Reviewerly/exHarmony huggingface dataset:
Human Pose Estimation Based on the Multistage Learning and the Dense Connection

The Human pose estimation is an important and challenging task for the computer vision. Existing human pose estimation networks often improve the accuracy of the pose estimation by feature fusion. However, most of them still suffer from information loss since they only consider high level features. In order to solve this problem, we propose a novel Multistage Learning Network named MS-Net. The key idea behind our approach is that different characteristics of joints may require different levels of feature. MS-Net first predicts joints at different stages of the network in a coarse-to-fine manner. By doing so, both geometric and semantic characteristics of the pose can be learned. Then, to deeper the networks understanding towards the pose, a dense connection is utilized to multiplex multi-level features, which further compensates for the loss of low-level features. We conduct comprehensive experiments on the coco dataset and results show that our model achieves remarkable improvements over state-of-the-art baselines.
Privacy-preserving machine learning for time series data

Machine learning has a lot of potential when applied to time series sensor data, yet a lot of this potential is currently not utilized, due to privacy concerns of parties in charge of this data. In this work I want to apply privacy-preserving techniques to machine learning for time series data, in order to unleash the dormant potential of this type of data.
Interferometric method of measuring external cylindrical surfaces

A new method of measuring external cylindrical surfaces is proposed, making use of a ring beam transform unit. The unit is composed of three cone mirrors, with which a parallel beam can be transformed into annular and convergent beams. The method has advantages over classic methods: It avoids physical contact with the cylinder surface and is fast and stitching-free. In experiments using a dynamic interferometer to demonstrate the feasibility of the method, the RMS difference of the axis contour from results using a Luphoscan is 0.0282 µm, while the roundness difference of the circumferential contour is 0.0962 µm.
Deep Learning for Multilingual POS Tagging

Various neural networks for sequence labeling tasks have been studied extensively in recent years. The main research focus on neural networks for the task are range from the feed-forward neural network to the long short term memory (LSTM) network with CRF layer. This paper summarizes the existing neural architectures and develop the most representative four neural networks for part-of-speech tagging and apply them on several typologically different languages. Experimental results show that the LSTM type of networks outperforms the feed-forward network in most cases and the character-level networks can learn the lexical features from characters within words, which makes the model achieve better results than no-character ones.
Using social media in the educational process

The article discusses the features of using distance learning in the modern educational environment of a university. This form of training is becoming more and more popular in modern educational organizations, therefore, it is advisable to adapt the usual, which have become classic teaching methods. The advantage of distance learning is determined by the use of innovative educational technologies, they are the foundation for designing and implementing of distance learning. Today, the Internet is one of the important channels of information transmission. Social media resources and the so-called social media (Rus. social network) are especially popular among young people and adolescents. In the article we have considered the term “social media”, studied the history of social networks, briefly analyzed the degree of effectiveness of the introduction of social networks in the educational process. It can be concluded that each social network has its own audience. In the educational process, they can be used at almost all stages of training. The article analyzes the possibilities of social networks in detail for implementing them as digital educational products, and also puts forward a number of features in the use of the obtained solutions for the learning process organizing. We came to the conclusion that the use of modern information technologies increases the effectiveness of distance learning. The study showed that each social network has its own target audience. Social networks can be used at almost all stages of training, because the function of social networks is not only to communicate between users in the form of sending audio, video, and text messages, but also to integrate social networks into the educational process, thereby becoming an educational tool. Social networks are becoming a new learning environment for modern students and offer them new ways of interaction. The positive impact of network technologies is caused by the modernization of traditional education.
Development of a VR STEAM Welding Project Course

This research aimed to develop the teaching mode, ability indicators, and course content of a “VR STEAM Welding Course” for the Engineering Department of Universities of Science and Technology. The students of the Electric Welding Course in Universities of Science and Technology were taken as the subjects for the integration of the STEAM education concept, in order to integrate the VR technology into the teaching of the welding course. The Fuzzy Delphi Method was adopted as the research method, and an expert questionnaire analysis was conducted. The conclusions are summarized as follows: (1) a three-part, “student-centered” teaching mode was developed, which included welding knowledge and skills training, STEAM integrated learning, and the practical application of VR; (2) Eight ability indicators of the “VR STEAM welding course” were established; (3) the “welding construction” ability indicator for VR-assisted welding teaching was the most feasible; (4) the ability indicator of “welding construction” for STEAM education had the highest integrality into welding teaching; and (5) the mobile learning platform of the VR STEAM welding course had high real-time characteristics. The findings can serve as a reference for the subsequent content design, teaching activity planning and the implementation of the experimental teaching of this course.
A simple device to measure bend limit of sheet metals

