Reviewerly/exHarmony huggingface dataset:
Fuzzy region-based active contour driven by global and local fitting energy for image segmentation

This paper presents a novel global and local fuzzy image fitting (GLFIF) based active contour model for image segmentation. First, we design two fitted images: global fuzzy fitted image (GFFI) and local fuzzy fitted image (LFFI). Next, the energy function including a global term and a fitting term is constructed. The global term comes from the fuzzy energy-based active contour (FEAC) model to balance the importance of the object and background while the fitting term is based on GFFI and LFFI to handle the intensity inhomogeneity in given images. Then, we prove the energy function to be convex, which can ensure the segmentation results independent of initialization. Finally, unlike the FEAC model by computing the change of pixel-by-pixel energy function, a direct method is utilized to calculate the difference between the old and new energy functions in the whole image for each iteration to update the pseudo level set function. Experiments on synthetic and real images show that the proposed model is more robust than the popular active contour models for segmenting the images with noise, blurred boundaries, and intensity inhomogeneity. The code is available at: https://github.com/fangchj2002/GLFIF.
UAV Path Planning under Dynamic Threats Using an Improved PSO Algorithm

This paper presents the method to solve the problem of path planning for an unmanned aerial vehicle (UAV) in adversarial environments including radar-guided surface-to-air missiles (SAMs) and unknown threats. SAM lethal envelope and radar detection for SAM threats and line-of-sight (LOS) calculation for unknown threats are considered to compute the cost for path planning. In particular, dynamic SAM lethal envelope is taken into account for path planning in that SAM lethal envelope does change its direction according to the flight direction of UAV. In addition, terrain masking, nonisotropic radar cross section (RCS), and dynamic constraints of UAV are considered to determine the cost of the path. An improved particle swarm optimization (PSO) algorithm is proposed for finding an optimal path. The proposed algorithm is composed of preprocessing steps, multi-swarm PSO algorithm, and postprocessing steps. The Voronoi diagram and Dijkstra algorithm as preprocessing steps provide the initial path for the multi-swarm PSO algorithm which uses multiple swarms with sub-swarms for the balance between exploration and exploitation. Postprocessing steps include waypoint insertion and 3D path smoothing. The computation time is reduced by using the map generation, the coordinate transformation, and the graphic processing unit (GPU) implementation of the algorithm. Various simulations are carried out to compare the performance of the proposed method according to the number of iterations, the number of swarms, and the number of cost evaluation points. The <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>t</mi> </math> -test results show that the suggested method is statistically better than existing methods.
A GAN-Based Image Transformation Scheme for Privacy-Preserving Deep Neural Networks

We propose a novel image transformation scheme using generative adversarial networks (GANs) for privacy-preserving deep neural networks (DNNs). The proposed scheme enables us not only to apply images without visual information to DNNs, but also to enhance robustness against ciphertext-only attacks (COAs) including DNN-based attacks. In this paper, the proposed transformation scheme is demonstrated to be able to protect visual information on plain images, and the visually-protected images are directly applied to DNNs for privacy-preserving image classification. Since the proposed scheme utilizes GANs, there is no need to manage encryption keys. In an image classification experiment, we evaluate the effectiveness of the proposed scheme in terms of classification accuracy and robustness against COAs.
Learning to Anticipate Egocentric Actions by Imagination

