Reviewerly/exHarmony huggingface dataset:
E-DNAS: Differentiable Neural Architecture Search for Embedded Systems

Designing optimal and light weight networks to fit in resource-limited platforms like mobiles, DSPs or GPUs is a challenging problem with a wide range of interesting applications, e.g. in embedded systems for autonomous driving. While most approaches are based on manual hyperparameter tuning, there exist a new line of research, the so-called NAS (Neural Architecture Search) methods, that aim to optimize several metrics during the design process, including memory requirements of the network, number of FLOPs, number of MACs (Multiply-ACcumulate operations) or inference latency. However, while NAS methods have shown very promising results, they are still significantly time and cost consuming. In this work we introduce E-DNAS, a differentiable architecture search method, which improves the efficiency of NAS methods in designing light-weight networks for the task of image classification. Concretely, E-DNAS computes, in a differentiable manner, the optimal size of a number of meta-kernels that capture patterns of the input data at different resolutions. We also leverage on the additive property of convolution operations to merge several kernels with different compatible sizes into a single one, reducing thus the number of operations and the time required to estimate the optimal configuration. We evaluate our approach on several datasets to perform classification. We report results in terms of the SoC (System on Chips) metric, typically used in the Texas Instruments TDA2x families for autonomous driving applications. The results show that our approach allows designing low latency architectures significantly faster than state-of-the-art.
Softer Pruning, Incremental Regularization

Network pruning is widely used to compress Deep Neural Networks (DNNs). The Soft Filter Pruning (SFP) method zeroizes the pruned filters during training while updating them in the next training epoch. Thus the trained information of the pruned filters is completely dropped. To utilize the trained pruned filters, we proposed a SofteR Filter Pruning (SRFP) method and its variant, Asymptotic SofteR Filter Pruning (ASRFP), simply decaying the pruned weights with a monotonic decreasing parameter. Our methods perform well across various networks, datasets and pruning rates, also transferable to weight pruning. On ILSVRC-2012, ASRFP prunes 40% of the parameters on ResNet-34 with 1.63% top-1 and 0.68% top-5 accuracy improvement. In theory, SRFP and ASRFP are an incremental regularization of the pruned filters. Besides, We note that SRFP and ASRFP pursue better results while slowing down the speed of convergence.
Social Distance Identification Using Optimized Faster Region-Based Convolutional Neural Network

In 2019, an aggressive coronavirus disease (COVID-19) has resulted in large-scale epidemic with its deadly outbreak in more than 190 countries and nearly 114 million confirmed cases as of February 2021, along with 2.52 million deaths worldwide. As no proper vaccinations are available, the only viable solution to fight this pandemic is physical distance or social distancing. Reducing the spread of COVID-19 in public areas, and to reduce the rate of losing helpless lives, social distancing is a primary and primitive proposed approach by the World Health Organization (WHO). In shopping malls, organizations, schools and other covered areas, the government and national healthcare authorities have set a 2-meter or 6-foot social distance in their surroundings as a required safety precaution. It is tough for authorities to manage people manually, whether the individuals maintain social distancing in public and crowded areas. Keeping this as our motivation, this research work proposes a simplified and optimized way to achieve social distancing detection between the individuals and notifying the higher officials if it is not maintained properly. This paper proposes OFRCNN -optimized faster region-based convolutional neural network methodology, which runs in real-time and is built using a Faster Region-Based Convolutional Neural Network (Faster R-CNN), which is used for object detection and COCO dataset is used for training.
Bayesian nonparametric learning of how skill is distributed across the mutual fund industry

Abstract In this paper, we use Bayesian nonparametric learning to estimate the skill of actively managed mutual funds and also to estimate the population distribution of skill. A nonparametric hierarchical prior, where the hyperprior distribution is unknown and modeled with a Dirichlet Process prior, is used to model the skill parameter, with its posterior predictive distribution being an estimate of the population distribution. Our nonparametric approach is equivalent to an infinitely ordered mixture of normals where we resolve the uncertainty in the number of mixture components by learning how to partition the funds into groups according to the average ability and the variability in the skill of a group. By resolving the mixture’s uncertainty, our nonparametric prior avoids having to sequentially estimate and test an array of pre-specified, finite ordered, mixture priors. Applying our Bayesian nonparametric learning approach to a panel of actively managed, domestic equity funds, we find the population distribution of skill to be fat-tailed, skewed towards higher levels of performance, with two distinct modes – a primary mode where the average ability covers the average fees charged by funds, and a secondary mode at a performance level where a fund loses money for its investors.
EADNet: Efficient Asymmetric Dilated Network For Semantic Segmentation