Abstract The analysis of bend fracture limits of sheet metals is important for the automotive industry. For high and mid-ductility materials, current methods are often unreliable due to the difficultly of detecting the initiation of a crack on the outer surface of the bend. Sophisticated techniques that rely on the strain evolution in the material are capable to detect the cracks and establish the bend limits using d igital i mage c orrelation (DIC) strain measurement systems. However, these techniques are complex and sometimes impractical for industrial use. This study uses a simplified bend test technique that uses optical image processing to detect crack initiation to then determine the bend limits of aluminum alloys using a m odified a ngled line m ethod (MALM). The findings of this study indicate that the optical method is inconsistent in detecting the crack initiation and likely to result in a large deviation in detected the bend limits.
Self-esteem Anak Sekolah Dasar dengan Disleksia

Dyslexia is the most common specific learning disorder. Around 5% - 15% of elementary school students have dyslexia (APA, 2013). Children with dyslexia might appear different at school and tend to get lower academic scores in comparison with their peers. Hence, this may increase the possibility for the children with dyslexia to have low self-esteem (Mash & Wolfe, 2016)because their peers’ opinions about them play an important role in affecting their self-esteem (Veenstra et al., 2007, dalam Taylor, Hume, & Welsh, 2010).The aim of this study is to give a representation about the effect of dyslexia to the self-esteem of elementary school students with the expectance to increase the awareness to detect and give early intervention to dyslexic children. The research method is literature review using articles obtained online from several databases. There were 5 selected research articles with the criteria of the participants in the study aged between 6 – 12 years old, and the used the instruments to measure specific learning disorder and also self-esteem. The result of this literature review shows that dyslexia can affect the self-esteem of elementary school students.
Designing Citizens Data Management Information System Social Guidance at UPT. Bina Laras Riau Provincial Social Service

So far the data management of socially assisted occupants at the UPT. Bina Laras Dinas Sosial Provinsi Riau is still sober and has not implemented an optimal computerized system. The data is only processed using Microsoft Word and Excel, with different files. This causes the search data to be long and the difficulty of managing these data. Even though there are quite a lot of data and are interrelated with each other. With the existence of these problems, the author feels the need to create an information system that can manage all the data contained in an application program. Information System for Data Management of Socially assisted Occupants at the UPT. Bina Laras Dinas Sosial Provinsi Riau was created using the Java language and MySQL as the database. Finally, with the existence of this information system, users can be used to get the information needed quickly, efficiently and effectively. Likewise, updating data will also become easier. Making reports that are relatively long will also be Faster.
Wage Dynamics of Vocational Education Employees in Far North

The article analyzes the dynamics of wages of teachers in the sphere of professional education of the Russian Federation, which was applied to the regions of the far North and equated localities as of 01.01.2018. Analyzed regional features of changes in wages of the teaching staff in 20132018 on two levels of education: elementary and secondary professional education and higher professional education, due to the necessity of execution of the decree of the President of the Russian Federation of May 7, 2012 No. 597 «On measures to implement the state social policy» (hereinafter referred to as the Decree of the President of the Russian Federation 2012). The factors that determined the degree of achievement by 2018 of target indicators on the level of remuneration of teachers of vocational education in the regions of the country are highlighted.
Mobile Learning Model for the Zimbabwe Higher Education Sector

With mobile technologies outpacing desktop computers this may be the ideal time for Zimbabwe universities to tap into m-learning. There is little research on m-learning implementation and adoption on a large scale in Zimbabwe universities. This study attempts to determine the feasibility of implementing m-learning in Zimbabwe by initially investigating students’ readiness for m-learning. A qualitative approach was employed by conducting focus group discussions with 20 university students. The study shows that some students are ready for m-learning based on their exposure to m-learning and opportunities of increased engagement. To further confirm the findings of the focus group discussions a survey with a large cohort of students to establish students’ readiness for m-learning in Zimbabwe universities should be conducted. With mobile technologies becoming more pervasive, this paper adds to the field of HCI as it discusses how students utilise mobile technologies to extend their learning. The paper is presented at MobileHCI 2020 Doctoral Consortium.
Sensing and Automation in the Future Maritime Environment

