Reviewerly/exHarmony huggingface dataset:
A deep learning approach for face recognition based on angularly discriminative features

Face recognition in digital images or video frames has several real-world applications in the modern zone of computer vision. Loss function plays a vital role in deep face recognition. Recently, several loss functions have been proposed in classification techniques to reduce the number of model errors. Among several loss functions, softmax loss implemented either multiplicative angular or additive cosine margin. These individual margins have less capacity to reduce the model`s errors. To fill this gap; we proposed a hybrid angularly discriminative features by combining multiplicative angular and additive cosine margin to improve the efficiency of angular softmax loss and large margin cosine. We trained proposed model using CASIA-WebFace dataset and testing has been performed on Labeled Face in the Wild (LFW), YouTube Faces (YTF), VGGFace1 and VGGFace2. The experimental result shows 99.77% accuracy on LFW dataset whereas 96.40% accuracy achieved on YTF dataset which is higher than the existing similar state-of-the-art techniques.
Source Compression with Bounded DNN Perception Loss for IoT Edge Computer Vision

IoT and deep learning based computer vision together create an immense market opportunity, but running deep neural networks (DNNs) on resource-constrained IoT devices remains challenging. Offloading DNN inference to an edge server is a promising solution, but limited wireless bandwidth bottlenecks its end-to-end performance and scalability. While IoT devices can adopt source compression to cope with the limited bandwidth, existing compression algorithms (or codecs) are not designed for DNN (but for human eyes), and thus, suffer from either low compression rates or high DNN inference errors. This paper presents GRACE, a DNN-aware compression algorithm that facilitates the edge inference by significantly saving the network bandwidth consumption without disturbing the inference performance. Given a target DNN, GRACE (i) analyzes DNN's perception model w.r.t both spatial frequencies and colors and (ii) generates an optimized compression strategy for the model -- one-time offline process. Next, GRACE deploys thus-generated compression strategy at IoT devices (or source) to perform online source compression within the existing codec framework, adding no extra overhead. We prototype GRACE on JPEG (the most popular image codec framework), and our evaluation results show that GRACE indeed achieves the superior compression performance over existing strategies for key DNN applications. For semantic segmentation tasks, GRACE reduces a source size by 23% compared to JPEG with similar interference accuracy (0.38% lower than GRACE). Further, GRACE even achieves 7.5% higher inference accuracy than JPEG with a commonly used quality level of 75 does. For classification tasks, GRACE reduces the bandwidth consumption by 90% over JPEG with the same inference accuracy.
DeepFlux for Skeletons in the Wild

Computing object skeletons in natural images is challenging, owing to large variations in object appearance and scale, and the complexity of handling background clutter. Many recent methods frame object skeleton detection as a binary pixel classification problem, which is similar in spirit to learning-based edge detection, as well as to semantic segmentation methods. In the present article, we depart from this strategy by training a CNN to predict a two-dimensional vector field, which maps each scene point to a candidate skeleton pixel, in the spirit of flux-based skeletonization algorithms. This ``image context flux'' representation has two major advantages over previous approaches. First, it explicitly encodes the relative position of skeletal pixels to semantically meaningful entities, such as the image points in their spatial context, and hence also the implied object boundaries. Second, since the skeleton detection context is a region-based vector field, it is better able to cope with object parts of large width. We evaluate the proposed method on three benchmark datasets for skeleton detection and two for symmetry detection, achieving consistently superior performance over state-of-the-art methods.
A Brief Review of Image Denoising Algorithms and Beyond

The recent advances in hardware and imaging systems made the digital cameras ubiquitous. Although the development of hardware has steadily improved the quality of images for the last several decades, image degradation is unavoidable due to the many factors affecting the image acquisition process and the subsequent post-processing. Image denoising, which aims to reconstruct a high quality image from its degraded observation, is a classical yet still very active topic in the area of low-level computer vision. It represents an important building block in real applications such as digital photography, medical image analysis, remote sensing, surveillance and digital entertainment. Also, image denoising constitutes an ideal test bed for evaluating image prior modeling methods. In this paper, we briefly review recent progresses in image denoising. We firstly present an overview of prior modeling approaches used in image denoising task. Then, we review conventional sparse representation based denoising algorithms, low-rank based denoising algorithms and recently proposed deep neural networks based approaches. At last, we discuss some emerging topics and open problems about image denoising.
Assessing information security risks in the cloud: A case study of Australian local government authorities

