Reviewerly/exHarmony huggingface dataset:
Automation of pipe defect detection and characterization by structured light

Abstract High quality tubular products are essential to the oil and gas industry. Quality control during their production focuses on the non-destructive detection of surface defects. The structured light technique is a candidate for the challenge to detect, monitor and evaluate such defects in real-time. In the present study the automatic processing of structured light measurements is performed and validated. The algorithm for the automatic analysis of inspection data has an advantage over current data evaluation methods based on individual assessments of operators.
A Comparative Study Between K-Nearest Neighbors and K-Means Clustering Techniques of Collaborative Filtering in e-Learning Environment

Data mining is an important phase for obtaining useful information from a data warehouse, through the use of a set of processes that generally rely on certain techniques and algorithms. Data mining is mainly used in recommendation systems based on collaborative filtering, with the aim of extracting useful and specific knowledge about the users of the system in order to recommend personalized items to them. Several data mining techniques have been considered in literature, namely: classification, clustering, regression, association, etc. Each one is based on a set of algorithms, e.g. classification with K-Nearest Neighbors, decision tree, naive bayes, neural networks, etc. and clustering with k-means. Our work is to make a comparative study between k-NN and k-means clustering in order to identify the most efficient algorithm in terms of prediction in e-learning recommender system. Mean Absolute Error (MAE) is the most widely used technique to measure the effectiveness of algorithm performance in terms of accuracy; The lower the MAE value, the more accuracy of prediction model.
Data-Dependent Scaling of CNN’s First Layer for Improved Image Manipulation Detection

Convolutional Neural Networks (CNNs) have become an effective tool to detect image manipulation operations, e.g., noise addition, median filtering and JPEG compression. In this paper, we propose a simple and practical method for adjusting the CNN’s first layer, based on a proper scaling of first-layer filters with a data-dependent approach. The key idea is to keep the stability of the variance of data flow in a CNN. We also present studies on the output variance for convolutional filter, which are the basis of our proposed scaling. The proposed method can cope well with different first-layer initialization algorithms and different CNN architectures. The experiments are performed with two challenging forensic problems, i.e., a multi-class classification problem of a group of manipulation operations and a binary detection problem of JPEG compression with high quality factor, both on relatively small image patches. Experimental results show the utility of our method with a noticeable and consistent performance improvement after scaling.
Are Neural Rankers still Outperformed by Gradient Boosted Decision Trees

Despite the success of neural models on many major machine learning problems, their effectiveness on traditional Learning-to-Rank (LTR) problems is still not widely acknowledged. We first validate this concern by showing that most recent neural LTR models are, by a large margin, inferior to the best publicly available Gradient Boosted Decision Trees (GBDT) in terms of their reported ranking accuracy on benchmark datasets. This unfortunately was somehow overlooked in recent neural LTR papers. We then investigate why existing neural LTR models under-perform and identify several of their weaknesses. Furthermore, we propose a unified framework comprising of counter strategies to ameliorate the existing weaknesses of neural models. Our models are the first to be able to perform equally well, comparing with the best tree-based baseline, while outperforming recently published neural LTR models by a large margin. Our results can also serve as a benchmark to facilitate future improvement of neural LTR models.
Hybrid Algorithm as alternative method for optimization, a combination Genetic Algorithm and Particle Swarm Optimization

Abstract Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are both known as a method for optimization. Here in these paper we will combine GA and PSO to create a hybrid algorithm. We compare GA, PSO and Hybrid to solve some optimization problem. These Hybrid Algorithm is an alternative method for optimization.
Context encoder guided self-supervised siamese depth estimation based on stereo laparoscopic images

This paper proposes a novel self-supervised depth estimation method guided by a context encoder. Depth estimation from stereo laparoscopic images is essential to robotic surgical navigation systems and robotic surgical platform. Recent work has shown that depth estimation of stereo image pairs can be formulated as a self- supervised learning task without ground-truth. However, most architectures based on convolutional neural lead to lose some spatial information because of the consecutive pooling and convolution operations. In order to tackle this problem, we add a contextual encoding module to the previous method. The context encoder module is formed by dense atrous convolution block and spatial pyramid pooling block that are used to extract and merge features on different scales. Also, we add the edge-awared smoothness for predicted disparity maps. In addition, we output multi-scale disparity predictions and corresponding image reconstruction for loss calculating. In the experiments, we showed that the proposed method has about 7.79% improvement in SSIM and about 17.76% improvement in PSNR for stereo image pairs compared with previous method. Also, the disparity maps and reconstructed images given by the proposed method have significant enhancements compared with the previous method.
Socio-cultural environment as a factor of differentiation in mathematicial reasoning