In this emerging era of great power competition, the goal of outpacing potential adversaries in the development of military technology takes on a new urgency. Evolving capabilities in sensing and automation are driven by a trade space that includes range and lethality versus close engagement and survivability; finders versus hiders; centralized command/control versus asset independence/dispersion; and planning and judgement versus reaction and autonomy. This paper explores this trade space first by describing sensing and automation innovations demonstrated during the 1990/1991 Gulf War and shortly thereafter, followed by discussion on current and emerging game-changing technologies. Capabilities projected for near and far term advantage include: weapons systems ensuring long-range lethality; unmanned cooperative networks of offboard systems; artificial intelligence and machine learning; and exploitation of advanced materials and quantum technologies. These will play a vital role in realizing a networked force of manned and unmanned systems with the ability to sense, comprehend, communicate, predict, plan, and take appropriate action in the future maritime environment.
Use of semi-volatile metals as a new vectoring tool for VMS exploration: Example from the Zn-rich McLeod deposit, Abitibi, Canada

Volcanogenic massive sulfide (VMS) deposits are small targets (100's of meters) hosted in a hectometric to kilometric-scale zoned alteration halo, comprising narrow proximal chlorite and wider distal sericite zones. For exploration, identification of the distal fine-grained sericite halo related to fertile VMS systems is critical but remains challenging. Sericitization is a low-temperature hydrothermal alteration and its geochemical signature could be very similar to clay assemblages produced as the results of simple low-temperature prolonged seawater interaction with volcanic rocks on the seafloor. Currently, classical lithogeochemical methods (e.g., mass balance, element ratios) are not very efficient to decipher sericite alteration related to distal fertile VMS systems. However, semi-volatile metals have good potential as pathfinders, as they have a wider dispersion than major, or trace elements commonly used due to their enhanced mobility in hydrothermal fluids. New analytical improvements, involving four-acid digestion and advanced ICP-MS technology, now allows the accurate determination of semi-volatile metals at the ppb level. Using the Zn-rich McLeod deposit in the Matagami district (Abitibi, Canada) as a case study, we tested first the representativeness and advantage of the new analytical method, and secondly, the capacity of the semi-volatile metals for vectoring exploration. The new method is both accurate and precise and provided good resolution with low detection limits. The performance of classical lithogeochemical methods was compared with semi-volatile metal distribution (As, Se, Cd, In, Sn, Sb, Te, Hg, Tl, Bi) in the footwall rhyolite. Although the classical tools efficiently identified both the proximal chlorite and distal sericite zones, most of them (except Rb/Sr) cannot efficiently vector, as there is no systematic increase or decrease towards the mineralization. Conversely, overlapping alteration halos were identified using semi-volatile metals. The proximal signature (chlorite zone) is characterized by enrichment of Sb, Li ± Mo, W and U. The distal signature (sericite zone) is defined by successive appearance of higher values of Sn, W, and Tl up to 750 m, 1000 m and 1400 m away from the mineralization, respectively. This zoned spatial distribution of semi-volatile metals clearly allows better localization of a given altered sample within a wide and distal alteration halo, thus providing a solid vectoring tool for VMS exploration. This is especially true since the signal of semi-volatile metals extends far beyond both the visual sericite alteration (up to 200 m from mineralization) and classical lithogeochemical tools (up to 1000 m away). Specifically, the Tl/Co ratio records the widest footprint of the mineralization, up to 1400 m away from the deposit. It is well known that Tl is enriched in Zn-rich VMS. Thallium in the sericite alteration halo is here considered as an indicator for discriminating fertile volcanogenic hydrothermal systems from barren low-temperature convective cells.
Glandular structure-guided classification of microscopic colorectal images using deep learning

In this work, we propose to automate the pre-cancerous tissue abnormality analysis by performing the classification of image patches using a novel two-stage convolutional neural network (CNN) based framework. Rather than training a model with features that may correlate among various classes, we propose to train a model using the features which vary across the different classes. Our framework processes the input image to locate the region of interest (glandular structures) and then feeds the processed image to a classification model for abnormality prediction. Our experiments show that our proposed approach improves the classification performance by up to 7% using CNNs and more than 10% while using texture descriptors. When testing with gland segmented images, our experiments reveal that the performance of our classification approach is dependent on the gland segmentation approach which is a key task in gland structure-guided classification.
A Systematic Comparison of Search Algorithms for Topic Modelling—A Study on Duplicate Bug Report Identification

Latent Dirichlet Allocation (LDA) has been used to support many software engineering tasks. Previous studies showed that default settings lead to sub-optimal topic modeling with a dramatic impact on the performance of such approaches in terms of precision and recall. For this reason, researchers used search algorithms (e.g., genetic algorithms) to automatically configure topic models in an unsupervised fashion. While previous work showed the ability of individual search algorithms in finding near-optimal configurations, it is not clear to what extent the choice of the meta-heuristic matters for SE tasks. In this paper, we present a systematic comparison of five different meta-heuristics to configure LDA in the context of duplicate bug reports identification. The results show that (1) no master algorithm outperforms the others for all software projects, (2) random search and PSO are the least effective meta-heuristics. Finally, the running time strongly depends on the computational complexity of LDA while the internal complexity of the search algorithms plays a negligible role.
Utilizing Machine Learning Techniques to Reveal VAT Compliance Violations in Accounting Data

