Reviewerly/exHarmony huggingface dataset:
FNNG: A High-Performance FPGA-based Accelerator for K-Nearest Neighbor Graph Construction

The k-nearest neighbor graph has emerged as the key data structure for many critical applications. However, it can be notoriously challenging to construct k-nearest neighbor graphs over large graph datasets, especially with a high-dimensional vector feature. Many solutions have been recently proposed to support the construction of k-nearest neighbor graphs. However, these solutions involve substantial memory access and computational overheads and an architecture-level solution is still absent. To address these issues, we architect FNNG, the first FPGA-based accelerator to support k-nearest neighbor graph construction. Specifically, FNNG is equipped with the block-based scheduling technique to exploit the inherent data locality between vertices. It divides the vertices that are close in space into blocks and process the vertices according to the granularity of the blocks during the construction process. FNNG also adopts the useless computation aborting technique to identify superfluous useless computations. It keeps the existing maximum similarity values of all vertices inside the computing unit. In addition, we propose an improved architecture in order to fully utilize both techniques. We implement FNNG on the Xilinx Alveo U280 FPGA card. The results show that FNNG achieves 190x and 2.1x speedups over the state-of-the-art CPU and GPU solutions, running on Intel Xeon Gold 5117 CPU and NVIDIA GeForce RTX 3090 GPU, respectively.
Robust Classification of Digitally Modulated Signals Using Capsule Networks and Cyclic Cumulant Features

The paper studies the problem of robust classification of digitally modulated signals using capsule networks and cyclic cumulant (CC) features extracted by cyclostationary signal processing (CSP). Two distinct datasets that contain similar classes of digitally modulated signals but that have been generated independently are used in the study, which reveals that capsule networks trained using CCs achieve high classification accuracy while also outperforming other deep learning-based approaches in terms of classification accuracy as well as generalization abilities.
A multi-task approach for contrastive learning of handwritten signature feature representations

In spite of recent advances in computer vision, the classic problem of offline handwritten signature verification still remains challenging. The signature verification task has a high intra-class variability because a given user often shows high variability between its samples. Besides, signature verification is harder in the presence of skilled forgeries. Recently, in order to tackle these challenges, the research community has investigated deep learning methods for learning feature representations of handwritten signatures. When mapping signatures to a feature space, it is desired to obtain dense clusters of signature’s representations, in order to deal with intra-class variability. Besides, not only dense clusters are required but also a larger separation between different user’s clusters in the feature space. Finally, it is also desired to move away feature representations of skilled forgeries in relation to the respective dense cluster of genuine representations. This last property is hard to achieve in the real-world scenario because skilled forgeries are not readily available during training. In this work, we hypothesize that such properties can be achieved by means of a multi-task framework for learning handwritten signature feature representations based on deep contrastive learning. The proposed framework is composed of two objective-specific tasks. The first task aims to map signature examples of the same user closer within the feature space, while separating the feature representations of signatures of different users. The second task aims to adjust the skilled forgeries representations by adopting contrastive losses with the ability to perform hard negative mining. Hard negatives are examples from different classes with some degree of similarity that can be applied for training. We evaluated models obtained with the proposed framework in terms of the equal error rate on GPDSsynthetic, CEDAR and MCYT-75 datasets in writer-dependent and writer-independent verification approaches. Using synthetic and real signature datasets, Friedman tests with Bonferroni–Dunn post hoc tests were performed to compare the proposed multi-task contrastive models against the popular SigNet model as a baseline. Experiments demonstrated an statistically significant improvement in signature verification with a multi-task contrastive model based on the Triplet loss. Implementation of the method is available for download at https://github.com/tallesbrito/contrastive_sigver.
An efficient entropy based dissimilarity measure to cluster categorical data

