Reviewerly/exHarmony huggingface dataset:
Single-Stage Rotation-Decoupled Detector for Oriented Object

Oriented object detection has received extensive attention in recent years, especially for the task of detecting targets in aerial imagery. Traditional detectors locate objects by horizontal bounding boxes (HBBs), which may cause inaccuracies when detecting objects with arbitrary oriented angles, dense distribution and a large aspect ratio. Oriented bounding boxes (OBBs), which add different rotation angles to the horizontal bounding boxes, can better deal with the above problems. New problems arise with the introduction of oriented bounding boxes for rotation detectors, such as an increase in the number of anchors and the sensitivity of the intersection over union (IoU) to changes of angle. To overcome these shortcomings while taking advantage of the oriented bounding boxes, we propose a novel rotation detector which redesigns the matching strategy between oriented anchors and ground truth boxes. The main idea of the new strategy is to decouple the rotating bounding box into a horizontal bounding box during matching, thereby reducing the instability of the angle to the matching process. Extensive experiments on public remote sensing datasets including DOTA, HRSC2016 and UCAS-AOD demonstrate that the proposed approach achieves state-of-the-art detection accuracy with higher efficiency.
An Efficient Artificial Bee Colony Algorithm With an Improved Linkage Identification Method

The artificial colony (ABC) algorithm shows a relatively powerful exploration search capability but is constrained by the curse of dimensionality, especially on nonseparable functions, where its convergence speed slows dramatically. In this article, based on an analysis of the difference between updating mechanisms that include both all-variable and one-variable updating mechanisms, we find that when equipped with the former strategy, the algorithm rapidly converges to an optimal region, while with the latter strategy, it searches the solution space thoroughly. To utilize multivariable and one-variable updating mechanisms on nonseparable and separable functions, respectively, we embed an improved linkage identification strategy into the ABC by detecting the linkage between variables more effectively. Then, we propose three common strategies for ABC to improve its performance. First, a new approach that considers the historic experiences of the population is proposed to balance exploration and exploitation. Second, a new strategy for initializing scout bees is used to reduce the number of function evaluations. Finally, the individual with the worst performance is updated with a defined probability on multiple dimensions instead of one dimension, causing it to follow the population steps on nonseparable functions. This article is the first to propose all these concepts, which could be adopted for other ABC variants. The effectiveness of our algorithm is validated through basic, CEC2010, CEC2013, and CEC2014 functions and real-world problems.
Semi-Siamese Training for Shallow Face Learning

Most existing public face datasets, such as MS-Celeb-1M and VGGFace2, provide abundant information in both breadth (large number of IDs) and depth (sufficient number of samples) for training. However, in many real-world scenarios of face recognition, the training dataset is limited in depth, i.e. only two face images are available for each ID. We define this situation as Shallow Face Learning, and find it problematic with existing training methods. Unlike deep face data, the shallow face data lacks intra-class diversity. As such, it can lead to collapse of feature dimension and consequently the learned network can easily suffer from degeneration and over-fitting in the collapsed dimension. In this paper, we aim to address the problem by introducing a novel training method named Semi-Siamese Training (SST). A pair of Semi-Siamese networks constitute the forward propagation structure, and the training loss is computed with an updating gallery queue, conducting effective optimization on shallow training data. Our method is developed without extra-dependency, thus can be flexibly integrated with the existing loss functions and network architectures. Extensive experiments on various benchmarks of face recognition show the proposed method significantly improves the training, not only in shallow face learning, but also for conventional deep face data.
Practical Recommendations for Stronger, More Usable Passwords Combining Minimum-strength, Minimum-length, and Blocklist Requirements

Multiple mechanisms exist to encourage users to create stronger passwords, including minimum-length and character-class requirements, prohibiting blocklisted passwords, and giving feedback on the strength of candidate passwords. Despite much research, there is little definitive, scientific guidance on how these mechanisms should be combined and configured to best effect. Through two online experiments, we evaluated combinations of minimum-length and character-class requirements, blocklists, and a minimum-strength requirement that requires passwords to exceed a strength threshold according to neural-network-driven password-strength estimates.
Online learning of quantum states

