Reviewerly/exHarmony huggingface dataset:
inTIME: A Machine Learning-Based Framework for Gathering and Leveraging Web Data to Cyber-Threat Intelligence

In today’s world, technology has become deep-rooted and more accessible than ever over a plethora of different devices and platforms, ranging from company servers and commodity PCs to mobile phones and wearables, interconnecting a wide range of stakeholders such as households, organizations and critical infrastructures. The sheer volume and variety of the different operating systems, the device particularities, the various usage domains and the accessibility-ready nature of the platforms creates a vast and complex threat landscape that is difficult to contain. Staying on top of these evolving cyber-threats has become an increasingly difficult task that presently relies heavily on collecting and utilising cyber-threat intelligence before an attack (or at least shortly after, to minimize the damage) and entails the collection, analysis, leveraging and sharing of huge volumes of data. In this work, we put forward inTIME, a machine learning-based integrated framework that provides an holistic view in the cyber-threat intelligence process and allows security analysts to easily identify, collect, analyse, extract, integrate, and share cyber-threat intelligence from a wide variety of online sources including clear/deep/dark web sites, forums and marketplaces, popular social networks, trusted structured sources (e.g., known security databases), or other datastore types (e.g., pastebins). inTIME is a zero-administration, open-source, integrated framework that enables security analysts and security stakeholders to (i) easily deploy a wide variety of data acquisition services (such as focused web crawlers, site scrapers, domain downloaders, social media monitors), (ii) automatically rank the collected content according to its potential to contain useful intelligence, (iii) identify and extract cyber-threat intelligence and security artifacts via automated natural language understanding processes, (iv) leverage the identified intelligence to actionable items by semi-automatic entity disambiguation, linkage and correlation, and (v) manage, share or collaborate on the stored intelligence via open standards and intuitive tools. To the best of our knowledge, this is the first solution in the literature to provide an end-to-end cyber-threat intelligence management platform that is able to support the complete threat lifecycle via an integrated, simple-to-use, yet extensible framework.
A survey of reversible data hiding in encrypted images – The first 12 years

In the last few years, with the increasing popularity of cloud computing and the availability of mobile smart devices as well as ubiquitous network connections, more and more users are uploading their personal data to remote servers. However, this can lead to significant security breaches, where confidentiality, integrity and authentication are constantly threatened. To overcome these multiple problems, multimedia data must be secured, for example by means of encryption before transmission and storage. In this survey, we look into the issues involved in handling encrypted multimedia data, and more specifically we focus on reversible data hiding in encrypted images (RDHEI). The aim of this survey is to present the birth and evolution of RDHEI methods over the last 12 years. We first highlight different classes and characteristics of RDHEI, then describe representative RDHEI methods. A comparison table is presented to summarize the key features and achievements of each representative RDHEI method considered in this survey. Finally, we share the future outlook of emerging applications and open research topics relevant to RDHEI for the next 12 years and beyond.
Attend and Discriminate

Wearables are fundamental to improving our understanding of human activities, especially for an increasing number of healthcare applications from rehabilitation to fine-grained gait analysis. Although our collective know-how to solve Human Activity Recognition (HAR) problems with wearables has progressed immensely with end-to-end deep learning paradigms, several fundamental opportunities remain overlooked. We rigorously explore these new opportunities to learn enriched and highly discriminating activity representations. We propose: i) learning to exploit the latent relationships between multi-channel sensor modalities and specific activities; ii) investigating the effectiveness of data-agnostic augmentation for multi-modal sensor data streams to regularize deep HAR models; and iii) incorporating a classification loss criterion to encourage minimal intra-class representation differences whilst maximising inter-class differences to achieve more discriminative features. Our contributions achieves new state-of-the-art performance on four diverse activity recognition problem benchmarks with large margins---with up to 6% relative margin improvement. We extensively validate the contributions from our design concepts through extensive experiments, including activity misalignment measures, ablation studies and insights shared through both quantitative and qualitative studies. The code base and trained network parameters are open-sourced on GitHub https://github.com/AdelaideAuto-IDLab/Attend-And-Discriminate to support further research.
Multi-Stream Attention-Aware Graph Convolution Network for Video Salient Object Detection

