Reviewerly/exHarmony huggingface dataset:
Transductive Learning for Zero-Shot Object Detection

Zero-shot object detection (ZSD) is a relatively unexplored research problem as compared to the conventional zero-shot recognition task. ZSD aims to detect previously unseen objects during inference. Existing ZSD works suffer from two critical issues: (a) large domain-shift between the source (seen) and target (unseen) domains since the two distributions are highly mismatched. (b) the learned model is biased against unseen classes, therefore in generalized ZSD settings, where both seen and unseen objects co-occur during inference, the learned model tends to misclassify unseen to seen categories. This brings up an important question: How effectively can a transductive setting address the aforementioned problems? To the best of our knowledge, we are the first to propose a transductive zero-shot object detection approach that convincingly reduces the domain-shift and model-bias against unseen classes. Our approach is based on a self-learning mechanism that uses a novel hybrid pseudo-labeling technique. It progressively updates learned model parameters by associating unlabeled data samples to their corresponding classes. During this process, our technique makes sure that knowledge that was previously acquired on the source domain is not forgotten. We report significant 'relative' improvements of 34.9% and 77.1% in terms of mAP and recall rates over the previous best inductive models on MSCOCO dataset.
Onion-Peel Networks for Deep Video Completion

We propose the onion-peel networks for video completion. Given a set of reference images and a target image with holes, our network fills the hole by referring the contents in the reference images. Our onion-peel network progressively fills the hole from the hole boundary enabling it to exploit richer contextual information for the missing regions every step. Given a sufficient number of recurrences, even a large hole can be inpainted successfully. To attend to the missing information visible in the reference images, we propose an asymmetric attention block that computes similarities between the hole boundary pixels in the target and the non-hole pixels in the references in a non-local manner. With our attention block, our network can have an unlimited spatial-temporal window size and fill the holes with globally coherent contents. In addition, our framework is applicable to the image completion guided by the reference images without any modification, which is difficult to do with the previous methods. We validate that our method produces visually pleasing image and video inpainting results in realistic test cases.
Governance quality, remittances and their implications for food and nutrition security in Sub-Saharan Africa

Despite impressive progress in the fight against malnutrition and hunger in recent years, food and nutrition insecurity remains a major concern in Sub-Saharan Africa (SSA) countries. In this study, we employ a panel data covering 15 SSA countries from 1996 to 2015 to investigate the growth effects of remittances and quality of governance on food and nutrition security, proxied by the average value of food production and the average dietary energy supply adequacy, respectively. We use a dynamic empirical model based on system GMM to control for unobserved heterogeneity and potential endogeneity of the explanatory variables. The empirical results emanating from our analysis show that the interaction of remittances and the composite index of governance quality exerts positive and significant effects on the average value of food production, and also contributes to the improvement of average dietary energy supply adequacy in SSA. In addition, the control of corruption, government effectiveness, political stability and rule of law scores increase both measures of food and nutrition security. Albeit, the contribution of control over corruption score is relatively the largest as compared to other indicators of governance.
Saliency-Guided Attention Network for Image-Sentence Matching

This paper studies the task of matching image and sentence, where learning appropriate representations to bridge the semantic gap between image contents and language appears to be the main challenge. Unlike previous approaches that predominantly deploy symmetrical architecture to represent both modalities, we introduce a Saliency-guided Attention Network (SAN) that is characterized by building an asymmetrical link between vision and language to efficiently learn a fine-grained cross-modal correlation. The proposed SAN mainly includes three components: saliency detector, Saliency-weighted Visual Attention (SVA) module, and Saliency-guided Textual Attention (STA) module. Concretely, the saliency detector provides the visual saliency information to drive both two attention modules. Taking advantage of the saliency information, SVA is able to learn more discriminative visual features. By fusing the visual information from SVA and intra-modal information as a multi-modal guidance, STA affords us powerful textual representations that are synchronized with visual clues. Extensive experiments demonstrate SAN can improve the state-of-the-art results on the benchmark Flickr30K and MSCOCO datasets by a large margin.
Vehicle Tracking Using Deep SORT with Low Confidence Track Filtering

Multi-object tracking (MOT) becomes an attractive topic due to its wide range of usability in video surveillance and traffic monitoring. Recent improvements on MOT has focused on tracking-by-detection manner. However, as a relatively complicated and integrated computer vision mission, state-of-the-art tracking-by-detection techniques are still suffering from issues such as a large number of false-positive tracks. To reduce the effect of unreliable detections on vehicle tracking, in this paper, we propose to incorporate a low confidence track filtering into the Simple Online and Realtime Tracking with a Deep association metric (Deep SORT) algorithm. We present a self-generated UA-DETRAC vehicle re-identification dataset which can be used to train the convolutional neural network of Deep SORT for data association. We evaluate our proposed tracker on UA-DETRAC test dataset. Experimental results show that the proposed method can improve the original Deep SORT algorithm with a significant margin. Our tracker outperforms the state-of-the-art online trackers and is comparable with batch-mode trackers.
Answering range queries under local differential privacy

