Reviewerly/exHarmony huggingface dataset:
A Dueling-DDPG Architecture for Mobile Robots Path Planning Based on Laser Range Findings

Planning an obstacle-free optimal path presents great challenges for mobile robot applications, the deep deterministic policy gradient (DDPG) algorithm offers an effective solution. However, when the original DDPG is applied to robot path planning, there remains many problems such as inefficient learning and slow convergence that can adversely affect the ability to acquire optimal path. In response to these concerns, we propose an innovative framework named dueling deep deterministic policy gradient (D-DDPG) in this paper. First of all, we integrate the dueling network into the critic network to improve the estimation accuracy of Q-value. Furthermore, we design a novel reward function by combining the cosine distance with the Euclidean distance to improve learning efficiency. Our proposed model is validated by several experiments conducted in the simulation platform Gazebo. Experiments results demonstrate that our proposed model has the better path planning capability even in the unknown environment.
Adinkras: Graphs of Clifford Algebra Representations, Supersymmetry, and Codes

An Adinkra is a graph from the study of supersymmetry in particle physics, but it can be adapted to study Clifford algebra representations. The graph in this context is called a Cliffordinkra, and puts some standard ideas in Clifford algebra representations in a geometric and visual context. In the past few years there have been developments in Adinkras that have shown how they are connected to error correcting codes, algebraic topology, algebraic geometry, and combinatorics. These connections also arise for Cliffordinkras. This paper introduces Cliffordinkras and describes the relationship to these subjects in that context. No previous knowledge of Adinkras and supersymmetry is assumed.
Continual Representation Learning via Auto-Weighted Latent Embeddings on Person ReID

Popular deep neural network models in artificial intelligence systems are found having catastrophic forgetting problem: when learning on a sequence of tasks, deep networks tend to only achieve high performance on the current task, while losing performance on previously learned tasks. This issue is often addressed by continual learning or lifelong learning. The majority of existing continual learning approaches adopt class incremental strategy, which will continuously expand the network structure. Representation learning, which only leverages the feature vector before classification layer, is able to maintain the model capacity in continual learning. However, recent continual representation learning methods are not well evaluated on unseen classes. In this paper, we pay attention to the performance of continual representation learning on unseen classes, and propose a novel auto-weighted latent embeddings method. For each task, autoencoders are developed to reconstruct feature maps from different levels in the neural network. The embeddings generated by these autoencoders on the manifolds are constrained when learning a new task so as to preserve the knowledge in previous tasks. An adapted auto-weighted approach is developed in this paper to assign different levels of importance to the embeddings based on reconstruction errors. Our experiments on three widely used Person Re-identification datasets expose the existence of catastrophic forgetting problem for representation learning on unseen classes, and demonstrate that our proposed method outperforms other related methods in continual representation learning setup.
Invariant Moments, Textural and Deep Features for Diagnostic MR and CT Image Retrieval

Image analysis in the medical field aims to offer tools for the diagnosis and detection of life-threatening illness. This study means to propose a novel content-based image retrieval system oriented to medical diagnosis. In particular, we exploit several classic and deep image descriptors together with different similarity measures on three different data set, containing computed tomography and magnetic resonance images. Experiments show that feature selection can bring benefit if applied to deep and texture features, contrary to what observed for invariant moments. Moreover, the cityblock distance emerged to be quite suitable overall in this domain, although some other distances also exhibit satisfying robustness.
XPL-CF

Collaborative filtering (CF) methods are making an impact on our daily lives in a wide range of applications, including recommender systems and personalization. Latent factor methods, e.g., matrix factorization (MF), have been the state-of-the-art in CF, however they lack interpretability and do not provide a straightforward explanation for their predictions. Explainability is gaining momentum in recommender systems for accountability, and because a good explanation can swing an undecided user. Most recent explainable recommendation methods require auxiliary data such as review text or item content on top of item ratings. In this paper, we address the case where no additional data are available and propose augmenting the classical MF framework for CF with a prior that encodes each user's embedding as a sparse linear combination of item embeddings, and vice versa for each item embedding. Our XPL-CF approach automatically reveals these user-item relationships, which underpin the latent factors and explain how the resulting recommendations are formed. We showcase the effectiveness of XPL-CF on real data from various application domains. We also evaluate the explainability of the user-item relationship obtained from XPL-CF through numeric evaluation and case study examples.
Adjacency Constraint for Efficient Hierarchical Reinforcement Learning

