Reviewerly/exHarmony huggingface dataset:
Advanced utilization of 3D digital image correlation for thermal and impact reliabilities of electronics components

Purpose This paper aims to present an adaptation of digital image correlation (DIC) to the electronics industry for reliability assessment of electronic packages. Two case studies are presented: one for warpage measurement of a micro-electro-mechanical system (MEMS) package under different temperature conditions and the other for the measurement of transient displacements on the surface of a printed circuit board (PCB) assembly under free-fall drop conditions, which is for explaining the typical camera setup requirement and comparing among different boundary conditions by fastening methods of PCB. Design/methodology/approach DIC warpage measurements on a small device, such as a MEMS package, require a special speckle pattern. A new method for the creation of speckle patterns was developed using carbon coating and aluminum evaporative deposition. To measure the transient response on the surface of a PCB during a free-fall impact event, three-dimensional (3D) DIC was integrated with synchronized stereo-high speed cameras. This approach enables the measurement of full-field displacement on the PCB surface during a free-fall impact event, contrary to the localized information that is obtained by the conventional strain gage and accelerometer method. Findings The authors suggest the proposed patterning method to the small-sized microelectronics packages for DIC measurements. More generally, the idea is to have a thin layer of the dark or bright color of the background and then apply the white or black colored pattern, respectively, so that the surface has high contrast. Also, to achieve a proper size of speckles, this paper does not want to expose the measuring objects to high temperatures or pressures during the sample preparation stage. Of course, it seems a complicated process to use aluminum evaporator, carbon coater and electroformed mesh. However, the authors intend to share one of the solutions to achieve a proper pattern on such small-sized electronic packages. Originality/value 3D DIC technique can be successfully implemented for the measurement of micro-scale deformations in small packages (such as MEMS) and for the analysis of dynamic deformation of complex PCB.
London-modified coherent states: statistical properties and interaction with a two-level atom

In this paper we discuss statistical properties of modified London coherent states, that were introduced in order build up a resolution of the identity for London coherent states. In particular, we show that there exist sub-Poissonian behaviour for a large interval of amplitudes and, because their oscillating photon distribution, they show ringing revivals of the atomic inversion.
Abstracting Stone Walls for Visualization and Analysis

An innovative abstraction technique to represent both mathematically and visually some geometric properties of the facing stones in a wall is presented. The technique has been developed within the W.A.L.(L) Project, an interdisciplinary effort to apply Machine Learning techniques to support and integrate archaeological research. More precisely the paper introduces an original way to "abstract" the complex and irregular 3D shapes of stones in a wall with suitable ellipsoids. A wall is first digitized into a unique 3D point cloud and it is successively segmented into the sub-meshes of its stones. Each stone mesh is then "summarized" by the inertial ellipsoid relative to the point cloud of its vertices. A wall is in this way turned into a "population" of ellipsoid shapes statistical properties of which may be processed with Machine Learning algorithms to identify typologies of the walls under study. The paper also reports two simple case studies to assess the effectiveness of the proposed approach.
Augmenting Control Policies with Motion Planning for Robust and Safe Multi-robot Navigation

This work proposes a novel method of incorporating calls to a motion planner inside a potential field control policy for safe multi-robot navigation with uncertain dynamics. The proposed framework can handle more general scenes than the control policy and has low computational costs. Our work is robust to uncertain dynamics and quickly finds high-quality paths in scenarios generated from real-world floor plans. In the proposed approach, we attempt to follow the control policy as much as possible, and use calls to the motion planner to escape local minima. Trajectories returned from the motion planner are followed using a path-following controller guaranteeing robustness. We demonstrate the utility of our approach with experiments based on floor plans gathered from real buildings.
Research on Intelligent Test Paper Generating System Based on Improved Genetic Algorithm

Abstract Intelligent test paper generation belongs to an important research topic in the field of computer-aided education. Among them, Genetic Algorithm (GA), as an algorithm with high efficiency and performance, is widely used in the intelligent test paper generating system. But generally speaking, traditional GA always has some problems, such as local optimal solution and prematurity. The traditional genetic algorithm is improved in the aspects of the initial population, fitness function and some genetic operators to make it more in line with the requirements of intelligent paper generation, improving the efficiency and success ratio.
Dimension Estimation Using Autoencoders with Applications to Financial Market Analysis