Clustering is an unsupervised learning technique that discovers intrinsic groups based on proximity between data points. Therefore, the performance of clustering techniques mainly relies on the proximity measures used to compute the (dis)similarity between the data objects. In general, it is relatively easier to compute the distance between numerical data points as numerical operations can directly be applied to values along features. However, for categorical datasets, computing the (dis)similarity between the data objects becomes a non-trivial problem. Therefore, in this paper, we propose a new distance metric based on the information theoretic approach to compute the dissimilarity between categorical data points. We compute entropy along each feature to capture the intra-attribute statistical information, based on which significance of attributes are decided during clustering. The proposed measure is free from any domain-dependent parameters and also does not rely on the distribution of data points. Experiment is conducted over diversified benchmark data sets, considering six competing proximity measures with three popular clustering algorithms and the clustering results are compared in terms of RI (Rand Index), ARI (Adjusted Rand Index), CA (Clustering Accuracy) and Cluster Discrimination Matrix (CDM). Over 85 percent of the data sets, the clustering accuracy of the proposed metric embedded with K-Mode and Weighted K-Mode outperforms its counterparts. Approximately, 0.2951 s is needed by the proposed metric to cluster a data set having 10,000 data points with 8 attributes and 2 clusters on a standard desktop machine. Overall, experimental results demonstrate the efficacy of the proposed metric to handle complex real datasets of different characteristics.
Image motion deblurring via attention generative adversarial network

Image motion deblurring methods based on deep learning have achieved promising performance. However, these methods ignore the global dependence of structural features, which leads to the problem of incomplete structure or the introduction of artifacts in deblurred images. To address this issue, we propose an image motion deblurring method based on attention mechanism and generative adversarial network. Firstly, a feature extraction strategy combining residual module and cascaded criss-cross attention module is proposed, which can collect abundant feature information along with contextual relationships among pixels. Secondly, a local–global dual-scale discriminator is adopted to supervise the generator to generate complete local details and global contours with a larger receptive field. Thirdly, a multi-component loss function is designed to guide the network to focus more on the relevant edge features rather than the remote interference points, thus improving the realism of deblurred images in terms of color and textures. Finally, the results of quantitative and qualitative experiments on deblurring benchmark datasets demonstrate that our method performs favorably against the state-of-the-art deep image deblurring methods.
A novel image encryption scheme based on Logistic cosine cascade maps

Abstract A two-dimensional Logistic cosine cascade map (2D-LCCM) and a three-dimensional Logistic cosine cascade map (3D-LCCM) are proposed based on a logistic map and infinite collapse cascade model. We analyze the chaotic properties of 2D-LCCM and 3D-LCCM, such as Lyapunov exponential spectrum, attractors, and complexity, which show that the 2D-LCCM and 3D-LCCM are hyperchaotic and have large chaotic regions. We also proposed a DNA encryption algorithm based on double chaotic maps. 2D-LCCM was used for scrambling and 3D-LCCM for diffusion. First, the sequence no-duplicate zigzag transform (SNZT) is used to scramble the plaintext. Z-transform is applied to the permutation sequence to make the sequence more random. Then a repetition-free permutation associated with the plaintext is applied to each block, and the pixel position is effectively scrambled. Finally, DNA diffusion is performed using DNA coding to diffuse pixel values. Simulation results show that the algorithm has a good encryption effect, high efficiency and anti-interference performance. Therefore, the proposed new hyperchaotic map and DNA encryption algorithm can be effectively applied to the process of image encryption.
Application of forensic accounting techniques in the South African banking industry for the purpose of fraud risk mitigation

The purpose of this study is to investigate the application of forensic accounting techniques in relation to fraud risk mitigation. This study employed an explanatory research design and a qualitative approach accompanied with a purposive sampling method. A primary data source was devised with a focus on the 17 licensed commercial banks registered in South Africa. By obtaining a true reflection of the situations in the banks, a conclusion was drawn following the outcome of the inferential statistical analysis. The research was conducted at the individual and organisational levels, with the bank consultants presenting their views. One hypothesis was formulated and non-parametric statistical analyses involving the use of Chi-square test, Fisher's Exact test and Spearman’s correlation were carried out. The results obtained substantiate that the loopholes created as a result of non-effective application of forensic techniques are partly responsible for some cyberfraud incidents in the banking industry. There is no sufficient evidence to ascertain whether the fraud risk assessment and management in the banking industry has a relationship with the effective application of forensic accounting techniques in terms of the identified causes of cyberfraud. However, the findings establish a positive correlation between fraud risk assessment and management as it relates to forensic accounting implementation. This study provides an insight into the significance of forensic accounting applications for fraud risk mitigation. There is still a death of information regarding the forensic accounting for fraud risk mitigation; hence, it is envisaged that this study will add to the existing literature in this regard.
TANet: Transformer‐based asymmetric network for RGB‐D salient object detection

