Reviewerly/exHarmony huggingface dataset:
A comprehensive review of moth-flame optimisation: variants, hybrids, and applications

Moth-flame Optimisation Algorithm (MFO) is a new metaheuristics optimisation algorithm presented by Mirjalili in 2015 which inspired by the navigation method of moths in nature. It has gained a huge interest due to its impressive characteristics mainly: no derivation information needed in the starting phase, few numbers of parameters, simple in implementation, scalable and flexible. Till now, different variants to solve various optimisation problems such as binary, real(continuous), constraint, single-objective, multi-objective, and multimodal MFO has been introduced. Many research papers have been presented and summarised. In this review, a general overview of MFO is presented at first. Then, different variants of MFO are described which are classified into three classes: modified, hybridised, and multi-objective. Furthermore, applications of MFO in Engineering, Computer Science, Wireless Sensor Networks, and other fields are discussed. Finally, many possible and future directions are provided.
Reachability Analysis of Networked Finite State Machine With Communication Losses: A Switched Perspective

Networked finite state machine takes into account communication losses in industrial communication interfaces due to the limited bandwidth. The reachability analysis of networked finite state machine is a fundamental and important research topic in blocking detection, safety analysis, communication system design and so on. This paper is concerned with the impact of arbitrary communication losses on the reachability of networked finite state machine from a switched perspective. First, to model the dynamics under arbitrary communication losses in communication interfaces (from the controller to the actuator), by resorting to the semi-tensor product (STP) of matrices, a switched algebraic model of networked finite state machine with arbitrary communication losses is proposed, and the reachability analysis can be investigated by using the constructed model under arbitrary switching signal. Subsequently, based on the algebraic expression and its transition matrix, necessary and sufficient conditions for the reachability are derived for networked finite state machine. Finally, some typical numerical examples are exploited to demonstrate the effectiveness of the proposed approach. Note that current results provide valuable clues to design reliable and convergent Internet-of-Things networks.
Efficient In-Loop Filtering Based on Enhanced Deep Convolutional Neural Networks for HEVC

The raw video data can be compressed much by the latest video coding standard, high efficiency video coding (HEVC). However, the block-based hybrid coding used in HEVC will incur lots of artifacts in compressed videos, the video quality will be severely influenced. To settle this problem, the in-loop filtering is used in HEVC to eliminate artifacts. Inspired by the success of deep learning, we propose an efficient in-loop filtering algorithm based on the enhanced deep convolutional neural networks (EDCNN) for significantly improving the performance of in-loop filtering in HEVC. Firstly, the problems of traditional convolutional neural networks models, including the normalization method, network learning ability, and loss function, are analyzed. Then, based on the statistical analyses, the EDCNN is proposed for efficiently eliminating the artifacts, which adopts three solutions, including a weighted normalization method, a feature information fusion block, and a precise loss function. Finally, the PSNR enhancement, PSNR smoothness, RD performance, subjective test, and computational complexity/GPU memory consumption are employed as the evaluation criteria, and experimental results show that when compared with the filter in HM16.9, the proposed in-loop filtering algorithm achieves an average of 6.45% BDBR reduction and 0.238 dB BDPSNR gains.
Improving Sequence-To-Sequence Speech Recognition Training with On-The-Fly Data Augmentation

Sequence-to-Sequence (S2S) models recently started to show state-of-the-art performance for automatic speech recognition (ASR). With these large and deep models overfitting remains the largest problem, outweighing performance improvements that can be obtained from better architectures. One solution to the overfitting problem is increasing the amount of available training data and the variety exhibited by the training data with the help of data augmentation. In this paper we examine the influence of three data augmentation methods on the performance of two S2S model architectures. One of the data augmentation method comes from literature, while two other methods are our own development - a time perturbation in the frequency domain and sub-sequence sampling. Our experiments on Switchboard and Fisher data show state-of-the-art performance for S2S models that are trained solely on the speech training data and do not use additional text data.
Deep Underwater Image Restoration and Beyond

Underwater image restoration is a challenging problem due to the multiple distortions. Degradation in the information is mainly due to the 1) light scattering effect 2) wavelength dependent color attenuation and 3) object blurriness effect. In this letter, we propose a novel end-to-end deep network for underwater image restoration. The proposed network is divided into two parts viz. channel-wise color feature extraction module and dense-residual feature extraction module. A custom loss function is proposed, which preserves the structural details and generates the true edge information in the restored underwater scene. Also, to train the proposed network for underwater image enhancement, a new synthetic underwater image database is proposed. Existing synthetic underwater database images are characterized by light scattering and color attenuation distortions. However, object blurriness effect is ignored. We, on the other hand, introduced the blurring effect along with the light scattering and color attenuation distortions. The proposed network is validated for underwater image restoration task on real-world underwater images. Experimental analysis shows that the proposed network is superior than the existing state-of-the-art approaches for underwater image restoration.
The (non-)application of blockchain technology in the Greek shipping industry

