Reviewerly/exHarmony huggingface dataset:
A 3D Deep Residual Convolutional Neural Network for Differential Diagnosis of Parkinsonian Syndromes on <sup>18</sup>F-FDG PET Images

Idiopathic Parkinsons disease and atypical parkinsonian syndromes have similar symptoms at early disease stages, which makes the early differential diagnosis difficult. Positron emission tomography with <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">18</sup> F-FDG shows the ability to assess early neuronal dysfunction of neurodegenerative diseases and is well established for clinical use. In the past decades, machine learning methods have been widely used for the differential diagnosis of parkinsonism based on metabolic patterns. Unlike these conventional machine learning methods relying on hand-crafted features, the deep convolutional neural networks, which have achieved significant success in medical applications recently, have the advantage of learning salient feature representations automatically and effectively. This advantage may offer more appropriate invisible features extracted from data for the enhancement of the diagnosis accuracy. Therefore, this paper develops a 3D deep convolutional neural network on <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">18</sup> F-FDG PET images for the automated early diagnosis. Furthermore, we depicted in saliency maps the decision mechanism of the deep learning method to assist the physiological interpretation of deep learning performance. The proposed method was evaluated on a dataset with 920 patients. In addition to improving the accuracy in the differential diagnosis of parkinsonism compared to state-of-the-art approaches, the deep learning methods also discovered saliency features in a number of critical regions (e.g., midbrain), which are widely accepted as characteristic pathological regions for movement disorders but were ignored in the conventional analysis of FDG PET images.
Soft biometrics‐based face image retrieval using improved grey wolf optimisation

Humans often use faces to recognise and identify individuals. Face recognition is one of the important tasks carried out by forensic examiners manually during their investigation, when there is an evidence image/video available from a crime scene. There is a growing demand for face recognition from unconstrained images, which is valuable for criminal investigators in identifying the victims. When an input face image is given to the proposed system, it filters from large scale face dataset to find the top-k similar faces. Deep convolutional neural network approach is employed to extract important features present in the input face image and improved grey wolf optimisation approach is proposed to select optimal features from the extracted features. It is then preceded by a soft biometric-based face matcher that helps in retrieving exact face image from the top-k similar faces matched using approximate nearest neighbour. The performance of the proposed system is evaluated using LFW, CASIA, Multi-pie and Color-Feret datasets. The proposed system addresses the challenge of searching face images from a large collection of unconstrained images by incorporating feature retrieval using DCNN and IGWO with soft biometric face matcher in cascaded framework which improves accuracy and reduces computation and retrieval time.
Context-Aware Temporal Knowledge Graph Embedding

Knowledge graph embedding (KGE) is an important technique used for knowledge graph completion (KGC). However, knowledge in practice is time-variant and many relations are only valid for a certain period of time. This phenomenon highlights the importance of temporal knowledge graph embeddings. Currently, existing temporal KGE methods only focus on one aspect of facts, i.e., the factual plausibility, while ignoring the other aspect, i.e., the temporal consistency. Temporal consistency models the interactions between a fact and its contexts, and thus is able to capture fine-granularity temporal relationships, such as temporal orders, temporal distances and overlapping. In order to determine the useful contexts for the fact to be predicted, we propose a two-way strategy for context selection. In particular, we decompose the target fact into two parts, relation and entities, and measure the usefulness of a context for each part respectively. Furthermore, we adopt deep neural networks to encode contexts and score the temporal consistency. This consistency is used with factual plausibility to model a fact. Due to the incorporation of temporal information and the interactions between facts and contexts, our model learns a more representative embeddings for temporal KG. We conduct extensive experiments on real world datasets and the experimental results verify the effectiveness of our proposals.
The 2-adic complexity of a class of binary sequences with optimal autocorrelation magnitude

Recently, a class of binary sequences with optimal autocorrelation magnitude has been presented by Su et al. based on Ding-Helleseth-Lam sequences and interleaving technique (Designs, Codes and Cryptography 86, 1329–1338, 2018). The linear complexity of this class of sequences has been proved to be large enough to resist the B-M Algorithm by Fan (Designs, Codes and Cryptography 86, 2441–2450, 2018). In this paper, we study the 2-adic complexities of these sequences with period 4p and show they are no less than 2p, i.e., its 2-adic complexity is large enough to resist the Rational Approximation Algorithm.
Attention-based fusion network for human eye-fixation prediction in 3D images

