Reviewerly/exHarmony huggingface dataset:
Exploring Racial Bias within Face Recognition via per-subject Adversarially-Enabled Data Augmentation

Whilst face recognition applications are becoming increasingly prevalent within our daily lives, leading approaches in the field still suffer from performance bias to the detriment of some racial profiles within society. In this study, we propose a novel adversarial derived data augmentation methodology that aims to enable dataset balance at a per-subject level via the use of image-to-image transformation for the transfer of sensitive racial characteristic facial features. Our aim is to automatically construct a synthesised dataset by transforming facial images across varying racial domains, while still preserving identity-related features, such that racially dependant features subsequently become irrelevant within the determination of subject identity. We construct our experiments on three significant face recognition variants: Softmax, CosFace and ArcFace loss over a common convolutional neural network backbone. In a side-by-side comparison, we show the positive impact our proposed technique can have on the recognition performance for (racial) minority groups within an originally imbalanced training dataset by reducing the per-race variance in performance.
MoBiNet: A Mobile Binary Network for Image Classification

MobileNet and Binary Neural Networks are two among the most widely used techniques to construct deep learning models for performing a variety of tasks on mobile and embedded platforms. In this paper, we present a simple yet efficient scheme to exploit MobileNet binarization at activation function and model weights. However, training a binary network from scratch with separable depth-wise and point-wise convolutions in case of MobileNet is not trivial and prone to divergence. To tackle this training issue, we propose a novel neural network architecture, namely MoBi- Net - Mobile Binary Network in which skip connections are manipulated to prevent information loss and vanishing gradient, thus facilitate the training process. More importantly, while existing binary neural networks often make use of cumbersome backbones such as Alex-Net, ResNet, VGG-16 with float-type pre-trained weights initialization, our MoBi- Net focuses on binarizing the already-compressed neural networks like MobileNet without the need of a pre-trained model to start with. Therefore, our proposal results in an effectively small model while keeping the accuracy comparable to existing ones. Experiments on ImageNet dataset show the potential of the MoBiNet as it achieves 54.40% top-1 accuracy and dramatically reduces the computational cost with binary operators.
An Integrative Review and Analysis of Cybersecurity Research: Current State and Future Directions

ABSTRACT Advances in information technology have greatly changed communications and business transactions between firms and their customers and suppliers. As a result, cybersecurity risk attracts ever increasing attention from firms, regulators, customers, shareholders, and academics. For instance, the Securities and Exchange Commission has released guidance on the disclosure of cybersecurity risks and incidents, along with potential internal control solutions, in the managers' discussion and analysis section of 10-K annual financial reports. Despite increasing interest in cybersecurity research, the literature lacks an integrative review of existing research identifying opportunities for future cybersecurity developments. In this study, we conduct an extensive analysis of cybersecurity-related papers in the accounting, information systems, computer science, and general business disciplines. Our review integrates and classifies 68 cybersecurity papers, examines cybersecurity determinants, consequences, and remedial strategies, and identifies future research opportunities based on the current state of the literature.
PrivateDL: Privacy‐preserving collaborative deep learning against leakage from gradient sharing

Large-scale data training is vital to the generalization performance of deep learning (DL) models. However, collecting data directly is associated with increased risk of privacy disclosure, particularly in special fields such as healthcare, finance, and genomics. To protect training data privacy, collaborative deep learning (CDL) has been proposed to enable joint training from multiple data owners while providing reliable privacy guarantee. However, recent studies have shown that CDL is vulnerable to several attacks that could reveal sensitive information about the original training data. One of the most powerful attacks benefits from the leakage from gradient sharing during collaborative training process. In this study, we present a new CDL framework, PrivateDL, to effectively protect private training data against leakage from gradient sharing. Unlike conventional training process that trains on private data directly, PrivateDL allows effective transfer of relational knowledge from sensitive data to public data in a privacy-preserving way, and enables participants to jointly learn local models based on the public data with noise-preserving labels. This way, PrivateDL establishes a privacy gap between the local models and the private datasets, thereby ensuring privacy against the attacks launched to the local models through gradient sharing. Moreover, we propose a new algorithm called Distributed Aggregation Stochastic Gradient Descent, which is designed to improve the efficiency and accuracy of CDL, especially in the asynchronous training mode. Experimental results demonstrate that PrivateDL preserves data privacy with reasonable performance overhead.
Blind Binocular Visual Quality Predictor Using Deep Fusion Network

