Reviewerly/exHarmony huggingface dataset:
Design of Two-Channel LED Stand-Alone Solar Lamp Driver Prototype for Biodynamic Application

This paper deals with design of LED driver as a part of an efficient solar-powered streetlamp based on biodynamic lighting. The contribution of this paper is experimental verification of the designed solution in form of a fully functional battery powered SSLS prototype.
Valid Inferential Models Offer Performance and Probativeness Assurances

Bayesians and frequentists are now largely focused on developing methods that perform well in a frequentist sense. But the widely-publicized replication crisis suggests that performance guarantees are not enough for good science. In addition to reliably detecting hypotheses that are incompatible with data, users require methods that can probe for hypotheses that are actually supported by the data. In this paper, we demonstrate that valid inferential models achieve both performance and probativeness properties. We also draw important connections between inferential models and Deborah Mayo's severe testing.
Facing the Void: Overcoming Missing Data in Multi-View Imagery

In some scenarios, a single input image may not be enough to allow the object classification. In those cases, it is crucial to explore the complementary information extracted from images presenting the same object from multiple perspectives (or views) in order to enhance the general scene understanding and, consequently, increase the performance. However, this task, commonly called multi-view image classification, has a major challenge: missing data. In this paper, we propose a novel technique for multi-view image classification robust to this problem. The proposed method, based on state-of-the-art deep learning-based approaches and metric learning, can be easily adapted and exploited in other applications and domains. A systematic evaluation of the proposed algorithm was conducted using two multi-view aerial-ground datasets with very distinct properties. Results show that the proposed algorithm provides improvements in multi-view image classification accuracy when compared to state-of-the-art methods. The code of the proposed approach is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Gabriellm2003/remote_sensing_missing_data</uri> .
Optimal Partition Assignment for Universal Object Detection

The label assignment problem is a core task in object detection, which mainly focuses on how to define the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$positive/negative$</tex-math></inline-formula> samples during the training phase. Recent works have proved that label assignment is significant for performance improvement of the detector. In this article, we propose an exquisite strategy that can dynamically assign labels according samples' joint scores (classification and location). Moreover, our strategy can apply to both 2D and 3D monocular detectors. In our strategy, we formulate label assignment as an optimization problem. Concretely, we first calculate the classification and location costs of each sample, which are treated as points in a 2-D coordinate system. Then an optimal divider line that minimizes the sum of point-to-line distances is designed to separate the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$positive/negative$</tex-math></inline-formula> samples. An iterative Genetic Algorithm is employed in acquiring the optimal solution. Furthermore, a GIoU auxiliary branch is devised to keep sample selection consistent during the training and testing phase. Benefitting from the non-maximum suppression (NMS) that utilizes the joint scores of classification and location, excellent detection performance is achieved. Extensive experiments conducted on MS COCO, PASCAL VOC (2D object detection), and KITTI (3D object detection) verify the effectiveness and universality of our proposed Optimal Partition Assignment (OPA).
Diffusion Particle Filtering on the Special Orthogonal Group Using Lie Algebra Statistics

In this paper, we introduce new distributed diffusion algorithms to track a sequence of hidden random matrices that evolve on the special orthogonal group. The algorithms are based on the Adapt-then-Combine and the Random Exchange methods, and diffuse Gaussian approximations of posterior densities computed in the Lie algebra of the special orthogonal group. Simulation results show that, in scenarios with nonlinear observation functions, the proposed algorithms perform closely to the centralized particle filter estimator and can outperform competing Extended Kalman Filters.
Universal adversarial perturbation for remote sensing images

Recently, with the application of deep learning in the remote sensing image (RSI) field, the classification accuracy of the RSI has been dramatically improved compared with traditional technology. However, even the state-of-the-art object recognition convolutional neural networks are fooled by the universal adversarial perturbation (UAP). The research on UAP is mostly limited to ordinary images, and RSIs have not been studied. To explore the basic characteristics of UAPs of RSIs, this paper proposes a novel method combining an encoder-decoder network with an attention mechanism to generate the UAP of RSIs. Firstly, the former is used to generate the UAP, which can learn the distribution of perturbations better, and then the latter is used to find the sensitive regions concerned by the RSI classification model. Finally, the generated regions are used to fine-tune the perturbation making the model misclassified with fewer perturbations. The experimental results show that the UAP can make the classification model misclassify, and the attack success rate of our proposed method on the RSI data set is as high as 97.09%.
Preliminary Analysis of Result and Log Data on Viat-map in English Reading Comprehension

