Reviewerly/exHarmony huggingface dataset:
Threat modeling – A systematic literature review

Cyber security is attracting worldwide attention. With attacks being more and more common and often successful, no one is spared today. Threat modeling is proposed as a solution for secure application development and system security evaluations. Its aim is to be more proactive and make it more difficult for attackers to accomplish their malicious intents. However, threat modeling is a domain that lacks common ground. What is threat modeling, and what is the state-of-the-art work in this field? To answer these questions, this article presents a review of threat modeling based on systematic queries in four leading scientific databases. This is the first systematic literature review on threat modeling to the best of our knowledge. 176 articles were assessed, and 54 of them were selected for further analysis. We identified three separate clusters: (1) articles making a contribution to threat modeling, e.g., introducing a new method, (2) articles using an existing threat modeling approach, and (3) introductory articles presenting work related to the threat modeling process. The three clusters were analyzed in terms of a set of criteria, for instance: Is the threat modeling approach graphical or formal? Is it focused on a specific attack type and application? Is the contribution validated empirically or theoretically? We observe from the results that, most threat modeling work remains to be done manually, and there is limited assurance of their validations. The results can be used for researchers and practitioners who want to know the state-of-the-art threat modeling methods, and future research directions are discussed.
In the Wild Human Pose Estimation Using Explicit 2D Features and Intermediate 3D Representations

Convolutional Neural Network based approaches for monocular 3D human pose estimation usually require a large amount of training images with 3D pose annotations. While it is feasible to provide 2D joint annotations for large corpora of in-the-wild images with humans, providing accurate 3D annotations to such in-the-wild corpora is hardly feasible in practice. Most existing 3D labelled data sets are either synthetically created or feature in-studio images. 3D pose estimation algorithms trained on such data often have limited ability to generalize to real world scene diversity. We therefore propose a new deep learning based method for monocular 3D human pose estimation that shows high accuracy and generalizes better to in-the-wild scenes. It has a network architecture that comprises a new disentangled hidden space encoding of explicit 2D and 3D features, and uses supervision by a new learned projection model from predicted 3D pose. Our algorithm can be jointly trained on image data with 3D labels and image data with only 2D labels. It achieves state-of-the-art accuracy on challenging in-the-wild data.
The Application of Unsupervised Clustering Methods to Alzheimer’s Disease

Clustering is a powerful machine learning tool for detecting structures in datasets. In the medical field, clustering has been proven to be a powerful tool for discovering patterns and structure in labeled and unlabeled datasets. Unlike supervised methods, clustering is an unsupervised method that works on datasets in which there is no outcome (target) variable nor is anything known about the relationship between the observations, that is, unlabeled data. In this paper, we focus on studying and reviewing clustering methods that have been applied to datasets of neurological diseases, especially Alzheimer's disease (AD). The aim is to provide insights into which clustering technique is more suitable for partitioning patients of AD based on their similarity. This is important as clustering algorithms can find patterns across patients that are difficult for medical practitioners to find. We further discuss the implications of the use of clustering algorithms in the treatment of AD. We found that clustering analysis can point to several features that underlie the conversion from early-stage AD to advanced AD. Furthermore, future work can apply semi-clustering algorithms on AD datasets, which will enhance clusters by including additional information.
A hybrid optimization algorithm based on cuckoo search and differential evolution for solving constrained engineering problems

Based on Cuckoo Search (CS) and Differential Evolution (DE), a novel hybrid optimization algorithm, called CSDE, is proposed in this paper to solve constrained engineering problems. CS has strong ability on global search and less control parameters, but easy to suffer from premature convergence and lower the density of population. DE specializes in local search and good robustness, however, its convergence rate is too late to find the satisfied solution. Furthermore, these two algorithms are both proved to be especially suitable for engineering problems. This work divides population into two subgroups and adopts CS and DE for these two subgroups independently. By division, these two subgroups can exchange useful information and these two algorithms can utilize each other’s advantages to complement their shortcoming, thus avoid premature convergence, balance the quality of solution and the computation consumption, and find satisfactory global optima. Due to the tremendous design variables and constrained conditions of engineering problems, single optimizer failed to meet the requirement of precision, so hybrid optimization algorithms (such like CSDE) is the most promising mean to complete this job. Simulation results reveal that CSDE has more ability to find promising results than other 12 algorithms (including traditional algorithms and state-of-the-art algorithm) on 30 unconstrained benchmark functions, 10 constrained benchmark functions and 6 constrained engineering problems.
Salient object detection for RGB-D image by single stream recurrent convolution neural network