Due to real-time image semantic segmentation needs on power constrained edge devices, there has been an increasing desire to design lightweight semantic segmentation neural network, to simultaneously reduce computational cost and increase inference speed. In this paper, we propose an efficient asymmetric dilated semantic segmentation network, named EADNet, which consists of multiple developed asymmetric convolution branches with different dilation rates to capture the variable shapes and scales information of an image. Specially, a multi-scale multi-shape receptive field convolution (MMRFC) block with only a few parameters is designed to capture such information. Experimental results on the Cityscapes dataset demonstrate that our proposed EADNet achieves segmentation mIoU of 67.1% with smallest number of parameters (only 0.35M) among mainstream lightweight semantic segmentation networks.
TISSUE CLASSIFICATION FOR COLORECTAL CANCER UTILIZING TECHNIQUES OF DEEP LEARNING AND MACHINE LEARNING

Since the introduction of image pattern recognition and computer vision processing, the classification of cancer tissues has been a challenge at pixel-level, slide-level, and patient-level. Conventional machine learning techniques have given way to Deep Learning (DL), a contemporary, state-of-the-art approach to texture classification and localization of cancer tissues. Colorectal Cancer (CRC) is the third ranked cause of death from cancer worldwide. This paper proposes image-level texture classification of a CRC dataset by deep convolutional neural networks (CNN). Simple DL techniques consisting of transfer learning and fine-tuning were exploited. VGG-16, a Keras pre-trained model with initial weights by ImageNet, was applied. The transfer learning architecture and methods responding to VGG-16 are proposed. The training, validation, and testing sets included 5000 images of 150 × 150 pixels. The application set for detection and localization contained 10 large original images of 5000 × 5000 pixels. The model achieved F1-score and accuracy of 0.96 and 0.99, respectively, and produced a false positive rate of 0.01. AUC-based evaluation was also measured. The model classified ten large previously unseen images from the application set represented in false color maps. The reported results show the satisfactory performance of the model. The simplicity of the architecture, configuration, and implementation also contributes to the outcome this work.
Tensor-Compensated Color Face Recognition

Making face recognition more reliable under uncontrolled lighting conditions is one of the most important challenges for practical recognition systems. The reasons come from the need for automatic recognitions and security systems. To overcome this problem, we propose a novel illumination compensation method called adaptive high-order singular value decomposition to enhance face images at the preprocessing step of the face recognition system. First, we present an RGB color face image as a third-order tensor. Then, adaptive high-order singular value decomposition is proposed to adjust the core tensor automatically by multiplying three frontal slices of the core tensor with their corresponding compensation weight coefficients while keeping the third inverse factor fixed. The experiments performed on five of the most famous public color face databases, namely CMU-PIE, Color FERET, FEI, LFW, and IJB-C reveal that adaptive high-order singular value decomposition not only yields compensated images that are clear, natural, and smooth but also considerably improves the accuracy and computing time of face recognition.
Align or attend? Toward More Efficient and Accurate Spoken Word Discovery Using Speech-to-Image Retrieval

Multimodal word discovery (MWD) is often treated as a byproduct of the speech-to-image retrieval problem. However, our theoretical analysis shows that some kind of alignment/attention mechanism is crucial for a MWD system to learn meaningful word-level representation. We verify our theory by conducting retrieval and word discovery experiments on MSCOCO and Flickr8k, and empirically demonstrate that both neural MT with self-attention and statistical MT achieve word discovery scores that are superior to those of a state-of-the-art neural retrieval system, outperforming it by 2% and 5% alignment F1 scores respectively.
Postdigital Marxism and education

We now live in a world where digital technology is no longer ‘separate, virtual, [or] “other” to a ‘natural’ human and social life’ (Jandric et al., 2018, p. 893). Contemporary information and comm...
Rechained: Sybil-Resistant Distributed Identities for the Internet of Things and Mobile Ad Hoc Networks

Today, increasing Internet of Things devices are deployed, and the field of applications for decentralized, self-organizing networks keeps growing. The growth also makes these systems more attractive to attackers. Sybil attacks are a common issue, especially in decentralized networks and networks that are deployed in scenarios with irregular or unreliable Internet connectivity. The lack of a central authority that can be contacted at any time allows attackers to introduce arbitrary amounts of nodes into the network and manipulate its behavior according to the attacker’s goals, by posing as a majority participant. Depending on the structure of the network, employing Sybil node detection schemes may be difficult, and low powered Internet of Things devices are usually unable to perform impactful amounts of work for proof-of-work based schemes. In this paper, we present Rechained, a scheme that monetarily disincentivizes the creation of Sybil identities for networks that can operate with intermittent or no Internet connectivity. We introduce a new revocation mechanism for identities, tie them into the concepts of self-sovereign identities, and decentralized identifiers. Case-studies are used to discuss upper- and lower-bounds for the costs of Sybil identities and, therefore, the provided security level. Furthermore, we formalize the protocol using Colored Petri Nets to analyze its correctness and suitability. Proof-of-concept implementations are used to evaluate the performance of our scheme on low powered hardware as it might be found in Internet of Things applications.
Forecasting the Early Market Movement in Bitcoin Using Twitter's Sentiment Analysis: An Ensemble-based Prediction Model