Human eye-fixation prediction in 3D images is important for many 3D applications, such as fine-grained 3D video object segmentation and intelligent bulletproof curtains. While the vast majority of existing 2D-based approaches cannot be applied, the main challenge lies in the inconsistency, or even conflict, between the RGB and depth saliency maps. In this paper, we propose a three-stream architecture to accurately predict human visual attention on 3D images end-to-end. First, a two-stream feature extraction network based on advanced convolutional neural networks is trained for RGB and depth, and hierarchical information is extracted from each ResNet-18. Then, these multi-level features are fed into the channel attention mechanism to suppress the feature space inconsistency and make the network focus on a significant target. The enhanced saliency map is fused step-by-step by VGG-16 to generate the final coarse saliency map. Finally, each coarse map is refined empirically through refinement blocks, and the network's own identification errors are corrected based on the acquired knowledge, thus converting the prediction saliency map from coarse to fine. The results of comparison of our model with six other state-of-the-art approaches on the NUS dataset (CC of 0.5579, KLDiv of 1.0903, AUC of 0.8339, and NSS of 2.3373) and the NCTU dataset (CC of 0.8614, KLDiv of 0.2681, AUC of 0.9143, and NSS of 2.3795) indicate that the proposed model consistently outperforms them by a considerable margin as it fully employs the channel attention mechanism.
Foreign direct investment and economic growth nexus in Africa

Purpose African countries are generally fragile. This and other related characteristics affect the potential for growth and development. The purpose of this paper is to investigate whether the effect of FDI on economic growth is contingent on a financial system that accounts for financial market fragility. An important point of departure from earlier studies is the adoption of a new measure of financial market fragility. Design/methodology/approach Given the uniqueness of the data set, the study uses a panel data and estimates an econometric model using an instrumental variable approach. For robustness purposes, a pooled ordinary least square is also estimated. Findings The study provides evidence that if the financial market is fragile as in the case of Africa, FDI inflows may have a marginally significant positive impact on economic growth. The findings suggest that fragility in the financial market is a key absorptive capacity and cannot be trivialised when exploring FDI–growth nexus in Africa. Research limitations/implications The uniqueness of the data set limited the time period of the study. Nonetheless, the findings are still crucial to policy makers in Africa and other developing countries with similar characteristics. Originality/value To the best of the authors’ knowledge, this is the first study in Africa to investigate the FDI–growth nexus which accounts for financial market fragility.
Infrared Image Super-Resolution Reconstruction Based on Quaternion and High-Order Overlapping Group Sparse Total Variation

Owing to the limitations of imaging principles and system imaging characteristics, infrared images generally have some shortcomings, such as low resolution, insufficient details, and blurred edges. Therefore, it is of practical significance to improve the quality of infrared images. To make full use of the information on adjacent points, preserve the image structure, and avoid staircase artifacts, this paper proposes a super-resolution reconstruction method for infrared images based on quaternion total variation and high-order overlapping group sparse. The method uses a quaternion total variation method to utilize the correlation between adjacent points to improve image anti-noise ability and reconstruction effect. It uses the sparsity of a higher-order gradient to reconstruct a clear image structure and restore smooth changes. In addition, we performed regularization by using the denoising method, alternating direction method of multipliers, and fast Fourier transform theory to improve the efficiency and robustness of our method. Our experimental results show that this method has excellent performance in objective evaluation and subjective visual effects.
Dimension independent bounds for general shallow networks

This paper proves an abstract theorem addressing in a unified manner two important problems in function approximation: avoiding curse of dimensionality and estimating the degree of approximation for out-of-sample extension in manifold learning. We consider an abstract (shallow) network that includes, for example, neural networks, radial basis function networks, and kernels on data defined manifolds used for function approximation in various settings. A deep network is obtained by a composition of the shallow networks according to a directed acyclic graph, representing the architecture of the deep network. In this paper, we prove dimension independent bounds for approximation by shallow networks in the very general setting of what we have called G-networks on a compact metric measure space, where the notion of dimension is defined in terms of the cardinality of maximal distinguishable sets, generalizing the notion of dimension of a cube or a manifold. Our techniques give bounds that improve without saturation with the smoothness of the kernel involved in an integral representation of the target function. In the context of manifold learning, our bounds provide estimates on the degree of approximation for an out-of-sample extension of the target function to the ambient space. One consequence of our theorem is that without the requirement of robust parameter selection, deep networks using a non-smooth activation function such as the ReLU, do not provide any significant advantage over shallow networks in terms of the degree of approximation alone.
A nonsubsampled countourlet transform based CNN for real image denoising