Salient object detection for RGB-D images aims to utilize color and depth information to automatically localize objects of human interest in the scene and reduce the complexity of visual analysis. Different from existing saliency detection model with double-stream network, salient object detection by Single Stream Recurrent Convolution Neural Network(SSRCNN) is proposed. First RGBD four-channels input is fed into VGG-16 net to generate multiple level features which express the most original feature for RGB-D image. The coarse saliency map from the deepest features can detect and localize salient objects, but loss the boundaries and subtle structures. So Depth Recurrent Convolution Neural Network (DRCNN) is then applied to each level feature for rendering salient object outline from deep to shallow hierarchically and progressively. With the help of deeper level feature, original depth cue and coarse saliency map, each level feature can accurately predict the salient objects in different scales. At last all the saliency maps from each level are fused together to generate final results. Extensive quantitative and qualitative experimental evaluations on four dataset demonstrate that the proposed method outperforms most state-of-the-art methods.
PoRX: A reputation incentive scheme for blockchain consensus of IIoT

Industrial Internet of Things (IIoT) refers to all kinds of equipment in industrial production, whether it is machine equipment in factories or engine in automobiles and airplanes, on which sensors are installed and connected to wireless network terminals to collect and share data. At present, because IIoT system is an open, distributed and heterogeneous system, it is a great challenge to implement trusted communication in IIoT. Blockchain technology has become a new way to solve the cooperative trust issues of industrial Internet of Things. Using blockchain, a tamper-proof system can be built, which can be used as an audit tool for hardware products of industrial Internet of Things from chip to whole equipment. Blockchain improve productivity and operational efficiency of IIoT through smart contract, allowing machines to manage themselves. To apply blockchain technology in IIoT, one of the main issues is to solve the security and efficiency problems of consensus protocols. In this paper, a reputation scheme is proposed to encourage normal nodes and abnormal nodes both to participate in network collaboration in a good way. In order to guide the behavior, a credit based incentive approach is proposed. The reward and punishment factor is designed in the revenue payment function of reputation. The effectiveness is that the cooperative behavior is rewarded, and the non cooperative behavior is punished. The main advantage of our scheme is that our reputation based incentive module can be implemented on state-of-the-art PoX protocols, which is called PoRX,and can make PoX protocols achieve better consensus states. Therefore, it would benefit the applications of IIoT with blockchain. A prototype has been implemented and the experimental results shows that our scheme can effectively encourage the cooperative behavior of the nodes in IIoT, that can benefit the network.
Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning

We propose a symmetric graph convolutional autoencoder which produces a low-dimensional latent representation from a graph.In contrast to the existing graph autoencoders with asymmetric decoder parts, the proposed autoencoder has a newly designed decoder which builds a completely symmetric autoencoder form.For the reconstruction of node features, the decoder is designed based on Laplacian sharpening as the counterpart of Laplacian smoothing of the encoder, which allows utilizing the graph structure in the whole processes of the proposed autoencoder architecture.In order to prevent the numerical instability of the network caused by the Laplacian sharpening introduction, we further propose a new numerically stable form of the Laplacian sharpening by incorporating the signed graphs.In addition, a new cost function which finds a latent representation and a latent affinity matrix simultaneously is devised to boost the performance of image clustering tasks.The experimental results on clustering, link prediction and visualization tasks strongly support that the proposed model is stable and outperforms various state-of-theart algorithms.
Characteristic analysis of the fractional-order hyperchaotic complex system and its image encryption application

To explore characteristic of a new system with fractional order and complex variables, based on Adomian decomposition method (ADM), its nonlinear dynamical behaviors are discussed by bifurcation diagram, Lyapunov exponent spectrum, spectral entropy (SE) complexity, and DSP implementation. It is found that, for each parameter and order, there exists a large interval to make the system remain a hyper-chaotic state, which illustrates that the hyper-chaotic sequence could be more random to be applied in image encryption. Especially, a novel image encryption algorithm is proposed based on the fractional-order hyper-chaotic complex system and Galois field (GF). The column cyclic shift is used to scrambling part, the GF(17) algorithm is to diffuse pixel value. The security performances of algorithm are also analyzed. Finally, experiment results indicate that the proposed algorithm not only can effectively encrypt image, but also has better security features.
A Comprehensive Survey for Intelligent Spam Email Detection

