Reviewerly/exHarmony huggingface dataset:
Robust Self-supervised Correspondence Learning for SAR-optical Image Matching

SAR-optical image matching is a prerequisite for the joint use of these two modalities in many remote sensing applications. Due to significant nonlinear radiometric difference, it is a challenging task to perform robust matching between SAR and optical images. Recently, supervised deep learning methods have become the mainstream in the task of image feature matching, demonstrating the great power in feature description. One drawback of supervised deep learning methods is the huge demand for a vast amount of labelled training data, causing a big obstacle to its application in remote sensing. For this problem, we propose a self-supervised deep learning method to find correspondences using local image structure feature for SAR-optical image matching. In order to learn to exploit image structure cues, our network operates on the gradient of the input image instead of raw image intensity to produce dense descriptors, which paves the way for the self-supervised training. Next, a 4-D correlation map of SAR-optical images is computed using these descriptors. Lastly, reliable point-to-point correspondences can be extracted from the correlation map by means of the mutual nearest neighbor (MNN) and the RANSAC-based outlier removal. Extensive experimental results demonstrate that our method can establish robust correspondences between SAR and optical images with no demand for labelled data.
Big Data and Deep Learning Techniques Applied in Intelligent Recommender Systems

With the rapid growth of big data and information all-round the internet, deep learning has become a solution to improve the quality of Recommender Systems (RS). Adapting to various kinds of data formats, modern deep learning methods can extract nontrivial hidden features from data in different fields, including deep semantic features from images and texts or relationships between users and items. However, some recently proposed algorithms have not yet been exploited by RS-related applications. In this paper, we will discuss several trendy deep learning networks with some of their notable variants in RS with respect to different kinds of data. In addition, we will provide an overview of approaches to choosing deep learning algorithms for specific recommendation tasks. Secondly, we will provide some possible research directions related to deep learning applications in RS.
HTML Code Generation from Website Images and Sketches using Deep Learning-Based Encoder-Decoder Model

Making mockups of the website’s numerous pages is the first step in website design. Mock-ups can be created manually, using graphic design software, or using specialist tools. Then, software engineers turn the mock-up into structured HTML code. It takes a lot of time and effort to build the required template by repeating this method several times. This work proposes two deep learning-based encoder-decoder models that automatically generate HTML (Hypertext Markup Language) code from screenshot images of web pages and hand-drawn sketches. The webpage images are pre-processed using grayscaling, binarization and contour detection techniques before being handed over to the model as input. With an F1score of 0.91, the CNN-LSTM-based encoder-decoder model has outclassed the RNN-LSTM-based encoder-decoder model, which has an F1-score of 0.74.
A Novel Approach for Improving Image Compression Ratio

Image compression is an important issue in image information processing domain. The size of the image data is a crucial factor while processing image. It can affect the architecture of the design or memory size required, time required to process huge data and may increase complexity in processing. Therefore it is necessary to reduce the image data size by reducing the redundancies in the image data while retaining its quality same. In this paper we propose a new approach to compress an image while retaining all its features. Our approach shows superior performance over other compression techniques already available in the market. Our approach gives a compression ratio of 185.9 and a peak signal to noise ratio equal to 80.12.
Symbol Position Recovery for Optical Camera Communication With High-Density Matrix Codes

Optical transmission of barcodes using handheld cameras offers a practical solution for transmitting small amounts of data without device pairing. In various application scenarios, 2D matrix codes such as QR Code and Data Matrix have become ubiquitous for broadcasting short information such as web links. In recent years, both the increasing demand for higher data throughput and the rapid development of on-board cameras in smartphones have driven research into high-capacity systems. While the reduction of 2D symbol size can quadratically increase the spatial data capacity, accurate localization of small symbols, each consisting of only a few pixels, is extremely difficult due to the non-idealities of the camera-based optical channel. As a result, the data throughput achieved by state-of-the-art systems employing pattern-based methods is still far from optimal. In this work, we propose, for the first time to the best of our knowledge, a novel patternless approach to symbol position recovery for high-density matrix codes. We adapt the even-law synchronizer to estimate the symbol positions in each spatial dimension separately, and compensate for the impact of lens distortion by a successive curve fitting process. Simulation and experimental results show that our method significantly outperforms pattern-based benchmark methods by providing excellent accuracy and reliability.
Average Activation Network for Weakly Supervised Semantic Segmentation

