Reviewerly/exHarmony huggingface dataset:
A Flexible Model Compression and Resource Allocation Scheme for Federated Learning

Communication overhead has become one of the major constraints on the application of federated learning (FL). To reduce the overhead by trading off between the number of communication rounds and per-round latency, significant research efforts have been devoted to investigating joint optimization of model compression, client scheduling, and resource allocation to reduce the total training time. In order to reduce the complexity of joint optimization, the existing methods only consider the same compression level, unchanged participating clients, and identical round duration during training, resulting in low resource usage efficiency. In this paper, we propose a flexible model compression and resource allocation scheme to minimize the total communication time for FL in mobile networks. The proposed scheme can assign adaptive compression levels and communication resources to each client in each round. Simulation results show that the proposed scheme outperforms the start-of-the-art methods and is robust to outdated channel information.
Select-and-Combine (SAC): A Novel Multi-stereo Depth Fusion Algorithm for Point Cloud Generation via Efficient Local Markov Netlets

Many practical systems for image-based surface reconstruction employ a stereo/multi-stereo paradigm, due to its ability to scale for large scenes and its ease of implementation for out-of-core operations. In this process, multiple and abundant depth maps from stereo matching must be combined and fused into a single, consistent, and clean point cloud. However, the noises and outliers caused by stereo matching and the heterogenous geometric errors of the poses present a challenge for existing fusion algorithms, since they mostly assume Gaussian errors and predict fused results based on data from local spatial neighborhoods, which may inherit uncertainties from multiple depths resulting in lowered accuracy. In this paper, we propose a novel depth fusion paradigm, that instead of numerically fusing points from multiple depth maps, selects the best depth map per point, and combines them into a single and clean point cloud. This paradigm, called select-and-combine (SAC), is achieved through modeling the point level fusion using local Markov Netlets, a micro-network over point across neighboring views for depth/view selection, followed by a Netlets collapse process for point combination. The Markov Netlets are optimized such that they can inherently leverage spatial consistencies among depth maps of neighboring views, thus they can address errors beyond Gaussian ones. Our experiment results show that our approach outperforms existing depth fusion approaches by increasing the F1 score that considers both accuracy and completeness by 2.07% compared to the best existing method. Finally, our approach generates clearer point clouds that are 18% less redundant while with a higher accuracy before fusion.
Novel Dilated Separable Convolution Networks for Efficient Video Salient Object Detection in the Wild

Appearance and motion are essential features in Video Salient Object Detection (VSOD) tasks. Most of the existing approaches utilize local features and thus fail to understand both the appearance as well as motion-specific semantics at the global level. Hence, these methods are unable to perform in unconstrained scenarios where multiple challenges, such as partial occlusion, motion blur, noise, clutter background, etc., exist. Moreover, these approaches require a large number of computational resources due to their complex structures, which limits their applicability to real-world deployment. To resolve these issues and to achieve a balance between accuracy and computational complexity, in this paper, a Dilation Separable Convolution Network (DSCNet) is proposed, which is equipped with Dilation Attention Fusion Module (DAFM), Bi-directional Cross-modality Fusion Module (BCFM), and Saliency Prediction Module (SPM) to extract enhanced multi-scaled motion and appearance features without increasing the model complexity. Further, a Bi-directional Separable Convolution Network (BSC-Net) equipped with a Separable Convolution Module (SCM)s and a FlowNet2.0 is proposed to utilize multi-scale contextual information across appearance cues and generate enhanced multi-scaled motion maps. For faster and better training of the DSCNet model, we propose a novel Stochastic Gradient-based Firefly Algorithm (SGFA), which adaptively balances the exploration and exploitation in multi-scaled, cross-modal embedded sub-spaces. With the help of the proposed SGFA algorithm, DSCNet+ model is constructed on top of DSCNet, which further improves the results in terms of the training speed as well as other evaluation metrics. The proposed models are evaluated on six benchmark datasets, and a detailed comparative study is provided with sixteen state-of-the-art (SOTA) models. One of the major highlights of the work is the significant performance of the proposed models on the most difficult DAVSOD-Diff dataset, which best reflects the challenging real-world scenarios.
Preserving the Privacy of Latent Information for Graph-Structured Data

Latent graph structure and stimulus of graph-structured data contain critical private information, such as brain disorders in functional magnetic resonance imaging data, and can be exploited to identify individuals. It is critical to perturb the latent information while maintaining the utility of the data, which, unfortunately, has never been addressed. This paper presents a novel approach to obfuscating the latent information and maximizing the utility. Specifically, we first analyze the graph Fourier transform (GFT) basis that captures the latent graph structures, and the latent stimuli that are the spectral-domain inputs to the latent graphs. Then, we formulate and decouple a new multi-objective problem to alternately obfuscate the GFT basis and stimuli. The difference-of-convex (DC) programming and Stiefel manifold gradient descent are orchestrated to obfuscate the GFT basis. The DC programming and gradient descent are employed to perturb the spectral-domain stimuli. Experiments conducted on an attention-deficit hyperactivity disorder dataset demonstrate that our approach can substantially outperform its differential privacy-based benchmark in the face of the latest graph inference attacks.
Generating Python Mutants From Bug Fixes Using Neural Machine Translation