The tremendously growing problem of phishing e-mail, also known as spam including spear phishing or spam borne malware, has demanded a need for reliable intelligent anti-spam e-mail filters. This survey paper describes a focused literature survey of Artificial Intelligence (AI) and Machine Learning (ML) methods for intelligent spam email detection, which we believe can help in developing appropriate countermeasures. In this paper, we considered 4 parts in the email's structure that can be used for intelligent analysis: (A) Headers Provide Routing Information, contain mail transfer agents (MTA) that provide information like email and IP address of each sender and recipient of where the email originated and what stopovers, and final destination. (B) The SMTP Envelope, containing mail exchangers' identification, originating source and destination domains\users. (C) First part of SMTP Data, containing information like from, to, date, subject - appearing in most email clients (D) Second part of SMTP Data, containing email body including text content, and attachment. Based on the number the relevance of an emerging intelligent method, papers representing each method were identified, read, and summarized. Insightful findings, challenges and research problems are disclosed in this paper. This comprehensive survey paves the way for future research endeavors addressing theoretical and empirical aspects related to intelligent spam email detection.
Mask-Refined R-CNN: A Network for Refining Object Details in Instance Segmentation

With the rapid development of flexible vision sensors and visual sensor networks, computer vision tasks, such as object detection and tracking, are entering a new phase. Accordingly, the more challenging comprehensive task, including instance segmentation, can develop rapidly. Most state-of-the-art network frameworks, for instance, segmentation, are based on Mask R-CNN (mask region-convolutional neural network). However, the experimental results confirm that Mask R-CNN does not always successfully predict instance details. The scale-invariant fully convolutional network structure of Mask R-CNN ignores the difference in spatial information between receptive fields of different sizes. A large-scale receptive field focuses more on detailed information, whereas a small-scale receptive field focuses more on semantic information. So the network cannot consider the relationship between the pixels at the object edge, and these pixels will be misclassified. To overcome this problem, Mask-Refined R-CNN (MR R-CNN) is proposed, in which the stride of ROIAlign (region of interest align) is adjusted. In addition, the original fully convolutional layer is replaced with a new semantic segmentation layer that realizes feature fusion by constructing a feature pyramid network and summing the forward and backward transmissions of feature maps of the same resolution. The segmentation accuracy is substantially improved by combining the feature layers that focus on the global and detailed information. The experimental results on the COCO (Common Objects in Context) and Cityscapes datasets demonstrate that the segmentation accuracy of MR R-CNN is about 2% higher than that of Mask R-CNN using the same backbone. The average precision of large instances reaches 56.6%, which is higher than those of all state-of-the-art methods. In addition, the proposed method requires low time cost and is easily implemented. The experiments on the Cityscapes dataset also prove that the proposed method has great generalization ability.
A systematic review of unsupervised learning techniques for software defect prediction

Unsupervised machine learners have been increasingly applied to software defect prediction. It is an approach that may be valuable for software practitioners because it reduces the need for labeled training data. Investigate the use and performance of unsupervised learning techniques in software defect prediction. We conducted a systematic literature review that identified 49 studies containing 2456 individual experimental results, which satisfied our inclusion criteria published between January 2000 and March 2018. In order to compare prediction performance across these studies in a consistent way, we (re-)computed the confusion matrices and employed the Matthews Correlation Coefficient (MCC) as our main performance measure. Our meta-analysis shows that unsupervised models are comparable with supervised models for both within-project and cross-project prediction. Among the 14 families of unsupervised model, Fuzzy CMeans (FCM) and Fuzzy SOMs (FSOMs) perform best. In addition, where we were able to check, we found that almost 11% (262/2456) of published results (contained in 16 papers) were internally inconsistent and a further 33% (823/2456) provided insufficient details for us to check. Although many factors impact the performance of a classifier, e.g., dataset characteristics, broadly speaking, unsupervised classifiers do not seem to perform worse than the supervised classifiers in our review. However, we note a worrying prevalence of (i) demonstrably erroneous experimental results, (ii) undemanding benchmarks and (iii) incomplete reporting. We therefore encourage researchers to be comprehensive in their reporting.
Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning

