Reviewerly/exHarmony huggingface dataset:
Investigation of the time elapsed effect on residual stress measurement in a composite plate by DIC method

Abstract In composite plates, there are generally amounts of residual stresses that can have adverse effects in final parts. So, it is very important to know about the residual stress's type, location and amounts in composite plates. The measurement methods of the residual stress are commonly indirect and the displacement and strain is calculated at first. Two-dimensional digital image correlation (2D DIC) is a practical and effective tool for quantitative in-plane deformation measurement of a planar object surface and now widely accepted and commonly used in the field of experimental mechanics. In the hole drilling method, the residual stress is measured immediately just after drilling and the other researchers have paid less attention to the amount and pattern of changes in released stress around the hole after drilling at different times. It can be stated that the novelty of this research may identify the optimal measurement time after the drilling and the holes with different diameters is considered and the displacement and strain fields are measured at different times. So in this research, the residual stress measurement of a composite plate in the time elapsed is discussed. Also, the effects of hole's diameter and elapsed time after drilling in amount of released residual stresses are investigated by 2D DIC method. The point-wise local least-squares fitting technique (PLS) is used for strain estimation from displacement fields. The residual stress is calculated by the estimated strain, determination of calibration constants in orthotropic materials and considering the properties of composite plate that has already been calculated. The finite element method (FEM) is used for determination of calibration constants and the correction factor in FEM is obtained by comparing of the FEM and experimental tensile test results.
Detection of Workers Without the Helments in Videos Based on YOLO V3

In the construction site with complex environment, there are a variety of potential risk factors threatening personal safety. Since the head is the most critical part of a human body and is the most vulnerable to fatal injuries, lots of accidents caused by workers not wearing safety helmets have occurred from time to time. So, the staff working in construction site has the requirement to wear the safety helmet. In order to reduce the incidence of safety accidents caused by not wearing a protective helmet, a realtime detection application for wearing helmet based on YOLO (You Look Only Once) v3 was proposed and encapsulated into a real-time detection software with alert function and simple efficient operation, which has been successfully deployed to several construction sites. YOLO v3 model has a good response to the identification of ąśpesonąs; Therefore, firstly, the workers in video are identified and intercepted by YOLO v3 model, and positive and negative samples are made. Then the worker in the dataset is identified whether wearing helmets or not. Compared with y-OLO v3, the mAP (Mean Average Precision) reached 93.5%, and the detection rate reached 35fps. Theoretical analysis and experimental results show that the proposed algorithm not only satisfies the helmet wearing detection task in real time, but also has high detection accuracy.
Risk and protective factors of identity theft victimization in the United States

Identity theft victimization is associated with serious physical and mental health morbidities. The problem is expanding as society becomes increasingly reliant on technology to store and transfer personally identifying information. Guided by lifestyle-routine activity theory, this study sought to identify risk and protective factors associated with identity theft victimization and determine whether individual-level behaviors, including frequency of online purchasing and data protection practices, are determinative of victimization. Data from sequential administrations of the U.S. National Crime Victimization Survey–Identity Theft Supplement (ITS) in 2012 and 2014 were combined (N = 128,419). Using multivariable logistic regression, risk and protective factors were examined for three subtypes: 1) unauthorized use of existing credit card/bank accounts, and unauthorized use of personal information to 2) open new accounts, or 3) engage in instrumental activities (e.g., applying for government benefits, receiving medical care, filing false tax returns). Existing credit card/bank accounts and new accounts identity theft victimization were associated with higher levels of online purchasing activity and prior identity theft victimization. All identity theft subtypes were associated with government/corporate data breaches and other crime victimization experiences. Routine individual-level preventive behaviors such as changing online passwords and shredding/destroying documents were protective. Identity theft subtypes showed divergent socio-demographic risk/protective profiles, with those of higher socioeconomic status more likely to be victims of existing credit card/bank account identity theft. Identity theft is a pervasive, growing problem with serious health and psychosocial consequences, yet individuals can engage in specific protective behaviors to mitigate victimization risk.
A Machine Learning Approach to Improve the Detection of CI Skip Commits

