Reviewerly/exHarmony huggingface dataset:
Real-Time 3D Multi-Object Detection and Localization Based on Deep Learning for Road and Railway Smart Mobility

For smart mobility, autonomous vehicles, and advanced driver-assistance systems (ADASs), perception of the environment is an important task in scene analysis and understanding. Better perception of the environment allows for enhanced decision making, which, in turn, enables very high-precision actions. To this end, we introduce in this work a new real-time deep learning approach for 3D multi-object detection for smart mobility not only on roads, but also on railways. To obtain the 3D bounding boxes of the objects, we modified a proven real-time 2D detector, YOLOv3, to predict 3D object localization, object dimensions, and object orientation. Our method has been evaluated on KITTI's road dataset as well as on our own hybrid virtual road/rail dataset acquired from the video game Grand Theft Auto (GTA) V. The evaluation of our method on these two datasets shows good accuracy, but more importantly that it can be used in real-time conditions, in road and rail traffic environments. Through our experimental results, we also show the importance of the accuracy of prediction of the regions of interest (RoIs) used in the estimation of 3D bounding box parameters.
Why Study Geysers?

Aside from captivating our senses, geysers have much to tell us about subsurface fluids, climate change effects, and the occurrence and limits of life on Earth and elsewhere in the solar system.
Communication Algorithm-Architecture Co-Design for Distributed Deep Learning

Large-scale distributed deep learning training has enabled developments of more complex deep neural network models to learn from larger datasets for sophisticated tasks. In particular, distributed stochastic gradient descent intensively invokes all-reduce operations for gradient update, which dominates communication time during iterative training epochs. In this work, we identify the inefficiency in widely used all-reduce algorithms, and the opportunity of algorithm-architecture co-design. We propose MultiTree all-reduce algorithm with topology and resource utilization awareness for efficient and scalable all-reduce operations, which is applicable to different interconnect topologies. Moreover, we co-design the network interface to schedule and coordinate the all-reduce messages for contention-free communications, working in synergy with the algorithm. The flow control is also simplified to exploit the bulk data transfer of big gradient exchange. We evaluate the co-design using different all-reduce data sizes for synthetic study, demonstrating its effectiveness on various interconnection network topologies, in addition to state-of-the-art deep neural networks for real workload experiments. The results show that MultiTree achieves 2.3× and 1.56× communication speedup, as well as up to 81% and 30% training time reduction compared to ring all-reduce and state-of-the-art approaches, respectively.
Making Order in User Experience Research to Support Its Application in Design and Beyond

The term User Experience (UX) was introduced to define the dynamics of the human-product interaction, and it was thought that design would have been a main recipient of UX research. However, it can be claimed that the outcomes of UX studies were not seamlessly transferred into design research and practice. Among the possible reasons, this paper addresses the fragmentary knowledge ascribable to the field of UX. The authors reviewed the literature analyzing the conceptual contributions that interpret UX, proposing definitions and/or a theoretical framework. This allowed the authors to provide an overview of recurring elements of UX, highlighting their relationships and affecting factors. This research aims to clarify the overall understanding of UX, along with its key components (the user, interaction, the system, and context) and dimensions (ergonomic, affective, and the cognitive experiences). The authors built a semantic construction inspired by the structure of a grammatical sentence to highlight the relationship between those components. Therefore, UX is defined by a subject/user who performs an action-interaction towards an object-system. A complement-context better defines the condition(s) where the action-interaction takes place. This work is expected to lay the foundations for the understanding of approaches and methods employed in UX studies, especially in design.
Color image encryption using RSA cryptosystem with a chaotic map in Hartley domain

This paper proposes an asymmetric encryption scheme for color images by introducing a new efficient triple-layered encryption scheme based on the RSA cryptosystem along with a chaotic map in the discrete Hartley domain. The present approach encrypts the image independently using the RSA cryptosystem and afterward transforms them into discrete Hartley domains to diffuse the image pixels. Further, the pixel positions are dislocated by applying a non-linear chaotic map to provide a complex structure of the scheme. The hardness of prime factorization of integers is intricacy in the RSA algorithm. The massive pixel confusion by a non-linear Hénon map gives rise to an efficient and robust system. Simulation results with performance analysis and the detailed comparison with extant systems assure robustness, validity, and security of the proposed scheme against various cryptanalytic attacks.
Self-Organized Variational Autoencoders (Self-Vae) For Learned Image Compression