Dimension Estimation (DE) and Dimension Reduction (DR) are two closely related topics, but with quite different goals. In DR, one attempts to project a random vector, either linearly or non-linearly, to a lower dimensional space that preserves the information contained in the original higher dimensional space. However, in DE, one attempts to estimate the intrinsic dimensionality or number of latent variables in a set of measurements of a random vector. DE and DR are closely linked because reducing the dimension to a smaller value than suggested by DE will likely lead to information loss. In particular, when considering linear methods, such as Principal Component Analysis (PCA), DE and DR are often accomplished simultaneously. However, in this paper we will focus on a particular class of deep neural networks called autoencoders which are used extensively for DR but are less well studied for DE. We show that several important questions arise when using AEs for DE, above and beyond those that arise for more classic DR/DE techniques such as PCA. We address AE architectural choices and regularization techniques that allow one to transform AE latent layer representations into estimates of intrinsic dimension. We demonstrate the effectiveness of our techniques on synthetic, image processing benchmark problems, and, most importantly, the analysis of financial markets.
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A subspace of F2n is called cyclically covering if every vector in F2n has a cyclic shift which is inside the subspace. Let h2(n) denote the largest possible codimension of a cyclically covering subspace of F2n. We show that h2(p)=2 for every prime p such that 2 is a primitive root modulo p, which, assuming Artin's conjecture, answers a question of Peter Cameron from 1991. We also prove various bounds on h2(ab) depending on h2(a) and h2(b) and extend some of our results to a more general set-up proposed by Cameron, Ellis and Raynaud.
Towards metrics-driven ontology engineering

The software engineering field is continuously making an effort to improve the effectiveness of the software development process. This improvement is performed by developing quantitative measures that can be used to enhance the quality of software products and to more accurately describe, better understand and manage the software development life cycle. Even if the ontology engineering field is constantly adopting practices from software engineering, it has not yet reached a state in which metrics are an integral part of ontology engineering processes and support making evidence-based decisions over the process and its outputs. Up to now, ontology metrics are mainly focused on the ontology implementation and do not take into account the development process or other artefacts that can help assessing the quality of the ontology, e.g. its requirements. This work envisions the need for a metrics-driven ontology engineering process and, as a first step, presents a set of metrics for ontology engineering which are obtained from artefacts generated during the ontology development process and from the process itself. The approach is validated by measuring the ontology engineering process carried out in a research project and by showing how the proposed metrics can be used to improve the efficiency of the process by making predictions, such as the effort needed to implement an ontology, or assessments, such as the coverage of the ontology according to its requirements.
Utilization of the field of data mining in mapping the area of the Human Development Index (HDI) in Indonesia

Abstract The Human Development Index (abbreviated as HDI) is an indicator used by a country to measure success in efforts to develop human quality. The purpose of this research is to make HDI mapping in areas in Indonesia by utilizing data mining techniques. Source of data used comes from official data from the Indonesian Central Statistics Agency (https://www.bps.go.id/) consisting of 34 data records (2018-2019). Indicators used in mapping are Life Expectancy at Birth (X1), Expectations of Old School (X2) and Average Length of School (X3). The data mining technique used is part of clustering, namely K-Medoids. The analysis process uses the help of RapidMiner software 5.3. Determination of the number of clusters (k) in the mapping using the Davies Bouldin (DBI) parameter with a maximum value (k = 4) = 0.856. By using four mapping labels (C1 = “very high” group; C2 = “high” group; C3 = “medium” group; C4 = “low” group), the results of C1 = 5 province; C2 = 16 provinces; C3 = 10 provinces and C4 = 3 provinces. Based on the results of the mapping of regions in Indonesia, Indonesia’s HDI is still far behind when compared to countries in ASEAN. In the future, this will be submitted to the government to make HDI a priority because it involves the welfare and quality of Indonesian people.
A Survey of Deep Learning Based Fully Automatic Bone Age Assessment Algorithms

