Reviewerly/exHarmony huggingface dataset:
FIAD net: a Fast SAR ship detection network based on feature integration attention and self-supervised learning

Synthetic Aperture Radar (SAR) Ship Detection (SSD) is an important application, and it has been widely used in commercial and military fields. With the development of remote sensing technology, the use of a large amount of increasing unlabeled data is a challenge for model training, and the interference caused by the reefs and the nearshore facilities is also a challenge for SAR ship detection. In this work, we propose a Feature Integration Attention Detection (FIAD) Network to detect ships in SAR images, namely the FIAD Net. The FIAD Net has two contributions: a ModiBYOL method based on self-supervised learning and a novel Feature Integration Attention (FIA) module. The FIA module could enhance the feature learning ability of the backbone network, and the ModiBYOL could utilize the unlabeled data as a source for backbone pre-training. Experiments on the OpenSARShip dataset, the SAR ship detection dataset (SSDD), and the SAR Ship dataset show that the proposed method achieves an accuracy comparable to the state-of-art methods with a speed of 47 Frames Per Second (FPS). Besides, the effectiveness of the proposed modules is studied through an ablation study.
Applications of blockchain for vaccine passport and challenges

Purpose The purpose of this study is to investigate the applications of blockchain in vaccine passport solution. The world is facing an unprecedented situation because of the COVID-19 pandemic. Many countries have witnessed sporadic lockdown and travel restrictions and it has marred trade and tourism. As the mass vaccination has started the life is slowly and steadily returning to true normal. Various countries are issuing vaccination passports to manage the immunization information and validate it. To realize vaccine-passport’s true potential, security and privacy concerns should be being taken care of. There is a need for studies to evaluate the emerging technology for the vaccine passport. Design/methodology/approach This study uses a mix of qualitative and quantitative methods to achieve its objective. This study uses a systematic literature review to analyze the potential of blockchain for vaccine passports. The case study of three different types of organizations implementing blockchain for vaccine passports was analyzed and results were presented. Last but not least, focus group discussion and search of secondary literature was to done to identify scientific, ethical and legal challenges associated with the use of vaccine passports. The method used for calculating the importance score of these challenges was analytical hierarchy process. Findings This study concludes that blockchain-based solutions are very suitable for vaccine passports and addresses the concern related to interoperability, privacy and security. The case study approach was used to elaborate the use of blockchain in three different options available for the vaccine. Last but not least, this study identifies the challenges faced by vaccine passport programs and suggests measures to overcome them. This study concludes that the ethical challenges associated with vaccine passports are more important and should be preferentially treated. Research limitations/implications This study is timely and will be he lpful for policymakers in designing the vaccine passport programs. It gives valuable insight to decision-makers evaluating technologies for the development of vaccine passport programs. This study identifies nine challenges to be tackled to making a vaccine passport program successful. Originality/value To the best of the author’s knowledge, this study is not able to find out a review on the use of blockchain technology for vaccine passports, and this study attempts to fill this gap. This study further discusses the cases of organizations that have implemented blockchain technology for vaccine passport programs.
GA3N: Generative adversarial AutoAugment network

Data augmentation is beneficial for improving robustness of deep meta-learning. However, data augmentation methods for the recent deep meta-learning are still based on photometric or geometric manipulations or combinations of images. This paper proposes a generative adversarial autoaugment network (GA3N) for enlarging the augmentation search space and improving classification accuracy. To achieve, we first extend the search space of image augmentation by using GANs. However, the main challenge is to generate images suitable for the task. For solution, we find the best policy by optimizing a target and GAN losses alternatively. We then use the manipulated and generated samples determined by the policy network as augmented samples for improving the target tasks. To show the effects of our method, we implement classification networks by combining our GA3N and evaluate them on CIFAR-100 and Tiny-ImageNet datasets. As a result, we achieve better accuracy than the recent AutoAugment methods on each dataset.
An Efficient Graph Based Approach for Reducing Coverage Loss From Failed Cameras of a Surveillance Network

