Reviewerly/exHarmony huggingface dataset:
Impulse Noise Classification Using Machine Learning Classifier and Robust Statistical Features

Identifying corrupted pixels in an image helps the denoising algorithm to perform better. In this paper, I propose a machine learning (ML) based classification algorithm which uses random decision forest classifiers and classical pixel-wise statistical parameters. The ML algorithm is trained to identify impulse noise by taking the computed statistical parameters from the corrupted image as input. From the experiment, it is clear that the identification of corrupted pixels depend on both the robustness of the chosen image parameters and the accuracy of the trained classifier. Particularly, random forest classifier which is an ensemble of random decision trees is employed as it suits better for this application. The implemented decision tree which is limited to 10, shows a better classification performance in the images corrupted with impulse noise. The improvement in the classification is better for random impulse noises rather than salt and pepper noises. Also, its significance is visible in low and medium noise intensities rather than high densities and hence I limit the noises by 50% for the discussion.
Automated Augmentation with Reinforcement Learning and GANs for Robust Identification of Traffic Signs using Front Camera Images

Traffic sign identification using camera images from vehicles plays a critical role in autonomous driving and path planning. However, the front camera images can be distorted due to blurriness, lighting variations and vandalism which can lead to degradation of detection performances. As a solution, machine learning models must be trained with data from multiple domains, and collecting and labeling more data in each new domain is time consuming and expensive. In this work, we present an end-to-end framework to augment traffic sign training data using optimal reinforcement learning policies and a variety of Generative Adversarial Network (GAN) models, that can then be used to train traffic sign detector modules. Our automated augmenter enables learning from transformed nightime, poor lighting, and varying degrees of occlusions using the LISA Traffic Sign and BDD-Nexar dataset. The proposed method enables mapping training data from one domain to another, thereby improving traffic sign detection precision/recall from 0.70/0.66 to 0.83/0.71 for nighttime images.
Object Detection Based on Multiple Information Fusion Net

Object detection has been playing a significant role in computer vision for a long time, but it is still full of challenges. In this paper, we propose a novel object detection framework based on relationship among different objects and the scene-level information of the whole image to cope with the problem that some strongly correlated objects are difficult to be recognized. Our motivation is to enrich the semantics of object detection feature by a scene-level information branch and a relationship branch. There are three important changes of our framework over traditional detection methods: representation of relationship, scene-level information as the prior knowledge and the fusion of the above two information. Extensive experiments are carried out on PASCAL VOC and MS COCO databases. The experimental results show that the detection performance can be improved by introducing relationship and scene-level information, and our proposed model achieve better performance than several classical and state-of-the-art methods.
Penerapan Teknologi Blockchain Sebagai Media Pengamanan Proses Transaksi E-Commerce

The application of blockchain technology in an E-commerce service is a necessity in the transaction processing required by a security system to ensure data confidentiality.Through the SWOT method, blockchain technology can be applied as a security medium in the e-commerce transaction process. Unfortunately there are still very few e-commerce industries that use a blockchain-based platform, so there is still a lack of trust in the security of safe and decentralized business transactions.Specifically, there are 2 benefits of this research, the first is the security system for transaction processing through the implementation of the blockchain and the second is the payment system that is easier, more efficient and well documented. Please for students, lecturers and other parties in the transaction process. The use of Blockchain technology to streamline identity management, build approved systems and manage product authenticity, and is expected to synchronize data stored in the blockchain to all user networks. can make payment systems easier, more efficient and well-defined. Please for students, lecturers and other parties in the transaction process.
Age Estimation using Disconnectedness Features in Handwriting

Real-time applications of handwriting analysis have increased drastically in the fields of forensic and information security because of accurate cues.One of such applications is human age estimation based on handwriting for the purpose of immigrant checking.In this paper, we have proposed a new method for age estimation using handwriting analysis using Hu invariant moments and disconnectedness features.To make the proposed method robust to both ruled and un-ruled documents, we propose to explore intersection point detection in Canny edge images of each input document, which results in text components.For each text component pair, we propose Hu invariant moments for extracting disconnectedness features, which in fact measure multi-shape components based on distance, shape and mutual position analysis of components.Furthermore, iterative k-means clustering is proposed for the classification of different age groups.Experimental results on our dataset and some standard datasets, namely, IAM and KHATT, show that the proposed method is effective and outperforms the state-of-the-art methods.
Active stabilization of unmanned aerial vehicle imaging platform