Due to the fast-paced development of technology, the software has become a crucial aspect of modern life, facilitating the operation and management of hardware devices. Nevertheless, using substandard software can result in severe complications for users, putting human lives at risk. This underscores the significance of error-free and premium-quality software. Verification and validation are essential in ensuring high-quality software development; software testing is integral to this process. Although code coverage is a prevalent method for assessing the efficacy of test suites, it has some limitations. Therefore, mutation testing is proposed as a remedy to tackle these limitations. Furthermore, mutation testing is recognized as a method for directing test case creation and evaluating the effectiveness of test suites. Our proposed method involves autonomously learning mutations from faults in real-world software applications. Firstly, our approach involves extracting bug fixes at the method-level, classifying them according to mutation types, and performing code abstraction. Subsequently, the approach utilizes a deep learning technique based on neural machine translation to develop mutation models. Our method has been trained and assessed using approximately ~588k bug fix commits extracted from GitHub. The results of our experimental assessment indicate that our models can forecast mutations resembling resolved bugs in 6% to 35% of instances. The models effectively revert fixed code to its original buggy version, reproducing the original bug and generating various other buggy codes with up to 94% accuracy. More than 96% of the generated mutants also demonstrate lexical and syntactic accuracy.
Cracking the Curious Case of the Cascade Protocol

With the nature-powered quantum revolution gaining momentum, Quantum Computation and Quantum Information are all set to transform the landscape of all areas of science and technology. Quantum Cryptography is one such field which has witnessed tremendous progress in theory as well as in its practical implementation. Quantum Key Distribution is one of its integral parts and it necessarily requires the usage of an information reconciliation protocol to correct the errors present in the key bits transmitted via the physical system involved. Cascade is one such protocol which enjoys popularity among such system developers due to its simplicity and high error-correction efficiency. In this work, we identify, improve, interpret, and implement key optimizations from a practical viewpoint and give an algorithmic implementation of the Cascade protocol which would serve to increase its overall efficiency without changing its foundational structure. We also dig deep into the math and physical interpretation behind the values and expressions of key parameters and quantities of Cascade, which effectively make use of the cascade of errors unearthed by it so as to not only ensure an exponentially decreasing probability of any errors in the key bits with the passes of Cascade but also ensure that only the minimum possible information is leaked to a potential eavesdropper. Finally, we also give a visual walk-through of Cascade in action and support it with explanations at every step. By doing so, we aim to equip researchers and developers with an insight driven error correction tool which is sure to be useful in practice.
A Backpropagation Multidimensional Taylor Network Model for Measured 3-D Data Fitting

To solve the nonlinear fitting problem of three-dimensional (3-D) data, an improved multi-dimensional Taylor network (MTN) structure based on the error backpropagation (BP) algorithm is proposed in this paper. Compared to the traditional MTN, the so-called BP-MtN has three main characteristics: 1) an additional full connection layer is added after the same dimension output layer of the traditional MTN to describe the dynamic evolution of the nonlinear system state; 2) adding activation function to improve the ability of network fitting; and 3) the weights of the BP-MTN are trained using the BP algorithm. Compared to the traditional artificial neural networks, the improved MTN has a simpler structure without complex calculations, thus can ensure better real-time operation performance. For verification, the measured 3-D nonlinear torque characteristics of a 5kW switched reluctance motor in an electric vehicle application are modeled with the proposed BP-MTN algorithm. Experimental results show that BP-MTN performs well in terms of model training time and fitting capability.
SalCor: A Hierarchical Saliency-Driven Segmentation Model With Local Correntropy for Medical Images

In image segmentation, noise and nonuniform intensity can lead to performance degradation in existing models, particularly when dealing with shadow artifacts. This study proposes a hierarchical saliency-driven segmentation model with local correntropy (SalCor) to address this problem, incorporating saliency information with local correntropy-based K-means clustering to formulate an energy function. This approach enables it to extract objects with complex backgrounds effectively regardless of noise and intensity inhomogeneity. An adaptive weight function is introduced to dynamically adjust the intensities of the energy functions (external and internal) based on the image information, resulting in enhanced model resilience to contour initialization and improved robustness. The SalCor model can handle noise robustly by leveraging the local correntropy-based K-means clustering. The proposed approach is evaluated on synthetic and real images, including medical images, such as brain and mammogram magnetic resonance imaging (MRI) and coronavirus disease 2019 (COVID-19) computed tomography images, and is compared with state-of-the-art models. The statistical analysis confirms the SalCor model’s exceptional precision and efficiency. These outcomes indicate that SalCor holds great potential for detecting brain tumors and mammogram tumors in MRIs and early diagnosis of COVID-19.
Behavior Driven Development: A Systematic Literature Review