In end-to-end optimized learned image compression, it is standard practice to use a convolutional variational autoencoder with generalized divisive normalization (GDN) to transform images into a latent space.Recently, Operational Neural Networks (ONNs) that learn the best non-linearity from a set of alternatives, and their "self-organized" variants, Self-ONNs, that approximate any non-linearity via Taylor series have been proposed to address the limitations of convolutional layers and a fixed nonlinear activation.In this paper, we propose to replace the convolutional and GDN layers in the variational autoencoder with self-organized operational layers, and propose a novel self-organized variational autoencoder (Self-VAE) architecture that benefits from stronger non-linearity.The experimental results demonstrate that the proposed Self-VAE yields improvements in both rate-distortion performance and perceptual image quality.
Comparing within- and cross-project machine learning algorithms for code smell detection

Code smells represent a well-known problem in software engineering, since they are a notorious cause of loss of comprehensibility and maintainability. The most recent efforts in devising automatic machine learning-based code smell detection techniques have achieved unsatisfying results so far. This could be explained by the fact that all these approaches follow a within-project classification, i.e. training and test data are taken from the same source project, which combined with the imbalanced nature of the problem, produces datasets with a very low number of instances belonging to the minority class (i.e. smelly instances). In this paper, we propose a cross-project machine learning approach and compare its performance with a within-project alternative. The core idea is to use transfer learning to increase the overall number of smelly instances in the training datasets. Our results have shown that cross-project classification provides very similar performance with respect to within-project. Despite this finding does not yet provide a step forward in increasing the performance of ML techniques for code smell detection, it sets the basis for further investigations.
Deep Learning: a Promising Method for Histological Class Prediction of Breast Tumors in Mammography

The objective of the study was to determine if the pathology depicted on a mammogram is either benign or malignant (ductal or non-ductal carcinoma) using deep learning and artificial intelligence techniques. A total of 559 patients underwent breast ultrasound, mammography, and ultrasound-guided breast biopsy. Based on the histopathological results, the patients were divided into three categories: benign, ductal carcinomas, and non-ductal carcinomas. The mammograms in the cranio-caudal view underwent pre-processing and segmentation. Given the large variability of the areola, an algorithm was used to remove it and the adjacent skin. Therefore, patients with breast lesions close to the skin were removed. The remaining breast image was resized on the Y axis to a square image and then resized to 512 × 512 pixels. A variable square of 322,622 pixels was searched inside every image to identify the lesion. Each image was rotated with no information loss. For data augmentation, each image was rotated 360 times and a crop of 227 × 227 pixels was saved, resulting in a total of 201,240 images. The reason why our images were cropped at this size is because the deep learning algorithm transfer learning used from AlexNet network has an input image size of 227 × 227. The mean accuracy was 95.8344% ± 6.3720% and mean AUC 0.9910% ± 0.0366%, computed on 100 runs of the algorithm. Based on the results, the proposed solution can be used as a non-invasive and highly accurate computer-aided system based on deep learning that can classify breast lesions based on changes identified on mammograms in the cranio-caudal view.
SEcure Similar Image Matching (SESIM): An Improved Privacy Preserving Image Retrieval Protocol over Encrypted Cloud Database

The emergence of cloud computing provides new dimension for the user to perform computations and store huge amount of data say images, video, audio etc,. However, the benefits of outsourcing the tasks bring privacy issues for the data that are outsourced. Consequently, to ensure privacy, multimedia data is encrypted and offloaded to the cloud database. Though images are encrypted, during retrieval, cloud server performs similarity computation on plaintext features. Fully homomorphic shows great results in computation over encrypted data yet due to its computation burden it is not applicable for practical usage. Thus, to guarantee the secrecy of outsourced image features, the proposed paper introduced an efficient SEcure Similar Image Matching (SESIM) protocol under encrypted domain. The computation overhead of the proposed SESIM protocol is compared with the existing secure distance metrics. The experiments and performance analysis show the effectiveness and security of the proposed scheme under encrypted cloud database.
An alternative way of evolutionary multimodal optimization: density-based population initialization strategy

Evolutionary algorithms rely on the population initialization strategy to determine a set of candidate solutions, which provide the preliminary knowledge of the problem landscape for the subsequent evolutionary process. However, the distribution of the initial population is rarely concerned in the multimodal evolutionary community. Moreover, a large initial population has not been attractive enough for evolutionary multimodal optimization, because it is difficult and challenging to achieve a balance between diversity and convergence within limited computing resources. As an extended version of our previous conference paper, this paper focuses on the construction of a large initial population that is beneficial for evolutionary multimodal optimization. First, we propose an improved density-based population initialization strategy to generate a uniform initial population with a certain degree of randomness. Then, after acquiring the raw fitness landscape of the problem, a fitness-weighted density-based population initialization strategy is proposed to explore potential global peaks. Finally, we design a multi-species cooperative coevolution algorithm for multimodal optimization, which balances the exploration and exploitation of such a population in the evolutionary process. Experimental results demonstrate that the proposed algorithm is better than the mainstream algorithms on the CEC2013 multimodal optimization benchmark.
CondNet: Conditional Classifier for Scene Segmentation