The implementation of blockchain technology (BCT) is gaining traction in supply chain networks, revolutionising the operation of contemporary supply chains and reshaping the potential of business relationships. Empirical studies on blockchain adoption are scant because implementation across networks is in fairly early phase of development, yet evidence from empirical studies is highly desirable. This is one of the first studies of blockchain adoption in the Greek shipping industry, which has not so far been examined by the literature, in direct comparison to early adopters in other European countries such as Norway. The research examined eight Greek shipping companies using workshops with experienced supply chain personnel. Qualitative analysis identified the current position of these organisations in terms of blockchain adoption, by considering possible benefits and inhibitors to implementation. Despite benefits of automated processes and reduced paperwork as a result of smart contracts, findings show a reluctance to adopt BCT. That is, enterprise resource planning (ERP) transformations have left organisations fatigued and disinclined towards further systems development and resistant to subsequent change. Also, the exposure of shared information in the shipping nexus is considered to cause a threat to competitive survival.
Toward Communication-Efficient Federated Learning in the Internet of Things With Edge Computing

Federated learning is an emerging concept that trains the machine learning models with the local distributed data sets, without sending the raw data to the data center. But, in the Internet of Things (IoT) where the wireless network resource is constrained, the key problem of federated learning is the communication overhead for parameter synchronization, which wastes bandwidth, increases training time, and even impacts the model accuracy. Gradient sparsification has received increasing attention, which only updates significant gradients and accumulates insignificant gradients locally. However, how to preserve the accuracy after a high ratio sparsification has been ignored in the literature. In this article, a general gradient sparsification (GGS) framework is proposed for adaptive optimizers, to correct the sparse gradient update process. It consists of two important mechanisms: 1) gradient correction and 2) batch normalization (BN) update with local gradients. With gradient correction, the optimizer can properly treat the accumulated insignificant gradients, which makes the model converge better. Furthermore, updating the BN layer with local gradients can relieve the impact of delayed gradients without increasing the communication overhead. We have conducted experiments on LeNet-5, CifarNet, DenseNet-121, and AlexNet with adaptive optimizers. Results show that when 99.9% gradients are sparsified, validation data sets are maintained with top-1 accuracy.
IMD2020: A Large-Scale Annotated Dataset Tailored for Detecting Manipulated Images

Witnessing impressive results of deep nets in a number of computer vision problems, the image forensic community has begun to utilize them in the challenging domain of detecting manipulated visual content. One of the obstacles to replicate the success of deep nets here is absence of diverse datasets tailored for training and testing of image forensic methods. Such datasets need to be designed to capture wide and complex types of systematic noise and intrinsic artifacts of images in order to avoid overfitting of learning methods to just a narrow set of camera types or types of manipulations. These artifacts are brought into visual content by various components of the image acquisition process as well as the manipulating process.In this paper, we introduce two novel datasets. First, we identified the majority of camera brands and models on the market, which resulted in 2,322 camera models. Then, we collected a dataset of 35,000 real images captured by these camera models. Moreover, we also created the same number of digitally manipulated images by using a large variety of core image manipulation methods as well we advanced ones such as GAN or Inpainting resulting in a dataset of 70,000 images. In addition to this dataset, we also created a dataset of 2,000 "real-life" (uncontrolled) manipulated images. They are made by unknown people and downloaded from Internet. The real versions of these images also have been found and are provided. We also manually created binary masks localizing the exact manipulated areas of these images. Both datasets are publicly available for the research community at http://staff.utia.cas.cz/novozada/db.
Highly efficient federated learning with strong privacy preservation in cloud computing

Federated learning is a new machine learning framework that allows mutually distrusting clients to reap the benefits from the joint training model without explicitly disclosing their private datasets. However, the high communication cost between the cloud server and clients has become the main challenge due to the limited network bandwidth. Moreover, the model parameters it shares may be utilized to perform model inversion attacks. Aimed at these problems, a new scheme for highly efficient federated learning with strong privacy preservation in cloud computing is presented. We design a lightweight encryption protocol to provide provably privacy preservation while maintaining desirable model utility. Additionally, an efficient optimization strategy is employed to enhance the training efficiency. Under the defined threat model, we prove the proposed scheme is secure against the honest-but-curious server and extreme collusion. We evaluate the effectiveness of our scheme and compare it with existing related works on MNIST and UCI Human Activity Recognition Dataset. Results show that our scheme reduces the execution time by 20% and transmitted ciphertext size by 85% on average while achieving similar accuracy as the compared secure multiparty computation (SMC) based methods.
Sara, the Lecturer: Improving Learning in Online Education with a Scaffolding-Based Conversational Agent