Learning process in the current era offer plenty of ways, one of it was involving technology. Technology Enhanced Learning evolutions are still growing faster and offer new solution and point of view that has never been discovered before. Learning English, especially for Reading Comprehension Subject for English as Foreign Language students is quite challenging. Many of them can understand the word separately but often failed to understand the meaning when it was formed into a sentence. Viat-map is an application developed for Reading Comprehension. It implemented the Toulmin Arguments Concept to extract the special and important sentences and form them into a graphical related text. The basic assumption built for this research is Students who are using viat-map will get a better understanding than the students who are using the summarization conventional method. Fifty-eight sophomore students were involved in the experiments, they are from the Information engineering department of State Polytechnic of Malang. They study English in class. The initial test was conducted as the basic score to divide groups. Students were divided into control groups and experimental groups. ANOVA Statistical analysis was used to make sure that the groups were equal. The result of the experiment was in line with the assumption that the experimental groups overachieved the control groups, there are also found an improvement in Pre-test to Post-test scores in the experimental groups while the control groups remain the same. Another interesting fact of log preliminary analysis found that novice students tend to have more steps to get the correct answer in the application.
Employing Offset-Attention for 3D Few-shot Semantic Segmentation

The existing deep 3D semantic segmentation methods mostly are trained with a large number of human annotations. However, due to the expensive labor for annotations label, few-shot 3D semantic segmentation is achieving more attention. In this work, we improve the performance of few-shot learning based on semantic segmentation of 3D point clouds using the offset attention method that has been successfully applied in natural language processing. Experiments demonstrate the superiority of the offset-attention in 3D semantic segmentation on the benchmark datasets S3DIS.
DEDIC: Density Estimation Clustering Method Using Directly Interconnected Cores

Numerous clustering algorithms become invalid when data classes are unbalanced and close to each other, or clusters are arbitrary shapes and have different densities. A novel region density based clustering algorithm, DEDIC, is proposed to meet this challenge. It is based on recognizing dense regions using mutual nearest neighbors and estimating region density of a cluster by computing maximum of the directly-reachable distances among core points within the cluster. DEDIC is superior to the popular density-based clustering algorithm DBSCAN in two aspects. First, since there is only one parameter (choice of k nearest neighbors), the algorithm complexity is reduced, and second, an improved ability to handle clusters with large density variations. The superiority of DEDIC is demonstrated on several artificial and real-world datasets with respect to nine known clustering approaches.
Nondestructive Data Acquisition Methodology for IoT Devices: A Case Study on Amazon Echo Dot Version 2

The smart speaker is becoming a common part of the modern household, which usually includes an AI-powered Intelligent Voice Assistant to communicate with its users. Amazon Echo Dot is a popular smart speaker that extends the above-stated functionality by acting as a communication hub for other Internet of Things (IoT) and mobile devices within its local network. The nature and volume of data that an Echo Dot handles make it a potential source of evidence, if one is seized for a digital forensics investigation. Researchers and practitioners have explored various techniques to extract data from these IoT devices. However, traditional methods make changes to the physical device and/or its data, which is undesirable from a digital forensics perspective. The current work focuses on developing a nondestructive methodology for extracting data from IoT devices, with Amazon Echo Dot version 2 as an example, which use embedded Multimedia Card (eMMC)/embedded Multichip Package (eMCP) chips as their primary storage. We identify all in-system programming (ISP) pins using the computed tomography (CT) Scan imagery of the main printed circuit board (PCB) of the device. We created a 3-D fixture that accommodates pogo pin connectors to create contact with the already identified ISP taps on the main PCB. The 3-D Test Probe Jig can extract data from an IoT device’s memory chip using an eMMC reader. The proposed nondestructive solution is reproducible, portable, and affordable.
Automated labeling and classification of business rules from software requirement specifications

Business Rules (BRs) are a critical artifact in the requirements elicitation phase of the software development life cycle. Several taxonomies have been proposed for classification of BRs. In this paper, we utilize Ross's BR classification schema and present an approach to automatically label and classify BRs along this schema. Our approach uses Data Programming (DP) for generating labeled training data needed for training two deep learning-based models to classify the BRs. We obtained an average labeling accuracy of 0.73 for all the BR classes using DP. Upon evaluating the approach on industry-specific dataset, we obtained highest weighted F-score (0.69) with a Bi-LSTM with attention-based model.
Deep Speaker Embedding Using Hybrid Network of Multi-Feature Aggregation and Multi-Loss Fusion for TI-SV