Federated learning is a distributed framework for training machine learning models over the data residing at mobile devices, while protecting the privacy of individual users. A major bottleneck in scaling federated learning to a large number of users is the overhead of secure model aggregation across many users. In particular, the overhead of the state-of-the-art protocols for secure model aggregation grows quadratically with the number of users. In this article, we propose the first secure aggregation framework, named Turbo-Aggregate, that in a network with N users achieves a secure aggregation overhead of O(NlogN), as opposed to O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), while tolerating up to a user dropout rate of 50%. Turbo-Aggregate employs a multi-group circular strategy for efficient model aggregation, and leverages additive secret sharing and novel coding techniques for injecting aggregation redundancy in order to handle user dropouts while guaranteeing user privacy. We experimentally demonstrate that Turbo-Aggregate achieves a total running time that grows almost linear in the number of users, and provides up to 40× speedup over the state-of-the-art protocols with up to N=200 users. Our experiments also demonstrate the impact of model size and bandwidth on the performance of Turbo-Aggregate.
Emotion Recognition Using Eye-Tracking: Taxonomy, Review and Current Challenges

The ability to detect users’ emotions for the purpose of emotion engineering is currently one of the main endeavors of machine learning in affective computing. Among the more common approaches to emotion detection are methods that rely on electroencephalography (EEG), facial image processing and speech inflections. Although eye-tracking is fast in becoming one of the most commonly used sensor modalities in affective computing, it is still a relatively new approach for emotion detection, especially when it is used exclusively. In this survey paper, we present a review on emotion recognition using eye-tracking technology, including a brief introductory background on emotion modeling, eye-tracking devices and approaches, emotion stimulation methods, the emotional-relevant features extractable from eye-tracking data, and most importantly, a categorical summary and taxonomy of the current literature which relates to emotion recognition using eye-tracking. This review concludes with a discussion on the current open research problems and prospective future research directions that will be beneficial for expanding the body of knowledge in emotion detection using eye-tracking as the primary sensor modality.
AutoPruner: An end-to-end trainable filter pruning method for efficient deep model inference

Channel pruning is an important method to speed up CNN model’s inference. Previous filter pruning algorithms regard importance evaluation and model fine-tuning as two independent steps. This paper argues that combining them into a single end-to-end trainable system will lead to better results. We propose an efficient channel selection layer, namely AutoPruner, to find less important filters automatically in a joint training manner. Our AutoPruner takes previous activation responses as an input and generates a true binary index code for pruning. Hence, all the filters corresponding to zero index values can be removed safely after training. By gradually erasing several unimportant filters, we can prevent an excessive drop in model accuracy. Compared with previous state-of-the-art pruning algorithms (including training from scratch), AutoPruner achieves significantly better performance. Furthermore, ablation experiments show that the proposed novel mini-batch pooling and binarization operations are vital for the success of model pruning.
Iota Tangle: A cryptocurrency to communicate Internet-of-Things data

The emergence of distributed ledger technologies (DLT) and design limitations of Blockchain systems for some types of applications led to the development of cryptocurrency alternatives for various purposes. Iota is a cryptocurrency with a new architecture called Tangle, which promises high scalability, no fees, and near-instant transfers, focused on the Internet-of-Things (IoT) solutions. This paper aims to present a systematic community’s visions of this new technology and to provide minimum background to understand the Iota Tangle and ecosystem generated by this distributed Ledger. The first parts of this article describe the ecosystem behind Iota, theoretical mathematical foundation, and its challenges and solutions for implementation. In the second part, we presented systematic research about Iota Tangle in academic databases: IEEE, ScienceDirect, Scopus, and Research Gate. We select the articles those of high impact which can be filtered with the H5-index indicator. This criterion aims to guarantee that the papers analyzed underwent a careful selection process, evaluated by peers. The methodology used helped have a global vision of Iota, including that this innovation is not only understood as a cryptocurrency but can be considered as a “distributed communication protocol”, absence of fees, low latency, and low computational cost for sending transactions. However, there exist several challenges in the vanguard of development of this ledger. It could also be identified that this technology enables many possibilities, however, it is fundamental to understand the potentials and limitations of this ecosystem to generate the best use cases.
Online Deep Clustering for Unsupervised Representation Learning

Joint clustering and feature learning methods have shown remarkable performance in unsupervised representation learning. However, the training schedule alternating between feature clustering and network parameters update leads to unstable learning of visual representations. To overcome this challenge, we propose Online Deep Clustering (ODC) that performs clustering and network update simultaneously rather than alternatingly. Our key insight is that the cluster centroids should evolve steadily in keeping the classifier stably updated. Specifically, we design and maintain two dynamic memory modules, i.e., samples memory to store samples' labels and features, and centroids memory for centroids evolution. We break down the abrupt global clustering into steady memory update and batch-wise label re-assignment. The process is integrated into network update iterations. In this way, labels and the network evolve shoulder-to-shoulder rather than alternatingly. Extensive experiments demonstrate that ODC stabilizes the training process and boosts the performance effectively.