Continuous integration (CI) frameworks, such as Travis CI, are growing in popularity, encouraged by market trends towards speeding up the release cycle and building higher-quality software. A key facilitator of CI is to automatically build and run tests whenever a new commit is submitted/pushed. Despite the many advantages of using CI, it is known that the CI process can take a very long time to complete. One of the core causes for such delays is the fact that some commits (e.g., cosmetic changes) unnecessarily kick off the CI process. Therefore, the main <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">goal</i> of this paper is to automate the process of determining which commits can be CI skipped through the use of machine learning techniques. We first extracted 23 features from historical data of ten software repositories. Second, we conduct a study on the detection of CI skip commits using machine learning where we built a decision tree classifier. We then examine the accuracy of using the decision tree in detecting CI skip commits. Our results show that the decision tree can identify CI skip commits with an average AUC equal to 0.89. Furthermore, the top node analysis shows that the number of developers who changed the modified files, the CI-Skip rules, and commit message are the most important features to detect CI skip commits. Finally, we investigate the generalizability of identifying CI skip commits through applying cross-project validation, and our results show that the general classifier achieves an average 0.74 of AUC values.
Smartphone-Based Human Activity Recognition Using Bagging and Boosting

In today’s healthcare applications, the use of mobile technologies brings together physicians and patients for intelligent and automatic monitoring of daily clinical activities, remote life assistants, and preventive care, especially for the elderly and those under medical control. As smartphones become an important part of our everyday life, they are ever more employed in human activities recognition (HAR) including the monitoring of personal health care and wellbeing. However, HAR is complex and it is important to use the best technology and learn about human activity using machine learning. The purpose of this paper is to develop a HAR system based on the smartphone sensors’ data using Bagging and Adaboost ensemble classifiers. The experimental results for the HAR data have been evaluated after performing different data mining techniques. For each subject, the total classification accuracy, the F-measure, and the ROC area were calculated. Adaboost ensemble classifiers algorithm improved significantly the performance of smartphone-based HAR, combined with SVM, it reached 97.44% accuracy compared to the rest of the classifiers. The proposed algorithm of Adaboost SVM can lead to an accurate HAR for elderly and disabled patients who need continuous care as well as it is a tool that supports the decisions of all medical practitioners.
Synaptic weighting in single flux quantum neuromorphic computing

Abstract Josephson junctions act as a natural spiking neuron-like device for neuromorphic computing. By leveraging the advances recently demonstrated in digital single flux quantum (SFQ) circuits and using recently demonstrated magnetic Josephson junction (MJJ) synaptic circuits, there is potential to make rapid progress in SFQ-based neuromorphic computing. Here we demonstrate the basic functionality of a synaptic circuit design that takes advantage of the adjustable critical current demonstrated in MJJs and implement a synaptic weighting element. The devices were fabricated with a restively shunted Nb/AlO x -Al/Nb process that did not include MJJs. Instead, the MJJ functionality was tested by making multiple circuits and varying the critical current, but not the external shunt resistance, of the oxide Josephson junction that represents the MJJ. Experimental measurements and simulations of the fabricated circuits are in good agreement.
Threshold optimization for F measure of macro-averaged precision and recall

There are two different approaches to macro-averaging F measure for multi-label classification. The first encloses averaging F measure over all classes, which makes it easy to optimize. The second, extensively investigated in this paper, comprises the F measure of macro precision and recall calculation. We examine and compare these two measures when applied to different multi-label datasets. To optimize the performance measure, we adopt a widely known and proven modular approach. Classifiers sort the instances in descending order, according to a real-valued score of belonging to a corresponding class. After that, thresholds are selected so as to optimize the performance measure. If the number of classes is sufficiently large and the second alternative of macro-averaging F measure is employed, then it becomes non-trivial to define the optimal number of instances to assign to each class. Cyclic optimization procedure is widely used for threshold optimization although it results in a maximum in a special coordinate-wise sense. For a micro averaged F measure, such a coordinate-wise optimum is a maximum in the conventional sense of this term but it is not true for the F measure of macro precision and recall, which is shown by a counterexample. We reduce the problem of selecting the optimal threshold for each class to the problem of obtaining a fixed point of a specifically introduced transformation of a unit square. The suggested algorithm lets us localize all possible coordinate-wise maximums and detect the optimal among them. The approach is applied to datasets from diverse application domains.
Multi-Client Sub-Linear Boolean Keyword Searching for Encrypted Cloud Storage with Owner-Enforced Authorization

To date, cloud computing has emerged as a primary utility for providing remote data storage services for users, since users can thus be relieved from cumbersome document maintenance. Despite of the benefits brought by data outsourcing, the unexpected data breaches raise concerns about data confidentiality and privacy. To deal with this, a straightforward and convincing strategy is to encrypt data before outsourcing them to the cloud. However, securely sharing and searching over outsourced encrypted data has turned into a challenge due to the hindrance led by data encryption. To address the challenge, this article proposes a new highly-scalable searchable encryption scheme for encrypted cloud storage. The scheme achieves sub-linear Boolean keyword searching, and moreover allows the data owner to authorize which clients could search or access the documents in the cloud. Technically, we revisit searchable symmetric encryption primitive by non-trivially combining it with a novel access control technique, and build an inverted index data structure for both attribute-based access control and sub-linear search process. Furthermore, we introduce a formalized security definition for the system, and prove its security in the simulation-based security model. Finally, we conduct a couple of experiments over a representative real-world dataset to show practicality.
Age Estimation by Super-Resolution Reconstruction Based on Adversarial Networks