Goal-conditioned Hierarchical Reinforcement Learning (HRL) is a promising approach for scaling up reinforcement learning (RL) techniques. However, it often suffers from training inefficiency as the action space of the high-level, i.e., the goal space, is large. Searching in a large goal space poses difficulty for both high-level subgoal generation and low-level policy learning. In this article, we show that this problem can be effectively alleviated by restricting the high-level action space from the whole goal space to a k-step adjacent region of the current state using an adjacency constraint. We theoretically prove that in a deterministic Markov Decision Process (MDP), the proposed adjacency constraint preserves the optimal hierarchical policy, while in a stochastic MDP the adjacency constraint induces a bounded state-value suboptimality determined by the MDP's transition structure. We further show that this constraint can be practically implemented by training an adjacency network that can discriminate between adjacent and non-adjacent subgoals. Experimental results on discrete and continuous control tasks including challenging simulated robot locomotion and manipulation tasks show that incorporating the adjacency constraint significantly boosts the performance of state-of-the-art goal-conditioned HRL approaches.
Robust Maximum Mixture Correntropy Criterion Based One-Class Classification Algorithm

One-class classification achieves anomaly/outlier detection by exploiting the characteristics of target data. As a local similarity measure defined in kernel space, correntropy is generally more robust than the mean square error (MSE) based criterion in dealing with large outliers. In this article, the maximum mixture correntropy criterion (MMCC) with multiple kernels are applied to the shallow and hierarchical one-class extreme learning machine to enhance the model robustness and learning speed. Experiments on benchmark University of California, Irvine (UCI) classification datasets, urban acoustic classification dataset, and four synthetic datasets are carried out to show the effectiveness and comparisons with several state-of-the-art methods are provided to demonstrate the superiority of the proposed algorithms.
When Punjab Cried Wolf: How a Rumor Triggered an “Earthquake” in India

Abstract In recent years, earthquake felt reports contributed via online systems have provided increasingly valuable sources of data to characterize earthquakes and their effects. Contributed felt reports are accompanied by increases in website traffic, which are themselves potentially useful for the early detection of seismic events. In February 2017 the European-Mediterranean Seismic Centre detected an unusual surge in traffic from the Punjab region in northwestern India, although no nearby seismic event was detected instrumentally. Had crowdsourcing detected a felt earthquake that instruments had missed? Or did Punjab cry wolf? In this Earthquake Lites report, we describe the sleuthing endeavor undertaken to find an answer.
COVID-19 Contact Tracing and Privacy Rights in Ghana: A Critical Analysis of the Establishment of Emergency Communications System Instrument, 2020 (EI 63)

Abstract In December 2019, the world woke up to the news of a novel coronavirus (COVID-19). Since then, governments across the globe have deployed various measures to contain the spread of the disease. The government of Ghana, among other measures, issued the Establishment of Emergency Communications System Instrument, 2020 (EI 63) to establish an emergency communications system to aid contact tracing during public health emergencies. This executive instrument has been criticized for illegally “legalizing” the breach of the privacy rights of electronic communications network subscribers in Ghana. This article critically analyses EI 63 in relation to the right to privacy of communication enshrined in Ghana's 1992 Constitution. It argues against the constitutionality of EI 63, calls for its revocation and replacement with an act of Parliament enacted with due regard for Ghana's legislative framework on the protection of the right to privacy of communication.
A Semidecentralized PKI System Based on Public Blockchains with Automatic Indemnification Mechanism

The PKI framework is a widely used network identity verification framework. Users will register their identity information with a certification authority to obtain a digital certificate and then show the digital certificate to others as an identity certificate. After others receive the certificate, they must check the revocation list from the CA to confirm whether the certificate is valid. Although this architecture has a long history of use on the Internet, significant doubt surrounds its security. Because the CA may be attacked by DDoS, the verifier may not obtain the revocation list to complete the verification process. At present, there are many new PKI architectures that can improve on the CA’s single point of failure, but since they still have some shortcomings, the original architecture is still used. In this paper, we proposed a semidecentralized PKI architecture that can easily prevent a single point of failure. Users can obtain cryptographic evidence through specific protocols to clarify the responsibility for the incorrect certificate and then submit the cryptographic evidence to the smart contract for automatic judgment and indemnification.
VR-test ViKi: VR Test with Visual and Kinesthetic Stimulation for Assessment Color Vision Deficiencies in Adults