Anticipating actions before they are executed is crucial for a wide range of practical applications, including autonomous driving and robotics. In this paper, we study the egocentric action anticipation task, which predicts future action seconds before it is performed for egocentric videos. Previous approaches focus on summarizing the observed content and directly predicting future action based on past observations. We believe it would benefit the action anticipation if we could mine some cues to compensate for the missing information of the unobserved frames. We then propose to decompose the action anticipation into a series of future feature predictions. We imagine how the visual feature changes in the near future and then predicts future action labels based on these imagined representations. Differently, our ImagineRNN is optimized in a contrastive learning way instead of feature regression. We utilize a proxy task to train the ImagineRNN, i.e., selecting the correct future states from distractors. We further improve ImagineRNN by residual anticipation, i.e., changing its target to predicting the feature difference of adjacent frames instead of the frame content. This promotes the network to focus on our target, i.e., the future action, as the difference between adjacent frame features is more important for forecasting the future. Extensive experiments on two large-scale egocentric action datasets validate the effectiveness of our method. Our method significantly outperforms previous methods on both the seen test set and the unseen test set of the EPIC Kitchens Action Anticipation Challenge.
Classical algorithms for quantum mean values

We consider the task of estimating the expectation value of an $n$-qubit tensor product observable $O_1\otimes O_2\otimes \cdots \otimes O_n$ in the output state of a shallow quantum circuit. This task is a cornerstone of variational quantum algorithms for optimization, machine learning, and the simulation of quantum many-body systems. Here we study its computational complexity for constant-depth quantum circuits and three types of single-qubit observables $O_j$ which are (a) close to the identity, (b) positive semidefinite, (c) arbitrary. It is shown that the mean value problem admits a classical approximation algorithm with runtime scaling as $\mathrm{poly}(n)$ and $2^{\tilde{O}(\sqrt{n})}$ in cases (a,b) respectively. In case (c) we give a linear-time algorithm for geometrically local circuits on a two-dimensional grid. The mean value is approximated with a small relative error in case (a), while in cases (b,c) we satisfy a less demanding additive error bound. The algorithms are based on (respectively) Barvinok's polynomial interpolation method, a polynomial approximation for the OR function arising from quantum query complexity, and a Monte Carlo method combined with Matrix Product State techniques. We also prove a technical lemma characterizing a zero-free region for certain polynomials associated with a quantum circuit, which may be of independent interest.
Benchmark for Evaluating Pedestrian Action Prediction

Pedestrian action prediction has been a topic of active research in recent years resulting in many new algorithmic solutions. However, measuring the overall progress towards solving this problem is difficult due to the lack of publicly available benchmarks and common training and evaluation procedures. To this end, we introduce a benchmark based on two public datasets for pedestrian behavior understanding. Using the proposed evaluation procedures, we rank a number of baseline and state-of-the-art models and analyze their performance with respect to various properties of the data. Based on these findings we propose a new model for pedestrian crossing action prediction that uses attention mechanisms to effectively combine implicit and explicit features and demonstrate new state-of-the-art results. The code for models and evaluation is available at https://github.com/ykotseruba/PedestrianActionBenchmark.
Self-Supervised Anomaly Detection for In-Vehicle Network Using Noised Pseudo Normal Data

As the risk of cyber and safety threats to vehicle systems has increased, the anomaly detection in in-vehicle networks (IVN) has received the attention of researchers. Although, machine-learning-based anomaly detection methods have been proposed, there are limitations in detecting unknown attacks that the model has not learned because general supervised learning-based approaches depend on training dataset. To solve this problem, we propose a novel self-supervised method for IVN anomaly detection using noised pseudo normal data. The proposed method consists of two deep-learning models of the generator and the detector, which generates noised pseudo normal data and detects anomalies, respectively. Firstly, the generator is trained with only normal network traffic to generate pseudo normal traffic data. Then, the anomaly detector is trained to classify normal traffic and noised pseudo normal traffic as normal and abnormal, respectively. The experimental results demonstrate that the anomaly detection models, trained with the proposed method, not only significantly improved in the detection of unknown attacks, but also outperformed other semi-supervised learning-based methods.
Multimodal Prototypical Networks for Few-shot Learning