Age estimation using facial images is applicable in various fields, such as age-targeted marketing, analysis of demand and preference for goods, skin care, remote medical service, and age statistics, for describing a specific place. However, if a low-resolution camera is used to capture the images, or facial images are obtained from the subjects standing afar, the resolution of the images is degraded. In such a case, information regarding wrinkles and the texture of the face are lost, and features that are crucial for age estimation cannot be obtained. Existing studies on age estimation did not consider the degradation of resolution but used only high-resolution facial images. To overcome this limitation, this paper proposes a deep convolutional neural network (CNN)-based age estimation method that reconstructs low-resolution facial images as high-resolution images using a conditional generative adversarial network (GAN), and then uses the images as inputs. An experiment is conducted using two open databases (PAL and MORPH databases). The results demonstrate that the proposed method achieves higher accuracy in high-resolution reconstruction and age estimation than the state-of-the art methods.
Deep CNN-Based Speech Balloon Detection and Segmentation for Comic Books

We develop a method for the automated detection and segmentation of speech balloons in comic books, including their carrier and tails. Our method is based on a deep convolutional neural network that was trained on annotated pages of the Graphic Narrative Corpus. More precisely, we are using a fully convolutional network approach inspired by the U-Net architecture, combined with a VGG-16 based encoder. The trained model delivers state-of-the-art performance with an F1-score of over 0.94. Qualitative results suggest that wiggly tails, curved corners, and even illusory contours do not pose a major problem. Furthermore, the model has learned to distinguish speech balloons from captions. We compare our model to earlier results and discuss some possible applications.
Seeing Behind Things: Extending Semantic Segmentation to Occluded Regions

Semantic segmentation and instance level segmentation made substantial progress in recent years due to the emergence of deep neural networks (DNNs). A number of deep architectures with Convolution Neural Networks (CNNs) were proposed that surpass the traditional machine learning approaches for segmentation by a large margin. These architectures predict the directly observable semantic category of each pixel by usually optimizing a cross-entropy loss. In this work we push the limit of semantic segmentation towards predicting semantic labels of directly visible as well as occluded objects or objects parts, where the network's input is a single depth image. We group the semantic categories into one background and multiple foreground object groups, and we propose a modification of the standard cross-entropy loss to cope with the settings. In our experiments we demonstrate that a CNN trained by minimizing the proposed loss is able to predict semantic categories for visible and occluded object parts without requiring to increase the network size (compared to a standard segmentation task). The results are validated on a newly generated dataset (augmented from SUNCG) dataset.
Blockchain-based Security Solutions to Preserve Data Privacy And Integrity

The emerging blockchain technology helps in the decentralization of transactions, where every participant network verifies and validates the transaction making it immutable. With the rapid expansion of the technology, transactional data which is stored and validated is also increasing. The blockchain technology came into prominence largely due to the bitcoin and the security aspects of the technology. This technology is comparatively fast, secure and efficient. This paper discusses the generalized overview, different algorithms to reach consensus, system workflows and various security aspects of handling, transacting and storing data. This paper also throws light Smart Contracts and their applications.
Real-Time Sentiment Analysis of 2019 Election Tweets using Word2vec and Random Forest Model

Sentiment analysis of social media data consists of attitudes, assessments, and emotions which can be considered a way human think. Understanding and classifying the large collection of documents into positive and negative aspects are a very difficult task. Social networks such as Twitter, Facebook, and Instagram provide a platform in order to gather information about people's sentiments and opinions. Considering the fact that people spend hours daily on social media and share their opinion on various different topics helps us analyze sentiments better. More and more companies are using social media tools to provide various services and interact with customers. Sentiment Analysis (SA) classifies the polarity of given tweets to positive and negative tweets in order to understand the sentiments of the public. This paper aims to perform sentiment analysis of real-time 2019 election twitter data using the feature selection model word2vec and the machine learning algorithm random forest for sentiment classification. Word2vec with Random Forest improves the accuracy of sentiment analysis significantly compared to traditional methods such as BOW and TF-IDF. Word2vec improves the quality of features by considering contextual semantics of words in a text hence improving the accuracy of machine learning and sentiment analysis.
SteReFo: Efficient Image Refocusing with Stereo Vision