Data collected from social media such as tweets, posts, and blogs can assist in an early indication of market sentiment in the financial field. This has frequently been conducted on Twitter data in particular. Using data mining techniques, opinion mining, machine learning, natural language processing (NLP), and knowledge management, the underlying public mood states and sentiment can be uncovered. As cryptocurrencies play an increasingly significant role in global economies, there is an evident relationship between Twitter sentiment and future price fluctuations in Bitcoin. This paper assesses Tweets' collection, manipulation, and interpretation to predict early market movements of cryptocurrency. More specifically, sentiment analysis and text mining methods, including Logistic Regressions, Binary Classified Vector Prediction, Support Vector Mechanism, and Naive Bayes, were considered. Each model was evaluated on their ability to predict public mood states as measured by `tweets' from Twitter during the era of covid-19. An XGBoost-Composite ensemble model is constructed, which achieved higher performance than the state-of-the-art prediction models.
HPERL: 3D Human Pose Estimation from RGB and LiDAR

In-the-wild human pose estimation has a huge potential for various fields, ranging from animation and action recognition to intention recognition and prediction for autonomous driving. The current state-of-the-art is focused only on RGB and RGB-D approaches for predicting the 3D human pose. However, not using precise LiDAR depth information limits the performance and leads to very inaccurate absolute pose estimation. With LiDAR sensors becoming more affordable and common on robots and autonomous vehicle setups, we propose an end-to-end architecture using RGB and LiDAR to predict the absolute 3D human pose with unprecedented precision. Additionally, we introduce a weakly-supervised approach to generate 3D predictions using 2D pose annotations from PedX [1]. This allows for many new opportunities in the field of 3D human pose estimation.
A fast and effective vision enhancement method for single foggy image

Images captured in foggy weather conditions suffers from inevitable problems such as low contrast, blurriness and less visibility. In many computer and vision applications like surveillance, object detection, object tracking and navigation, these low-quality images cannot be used and it requires improvement in the image quality. Different algorithms have been proposed in this direction and to upgrade the quality of a foggy image. Most of the existing methods gives good quality image but with high time complexity. In this paper, a novel and effective method is proposed to remove fog from a single image. The proposed method is based on principal component analysis and modified dark channel prior. In proposed algorithm, foggy image is pre-processed using principal component analysis. This pre-processed image is further enhanced using fast global smoothening filter. Time complexity of the proposed method is much less as compared to the various existing methods and at the same time, quality is also maintained. Also, proposed algorithm does not require a large data set and specific hardware. To see the effectiveness of the proposed technique, both qualitative and quantitative analysis has been done on synthetic data set as well as on natural dataset.
Semantic Segmentation of Autonomous Driving Images by the Combination of Deep Learning and Classical Segmentation

One of the bold issues in autonomous driving is considered semantic image segmentation, which must be done with high accuracy and speed. Semantic segmentation is used to understand an image at the pixel level. In this regard, various architectures based on deep neural networks have been proposed for semantic segmentation of autonomous driving image datasets. In this paper, we proposed a novel combination method in which dividing the image into its constituent regions with the help of classical segmentation brings about achieving beneficial information that improves the DeepLab v3+ network results. The proposed method with the two backbones, Xception and MobileNetV2, obtains the mIoU of 81.73% and 76.31% on the Cityscapes dataset, respectively, which shows promising results compared to the model without post-processing.
FourierNet: Compact Mask Representation for Instance Segmentation Using Differentiable Shape Decoders