In recent years, compliance management has gained more and more interest from a practice and research point of view. The financial service industry, in general, is strongly regulated and has to follow specific laws, standards and guidelines. However, research has shown that little attention is being paid to Value Added Tax (VAT) issues, although there is a high cost and risk exposure, especially in large international companies which use large IT-Infrastructures for tax handling. In this paper, we examine a commonly applied approach for the verification of VAT regulations within Enterprise Resource Planning systems (ERP) and highlight weaknesses as well as error susceptibilities. Furthermore, we show that machine learning techniques can be utilized to minimize risks and increase VAT compliance. We use a supervised learning classifier to predict tax subjects and corresponding tax rates based on related voucher information of journal reports. By comparing the results of our model with the existing rule-based system of an ERP system, we reveal potential anomalies and compliance issues. Our approach was evaluated on a given real-world data set of a leading chemical industry company that was exported from its ERP system. The results were validated by VAT experts of the company.
An Approach Combined the Faster RCNN and Mobilenet for Logo Detection

Abstract Although deep learning object detection tools such as Faster Recurrent Convolution Neural Network (Faster R-CNN) has demonstrated good performances in object detection, they also have a limited success rate for some applications. It is due to the lack of refinedness of feature maps for accurate localization, the insensitivity for small scale objects and fixed-window feature extraction in Region Proposal Network (RPN). In this paper, we performed a meticulous examination of both the proposal and the classification process by evaluating the adequacy of feature representations from different stages of the feature sequencing. We presented an approach to improve the Regional Proposal Network (RPN) by appropriate anchors selection, and proposed a modification by combining Faster R-CNN and MobileNet which influences higher-resolution feature maps for mobile devices. The results demonstrate that Faster R-CNN architecture with MobileNet has the best detection accuracy. The experiment result showed that we managed to achieve a final accuracy of 92.4% on a NVIDIA GeForce Gtx 1070 compare to previous work that achieved 90.8% of accuracy and found that our models performed well at the detection, with very low false positive rates possible for a fairly reasonably.
Degenerate Fault Attacks on Elliptic Curve Parameters in OpenSSL

In this paper, we describe several practically exploitable fault attacks against OpenSSL's implementation of elliptic curve cryptography, related to the singular curve point decompression attacks of Blömer and G ünther (FDTC2015) and the degenerate curve attacks of Neves and Tibouchi (PKC 2016).In particular, we show that OpenSSL allows to construct EC key files containing explicit curve parameters with a compressed base point.A simple single fault injection upon loading such a file yields a full key recovery attack when the key file is used for signing with ECDSA, and a complete recovery of the plaintext when the file is used for encryption using an algorithm like ECIES.The attack is especially devastating against curves with j-invariant equal to 0 such as the Bitcoin curve secp256k1, for which key recovery reduces to a single division in the base field.Additionally, we apply the present fault attack technique to OpenSSL's implementation of ECDH, by combining it with Neves and Tibouchi's degenerate curve attack.This version of the attack applies to usual named curve parameters with nonzero j-invariant, such as P192 and P256.Although it is typically more computationally expensive than the one against signatures and encryption, and requires multiple faulty outputs from the server, it can recover the entire static secret key of the server even in the presence of point validation.These various attacks can be mounted with only a single instruction skipping fault, and therefore can be easily injected using low-cost voltage glitches on embedded devices.We validated them in practice using concrete fault injection experiments on a Rapsberry Pi single board computer running the up to date OpenSSL command line tools-a setting where the threat of fault attacks is quite significant.
Locality sensitive discriminant projection for feature extraction and face recognition

As an effective feature extraction method, locality sensitive discriminant analysis (LSDA) utilizes the neighbor relationship of data to characterize the manifold structure of data and uses label information of data to adapt to classification tasks. However, the performance of LSDA is affected by outliers and the destruction of local structure. Aiming at solving the limitations of LSDA, a locality sensitive discriminant projection (LSDP) algorithm is proposed. LSDP minimizes the distance of intraclass neighbor samples to maintain local structure and minimizes the intraclass non-neighbor samples to increase the compactness of intraclass samples after projection. The problem of outliers is alleviated by increasing the compactness of intraclass samples in subspace. At the same time, we redefine the weights of interclass neighbor samples to maintain the neighbor relationship of different labels samples. Holding the local structure of interclass samples maintains the manifold structure of data. Experiments on face datasets demonstrate the effectiveness of the LSDP algorithm.