ABSTRACT IS AVAILABLE IN THE PDF
Redesign website of engineering faculty in university of Indonesia using fuzzy delphi method and fuzzy cognitive maps

Abstract Utilizing website in business process has become essential. But lot of web designer have no respect of using methodology in developing a website cause the quality that website do not meet the needs of its users. On the other hand, usability level of the website could not attract a lot of visitation from any user. On this paper, researcher using Engineering Faculty in University of Indonesia’s website to be studied. To increase the usability of this website, research conducted by redesign the website using Fuzzy Delphi Method to determine the media type to presenting each service and also to determine the services layout of each web page. In addition, this research also uses Fuzzy Cognitive Maps to models the interrelationship between each services and media type. Resulting the prototype of 12 web page for website owned by Engineering Faculty in University of Indonesia with total 36 services provided in this website to gives an optimal information for the user.
Estimating near term breast cancer risk from sequential mammograms using deep learning, radon cumulative distribution transform, and a clinical risk factor: preliminary analysis

Our ultimate goal is to develop a personalized breastscreening model. This model will provide information to an individual woman for planning her own screening options based on her imaging and other characteristics. To do so, we are developing a computerized method to estimate a near term risk of screen detectable breast cancer (sBCa) by analyzing lateral and temporal differences in screening mammograms using the Radon Cumulative Distribution Transform (RCDT), a deep convolutional neural network (dCNN). The RCDT can highlight subtle signals associated with future sBCa by exploiting lateral and temporal differences in screening mammograms of women. We used a dCNN to build a robust classification marker of having sBCa within a year from a negative screening. We explored the potential for incremental improvement over known risk factors by combining the dCNN with age and showed consistent gains in identifying asymptomatic women with a low risk of having sBCa (who could use the personalized screening option to safely skip their next screening). We used 4296 screening mammogram images of 537 women with 151 sBCa cases and fine-tuned the VGG16 network using a data split ratio of 0.64:0.16:0.2 for training, validation, and testing. The final model on the independent testing dataset achieved an AUC of 0.82, and a specificity at a sensitivity level of 98% (Sp@Se98) of 0.51 with a 95% confidence interval of 0.40 to 0.65. This indicates that the model could reliably identify at least 40% of low-risk women with a 2% error for a false negative rate.
SAR Image Ship Detection Based on Scene Interpretation

Ship detection from SAR images is an important remote sensing application. However, in complex scenes, i.e., the shore or harbor area, traditional ship detection methods cannot disentangle background information from the target ship, and the detection performance drops dramatically. Moreover, the severe coherent speckle noise also challenges ship detection from SAR images. In order to address the aforementioned issues, this paper proposes a SAR image ship detection method based on scene interpretation to improve the performance of ship detection in complex scenes. Firstly, segmentation algorithm based on Mask R-CNN is utilized to interpret the scene into two catalogs, i.e., the sea and the land. Then, ship detection algorithm based on Faster R-CNN is performed on the sea area and the land area respectively. Finally, non-maximum suppression is used to integrate detection results. Experimental results on SAR ship detection dataset illustrate that the proposed method produces high detection accuracy and low false alarm rate in complex scenes.
Explainable Models with Consistent Interpretations

This material is based upon work partially supported by the United States Air Force under Contract No. FA8750-19-C-0098, funding from NSF grant number 1845216, SAP SE, and Northrop Grumman. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do notnecessarily reflect the views of the United States Air Force,DARPA, or other funding agencies.
On Interaction Between Augmentations and Corruptions in Natural  Corruption Robustness

Invariance to a broad array of image corruptions, such as warping, noise, or color shifts, is an important aspect of building robust models in computer vision. Recently, several new data augmentations have been proposed that significantly improve performance on ImageNet-C, a benchmark of such corruptions. However, there is still a lack of basic understanding on the relationship between data augmentations and test-time corruptions. To this end, we develop a feature space for image transforms, and then use a new measure in this space between augmentations and corruptions called the Minimal Sample Distance to demonstrate a strong correlation between similarity and performance. We then investigate recent data augmentations and observe a significant degradation in corruption robustness when the test-time corruptions are sampled to be perceptually dissimilar from ImageNet-C in this feature space. Our results suggest that test error can be improved by training on perceptually similar augmentations, and data augmentations may not generalize well beyond the existing benchmark. We hope our results and tools will allow for more robust progress towards improving robustness to image corruptions. We provide code at https://github.com/facebookresearch/augmentation-corruption.
Application of Virtual Reality Technology in Marketing Training

