Reviewerly/exHarmony huggingface dataset:
Theory versus practice in annealing-based quantum computing

This paper introduces basic concepts of annealing-based quantum computing, also known as adiabatic quantum computing (AQC) and quantum annealing (QA), and surveys what is known about this novel computing paradigm. Extensive empirical research on physical quantum annealing processers built by D-Wave Systems has exposed many interesting features and properties. However, because of longstanding differences between abstract and empirical approaches to the study of computational performance, much of this work may not be considered relevant to questions of interest to complexity theory; by the same token, several theoretical results in quantum computing may be considered irrelevant to practical experience. To address this communication gap, this paper proposes models of computation and of algorithms that lie on a scale of instantiation between pencil-and-paper abstraction and physical device. Models at intermediate points on these scales can provide a common language, allowing researchers from both ends to communicate and share their results. The paper also gives several examples of common terms that have different technical meanings in different regions of this highly multidisciplinary field, which can create barriers to effective communication across disciplines.
Varying-Parameter Convergent-Differential Neural Solution to Time-Varying Overdetermined System of Linear Equations

To solve a time-varying overdetermined problem, a novel varying-parameter convergent-differential neural network (VP-CDNN) is proposed, designed, and discussed. Specifically, a vector-error function is first defined. Second, according to neural dynamic design method, an implicit-dynamic equation with a time-varying parameter is derived. Mathematics analysis and theoretical proof verify that the VP-CDNN can obtain the least-squares solution with a super exponential convergence rate. In addition, it is also proved that VP-CDNN can restrain the noise efficiently. Simulations among the VP-CDNN, gradient-based recurrent neural network and zeroing neural network verify that the VP-CDNN has faster speed, higher accuracy, and stronger robustness. At last, applications to data fitting and system identification further verify the high effectiveness and efficiency of the VP-CDNN.
Hate Knows No Boundaries: Online Hate in Six Nations

This paper examines cross-national commonalities and differences in online hate speech content, exposure, and emotional reaction. Using online surveys from 18 to 25-year-old respondents in six countries, we find a majority of respondents were exposed to online hate in the preceding 3 months. Commonalities across countries are the platform where the respondents were exposed and how they arrived at such content. Unique national cultures of hate speech also exist, including the common targets and respondents’ emotional reactions. A majority of respondents report feeling angry, sad, or ashamed, but most worrisome may be the substantial numbers who report feelings of hatred or pride after seeing online hate. Given the potential for repeated exposure and the recent increase in hate crimes in the US. and Europe, this finding should serve as a reminder of the dangers of online hate and its potential link to offline violence.
LLR: Learning learning rates by LSTM for training neural networks

In the training process of the deep neural networks, the learning rate plays an important role in whether the training process can converge and how fast it can achieve converge. In order to ensure convergence, most of the existing optimization methods adopt a multi-stage descending small learning rate which is hand-designed. However, this method converges slowly especially in the early stage of training. Based on this, a learning rate adjustment strategy that can automatically adjust and has a faster speed of loss decline will be helpful to the training of the deep model. In this paper, a dynamic adjustment strategy of learning rate is developed based on the Long Short Term Memory(LSTM) model and the gradients of loss function. This method effectively utilizes the advantages of the LSTM model considering the multi-step learning rate as a whole, and generates the learning rate of the current step based on the memory information of the previous learning rate. Three datasets and four architectures are used in the experiments. We applied the learning rate adjustment method to various optimization methods and achieved good results that our method can achieve even smaller loss under the same number of iterations.
SDCT-AuxNet : DCT augmented stain deconvolutional CNN with auxiliary classifier for cancer diagnosis

Acute lymphoblastic leukemia (ALL) is a pervasive pediatric white blood cell cancer across the globe. With the popularity of convolutional neural networks (CNNs), computer-aided diagnosis of cancer has attracted considerable attention. Such tools are easily deployable and are cost-effective. Hence, these can enable extensive coverage of cancer diagnostic facilities. However, the development of such a tool for ALL cancer was challenging so far due to the non-availability of a large training dataset. The visual similarity between the malignant and normal cells adds to the complexity of the problem. This paper discusses the recent release of a large dataset and presents a novel deep learning architecture for the classification of cell images of ALL cancer. The proposed architecture, namely, SDCT-AuxNet$^{\theta}$ is a 2-module framework that utilizes a compact CNN as the main classifier in one module and a Kernel SVM as the auxiliary classifier in the other one. While CNN classifier uses features through bilinear-pooling, spectral-averaged features are used by the auxiliary classifier. Further, this CNN is trained on the stain deconvolved quantity images in the optical density domain instead of the conventional RGB images. A novel test strategy is proposed that exploits both the classifiers for decision making using the confidence scores of their predicted class labels. Elaborate experiments have been carried out on our recently released public dataset of 15114 images of ALL cancer and healthy cells to establish the validity of the proposed methodology that is also robust to subject-level variability. A weighted F1 score of 94.8$\%$ is obtained that is best so far on this challenging dataset.
Aerial image semantic segmentation using DCNN predicted distance maps