Most current image-level weakly supervised semantic segmentation (WSSS) methods are based on class activation map (CAM). However, the main limitation of weakly supervised semantic segmentation is that the CAMs generated by the WSSS network always focus on the most discriminative parts of the object, limiting the CAM to capture the holistic object. So we propose an average activation network to generate CAMs of the holistic object with weakly supervision. We restrain the highest activation regions of the CAM by continuously splitting the training image on the maximum activation point of the CAM during the training process. In this way, our network pays more attention to the whole of the object. In addition, we propose a CAM similarity loss function to narrow the gap between fully-supervised semantic segmentation (FSSS) and WSSS. We conducted experiments on the PASCAL VOC 2012 dataset to validate the effectiveness of our method.
A Universal Adversarial Attack on Time-Series DNNs for Connected Autonomous Vehicles

In the Connected Autonomous Vehicle (CAV) scenarios, each autonomous vehicle uses the Deep Neural Network (DNN) to process time-series driving signals (e.g., speed, steering wheel angle) sent by its nearby vehicle to output its driving maneuver. Though DNNs are effective in aiding the CAVs, DNNs are vulnerable to adversarial threats. To meet the time-efficiency requirements, a previous CAV black-box adversarial attack method creates an offline perturbation for each traffic context offline and searches the perturbation list for the current context to generate an adversarial attack online. However, searching a long list of traffic contexts consumes a significant amount of time. To further improve the time-efficiency performance, in this paper, we propose a method that generates one universal offline perturbation irrespective of the contexts in producing an untargeted adversarial attack. We first formulate finding the universal perturbation problem as an optimization problem. To solve this problem, we find the perturbation direction that mis-classifies the greatest number of input signals, and then apply a binary search to find the minimal perturbation amount along the chosen optimal direction. The Zeroth Order Adaptive Momentum Method (ZO-AdaMM) is applied to generate the offline universal perturbation. We evaluate the universal perturbation method on real driving dataset. The experiments show that the proposed universal perturbation method requires 52.8% smaller number of queries, 30.7% less perturbation amount and 42.02% less time than other existing universal methods while keeping a high success rate of 87.09%.
Particle Swarm Optimization with Enhanced Neighborhood Search for Task Scheduling in Cloud Computing

Due to cloud computing services' dynamic and elastic nature, implementing efficient task scheduling methods becomes primordial for cloud providers to handle the ever-growing demands and meet the Service Level Agreements (SLA) cost-effectively. In this paper, we propose a novel task scheduling approach, named ENS-PSO, that enhances Particle Swarm Op-timization (PSO) with an efficient neighborhood search strategy. We evaluate ENS-PSO using the CloudSim toolkit. Simulation results demonstrate that the proposed task scheduling with en-hanced neighborhood search outperforms other task scheduling algorithms in terms of makespan, energy consumption, and degree of imbalance.
Advanced Object Detection in Bio-Medical X-Ray Images for Anomaly Detection and Recognition

The human vision system is mimicked in the format of videos and images in the area of computer vision. As humans can process their memories, likewise video and images can be processed and perceptive with the help of computer vision technology. There is a broad range of fields that have great speculation and concepts building in the area of application of computer vision, which includes automobile, biomedical, space research, etc. The case study in this manuscript enlightens one about the innovation and future scope possibilities that can start a new era in the biomedical image-processing sector. A pre-surgical investigation can be perused with the help of the proposed technology that will enable the doctors to analyses the situations with deeper insight. There are different types of biomedical imaging such as magnetic resonance imaging (MRI), computerized tomographic (CT) scan, x-ray imaging. The focused arena of the proposed research is x-ray imaging in this subset. As it is always error-prone to do an eyeball check for a human when it comes to the detailing. The same applied to doctors. Subsequently, they need different equipment for related technologies. The methodology proposed in this manuscript analyses the details that may be missed by an expert doctor. The input to the algorithm is the image in the format of x-ray imaging; eventually, the output of the process is a label on the corresponding objects in the test image. The tool used in the process also mimics the human brain neuron system. The proposed method uses a convolutional neural network to decide on the labels on the objects for which it interprets the image. After some pre-processing the x-ray images, the neural network receives the input to achieve an efficient performance. The result analysis is done that gives a considerable performance in terms of confusion factor that is represented in terms of percentage. At the end of the narration of the manuscript, future possibilities are being traces out to the limelight to conduct further research.
A Novel Quaternion-based Method to Calculate Solar Irradiation on a Surface