Counting the fraction of a population having an input within a specified interval i.e. a range query, is a fundamental data analysis primitive. Range queries can also be used to compute other core statistics such as quantiles, and to build prediction models. However, frequently the data is subject to privacy concerns when it is drawn from individuals, and relates for example to their financial, health, religious or political status. In this paper, we introduce and analyze methods to support range queries under the local variant of differential privacy [23], an emerging standard for privacy-preserving data analysis. The local model requires that each user releases a noisy view of her private data under a privacy guarantee. While many works address the problem of range queries in the trusted aggregator setting, this problem has not been addressed specifically under untrusted aggregation (local DP) model even though many primitives have been developed recently for estimating a discrete distribution. We describe and analyze two classes of approaches for range queries, based on hierarchical histograms and the Haar wavelet transform. We show that both have strong theoretical accuracy guarantees on variance. In practice, both methods are fast and require minimal computation and communication resources. Our experiments show that the wavelet approach is most accurate in high privacy settings, while the hierarchical approach dominates for weaker privacy requirements.
How Saudi Crackdowns Fail to Silence Online Dissent

Saudi Arabia has imprisoned and tortured activists, religious leaders, and journalists for voicing dissent online. This reflects a growing worldwide trend in the use of physical repression to censor online speech. In this paper, we systematically examine the consequences of imprisoning well-known Saudis for online dissent by analyzing over 300 million tweets as well as detailed Google search data from 2010 to 2017 using automated text analysis and crowd-sourced human evaluation of content. We find that repression deterred imprisoned Saudis from continuing to dissent online. However, it did not suppress dissent overall. Twitter followers of the imprisoned Saudis engaged in more online dissent, including criticizing the ruling family and calling for regime change. Repression drew public attention to arrested Saudis and their causes, and other prominent figures in Saudi Arabia were not deterred by the repression of their peers and continued to dissent online.
Stacked Lstm Network for Human Activity Recognition Using Smartphone Data

Sensor-based human activity recognition is an essential task for automatic behavior analysis for sports player, senior citizens, and IoT applications. The traditional approaches are based on hand-crafted features which use fixed mathematical rules to extract the features from the input data and are not capable of incremental learning. In this paper, we proposed a stacked long Short-term memory (LSTM) network for recognizing six human behaviors from the smartphone data. The network consists of a five LSTM cell that is trained end-to-end on the sensor data. The network is preceded by a single layer neural network that pre-processes the data for the stacked LSTM network. An L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> regularizer is used in the cost function which helps the network in generalization. The network is evaluated on public domain UCI dataset and quantitative results are compared against six state-of-the-art methods. The performance is calculated in terms of precision-recall and the average accuracy. The proposed network improves the average accuracy by 0.93% as compared to the closest state-of-the-art method without any manual feature engineering.
Informed RRT*-Connect: An Asymptotically Optimal Single-Query Path Planning Method

Rapidly-exploring Random Trees (RRTs) are successful in single-query motion planning problems. The standard version of RRT grows a tree from a start location and stops once it reached the goal configuration. RRT-Connect is the bidirectional version of RRT, which grows two trees simultaneously. These two trees try to establish a connection to stop searching. RRT-Connect finds solutions faster than RRT. Following that, an asymptotically optimal version of RRT-Connect called RRT*-Connect has been introduced. It not only rewires both trees while they are growing, but also it keeps searching the state space for better solutions than the current one. However, it is inefficient and inconsistent to search all over the state space in order to find better solutions than the current one concerning its single-query nature. The better way is to look through states that can provide a better solution. In this paper, we propose Informed RRT*-Connect, which is the informed version of RRT*-Connect that uses direct sampling after the first solution found. Unlike RRT*-Connect, the proposed method checks only the states that can potentially provide better solutions than the current solution. The proposed method benefited from the properties of RRT*-Connect and informed sampling, which offers low-cost solutions with fewer iterations in comparison to RRT*-Connect. Different simulations in OMPL have been carried out to show the significance of Informed RRT*-Connect in comparison with RRT*, Informed RRT*, and RRT*-Connect.
Spatial and Spectral Joint Super-Resolution Using Convolutional Neural Network