Abstract Existing RGB‐D salient object detection methods mainly rely on a symmetric two‐stream Convolutional Neural Network (CNN)‐based network to extract RGB and depth channel features separately. However, there are two problems with the symmetric conventional network structure: first, the ability of CNN in learning global contexts is limited; second, the symmetric two‐stream structure ignores the inherent differences between modalities. In this study, a Transformer‐based asymmetric network is proposed to tackle the issues mentioned above. The authors employ the powerful feature extraction capability of Transformer to extract global semantic information from RGB data and design a lightweight CNN backbone to extract spatial structure information from depth data without pre‐training. The asymmetric hybrid encoder effectively reduces the number of parameters in the model while increasing speed without sacrificing performance. Then, a cross‐modal feature fusion module which enhances and fuses RGB and depth features with each other is designed. Finally, the authors add edge prediction as an auxiliary task and propose an edge enhancement module to generate sharper contours. Extensive experiments demonstrate that our method achieves superior performance over 14 state‐of‐the‐art RGB‐D methods on six public datasets. The code of the authors will be released at https://github.com/lc012463/TANet .
Application of Feature Pyramid Network and Feature Fusion Single Shot Multibox Detector for Real-Time Prostate Capsule Detection

In the process of feature propagation, the low-level convolution layers of the forward feature propagation network lack semantic information, and information loss occurs when fine-grained information is transferred to higher-level convolution; therefore, multi-stage feature fusion networks are needed to solve the interaction between low-level convolution layers and high-level convolution layers. Based on a two-way feature feedback network and feature fusion mechanism, we created a new object detection network called Feature Pyramid Network (FPN)-based Feature Fusion Single Shot Multibox Detector (FFSSD). A bottom-up and top-down architecture with lateral connections enhances the detector’s ability to extract features, then high-level multi-scale semantic feature maps are utilized to generate a feature pyramid network. The results show that the proposed network the mAP for prostate capsule image detection reaches 83.58%, providing real-time detection ability. The context interaction mechanism can transfer high-level semantic information to low-level convolution, and the resulting convolution after low-level and high-level fusion contains richer location and semantic information.
Sparse feature selection via fast embedding spectral analysis

Feature selection has been a research hotspot in many fields. Models based on graph learning are currently the most popular approaches. However, the sparsity of most models is not strong, and graph learning for pair-sample evaluation takes a lot of time. ℓ2,1-norm regularization is the sparsity strategy adopted in most sparse models at present since the convex function is easy to solve. Nevertheless, the sparsity of ℓ2,1-norm is insufficient, and there exist parameter adjustment problems. ℓ2,0-norm is a better choice, which can strengthen the sparse constraints of the subspace. In this paper, the Sparse feature selection via Fast Embedding Spectral Analysis (SFESA) is proposed. Firstly, an adaptive anchor nearest neighbor graph is constructed to avoid the high time cost of learning pairwise nearest neighbor graphs to a certain extent. The low-dimensional embedding of data manifold structure is maintained by performing spectral analysis for the constructed graph. Secondly, the projected data is approximated to the low-dimensional embedding structure via a regularization term. Finally, ℓ2,0-norm is employed to constrain the projection matrix to enhance the subspace sparsity. Furthermore, a fast iterative algorithm is presented to solve this non-convex optimization problem. Extensive experiments on multiple public datasets show that SFESA can obtain excellent performance in less time.
COM: Contrastive Masked-attention model for incomplete multimodal learning

Most multimodal learning methods assume that all modalities are always available in data. However, in real-world applications, the assumption is often violated due to privacy protection, sensor failure etc. Previous works for incomplete multimodal learning often suffer from one of the following drawbacks: introducing noise, lacking flexibility to missing patterns and failing to capture interactions between modalities. To overcome these challenges, we propose a COntrastive Masked-attention model (COM). The framework performs cross-modal contrastive learning with GAN-based augmentation to reduce modality gap, and employs a masked-attention model to capture interactions between modalities. The augmentation adapts cross-modal contrastive learning to suit incomplete case by a two-player game, improving the effectiveness of multimodal representations. Interactions between modalities are modeled by stacking self-attention blocks, and attention masks limit them on the observed modalities to avoid extra noise. All kinds of modality combinations share a unified architecture, so the model is flexible to different missing patterns. Extensive experiments on six datasets demonstrate the effectiveness and robustness of the proposed method for incomplete multimodal learning.
PO-GNN: Position-observant inductive graph neural networks for position-based prediction