This paper addresses the challenge of learning spatial context for the semantic segmentation of high-resolution aerial images using Deep Convolutional Neural Networks (DCNNs). The proposed solution involves deriving a signed distance map for each semantic class from a ground truth label map and training a DCNN to predict this distance map instead of a score map for each class. Since the distance between a target pixel and its nearest object boundary measures how far the pixel penetrates an object, the distance maps encode spatial context, particularly spatial smoothness. Positive pixel values in the distance maps correspond to the correct class and negative values correspond to the incorrect class. A final label map is derived from the predicted distance maps by selecting the class with the maximum distance. Since neighboring pixels in the distance maps have similar values, the segmentation results are smoother than current approaches. The results are shown to be even better than performing post-processing using fully connected Conditional Random Fields (CRFs), a common approach to smoothing the segmentations produced DCNNs. Experimental results on the semantic labeling challenge dataset show the proposed approach outperforms most state-of-the-art methods. Our main contribution, though, is the novel idea of replacing the pixel-wise class score maps of DCNNs with distance maps. This is therefore orthogonal and complementary to other techniques employed by the state-of-the-art methods and could therefore be used to improve upon them.
Deep Multiscale Fusion Hashing for Cross-Modal Retrieval

Owing to the rapid development of deep learning and the high efficiency of hashing, hashing methods based on deep learning models have been extensively adopted in the area of cross-modal retrieval. In general, in existing deep model-based methods, modality-specific features play an important role during the hash learning. However, most existing methods only use the modality-specific features from the final fully connected layer, ignoring the semantic relevance among modality-specific features with different scales in multiple layers. To address this issue, in this study, we put forward an end-to-end deep hashing method called deep multiscale fusion hashing (DMFH) for cross-modal retrieval. For the proposed DMFH, we first design different network branches for two modalities and then adopt multiscale fusion models for each branch network to fuse the multiscale semantics, which can be used to explore the semantic relevance. Furthermore, the multi-fusion models also embed the multiscale semantics into the final hash codes, making the final hash codes more representative. In addition, the proposed DMFH can learn common hash codes directly without a relaxation, thereby avoiding a loss in accuracy during hash learning. Experimental results on three benchmark datasets prove the relative superiority of the proposed method.
Combination of compositional data analysis and machine learning approaches to identify sources and geochemical associations of potentially toxic elements in soil and assess the associated human health risk in a mining city

Mining activities change the chemical composition of the environment and have negative reflection on people's health and there is no single measure to deal with adverse consequences of mining activities, as each case is specific and needs to be understood and mitigated in a unique way. In this study, the combination of compositional data analysis (CoDA), k-means algorithm, hierarchical cluster analysis applied to reveal the geochemical associations of potentially toxic elements (PTE) in soil of Alaverdi city (Armenia) (Ti, Fe, Ba, Mn, Co, V, Pb, Zn, Cu, Cr, Mo, As). Additionally, to assess PTE-induced health risk, two commonly used approaches were used. The obtained results show that the combination of CoDA and machine learning algorithms allow to identify and describe three geochemical associations of the studied elements: the natural, manmade and hybrid. Moreover, the revealed geochemical associations were linked to the natural pattern of distribution of the element concentrations including the influence of the natural mineralization of the parent rocks, as well as the emission from the copper smelter and urban management related activities. The health risk assessment using the US EPA method demonstrated that the observed contents of studied elements are posing a non-carcinogenic risk to children in the entire territory of the city. In the case of adults, the non-carcinogenic risk was identified in areas situated close to the copper smelter. The Summary pollution index (Zc) values were in line with the results of the US EPA method and indicated that the main residential part of the city was under the hazardous pollution level suggesting the possibility of increase in the overall incidence of diseases among frequently ill individuals, children with chronic diseases and functional disorders of vascular system. The obtained results indicated the need for further in-depth studies with special focus on the synergic effect of PTE.
Taming an Autonomous Surface Vehicle for Path Following and Collision Avoidance Using Deep Reinforcement Learning