Enrollment in online courses has sharply increased in higher education. Although online education can be scaled to large audiences, the lack of interaction between educators and learners is difficult to replace and remains a primary challenge in the field. Conversational agents may alleviate this problem by engaging in natural interaction and by scaffolding learners' understanding similarly to educators. However, whether this approach can also be used to enrich online video lectures has largely remained unknown. We developed Sara, a conversational agent that appears during an online video lecture. She provides scaffolds by voice and text when needed and includes a voice-based input mode. An evaluation with 182 learners in a 2 x 2 lab experiment demonstrated that Sara, compared to more traditional conversational agents, significantly improved learning in a programming task. This study highlights the importance of including scaffolding and voice-based conversational agents in online videos to improve meaningful learning.
Improved Salp Swarm Algorithm Based on Levy Flight and Sine Cosine Operator

The salp swarm algorithm (SSA) is a swarm intelligence optimization algorithm that simulates the chain movement behavior of salp populations in the sea.Aiming at the shortcomings of the SSA, such as low precision, low optimization dimension and slow convergence speed, an improved salp swarm algorithm based on Levy flight and sine cosine operator (LSC-SSA) was proposed.The Levy flight mechanism uses the route of short walks combined with long jumps to search the solution space, which can effectively improve the global exploration capability of the algorithm.Improved sine cosine operator use sine search for global exploration and cosine search for local exploitation.At the same time, an adaptively switching between the two function search methods can achieve a smooth transition between global exploration and local exploitation.In the simulation experiment, salp swarm algorithm (SSA), whale optimization algorithm (WOA), particle swarm algorithm (PSO), sine cosine algorithm (SCA), firefly algorithm (FA) and LSC-SSA were adopted for solving function optimization problems.Then, the feasibility of the improved algorithm for solving high-dimensional large-scale optimization problems and the effectiveness of the improvement strategy are evaluated.Finally, LSC-SSA was applied to train muti-layer perceptron neural network.Simulation results show that the introduction of Levy flight and improved sine cosine operator in LSC-SSA significantly improves optimization accuracy and convergence speed compared with other swarm optimization algorithms.In addition, the improved algorithm can effectively solve high-dimensional largescale optimization problems.In the application of training muti-layer perceptron NN, the improved algorithm can avoid falling into the local optimal value and obtain the ideal classification accuracy.
Binary coyote optimization algorithm for feature selection

The Coyote Optimization Algorithm (COA) is a bio-inspired optimization algorithm based on the intelligent behavior of coyotes. COA was proposed recently and it considers the social organization of the coyotes and its adaptation to the environment in order to solve continuous optimization problems. In addition, it is a population-based algorithm and it can be classified as both, swarm intelligence and evolutionary heuristics, because contributes with a different algorithmic structure. This paper proposes a binary version of the COA, named Binary COA (BCOA) applying to select the optimal feature subset for classification, based on the hyperbolic transfer function in a wrapper model. By this way, the features are selected based on the performance evaluation of a classification algorithm. We tested the effectiveness of the BCOA wrapper with the Naïve Bayes classifier and were used seven public domain benchmark datasets to compare the proposed approach in terms of classification accuracy, number of selected features and computational cost with other state-of-art algorithms of the literature. The results shown that BCOA was able to find subsets with few features while it still performs well in terms of classification accuracy.
Unsupervised Domain Adaptation With Hierarchical Gradient Synchronization

Domain adaptation attempts to boost the performance on a target domain by borrowing knowledge from a well established source domain. To handle the distribution gap between two domains, the prominent approaches endeavor to extract domain-invariant features. It is known that after a perfect domain alignment the domain-invariant representations of two domains should share the same characteristics from perspective of the overview and also any local piece. Inspired by this, we propose a novel method called Hierarchical Gradient Synchronization to model the synchronization relationship among the local distribution pieces and global distribution, aiming for more precise domain-invariant features. Specifically, the hierarchical domain alignments including class-wise alignment, group-wise alignment and global alignment are first constructed. Then, these three types of alignment are constrained to be consistent to ensure better structure preservation. As a result, the obtained features are domain invariant and intrinsically structure preserved. As evaluated on extensive domain adaptation tasks, our proposed method achieves state-of-the-art classification performance on both vanilla unsupervised domain adaptation and partial domain adaptation.
Fashion Outfit Complementary Item Retrieval