The advancement in technology has seen a rapid increase in the use of unmanned aerial vehicles for various applications. These unmanned aerial vehicles are often equipped with the imaging platform like a camera. During the unmanned aerial vehicle flight, the camera is subjected to vibrations which hamper the quality of the captured images/videos. The high-frequency vibrations from the unmanned aerial vehicle are transmitted to the camera. Conventionally, passive rubber mounts are used to isolate the camera from the drone vibrations. The passive mounts are able to provide reduction in response near the resonance. However, this comes at the cost of amplification of response at the higher frequency. This article proposes an active vibration isolation system which exhibits improved performance at the higher frequencies than the conventional system. The active isolation system consists of a contact-less voice coil actuator supported by four springs. Experiments are carried out to study the effect of vibrations on the quality of images captured. The characterization of drone vibrations is also carried out by recording the acceleration during different flight modes. The performance of the proposed isolation system is experimentally validated on a real drone camera subjected to the recorded drone acceleration spectrum. The isolation system is found to perform better than the conventional rubber mounts and is able to reduce the vibrations to a factor of one-fourth. It can be effectively used to improve the image acquisition quality of the unmanned aerial vehicles.
A hybrid DevOps process supporting software reuse: A pilot project

Abstract Large software development organizations manage reusable software components through a reusable software repository in order to reduce development time and cost and to improve software quality and productivity. This paper presents a hybrid DevOps process with a systematic reuse‐based software development and management process to reduce the effort and cost required for the rework and to increase productivity. The proposed approach promotes the systematic reuse of software components based on both information retrieval and ontology‐based retrieval techniques. The reusable assets are presented in different styles to ease and support the reuse process with fine‐grained reusable artifacts. To evaluate our proposed process, a pilot project, aiming to monitor the health of a patient, was developed and monitored the reuse activities throughout the whole experiment. The results revealed that our proposed process got an average gain of 35.2% in terms of developed function points by reusing 30.63% of reusable artifacts available in the reuse repository.
EspeleoRobô - a robotic device to inspect confined environments

Robotic devices operating in confined environments such as underground, tunnel systems, and cave networks are currently receiving particular attention from research and industry. One example is the Brazilian mining company Vale S.A., which is employing a robot - Espeleolcobô - to access restricted areas. The robot was designed initially to inspect natural caves during teleoperated missions and is now being used to monitor dam galleries and other confined or dangerous environments. This paper describes recent developments regarding locomotion mechanisms and mobility analyses, localization, and path planning strategies aiming autonomous robot operation. Preliminary results from simulation and field experiments validate the robotic device concept. Lessons learned from multiple field tests in industrial scenarios are also described.
Determination of Hydrothermal Prospects in Paipa Geothermal Region (Boyacá, Colombia) Using Remote Sensing and Field Data

Geothermal resources (e.g. hot springs) are found with the help of field techniques, such as geological, geochemistry and geophysical. These techniques in some occasions are difficult to apply because of the limit access to the research area, rising operational costs and constrained spatially the exploration areas. The thermal infrared (TIR) remote sensing is an important tool for the exploration of geothermal resources, due to the low cost and high efficiency in the study of large geographic areas. The aim of this study is to use thermal imagery of satellite remote sensing and combined with geological-geophysical data, for spatial determination of exploratory prospects of hot springs in the geothermal region of Paipa, Boyacá. The images used in this study are from satellites Landsat-7 ETM+, Landsat-8 OLI/TIRS, MODIS, ALOS-PALSAR and Pléiades. Also, field data is used, such as soil temperature, surface temperature, air temperature, relative humidity, atmospheric pressure and thermal imagery of surface geothermal manifestations. The Landsat thermal bands were radiometrically calibrated, then atmospherically and surface emissivity corrected, applying single channel and split window algorithms, for Landsat-7 ETM+ and Landsat-8 TIRS, respectively. The field data helped to correct the thermal bands. And the soil temperature data are used to create a subsurface temperature map at 1-meter depth. Once primary and secondary data is had, in a geographic information system (GIS) is implemented an unweighted spatial model, which use four input indicators (satellite temperature index, soil temperature index, structural lineaments index and iso-resistivity index) to determine the areas with higher probability to find geothermal fluids. Six prospects are highlighted for hydrothermal fluid extraction, in which two of them are already known. Results allow to concluded that thermal remote sensing are useful to map geothermal anomalies in the Paipa region, and by using these anomalies plus geological-geophysical information is possible to determine exact exploration areas.
Maximal Correlation Regression