In this article, we explore the feasibility of applying proximal policy optimization, a state-of-the-art deep reinforcement learning algorithm for continuous control tasks, on the dual-objective problem of controlling an underactuated autonomous surface vehicle to follow an a priori known path while avoiding collisions with non-moving obstacles along the way. The AI agent, which is equipped with multiple rangefinder sensors for obstacle detection, is trained and evaluated in a challenging, stochastically generated simulation environment based on the OpenAI gym Python toolkit. Notably, the agent is provided with real-time insight into its own reward function, allowing it to dynamically adapt its guidance strategy. Depending on its strategy, which ranges from radical path-adherence to radical obstacle avoidance, the trained agent achieves an episodic success rate close to 100%
Open DNN Box by Power Side-Channel Attack

Deep neural networks are becoming popular and important assets of many AI companies. However, recent studies indicate that they are also vulnerable to adversarial attacks. Adversarial attacks can be either white-box or black-box. The white-box attacks assume full knowledge of the models while the black-box ones assume none. In general, revealing more internal information can enable much more powerful and efficient attacks. However, in most real-world applications, the internal information of embedded AI devices is unavailable. Therefore, in this brief, we propose a side-channel information based technique to reveal the internal information of black-box models. Specifically, we have made the following contributions: (1) different from previous works, we use side-channel information to reveal internal network architecture in embedded devices; (2) we construct models for internal parameter estimation that no research has been reached yet; and (3) we validate our methods on real-world devices and applications. The experimental results show that our method can achieve 96.50% accuracy on average. Such results suggest that we should pay strong attention to the security problem of many AI devices, and further propose corresponding defensive strategies in the future.
Cloud Gaming with Foveated Video Encoding

Cloud gaming enables playing high-end games, originally designed for PC or game console setups, on low-end devices such as netbooks and smartphones, by offloading graphics rendering to GPU-powered cloud servers. However, transmitting the high-resolution video requires a large amount of network bandwidth, even though it is a compressed video stream. Foveated video encoding (FVE) reduces the bandwidth requirement by taking advantage of the non-uniform acuity of human visual system and by knowing where the user is looking. Based on a consumer-grade real-time eye tracker and an open source cloud gaming platform, we provide a cloud gaming FVE prototype that is game-agnostic and requires no modifications to the underlying game engine. In this article, we describe the prototype and its evaluation through measurements with representative games from different genres to understand the effect of parametrization of the FVE scheme on bandwidth requirements and to understand its feasibility from the latency perspective. We also present results from a user study on first-person shooter games. The results suggest that it is possible to find a “sweet spot” for the encoding parameters so the users hardly notice the presence of foveated encoding but at the same time the scheme yields most of the achievable bandwidth savings.
Local segmentation of images using an improved fuzzy C-means clustering algorithm based on self-adaptive dictionary learning

Image segmentation is an active research topic in image processing. The Fuzzy C-means (FCM) clustering analysis has been widely used in image segmentation. As there is a large amount of delicate tissues such as blood vessels and nerves in medical images, noise generated during imaging process can easily affect successful segmentation of these tissues. The traditional FCM algorithm is not ideal for segmentation of images containing strong noise. In this study, we proposed an improved FCM algorithm with anti-noise capability. We first discussed the algorithm of dictionary learning for noise reduction. Then we developed a new image segmentation algorithm as a combination of the dictionary learning for noise reduction and the improved fuzzy C-means clustering. Lastly we used the algorithm of the improved FCM to segment images, during which we removed the non-target areas making use of the grayscale features of images and extracted accurately the areas of interests. The algorithm was tested using synthetic Shepp-Logan images and real medical magnetic resonance imaging (MRI) and computed tomography (CT) images. Compared to the synthetic data and real medical images segmented by the fuzzy C-means (FCM) clustering algorithm, the Kernel Fuzzy C-mean (KFCM) clustering algorithm, spectral clustering algorithm, the sparse learning based fuzzy C-means (SL_FCM) clustering algorithm, and the modified spatial KFCM (MSFCM) algorithm, the images segmented by the dictionary learning Fuzzy C-mean clustering (DLFCM) algorithm have higher partition coefficient, lower partition entropy, better visual perception, better clustering accuracy, and clustering purity.
Semantic Segmentation of Underwater Images Based on Improved Deeplab

Image semantic segmentation technology has been increasingly applied in many fields, for example, autonomous driving, indoor navigation, virtual reality and augmented reality. However, underwater scenes, where there is a huge amount of marine biological resources and irreplaceable biological gene banks that need to be researched and exploited, are limited. In this paper, image semantic segmentation technology is exploited to study underwater scenes. We extend the current state-of-the-art semantic segmentation network DeepLabv3 + and employ it as the basic framework. First, the unsupervised color correction method (UCM) module is introduced to the encoder structure of the framework to improve the quality of the image. Moreover, two up-sampling layers are added to the decoder structure to retain more target features and object boundary information. The model is trained by fine-tuning and optimizing relevant parameters. Experimental results indicate that the image obtained by our method demonstrates better performance in improving the appearance of the segmented target object and avoiding its pixels from mingling with other class’s pixels, enhancing the segmentation accuracy of the target boundaries and retaining more feature information. Compared with the original method, our method improves the segmentation accuracy by 3%.
Detecting Misogyny and Xenophobia in Spanish Tweets Using Language Technologies