Visual colorCárdenas-Delgado, Sonia perception in humans isLoachamín-Valencia, Mauricio produced in the sensitiveRodríguez-Reyes, Bryan cells of the retina, which are called cones. There are three types of cones: those that capture red, green, and blue light. The deficiency of color perception implies that the person sees different colors from other people, and “confuses” or interchanges some of them, or that sees directly in black and white. Some professions such as pilots, surgeons, electricians, dentists, designers, among others, require an accurate perception of color to be able to safely perform their functions. There are several traditional test for diagnosing color vision deficiencies and applications using conventional devices. In this work, we propose a new approach to assess the color vision deficiencies using VR devices. We are developing a visual color test (VR-test ViKi) based on the Ishihara test including visual and kinaesthetic stimulation. It includes a virtual environment, ten 3D objects, a predetermined short path, a bridge, four circular figures with numbers, 3D objects, a hummer modeled on the inside and outside, and a virtual task. Also, the virtual environment shows three conditions: sunny, rainy, and night. Two types of HMDs will be used. Assessment methods will include two questionnaires (SSQ and PQ), the Ishihara test, remember the numbers seen, and time task. Also, we will compare the performance between the Ishihara test and VR-test ViKi. This research will contribute to improve the visual evaluation process in access to aviation schools. We will contribute with a complementary tool to assess color vision deficiency.
Border Security and Surveillance System Using IoT

Security along the international border is a critical process in security assessment; It must be exercised the 24x7. With the advancements in wireless IoT technology, it has become much easier to design, develop and deploy a cost-effective, automatic and efficient system for intrusion detection in the context of surveillance. This paper set up to set up the most efficient surveillance solution, we propose a Border Surveillance Systems and sensitive sites. this surveillance and security system is to detect and track intruders trespassing into the monitoring area along the border, it able which triggers off precocious alerts and valuation necessary for the catch of efficient measurements in case of a threat. Our system is based on the classification of the human gestures drawn from videos envoy by Drones equipped with cameras and sensors in real-time. All accomplished experimentation and acquired results showed the benefit diverted from the use of our system and therefore it enables our soldiers to watch the borders at each and every moment to effectively and at low cost.
Denoising Letter Images from Scanned Invoices Using Stacked Autoencoders

Invoice document digitization is crucial for efficient management in industries. The scanned invoice image is often noisy due to various reasons. This affects the OCR (optical character recognition) detection accuracy. In this paper, letter data obtained from images of invoices are denoised using a modified autoencoder based deep learning method. A stacked denoising autoencoder (SDAE) is implemented with two hidden layers each in encoder network and decoder network. In order to capture the most salient features of training samples, a undercomplete autoencoder is designed with non-linear encoder and decoder function. This autoencoder is regularized for denoising application using a combined loss function which considers both mean square error and binary cross entropy. A dataset consisting of 59,119 letter images, which contains both English alphabets (upper and lower case) and numbers (0 to 9) is prepared from many scanned invoices images and windows true type (.ttf) files, are used for training the neural network. Performance is analyzed in terms of Signal to Noise Ratio (SNR), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM) and Universal Image Quality Index (UQI) and compared with other filtering techniques like Nonlocal Means filter, Anisotropic diffusion filter, Gaussian filters and Mean filters. Denoising performance of proposed SDAE is compared with existing SDAE with single loss function in terms of SNR and PSNR values. Results show the superior performance of proposed SDAE method.
Evaluation of Reanalysis Datasets for Solar Radiation with In Situ Observations at a Location over the Gobi Region of Xinjiang, China

Solar radiation is the most important source of energy on the Earth. The Gobi area in the eastern Xinjiang region, due to its geographic location and climate characteristics, has abundant solar energy resources. In order to provide detailed scientific data supporting solar energy development in this area, we used ground-based data to evaluate the applicability of the five reanalysis data sources: the Clouds and the Earth’s Radiant Energy System (CERES), the European Center for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5), the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA2), and the Japanese 55-year Reanalysis (JRA-55). Our results indicated that the CERES data show underestimated short-wave radiation and overestimated long-wave radiation. The correlation coefficients (r) between the ERA5 dataset and the net long-wave and short-wave radiation in observation were 0.92 and 0.91, respectively, and the r between the MERRA2 dataset and the net long-wave and short-wave radiation in observation were both 0.88. The JRA-55 dataset overestimated the long-wave radiation flux and underestimated the short-wave radiation flux. The clearness index (kt) of all datasets was poor during autumn and winter, the ERA5 estimates were cloudy when the actual condition was sunny, while the JRA-55 estimates were sunny when the actual condition was cloudy. Overall, the radiation flux in the ERA5 dataset had the highest applicability in the Gobi region.
A Spherical Harmonics-LSB-quantification Adaptive Watermarking Approach for 3D Multiresolution Meshes Security