Although providing exceptional results for many computer vision tasks, state-of-the-art deep learning algorithms catastrophically struggle in low data scenarios. However, if data in additional modalities exist (e.g. text) this can compensate for the lack of data and improve the classification results. To overcome this data scarcity, we design a cross-modal feature generation framework capable of enriching the low populated embedding space in few-shot scenarios, leveraging data from the auxiliary modality. Specifically, we train a generative model that maps text data into the visual feature space to obtain more reliable prototypes. This allows to exploit data from additional modalities (e.g. text) during training while the ultimate task at test time remains classification with exclusively visual data. We show that in such cases nearest neighbor classification is a viable approach and outperform state-of-the-art single-modal and multimodal few-shot learning methods on the CUB-200 and Oxford-102 datasets.
Context-Aware Domain Adaptation in Semantic Segmentation

In this paper, we consider the problem of unsupervised domain adaptation in the semantic segmentation. There are two primary issues in this field, i.e., what and how to transfer domain knowledge across two domains. Existing methods mainly focus on adapting domain-invariant features (what to transfer) through adversarial learning (how to transfer). Context dependency is essential for semantic segmentation, however, its transferability is still not well understood. Furthermore, how to transfer contextual information across two domains remains unexplored. Motivated by this, we propose a cross-attention mechanism based on self-attention to capture context dependencies between two domains and adapt transferable context. To achieve this goal, we design two cross-domain attention modules to adapt context dependencies from both spatial and channel views. Specifically, the spatial attention module captures local feature dependencies between each position in the source and target image. The channel attention module models semantic dependencies between each pair of cross-domain channel maps. To adapt context dependencies, we further selectively aggregate the context information from two domains. The superiority of our method over existing state-of-the-art methods is empirically proved on "GTA5 to Cityscapes" and "SYNTHIA to Cityscapes".
Multi-modal aggression identification using Convolutional Neural Network and Binary Particle Swarm Optimization

Aggressive posts containing symbolic and offencive images, inappropriate gestures along with provocative textual comments are growing exponentially in social media with the availability of inexpensive data services. These posts have numerous negative impacts on the reader and need an immediate technical solution to filter out aggressive comments. This paper presents a model based on a Convolutional Neural Network (CNN) and Binary Particle Swarm Optimization (BPSO) to classify the social media posts containing images with associated textual comments into non-aggressive, medium-aggressive and high-aggressive classes. A dataset containing symbolic images and the corresponding textual comments was created to validate the proposed model. The framework employs a pre-trained VGG-16 to extract the image features and a three-layered CNN to extract the textual features in parallel. The hybrid feature set obtained by concatenating the image and the text features were optimized using the BPSO algorithm to extract the more relevant features. The proposed model with optimized features and Random Forest classifier achieves a weighted F1-Score of 0.74, an improvement of around 3% over unoptimized features.
Responsible use of crop protection products and Nigeria's growth enhancement support scheme

This article examines the impact of the federal government of Nigeria's growth enhancement support scheme (GESS) on responsible use of crop protection products (CPPs) in rural Nigeria. Results from the logistic regressions show that GESS significantly impact on farmers’ access to CPPs but does not significantly impact on farmers’ knowledge and skill of CPP application, and that in several cases misuse has led to deterioration of soil fertility. Findings suggests that embracing information on recommended CPPs, dose rates, dilutions, timing, frequency of applications, and precautions should form the foundation of GESS activity on CPPs in sub-Saharan Africa.
Referring Segmentation in Images and Videos with Cross-Modal Self-Attention Network

We consider the problem of referring segmentation in images and videos with natural language. Given an input image (or video) and a referring expression, the goal is to segment the entity referred by the expression in the image or video. In this paper, we propose a cross-modal self-attention (CMSA) module to utilize fine details of individual words and the input image or video, which effectively captures the long-range dependencies between linguistic and visual features. Our model can adaptively focus on informative words in the referring expression and important regions in the visual input. We further propose a gated multi-level fusion (GMLF) module to selectively integrate self-attentive cross-modal features corresponding to different levels of visual features. This module controls the feature fusion of information flow of features at different levels with high-level and low-level semantic information related to different attentive words. Besides, we introduce cross-frame self-attention (CFSA) module to effectively integrate temporal information in consecutive frames which extends our method in the case of referring segmentation in videos. Experiments on benchmark datasets of four referring image datasets and two actor and action video segmentation datasets consistently demonstrate that our proposed approach outperforms existing state-of-the-art methods.
Foreign Aid, Instability, and Governance in Africa