Text-independent speaker verification (TI-SV) refers to the process of verifying an individual’s claimed identity from a given speech utterance with unfixed content. Most deep speaker embedding networks of TI-SV apply temporal pooling or similar techniques for frame-level feature aggregation, and adopt a single loss function for training. In this paper, we propose a powerful hybrid network, named HN-MFML, which consists of a backbone and two sub-networks: speaker embedding extraction and speaker classification. The hybrid network not only incorporates global and local features, but also assigns adaptive weights to them. It adopts a modified ResNet-50 as backbone, using speaker embedding extraction sub-network to aggregate global and local features adaptively in frequency-time domain, which can be trained end-to-end by a loss. In addition, we add a speaker classification sub-network with another loss and explore a multi-loss fusion to jointly train for improving generalization. We demonstrate that our multi-feature aggregation and multi-loss fusion are superior for obtaining discriminative utterance-level embedding descriptors. We also show that HN-MFML achieves state-of-the-art performance by a significant margin compared with previous methods.
Image Aesthetic Assessment Based on Attention Mechanisms and Holistic Nested Edge Detection

Aesthetic assessment of images has attracted a great deal of research interests as one of the hot spots in the field of computer vision. This paper proposed a new method for classifying image quality from an aesthetic perspective, by classifying images into two categories: high aesthetic quality and low aesthetic quality. Since the local features and edge features of images affect detail and holistic perception of aesthetic, we propose an image aesthetic assessment model based on attention mechanism and holistic nested edge detection. The model consists of two parallel deep convolutional neural networks named upper and lower branches. The upper branch uses an attention mechanism for extracting local features of RGB images and the lower branch is used to extract edge features of images. Experimental results indicate that the proposed method can predict image aesthetic with the accuracies of 88.13% and 94.10% in AVA dataset and CUHKPQ dataset respectively. Compared with the other methods, our approach takes into account both the details and the holistic information of images and performs more efficiently in assessing visual aesthetic of images.
Modeling flexible Monitoring Systems with a Role-Based Control Loop

Monitoring information technology systems during operation is one of the few methods available to help administrators keep track of the monitored system’s state, predict and identify errors, and assist in system repair and error avoidance. Monitoring solutions to date, however, still suffer from various trade-offs as current implementations impose architectural restrictions on monitored systems, which lead to reduced flexibility in deployment and operation. Most monitoring software is specialized to function with fixed specific data formats, protocols, and data acquisition mechanisms, further reducing their flexibility. The role-based approach for modeling and implementing software promises an intuitive way of increasing the flexibility of information technology systems’ modeling and implementation. Paired with technology on control loops from the domain of self-adaptive systems, we can create a reusable framework made from static role-playing building blocks that allow overcoming those limitations. In this paper, a novel concept and model for a flexible monitoring solution on this basis is presented, which provides functioning in most application domains while minimizing architectural and functional constraints on the monitored system. We compare the flexibility of our concept with a collection of 15 monitoring systems based on 11 criteria. We further illustrate the concept by presenting and discussing a uniform role-based model and, finally, discussing one possible implementation method.
3D Face Cartoonizer: Generating Personalized 3D Cartoon Faces from 2D Real Photos with a Hybrid Dataset

Cartoon face is a prevalent kind of stylized face, which is widely used in movies, TVs and advertisements. Although plenty of methods have been proposed to generate 2D cartoon faces, it is still challenging to learn personalized 3D cartoon faces directly from 2D real photos. To solve this problem, we contribute the first 3D cartoon face hybrid dataset with both large amounts of low-quality and a small number of high-quality face triplets. Each triplet contains a 2D real face, as well as its corresponding 2D and 3D cartoon faces. To leverage the hybrid dataset, we propose Recon2AGen which first pretrains our network with low-quality triplets in a reconstruction-then-generation manner and then finetunes it with high-quality triplets in an adversarial manner. In this way, we solve the 2D-to-3D ambiguity and the real-to-cartoon transformation by disentangling the task into three progressively learned sub-tasks. And the hybrid dataset is fully explored to achieve generalizable and high accuracy results. Extensive experiments show that our generated 3D cartoon faces are of high quality and can be easily edited and animated, enabling extensive practical applications. Code and dataset will be available at https://github.com/mingsjtu/3DCartoonGenerator .
SCAR U-Net: A 3D Spatial-Channel Attention ResU-Net for Brain Tumor Segmentation