Recent advances in deep convolution neural networks (CNNs) boost the development of video salient object detection (SOD), and many remarkable deep-CNNs video SOD models have been proposed. However, many existing deep-CNNs video SOD models still suffer from coarse boundaries of the salient object, which may be attributed to the loss of high-frequency information. The traditional graph-based video SOD models can preserve object boundaries well by conducting superpixels/supervoxels segmentation in advance, but they perform weaker in highlighting the whole object than the latest deep-CNNs models, limited by heuristic graph clustering algorithms. To tackle this problem, we find a new way to address this issue under the framework of graph convolution networks (GCNs), taking advantage of graph model and deep neural network. Specifically, a superpixel-level spatiotemporal graph is first constructed among multiple frame-pairs by exploiting the motion cues implied in the frame-pairs. Then the graph data is imported into the devised multi-stream attention-aware GCN, where a novel Edge-Gated graph convolution (GC) operation is proposed to boost the saliency information aggregation on the graph data. A novel attention module is designed to encode the spatiotemporal sematic information via adaptive selection of graph nodes and fusion of the static-specific and the motion-specific graph embedding. Finally, a smoothness-aware regularization term is proposed to enhance the uniformity of salient object. Graph nodes (superpixels) inherently belonging to the same class will be ideally clustered together in the learned embedding space. Extensive experiments have been conducted on three widely used datasets. Compared with fourteen state-of-the-art video SOD models, our proposed method can well retain the salient object boundaries and possess a strong learning ability, which shows that this work is a good practice for designing GCNs for video SOD.
A deep learning model for intra-day forecasting of solar irradiance using satellite-based estimations in the vicinity of a PV power plant

This work proposes an intra-day forecasting model, which does not require to be trained or fed with real-time data measurements, for global horizontal irradiance (GHI) at a given location. The proposed model uses a series of time-dependant irradiance estimates near the target location as the main input. These estimates are derived from satellite images and are combined with other secondary inputs in an advanced neural network, which features convolutional and dense layers and is trained using a deep learning approach. For the various input combinations, the performance of the model is validated with a quantitative analysis on the forecast accuracy using different error metrics. Accuracies are compared with a commercial solution for irradiance forecasting made by the European Centre for Medium-Range Weather Forecasts (ECMWF) and publications with similar approaches and forecasting horizons, showing state-of-the-art performance even without irradiance measurements.
Inflation and Bitcoin: A descriptive time-series analysis

This study examines the time-series relation between Bitcoin and forward inflation expectation rates. Using a vector autoregressive process, we find that changes in Bitcoin Granger cause changes in the forward inflation rate. Furthermore, imposing an exogenous shock to Bitcoin’s price results in a persistent increase in the forward inflation rate. Our findings provide support for the notion that Bitcoin may be used as a hedge against inflation as changes in the price of Bitcoin tend to lead changes in the expected inflation.
An anomaly detection approach to identify chronic brain infarcts on MRI

Abstract The performance of current machine learning methods to detect heterogeneous pathology is limited by the quantity and quality of pathology in medical images. A possible solution is anomaly detection; an approach that can detect all abnormalities by learning how ‘normal’ tissue looks like. In this work, we propose an anomaly detection method using a neural network architecture for the detection of chronic brain infarcts on brain MR images. The neural network was trained to learn the visual appearance of normal appearing brains of 697 patients. We evaluated its performance on the detection of chronic brain infarcts in 225 patients, which were previously labeled. Our proposed method detected 374 chronic brain infarcts (68% of the total amount of brain infarcts) which represented 97.5% of the total infarct volume. Additionally, 26 new brain infarcts were identified that were originally missed by the radiologist during radiological reading. Our proposed method also detected white matter hyperintensities, anomalous calcifications, and imaging artefacts. This work shows that anomaly detection is a powerful approach for the detection of multiple brain abnormalities, and can potentially be used to improve the radiological workflow efficiency by guiding radiologists to brain anomalies which otherwise remain unnoticed.
A meta-analysis of virtual reality training programs

Virtual reality (VR) is the three-dimensional digital representation of a real or imagined space with interactive capabilities. The application of VR for organizational training purposes has been surrounded by much fanfare; however, mixed results have been provided for the effectiveness of VR training programs, and the attributes of effective VR training programs are still unknown. To address these issues, we perform a meta-analysis of controlled experimental studies that tests the effectiveness of VR training programs. We obtain an estimate of the overall effectiveness of VR training programs, and we identify features of VR training programs that systematically produce improved results. Our meta-analytic findings support that VR training programs produce better outcomes than tested alternatives. The results also show that few moderating effects were significant. The applied display hardware, input hardware, and inclusion of game attributes had non-significant moderating effects; however, task-technology fit and aspects of the research design did influence results. We suggest that task-technology fit theory is an essential paradigm for understanding VR training programs, and no set of VR technologies is "best" across all contexts. Future research should continue studying all types of VR training programs, and authors should more strongly integrate research and theory on employee training and development.
Information-Utilization-Method-Assisted Multimodal Multiobjective Optimization and Application to Credit Card Fraud Detection