This article contributes to the attendant literature by bundling governance dynamics and focusing on foreign aid instability instead of foreign aid. We assess the role of foreign aid instability on governance dynamics in 53 African countries for the period 1996‐2010. An autoregressive endogeneity‐robust generalized method of moments is employed. Instabilities are measured in terms of variance of the errors and standard deviations. Three main aid indicators are used, namely: total aid, aid from multilateral donors, and bilateral aid. Principal component analysis is used to bundle governance indicators, namely: political governance (voice and accountability and political stability/no violence), economic governance (regulation quality and government effectiveness), institutional governance (rule of law and corruption control), and general governance (political, economic, and institutional governance). Our findings show that foreign aid instability increases governance standards, especially political and general governance. Policy implications are discussed. Related Articles Beal, Amanda L., and Leah Graham. 2014. “Foundations for Change: Rule of Law, Development, and Democratization.” Politics &amp; Policy 42 (3): 311‐345. https://doi.org/10.1111/polp.12077 Fails, Matthew D. 2014. “Leader Turnover, Volatility, and Political Risk.” Politics &amp; Policy 42 (3): 369‐399. https://doi.org/10.1111/polp.12078 Jakubow, Alexander. 2014. “State Intervention and Life Satisfaction Reconsidered: The Role of Governance Quality and Resource Misallocation.” Politics &amp; Policy 42 (1): 3‐36. https://doi.org/10.1111/polp.12057
Self-sovereign identity on public blockchains and the GDPR

This paper studies three existing technical solutions for a self-sovereign identity on blockchains and analyzes the arising issues related to the General Data Protection Regulation (GDPR) of the European Union (EU). In particular, the paper provides an overview of the existing Sovrin self-sovereign identity on the Hyperledger Indy public permissioned blockchain as well as uPort and Jolocom on the Ethereum public permissionless blockchain. The paper then concludes with a discussion on the GDPR-compliance of the blockchain-based identity concepts.
Audee

Deep learning (DL) has been applied widely, and the quality of DL system becomes crucial, especially for safety-critical applications. Existing work mainly focuses on the quality analysis of DL models, but lacks attention to the underlying frameworks on which all DL models depend. In this work, we propose Audee, a novel approach for testing DL frameworks and localizing bugs. Audee adopts a search-based approach and implements three different mutation strategies to generate diverse test cases by exploring combinations of model structures, parameters, weights and inputs. Audee is able to detect three types of bugs: logical bugs, crashes and Not-a-Number (NaN) errors. In particular, for logical bugs, Audee adopts a cross-reference check to detect behavioural inconsistencies across multiple frameworks (e.g., TensorFlow and PyTorch), which may indicate potential bugs in their implementations. For NaN errors, Audee adopts a heuristic-based approach to generate DNNs that tend to output outliers (i.e., too large or small values), and these values are likely to produce NaN. Furthermore, Audee leverages a causal-testing based technique to localize layers as well as parameters that cause inconsistencies or bugs. To evaluate the effectiveness of our approach, we applied Audee on testing four DL frameworks, i.e., TensorFlow, PyTorch, CNTK, and Theano. We generate a large number of DNNs which cover 25 widely-used APIs in the four frameworks. The results demonstrate that Audee is effective in detecting inconsistencies, crashes and NaN errors. In total, 26 unique unknown bugs were discovered, and 7 of them have already been confirmed or fixed by the developers.
Adversarial Attacks to Machine Learning-Based Smart Healthcare Systems