Cloud computing enables cost-effective and scalable growth of IT services that can enhance government services. Despite the Australian Federal Government's ‘cloud-first’ strategy and policies, and the Queensland State Government's ‘digital-first’ strategy, cloud services adoption at local government level has been limited—largely due to data security concerns. We reviewed the ISO 27002 Information Security standard with extant literature and found that operational security, individual awareness and compliance matters pose more significant government challenges than the often-highlighted technical and process-oriented cloud security requirements. This study identifies and explores the critical factors associated with information security requirements of cloud services within the Australian regional local government context. We conducted 21 field interviews with IT managers, and surveyed 480 IT staff from Australia's 47 regional local governments. We propose a conceptual cloud computing security requirements model with four components – data security; risk assessment; legal & compliance requirements; and business & technical requirements – in order to promote a balanced view on cloud security for governments. Using this model, governments can work together to demand uniform security requirements for adopting cloud services.
BlueConnect: Decomposing all-reduce for deep learning on heterogeneous network hierarchy

As deep neural networks get more complex and input datasets get larger, it can take days or even weeks to train a deep neural network to the desired accuracy. Therefore, enabling distributed deep learning at a massive scale is critical since it offers the potential to reduce the training time from weeks to hours. In this article, we present BlueConnect, an efficient communication library for distributed deep learning that is highly optimized for popular GPU-based platforms. BlueConnect decomposes a single all-reduce operation into a large number of parallelizable reduce–scatter and all-gather operations to exploit the tradeoff between latency and bandwidth and adapt to a variety of network configurations. Therefore, each individual operation can be mapped to a different network fabric and take advantage of the best performing implementation for the corresponding fabric. According to our experimental results on two system configurations, BlueConnect can outperform the leading industrial communication library by a wide margin, and the BlueConnect-integrated Caffe2 can significantly reduce synchronization overhead by 87% on 192 GPUs for Resnet-50 training over prior schemes.
Wearable Augmented Reality Application for Shoulder Rehabilitation

Augmented reality (AR) technology is gaining popularity and scholarly interest in the rehabilitation sector because of the possibility to generate controlled, user-specific environmental and perceptual stimuli which motivate the patient, while still preserving the possibility to interact with the real environment and other subjects, including the rehabilitation specialist. The paper presents the first wearable AR application for shoulder rehabilitation, based on Microsoft HoloLens, with real-time markerless tracking of the user’s hand. Potentialities and current limits of commercial head-mounted displays (HMDs) are described for the target medical field, and details of the proposed application are reported. A serious game was designed starting from the analysis of a traditional rehabilitation exercise, taking into account HoloLens specifications to maximize user comfort during the AR rehabilitation session. The AR application implemented consistently meets the recommended target frame rate for immersive applications with HoloLens device: 60 fps. Moreover, the ergonomics and the motivational value of the proposed application were positively evaluated by a group of five rehabilitation specialists and 20 healthy subjects. Even if a larger study, including real patients, is necessary for a clinical validation of the proposed application, the results obtained encourage further investigations and the integration of additional technical features for the proposed AR application.
Whole Stomach 3D Reconstruction and Frame Localization From Monocular Endoscope Video

Gastric endoscopy is a common clinical practice that enables medical doctors to diagnose various lesions inside a stomach. In order to identify the location of a gastric lesion such as early cancer and a peptic ulcer within the stomach, this work addresses to reconstruct the color-textured 3D model of a whole stomach from a standard monocular endoscope video and localize any selected video frame to the 3D model. We examine how to enable structure-from-motion (SfM) to reconstruct the whole shape of a stomach from endoscope images, which is a challenging task due to the texture-less nature of the stomach surface. We specifically investigate the combined effect of chromo-endoscopy and color channel selection on SfM to increase the number of feature points. We also design a plane fitting-based algorithm for 3D point outliers removal to improve the 3D model quality. We show that whole stomach 3D reconstruction can be achieved (more than 90% of the frames can be reconstructed) by using red channel images captured under chromo-endoscopy by spreading indigo carmine (IC) dye on the stomach surface. In experimental results, we demonstrate the reconstructed 3D models for seven subjects and the application of lesion localization and reconstruction. The methodology and results presented in this paper could offer some valuable reference to other researchers and also could be an excellent tool for gastric surgeons in various computer-aided diagnosis applications.
CAMP: Co-Attention Memory Networks for Diagnosis Prediction in Healthcare