Although surgical resection is the best option for treating gliomas, it might be difficult to minimize the harm done to healthy brain regions. As a result, segmenting brain tumors in medical image analysis has become essential. A number of segmentation investigations utilizing CNNs have lately demonstrated promising performance thanks to the advancement of image equipment and deep learning techniques. In this study, we propose the SCAR U-Net, an end-to-end 3D residual U-Net model incorporating attention mechanisms for the segmentation of brain tumors. The SCAR U-Net employs channel and spatial attention processes and has a 3D U-Net architecture with residual blocks. We evaluate the model on a subset of the BraTS 2021 dataset. And the model outperforms the baseline significantly by ET, TC, and WT in the test set. Finally, we use ablation tests to confirm the beneficial effects of the residual connections and the attention modules.
Multiontology Construction and Application of Threat Model Based on Adversarial Attack and Defense Under ISO/IEC 27032

Research pertaining to threat modeling is significant. However, the existing threat modeling methods suffer from ambiguity, heterogeneity and incompleteness; furthermore, the threat models at different abstraction levels are separated from each other, and the model elements are fragmented. In the knowledge engineering community, an ontology is an explicit specification of a conceptualization. Introducing the ontology method into the study of threat models is an effective way to solve the above problems. This paper creates a multiontology framework for the threat model of information systems (IS) based on domain knowledge (attack and defense knowledge), engineering experience, and industry standards (ISO/IEC 27032). The multiontology framework includes a generalized ontology (GO), a domain ontology (DO), and an application ontology (AO). This paper builds the ontology of each layer and ultimately presents case studies. The results show that the multiontology threat model based on adversarial attack and defense effectively solves the above problems of the existing threat modeling methods. In addition, systematic threat modeling using the multiontology method can be used not only for attack path-based threat analysis but also for adversarial attack and defense-based threat analysis. This method can help detect security issues and effectively guide security personnel.
Robust zero-watermarking algorithm for medical images based on &lt;em&gt;K&lt;/em&gt;-means and DCT

To better protect patient information in medical images and improve the security of medical image transmission, this paper studies a robust watermarking algorithm for medical images based on K-means and Discrete Cosine Transform (DCT). Firstly, the watermark is pre-processed by chaotic encryption to make it more secure. Then, the K-means clustering algorithm is used to classify the grey values of the pixels in the medical image to obtain the feature image after the cluster segmentation; then the DCT is used to extract the feature coefficient matrix and transform it into the feature hash sequence of the image. Finally, the zero-watermark technology is used to combine the feature hash sequence with the encrypted watermark to realise the embedding and extraction of the watermark. Experiments show that the algorithm not only can resist conventional attacks, but also has good robustness against geometric attacks.
Deep Graph Convolutional Networks Based on Contrastive Learning: Alleviating Over-smoothing Phenomenon

Graphs are a common and important data structure, and networks such as the Internet and social networks can be represented by graph structures. The proposal of Graph Convolutional Network (GCN) brings graph research into the era of deep learning and has achieved better results than traditional methods on various tasks. For ordinary neural networks, more layers can often achieve better results. However, for GCN, the deepening of the number of layers will cause a catastrophic decline in performance, including the gradual indistinguishability of node features, the disappearance of gradients, and the inability to update weights. This phenomenon is called over-smoothing. The occurrence of over-smoothing makes training deep GCNs a difficult problem. Compared with deep GCNs, shallow GCNs tend to perform better. Therefore, we design a contrastive learning model such that the deep GCN learns the features of the same node (positive samples) of the shallow GCN while alienating the features of other nodes (negative samples) , so that the deep GCN can learn the performance of the shallow GCN. Experiments show that our method can effectively alleviate the over-smoothing phenomenon. At the same time, we apply this model to other over-smoothing methods, and also achieve better results.
Generative Negative Text Replay for Continual Vision-Language Pretraining

Vision-language pre-training (VLP) has attracted increasing attention recently. With a large amount of image-text pairs, VLP models trained with contrastive loss have achieved impressive performance in various tasks, especially the zero-shot generalization on downstream datasets. In practical applications, however, massive data are usually collected in a streaming fashion, requiring VLP models to continuously integrate novel knowledge from incoming data and retain learned knowledge. In this work, we focus on learning a VLP model with sequential chunks of image-text pair data. To tackle the catastrophic forgetting issue in this multi-modal continual learning setting, we first introduce pseudo text replay that generates hard negative texts conditioned on the training images in memory, which not only better preserves learned knowledge but also improves the diversity of negative samples in the contrastive loss. Moreover, we propose multi-modal knowledge distillation between images and texts to align the instance-wise prediction between old and new models. We incrementally pre-train our model on the both instance and class incremental splits of Conceptual Caption dataset, and evaluate the model on zero-shot image classification and image-text retrieval tasks. Our method consistently outperforms the existing baselines with a large margin, which demonstrates its superiority. Notably, we realize an average performance boost of $$4.60\%$$ on image-classification downstream datasets for class incremental split.