The increasing availability of healthcare data requires accurate analysis of disease diagnosis, progression, and real-time monitoring to provide improved treatments to the patients. In this context, Machine Learning (ML) models are used to extract valuable features and insights from high-dimensional and heterogeneous healthcare data to detect different diseases and patient activities in a Smart Healthcare System (SHS). However, recent researches show that ML models used in different application domains are vulnerable to adversarial attacks. In this paper, we introduce a new type of adversarial attacks to exploit the ML classifiers used in a SHS. We consider an adversary who has partial knowledge of data distribution, SHS model, and ML algorithm to perform both targeted and untargeted attacks. Employing these adversarial capabilities, we manipulate medical device readings to alter patient status (disease-affected, normal condition, activities, etc.) in the outcome of the SHS. Our attack utilizes five different adversarial ML algorithms (HopSkipJump, Fast Gradient Method, Crafting Decision Tree, Carlini & Wagner, Zeroth Order optimization) to perform different malicious activities (e.g., data poisoning, misclassify outputs, etc.) on a SHS. Moreover, based on the training and testing phase capabilities of an adversary, we perform white box and black box attacks on a SHS. We evaluate the performance of our work in different SHS settings and medical devices. Our extensive evaluation shows that our proposed adversarial attack can significantly degrade the performance of a ML-based SHS in detecting diseases and normal activities of the patients correctly, which eventually leads to erroneous treatment.
Cyber security risk assessment for seaports: A case study of a container port

Seaports are fixed infrastructures of maritime transportation systems. Through Industry 3.0 and Industry 4.0, ports have faced with digital transformation based on networked cyber physical systems to be a part of smart and intelligent transportation systems. However, besides the advantages, this transformation has brought cyber security gaps and threats which can be resulted in breakdowns in maritime transportation domain. Therefore, port and port facilities should be prepared for cyber threats through holistic risk assessment frameworks for developing proactive actions. Based on these facts, this study has proposed to apply an integrated cyber risk assessment method for a container port with a cyber-physical perspective through analyzing four exemplary cyber-attack scenarios. For each cyber-attack scenario, risk assessment methodology has been applied using integrated cyber security management approach by taking into account the cyber physical assets of the container port. Results show that for the specified cyber threats, the risks have been evaluated non acceptable. Mitigation strategies have also been presented briefly in conclusion.
Regularizing Deep Networks with Semantic Data Augmentation

Data augmentation is widely known as a simple yet surprisingly effective technique for regularizing deep networks. Conventional data augmentation schemes, e.g., flipping, translation or rotation, are low-level, data-independent and class-agnostic operations, leading to limited diversity for augmented samples. To this end, we propose a novel semantic data augmentation algorithm to complement traditional approaches. The proposed method is inspired by the intriguing property that deep networks are effective in learning linearized features, i.e., certain directions in the deep feature space correspond to meaningful semantic transformations, e.g., changing the background or view angle of an object. Based on this observation, translating training samples along many such directions in the feature space can effectively augment the dataset for more diversity. To implement this idea, we first introduce a sampling based method to obtain semantically meaningful directions efficiently. Then, an upper bound of the expected cross-entropy (CE) loss on the augmented training set is derived by assuming the number of augmented samples goes to infinity, yielding a highly efficient algorithm. In fact, we show that the proposed implicit semantic data augmentation (ISDA) algorithm amounts to minimizing a novel robust CE loss, which adds minimal extra computational cost to a normal training procedure. In addition to supervised learning, ISDA can be applied to semi-supervised learning tasks under the consistency regularization framework, where ISDA amounts to minimizing the upper bound of the expected KL-divergence between the augmented features and the original features. Although being simple, ISDA consistently improves the generalization performance of popular deep models (e.g., ResNets and DenseNets) on a variety of datasets, i.e., CIFAR-10, CIFAR-100, SVHN, ImageNet, and Cityscapes. Code for reproducing our results is available at https://github.com/blackfeather-wang/ISDA-for-Deep-Networks.