Abstract With the development of the times and the rapid development of network technology, virtual reality technology is a new technology developed in recent years, and it has great practical value when applied to emerging network marketing. The purpose of this article is to combine virtual reality technology to research marketing training. This article is based on virtual reality technology security rules and data security protection, and aims to improve the computing power of virtual reality technology while taking into account the process results of marketing training, and then collect, sort and analyze relevant information, using sandbox simulation calculations, Modeling, and multiple intelligent algorithms finally get the result. This article mainly uses the AdaBoost algorithm for experimentation. Experimental results show that the use of appropriate algorithms can improve the application of virtual reality technology in marketing training.
Introduction to artificial neural networks (ANN)

This chapter gives an introduction that covers some important mathematical concepts behind ANN. It discusses the meaning of weight and bias, and how they are useful in neural networks. By starting with the simplest model Y = X, which has no parameters at all, some parameters will be added gradually until we build a single neuron. This neuron will be made to accept one or more inputs. The math behind building the neuron will then be mapped to a graphical representation. By connecting multiple neurons together, a complete ANN can be created.
Wearable and controllable protective system design for elderly falling

This paper focuses on our understanding and design of fall protection devices for the elderly. We collected the information of most products of the same type on the market and analyzed the alignment. As there are some challenges existing in this kind of products, such as not comfortable enough to wear, not comprehensive enough for protection, not reasonable enough design and high price. We explained our design concept in the following article, such as hiding the airbag inside the clothes to improve the wear comfort. At the beginning, we optimized the inflatable mechanical structure, making it smaller in size and lighter in weight. The hydrodynamic design of the pinhole improves the gas flow rate and accelerates the airbag filling speed. Materials as far as possible to choose strong interchangeability of materials, further reduce the cost of manufacturing. Next, we use 3d modeling to design hip and head airbags to protect the affected area. On the other hand, the designed size of the airbag, we calculated the volume of the airbag and selected the cylinder. And check the strength of the air pressure so that the pressure is not so high as to cause the balloon to explode. It should not be too small, resulting in insufficient carrying capacity and reduced protection performance. Finally, we explained the system design of the protective mechanism, which could recognize real falling and provide effect protection to elderly people.
Digital Evidence Identification of Android Device using Live Forensics Acquisition on Cloud Storage (iDrive)

The use of the internet cannot be separated in the era of the industrial revolution 4.0 because all lines are experiencing a digital transformation.Digital transformation cannot be separated from cloud computing, so cloud computing is increasingly diverse and widely used, especially in cloud storage.Cloud storage is widely used both on computers and smartphones and several incidents have shown it is used as a crime.So there is a need for research on how to first identify digital evidence on an Android smartphone, because Android is increasingly being used.By using experimental research, a series of forensic investigations were able to identify digital evidence on an android smartphone with live forensic acquisitions on partitions, applications, and the android ram process that was still on.
Synthesis of Modality Definitions and a Theorem Prover for Epistemic Intuitionistic Logic

We propose a mechanism for automating discovery of definitions, that, when added to a logic system for which we have a theorem prover, extends it to support an embedding of a new logic system into it. As a result, the synthesized definitions, when added to the prover, implement a prover for the new logic. As an instance of the proposed mechanism, we derive a Prolog theorem prover for an interesting but unconventional epistemic Logic by starting from the sequent calculus G4IP that we extend with operator definitions to obtain an embedding in intuitionistic propositional logic (IPC). With help of a candidate definition formula generator, we discover epistemic operators for which axioms and theorems of Artemov and Protopopescu’s Intuitionistic Epistemic Logic (IEL) hold and formulas expected to be non-theorems fail. We compare the embedding of IEL in IPC with a similarly discovered successful embedding of Dosen’s double negation modality, judged inadequate as an epistemic operator. Finally, we discuss the failure of the necessitation rule for an otherwise successful S4 embedding and share our thoughts about the intuitions explaining these differences between epistemic and alethic modalities in the context of the Brouwer-Heyting-Kolmogorov semantics of intuitionistic reasoning and knowledge acquisition.
Estimation of Target Defect Prediction Coverage in Heterogeneous Cross Software Projects