In this paper, we propose a novel regression analysis approach, called maximal correlation regression, by exploiting the ideas from the Hirschfeld-Gebelein-Rényi (HGR) maximal correlation.We show that in supervised learning problems, the optimal weights in maximal correlation regression can be expressed analytically with the relationships to the HGR maximal correlation functions, which reveals theoretical insights for our approach.In addition, we apply the maximal correlation regression to deep learning, in which efficient training algorithms are proposed for learning the weights in hidden layers.Furthermore, we illustrate that the maximal correlation regression is deeply connected to several existing approaches in information theory and machine learning, including the universal feature selection problem, linear discriminant analysis, and the softmax regression.Finally, experiments on real datasets demonstrate that our approach can obtain performance comparable to the widely used softmax regression based-method.
Best of Both Worlds: AutoML Codesign of a CNN and its Hardware Accelerator

Neural architecture search (NAS) has been very successful at outperforming human-designed convolutional neural networks (CNN) in accuracy, and when hardware information is present, latency as well. However, NAS-designed CNNs typically have a complicated topology, therefore, it may be difficult to design a custom hardware (HW) accelerator for such CNNs. We automate HW-CNN codesign using NAS by including parameters from both the CNN model and the HW accelerator, and we jointly search for the best model-accelerator pair that boosts accuracy and efficiency. We call this Codesign-NAS. In this paper we focus on defining the Codesign-NAS multiobjective optimization problem, demonstrating its effectiveness, and exploring different ways of navigating the codesign search space. For CIFAR-10 image classification, we enumerate close to 4 billion model-accelerator pairs, and find the Pareto frontier within that large search space. This allows us to evaluate three different reinforcement-learning-based search strategies. Finally, compared to ResNet on its most optimal HW accelerator from within our HW design space, we improve on CIFAR-100 classification accuracy by 1.3% while simultaneously increasing performance/area by 41% in just~1000 GPU-hours of running Codesign-NAS.
Systematic Review of Outdoor Science Learning Activities with the Integration of Mobile Devices

The purpose of this systematic study review was to describe how researchers integrated mobile devices into outdoor science learning, assessment of those activities, and alignment of purpose, integration, and assessment. From initial 980 search results, the authors selected 45 articles based on the eligibility criteria of: (a) empirical study; (b) learning activity with science content; (c) outdoor setting; (d) mobile device integration; and (e) assessement. Researchers designed outdoor science learning activities integrated with mobile devices for the purposes of science knowledge gain, affective domain gain, and scientific inquiry. Researchers aligned components of scientific inquiry including hypothesis formation, observation, data collection and interpretation, and communication and collaboration. Conclusions describe benefits to integrating mobile devices with outdoor science learning activities by supporting scientific inquiry skill development. Alignment of purpose and assessment provides evidence of student learning important in meeting accountability standards.
A Fast Characterization Method for Semi-invasive Fault Injection Attacks

Semi-invasive fault injection attacks are powerful techniques well-known by attackers and secure embedded system designers. When performing such attacks, the selection of the fault injection parameters is of utmost importance and usually based on the experience of the attacker. Surprisingly, there exists no formal and general approach to characterize the target behavior under attack. In this work, we present a novel methodology to perform a fast characterization of the fault injection impact on a target, depending on the possible attack parameters. We experimentally show our methodology to be a successful one when targeting different algorithms such as DES and AES encryption and then extend to the full characterization with the help of deep learning. Finally, we show how the characterization results are transferable between different targets.
How to Evaluate Mobile 360° Video Streaming Systems?

360° video brings an immersive experience to users by projecting panoramic content onto the display. Viewing 360° videos on untethered mobile platforms further enhances the immersive user experience. As a result, a large number of systems for optimized high-resolution 360° video streaming to mobile devices have been proposed over the past few years. In this paper, we review the diverse set of research methodologies in the system design and evaluation of recently proposed mobile 360° video streaming systems and discuss a number of pitfalls that prevent a fair and meaningful comparison among different systems. Our discussion suggests that there is an urgent need to redefine the design objectives and to develop an effective methodology for a meaningful evaluation and comparison of different systems. We finish with a set of concrete guidelines on the design and evaluation methodology of future mobile 360° video systems.
Revisiting Training Strategies and Generalization Performance in Deep Metric Learning

Deep Metric Learning (DML) is arguably one of the most influential lines of research for learning visual similarities with many proposed approaches every year. Although the field benefits from the rapid progress, the divergence in training protocols, architectures, and parameter choices make an unbiased comparison difficult. To provide a consistent reference point, we revisit the most widely used DML objective functions and conduct a study of the crucial parameter choices as well as the commonly neglected mini-batch sampling process. Under consistent comparison, DML objectives show much higher saturation than indicated by literature. Further based on our analysis, we uncover a correlation between the embedding space density and compression to the generalization performance of DML models. Exploiting these insights, we propose a simple, yet effective, training regularization to reliably boost the performance of ranking-based DML models on various standard benchmark datasets. Code and a publicly accessible WandB-repo are available at https://github.com/Confusezius/Revisiting_Deep_Metric_Learning_PyTorch.
Improving monarch butterfly optimization through simulated annealing strategy