The optimal placement of surveillance cameras to maximize the total coverage of camera networks is of significant research interest due to the proliferation of camera networks in diverse application scenarios. Existing camera placement algorithms are mostly designed to optimize coverage. In the event of failure of one or more cameras, these optimization algorithms reorient all the remaining cameras in the surveillance network to regain the lost coverage; and hence are not cost-effective. A simple and straightforward solution to regain the lost coverage is by reorienting the field of view of active cameras to overlap with those of the damaged cameras. In this paper, we propose the Visibility Graph Reduction (VGR) algorithm, a novel graph-based approach to select potential cameras amongst the active cameras, that can optimally alleviate the loss in coverage resulting from the failure of one or more cameras. We validate our algorithm on map images across diverse indoor and outdoor surveillance scenarios with existing camera networks. Experimental results show that our algorithm minimizes the number of cameras that need to be reoriented, and produces similar coverage results as that of reorientating all the available active cameras in the surveillance network. Our approach is both cost-effective and computationally efficient, and thus minimizes the human effort involved in reorienting cameras across practical scenarios.
An adaptive pixel mapping based approach for reversible data hiding in encrypted images

Reversible data hiding (RDH) is a widely studied research domain due to the extensive applications in medical image transmission, military image transmission, and cloud computing. In this paper, we propose a novel RDH scheme in encrypted images which is having a high embedding rate without affecting the bit error rate. To perform data hiding, all the non-overlapping pixel sub-arrays from the encrypted image will be considered in a predefined order, and one-bit from the secret message can be hidden in each of the pixel sub-arrays through a controlled pixel mapping technique. The pixels in a selected sub-array P will be split into two groups: odd pixels and even pixels according to the parity of the index of the pixels. All the even pixels will be replaced by a new pixel value based on a predefined mapping function to hide a secret bit 1. To embed bit-value 0, no need to do any modifications in the pixel sub-array P. A new correlation measure between adjacent pixels is introduced in this manuscript to extract the secret message bit and to restore the original image. All the simulations of the proposed scheme are done on the standard images from USC-SIPI image dataset. The results show that the proposed scheme achieved a very good embedding rate without affecting the bit error rate or image recoverability.
A new adaptive sampling algorithm for big data classification

The exponential growth of the quantity of data that circulates on the web led to the emergence of the big data phenomenon. This fact is a natural consequence of the proliferation of social media, mobile devices, the abundance of free online storage, and new technologies like the internet of things. Subsequently, big data has created several challenges to the computer science community, among which the large size of data is the most challenging. Traditional machine learning algorithms used mostly for insight extraction find themselves inadequate, even on high-performance computer architectures. For instance, big data analytics algorithms can overcome the size issue by either: (1) adapting the existing machine learning techniques to the scale of the big data; or, (2) by sampling big datasets, choosing randomly much smaller subsets of the data population, to meet what current algorithms can handle. In the present work, we aim to proceed through the second alternative to address the size challenge in the big data context. We propose intelligent sampling techniques based on Scalable Simple Random Sampling (ScaSRS) and Subsampled Double Bootstrap (SDB). Test results carried out on public generic datasets show that our proposal is able to address the size dimension efficiently. The proposed algorithms were evaluative on a classification task where the obtained results provided significant improvement compared to the state-of-the-art.
Understanding and Improvement of Adversarial Training for Network Embedding from an Optimization Perspective

Network Embedding aims to learn a function mapping the nodes to Euclidean space contribute to multiple learning analysis tasks on networks. However, both the noisy information behind the real-world networks and the overfitting problem negatively impact the quality of embedding vectors. To tackle these problems, researchers utilize Adversarial Perturbations on Parameters (APP) and achieve state-of-the-art performance. Unlike the mainstream methods introducing perturbations on the network structure or the data feature, Adversarial Training for Network Embedding (AdvTNE) adopts APP to directly perturb the model parameters, thus providing a new chance to understand the mechanism behind it. In this paper, we explain APP theoretically from an optimization perspective. Considering the Power-law property of networks and the optimization objective, we analyze the reason for its remarkable results on network embedding. Based on the above analysis and the Sigmoid saturation region problem, we propose a new Sine-base activation to enhance the performance of AdvTNE. We conduct extensive experiments on four real networks to validate the effectiveness of our method in node classification and link prediction. The results demonstrate that our method is competitive with state-of-the-art methods.
On the adequacy of static analysis warnings with respect to code smell prediction