Suppose we have many copies of an unknown $n$-qubit state $\rho$. We measure some copies of $\rho$ using a known two-outcome measurement $E_{1}$, then other copies using a measurement $E_{2}$, and so on. At each stage $t$, we generate a current hypothesis $\sigma_{t}$ about the state $\rho$, using the outcomes of the previous measurements. We show that it is possible to do this in a way that guarantees that $|\operatorname{Tr}(E_{i} \sigma_{t}) - \operatorname{Tr}(E_{i}\rho) |$, the error in our prediction for the next measurement, is at least $\varepsilon$ at most $\operatorname{O}\!\left(n / \varepsilon^2 \right) $ times. Even in the "non-realizable" setting---where there could be arbitrary noise in the measurement outcomes---we show how to output hypothesis states that do significantly worse than the best possible states at most $\operatorname{O}\!\left(\sqrt {Tn}\right) $ times on the first $T$ measurements. These results generalize a 2007 theorem by Aaronson on the PAC-learnability of quantum states, to the online and regret-minimization settings. We give three different ways to prove our results---using convex optimization, quantum postselection, and sequential fat-shattering dimension---which have different advantages in terms of parameters and portability.
Hybrid calibration procedure for fringe projection profilometry based on stereo vision and polynomial fitting

The key to accurate 3D shape measurement in fringe projection profilometry (FPP) is the proper calibration of the measurement system. Current calibration techniques rely on phase-coordinate mapping (PCM) or back-projection stereo vision (SV) methods. PCM methods are cumbersome to implement as they require precise positioning of the calibration target relative to the FPP system, but they produce highly accurate measurements within the calibration volume. SV methods generally do not achieve the same accuracy level. However, the calibration is more flexible in that the calibration target can be arbitrarily positioned. In this work, we propose a hybrid calibration method that leverages the SV calibration approach using a PCM method to achieve higher accuracy. The method has the flexibility of SV methods, is robust to lens distortions, and has a simple relation between the recovered phase and the metric coordinates. Experimental results show that the proposed hybrid method outperforms the SV method in terms of accuracy and reconstruction time due to its low computational complexity.
Garbage in, garbage out?: do machine learning application papers in social computing report where human-labeled training data comes from?

Many machine learning projects for new application areas involve teams of humans who label data for a particular purpose, from hiring crowdworkers to the paper's authors labeling the data themselves. Such a task is quite similar to (or a form of) structured content analysis, which is a longstanding methodology in the social sciences and humanities, with many established best practices. In this paper, we investigate to what extent a sample of machine learning application papers in social computing --- specifically papers from ArXiv and traditional publications performing an ML classification task on Twitter data --- give specific details about whether such best practices were followed. Our team conducted multiple rounds of structured content analysis of each paper, making determinations such as: Does the paper report who the labelers were, what their qualifications were, whether they independently labeled the same items, whether inter-rater reliability metrics were disclosed, what level of training and/or instructions were given to labelers, whether compensation for crowdworkers is disclosed, and if the training data is publicly available. We find a wide divergence in whether such practices were followed and documented. Much of machine learning research and education focuses on what is done once a gold standard of training data is available, but we discuss issues around the equally-important aspect of whether such data is reliable in the first place.
Graph-to-Tree Neural Networks for Learning Structured Input-Output Translation with Applications to Semantic Parsing and Math Word Problem

The celebrated Seq2Seq technique and its numerous variants achieve excellent performance on many tasks such as neural machine translation, semantic parsing, and math word problem solving.However, these models either only consider input objects as sequences while ignoring the important structural information for encoding, or they simply treat output objects as sequence outputs instead of structural objects for decoding.In this paper, we present a novel Graph-to-Tree Neural Networks, namely Graph2Tree consisting of a graph encoder and a hierarchical tree decoder, that encodes an augmented graph-structured input and decodes a tree-structured output.In particular, we investigated our model for solving two problems, neural semantic parsing and math word problem.Our extensive experiments demonstrate that our Graph2Tree model outperforms or matches the performance of other state-of-the-art models on these tasks.
On the (Im)Practicality of Adversarial Perturbation for Image Privacy

Abstract Image hosting platforms are a popular way to store and share images with family members and friends. However, such platforms typically have full access to images raising privacy concerns. These concerns are further exacerbated with the advent of Convolutional Neural Networks (CNNs) that can be trained on available images to automatically detect and recognize faces with high accuracy. Recently, adversarial perturbations have been proposed as a potential defense against automated recognition and classification of images by CNNs. In this paper, we explore the practicality of adversarial perturbation-based approaches as a privacy defense against automated face recognition. Specifically, we first identify practical requirements for such approaches and then propose two practical adversarial perturbation approaches – (i) learned universal ensemble perturbations (UEP) , and (ii) k-randomized transparent image overlays (k-RTIO) that are semantic adversarial perturbations . We demonstrate how users can generate effective transferable perturbations under realistic assumptions with less effort. We evaluate the proposed methods against state-of-theart online and offline face recognition models, Clarifai.com and DeepFace, respectively. Our findings show that UEP and k-RTIO respectively achieve more than 85% and 90% success against face recognition models. Additionally, we explore potential countermeasures that classifiers can use to thwart the proposed defenses. Particularly, we demonstrate one effective countermeasure against UEP.
A taxonomy of service identification approaches for legacy software systems modernization