Abstract Currently, a novel of meta-heuristic algorithm called monarch butterfly optimization (MBO) is presented for solving machine learning and continuous optimization problems. It has been proved experimentally that MBO is superior to artificial bee colony algorithm (ABC), ant colony optimization algorithm (ACO), Biogeography-based optimization (BBO), differential evolution algorithm (DE) and simple genetic algorithm (SGA) algorithms on most test functions. This paper presents a new version of MBO with simulated annealing (SA) strategy called SAMBO. The SA strategy is put in the migration operator and butterfly adjusting operator. So the newly proposed algorithm has two features: One is that the algorithm accepts all the butterfly individuals whose fitness are better than their parents. The other is that the algorithm randomly selects some individuals which are worse than their parents to disturbance the convergence of algorithm. In this way, the SAMBO algorithm can escape from local optima. Finally, the experiments are carried on 14 continuous nonlinear functions, and results show that SAMBO method exceeds the MBO algorithm on most test functions.
Quantifying the Distribution Characteristics of Geochemical Elements and Identifying Their Associations in Southwestern Fujian Province, China

The distribution of geochemical elements in the surficial media is the end product of geochemical dispersion under complex geological conditions. This study explored the frequency and spatial distribution characteristics of geochemical elements and their associations. It quantifies the frequency distribution via mean, variance, skewness and kurtosis, followed by measuring the spatial distribution characteristics (i.e., spatial autocorrelation, heterogeneity and self-similarity) via semivariogram, q-statistic and multifractal spectrum, and further identify the elemental associations based on these distribution parameters using hierarchical clustering. A criterion was defined to identify the importance of parameters in the clustering procedure. A case study processing a geochemical dataset of stream sediment samples collected in southwestern Fujian province of China was carried out to illustrate and validate the procedure. The results indicate that studies of the frequency and spatial distribution characteristics of geochemical elements can enhance the knowledge of geochemical dispersions. The associations identified based on the frequency and spatial distribution parameters are different from those obtained by conventional cluster analysis. Spatial distribution characteristics cannot be neglected when investigating the distribution patterns of geochemical elements and their associations. The findings can enhance the knowledge of the geochemical dispersion in the study area and might benefit the following-up mineral exploration.
Virtual Anatomical and Endoscopic Exploration Method of Internal Human Body for Training Simulator

Virtual environments have brought the use of realistic training closer to many different fields of education. In medical education, several visualization methods for studying inside the human body have been introduced as a way to verify the structure of internal organs. However, these methods are insufficient for realistic training simulators because they do not provide photorealistic scenes or offer an intuitive perception to the user. In addition, they are used in limited environments within a classroom setting.We have developed a virtual dissection exploration system that provides realistic three-dimensional images and a virtual endoscopic experience. This system enables the user to manipulate a virtual camera through a human organ, using gesture-sensing technology. We can make a virtual dissection image of the human body using a virtual dissection simulator and then navigate inside an organ using a virtual endoscope. To improve the navigation performance during virtual endoscopy, our system warns the user about any potential collisions that may occur against the organ's wall by taking the virtual control sphere at the virtual camera position into consideration.Experimental results show that our system efficiently provides high-quality anatomical visualization. We can simulate anatomic training using virtual dissection and endoscopic images.Our training simulator would be helpful in training medical students because it provides an immersive environment.
A Novel Low-Bit Quantization Strategy for Compressing Deep Neural Networks

The increase in sophistication of neural network models in recent years has exponentially expanded memory consumption and computational cost, thereby hindering their applications on ASIC, FPGA, and other mobile devices. Therefore, compressing and accelerating the neural networks are necessary. In this study, we introduce a novel strategy to train low-bit networks with weights and activations quantized by several bits and address two corresponding fundamental issues. One is to approximate activations through low-bit discretization for decreasing network computational cost and dot-product memory. The other is to specify weight quantization and update mechanism for discrete weights to avoid gradient mismatch. With quantized low-bit weights and activations, the costly full-precision operation will be replaced by shift operation. We evaluate the proposed method on common datasets, and results show that this method can dramatically compress the neural network with slight accuracy loss.