Blind binocular visual quality prediction (BVQP) is more challenging than blind monocular visual quality prediction (MVQP). Recently, the application of convolutional neural networks (CNNs) to blind MVQP has resulted in significant progress in that area. In contrast, the adoption of deep learning for blind BVQP has received scant attention. In this study, we devised an end-to-end deep fusion network (DFNet) model trained in a unified framework for blind BVQP. This core prediction engine comprises monocular feature encoding networks and binocular feature fusion networks, followed by a quality prediction layer. The monocular feature encoding networks are first established to capture the low- and high-level monocular features of the left and right retinal views, respectively. Subsequently, these monocular features are integrated by the binocular feature fusion networks to obtain binocular deep features. Finally, the final binocular visual quality is predicted by quality prediction networks. Comparisons via experiments using two standard subject-rated BVQP datasets indicate that the proposed DFNet architecture achieves highly consistent alignment with human assessment and outperforms most relevant existing models.
An Intriguing Struggle of CNNs in JPEG Steganalysis and the OneHot Solution

Deep convolutional neural networks (CNNs) have become the tool of choice for steganalysis because they outperform older feature-based detectors by a large margin. However, recent work points at cases where feature-based detectors perform better than CNNs due to their failure to compute simple statistics of DCT coefficients. We introduce a shallow “OneHot” CNN, which encodes DCT coefficients using clipped one-hot encoding into a binary volumetric representation of the DCT plane fed to a convolutional block designed to learn relevant intra-block and inter-block relationships using vanilla and dilated convolutions. Methodology for plugging the “OneHot” network into conventional steganalysis CNNs is also introduced for an end-to-end learnable detector with improved performance.
Disentangling Human Dynamics for Pedestrian Locomotion Forecasting with Noisy Supervision

We tackle the problem of Human Locomotion Forecasting, a task for jointly predicting the spatial positions of several keypoints on human body in the near future under an egocentric setting. In contrast to the previous work that aims to solve either the task of pose prediction or trajectory forecasting in isolation, we propose a framework to unify these two problems and address the practically useful task of pedestrian locomotion prediction in the wild. Among the major challenges in solving this task is the scarcity of annotated egocentric video datasets with dense annotations for pose, depth, or egomotion. To surmount this difficulty, we use state-of-the-art models to generate (noisy) annotations and propose robust forecasting models that can learn from this noisy supervision. We present a method to disentangle the overall pedestrian motion into easier to learn subparts by uti-lizing a pose completion and a decomposition module. The completion module fills in the missing key-point annotations and the decomposition module breaks the cleaned locomotion down to global (trajectory) and local (pose keypoint movements). Further, with Quasi RNN as our backbone, we propose a novel hierarchical trajectory forecasting network that utilizes low-level vision domain specific signals like egomotion and depth to predict the global trajectory. Our method leads to state-of-the-art results for the prediction of human locomotion in the egocentric view.
Multi-source information fusion based heterogeneous network embedding

Heterogeneous network embedding aims to learn a mapping between network data in original topological space and vectored data in low dimensional latent space, while encoding valuable information, such as structural and semantic information. The resulting vector representation has shown promising performance for extensive real-world applications, such as node classification and node clustering. However, most of existing methods merely focus on modeling network structural information, ignoring the rich multi-source information of different types of nodes. In this paper, we propose a novel Multi-source Information Fusion based Heterogeneous Network Embedding (MIFHNE) approach. We first capture the semantic information using the strategy of meta-graph based random walk. Subsequently, we jointly model the structural proximity, attribute information and label information in the framework of Nonnegative Matrix Factorization (NMF). Theoretical proofs and comprehensive experiments on two real-world heterogeneous network datasets demonstrate the feasibility and effectiveness of our approach.
Despeckling of clinical ultrasound images using deep residual learning

Abstract Background and objective Ultrasound is the non-radioactive imaging modality used in the diagnosis of various diseases related to the internal organs of the body. The presence of speckle noise in ultrasound image (UI) is inevitable and may affect resolution and contrast of the image. Existence of the speckle noise degrades the visual evaluation of the image. The despeckling of UI is a desirable pre-processing step in computer-aided UI based diagnosis systems. Methods This paper proposes a novel method for despeckling UIs using pre-trained residual learning network (RLN). Initially, RLN is trained with pristine and its corresponding noisy images in order to achieve a better performance. The developed method chooses a pre-trained RLN for despeckling UIs with less computational resources. But the training procedure of RLN from scratch is computationally demanding. The pre-trained RLN is a blind despeckling approach and does not require any fine tuning and noise level estimation. The presented approach shows superiority in the removal of speckle noise as compared to the existing state-of-art methods. Results To highlight the effectiveness of the proposed method the pristine images from the Waterloo dataset has been considered. The proposed pre-trained RLN based UI despeckling method resulted in a better peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM) at different speckle noise levels. The no-reference image quality approach is adopted to ensure robustness of the established method for real time UI. From results it is obvious that, the performance of the proposed method is superior than the existing methods in terms of naturalness image quality evaluator (NIQE). Conclusions From the experimental results, it is clear that the proposed method outperforms the existing despeckling methods in terms of both artificially added and naturally occurring speckle images.
OntoPowSys: A power system ontology for cross domain interactions in an eco industrial park