Many applications have benefited from the images with both high spatial and spectral resolution, such as mineralogy and surveillance. However, it is difficult to acquire such images due to the limitation of sensor technologies. Recently, super-resolution (SR) techniques have been proposed to improve the spatial or spectral resolution of images, e.g., improving the spatial resolution of hyperspectral images (HSIs) or improving spectral resolution of color images (reconstructing HSIs from RGB inputs). However, none of the researches attempted to improve both spatial and spectral resolution together. In this article, these two types of resolution are jointly improved using convolutional neural network (CNN). Specifically, two kinds of CNN-based SR are conducted, including a simultaneous spatial-spectral joint SR (SimSSJSR) that conducts SR in spectral and spatial domain simultaneously and a separated spatial-spectral joint SR (SepSSJSR) that considers spectral and spatial SR sequentially. In the proposed SimSSJSR, a full 3-D CNN is constructed to learn an end-to-end mapping between a low spatial-resolution mulitspectral image (LR-MSI) and the corresponding high spatial-resolution HSI (HR-HSI). In the proposed SepSSJSR, a spatial SR network and a spectral SR network are designed separately, and thus two different frameworks are proposed for SepSSJSR, namely SepSSJSR1 and SepSSJSR2, according to the order that spatial SR and spectral SR are applied. Furthermore, the least absolute deviation, instead of mean square error (MSE) in traditional SR networks, is chosen as the loss function for the proposed networks. Experimental results over simulated images from different sensors demonstrated that the proposed SepSSJSR1 is most effective to improve spatial and spectral resolution of MSIs sequentially by conducting spatial SR prior to spectral SR. In addition, validation on real Landsat images also indicates that the proposed SSJSR techniques can make full use of available MSIs for high-resolution-based analysis or applications.
Object detection based on RGC mask R‐CNN

Object detection is a crucial topic in computer vision. Mask Region-Convolution Neural Network (R-CNN) based methods, wherein a large intersection over union (IoU) threshold is chosen for high quality samples, have often been employed for object detection. However, the detection performance of such methods deteriorates when samples are reduced. To address this, the authors propose an improved Mask R-CNN-based method: the ResNet Group Cascade (RGC) Mask R-CNN. First, they compared ResNet with different layers, finding that ResNeXt-101-64 × 4d is superior to other backbone networks. Secondly, during the training of the test model, the performance of Mask R-CNN suffered from a small batch processing scale, resulting in inaccurately calculated mean and variance; thus, group normalisation was added to the backbone, feature pyramid network neck and bounding box head of the network. Finally, the higher the intersection of Mask R-CNN than the threshold, the easier it is to obtain high-quality samples. However, blindly selecting a high threshold leads to sample reduction and overfitting. Thus, a proposed cascade network configuration with three IoU thresholds was utilised in the process of model training. The model was trained and tested on the COCO and PASCAL VOC07 datasets. Their proposed algorithm demonstrated superior performance compared to that of the Mask R-CNN.
Reducing the Cost of Implementing the Advanced Encryption Standard as a Quantum Circuit

To quantify security levels in a postquantum scenario, it is common to use the quantum resources needed to attack the Advanced Encryption Standard (AES) as a reference value. Specifically, in the National Institute of Standards and Technology's ongoing postquantum standardization effort, different security categories are defined that reflect the quantum resources needed to attack AES-128, AES-192, and AES-256. This article presents a quantum circuit to implement the S-box of AES. Also, leveraging an improved implementation of the key expansion, we identify new quantum circuits for all three AES key lengths. For AES-128, the number of Toffoli gates can be reduced by more than 88% compared to Almazrooie et al.'s and Grassl et al.'s estimates while simultaneously reducing the number of qubits. Our circuits can be used to simplify a Grover-based key search for AES.
Blockchain for smart homes: Review of current trends and research challenges

The increasing popularity of smart home applications has introduced unprecedented demand for improving the underlying information technology infrastructure to ensure the transparency, security, and privacy of user data. Blockchain is a promising technology capable of addressing such demand. This paper provides a comprehensive review of current advancements and highlights the major progress of employing blockchain in smart home systems. We first discuss blockchain techniques and the prerequisites of a smart home to adapt blockchain technology. Then, we present various applications of blockchain for smart homes and discuss the most commonly used methods in the literature. We also present two case studies to show how blockchain empowers smart home transactions by eliminating the middleman. This paper also reports on the major challenges pertaining to interoperability protocols, security and privacy, data collection and sharing, data analytics, and latency. Finally, the paper identifies potentially promising areas for future research.
Characterizing the Deployment of Deep Neural Networks on Commercial Edge Devices

