Reviewerly/exHarmony huggingface dataset:
SAR2OPT: Image Alignment Between Multi-Modal Images Using Generative Adversarial Networks

This work proposes an image-alignment method for multi-modal images (e.g., synthetic aperture radar (SAR) and optical satellite images) using an image-feature-based keypoint-matching algorithm. In applying the matching algorithm to multi-modal images, common features need to be obtained at the corresponding positions. However, the appearances of features among images are different. We solve this issue by translating the appearance of one modal image to the other using generative adversarial networks (GANs). In this work, we attempt to generate optical images from SAR images as a way to extract common features. Through an experiment, we confirm that the proposed method can estimate accurate correspondences between SAR and optical images.
FPGA-Based Training Accelerator Utilizing Sparseness of Convolutional Neural Network

Training of convolutional neural networks (CNNs) is almost exclusively performed on large clusters of GPUs. However, it consumes vast amounts of power. Thus, high-speed training systems superior in low-power consumption are desired. This paper proposes an FPGA-based training accelerator utilizing a sparseness of a CNN, which consists of universal convolutional units and pooling units with distributed stacks. The proposed universal convolution architecture supports various convolution operations, such as the point-wise, depth-wise, large kernel and atrous convolutions used in the modern CNN. Additionally, we utilize a fine-tuning scheme, which loads a pre-trained dense CNN to reduce the memory size for the training process, while it considers important connectivity to preserve recognition accuracy. Our training scheme reduces 85% parameters to accelerate the training computation and reduce on-chip size. Thus, it eliminates energy-consuming DRAM accesses. We implemented the proposed training accelerator on a Xilinx Virtex UltraScale+ VC1525 acceleration development board. Experimental results show that the proposed sparse CNN training accelerator on the FPGA can achieve four times faster, 2.9 times lower power consumption, and 11.6 times better performance per power, compared to the existing NVIDIA RTX2080Ti GPU.
Fast Convolutional Neural Networks in Low Density FPGAs Using Zero-Skipping and Weight Pruning

Edge devices are becoming smarter with the integration of machine learning methods, such as deep learning, and are therefore used in many application domains where decisions have to be made without human intervention. Deep learning and, in particular, convolutional neural networks (CNN) are more efficient than previous algorithms for several computer vision applications such as security and surveillance, where image and video analysis are required. This better efficiency comes with a cost of high computation and memory requirements. Hence, running CNNs in embedded computing devices is a challenge for both algorithm and hardware designers. New processing devices, dedicated system architectures and optimization of the networks have been researched to deal with these computation requirements. In this paper, we improve the inference execution times of CNNs in low density FPGAs (Field-Programmable Gate Arrays) using fixed-point arithmetic, zero-skipping and weight pruning. The developed architecture supports the execution of large CNNs in FPGA devices with reduced on-chip memory and computing resources. With the proposed architecture, it is possible to infer an image in AlexNet in 2.9 ms in a ZYNQ7020 and 1.0 ms in a ZYNQ7045 with less than 1% accuracy degradation. These results improve previous state-of-the-art architectures for CNN inference.
Intelligent Chatbot Model to Enhance the Emotion Detection in social media using Bi-directional Recurrent Neural Network

Abstract A Chatbot is like a digital assistant, can be pretrained or have self learning capability. It can be trained to understand the user queries and respond in natural language of the user during a conversation. Chatbot can actively help human to involve in a digital conversation since it can make use of the natural language processing. The existing Chatbot enhances the communication but it does not improvise the information when it is needed. And sometimes they go beyond the topic during conversation which is a major drawback. The proposed bot could identify the emotional status of the users and can provide suggestions using Bi-directional Recurrent Neural Network and tensor flow library functions. The popularity of the chat bots lets our system to be used for much variety of applications. These chat bots be used to automate the various fields like e-learning, e-government and web based models.
Mesh Defiltering via Cascaded Geometry Recovery

Abstract This paper addresses the nontraditional but practically meaningful reversibility problem of mesh filtering. This reverse‐filtering approach (termed a DeFilter) seeks to recover the geometry of a set of filtered meshes to their artifact‐free status. To solve this scenario, we adapt cascaded normal regression (CNR) to understand the commonly used mesh filters and recover automatically the mesh geometry that was lost through various geometric operations. We formulate mesh defiltering by an extreme learning machine (ELM) on the mesh normals at an offline training stage and perform it automatically at a runtime defiltering stage. Specifically, (1) to measure the local geometry of a filtered mesh, we develop a generalized reverse Filtered Facet Normal Descriptor (grFND) in the consistent neighbors; (2) to map the grFNDs to the normals of the ground‐truth meshes, we learn a regression function from a set of filtered meshes and their ground‐truth counterparts; and (3) at runtime, we reversely filter the normals of a filtered mesh, using the learned regression function for recovering the lost geometry. We evaluate multiple quantitative and qualitative results on synthetic and real data to verify our DeFilter's performance thoroughly. From a practical point of view, our method can recover the lost geometry of denoised meshes without needing to know the exact filter used previously, and can act as a geometry‐recovery plugin for most of the state‐of‐the‐art methods of mesh denoising.
A Comparative Study of Hand Gesture Recognition Devices in the Context of Game Design