Bone age assessment (BAA) is a technique for assessing the maturity of individual skeletal development, and it is the most accurate and objective method for assessing the deviation of individual development in clinical practice. It is used in the diagnosis of pediatric endocrine diseases, age determination of suspects in juvenile delinquency cases, height prediction, and athlete selection. Early computer-aided BAA mainly segments the skeleton of the hand and gives a final bone age by comparing the morphological descriptions of bones to standard atlas. In recent years, deep learning methods have developed rapidly, and a large number of end-to-end BAA methods based on deep learning have emerged, which have brought new development to the automatic BAA technology. This paper summarizes the technical basis, research status of automatic BAA. Firstly, the basic theory of medical BAA is introduced, then the commonly used traditional segmentation methods in BAA are analyzed. After that, we summarized the most popular methods of BAA based on deep learning, including the network model, data set and the assessment results. This survey also draws attention to a number of research challenges in the fully automatic BAA with deep learning.
A Defense Method against Poisoning Attacks on IoT Machine Learning Using Poisonous Data

Machine learning is a technology with the potential to enrich our lives in many ways. It is expected to be used in various situations. However, the value of attacks on machine learning models is also increasing. Therefore, it is considered to be dangerous to use machine learning without proper planning. Poisoning attacks are one of the attacks that can be launched against machine learning models. Poisoning attacks reduce the accuracy of machine learning models by mixing training data with data created with malicious intent to attack the models. Depending on the scenario, the damage caused by poisoning attacks may lead to large-scale accidents. In this study, we propose a method to protect machine learning models from poisoning attacks. In this paper, we assume an environment in which data obtained from multiple sources is used as training data for machine learning models and present a method suitable for defending against poisoning attacks in such an environment. The proposed method computes the influence of the data obtained from each source on the accuracy of the machine learning model to understand how good each source is. The impact of replacing the data from each source with poisonous data is also calculated. Based on the results of these calculations, the proposed method determines the data removal rate for each data source, which represents the confidence level for determining the degree of harmfulness of the data. The proposed method prevents poisonous data from being mixed with the normal data by removing it according to the removal rate. To evaluate the performance of the proposed method, we compared existing methods with the proposed method based on the accuracy of the model after applying the proposed defensive measure. In this experiment, under the condition that the training data contains 17% of poisonous data, the accuracy of the defended model of the proposed method is 89%, which is higher than 83% obtained using the existing method. This shows that the proposed method improved the performance of the model against poisoning attacks.
3D Face Identification Using HOG Features and Collaborative Representation

Scientists have attempted to enhance face recognition accuracy by using 3D images to solve pose variability, lighting issues, and occlusion; but the applicability of these approaches is insufficient in realistic applications due to the slowness and high cost of 3D sensors. In this paper, we developed a face recognition process for low-cost and low-quality Microsoft Kinect sensor based on the Histogram of Oriented Gradient (HOG) descriptor. After locating the nose tip, the face is extracted and filtered to reduce the noise, the feature vectors are extracted by first computing the shape index map and then applying the HOG descriptor. For identification, the Collaborative Representation Classifier (CRC) is used. We compare our approach results with previous ones obtained on the publicly available CurtinFaces database, achieving rank one recognition rates of 99.87% and 82.53% when testing against expression and pose variations respectively.
HPCF: Hybrid Music Group Recommendation System based on Item Popularity and Collaborative Filtering

Group recommendation system is designed to pleasure a certain group of people by providing recommendation lists to prevent information overload. This paper proposes HPCF, which combines item popularity and preference of the group members to optimize the quality of recommendation. Evaluation results show that the HPCF outperforms popularity-based methods and Singular Value Decomposition based methods. In our experiments, HPCF scheme improves the accuracy of recommendation by 8.7% to 19.8% in different conditions.
Hybrid Vessel Extraction Method Based on Tight-Frame and EM Algorithms by Using 2D Dual Tree Complex Wavelet

The vessel extraction is very important for the vascular disease diagnosis and grading of the stenoses and aneurysms in vessels. This aids in brain surgery and making angioplasty. The presence of noise in the MRA image, etc., turns the vessel extraction into a difficult problem. In this paper, we derive a vessel extraction algorithm based on TFA and EMS algorithms. We prove the convergence of the proposed method within a few iterations. Results of applying the presented method on real 2D MRA images demonstrate that our method is very efficient.
Improvement of Face Detection Algorithm Based on Lightweight Convolutional Neural Network