The great success of deep neural networks (DNNs) has significantly assisted humans in numerous applications such as computer vision. DNNs are widely used in today's applications and systems. However, in-the-edge inference of DNNs is still a severe challenge mainly because of the contradiction between the inherent intensive resource requirements of DNNs and the tight resource availability of edge devices. Nevertheless, in-the-edge inferencing preserves privacy in several user-centric domains and applies in several scenarios with limited Internet connectivity (e.g., drones, robots, autonomous vehicles). That is why several companies have released specialized edge devices for accelerating the execution performance of DNNs in the edge. Although preliminary studies have characterized such edge devices separately, a unified comparison with the same set of assumptions has not been fully performed. In this paper, we endeavor to address this knowledge gap by characterizing several commercial edge devices on popular frameworks using well-known convolution neural networks (CNNs), a type of DNN. We analyze the impact of frameworks, their software stack, and their implemented optimizations on the final performance. Moreover, we measure energy consumption and temperature behavior of these edge devices.
Unsupervised Anomaly Detection in Time Series Using LSTM-Based Autoencoders

Automatic anomaly detection in data mining has a wide range of applications such as fraud detection, system health monitoring, fault detection, event detection systems in sensor networks, and so on. The main challenge related to such problem is unknown nature of the anomaly. Therefore, it is impossible to use classical machine learning techniques to train the model, as we don't have labels of time series with anomaly. For periodic time series it is advisable to use STL decomposition of the signal. In such case anomaly detection task is reduced to residuals peak detection. If time series is not periodic (for example, forex price or sound) the only way is using machine learning methods. One of the best machine learning methods is autoencoder-based anomaly detection. An autoencoder is a type of artificial neural network used to learn efficient data encodings in an unsupervised manner. The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for dimensionality reduction, by training the network to ignore signal “noise”. Unsupervised anomaly detection method based on autoencoders was tested on two types of data: various artificial signal datasets and detection of rare sound events dataset.
An Efficient Image Encryption Scheme Based on S-Boxes and Fractional-Order Differential Logistic Map

In this work, an efficient image encryption based on S-boxes and fractional-order logistic map is proposed. The features of the fractional-order chaotic system in dynamical behaviors are exhibited. By simulation and comparison with the traditional logistic map, it is proved that the fractional-order logistic map contains larger key space and more parameters. Therefore, the fractional-order logistic system has better efficiency and security against cryptanalyst attacks. The S-boxes construction algorithm is proposed. By comparing with the S-boxes of the former schemes, the proposed S-boxes have good performance under Bits Independence Criterion (BIC), the Strict Avalanche Criterion (SAC) and the nonlinearity. Finally, the image encryption scheme is proposed for the verification. In the encryption process, the proposed S-boxes are used for scrambling and confusion. The simulation and experimental results indicate that the fractional-order method is a preferred approach to integer-order chaotic system.
Adversarial Attacks on Graph Neural Networks via Node Injections: A Hierarchical Reinforcement Learning Approach

Graph Neural Networks (GNN) offer the powerful approach to node classification in complex networks across many domains including social media, E-commerce, and FinTech. However, recent studies show that GNNs are vulnerable to attacks aimed at adversely impacting their node classification performance. Existing studies of adversarial attacks on GNN focus primarily on manipulating the connectivity between existing nodes, a task that requires greater effort on the part of the attacker in real-world applications. In contrast, it is much more expedient on the part of the attacker to inject adversarial nodes, e.g., fake profiles with forged links, into existing graphs so as to reduce the performance of the GNN in classifying existing nodes.
Single underwater image enhancement by attenuation map guided color correction and detail preserved dehazing

The attenuation (sum of absorption and scattering), which caused by the dense and non-uniform medium, generally leads to problems of color degradation and detail loss in underwater imaging. To address these problems, we propose a systematic underwater image enhancement method, which includes an attenuation map guided underwater image color correction approach and a detail preserved dehazing approach. The color correction approach fully considers the main causes of color degradation in underwater imaging, namely wavelength-dependent attenuation of different colors. According to the attenuation map of each color channel, a piece-wise linear transform is used to process the information of each color channel. Then, the detail preserved dehazing approach based on multi-scale decomposition is proposed to compensate for the lost details while eliminating the effects of haze. Especially, an adaptive Maximum Intensity Prior (MIP) measurement based on maximum attenuation identification is proposed to estimate transmission of the medium. Experiments on a variety types of degraded underwater images have proven that our proposed method can produce accurate results with vivid color and fine details, even better than other state-of-the-art underwater image dehazing methods.