Most graph neural networks (GNNs) map local neighborhood information into node embeddings but lack positional information. It results in similar node embeddings for nodes in similar local neighborhoods, leading to poor performance. We propose a position-observant inductive graph neural network (PO-GNN) architecture that captures the global positions of the nodes using the proposed novel closeness-distance metric. We demonstrate that the problem of anchor node selection, a significant component of position-aware GNNs, is NP-hard. Additionally, we utilize a heuristic anchor selection approach to obtain equitably distributed and asymmetric reference nodes over the given graph. In particular, PO-GNN significantly achieved a 50% relative improvement in the AUC metric for the Link Prediction (LP) over five different datasets and 85% for the Pairwise Node Classification (PNC) tasks over four different datasets and compared to the common GNN architectures and 1.805% and 15.29% improvement over state-of-the-art position-aware GNNs in LP and PNC tasks respectively. Furthermore, we also perform ablation studies over the length and number of random walks along with five aggregation methods and four update methods.
Dual-distribution discrepancy with self-supervised refinement for anomaly detection in medical images

Medical anomaly detection is a crucial yet challenging task aimed at recognizing abnormal images to assist in diagnosis. Due to the high-cost annotations of abnormal images, most methods utilize only known normal images during training and identify samples deviating from the normal profile as anomalies in the testing phase. Many readily available unlabeled images containing anomalies are thus ignored in the training phase, restricting the performance. To solve this problem, we introduce one-class semi-supervised learning (OC-SSL) to utilize known normal and unlabeled images for training, and propose Dual-distribution Discrepancy for Anomaly Detection (DDAD) based on this setting. Ensembles of reconstruction networks are designed to model the distribution of normal images and the distribution of both normal and unlabeled images, deriving the normative distribution module (NDM) and unknown distribution module (UDM). Subsequently, the intra-discrepancy of NDM and inter-discrepancy between the two modules are designed as anomaly scores. Furthermore, we propose a new perspective on self-supervised learning, which is designed to refine the anomaly scores rather than directly detect anomalies. Five medical datasets, including chest X-rays, brain MRIs and retinal fundus images, are organized as benchmarks for evaluation. Experiments on these benchmarks comprehensively compare a wide range of anomaly detection methods and demonstrate that our method achieves significant gains and outperforms the state-of-the-art. Code and organized benchmarks are available at https://github.com/caiyu6666/DDAD-ASR.
Interactive Peer Instruction Method Applied to Classroom Environments Considering a Learning Engineering Approach to Innovate the Teaching–Learning Process

Faced with the problem of a need for more student participation and motivation in the teaching–learning process (TLP) due to the persistence of traditional methods, peer instruction (PI) has emerged as an interactive teaching method. It is based on a dynamic of questions and answers to promote student reflection and discussion. Thus, this article shows the applicability of PI, considering a learning engineering approach to innovating the TLP. For this, the historical-descriptive method is used to conduct a literature review and a bibliometric study, evaluating scientific articles in Web of Science (WoS) and Scopus between 2018 and 2022. In addition, in the second stage, the experimental method is used to apply PI in two educational institutions and evaluate its applicability with Likert scales for teachers and students following a quantitative methodology. Consequently, following the analytical-synthetic method, the results indicate that PI the stages, the most relevant aspects, and the conditions to consider in a classroom environment are highly relevant to enhancing its effects. Thus, such applicability is reflected in its positive results in the TLP, considering the learning engineering, and its representation as a flexible and innovative alternative to traditional methods. This is because PI generated benefits for teachers and students, thus encouraging greater satisfaction, motivation, interest, understanding, and student participation.
Lightweight Kubernetes Distributions: A Performance Comparison of MicroK8s, k3s, k0s, and Microshift

With containers becoming a prevalent method of software deployment, there is an increasing interest to use container orchestration frameworks not only in data centers, but also on resource-constrained hardware, such as Internet-of-Things devices, Edge gateways, or developer workstations. Consequently, software vendors have released several lightweight Kubernetes (K8s) distributions for container orchestration in the last few years, but it remains difficult for software developers to select an appropriate solution. Existing studies on lightweight K8s distribution performance tested only small workloads, showed inconclusive results, and did not cover recently released distributions. The contribution of this paper is a comparison of MicroK8s, k3s, k0s, and MicroShift, investigating their minimal resource usage as well as control plane and data plane performance in stress scenarios. While k3s and k0s showed by a small amount the highest control plane throughput and MicroShift showed the highest data plane throughput, usability, security, and maintainability are additional factors that drive the decision for an appropriate distribution.