The fully convolutional network (FCN) has achieved tremendous success in dense visual recognition tasks, such as scene segmentation. The last layer of FCN is typically a global classifier (1x1 convolution) to recognize each pixel to a semantic label. We empirically show that this global classifier, ignoring the intra-class distinction, may lead to sub-optimal results. In this work, we present a conditional classifier to replace the traditional global classifier, where the kernels of the classifier are generated dynamically conditioned on the input. The main advantages of the new classifier consist of: (i) it attends on the intra-class distinction, leading to stronger dense recognition capability; (ii) the conditional classifier is simple and flexible to be integrated into almost arbitrary FCN architectures to improve the prediction. Extensive experiments demonstrate that the proposed classifier performs favourably against the traditional classifier on the FCN architecture. The framework equipped with the conditional classifier (called CondNet) achieves new state-of-the-art performances on two datasets. The code and models are available at https://git.io/CondNet.
Privacy-enhanced momentum federated learning via differential privacy and chaotic system in industrial Cyber–Physical systems

By leveraging Industrial Cyber-Physical Systems (ICPSs), deep learning-based methods are applied to address various industrial issues. Due to privacy policy reasons, conventional centralized learning (CL) may be improper for some industrial scenarios with sensitive data, such as smart medicine. Recently, federated learning (FL) as a novel collaboration learning approach has received extensive attention, which can break data barriers between different institutions to improve the model performance. However, the privacy information of the industrial agents may be inferred from their shared parameters. In this paper, we propose a Privacy-Enhanced Momentum Federated Learning framework, named PEMFL, that amalgamates differential privacy (DP), Momentum FL (MFL) and chaos-based encryption method. During the training, differentially privacy is used to disturb the industrial agents' gradient parameters in order to preserve their privacy information. Meanwhile, each industrial agent uses the chaos system-based encryption method to encrypt the weight parameters of their local models, which has two advantages: (1) the encryption method can enhance privacy protection; (2) the cloud server cannot access the truth value of the global model parameters which is a vital asset to the industrial agents. In addition, Momentum Gradient Descent (MGD) and an adjusting learning rate schedule are adopted to improve training efficiency for the PEMFL. The performance of the PEMFL is evaluated based on two non-i.i.d datasets. Theoretical analysis and experimental results demonstrate the excellent performance of the PEMFL in terms of accuracy and privacy security.
A Hierarchical Approach to Activity Recognition and Fall Detection Using Wavelets and Adaptive Pooling

Human activity recognition has been a key study topic in the development of cyber physical systems and assisted living applications. In particular, inertial sensor based systems have become increasingly popular because they do not restrict users' movement and are also relatively simple to implement compared to other approaches. In this paper, we present a hierarchical classification framework based on wavelets and adaptive pooling for activity recognition and fall detection predicting fall direction and severity. To accomplish this, windowed segments were extracted from each recording of inertial measurements from the SisFall dataset. A combination of wavelet based feature extraction and adaptive pooling was used before a classification framework was applied to determine the output class. Furthermore, tests were performed to determine the best observation window size and the sensor modality to use. Based on the experiments the best window size was found to be 3 s and the best sensor modality was found to be a combination of accelerometer and gyroscope measurements. These were used to perform activity recognition and fall detection with a resulting weighted F1 score of 94.67%. This framework is novel in terms of the approach to the human activity recognition and fall detection problem as it provides a scheme that is computationally less intensive while providing promising results and therefore can contribute to edge deployment of such systems.
Beyond Low-Pass Filters: Adaptive Feature Propagation on Graphs

Graph neural networks (GNNs) have been extensively studied for prediction tasks on graphs. As pointed out by recent studies, most GNNs assume local homophily, i.e., strong similarities in local neighborhoods. This assumption however limits the generalizability power of GNNs. To address this limitation, we propose a flexible GNN model, which is capable of handling any graphs without being restricted by their underlying homophily. At its core, this model adopts a node attention mechanism based on multiple learnable spectral filters; therefore, the aggregation scheme is learned adaptively for each graph in the spectral domain. We evaluated the proposed model on node classification tasks over eight benchmark datasets. The proposed model is shown to generalize well to both homophilic and heterophilic graphs. Further, it outperforms all state-of-the-art baselines on heterophilic graphs and performs comparably with them on homophilic graphs.
Noise estimation in 2D MRI using DWT coefficients and optimized neural network

