have applied their online learning method synronically, namely using WA-based applications as media that help the learning process and its implementation. From the results of the Implementation of Online Learning on the understanding of grade VI students, everything is complete.
The Continuous Wavelet Transform

The wavelet transform was designed to estimate the power spectra of non-stationary signals, that is, those whose frequency content varies over time or space. This chapter introduces the continuous—as opposed to discrete—wavelet transform in one and two dimensions and explores some properties of wavelets, focussing on the 2D Morlet wavelet. This particular wavelet is used in the so-called fan wavelet method of elastic thickness estimation. The fan wavelet geometry enables the estimation of spectra that are both isotropic and complex-valued, which can then be used in the determination of the admittance and coherency.
AMR-to-Text Generation with Graph Structure Reconstruction and Coverage Mechanism

Generating text from abstract meaning repre-sentation (AMR) is a challenging task. Graph-to-sequence (Graph2Seq-based) methods and pre-trained-based methods are proposed for this task. However, both methods have advantages and disadvantages. Graph2Seq-based methods can make use of the structural information of the graph but not the extra knowledge, while pre-trained-based methods have the advantage of the utilization of extra knowledge but may lose the structural information. In addition, both types of methods often suffer from the under- and over-translation problem. To address these prob-lems, we propose a graph structure reconstruction and coverage enhanced model for this task. The graph structure reconstruction uses two auxiliary objectives, relationship prediction and distance prediction of nodes in AMR graphs to enhance the information of graph structure. In addition, we design a coverage mechanism to solve the problem of information under-translation or over-translation in AMR-to-text generation. Experimental results on three datasets show that our proposed method outperforms the existing methods significantly.
Groupwise Non-Rigid Image Alignment Using Few Parameters

Groupwise non-rigid image alignment is a difficult non-linear optimization problem involving many parameters and often large datasets. Previous methods have explored various metrics and optimization strategies. Good results have been previously achieved with simple metrics, requiring complex optimization, often with many unintuitive parameters that require careful tuning for each dataset. In this chapter, the problem is restructured to use a simpler, iterative optimization algorithm, with very few free parameters. The warps are refined using an iterative Levenberg-Marquardt minimization to the mean, based on updating the locations of a small number of points and incorporating a stiffness constraint. This optimization approach is efficient, has very few free parameters to tune, and the authors show how to tune the few remaining parameters. Results show that the method reliably aligns various datasets including two facial datasets and two medical datasets of prostate and brain MRI images and demonstrates efficiency in terms of performance and a reduction of the computational cost.
Optimization Path Planning Algorithm Rapidly-Exploring Random Trees* (RRT*) with Fixed Node, Mesh Grid and Visibility Graph Sampling-Based Method

Path planning is very necessary in an autonomous mobile robot system to ensure a planned collision- free path from the starting point to the destination point in the navigation environment In general, there are two methods for finding paths, namely Deterministic and Random Graph Search. One of the random graph search algorithms that is often used for path planning is Rapidly Exploring Random Trees (RRT). RRT is a path planning algorithm that forms nodes randomly from the start point to the end point in the navigation environment. RRT can ensure probabilistic completeness but does not guarantee that the specified path is the shortest path in the navigation environment The proposed extension of the RRT and guaranteeing a shorter path length is RRT*. RRT* claims a more optimal solution than RRT but the path formed is not yet the most optimal path. This article introduces an RRT* algorithm with several innovative methods that can form optimal paths with shorter paths, less memory usage, and fewer iterations. The fixed node method is used to select the node to be selected and stored when searching for nodes, so that it uses less memory. Meanwhile, to reduce the number of iterations needed to form a path from the starting point to the destination point, a sampling-based method is used when looking for q <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">new</inf> from q <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">near</inf> and q <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">grand</inf> .
Teragraph Heterogeneous System for Ultra-large Graph Processing

The interest to the complex data models analysis has grown significantly in the last decade. Graphs are known as the most adequate form of the real data representation in social networks, computer program's structure, the topology of integrated circuits, to the bio-informatics and knowledge databases and other areas. As the size of datasets increases, the need to find more efficient computer architecture and software tools for analyzing and visualization of large graphs, including improved hardware, becomes obvious. Modern CPUs and GPUs have a great parallelism and performance level, but cannot resolve the fundamental architectural flaws of the graphs processing: the data dependency, the distribution of irregular graphs computing workload on multiple processing devices, as well as memory access conflicts for multiple cores. We introduce the new version of multicore Leonhard x64 microprocessor based on the Discrete mathematics Instruction Set Computer (DISC) instruction set for an ultra-scale graph processing. We show some advantages of DISC and compare the efficiency of universal computing systems with Leonhard x64 based system. Also, we introduce the Teragraph supercomputer architecture based on multiple heterogeneous Graph Processing Cores.