The state of the art deep learning based denoising methods can achieve great denoising results. However, due to the lack of clean training data, the ground truth and noise level are unknown, traditional denoising methods are difficult to remove blind noise in general images. Furthermore, deep learning methods require specific noise levels to train the model, and specific models are built only deal with one noise level. In this paper, we propose a Nonsubsampled Countourlet Transform based convolutional network model (CTCNN) to deal with Gaussian noise and the noise of real images. The model is modified by U-Net, nonsubsampled Countourlet Transform (NSCT) and inverse NSCT are used to instead of sum pooling layer and up-convolution operation. NSCT can decrease the size of feature maps and preserve details of images without information loss. Different training strategies are adopted to train models in order to handle blinding noise such as underwater images which contain noise naturally. Simulation results show the proposed method is effective in standard images dataset and blind noisy images. The model we proposed has been compared with other state of the art denoising methods, and better subjective representation and PSNR improvement are obtained.
Distance-Based Learning from Errors for Confidence Calibration

Deep neural networks (DNNs) are poorly-calibrated when trained in conventional ways. To improve confidence calibration of DNNs, we propose a novel training method, distance-based learning from errors (DBLE). DBLE bases its confidence estimation on distances in the representation space. We first adapt prototypical learning for training of a classification model for DBLE. It yields a representation space where a test sample's distance to its ground-truth class center can calibrate the model's performance. At inference, however, these distances are not available due to the lack of ground-truth label. To circumvent this by approximately inferring the distance for every test sample, we propose to train a confidence model jointly with the classification model, by merely learning from mis-classified training samples, which we show to be highly-beneficial for effective learning. On multiple data sets and DNN architectures, we demonstrate that DBLE outperforms alternative single-modal confidence calibration approaches. DBLE also achieves comparable performance with computationally-expensive ensemble approaches with lower computational cost and lower number of parameters.
A FRAMEWORK FOR ROBUSTNESS CERTIFICATION OF SMOOTHED CLASSIFIERS USING F-DIVERGENCES

Formal verification techniques that compute provable guarantees on properties of machine learning models, like robustness to norm-bounded adversarial perturbations, have yielded impressive results. Although most techniques developed so far requires knowledge of the architecture of the machine learning model and remains hard to scale to complex prediction pipelines, the method of randomized smoothing has been shown to overcome many of these obstacles. By requiring only black-box access to the underlying model, randomized smoothing scales to large architectures and is agnostic to the internals of the network. However, past work on randomized smoothing has focused on restricted classes of smoothing measures or perturbations (like Gaussian or discrete) and has only been able to prove robustness with respect to simple norm bounds. In this paper we introduce a general framework for proving robustness properties of smoothed machine learning models in the black-box setting. Specifically, we extend randomized smoothing procedures to handle arbitrary smoothing measures and prove robustness of the smoothed classifier by using $f$-divergences. Our methodology achieves state-of-the-art}certified robustness on MNIST, CIFAR-10 and ImageNet and also audio classification task, Librispeech, with respect to several classes of adversarial perturbations.
Knowledge-Enriched Visual Storytelling

Stories are diverse and highly personalized, resulting in a large possible output space for story generation. Existing end-to-end approaches produce monotonous stories because they are limited to the vocabulary and knowledge in a single training dataset. This paper introduces KG-Story, a three-stage framework that allows the story generation model to take advantage of external Knowledge Graphs to produce interesting stories. KG-Story distills a set of representative words from the input prompts, enriches the word set by using external knowledge graphs, and finally generates stories based on the enriched word set. This distill-enrich-generate framework allows the use of external resources not only for the enrichment phase, but also for the distillation and generation phases. In this paper, we show the superiority of KG-Story for visual storytelling, where the input prompt is a sequence of five photos and the output is a short story. Per the human ranking evaluation, stories generated by KG-Story are on average ranked better than that of the state-of-the-art systems. Our code and output stories are available at https://github.com/zychen423/KE-VIST.
Technology-mediated Control: Case Examples and Research Directions for the Future of Organizational Control

This study explores the emerging topic of technology-mediated control (TMC), which refers to an organization’s using digital technologies to influence workers to behave in a manner consistent with organizational objectives. The popular press has discussed many mobile apps, digital sensors, software algorithms, and other technologies that support, or automate, managerial control processes. Building on the rich history of research on organizational and information systems (IS) control and on ubiquitous technology, we explore how TMC approaches have increasingly begun to replace traditional, face-to-face control relationships. In particular, we analyze four illustrative case examples (UPS, Uber, Rationalizer, and Humanyze) to propose a detailed research agenda for future study in this important new topic area.
Reinforcement learning-based controller for adaptive workflow scheduling in multi-tenant cloud computing