The success of modernizing legacy software systems to Service-Oriented Architecture (SOA) depends on Service Identification Approaches (SIAs), which identify reusable functionalities that could become services. The literature describes several SIAs. However, the selection of an identification approach that is suitable for a practitioner is difficult because it depends on several factors, including the goal of modernization, the available legacy artifacts, the organization’s development process, the desired output, and the usability of the approach. Accordingly, to select a suitable service identification approach, a practitioner must have a comprehensive view of existing techniques. We report a systematic literature review (SLR) that covers 41 SIAs based on software-systems analyses. Based on this SLR, we create a taxonomy of SIAs and build a multi-layer classification of existing identification approaches. We start from a high-level classification based on the used inputs, the applied processes, the given outputs, and the usability of the SIAs. We then divide each category into a fine-grained taxonomy that helps practitioners in selecting a suitable approach for identifying services in legacy software systems. We build our SLR based on our experience with legacy software modernization, on discussions and experiences working with industrial partners, and analyses of existing SIAs. We validate the correctness and the coverage of our review with industrial experts who modernize(d) legacy software systems to SOA. The results show that our classification conforms to the industrial experts’ experiences. We also show that most of the studied SIAs are still at their infancy. Finally, we identify the main challenges that SIAs need to address, to improve their quality.
Overcoming Catastrophic Forgetting in Graph Neural Networks

Catastrophic forgetting refers to the tendency that a neural network ``forgets'' the previous learned knowledge upon learning new tasks. Prior methods have been focused on overcoming this problem on convolutional neural networks (CNNs), where the input samples like images lie in a grid domain, but have largely overlooked graph neural networks (GNNs) that handle non-grid data. In this paper, we propose a novel scheme dedicated to overcoming catastrophic forgetting problem and hence strengthen continual learning in GNNs. At the heart of our approach is a generic module, termed as topology-aware weight preserving (TWP), applicable to arbitrary form of GNNs in a plug-and-play fashion. Unlike the main stream of CNN-based continual learning methods that rely on solely slowing down the updates of parameters important to the downstream task, TWP explicitly explores the local structures of the input graph, and attempts to stabilize the parameters playing pivotal roles in the topological aggregation. We evaluate TWP on different GNN backbones over several datasets, and demonstrate that it yields performances superior to the state of the art. Code is publicly available at https://github.com/hhliu79/TWP.
Task-Aware Representation of Sentences for Generic Text Classification

State-of-the-art approaches for text classification leverage a transformer architecture with a linear layer on top that outputs a class distribution for a given prediction problem.While effective, this approach suffers from conceptual limitations that affect its utility in few-shot or zero-shot transfer learning scenarios.First, the number of classes to predict needs to be pre-defined.In a transfer learning setting, in which new classes are added to an already trained classifier, all information contained in a linear layer is therefore discarded, and a new layer is trained from scratch.Second, this approach only learns the semantics of classes implicitly from training examples, as opposed to leveraging the explicit semantic information provided by the natural language names of the classes.For instance, a classifier trained to predict the topics of news articles might have classes like "business" or "sports" that themselves carry semantic information.Extending a classifier to predict a new class named "politics" with only a handful of training examples would benefit from both leveraging the semantic information in the name of a new class and using the information contained in the already trained linear layer.This paper presents a novel formulation of text classification that addresses these limitations.It imbues the notion of the task at hand into the transformer model itself by factorizing arbitrary classification problems into a generic binary classification problem.We present experiments in few-shot and zero-shot transfer learning that show that our approach significantly outperforms previous approaches on small training data and can even learn to predict new classes with no training examples at all.The implementation of our model is publicly available at:
Human Action Recognition in Videos using Convolution Long Short-Term Memory Network with Spatio-Temporal Networks

Two-stream convolutional networks plays an essential role as a powerful feature extractor in human action recognition in videos. Recent studies have shown the importance of two-stream Convolutional Neural Networks (CNN) to recognize human action recognition. Recurrent Neural Networks (RNN) has achieved the best performance in video activity recognition combining CNN. Encouraged by CNN's results with RNN, we present a two-stream network with two CNNs and Convolution Long-Short Term Memory (CLSTM). First, we extricate Spatio-temporal features using two CNNs using pre-trained ImageNet models. Second, the results of two CNNs from step one are combined and fed as input to the CLSTM to get the overall classification score. We also explored the various fusion function performance that combines two CNNs and the effects of feature mapping at different layers. And, conclude the best fusion function along with layer number. To avoid the problem of overfitting, we adopt the data augmentation techniques. Our proposed model demonstrates a substantial improvement compared to the current two-stream methods on the benchmark datasets with 70.4% on HMDB-51 and 95.4% on UCF-101 using the pre-trained ImageNet model. Doi: 10.28991/esj-2021-01254 Full Text: PDF
A Novel Blockchain-as-a-Service (BaaS) Platform for Local 5G Operators