Whether to attract viewer attention to a particular object, give the impression of depth or simply reproduce human-like scene perception, shallow depth of field images are used extensively by professional and amateur photographers alike. To this end, high quality optical systems are used in DSLR cameras to focus on a specific depth plane while producing visually pleasing bokeh. We propose a physically motivated pipeline to mimic this effect from all-in-focus stereo images, typically retrieved by mobile cameras. It is capable to change the focal plane a posteriori at 76 FPS on KITTI images to enable real-time applications. As our portmanteau suggests, SteReFo interrelates stereo-based depth estimation and refocusing efficiently. In contrast to other approaches, our pipeline is simultaneously fully differentiable, physically motivated, and agnostic to scene content. It also enables computational video focus tracking for moving objects in addition to refocusing of static images. We evaluate our approach on the publicly available datasets SceneFlow, KITTI, CityScapes and quantify the quality of architectural changes.
Software fault prediction using particle swarm algorithm with genetic algorithm and support vector machine classifier

Summary Software fault prediction is a process of developing modules that are used by developers in order to help them to detect faulty classes or faulty modules in early phases of the development life cycle and to determine the modules that need more refactoring in the maintenance phase. Software reliability means the probability of failure has occurred during a period of time, so when we describe a system as not reliable, it means that it contains many errors, and these errors can be accepted in some systems, but it may lead to crucial problems in critical systems like aircraft, space shuttle, and medical systems. Therefore, locating faulty software modules is an essential step because it helps defining the modules that need more refactoring or more testing. In this article, an approach is developed by integrating genetics algorithm (GA) with support vector machine (SVM) classifier and particle swarm algorithm for software fault prediction as a stand though for better software fault prediction technique. The developed approach is applied into 24 datasets (12‐NASA MDP and 12‐Java open‐source projects), where NASA MDP is considered as a large‐scale dataset and Java open‐source projects are considered as a small‐scale dataset. Results indicate that integrating GA with SVM and particle swarm algorithm improves the performance of the software fault prediction process when it is applied into large‐scale and small‐scale datasets and overcome the limitations in the previous studies.
A systematic review of studies on use case points and expert‐based estimation of software development effort

Abstract In recent years, due to significant evolution in adopting new technologies and development methodologies in the field of software engineering, the developers and researchers are striving to optimize the accuracy of software effort estimation (SEE). The overestimation and underestimation both are the key challenges for software progress; hence, there is a continuous need for an accurate SEE. This paper highlights a systematic review of studies associated with the best practices of use case point (UCP) and expert judgment–based software development effort estimation techniques. The primary aim and contribution of this paper are to support the researchers through an extensive review to ease to other researcher's search for effort estimation studies. We have performed state‐of‐the‐art review from five viewpoints of reference: (a) review of studies concerning UCPs and expert judgment–based effort estimation, (b) research contribution and future recommendation in different novelties, (c) usage of the dataset, (d) availability of accuracy metrics, and (e) findings of the studies. We have performed a systematic review of studies which are published in the period of 2000 to 2019. We have selected a total of 34 primary studies of UCP and expert judgment–based estimation techniques to report the research questions stated in this review.
A Bitcoin Transaction Network Analytic Method for Future Blockchain Forensic Investigation

Popular Blockchain-based cryptocurrencies, like Bitcoin, are increasingly being used maliciously for illegal trades. In order to trace and analyze suspected Bitcoin transactions and addresses, address clustering methods and Bitcoin flow analysis methods are gaining attention recently. However, existing methods only focus on Bitcoin addresses and flow, and neglect other important information, such as transaction structure and behavior features. In order to exploit all useful features of transactions, this paper proposes a Bitcoin transaction network analytic method for facilitating Blockchain forensic investigation based on an extended safe Petri Net. The structural features and dynamic semantics of Petri net are used in our proposed model to define the static and dynamic features of Bitcoin transactions. Nineteen features have been identified to define Bitcoin transaction patterns for analyzing and finding suspected addresses. Bitcoin gene has been embedded into the Petri net transitions to trace and analyze Bitcoin flow accurately. Finally, marginal distribution analysis of Bitcoin transaction features and data visualization techniques are used to eliminate some false positive samples further and to improve the accuracy of identifying suspected addresses. The proposed Bitcoin transaction network analytic method provides a reliable forensic investigation model along with a prototype platform which is beneficial for financial security. The efficiency of our proposed method is empirically verified based on a real-life case study analysis.