Gesture recognition devices provide a new means for natural human-computer interaction. However, when selecting these devices for games, designers might find it challenging to decide which gesture recognition device will work best. In the present research, we compare three vision-based, hand gesture devices: Leap Motion, Microsoft's Kinect, and Intel's RealSense. We developed a simple hand-gesture based game to evaluate performance, cognitive demand, comfort, and player experience of using these gesture devices. We found that participants' preferred and performed much better using Leap Motion and Kinect compared to using RealSense. Leap Motion also outperformed or was equivalent to Kinect. These findings suggest that not all gesture recognition devices can be suitable for games and that designers need to make better decisions when selecting gesture recognition devices and designing gesture based games to insure the usability, accuracy, and comfort of such games.
Characterization of tension set behavior of a silicone rubber at different loads and temperatures via digital image correlation

This work aims to determine the tensile set behavior of a silicone rubber under different stress magnitudes and temperatures through digital image correlation implemented in an improved creep experimental set-up. Creep-recovery strains were measured with time at 20, 40 and 60 °C under tensile strengths of 98.1, 196.2 and 394.3 kPa, respectively. The behavior of creep and recovery strain with time at the different stress magnitudes and temperatures was successfully obtained by the experiments. The corresponding elastic and viscous components of the material for each condition were determined from the results. Overall, all obtained creep behaviors matched with the behavior of a four-element model of creep-recovery. The increase of temperature generated an increase of creep compliance at the three loads, but the increase of tensile load produced a decrease of creep compliance for the three temperatures. The strain was not recovered entirely in any case for the test time stated.
On the self-dual codes with an automorphism of order 5

For lengths 60, 62, and 64, by applying the method for constructing self-dual codes having an automorphism of odd prime order, we classify all optimal singly even self-dual codes with an automorphism of order 5 with 12 cycles. For the binary self-dual [62, 31, 12] codes we have found five new values of the parameter in the weight enumerator thus doubling the number of know values. For length 64 we have found codes with 14 new parameter values for both known weight enumerators. By shortening all binary self-dual [60, 30, 12] codes having an automorphism of order 5 we construct many new [58, 29, 10] self-dual codes. We have found a new value of the parameter in the weight enumerator of these codes.
Human Activities Recognition Using Accelerometer and Gyroscope

Plenty of supervised machine learning techniques that use accelerometer and gyroscope signals for automatic Human Activity Recognition (HAR) has been proposed in the last decade. According to recent studies, the combination of accelerometer and gyroscope signals, also called multimodal recognition, increases the accuracy in HAR with respect to the use of each signal alone. This paper presents the results of an analysis we performed in order to compare the effectiveness of machine learning techniques when used separately or jointly on accelerometer and gyroscope signals. We compare SVM and $$k-$$ NN classifiers (combined with hand-crafted features) with a deep residual network using three publicly available datasets. The results show that the use of deep learning techniques in multimodal mode (i.e., using accelerometer and gyroscope signals jointly) outperforms other strategies of at least 10%.
Multimodal Image Super-resolution via Deep Unfolding with Side Information

Deep learning methods have been successfully applied to various computer vision tasks. However, existing neural network architectures do not per se incorporate domain knowledge about the addressed problem, thus, understanding what the model has learned is an open research topic. In this paper, we rely on the unfolding of an iterative algorithm for sparse approximation with side information, and design a deep learning architecture for multimodal image super-resolution that incorporates sparse priors and effectively utilizes information from another image modality. We develop two deep models performing reconstruction of a high-resolution image of a target image modality from its low-resolution variant with the aid of a high-resolution image from a second modality. We apply the proposed models to super-resolve near-infrared images using as side information high-resolution RGB images. Experimental results demonstrate the superior performance of the proposed models against state-of-the-art methods including unimodal and multimodal approaches.
Unsupervised 3D Reconstruction Networks

In this paper, we propose 3D unsupervised reconstruction networks (3D-URN), which reconstruct the 3D structures of instances in a given object category from their 2D feature points under an orthographic camera model. 3D-URN consists of a 3D shape reconstructor and a rotation estimator, which are trained in a fully-unsupervised manner incorporating the proposed unsupervised loss functions. The role of the 3D shape reconstructor is to reconstruct the 3D shape of an instance from its 2D feature points, and the rotation estimator infers the camera pose. After training, 3D-URN can infer the 3D structure of an unseen instance in the same category, which is not possible in the conventional schemes of non-rigid structure from motion and structure from category. The experimental result shows the state-of-the-art performance, which demonstrates the effectiveness of the proposed method.
Generating 3D Faces using Multi‐column Graph Convolutional Networks