Different from multiobjective optimization problems (MOPs), multimodal MOPs (MMOPs) focus on both decision and objective spaces rather than only objective one. Thus, finding a good Pareto front approximation and finding the maximal number of equivalent Pareto optimal solutions for each objective vector in the Pareto front are two core tasks for them. Although some multimodal multiobjective evolutionary algorithms have been proposed to handle them, they can quickly converge to the easy-to-find equivalent Pareto optimal solutions, thereby losing their ability to improve solution diversity in decision space and performance in objective space. To address the above issues, this work proposes a new information utilization method. Its core idea is to randomly extract a certain amount of decision variable information from the current optimal solutions to construct an information vector, which is, in turn, used to assist the generation of elite solutions. The proposed method can assist any available intelligent optimizers to improve their performance in solving MMOPs. This is confirmed by experimental results obtained from solving 22 such problems from CEC2019 and 12 scalable imbalanced distance minimization problems through a number of optimizers. Finally, we apply the proposed method to credit card fraud detection problems to show its practical significance.
The Additive Input-Doubling Method Based on the SVR with Nonlinear Kernels: Small Data Approach

The problem of effective intellectual analysis in the case of handling short datasets is topical in various application areas. Such problems arise in medicine, economics, materials science, science, etc. This paper deals with a new additive input-doubling method designed by the authors for processing short and very short datasets. The main steps of the method should include the procedure of data augmentation within the existing dataset both in rows and columns (without training), the use of nonlinear SVR to implement the training procedure, and the formation of the result based on the author’s procedure. The authors show that the developed data augmentation procedure corresponds to the principles of axial symmetry. The training and application procedures of the method developed are described in detail, and two algorithmic implementations are presented. The optimal parameters of the method operation were selected experimentally. The efficiency of its work during the processing of short datasets for solving the prediction task was established experimentally by comparison with other methods of this class. The highest prediction accuracy based on both proposed algorithmic implementations of a method among all of the investigated ones was defined. The main areas of application of the developed method are described, and its shortcomings and prospects of further research are given.
Cross-Layer Feature Pyramid Network for Salient Object Detection

Feature pyramid network (FPN) based models, which fuse the semantics and salient details in a progressive manner, have been proven highly effective in salient object detection. However, it is observed that these models often generate saliency maps with incomplete object structures or unclear object boundaries, due to the \emph{indirect} information propagation among distant layers that makes such fusion structure less effective. In this work, we propose a novel Cross-layer Feature Pyramid Network (CFPN), in which direct cross-layer communication is enabled to improve the progressive fusion in salient object detection. Specifically, the proposed network first aggregates multi-scale features from different layers into feature maps that have access to both the high- and low-level information. Then, it distributes the aggregated features to all the involved layers to gain access to richer context. In this way, the distributed features per layer own both semantics and salient details from all other layers simultaneously, and suffer reduced loss of important information. Extensive experimental results over six widely used salient object detection benchmarks and with three popular backbones clearly demonstrate that CFPN can accurately locate fairly complete salient regions and effectively segment the object boundaries.
A hybrid deep learning approach for gland segmentation in prostate histopathological images

In digital pathology, the morphology and architecture of prostate glands have been routinely adopted by pathologists to evaluate the presence of cancer tissue. The manual annotations are operator-dependent, error-prone and time-consuming. The automated segmentation of prostate glands can be very challenging too due to large appearance variation and serious degeneration of these histological structures. A new image segmentation method, called RINGS (Rapid IdentificatioN of Glandural Structures), is presented to segment prostate glands in histopathological images. We designed a novel glands segmentation strategy using a multi-channel algorithm that exploits and fuses both traditional and deep learning techniques. Specifically, the proposed approach employs a hybrid segmentation strategy based on stroma detection to accurately detect and delineate the prostate glands contours. Automated results are compared with manual annotations and seven state-of-the-art techniques designed for glands segmentation. Being based on stroma segmentation, no performance degradation is observed when segmenting healthy or pathological structures. Our method is able to delineate the prostate gland of the unknown histopathological image with a dice score of 90.16 % and outperforms all the compared state-of-the-art methods. To the best of our knowledge, the RINGS algorithm is the first fully automated method capable of maintaining a high sensitivity even in the presence of severe glandular degeneration. The proposed method will help to detect the prostate glands accurately and assist the pathologists to make accurate diagnosis and treatment. The developed model can be used to support prostate cancer diagnosis in polyclinics and community care centres.
On the vanishing and exploding gradient problem in Gated Recurrent Units