Code smells are poor implementation choices that developers apply while evolving source code and that affect program maintainability. Multiple automated code smell detectors have been proposed: while most of them relied on heuristics applied over software metrics, a recent trend concerns the definition of machine learning techniques. However, machine learning-based code smell detectors still suffer from low accuracy: one of the causes is the lack of adequate features to feed machine learners. In this paper, we face this issue by investigating the role of static analysis warnings generated by three state-of-the-art tools to be used as features of machine learning models for the detection of seven code smell types. We conduct a three-step study in which we (1) verify the relation between static analysis warnings and code smells and the potential predictive power of these warnings; (2) build code smell prediction models exploiting and combining the most relevant features coming from the first analysis; (3) compare and combine the performance of the best code smell prediction model with the one achieved by a state of the art approach. The results reveal the low performance of the models exploiting static analysis warnings alone, while we observe significant improvements when combining the warnings with additional code metrics. Nonetheless, we still find that the best model does not perform better than a random model, hence leaving open the challenges related to the definition of ad-hoc features for code smell prediction.
The use of online dictionaries in video-mediated L2 interactions for the social accomplishment of Virtual Exchange tasks

Mobile applications and dedicated websites as online dictionaries have been common resources in language learning and teaching settings for years. Primarily used for looking up unknown words in reading, writing, and vocabulary learning activities, online dictionaries have been considered highly feasible, individual learning materials. However, their situated use in synchronous video-mediated interactions has remained largely unexplored despite their potential to help L2 learners resolve word-knowledge-related troubles, thus creating opportunities for meaning negotiation. Using Multimodal Conversation Analysis, this study describes the active use of online dictionaries in task-oriented video-mediated L2 interactions of Virtual Exchange participants in higher education. The close examination of the screen-recorded interactions shows that online dictionaries play an essential role in the social accomplishment of intercultural tasks. The findings indicate that L2 learners do not only look up unknown words, but they also look up the synonyms of already known words and validate their existing knowledge. Moreover, online dictionaries operate in a context-specific sequential environment consisting of diverse participant roles (describer/recipient), embodied actions, and grammatical action formats. Additionally, we observe that Wikipedia and image search emerge as supplementary tools to dictionary look-ups. The findings bring new insights into computer assisted language learning, video-mediated interactions, and Virtual Exchange.
A two-point machine learning method for the spatial prediction of soil pollution

Heavy metal soil pollution is a worldwide problem. It is affected by many natural and human factors through heterogeneous relationships. Accurate prediction at unobserved locations using a limited number of observations hence remains a challenge. This study proposes a two-point machine learning method to fully utilize the information in spatial neighbors and high-dimensional covariates to improve prediction accuracy. It models the difference between pairs of points, predicts concentration differences between observation points and unobserved points, and uses those for neighbor selection. This supervised learning method integrates both spatial autocorrelation and property similarity. Method performance, illustrated in a case study of soil Pb, confirms that our method can greatly improve prediction accuracy for different sample sizes. The improvements vary with the sample size and have a decreasing trend as the sample size increases. Compared with ordinary kriging, kriging with external drift, random forest, and random forest-based regression kriging, the average improvements on RMSE are 1.49, 0.95, 0.93 and 0.62 respectively, and on MAE are 1.29, 1.17, 0.87 and 0.65 respectively. In the future, the method may be applied to the spatial prediction of other variables of the earth system, while the supervised learning method can be adjusted to new applications.
A Novel Deep Learning Model Compression Algorithm

In order to solve the problem of large model computing power consumption, this paper proposes a novel model compression algorithm. Firstly, this paper proposes an interpretable weight allocation method for the loss between a student network (a network model with poor performance), a teacher network (a network model with better performance) and real label. Then, different from the previous simple pruning and fine-tuning, this paper performs knowledge distillation on the pruned model, and quantifies the residual weights of the distilled model. The above operations can further reduce the model size and calculation cost while maintaining the model accuracy. The experimental results show that the weight allocation method proposed in this paper can allocate a relatively appropriate weight to the teacher network and real tags. On the cifar-10 dataset, the pruning method combining knowledge distillation and quantization can reduce the memory size of resnet32 network model from 3726 KB to 1842 KB, and the accuracy can be kept at 93.28%, higher than the original model. Compared with similar pruning algorithms, the model accuracy and operation speed are greatly improved.
Loss-Privacy Tradeoff in Federated Edge Learning