The calculation of the solar irradiance is crucial in evaluating the performance of solar power plants and can ground their malfunction-analysis. In this paper, a new solar irradiance calculation method is presented to support these aims. The proposed method uses quaternions to describe the interaction between the Earth and the Sun regarding their motion to determine the angle of incidence of solar radiation on a given surface. The angular values obtained can be used to determine the estimated solar irradiance for a given surface using an arbitrary meteorological model. The method is highly scalable, thus the orientation and tilt of the surface can be varied by adding additional quaternion rotations. By changing the parameters and the meteorological model, it can be used to calculate the irradiance of a given surface of any object or device in the solar system. To show the accuracy of the proposed model, the generated data using the proposed method was compared with measured data from a meteorological station. The Pearson correlation coefficient showed a strong correlation (r=0.9613) between the measured and calculated data, confirming the proposed method's usefulness.
Bipartite Graph based Spectral Clustering with Comprehensive Structure Information Learning

Spectral clustering has received increasing attention due to its promising performance on non-linear data. However, the problem of high computational complexity hinders its application on large-scale data sets. Some bipartite graph based spectral clustering methods have been developed to alleviate this problem in recent years. Unfortunately, the completeness of structure information of data in bipartite graph is always ignored, which may lead to a sub-optimal clustering result. In this paper, we proposed a new bipartite graph based method with comprehensive structure information learning (CBGSC) to fill this gap. In our method, the bipartite graph is learned by a joint optimization problem, which combines two types of similarity learning strategies into the same objective function to capture the local and global structure information of data comprehensively. Thus, our bipartite graph would contain complete structure information of data. Moreover, the Laplacian rank constraint is added into the joint optimization problem to ensure that the bipartite graph has a desired cluster structure. An efficient iterative algorithm is developed to solve our method, whose computational complexity is linear to the number of data points. Extensive experiments on real world data set demonstrate the efficiency and effectiveness of our method with respect to other state-of-the-art clustering methods.
Offline handwriting image analysis to predict Alzheimer’s disease via deep learning

In the framework of Alzheimer’s disease prediction systems, it is widely agreed that handwriting seems to be one of the first skills to be influenced by the onset of such a disease. In the large majority of cases, the above systems consider information relating to the dynamics of the handwriting process, directly derived from online handwriting samples. This kind of features, however, are not able to capture the alterations in the shape, size and thickness of the handwritten traits, which may be produced by the alterations in motor control due to neurodegenerative disorders. Following this line of thought, in a previous study we combined shape and dynamic information by generating synthetic color images from online handwriting samples, where the color of each elementary trait encodes, in the three RGB channels, the dynamic information associated to that trait. Finally, we exploited the capability of Deep Neural Networks to automatically extract features from raw images, following the Transfer Learning approach. The results obtained with this approach did not show significant improvements compared to those obtained with the use of dynamic information only, probably because approximating the original traits with straight lines of predefined thickness results in a loss of information on their actual shape and thickness. Moving from these considerations, the purpose of our study is to verify whether automatically extracting features directly from offline handwriting images, thus considering the original shape of the handwritten trace, could provide better results. Again, we exploited the capability of Deep Neural Networks to automatically extract features from raw images. The preliminary experimental results confirmed the effectiveness of the proposed approach.
Boosting Initial Population in Multiobjective Feature Selection with Knowledge-Based Partitioning

The quality of features is one of the main factors that affect classification performance. Feature selection aims to remove irrelevant and redundant features from data in order to increase classification accuracy. However, identifying these features is not a trivial task due to a large search space. Evolutionary algorithms have been proven to be effective in many optimization problems, including feature selection. These algorithms require an initial population to start their search mechanism, and a poor initial population may cause getting stuck in local optima. Diversifying the initial population is known as an effective approach to overcome this issue; yet, it may not suffice as the search space grows exponentially with increasing feature sizes. In this study, we propose an enhanced initial population strategy to boost the performance of the feature selection task. In our proposed method, we ensure the diversity of the initial population by partitioning the candidate solutions according to their selected number of features. In addition, we adjust the chances of features being selected into a candidate solution regarding their information gain values, which enables wise selection of features among a vast search space. We conduct extensive experiments on many benchmark datasets retrieved from UCI Machine Learning Repository. Moreover, we apply our algorithm on a real-world, large-scale dataset, i.e., Stanford Sentiment Treebank. We observe significant improvements after the comparisons with three off-the-shelf initialization strategies.
Incomplete Multi-view Domain Adaptation via Channel Enhancement and Knowledge Transfer