Multi-tenancy is an essential feature in cloud computing and is a major component to achieve scalability and energy-efficient solution to gain high level of economic benefits. As the cloud, computing is gaining more audiences and high user base, the problem of scheduling the computational workflow for multi-tenant cloud scheduling is becoming a difficult task to achieve. In this study, we present a learning-based scheduler for catering heterogeneous software and hardware resources in the context of multi-tenant cloud computing. The experimentation has been carried out with the help of green cloud simulator and the results are compared with the state of the art techniques like minimum completion time, first come first serve and backfilling. The experimental results exhibit that the presented algorithm provides an effective means of utilizing cloud resources in addition with drastic reduction in cost of operation.
The virtual lands of Oz: testing an agribot in simulation

Testing autonomous robots typically requires expensive test campaigns in the field. To alleviate them, a promising approach is to perform intensive tests in virtual environments. This paper presents an industrial case study on the feasibility and effectiveness of such an approach. The subject system is Oz, an agriculture robot for autonomous weeding. Its software was tested with weeding missions in virtual crop fields, using a 3D simulator based on Gazebo. The case study faced several challenges: the randomized generation of complex 3D environments, the automated checking of the robot behavior (test oracle), and the imperfect fidelity of simulation with respect to real-world behavior. We describe the test approach we developed, and compare the results with the ones of the industrial field tests. Despite the low-fidelity physics of the robot, the virtual tests revealed most software issues found in the field, including a major one that caused the majority of failures; they also revealed a new issue missed in the field. On the downside, the simulation could introduce spurious failures that would not occur in the real world.
3D Mineral Prospectivity Modeling for the Low-Sulfidation Epithermal Gold Deposit: A Case Study of the Axi Gold Deposit, Western Tianshan, NW China

The Axi low-sulfidation (LS) epithermal deposit in northwestern China is the result of geological controls on hydrothermal fluid flow through strike-slip faults. Such controls occur commonly in LS epithermal deposits worldwide, but unfortunately, these have not been quantitatively analyzed to determine their spatial relationships with gold distribution and further guide mineral prospecting. In this study, we conduct a 3D mineral prospectivity modeling approach for the Axi deposit involving 3D geological modeling, 3D spatial analysis, and prospectivity modeling. The spatial analysis of geometric features revealed the gold mineralization trends in convex segments (0–20 m) with a specific distance from fault 2, the lower interface of late volcanic phase, and the upper interface of phyllic alteration with steep slopes (&gt;65°), implying that gold deposition was significantly controlled by the morphological characteristics and distance fields of geologic features. The present alteration–mineralization zone at Axi has a larger width in bending sites (sections No. 35–15 and No. 40–56) than elsewhere, indicating the location of two fluid conduits extending to depth. The prediction-area plots and receiver operating characteristic curves demonstrated that (genetic algorithm optimized support vector regression (GA-SVR)) outperformed multiple nonlinear regression and fuzzy weights-of-evidence, which was proposed as a robust method to solve complicated nonlinear and high-dimensional issues in prospectivity modeling. Our study manifests spatial controls of structure, host rock, and alteration on LS epithermal gold deposition, and highlights the capability of GA-SVR for identifying deposit-scale potential epithermal gold mineralization.
A Privacy-Preserving and Efficient k-Nearest Neighbor Query and Classification Scheme Based on k-Dimensional Tree for Outsourced Data

Cloud computing technology has attracted the attention of researchers and organizations due to its computing power, computing efficiency and flexibility. Using cloud computing technology to analysis outsourced data has become a new data utilization model. However, due to the severe security risks that appear in cloud computing, most organizations now encrypt data before outsourcing data. Therefore, in recent years, many new works on the k-Nearest Neighbor (denoted by k-NN) algorithm for encrypted data has appeared. However, two main problems are existing in the current research: either the program is not secure enough or inefficient. In this paper, based on the existing problems, we design a non-interactive privacy-preserving k-NN query and classification scheme. Our proposed scheme uses two existing encryption schemes: Order Preserving Encryption and the Paillier cryptosystem, to preserve the confidentiality of encrypted outsourced data, data access patterns, and the query record, and utilizes the encrypted the k-dimensional tree (denoted by kd-tree) to optimize the traditional k-NN algorithm. Our proposed scheme strives to achieve high query efficiency while ensuring data security. Extensive experimental results prove that this scheme is very close to the scheme using plaintext data and the existing non-interactive encrypted data query scheme in terms of classification accuracy. The query runtime of our scheme is superior to the existing non-interactive k-NN query scheme.