Today, misogyny and xenophobia are some of the most important social problems. With the increase in the use of social media, this feeling of hatred toward women and immigrants can be more easily expressed, and therefore it can have harmful effects on social media users. For this reason, it is important to develop systems capable of detecting hateful comments automatically. In this article, we analyze the hate speech in Spanish tweets against women and immigrants conducting classification experiments using different approaches. Moreover, we create appropriate language resources for hate speech detection in Spanish.
Small Object Augmentation of Urban Scenes for Real-Time Semantic Segmentation

Semantic segmentation is a key step in scene understanding for autonomous driving. Although deep learning has significantly improved the segmentation accuracy, current highquality models such as PSPNet and DeepLabV3 are inefficient given their complex architectures and reliance on multi-scale inputs. Thus, it is difficult to apply them to real-time or practical applications. On the other hand, existing real-time methods cannot yet produce satisfactory results on small objects such as traffic lights, which are imperative to safe autonomous driving. In this paper, we improve the performance of real-time semantic segmentation from two perspectives, methodology and data. Specifically, we propose a real-time segmentation model coined Narrow Deep Network (NDNet) and build a synthetic dataset by inserting additional small objects into the training images. The proposed method achieves 65.7% mean intersection over union (mIoU) on the Cityscapes test set with only 8.4G floatingpoint operations (FLOPs) on 1024×2048 inputs. Furthermore, by re-training the existing PSPNet and DeepLabV3 models on our synthetic dataset, we obtained an average 2% mIoU improvement on small objects.
Pose-Guided Tracking-by-Detection: Robust Multi-Person Pose Tracking

Multi-person pose tracking task aims to estimate and track person keypoints in videos. Most of the previous methods follow the general track-by-detection strategy that ignores the consistent pose information during the whole framework. Thus, they often suffer from missing detections or inaccurate human association in challenging scenes with motion blur or person occlusion. To handle those problems, we propose a pose-guided tracking-by-detection framework that fuses pose information into both video human detection and human association procedures. In the video human detection stage, we adopt the pose-guided person location prediction exploiting the temporal information to make up missing detections. Technically, pose heatmaps are utilized to cope with the person-specific intra-class distractors. Furthermore, in the human association stage, we propose an appearance discriminative model based on the hierarchical pose-guided graph convolutional networks (PoseGCN). The PoseGCN-based model exploits human structural relations to boost person representation. Extensive experiments show the superiority of our method on the challenging pose tracking benchmark. Our proposed method ranks first on the PoseTrack leaderboard. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> http://posetrack.net/leaderboard.php till the submission date (22-Aug-2019) of this paper. Our code has been publicly available at https://github.com/human-centric982/PGPT.
S2DNet: Depth Estimation From Single Image and Sparse Samples

Depth prediction from single image is a challenging task due to the intra scale ambiguity and unavailability of prior information. The prediction of an unambiguous depth from single RGB image is very important aspect for computer vision applications. In this paper, an end-to-end sparse-to-dense network (S2DNet) is proposed for single image depth estimation (SIDE). The proposed network processes single image along with the additional sparse depth samples for depth estimation. The additional sparse depth sample are acquired either with a low-resolution depth sensor or calculated by visual simultaneous localization and mapping (SLAM) algorithms. In first stage, the proposed S2DNet estimates coarse-level depth map using sparse-to-dense coarse network (S2DCNet). In second stage, the estimated coarse-level depth map is concatenated with the input image and used as an input to the sparse-to-dense fine network (S2DFNet) for fine-level depth map estimation. The proposed S2DFNet comprises of attention map architecture which helps to estimate the prominent depth information. The quantitative and qualitative performance evaluation of the proposed network has been carried out using the error metrics. We perform complete evaluation of S2DNet on four publically available benchmark data sets i.e. NYU Depth-V2 indoor dataset [1], KITTI odometry outdoor dataset [2], KITTI depth completion test database [3] and SUN-RGB database [4]. Further, we have extended the proposed S2DNet for image de-hazing. The experimental analysis shows that the proposed S2DNet outperforms the existing state-of-the-art methods for both single image depth estimation and image de-hazing.