Complementary fashion item recommendation is critical for fashion outfit completion. Existing methods mainly focus on outfit compatibility prediction but not in a retrieval setting. We propose a new framework for outfit complementary item retrieval. Specifically, a category-based subspace attention network is presented, which is a scalable approach for learning the subspace attentions. In addition, we introduce an outfit ranking loss that better models the item relationships of an entire outfit. We evaluate our method on the outfit compatibility, FITB and new retrieval tasks. Experimental results demonstrate that our approach outperforms state-of-the-art methods in both compatibility prediction and complementary item retrieval.
Solving Mixed-Modal Jigsaw Puzzle for Fine-Grained Sketch-Based Image Retrieval

ImageNet pre-training has long been considered crucial by the fine-grained sketch-based image retrieval (FG-SBIR) community due to the lack of large sketch-photo paired datasets for FG-SBIR training. In this paper, we propose a self-supervised alternative for representation pre-training. Specifically, we consider the jigsaw puzzle game of recomposing images from shuffled parts. We identify two key facets of jigsaw task design that are required for effective FG-SBIR pre-training. The first is formulating the puzzle in a mixed-modality fashion. Second we show that framing the optimisation as permutation matrix inference via Sinkhorn iterations is more effective than the common classifier formulation of Jigsaw self-supervision. Experiments show that this self-supervised pre-training strategy significantly outperforms the standard ImageNet-based pipeline across all four product-level FG-SBIR benchmarks. Interestingly it also leads to improved cross-category generalisation across both pre-train/fine-tune and fine-tune/testing stages.
There and Back Again: Revisiting Backpropagation Saliency Methods

Saliency methods seek to explain the predictions of a model by producing an importance map across each input sample. A popular class of such methods is based on backpropagating a signal and analyzing the resulting gradient. Despite much research on such methods, relatively little work has been done to clarify the differences between such methods as well as the desiderata of these techniques. Thus, there is a need for rigorously understanding the relationships between different methods as well as their failure modes. In this work, we conduct a thorough analysis of backpropagation-based saliency methods and propose a single framework under which several such methods can be unified. As a result of our study, we make three additional contributions. First, we use our framework to propose NormGrad, a novel saliency method based on the spatial contribution of gradients of convolutional weights. Second, we combine saliency maps at different layers to test the ability of saliency methods to extract complementary information at different network levels (e.g.~trading off spatial resolution and distinctiveness) and we explain why some methods fail at specific layers (e.g., Grad-CAM anywhere besides the last convolutional layer). Third, we introduce a class-sensitivity metric and a meta-learning inspired paradigm applicable to any saliency method for improving sensitivity to the output class being explained.
Efficient Adversarial Training With Transferable Adversarial Examples

Adversarial training is an effective defense method to protect classification models against adversarial attacks. However, one limitation of this approach is that it can require orders of magnitude additional training time due to high cost of generating strong adversarial examples during training. In this paper, we first show that there is high transferability between models from neighboring epochs in the same training process, i.e., adversarial examples from one epoch continue to be adversarial in subsequent epochs. Leveraging this property, we propose a novel method, Adversarial Training with Transferable Adversarial Examples (ATTA), that can enhance the robustness of trained models and greatly improve the training efficiency by accumulating adversarial perturbations through epochs. Compared to state-of-the-art adversarial training methods, ATTA enhances adversarial accuracy by up to 7.2% on CIFAR10 and requires 12~14x less training time on MNIST and CIFAR10 datasets with comparable model robustness.
Boosting Semantic Human Matting With Coarse Annotations

Semantic human matting aims to estimate the per-pixel opacity of the foreground human regions. It is quite challenging that usually requires user interactive trimaps and plenty of high quality annotated data. Annotating such kind of data is labor intensive and requires great skills beyond normal users, especially considering the very detailed hair part of humans. In contrast, coarse annotated human dataset is much easier to acquire and collect from the public dataset. In this paper, we propose to leverage coarse annotated data coupled with fine annotated data to boost end-to-end semantic human matting without trimaps as extra input. Specifically, We train a mask prediction network to estimate the coarse semantic mask using the hybrid data, and then propose a quality unification network to unify the quality of the previous coarse mask outputs. A matting refinement network takes the unified mask and the input image to predict the final alpha matte. The collected coarse annotated dataset enriches our dataset significantly, allows generating high quality alpha matte for real images. Experimental results show that the proposed method performs comparably against state-of-the-art methods. Moreover, the proposed method can be used for refining coarse annotated public dataset, as well as semantic segmentation methods, which reduces the cost of annotating high quality human data to a great extent.