Reviewerly/exHarmony huggingface dataset:
Fuzzy graph clustering

Spectral clustering is a group of graph-based clustering methods in which the columns of the scaled cluster indicator matrix can be obtained by stacking the eigenvectors of the Laplacian matrix corresponding to the top c smallest eigenvalues ( c is the number of clusters). This leads to the possible existence of negative values in the scaled indicator matrix and therefore a post-processing step such as K means clustering or spectral rotation is necessary to get the discrete cluster assignments. Moreover, such obtained results lack of the interpretability for data points in the boundary area of multiple clusters. To simultaneously address both limitations, we propose a two-stage clustering model, termed FGC (fuzzy graph clustering) in this paper. In FGC, we first construct a doubly stochastic graph affinity matrix which is then approximated by the scaled product of the fuzzy cluster indicator matrices. The newly designed fuzzy cluster indicator matrix has two desirable properties of non-negativity and row normalization, which can bring us two benefits. On one hand, we can directly get the cluster assignment of a certain data point by checking the largest value in the corresponding row of the fuzzy cluster indicator matrix; and on the other hand, we can obtain the membership of each data point to different clusters. An iterative method under the alternative optimization framework is proposed to solve the objective function of FGC. We conduct data clustering experiments on both synthetic and benchmark data sets and the results demonstrate the effectiveness of our proposed FGC model.
Mitigating Gender Bias in Captioning Systems

Image captioning has made substantial progress with huge supporting image collections sourced from the web. However, recent studies have pointed out that captioning datasets, such as COCO, contain gender bias found in web corpora. As a result, learning models could heavily rely on the learned priors and image context for gender identification, leading to incorrect or even offensive errors. To encourage models to learn correct gender features, we reorganize the COCO dataset and present two new splits COCO-GB V1 and V2 datasets where the train and test sets have different gender-context joint distribution. Models relying on contextual cues will suffer from huge gender prediction errors on the anti-stereotypical test data. Benchmarking experiments reveal that most captioning models learn gender bias, leading to high gender prediction errors, especially for women. To alleviate the unwanted bias, we propose a new Guided Attention Image Captioning model (GAIC) which provides self-guidance on visual attention to encourage the model to capture correct gender visual evidence. Experimental results validate that GAIC can significantly reduce gender prediction errors with a competitive caption quality. Our codes and the designed benchmark datasets are available at https://github.com/datamllab/Mitigating_Gender_Bias_In_Captioning_System.
Generalizable No-Reference Image Quality Assessment via Deep Meta-Learning

Recently, researchers have shown great interest in using convolutional neural networks (CNNs) for no-reference image quality assessment (NR-IQA). Due to the lack of big training data, the efforts of existing metrics in optimizing CNN-based NR-IQA models remain limited. Furthermore, the diversity of distortions in images result in the generalization problem of NR-IQA models when trained with known distortions and tested on unseen distortions, which is an easy task for human. Hence, we propose a NR-IQA metric via deep meta-learning, which is highly generalizable in the face of unseen distortions. The fundamental idea is to learn the meta-knowledge shared by human when evaluating the quality of images with diversified distortions. Specifically, we define NR-IQA of different distortions as a series of tasks and propose a task selection strategy to build two task sets, which are characterized by synthetic to synthetic and synthetic to authentic distortions, respectively. Based on these two task sets, an optimization-based meta-learning is proposed to learn the generalized NR-IQA model, which can be directly used to evaluate the quality of images with unseen distortions. Extensive experiments demonstrate that our NR-IQA metric outperforms the state-of-the-arts in terms of both evaluation performance and generalization ability.
Hardware-Software Contracts for Secure Speculation

Since the discovery of Spectre, a large number of hardware mechanisms for secure speculation has been proposed. Intuitively, more defensive mechanisms are less efficient but can securely execute a larger class of programs, while more permissive mechanisms may offer more performance but require more defensive programming. Unfortunately, there are no hardware-software contracts that would turn this intuition into a basis for principled co-design.In this paper, we put forward a framework for specifying such contracts, and we demonstrate its expressiveness and flexibility.On the hardware side, we use the framework to provide the first formalization and comparison of the security guarantees provided by a representative class of mechanisms for secure speculation.On the software side, we use the framework to characterize program properties that guarantee secure co-design in two scenarios traditionally investigated in isolation: (1) ensuring that a benign program does not leak information while computing on confidential data, and (2) ensuring that a potentially malicious program cannot read outside of its designated sandbox. Finally, we show how the properties corresponding to both scenarios can be checked based on existing tools for software verification, and we use them to validate our findings on executable code.
A modified adaptive formation of UAV swarm by pigeon flock behavior within local visual field