Abstract In this work, we introduce multi‐column graph convolutional networks (MGCNs), a deep generative model for 3D mesh surfaces that effectively learns a non‐linear facial representation. We perform spectral decomposition of meshes and apply convolutions directly in the frequency domain. Our network architecture involves multiple columns of graph convolutional networks (GCNs), namely large GCN (L‐GCN), medium GCN (M‐GCN) and small GCN (S‐GCN), with different filter sizes to extract features at different scales. L‐GCN is more useful to extract large‐scale features, whereas S‐GCN is effective for extracting subtle and fine‐grained features, and M‐GCN captures information in between. Therefore, to obtain a high‐quality representation, we propose a selective fusion method that adaptively integrates these three kinds of information. Spatially non‐local relationships are also exploited through a self‐attention mechanism to further improve the representation ability in the latent vector space. Through extensive experiments, we demonstrate the superiority of our end‐to‐end framework in improving the accuracy of 3D face reconstruction. Moreover, with the help of variational inference, our model has excellent generating ability.
CEOS Analysis Ready Data For Land – An Overview on the Current and Future Work

Analysis Ready Data (ARD) products are enabling users to get first hand satellite data that are `ready to use' for a wide range of applications, including time-series analysis and the way forward to multi-sensor interoperability. The Committee on Earth Observation Satellites (CEOS) is leading the CEOS Analysis Ready Data for Land (CARD4L) initiative. The rationale behind this initiative is to enable users to access products that have been already processed to a certain level. This will allow users to carry out immediate analysis on the data and to address a variety of applications without the extra time and cost that is associated with the data pre-processing steps. The aim of the proposed paper is to give an overview on the work already carried out by the CEOS community on the CARD4L framework, to introduce the processes and steps that Earth Observation (EO) data providers should undertake to have a CARD4L level product as well as the planned steps that CEOS LSIVC will be undertaking to accomplish the CARD4L initiative.
Blockchain-based RBAC for user authentication with anonymity

RBAC is a traditional approach of controlling who wants to access resources in a computer system through a permission to a role. Blockchain is an open, distributed ledger that can permanently record transactions. Smart Contract facilitates negotiations for a trustless transaction by enabling automatic payment. It is currently being applied to various kinds of business applications. In a smart contract that requires payment with cryptocurrency, if access control is performed only without individual authentication, progress for applications to various fields will be restricted. In this paper, we propose a blockchain-based RBAC with an authentication mechanism that can provides role-based access control while also providing individual account-based authentication. We have defined three interfaces based on blockchain to realize it. The proposed mechanism provides anonymity-based authentication by using a hash function with a pass that is indistinguishable from the owner. This was simulated on an Ethereum-based test network and compared with existing methods. The proposed mechanism not only added authentication methods, but also was more efficient in gas use and performance time than the existing RBAC model.
Scalable Causal Graph Learning through a Deep Neural Network

Learning the causal graph in a complex system is crucial for knowledge discovery and decision making, yet it remains a challenging problem because of the unknown nonlinear interaction among system components. Most of the existing methods either rely on predefined kernel or data distribution, or they focus simply on the causality between a single target and the remaining system. This work presents a deep neural network for scalable causal graph learning (SCGL) through low-rank approximation. The SCGL model can explore nonlinearity on both temporal and intervariable relationships without any predefined kernel or distribution assumptions. Through low-rank approximation, the noise influence is reduced, and better accuracy and high scalability are achieved. Experiments using synthetic and real-world datasets show that our SCGL algorithm outperforms existing state-of-the-art methods for causal graph learning.
Deep Learning and the Future of Biomedical Image Analysis

Deep Learning (DL) is popular among the researchers and academicians due to its reliability and accuracy, especially in the field of engineering and medical sciences. In the field of medical imaging for the diagnosis of disease, DL techniques are very helpful for early detection. Most important features of DL techniques are that they are uncomplicated with lower complexity, which ultimately saves the time and money and tackle many tough tasks simultaneously. Artificial Intelligence (AI) and Deep Learning (DL) technologies have rapidly improved in recent years. These techniques played an important role in every field of application, especially in the medical field such as in image processing, image fusion, image segmentation, image retrieval, image analysis, computer aided diagnosis (CAD), image registration and, image-guided therapy and many more. The aim of writing this chapter is to describe the DL methods and, the future of biomedical imaging using DL in detail and discuss the issues and challenges.
MrMine