Behavior Driven Development (BDD) is a widely adopted agile methodology for software development that emphasizes the behavior of an application as a series of test cases, using the keywords, which include "Given," "When," and "Then." It involves writing requirements in a structured and testable format that can be evaluated to ensure compliance with the expected behavior. Although a significant amount of research has been conducted to examine the impact of using BDD on software development process yet rare work is observed to synthesize these studies and identify areas for future exploration. This study presents a review of the state-of-the-art BDD by synthesizing the recent advancements in its uses and applications. It aims to systematically investigate the impact of BDD on software development process as well as on product quality by aiding to bridge the communication gap between the stakeholders. The results reveal that BDD is an effective technique to clarify requirements during the software development process as it helps minimizing the intrinsic ambiguities. This work proposes a taxonomy based on the role and applications of BDD in various contexts. It suggests a framework for applying BDD in software development and defines a workflow for its application in software development. Finally, this work highlights some pertinent future directions for the use of BDD in software development.
Two-Stream Multiplicative Heavy-Tail Noise Despeckling Network With Truncation Loss

In recent years, deep learning algorithms for speckle noise removal have attracted much attention. However, speckle noise is strongly heavy-tailed and signal dependent, which makes it difficult to remove. In this paper, we propose a two-stream convolutional neural network with hybrid truncation loss to eliminate multiplicative noise (HTNet). HTNet combines the major task of multiplicative noise removal and the auxiliary task of noise estimation to improve the despeckling effect while preserving texture details. The main branch of HTNet is composed of a feature extraction block and an improved U-Net that can extract multi-scale information, which is mainly used for speckle noise removal. The noise estimation auxiliary branch is designed to fit the speckle noise. A hybrid truncation loss function is to applied for robust estimation of heavy-tailed distribution instead of mean squared error. Extensive experimental results show that HTNet can effectively remove speckle noise and outperforms the state-of-the-art methods on both simulated and real SAR images. In addition, HTNet has advantages on textured images.
Patients Arms Segmentation and Gesture Identification Using Standalone 3-D LiDAR Sensors

The intelligent and autonomous learning of patients' activities will lead to an incredible progression toward future smart e-health systems. With the recent advances in artificial intelligence, signal processing, and computational capabilities; light detection and ranging (LiDAR) technology can play a significant role in enhancing the current patients' activity recognition (PAR) systems. In this letter, we propose confidential and accurate patient arms behavior monitoring using a standalone 3-D LiDAR sensor. Due to the unavailability of LiDAR data, we use a computer-programmed 3-D simulator to generate virtual-LiDAR (VLiDAR) 3-D point cloud data that simulates real patient movements. These virtual data are used to train a multilayer-perception (MLP) model to segment the data points of the patient's body into arms versus not arms. We further propose a subsegmentation technique to segment patient's arms point cloud data into upper or lower arms. Finally, we demonstrate uses of arms gesture identification using the proposed scheme. The numerical results show that the proposed MLP model achieves a test accuracy of 90.8% and a cross-validation accuracy of 87.4%.
Deep Learning-Based Phase Unwrapping Method

A phase unwrapping method based on spatial and channel attention network is proposed to retrieve true phases from interferograms with various levels of noise.First, we propose a network that is suitable for unwrapping wrapped phase images.This network utilizes Deeplabv3+ as the backbone, adopts a serial-parallel atrous spatial pyramid pooling module, implements multi-scale skip connections between the encoder-decoder models, and fuses a convolutional block attention module.Second, datasets with different noise levels are used to train the network employing an existing noise level evaluation system, and the trained networks effectively handle the phase unwrapping for interferograms.Finally, the interferograms are unwrapped by the networks with the same noise level as the interferograms.The experimental results of phase unwrapping for interferograms fully verify the performance of this method.
Low-Altitude Slow Small Target Threat Assessment Algorithm by Exploiting Sequential Multifeature With Long Short-Term Memory