UAV swarm track planning is crucial to ensure the smooth completion of the complex assigned tasks. UAV formation is of great significance to UAV swarm track planning. At present, UAV formation control methods have the defects of only set one formation type in the whole process, and poor adaptability in complex environment. This paper incorporates the formation decision function into the UAV control mode of virtual point formation, and classifies it at the expected angle based on the track planning model of pigeon swarm behavior, and puts forward a hierarchical early warning mechanism. In simulation experiment the method proposed in this paper can make UAV adjust its formation according to the environment, and avoid collisions between UAVs and various obstacles. In the adaptability of the algorithm, we fully consider the addition and deletions of UAV, static and dynamic obstacles, the interference of wind phenomenon and the diversity of tasks in the actual environment. The method proposed in this paper has made a beneficial attempt in the field of UAV control and expanded the application of UAV swarm.
MultiLevel Composite Stochastic Optimization via Nested Variance Reduction

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 4 September 2019Accepted: 29 January 2021Published online: 13 April 2021Keywordscomposite stochastic optimization, proximal gradient method, variance reductionAMS Subject Headings68Q25, 68W20, 90C15, 90C26, 90C30Publication DataISSN (print): 1052-6234ISSN (online): 1095-7189Publisher: Society for Industrial and Applied MathematicsCODEN: sjope8
Practical quantum-enhanced receivers for classical communication

Communication is an integral part of human life. Today, optical pulses are the preferred information carriers for long-distance communication. The exponential growth in data leads to a “capacity crunch” in the underlying physical systems. One of the possible methods to deter the exponential growth of physical resources for communication is to use quantum, rather than classical measurement at the receiver. Quantum measurement improves the energy efficiency of optical communication protocols by enabling discrimination of optical coherent states with the discrimination error rate below the shot-noise limit. In this review article, the authors focus on quantum receivers that can be practically implemented at the current state of technology, first and foremost displacement-based receivers. The authors present the experimentalist view on the progress in quantum-enhanced receivers and discuss their potential.
A self-adaptive strategy based firefly algorithm for constrained engineering design problems

Firefly algorithm (FA) is an effective meta-heuristic method, which has drawn extensive attentions and inspired many variants. However, it is difficult to balance the relation of the exploration and exploitation of FA. To this end, FA with self-adaptive strategy (SAFA) is proposed to improve the performance of the algorithm for constrained engineering design problems. Firstly, the characteristics of the standard FA are analyzed, and the essence of FA easily trapped into the local optima is revealed by theory analysis and case study. Secondly, a self-adaptive strategy for attraction model is proposed to improve the exploitation ability, and another self-adaptive strategy for stochastic model is developed to ensure a better balance between the exploration and exploitation. Thirdly, a self-adaptive penalty function is presented to treat constraint conditions. Then, the initial parameter setting is investigated, and the proper initial parameter value corresponding to the optimal performance of SAFA is obtained. And then, SAFA and other FA variants are used to optimize a set of classical and Congress on Evolutionary Computation (CEC) 2015 benchmark functions. Meanwhile, the contributions of self-adaptive strategies for attraction model and stochastic model are investigated with experimental analysis, respectively Finally, SAFA and other meta-heuristic methods are employed to solve constrained engineering design problems. The effects of SAFA with standard and self-adaptive penalty function are compared. Numerical experimental results show that self-adaptive strategies for attraction model and stochastic model improve the performance of FA, and SAFA obtains the best solutions on most of the benchmark functions. In addition, SAFA has the advantage in solving problems with different kind of dimensions, and maintains reasonable population diversity throughout the iteration compared with the other FA variants. Meanwhile, SAFA can reduce the computational complexity and ensure the solution accuracy in solving constrained optimization problems.
Joint Luminance and Chrominance Learning for Underwater Image Enhancement

Recently, learning-based works have been widely-investigated to enhance underwater images. However, interactions between various degradation factors (e.g., color distortion and haze effects) inevitably cause negative interference during the inference phase. Thus, these works cannot fully remove degraded factors. To address this problem, we propose a novel Joint Luminance and Chrominance Learning Network (JLCL-Net). Concretely, we reformulate the task as luminance reconstruction (for haze removal), and chrominance correction (for color correction) sub-tasks by separating the luminance and chrominance (i.e., color appearance) of the underwater images. In this way, we successfully realize the disentanglement in degraded factors to avoid introducing interference. We specify the reconstruction by integrating the atmospheric scattering model, which endows the adaptive dehazing ability over different scenarios. The correction learns to compensate for color by a simple network to reverse the color attenuation process. To this end, we obtain our JLCL-Net. To better train it, we design a new multi-stage cross-space training strategy, which progressively updates the network parameters to enlarge the network potentiality. Extensive evaluations are presented to fully verify our superiority against other methods.
Sequential Recommendation for Cold-start Users with Meta Transitional Learning