Heterogeneous cross-project defect prediction (HCPDP) is an evolving area under quality assurance domain which aims to predict defects in a target project that has restricted historical defect data as well as completely non-uniform software metrics from other projects using a model built on another source project. The article discusses a particular source project group's problem of defect prediction coverage (DPC) and also proposes a novel two phase model for addressing this issue in HCPDP. The study has evaluated DPC on 13 benchmarked datasets in three open source software projects. One hundred percent of DPC is achieved with higher defect prediction accuracy for two project group pairs. The issue of partial DPC is found in third prediction pairs and a new strategy is proposed in the research study to overcome this issue. Furthermore, this paper compares HCPDP modeling with reference to with-in project defect prediction (WPDP), both empirically and theoretically, and it is found that the performance of WPDP is highly comparable to HCPDP and gradient boosting method performs best among all three classifiers.
An Agent-Based Architecture for Multi-modal Transportation Using Prometheus Methodology Design

In recent years, the demand for public transport and the need for travel have increased considerably. The movement of travelers is becoming increasingly difficult and complex, hence the need to develop an intelligent solution that will facilitate decision-making and route planning. In addition, ITS has become essential for the proper management of urban mobility. However, the data processed in the ITS is large and diverse, which makes it less usable, before obtaining the desired information. For that, the authors propose to set up a semantic approach in order to locate the various WEB resources, to standardize them and to make them comprehensible and usable by the various agents of the system. The objective of this work is to present a new approach for the development of an architecture based on multi-agent systems coupled with semantic web services (SWS) and this in order to help decision making in the context of urban mobility. The design of this architecture is also presented using the Prometheus methodology to effectively manage the route planning process in multimodal transport.
Exploring the Contributions of Low-Light Image Enhancement to Network-Based Object Detection

Low-light is a challenging environment for both human and computer vision to perform tasks such as object classification and detection. Recent works have shown potential in employing enhancements algorithms to support and improve such tasks in low-light, however there has not been any focused analysis to understand the direct effects that low-light enhancement have on an object detector. This work aims to quantify and visualize such effects on the multi-level abstractions involved in network-based object detection. First, low-light image enhancement algorithms are employed to enhance real low-light images, and then followed by deploying an object detection network on the low-light as well as the enhanced counterparts. A comparison of the activations in different layers, representing the detection features, are used to generate statistics in order to quantify the enhancements’ contribution to detection. Finally, this framework was used to analyze several low-light image enhancement algorithms and identify their impact on the detection model and task. This framework can also be easily generalized to any convolutional neural network-based models for the analysis of different enhancements algorithms and tasks.
Image Denoising Based on Adaptive Sector Rotation Median Filter

Abstract By analyzing the characteristics of different median filtering algorithms, an adaptive fan rotation median filtering method based on the standard median filtering method is proposed to restore the details of the image to the maximum extent for achieving better pepper and salt noise removal effect. The proposed method can change the window size adaptively according to the pollution degree in the window, and calculate the gray difference value for different areas, judge the correlation between the center point and different areas according to the gray difference value, and take the median value of the highest correlation area as output, which can restore the details of the image while removing the noise. The experimental results show that the new algorithm has a high similarity with the original image structure and a high peak signal-to-noise ratio after filtering, and can better recover the image details. Compared with other methods, the noise removal effect and detail recovery effect are better.
Certified Defenses: Why Tighter Relaxations May Hurt Training?

Certified defenses based on convex relaxations are an established technique for training provably robust models. The key component is the choice of relaxation, varying from simple intervals to tight polyhedra. Paradoxically, however, training with tighter relaxations can often lead to worse certified robustness. The poor understanding of this paradox has forced recent state-of-the-art certified defenses to focus on designing various heuristics in order to mitigate its effects. In contrast, in this paper we study the underlying causes and show that tightness alone may not be the determining factor. Concretely, we identify two key properties of relaxations that impact training dynamics: continuity and sensitivity. Our extensive experimental evaluation demonstrates that these two factors, observed alongside tightness, explain the drop in certified robustness for popular relaxations. Further, we investigate the possibility of designing and training with relaxations that are tight, continuous and not sensitive. We believe the insights of this work can help drive the principled discovery of new and effective certified defense mechanisms.