Federated learning has been recognized as a promising scheme to tackle the privacy issues in multi-access edge computing through periodically uploading machine learning (ML) model updates instead of the original user data to the edge server. However, there still remains privacy leakage in such federated edge learning (FEL) systems since the model updates accessed by the server can be utilized to recover the original data. In this paper, we consider a personalized differential privacy based FEL scheme to alleviate the privacy leakage by adding different noise perturbations to the model updates of each edge device. Note that the noise perturbations may degrade the ML model performance, which is depicted by the global loss function. It is thus necessary to achieve a loss-privacy tradeoff in FEL by determining the noise scales and the numbers of local model updates. To address this challenge, we first derive the convergence upper bound of the global loss function as well as the closed-form privacy leakage from an adversarial perspective. We then propose a distributed mechanism in which the choices of noise scales and numbers of local model updates are optimized, even when the server is unaware of the personalized privacy preferences of different edge devices. Extensive theoretical analysis and numerical evaluations demonstrate the effectiveness of our proposed method in terms of privacy preservation and the global loss of the learned model.
UI-UX Design Using User Centred Design (UCD) Method

As businesses extend their online offerings, demand for UI-UX and digital services is increasing. Both in-house teams and agencies will be under pressure to offer services on time, with the total number of internet users worldwide increasing by 301 million since April 2019 (We are Social). After the covid-19 it&#x0027;s demand in e-commerce market is also increasing exponentially. In this paper User-centred design (UCD) method is used to process the whole prototyping. User flow is also discussed based on 3 levels(launch screen, create and account, home screen). The test result for the sample prototyping were the app&#x0027;s design elements with 4 points being the most common response on the scale of 5, the main menu page and its experience with 5 points, and the overall UI-UX of the app with 5 points.
Identification of ore-finding targets using the anomaly components of ore-forming element associations extracted by SVD and PCA in the Jiaodong gold cluster area, Eastern China

Multi-stage geological processes (i.e. magma and hydrothermal processes) produce generally regional and local element concentration anomalies of ore-forming elements. The Principal Component Analysis (PCA) can be applied to establish various types of ore-forming element associations, which can be further divided into two different anomalous components with the help of the Singular Value Decomposition (SVD). One can be defined as a regional geochemical anomaly generated by geological processes (i.e. magmatism); and the other can be regarded as a local geochemical anomaly created by ore forming events. Three aspects are illustrated in this paper: (a) The ore-forming element associations are established by PCA; (b) The geochemical anomaly components created by both magmatism and hydrothermal ore-forming processes are respectively identified by the SVD from the ore-forming element associations; and (c) The regional and the local geochemical anomalies are delineated using the geochemical anomaly components corresponding to different eigenspaces. The PCA and SVD methods were applied gold polymetallic ore-prospecting target areas and molybdenum polymetallic ore-prospecting target areas in the Jiaodong gold cluster area, China by delineating their regional and local geochemical anomalies. The results show that the SVD can extract not only the regional anomalies associated with magmatism, but also the local anomalies related to the hydrothermal processes from multi-element associations produced by PCA.
CrabNet: Fully Task-Specific Feature Learning for One-Stage Object Detection

Object detection is usually solved by learning a deep architecture involving classification and localization tasks, where feature learning for these two tasks is shared using the same backbone model. Recent works have shown that suitable disentanglement of classification and localization tasks has the great potential to improve performance of object detection. Despite the promising performance, existing feature disentanglement methods usually suffer from two limitations. First, most of them only focus on the disentangled proposals or predication heads for classification and localization tasks after RPN. While little consideration has been given to that the features for these two different tasks actually are obtained by a shared backbone model before RPN. Second, they are suggested for two-stage objectors and are not applicable to one-stage methods. To overcome these limitations, this paper presents a novel fully task-specific feature learning method for one-stage object detection. Specifically, our method first learns disentangled features for classification and localization tasks using two separated backbone models, where auxiliary classification and localization heads are inserted at the end of the two backbone models for providing a fully task-specific features for classification and localization. Then, a feature interaction module is developed for aligning and fusing task-specific features, which are further used to produce the final detection result. Experiments on MS COCO show that our proposed method (dubbed CrabNet) can achieve clear improvement over counterparts with increasing limited inference time, while performing favorably against state-of-the-arts.