A fundamental challenge for sequential recommenders is to capture the sequential patterns of users toward modeling how users transit among items. In many practical scenarios, however, there are a great number of cold-start users with only minimal logged interactions. As a result, existing sequential recommendation models will lose their predictive power due to the difficulties in learning sequential patterns over users with only limited interactions. In this work, we aim to improve sequential recommendation for cold-start users with a novel framework named MetaTL, which learns to model the transition patterns of users through meta-learning. Specifically, the proposed MetaTL: (i) formulates sequential recommendation for cold-start users as a few-shot learning problem; (ii) extracts the dynamic transition patterns among users with a translation-based architecture; and (iii) adopts meta transitional learning to enable fast learning for cold-start users with only limited interactions, leading to accurate inference of sequential interactions.
Image denoising based on nonconvex anisotropic total-variation regularization

Image denoising models based on the total variation (TV) regularization have been used in many fields of image processing. The main advantage of the TV regularization can preserve the edge efficiently when restoring the degraded image. However, the main flaw can not describe the local feature due to the same weight for the gradient subvariable in the TV term. To this end, we propose a nonconvex anisotropic total variation (NCATV)-based image denoising model. In the proposed model, the weighted matrix depends on the restored image, so we can expect that it can describe local features to be more robust. Due to the nonconvexity of the proposed model, first we need to use the successive replacement scheme to decouple with the weighted matrix from the TV term. With this operation, the proposed model is transformed into the nonsmooth and convex optimization problem. Then we can employ the alternating direction method of multipliers (ADMM) to transform this problem into several easily solvable subproblems. Numerical experiments show that our proposed model yields an improvement in performance both visually and quantitatively compared with some state-of-the-art methods.
A New Two-Stage Algorithm for Solving Optimization Problems

Optimization seeks to find inputs for an objective function that result in a maximum or minimum. Optimization methods are divided into exact and approximate (algorithms). Several optimization algorithms imitate natural phenomena, laws of physics, and behavior of living organisms. Optimization based on algorithms is the challenge that underlies machine learning, from logistic regression to training neural networks for artificial intelligence. In this paper, a new algorithm called two-stage optimization (TSO) is proposed. The TSO algorithm updates population members in two steps at each iteration. For this purpose, a group of good population members is selected and then two members of this group are randomly used to update the position of each of them. This update is based on the first selected good member at the first stage, and on the second selected good member at the second stage. We describe the stages of the TSO algorithm and model them mathematically. Performance of the TSO algorithm is evaluated for twenty-three standard objective functions. In order to compare the optimization results of the TSO algorithm, eight other competing algorithms are considered, including genetic, gravitational search, grey wolf, marine predators, particle swarm, teaching-learning-based, tunicate swarm, and whale approaches. The numerical results show that the new algorithm is superior and more competitive in solving optimization problems when compared with other algorithms.
Aircraft Fleet Health Monitoring with Anomaly Detection Techniques

Predictive maintenance has received considerable attention in the aviation industry where costs, system availability and reliability are major concerns. In spite of recent advances, effective health monitoring and prognostics for the scheduling of condition-based maintenance operations is still very challenging. The increasing availability of maintenance and operational data along with recent progress made in machine learning has boosted the development of data-driven prognostics and health management (PHM) models. In this paper, we describe the data workflow in place at an airline for the maintenance of an aircraft system and highlight the difficulties related to a proper labelling of the health status of such systems, resulting in a poor suitability of supervised learning techniques. We focus on investigating the feasibility and the potential of semi-supervised anomaly detection methods for the health monitoring of a real aircraft system. Proposed methods are evaluated on large volumes of real sensor data from a cooling unit system on a modern wide body aircraft from a major European airline. For the sake of confidentiality, data has been anonymized and only few technical and operational details about the system had been made available. We trained several deep neural network autoencoder architectures on nominal data and used the anomaly scores to calculate a health indicator. Results suggest that high anomaly scores are correlated with identified failures in the maintenance logs. Also, some situations see an increase in the anomaly score for several flights prior to the system’s failure, which paves a natural way for early fault identification.
Low Area PRESENT Cryptography in FPGA Using TRNG-PRNG Key Generation

Lightweight Cryptography (LWC) is widely used to provide integrity, secrecy and authentication for the sensitive applications. However, the LWC is vulnerable to various constraints such as high-power consumption, time consump... | Find, read and cite all the research you need on Tech Science Press
A Novel Focal Phi Loss for Power Line Segmentation with Auxiliary Classifier U-Net