5G is a promising technology that has the potential to support verticals and applications such as Industrial Internet of Things IoT (IIoT), smart cities, autonomous vehicles, remote surgeries, virtual and augmented realities, and so on. These verticals have a diverse set of network connectivity requirements, and it is challenging to deliver customized services for each by using a common 5G infrastructure. Thus, the operation of Local 5G operator (L5GO) networks or private 5G networks are a viable option to tackle this challenge. A L5GO network is a localized small cell network which can offer tailored service delivery. The adaptation of network softwarization in 5G allows vertical owners to deploy and operate L5GO networks. However, the deployment of L5GOs raises various issues related to management of subscribers, roaming users, spectrum, security, and also the infrastructure. This paper proposes a blockchain-based platform to address these issues. The paper introduces a set of blockchain-based modularized functions such as service rating systems, bidding techniques, and selection functions, which can be used to deploy different services for L5GOs. Exploitation of blockchain technology ensures availability, non-reliance on trusted third parties, secure transfer payments, and stands to gain many more advantages. The performance and the viability of the proposed platform are analyzed by using simulations and a prototype implementation.
Dual Metric Learning for Effective and Efficient Cross-Domain Recommendations

Cross domain recommender systems have been increasingly valuable for helping consumers identify useful items in different applications. However, existing cross-domain models typically require large number of overlap users, which can be difficult to obtain in some applications. In addition, they did not consider the duality structure of cross-domain recommendation tasks, thus failing to take into account bidirectional latent relations between users and items and achieve optimal recommendation performance. To address these issues, in this paper we propose a novel cross-domain recommendation model based on dual learning that transfers information between two related domains in an iterative manner until the learning process stabilizes. We develop a novel latent orthogonal mapping to extract user preferences over multiple domains while preserving relations between users across different latent spaces. Furthermore, we combine the dual learning method with the metric learning approach, which allows us to significantly reduce the required common user overlap across the two domains and leads to even better cross-domain recommendation performance. We test the proposed model on three large-scale industrial datasets and demonstrate that it consistently and significantly outperforms the state-of-the-art baselines. We also show that the proposed model works well with very few overlap users to obtain recommendation performance comparable to the state-of-the-art baselines that use many overlap users.
Entropy K-Means Clustering With Feature Reduction Under Unknown Number of Clusters

The k-means algorithm with its extensions is the most used clustering method in the literature. But, the k-means and its various extensions are generally affected by initializations with a given number of clusters. On the other hand, most of k-means always treat data points with equal importance for feature components. There are several feature-weighted k-means proposed in literature, but, these feature-weighted k-means do not give a feature reduction behavior. In this paper, based on several entropy-regularized terms we can construct a novel k-means clustering algorithm, called Entropy-k-means, such that it can be free of initializations without a given number of clusters, and also has a feature reduction behavior. That is, the proposed Entropy-k-means algorithm can eliminate irrelevant features with feature reduction under free of initializations with automatically finding an optimal number of clusters. Comparisons between the proposed Entropy-k-means and other methods are made. Experimental results and comparisons actually demonstrate these good aspects of the proposed Entropy-k-means with its effectiveness and usefulness in practice.
Future Technology: Software-Defined Network (SDN) Forensic

The software-defined networking (SDN) paradigm has recently emerged as a trend to build various protocols, develop more reliable networks, enhance the data flow controlling, and provide security in a much simpler and flexible way. SDN helps to ease management and handle asymmetric connectivity across various nodes. It solves the problems of network and cloud security and hence provides the best solution for the safety of data on the network. Therefore, we feel the urge to research more and provide the basics of SDN forensics, mention its advantages in network especially in the cloud, and present its elaborate prospects in context with Network Forensic (NF) and Cloud Forensic (CF). In this research article, we explained in detail the NF and CF with emphasis on Network security (NS) and Cloud Security (CS). The paper also provided the various security approaches and categories. Then, an overview of the software-defined networking (SDN) is mentioned. We also discussed the use of SDN in Network Forensic and Cloud Forensic. Furthermore, to aid the SDN forensic, we presented the advantages, challenges, and issues along with future research directions of SDN in network forensic and cloud forensic, and at last, we thus express and explore the need for security in forensic based on the SDN paradigm in the form of a set of suggested recommendations.