Knowledge management in multi-domain, heterogeneous industrial networks like an Eco-Industrial Park (EIP) is a challenging task. In this paper, an ontology-based management system has been proposed for addressing this challenge. It focuses on the power systems domain and provides a framework for integrating this knowledge with the other domains of an EIP. The proposed ontology, OntoPowSys is expressed using a Description Logics (DL) syntax and the OWL2 language was used to make it alive. It is then used as a part of the Knowledge Management System (KMS) in a virtual EIP called the J-Park Simulator (JPS). The advantages of the proposed approach are demonstrated by conducting two case studies on the JPS. The first case study illustrates the application of optimal power flow (OPF) in the electrical network of the JPS. The second case study plays an important role in understanding the cross-domain interactions between the chemical and electrical engineering domains in a bio-diesel plant of the JPS. These case studies are available as web services on the JPS website. The results showcase the advantages of using ontologies in the development of decision support tools. These tools are capable of taking into account contextual information on top of data during their decision-making processes. They are also able to exchange knowledge across different domains without the need for a communication interface.
MBFT: A New Consensus Algorithm for Consortium Blockchain

Blockchain technology is an emerging distributed ledger technology that has exploratory applications in many areas.The consensus algorithm, as the core module of the blockchain, has an important impact on the security, scalability, and efficiency of the blockchain network.The consensus algorithm is also a popular topic in current blockchain technology research.In existing consortium blockchains and public blockchains, the blockchain has low efficiency or poor fault tolerance because of the limitations of the consensus algorithm.To both ensure the fault tolerance of the blockchain and improve the scalability and throughput, in this paper, we propose a new type of consensus algorithm: mixed Byzantine fault tolerance (MBFT).MBFT uses sharding and layered technology.MBFT functionally partitions the nodes that participate in the consensus process and improves the scalability and efficiency without sacrificing security.MBFT also introduces a random node selection mechanism and a credit mechanism to improve security and fault tolerance.We analyze the security and experiment on transaction throughput in a real network environment.The results prove that MBFT has good security and scalability and high throughput.
R-Net: A Relationship Network for Efficient and Accurate Scene Text Detection

This paper introduces a novel bi-directional con-volutional framework to cope with the large-variance scale problem in scene text detection. Due to the lack of scale normalization in recent CNN-based methods, text instances with large-variance scale are activated inconsistently in feature maps, which makes it hard for CNN-based methods to accurately locate multi-size text instances. Thus, we propose the relationship network (R-Net) that maps multi-scale convolutional features to a scale-invariant space to obtain consistent activation of multi-size text instances. Firstly, we implement an FPN-like backbone with a Spatial Relationship Module (SPM) to extract multi-scale features with powerful spatial semantics. Then, a Scale Relationship Module (SRM) constructed on feature pyramid propagates contextual scale information in sequential features through a bi-directional convolutional operation. SRM supplements the multi-scale information in different feature maps to obtain consistent activation of multi-size text instances. Compared with previous approaches, R-Net effectively handles the large-variance scale problem without complicated post processing and complex hand-crafted hyperparameter setting. Extensive experiments conducted on several benchmarks verify that our R-Net obtains state-of-the-art performance on both accuracy and efficiency. More specifically, R-Net achieves an F-measure of 85.6% at 21.4 frames/s and an F-measure of 81.7% at 11.8 frames/s for ICDAR 2015 and MSRA-TD500 datasets respectively, which is the latest SOTA. The code is available on https://github.com/wangyuxin87/R-Net.
A New Linguistic Petri Net for Complex Knowledge Representation and Reasoning