Diagnosis prediction, which aims to predict future health information of patients from historical electronic health records (EHRs), is a core research task in personalized healthcare. Although some RNN-based methods have been proposed to model sequential EHR data, these methods have two major issues. First, they cannot capture fine-grained progression patterns of patient health conditions. Second, they do not consider the mutual effect between important context (e.g., patient demographics) and historical diagnosis. To tackle these challenges, we propose a model called Co-Attention Memory networks for diagnosis Prediction (CAMP), which tightly integrates historical records, fine-grained patient conditions, and demographics with a three-way interaction architecture built on co-attention. Our model augments RNNs with a memory network to enrich the representation capacity. The memory network enables analysis of fine-grained patient conditions by explicitly incorporating a taxonomy of diseases into an array of memory slots. We instantiate the READ/WRITE operations of the memory network so that the memory cooperates effectively with the patient demographics through co-attention mechanism. Experiments on real-world datasets demonstrate that CAMP consistently performs better than state-of-the-art methods.
Hierarchical Global-Local Temporal Modeling for Video Captioning

In this paper, a Hierarchical Temporal Model (HTM) is proposed for the video captioning task, based on exploring the global and local temporal structure to better recognize fine-grained objects and actions. In our HTM, the encoder and decoder are hierarchically aligned according to different levels of features. The encoder applies two LSTM layers to construct temporal structures at both frame-level and object-level where the attention mechanism is applied to locate objects of interest, and the decoder uses corresponding LSTM layers to extract pivotal features from global to local through multi-level attention mechanism. Moreover, the local temporal structure is constructed implicitly from candidate object-oriented features under the guidance of global temporal-spatial representation, that could generate more accurate descriptions in handling shot-switching problems. Experiments on the widely used Microsoft Video Description Corpus (MSVD) and Charades datasets demonstrate the effectiveness of our proposed approach when compared to the state-of-the-art methods.
Robust Subspace Discovery by Block-diagonal Adaptive Locality-constrained Representation

We propose a novel and unsupervised representation learning model, i.e., Robust Block-Diagonal Adaptive Locality-constrained Latent Representation (rBDLR). rBDLR is able to recover multi-subspace structures and extract the adaptive locality-preserving salient features jointly. Leveraging on the Frobenius-norm based latent low-rank representation model, rBDLR jointly learns the coding coefficients and salient features, and improves the results by enhancing the robustness to outliers and errors in given data, preserving local information of salient features adaptively and ensuring the block-diagonal structures of the coefficients. To improve the robustness, we perform the latent representation and adaptive weighting in a recovered clean data space. To force the coefficients to be block-diagonal, we perform auto-weighting by minimizing the reconstruction error based on salient features, constrained using a block-diagonal regularizer. This ensures that a strict block-diagonal weight matrix can be obtained and salient features will possess the adaptive locality preserving ability. By minimizing the difference between the coefficient and weights matrices, we can obtain a block-diagonal coefficients matrix and it can also propagate and exchange useful information between salient features and coefficients. Extensive results demonstrate the superiority of rBDLR over other state-of-the-art methods.
Real-Time Fault Detection for IIoT Facilities Using GBRBM-Based DNN

Fault detection is a fundamental requirement for Industrial Internet of Things (IIoT), such as the process industry. This article first reviews the recent studies focusing on applying the fault detection techniques to the IIoT networks. However, we find that numerous studies focus on the resource utilization and workload allocation. The fault detection toward IIoT facilities is still in its immature stage because the existing approaches are not accurate enough for the stringent fault detection in IIoT networks. To this end, we present a novel algorithm, named Gaussian Bernoulli restricted Boltzmann machines (GBRBMs)-based deep neural network (DNN), to transform the fault detection into a classification problem. The real trace-driven experiments show that the proposed scheme outperforms other baseline machine learning methods. We anticipate that this article can inspire blooming studies on the related topics of smart IIoT networks.
An Axiomatic Approach to Block Rewards

Proof-of-work blockchains reward each miner for one completed block by an amount that is, in expectation, proportional to the number of hashes the miner contributed to the mining of the block. Is this proportional allocation rule optimal? And in what sense? And what other rules are possible? In particular, what are the desirable properties that any "good" allocation rule should satisfy? To answer these questions, we embark on an axiomatic theory of incentives in proof-of-work blockchains at the time scale of a single block. We consider desirable properties of allocation rules including: symmetry; budget balance (weak or strong); sybil-proofness; and various grades of collusion-proofness. We show that Bitcoin's proportional allocation rule is the unique allocation rule satisfying a certain system of properties, but this does not hold for slightly weaker sets of properties, or when the miners are not risk-neutral. We also point out that a rich class of allocation rules can be approximately implemented in a proof-of-work blockchain.
Testing the Robustness of Attribution Methods for Convolutional Neural Networks in MRI-Based Alzheimer’s Disease Classification