For the preservation of low-airspace security, a comprehensive threat assessment of low-altitude slow small (LSS) flying targets is essential. Threat assessment intrinsically depends on effectively extracting the optimal attributes from tracks, which represent the target’s motion intention and specific characteristics. However, unavoidable measured noise and track jitters during observation impact assessment performance. To address this problem, long short-term memory (LSTM) is introduced due to its good performance on sequential perception. LSTM captures the permanent intent traits and reduces noise interference utilizing temporal correlations within its novel “memory” units. Compared to traditional algorithms, the proposed technique substantially reduces measurement noise, enhancing the accuracy of threat evaluations. Simulation experiments confirm the effectiveness and robustness of the proposed algorithms.
Rating Proportion-Aware Binomial Matrix Factorization for Collaborative Filtering

Addressing biases in observed data is a major challenge in statistical and machine learning applications. This challenge also exists in recommendation systems, and various methods based on causal inference are being investigated. We investigate a collaborative filtering technique that robustly predicts ratings from biased observation. Utilizing the proportion of unbiased ratings in the different data sources, we extend collaborative filtering by adding a term for the proportion of ratings estimates from collaborative filtering to be closer to the proportion of ratings in unbiased data. Our aim is to obtain less biased estimates from observations that include bias. The proposed method is based on collaborative filtering with binomial matrix factorization, which treats observations and predictions as discrete variables. By treating the proportion of ratings in the unbiased case as a probability distribution, we introduce a constraint term that minimizes the KL divergence with the estimates by collaborative filtering. The binomial matrix factorization allows for direct calculation of the KL term due to the discrete assumption. The simple extension by adding the constraint term can be straightforwardly combined with various existing methods, such as inverse propensity weighting matrix factorization. Experimental results show that the standalone proposed method improves MSE from the conventional method, and the proposed method combined with inverse propensity score weighting also still improves slightly.
An Efficient CNN Accelerator Achieving High PE Utilization Using a Dense-/Sparse-Aware Redundancy Reduction Method and Data–Index Decoupling Workflow

To adapt to complex scenes and strict accuracy requirements, evolutions have unstoppably occurred in current convolutional neural networks (CNNs). However, these evolutions bring changes to filter size, convolution type, and sparsity, and such diversity leads to difficulties when adopting evolving CNNs in field-programmable gate array (FPGA)-based accelerators. This article proposes a dense-/sparse-aware CNN accelerator to achieve high PE utilization and configurability. First, a filter-based decomposition and clustering algorithm (FDCA) is proposed to change the various-sized filters into unified size filters. In addition, a sparse-aware filter transformation scheme (SFTS) is presented to dynamically eliminate invalid weights for sparse filters and accelerate dense filters. Based on the elimination of sparsity dependency, a hardware accelerator with a data–index decoupling workflow and an input channel schedule-distribution system is designed to take advantage of FDCA and SFTS. The proposed accelerator is implemented on a Xilinx ZCU102 platform at 300 MHz. With different CNN configurations, the digital signal processor (DSP) efficiencies for dense and unstructured sparse AlexNet and dense and structured sparse MobileNetV2 are 0.987, 2.025, 0.547, and 1.278 GOPS/DSP, respectively. Compared with previous dense- and sparse-based designs, the accelerator achieves up to a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.263\times $ </tex-math></inline-formula> speedup in DSP efficiency.
Implementation and Evaluation of Algorithms for Realizing Explainable Autonomous Robots

For autonomous robots to gain the trust of humans and maximize their abilities in society, they must be able to explain the reasons for their behavioral decisions. Defining explainable autonomous robots (XAR) as robots with such explanatory capabilities, we can summarize four requirements for their realization: 1) obtaining an interpretable decision space, 2) estimating the user’s world model, 3) extracting important information for conveying the policy in the robot, and 4) generating explanations based on explanatory factors. So far, these four elements have been studied independently. In this paper, we first implement an explanatory algorithm that integrates these four elements. Then, we evaluate the implemented explanatory algorithm by conducting a large-scale subject experiment. The implemented explanation algorithm is shown to generate human-acceptable explanations; the results provide many insights and suggestions for future research on XAR.
DANS: Deep Attention Network for Single Image Super-Resolution

The current advancements in image super-resolution have explored different attention mechanisms to achieve better quantitative and perceptual results.The critical challenge recently is to utilize the potential of attention mechanisms to reconstruct high-resolution images from their low-resolution counterparts.This research proposes a novel method that combines inception blocks, non-local sparse attention, and a U-Net network architecture.The network incorporates the non-local sparse attention on the backbone of symmetric encoder-decoder U-Net structure, which helps to identify long-range dependencies and exploits contextual information while preserving global context.By incorporating skip connections, the network can leverage features at different scales, enhancing the reconstruction of high-frequency information.Additionally, we introduce inception blocks allowing the model to capture information at various levels of abstraction to enhance multi-scale representation learning further.Experimental findings show that our suggested approach produces superior quantitative measurements, such as peak signal-tonoise ratio (PSNR), structural similarity index (SSIM), visual information fidelity (VIF), and visually appealing high-resolution image reconstructions.