Fuzzy Petri nets (FPNs) are a useful instrument for modelling expert systems to conduct knowledge representation and reasoning. Many studies have been carried out for improving the performance of FPNs in terms of their accurate representation of knowledge and power of approximate reasoning. Nevertheless, the current representation methods with FPNs are unable to handle the uncertain linguistic knowledge given by domain experts and the reliability of their judgments. In addition, the existing reasoning algorithms have no way to capture the interrelationship of the propositions with the same output transition. Therefore, we present a new type of FPNs, called 2-dimensional uncertain linguistic Petri nets (2DULPNs). The 2-dimensional uncertain linguistic variables (2DULVs) and Choquet integral are combined for knowledge representation and reasoning for the first time. The truth degrees of propositions, thresholds and certainty values of linguistic production rules are denoted as 2DULVs. Some new aggregated operators based on Choquet integral are proposed and used in the approximate reasoning to capture the interactions among antecedent propositions. Finally, an equipment fault diagnosis example is provided to illustrate the correctness and effectiveness of the proposed 2DULPN model.
An end-to-end joint model for evidence information extraction from court record document

Information extraction is one of the important tasks in the field of Natural Language Processing (NLP). Most of the existing methods focus on general texts and little attention is paid to information extraction in specialized domains such as legal texts. This paper explores the task of information extraction in the legal field, which aims to extract evidence information from court record documents (CRDs). In the general domain, entities and relations are mostly words and phrases, indicating that they do not span multiple sentences. In contrast, evidence information in CRDs may span multiple sentences, while existing models cannot handle this situation. To address this issue, we first add a classification task in addition to the extraction task. We then formulate the two tasks as a multi-task learning problem and present a novel end-to-end model to jointly address the two tasks. The joint model adopts a shared encoder followed by separate decoders for the two tasks. The experimental results on the dataset show the effectiveness of the proposed model, which can obtain 72.36% F1 score, outperforming previous methods and strong baselines by a large margin.
Quantifying uncertainty for remote spectroscopy of surface composition

Remote surface measurements by imaging spectrometers play an important role in planetary and Earth science. To make these measurements, investigators calibrate instrument data to absolute units, invert physical models to estimate atmospheric effects, and then determine surface properties from the spectral reflectance. This study quantifies the uncertainty in this process. Global missions demand predictive uncertainty models that can estimate future errors for varied environments and observing conditions. Here we validate uncertainty predictions with remote surface composition retrievals and in situ measurements in a field analogue of Earth and planetary exploration. We consider rover transects at Cuprite, Nevada, and remote observations by NASA's Next-Generation Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG). We show that accounting for input uncertainties can benefit mineral detection methods such as constrained spectrum fitting. This suggests that operational uncertainty estimates could improve future NASA missions like the Earth Mineral dust source InvesTigation (EMIT) and the Lunar Trailblazer mission, as well as NASA's Decadal Surface Biology and Geology (SBG) Investigation.
Coactive design of explainable agent-based task planning and deep reinforcement learning for human-UAVs teamwork

Unmanned Aerial Vehicles (UAVs) are useful in dangerous and dynamic tasks such as search-and-rescue, forest surveillance, and anti-terrorist operations. These tasks can be solved better through the collaboration of multiple UAVs under human supervision. However, it is still difficult for human to monitor, understand, predict and control the behaviors of the UAVs due to the task complexity as well as the black-box machine learning and planning algorithms being used. In this paper, the coactive design method is adopted to analyze the cognitive capabilities required for the tasks and design the interdependencies among the heterogeneous teammates of UAVs or human for coherent collaboration. Then, an agent-based task planner is proposed to automatically decompose a complex task into a sequence of explainable subtasks under constrains of resources, execution time, social rules and costs. Besides, a deep reinforcement learning approach is designed for the UAVs to learn optimal policies of a flocking behavior and a path planner that are easy for the human operator to understand and control. Finally, a mixed-initiative action selection mechanism is used to evaluate the learned policies as well as the human’s decisions. Experimental results demonstrate the effectiveness of the proposed methods.
ARMath: Augmenting Everyday Life with Math Learning

We introduce ARMath, a mobile Augmented Reality (AR) system that allows ch ildren to discover mathematical concepts in familiar, ord inary objects and engage with math problems in meaningful contexts. Leveraging advanced computer vision, ARMath recognizes everyday objects, visualizes their mathematical attributes, and turns them into tangible or virtual manipulatives. Using the manipulatives, children can solve problems that situate math operations or concepts in specific everyday contexts. Informed by four participatory design sessions with teachers and children, we developed five ARMath modules to support basic arithmetic and 2D geometry. We also conducted an exploratory evaluation of ARMath with 27 children (ages 5-8) at a local children's museum. Our findings demonstrate how ARMath engages children in math learning, how failures in AI can be used as learning opportunities, and challenges that children face when using ARMath.