Network embedding has become the cornerstone of a variety of mining tasks, such as classification, link prediction, clustering, anomaly detection and many more, thanks to its superior ability to encode the intrinsic network characteristics in a compact low-dimensional space. Most of the existing methods focus on a single network and/or a single resolution, which generate embeddings of different network objects (node/subgraph/network) from different networks separately. A fundamental limitation with such methods is that the intrinsic relationship across different networks (e.g., two networks share same or similar subgraphs) and that across different resolutions (e.g., the node-subgraph membership) are ignored, resulting in disparate embeddings. Consequentially, it leads to sub-optimal performance or even becomes inapplicable for some downstream mining tasks (e.g., role classification, network alignment. etc.). In this paper, we propose a unified framework MrMine to learn the representations of objects from multiple networks at three complementary resolutions (i.e., network, subgraph and node) simultaneously. The key idea is to construct the cross-resolution cross-network context for each object. The proposed method bears two distinctive features. First, it enables and/or boosts various multi-network downstream mining tasks by having embeddings at different resolutions from different networks in the same embedding space. Second, Our method is efficient and scalable, with a O(nlog(n)) time complexity for the base algorithm and a linear time complexity w.r.t. the number of nodes and edges of input networks for the accelerated version. Extensive experiments on real-world data show that our methods (1) are able to enable and enhance a variety of multi-network mining tasks, and (2) scale up to million-node networks.
Manual IoT Forensics of a Samsung Gear S3 Frontier Smartwatch

Internet of Things (IoT) devices such as Samsung Gear S3 Frontier smartwatch are great sources of potential digital evidence, due to their constant daily use. The main aim of this paper is to analyze the capabilities and limitations of IoT forensics of a Samsung Gear S3 Frontier smartwatch. Main concepts of IoT forensics, a summary of the current and future research progress and challenges, is given. A scenario of watch events during 3 hours of usage was recorded, which forensic analysis had to restore. Manual extraction and analysis of data, along with the detailed look at the discovered relevant files, and achieved results are presented. The primary contribution of this paper consists of a detailed approach to a particular smartwatch forensic, which supports future forensic investigations.
A3C Based Motion Learning for an Autonomous Mobile Robot in Crowds

The paper proposes a motion planning method using a deep reinforcement learning algorithm, Asynchronous Advantage Actor-Critic (A3C). For mobile robot navigation tasks in crowds, existing path planning based approaches are limited because the surrounding environments change dynamically. The correct motion in such a dynamic environment is underspecified, and a reinforcement learning approach is suitable for generating applicable motion. We propose an A3C based motion planning method for acquiring robot motion for a robot moving through crowds. The proposed method is evaluated in simulated crowds of pedestrians. The experiment section shows the basic performance depending on training parameters and some generated motion examples in the simulator. The learning results using real pedestrian motion are also shown.
Do I Smell Coffee? The Tale of a 360° Mulsemedia Experience

One of the main challenges in current multimedia networking environments is to find solutions to help accommodate the next generation of mobile application classes with stringent Quality of Service (QoS) requirements while enabling the Quality of Experience (QoE) provisioning for users. One such application class, featured in this article, is 360° mulsemedia—multiple sensorial media—which enriches 360° video by adding sensory effects that stimulate human senses beyond those of sight and hearing, such as the tactile and olfactory ones. In this article, we present a conceptual framework for 360° mulsemedia delivery and a 360° mulsemedia-based prototype that enables users to experience 360° mulsemedia content. User evaluations revealed that higher video resolutions do not necessarily lead to the highest QoE levels in our experimental setup. Therefore, bandwidth savings can be leveraged with no detrimental impact on QoE.
DETECTION MODELS FOR UNINTENTIONAL FINANCIAL RESTATEMENTS

The aim of manuscript is to analyze and identify determinants of honest accounting errors leading to financial restatements based on data from SEC database and from annual reports. Reason for this study is that accounting errors are expensive for companies that need to change already published financial statements and have impact on company reputation and stock price. Most of authors focus on prediction of accounting frauds and financial restatements remain in the background of research. This study initially tests existing accounting fraud detection model of Beneish on a sample of 40 financial restatement companies over 10 years and develops two new pioneer prediction models, one based on linear discriminant analysis (LDA) and another based on logistic regression. In testing dataset, LDA model has achieved accuracy 70.96%, specificity 25.00% and sensitivity 79.83% and logistic regression model has achieved accuracy 62.22%, specificity 41.66% and sensitivity 66.67%, performance of both models is better than existing Beneish model or other studies in this field. Developed models can be widely used by both internal and external users of financial statements, who would like to determine if financial statements of analyzed company include accounting errors or not, thanks to easily interpretable results in equation form.