Since the release of the first 3D watermarking algorithm in the last decade, 3D meshes applying such an algorithm have been growing up with a diversity of techniques during the embedding of information into meshes. The main objective is always to secure data shared by remote users. The originality of the present work is issued from the use of spherical harmonics to transform the mesh to the frequency domain to be watermarked, and the entropy concept to estimate the optimal order of the reconstruction. Our algorithm includes three parts. The first is the data pre-processing based on a combination of a signature generated by the well-known Secure Hash Algorithm, and a mesh description, subject to a convolutional error correcting code. The second step is the mesh treatment, consisting in extracting spherical harmonics coefficients to be watermarked. Finally, the third part consists in inserting data into coefficients using LSB and quantification method, and next to reconstruct the watermarked mesh. The experimentation of our approach has shown a high insertion rate, while maintaining the mesh quality. It is also robust against the most popular attacks. Our results showed that the present approach improves the existing works.
Semi-supervised Single Image Deraining with Discrete Wavelet Transform

In recent years, single image deraining has received considerable research interests. Supervised learning is widely adopted for training dedicated deraining networks to achieve promising results on synthetic datasets, while limiting in handling real-world rainy images. Unsupervised and semi-supervised learning-based deranining methods have been studied to improve the performance on real cases, but their quantitative results are still inferior. In this paper, we propose to address this crucial issue for image deraining in terms of backbone architecture and the strategy of semi-supervised learning. First, in terms of network architecture, we propose an attentive image deraining network (AIDNet), where residual attention block is proposed to exploit the beneficial deep feature from the rain streak layer to background image layer. Then, different from the traditional semi-supervised method by enforcing the consistency of rain pattern distribution between real rainy images and synthetic rainy images, we explore the correlation between the real clean images and the predicted background image by imposing adversarial losses in wavelet space \(I _{HH}\), \(I _{HL}\), and \(I _{LH}\), resulting in the final AID-DWT model. Extensive experiments on both synthetic and real-world rainy images have validated that our AID-DWT can achieve better deraining results than not only existing semi-supervised deraining methods qualitatively but also outperform state-of-the-art supervised deraining methods quantitatively. All the source code and pre-trained models are available at https://github.com/cuiyixin555/DeRain-DWT.
SAR Ship Detection from Complex Background Based on Dynamic Shrinkage Attention Mechanism

With the development of synthetic aperture radar (SAR) satellite, more high-resolution SAR imageries can be obtained, which have been widely used for ship detection. SAR ship detection based on convolution neural network has shown more significant potential. However, several challenges due to its imaging characteristic remain to be addressed: 1) SAR images are severely polluted by speckles under normal conditions. 2) Complex background such as inshore building and harbor may cause background to be brighter than targets. These problems result in the blurred edges of objects in SAR images and the inefficiency of ship feature extraction, which limit the performance of marine ship detection. To solve these issues, we first analyzed the impact of these issues on the feature level and proposed a feature-level denoising method called dynamic shrinkage attention (DSA). This method can adaptively suppress the irrelevant spatial response. Experiments on the SSDCB dataset show the efficiency and feasibility of our algorithm for SAR marine ship detection in complex background,
Software Defect Prediction Model Based on Attention Mechanism

In recent years, with the gradual expansion of modern software, software components have become more and more complex, and software defects have also increased. The existence of defects often leads to software errors or even crashes, and brings huge economic losses to enterprises. Therefore, how to use software defect prediction technology to find defects as early as possible to improve software reliability has become an important guarantee activity in the process of software projects. Different from the traditional defect prediction methods, this article takes program semantics as the point of penetration, constructs a software defect prediction model based on the attention mechanism on the basis of the abstract syntax tree, and introduces a mask model for the correlation between the function methods in the program files. The evaluation results on the PROMISE data set show that the model proposed in this paper is superior to the two state-of-the-art defect prediction model for both within-project defect prediction and cross-project defect prediction. And it also verifies the necessity of introducing masking model into attention mechanism.
Gaze prediction for first-person videos based on inverse non-negative sparse coding with determinant sparse measure

Gaze prediction is a significant approach for processing a large amount of incoming visual information of videos. Recent gaze prediction algorithms often employ sparse models with the assumption that every superpixel in the video frames can be represented as linear combinations of a few salient superpixels. However, they are not actuated enough because of the insufficient knowledge that video signals contain a non-negative request. Hence, we develop a novel gaze prediction based on an inverse sparse coding framework with a determinant sparse measure. By introducing this sparse measure, the solutions are non-negative and sparser than conventional sparse constraints. However, the proposed optimization problem becomes nonconvex, which is difficult to solve. To efficiently address the corresponding nonconvex optimization problem, we propose a novel algorithm based on the difference in convex function programming, which can yield the global solutions. Experimental results indicate the improved accuracy of the proposed approach compared with state-of-the-art algorithms.