Attribution methods are an easy to use tool for investigating and validating machine learning models. Multiple methods have been suggested in the literature and it is not yet clear which method is most suitable for a given task. In this study, we tested the robustness of four attribution methods, namely gradient * input, guided backpropagation, layer-wise relevance propagation and occlusion, for the task of Alzheimer’s disease classification. We have repeatedly trained a convolutional neural network (CNN) with identical training settings in order to separate structural MRI data of patients with Alzheimer’s disease and healthy controls. Afterwards, we produced attribution maps for each subject in the test data and quantitatively compared them across models and attribution methods. We show that visual comparison is not sufficient and that some widely used attribution methods produce highly inconsistent outcomes.
SynDeMo: Synergistic Deep Feature Alignment for Joint Learning of Depth and Ego-Motion

Despite well-established baselines, learning of scene depth and ego-motion from monocular video remains an ongoing challenge, specifically when handling scaling ambiguity issues and depth inconsistencies in image sequences. Much prior work uses either a supervised mode of learning or stereo images. The former is limited by the amount of labeled data, as it requires expensive sensors, while the latter is not always readily available as monocular sequences. In this work, we demonstrate the benefit of using geometric information from synthetic images, coupled with scene depth information, to recover the scale in depth and ego-motion estimation from monocular videos. We developed our framework using synthetic image-depth pairs and unlabeled real monocular images. We had three training objectives: first, to use deep feature alignment to reduce the domain gap between synthetic and monocular images to yield more accurate depth estimation when presented with only real monocular images at test time. Second, we learn scene specific representation by exploiting self-supervision coming from multi-view synthetic images without the need for depth labels. Third, our method uses single-view depth and pose networks, which are capable of jointly training and supervising one another mutually, yielding consistent depth and ego-motion estimates. Extensive experiments demonstrate that our depth and ego-motion models surpass the state-of-the-art, unsupervised methods and compare favorably to early supervised deep models for geometric understanding. We validate the effectiveness of our training objectives against standard benchmarks thorough an ablation study.
Model-Agnostic Interpretability with Shapley Values

The ability to explain in understandable terms, why a machine learning model makes a certain prediction is becoming immensely important, as it ensures trust and transparency in the decision process of the model. Complex models, such as ensemble or deep learning models, are hard to interpret. Various methods have been proposed that deal with this matter. Shapley values provide accurate explanations, as they assign each feature an importance value for a particular prediction. However, the exponential complexity of their calculation is dealt efficiently only in decision tree-based models. Another method is surrogate models, which emulate a black-box model's behavior and provide explanations effortlessly, since they are constructed to be interpretable. Surrogate models are model-agnostic, but they produce only approximate explanations, which cannot always be trusted. We propose a method that combines these two approaches, so that we can take advantage of the model-agnostic part of the surrogate models, as well as the explanatory power of the Shapley values. We introduce a new metric, Top <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">j</sub> Similarity, that measures the similitude of two given explanations, produced by Shapley values, in order to evaluate our work. Finally, we recommend ways on how this method could be improved further.
AM-LFS: AutoML for Loss Function Search

Designing an effective loss function plays an important role in visual analysis. Most existing loss function designs rely on hand-crafted heuristics that require domain experts to explore the large design space, which is usually sub-optimal and time-consuming. In this paper, we propose AutoML for Loss Function Search (AM-LFS) which leverages REINFORCE to search loss functions during the training process. The key contribution of this work is the design of search space which can guarantee the generalization and transferability on different vision tasks by including a bunch of existing prevailing loss functions in a unified formulation. We also propose an efficient optimization framework which can dynamically optimize the parameters of loss function's distribution during training. Extensive experimental results on four benchmark datasets show that, without any tricks, our method outperforms existing hand-crafted loss functions in various computer vision tasks.
Efficient construction of a substitution box based on a Mordell elliptic curve over a finite field

Elliptic curve cryptography has been used in many security systems due to its small key size and high security compared with other cryptosystems. In many well-known security systems, a substitution box (S-box) is the only non-linear component. Recently, it has been shown that the security of a cryptosystem can be improved using dynamic S-boxes instead of a static S-box. This necessitates the construction of new secure S-boxes. We propose an efficient method to generate S-boxes that are based on a class of Mordell elliptic curves over prime fields and achieved by defining different total orders. The proposed scheme is developed in such a way that for each input it outputs an S-box in linear time and constant space. Due to this property, our method takes less time and space than the existing S-box construction methods over elliptic curves. Computational results show that the proposed method is capable of generating cryptographically strong S-boxes with security comparable to some of the existing S-boxes constructed via different mathematical structures.