Recurrent Neural Networks are applied in areas such as speech recognition, natural language and video processing, and the identification of nonlinear state space models. Conventional Recurrent Neural Networks, e.g. the Elman Network, are hard to train. A more recently developed class of recurrent neural networks, so-called Gated Units, outperform their counterparts on virtually every task. This paper aims to provide additional insights into the differences between RNNs and Gated Units in order to explain the superior perfomance of gated recurrent units. It is argued, that Gated Units are easier to optimize not because they solve the vanishing gradient problem, but because they circumvent the emergence of large local gradients.
Adapting Gaussian YOLOv3 with transfer learning for overhead view human detection in smart cities and societies

Nowadays, deep neural networks are widely applied in sustainable smart cities and societies, including smart manufacturing, healthcare, industries, agriculture, surveillance, and various artificial intelligence-based real-life applications. In this regard, the human detection system has gained notable attention since it is recognized as a crucial task in intelligent surveillance applications. Researchers practiced a variety of computer vision and deep neural networks-based techniques for human detection-based applications; however, they often focused on the frontal view camera perspective. Thus, in this work, we have introduced a human detection system for intelligent surveillance in smart cities and societies with a completely distinct perspective, i.e., an overhead perspective that can provide sufficient visibility and coverage of a scene in congested and obstructed environments. However, human appearance can be difficult from such an extreme point of view, as there are significant variations in humans’ poses and appearances. Therefore, in this work, leveraging the deep neural network-based object detection technique, the Gaussian YOLOv3 algorithm is used for human detection. The algorithm determines the bounding box uncertainty by modeling its coordinates as a Gaussian parameter, improving accuracy and reducing false positives. A Gaussian YOLOv3 is combined with channel attention and feature intertwine modules to improve specific feature maps. The channel attention module is combined with the feature map to learn each channel's weight autonomously, improve the key features, and enhance the network's ability to discriminate between humans and background. At the same time, different channels of the feature map are intertwined to obtain more representative features. Finally, the features obtained from the attention and feature intertwine modules are fused to form an improved feature map. In addition, to further increase the detection accuracy of the algorithm for human detection, transfer learning is adopted. The experimental outcomes reveal that training improves the Gaussian YOLOv3 algorithm's potential for human detection with an overall detection accuracy of 94%.
Heterogeneous Attention Network for Effective and Efficient Cross-modal Retrieval

Traditionally, the task of cross-modal retrieval is tackled through joint embedding. However, the global matching used in joint embedding methods often fails to effectively describe matchings between local regions of the image and words in the text. Hence they may not be effective in capturing the relevance between the text and the image. In this work, we propose a heterogeneous attention network (HAN) for effective and efficient cross-modal retrieval. The proposed HAN represents an image by a set of bounding box features and a sentence by a set of word features. The relevance between the image and the sentence is determined by the set-to-set matching between the set of word features and the set of bounding box features. To enhance the matching effectiveness, we exploit the proposed heterogeneous attention layer to provide the cross-modal context for word features as well as bounding box features. Meanwhile, to optimize the metric more effectively, we propose a new soft-max triplet loss, which adaptively gives more attention to harder negatives and thus trains the proposed HAN in a more effective manner compared with the original triplet loss. Meanwhile, the proposed HAN is efficient, and its lightweight architecture only needs a single GPU card for training. Extensive experiments conducted on two public benchmarks demonstrate the effectiveness and efficiency of our HAN. This work has been deployed in production Baidu Search Ads and is part of the "PaddleBox'' platform.
A Systematic Review on Harmony Search Algorithm: Theory, Literature, and Applications

Harmony search algorithm is the recently developed metaheuristic in the last decade. It mimics the behavior of a musician producing a perfect harmony. It has been used to solve the wide variety of real-life optimization problems due to its easy implementation over other metaheuristics. It has an ability to provide the balance between exploration and exploitation during search. In this paper, a systematic review on harmony search algorithm (HSA) is presented. The natural inspiration and conceptual framework of HSA are discussed. The control parameters of HSA are described with their mathematical foundation. The improvement and hybridization in HSA with other metaheuristics are discussed in detail. The applicability of HSA in different problem domains is studied. The future research directions of HSA are also investigated.