The segmentation of power lines (PLs) from aerial images is a crucial task for the safe navigation of unmanned aerial vehicles (UAVs) operating at low altitudes. Despite the advances in deep learning-based approaches for PL segmentation, these models are still vulnerable to the class imbalance present in the data. The PLs occupy only a minimal portion (1-5%) of the aerial images as compared to the background region (95-99%). Generally, this class imbalance problem is addressed via the use of PL-specific detectors in conjunction with the popular class balanced cross entropy (BBCE) loss function. However, these PL-specific detectors do not work outside their application areas and a BBCE loss requires hyperparameter tuning for class-wise weights, which is not trivial. Moreover, the BBCE loss results in low dice scores and precision values and thus, fails to achieve an optimal trade-off between dice scores, model accuracy, and precision-recall values. In this work, we propose a generalized focal loss function based on the Matthews correlation coefficient (MCC) or the Phi coefficient to address the class imbalance problem in PL segmentation while utilizing a generic deep segmentation architecture. We evaluate our loss function by improving the vanilla U-Net model with an additional convolutional auxiliary classifier head (ACU-Net) for better learning and faster model convergence. The evaluation of two PL datasets, namely the Mendeley Power Line Dataset and the Power Line Dataset of Urban Scenes (PLDU), where PLs occupy around 1% and 2% of the aerial images area, respectively, reveal that our proposed loss function outperforms the popular BBCE loss by 16% in PL dice scores on both the datasets, 19% in precision and false detection rate (FDR) values for the Mendeley PL dataset and 15% in precision and FDR values for the PLDU with a minor degradation in the accuracy and recall values. Moreover, our proposed ACU-Net outperforms the baseline vanilla U-Net for the characteristic evaluation parameters in the range of 1-10% for both the PL datasets. Thus, our proposed loss function with ACU-Net achieves an optimal trade-off for the characteristic evaluation parameters without any bells and whistles. Our code is available at Github.
Redundant Entanglement Provisioning and Selection for Throughput Maximization in Quantum Networks

Quantum communication using qubits based on the principle of entangled photons is a promising solution to improve network security. However, it is difficult to successfully create an entanglement link or connection between two nodes, especially when they are far apart from each other. In addition, only one qubit can be exchanged over an established entanglement connection, resulting in a low throughput.In this paper, we propose Redundant Entanglement Pro-visioning and Selection (REPS) to maximize the throughput for multiple source-destination (SD) pairs in a circuit-switched, multi-hop quantum network. REPS has two distinct features: (i). It provisions backup resources for extra entanglement links between adjacent nodes for failure-tolerance; and (ii). It provides flexibility in selecting successfully created entanglement links to establish entanglement connections for the SD pairs to achieve network-wide optimization. Extensive analysis and simulations show that REPS can achieve optimal routing with a high probability, and improves the throughput by up to 68.35% over the highest-performing algorithms in existence. In addition, it also improves the fairness among the SD pairs in the networks.
Feature pyramid network for diffusion-based image inpainting detection

Inpainting is a technique that can be employed to tamper with the content of images. In this paper, we propose a novel forensics analysis method for diffusion-based image inpainting based on a feature pyramid network (FPN). Our method features an improved u-shaped net to migrate FPN for multi-scale inpainting feature extraction. In addition, a stagewise weighted cross-entropy loss function is designed to take advantage of both the general loss and the weighted loss to improve the prediction rate of inpainted regions of all sizes. The experimental results demonstrate that the proposed method outperforms several state-of-the-art methods, especially when the size of the inpainted region is small.
Performance Evaluation of Permissioned Blockchain Platforms

Blockchain is a technology for storing an immutable history of transactions in a decentralized platform by using cryptographic principles. Many industries have become interested in adopting blockchain within their IT systems. However, the accessibility, privacy, performance, and scalability aspects of different blockchain-based platforms are still legitimate concerns when designing an enterprise solution. Permissioned blockchain frameworks facilitate a way to immutably store confidential records. Numerous research studies have been carried out on the opportunities, challenges, application areas, and performance analysis of different public and permissioned blockchain-based platforms. However, the implication of blockchain in recent private enterprise solution requires detailed comparative analysis. This paper conducts a performance and scalability analysis of popular private blockchain platforms, including Ethereum (private deployment), Quorum, Corda, and Hyperledger Fabric. Each of these platforms is assessed by varying the workloads (no. of transactions and nodes) and determining the performance evaluation metrics such as throughput and network latency.