Robust estimation of noise strength in Magnetic Resonance Images (MRIs) is a task of great importance due to its extensive applications in image post-processing techniques. Many noise estimation algorithms have been proposed in the past to retrieve noise characteristics of the magnitude image. These algorithms rely on either the spatial or transform domain information of the image. In this research article, we propose a noise estimation algorithm that utilizes a hybrid Discrete Wavelet Transform (DWT) and edge information suppression based algorithm to estimate the strength of noise in magnitude MR images. The wavelet coefficients corresponding to spatial domain edges are eliminated using a down-sampled complement of the Sobel edge map. The distribution of average noise energy in spatial and transform domain which follows Parseval’s theorem is utilized for calculating the initial noise estimate. Further, the robustness of the proposed algorithm is enhanced using Feedforward Neural Network. The proposed algorithm is observed to be computationally fast and accurate. Results on synthetic and clinical T1- and T2-w brain MRIs following Gaussian or Rician distribution show better performance than the existing benchmark algorithms over a wide range of input noise.
Task-Driven Deep Image Enhancement Network for Autonomous Driving in Bad Weather

Visual perception in autonomous driving is a crucial part of a vehicle to navigate safely and sustainably in different traffic conditions. However, in bad weather such as heavy rain and haze, the performance of visual perception is greatly affected by several degrading effects. Recently, deep learning-based perception methods have addressed multiple degrading effects to reflect real-world bad weather cases but have shown limited success due to 1) high computational costs for deployment on mobile devices and 2) poor relevance between image enhancement and visual perception in terms of the model ability. To solve these issues, we propose a task-driven image enhancement network connected to the high-level vision task, which takes in an image corrupted by bad weather as input. Specifically, we introduce a novel low memory network to reduce most of the layer connections of dense blocks for less memory and computational cost while maintaining high performance. We also introduce a new task-driven training strategy to robustly guide the high-level task model suitable for both high-quality restoration of images and highly accurate perception. Experiment results demonstrate that the proposed method improves the performance among lane and 2D object detection, and depth estimation largely under adverse weather in terms of both low memory and accuracy.
Are Microcontrollers Ready for Deep Learning-Based Human Activity Recognition?

The last decade has seen exponential growth in the field of deep learning with deep learning on microcontrollers a new frontier for this research area. This paper presents a case study about machine learning on microcontrollers, with a focus on human activity recognition using accelerometer data. We build machine learning classifiers suitable for execution on modern microcontrollers and evaluate their performance. Specifically, we compare Random Forests (RF), a classical machine learning technique, with Convolutional Neural Networks (CNN), in terms of classification accuracy and inference speed. The results show that RF classifiers achieve similar levels of classification accuracy while being several times faster than a small custom CNN model designed for the task. The RF and the custom CNN are also several orders of magnitude faster than state-of-the-art deep learning models. On the one hand, these findings confirm the feasibility of using deep learning on modern microcontrollers. On the other hand, they cast doubt on whether deep learning is the best approach for this application, especially if high inference speed and, thus, low energy consumption is the key objective.
A Visual Data Science Solution for Visualization and Visual Analytics of Big Sequential Data

In the current era of big data, huge volumes of valuable data have been generated and collected at a rapid velocity from a wide variety of rich data sources. In recent years, the initiates of open data also led to the willingness of many government, researchers, and organizations to share their data and make them publicly accessible. An example of open big data is healthcare, disease and epidemiological data such as privacy-preserving statistics on patients who suffered from epidemic diseases like the coronavirus disease 2019 (COVID-19). Analyzing these open big data can be for social good. For instance, analyzing and mining the disease statistics helps people to get a better understanding of the disease, which may inspire them to take part in preventing, detecting, controlling and combating the disease. As “a picture is worth a thousand words”, having the pictorial representation further enhances people’s understanding of the data and the corresponding results for the analysis and mining. Hence, in this paper, we present a visual data science solution for the visualization and visual analytics of big sequential data. We illustrate the ideas through the visualization and visual analytics of sequences of real-life COVID-19 epidemiological data. Our solution enables people to visualize COVID-19 epidemiological data and their temporal trends. It also allows people to visually analyze the data and discover relationships among popular features associated with the COVID-19 cases. Evaluation of these real-life sequential COVID-19 epidemiological data demonstrates the effectiveness of our visual data science solution in enhancing user experience in the visualization and visual analytics of big sequential data.