We present FourierNet, a single shot, anchor-free, fully convolutional instance segmentation method that predicts a shape vector. Consequently, this shape vector is converted into the masks' contour points using a fast numerical transform. Compared to previous methods, we introduce a new training technique, where we utilize a differentiable shape decoder, which manages the automatic weight balancing of the shape vector's coefficients. We used the Fourier series as a shape encoder because of its coefficient interpretability and fast implementation. FourierNet shows promising results compared to polygon representation methods, achieving 30.6 mAP on the MS COCO 2017 benchmark. At lower image resolutions, it runs at 26.6 FPS with 24.3 mAP. It reaches 23.3 mAP using just eight parameters to represent the mask (note that at least four parameters are needed for bounding box prediction only). Qualitative analysis shows that suppressing a reasonable proportion of higher frequencies of Fourier series, still generates meaningful masks. These results validate our understanding that lower frequency components hold higher information for the segmentation task, and therefore, we can achieve a compressed representation. Code is available at: github.com/cogsys-tuebingen/FourierNet.
A Quantitative Metric for Privacy Leakage in Federated Learning

In the federated learning system, parameter gradients are shared among participants and the central modulator, while the original data never leave their protected source domain. However, the gradient itself might carry enough information for precise inference of the original data. By reporting their parameter gradients to the central server, client datasets are exposed to inference attacks from adversaries. In this paper, we propose a quantitative metric based on mutual information for clients to evaluate the potential risk of information leakage in their gradients. Mutual information has received increasing attention in the machine learning and data mining community over the past few years. However, existing mutual information estimation methods cannot handle high-dimensional variables. In this paper, we propose a novel method to approximate the mutual information between the high-dimensional gradients and batched input data. Experimental results show that the proposed metric reliably reflect the extent of information leakage in federated learning. In addition, using the proposed metric, we investigate the influential factors of risk level. It is proven that, the risk of information leakage is related to the status of the task model, as well as the inherent data distribution.
Solving a Class of Non-Convex Min-Max Games Using Adaptive Momentum Methods

Adaptive momentum methods have recently attracted a lot of attention for training of deep neural networks. They use an exponential moving average of past gradients of the objective function to update both search directions and learning rates. However, these methods are not suited for solving min-max optimization problems that arise in training generative adversarial networks. In this paper, we propose an adaptive momentum min-max algorithm that generalizes adaptive momentum methods to the non-convex min-max regime. Further, we establish non-asymptotic rates of convergence for it when used in a reasonably broad class of non-convex min-max optimization problems. Experimental results illustrate its superior performance vis-a-vis benchmark methods for solving such problems.
Weighted regularized extreme learning machine to model the discharge coefficient of side slots

In the current research, a modern learning machine algorithm named "Weighted Regularized Extreme Learning Machine (WRELM)" is implemented for the first time for the simulation of the coefficient of discharge of side slots. For this purpose, an effective variable on the coefficient of discharge of side slots is firstly introduced, then five distinctive WRELM models are produced by it for the estimation of the coefficient. In the next stage, a database is created for verification of WRELM results. it should be mentioned that 70% of the data are utilized for training the WRELM models, while the rest (i.e. 30%) for testing them. After that, the optimal number of hidden layer neurons as well as the best activation function of the WRELM algorithm are chosen. In addition, the best regularization parameter and also the weight function of the WRELM are achieved. By conducting a sensitivity analysis, the most effective variable for the simulation of the coefficient of discharge along with the WRELM superior model is introduced. The WRELM superior model estimates values of the coefficient of discharge with the maximum exactness and the highest correlation. For instance, the estimations of the correlation coefficient and scatter index for this model are computed to be 0.930 and 0.051, respectively. The sensitivity analysis shows that the ratio of the side slot crest height to its length and the Froude number should be considered as the most important input variables. A comparison between the WRELM with the ELM displays that the former works much better. Furthermore, an uncertainty analysis is executed for both models. Eventually, an equation is suggested for the estimation of the coefficient of discharge and a partial derivative sensitivity analysis is performed on it.
Bayesian sparse hierarchical model for image denoising

Sparse models and their variants have been extensively investigated, and have achieved great success in image denoising. Compared with recently proposed deep-learning-based methods, sparse models have several advantages: (1) Sparse models do not require a large number of pairs of noisy images and the corresponding clean images for training. (2) The performance of sparse models is less reliant on the training data, and the learned model can be easily generalized to natural images across different noise domains. In sparse models, ℓ0 norm penalty makes the problem highly non-convex, which is difficult to be solved. Instead, ℓ1 norm penalty is commonly adopted for convex relaxation, which is considered as the Laplacian prior from the Bayesian perspective. However, many previous works have revealed that ℓ1 norm regularization causes a biased estimation for the sparse code, especially for high-dimensional data, e.g., images. In this paper, instead of using the ℓ1 norm penalty, we employ an improper prior in the sparse model and formulate a hierarchical sparse model for image denoising. Compared with other competitive methods, experiment results show that our proposed method achieves a better generalization for images with different characteristics across various domains, and achieves state-of-the-art performance for image denoising on several benchmark datasets.