Unsupervised domain adaptation (UDA) borrows well-labeled source knowledge to solve the specific task on unlabeled target domain with the assumption that both domains are from a single sensor, e.g., RGB or depth images. To boost model performance, multiple sensors are deployemd on new-produced devices like autonomous vehicles to benefit from enriched information. However, the model trained with multi-view data difficultly becomes compatible with conventional devices only with a single sensor. This scenario is defined as incomplete multi-view domain adaptation (IMVDA), which considers that the source domain consists of multi-view data while the target domain only includes single-view instances. To overcome this practical demand, this paper proposes a novel Channel Enhancement and Knowledge Transfer (CEKT) framework with two modules. Concretely, the source channel enhancement module distinguishes view-common from view-specific channels and explores channel similarity to magnify the representation of important channels. Moreover, the adaptive knowledge transfer module attempts to enhance target representation towards multi-view semantic through implicit missing view recovery and adaptive cross-domain alignment. Extensive experimental results illustrate the effectiveness of our method in solving the IMVDA challenge.
Shared Autonomy Based on Human-in-the-loop Reinforcement Learning with Policy Constraints

In shared autonomous systems, humans and agents cooperate to complete tasks. Since reinforcement learning enables agents to obtain good policies through trial and error without knowing the dynamic model of the environment, it has been well applied in shared autonomous systems. After inferring the target from human inputs, agents trained by RL can accurately act accordingly. However, existing methods of this kind have big problems: the training of reinforcement learning algorithms require lots of exploration, which is time-consuming, lack of security guarantee and likely to cause great losses in the training process. Moreover, most of shared control methods are human-oriented, and do not consider the situation that humans may make wrong actions. In view of the above problems, this paper proposes human-in-the-loop reinforcement learning with policy constraints. In the training process, human prior knowledge is used to constrain the exploration of agents to achieve fast and efficient learning. In the process of testing we incorporate policy constraints in the arbitration to avoid serious consequences caused by human mistakes.
Formation of functional mathematical literacy of future primary school teachers by means of mathematical and informational tasks

Abstract. The paper presents the theoretical justification and practical implementation of the educational environment of studying mathematics by students – future primary school teachers, focused on the formation of functional mathematical literacy of students by means of mathematical and information tasks. The purpose of the research is to theoretically substantiate, practically define and experimentally create an educational environment aimed at developing functional mathematical literacy in future primary school teachers. Research methods: 1) theoretical: analysis, generalization, interpretation of scientific literature on research issue; 2) empirical: pedagogical experiment, questionnaire. Conclusions and recommendations: we describe in the course of the theoretical study the main approaches to the definition of the categories “functional literacy”, “functional mathematical literacy”, expediency of forming functional mathematical literacy of future primary school teachers for their further successful pedagogical activity. We present a theoretically revealed set of didactic conditions for the selection of mathematical and informational tasks as a means of forming the functional mathematical literacy of a future teacher. The effectiveness of using a complex of mathematical and informational tasks as a means of forming functional mathematical literacy of students – future primary school teachers in the course of studying mathematics has been experimentally proved. The practical significance of the research consists in the development of didactic conditions for the selection of mathematical and informational tasks that can serve as a means of integrating mathematical, professional and cultural knowledge, forming students' ideas about a unified picture of the world and the ability to apply the mathematical knowledge obtained in solving professional problems. The practical results of the study can be used in the practice of mathematics teachers in pedagogical specialties of universities in order to form functional mathematical literacy in the study of the discipline “Mathematics”.
Synthetic Speech Classification using Bidirectional LSTM Networks

With the invention of generative adversarial networks, synthetic man-made manipulation of speech has been exponentially increasing. The detection of synthetic or bonafide speech is a challenging problem due to the availability of plenty of algorithms generating synthetic speech signals, This becomes more challenging if we want to know the algorithm used to generate the synthetic speech. In this work, a set of synthetic speech signals generated by five different commonly known algorithms and an another set belonging to unknown algorithm class are used for classification. The objective is to classify the algorithms used to generate synthetic speech. Recurrent neural network based bidirectional long short term memory (B-LSTM) sequence to label classifier is employed for training and evaluation. Eight different feature sets are used for analyzing the performance of classification accuracy. It is observed that the feature set produced from dynamic time warping and spectral characteristics provide the best classification accuracy.
SAT-Based Invariant Inference and Its Relation to Concept Learning

This paper surveys results that establish formal connections and distinctions between SAT-based invariant inference and exact concept learning with queries, showing that learning techniques and algorithms can clarify foundational questions, illuminate existing algorithms, and suggest new directions for efficient invariant inference.