In recent years, the application of visual processing algorithm based on deep network on mobile terminal has become a popular research problem. In this paper, a new face loss function is designed to solve the problems of limited computing power and storage resource of mobile devices when face detection is applied to mobile terminal. The face detection algorithm based on SSH (single stage headless face detector) is improved in two aspects: firstly, the feature of sample data is extracted by using the lightweight convolutional neural network based on MobileNet, which can effectively reduce the amount of parameters and calculation of the model; Secondly, the deformable convolution layer is introduced into the detection module of SSH network to improve the modeling ability of face deformation. At the same time, the method of knowledge distillation is introduced. The high-precision large network model is used as the teacher network, and the small network model of the improved SSH algorithm is used as the student network to further improve performance of the detection model. Experimental results on the wider face dataset show that the proposed method can significantly reduce the complexity of the model, effectively reduce the size of the face detection model and improve the speed of model detection, while ensuring the detection accuracy. Therefore, the proposed method is expected to be better applied to face detection equipment in mobile terminals.
A Curated Set of Labeled Code Tutorial Images for Deep Learning

As more work is conducted in mining software repositories at a large scale, aggregating data from multiple modalities beyond textual representations will prove beneficial. Online video content related to software engineering offers another, thus far largely untapped, venue for source code. In this paper, we present a novel dataset comprised of 111,229 frames extracted from a sample of 100 technology-related YouTube tutorials, taught in the Java and Python programming languages. These videos span a diverse range of Integrated Development Environments (IDEs) and modes of information presentation, lending heterogeneity of image features to our dataset. Hand-labeled for the presence, or lack thereof, of typeset, partially visible typeset, or handwritten code, the dataset provides a uniquely consistent and accurate data source for supervised machine learning ventures in extracting and processing code from images.
Medical Image Encryption Algorithm based on Dynamic DNA Sequencing and 2D-3D Chaotic Equations

Abstract Digital images are now widely used in modern clinic diagnosis. The diagnostic images with confidential information related to patients’ privacy are stored and transmitted via public networks. Secured schemes to guarantee confidentiality of patients’ privacy are becoming more and more vital. Medical images are considered as one of the most important and sensitive data in information systems. Sending medical images over the network requires a strong encryption algorithm such that it is resistant against cryptographic attacks. Among the three security objectives for the security of information systems namely confidentiality, integrity and availability, confidentiality is the most important aspect that need to be taken much care for the secure storage and transmission of medical images. This paper proposes an adaptive medical image encryption algorithm based on Dynamic DNA Sequencing and 2D-3D Chaotic Equations that encrypts any medical image with same uniformity and efficiency.
A loss‐based prior for Gaussian graphical models

Summary Gaussian graphical models play an important role in various areas such as genetics, finance, statistical physics and others. They are a powerful modelling tool, which allows one to describe the relationships among the variables of interest. From the Bayesian perspective, there are two sources of randomness: one is related to the multivariate distribution and the quantities that may parametrise the model, and the other has to do with the underlying graph, G , equivalent to describing the conditional independence structure of the model under consideration. In this paper, we propose a prior on G based on two loss components. One considers the loss in information one would incur in selecting the wrong graph, while the second penalises for large number of edges, favouring sparsity. We illustrate the prior on simulated data and on real datasets, and compare the results with other priors on G used in the literature. Moreover, we present a default choice of the prior as well as discuss how it can be calibrated so as to reflect available prior information.
<i>Sprekend Nederland</i>, a multi-purpose collection of Dutch speech

Abstract This contribution sketches the aims, participant recruitment and methodology of the Sprekend Nederland project, the amount and types of data, the research possibilities they offer and suggestions for similar future projects.
Design of Learning Application using Trivia Method based on Google Assistant for Vision Impairment Disability

This mobile based application called Natasha Bot is a virtual learning media aimed at people with disabilities especially blind people. Natasha Bot contains questions about subjects in schools designed for elementary school students in grade 6 in the form of guessing or trivia. The system and infrastructure supporting learning activities for people with disabilities that have been facilitated by the government are still lacking. Some problems have arisen such as the inconvenience of students with disabilities in the learning environment because they feel different. Teachers in inclusive schools also have not been equipped with special skills or techniques for people with disabilities. The Natasha Bot application comes as an innovation that utilizes open source technology from Google. It is designed to make students with disabilities, especially the blind, feel comfortable in learning activities. Natasha Bot offers voice features as a two-way communication between the student and Bot. This research uses the design thinking method. To create Natasha Bot as a virtual friend as well as a learning media. Natasha Bot comes with Artificial Intelligence technology and is designed using the Google Assistant framework with the method of trivia or guessing. Natasha Bot is expected to help advance the world of education, especially for people with disabilities on visual impairments with a more teachable and psychological approach.