Reviewerly/exHarmony huggingface dataset:
Technological Competencies: A Study on the Acceptance of Digital Technology on Vocational Teachers in Indonesia

Changes in the flow of globalization are influenced by the presence of the Industrial Revolution 4.0, including the role of digital technology (DT) in the education sector, namely cyber technology-based learning.The main challenge for vocational teachers is the mastery of skills in learning using DT.The acceptance of DT is categorized into five clusters, namely awareness, literacy, capability, creativity, and criticism adapted from Pavlova's.The study was conducted to measure the level of ability of productive class vocational teachers.The study approach used quantitative with descriptive statistical analysis.A total of 50 productive classroom teachers in Yogyakarta were involved in filling out a questionnaire that had been tested and validated by content, experts, and calculation of product-moment correlation.The results of the study show (1) technological awareness 83.08%; (2) technological literacy 66.32%; (3) technological capability of 39.28%; (4) technological creativity of 21.92%; and (5) technological criticism at 20.84%.The use of DT is clearly in the low category and shows the capacity of vocational teachers needs to be improved.Training programs, self-development, and motivation are important factors in improving digital technology skills.
CNN-based Gender Prediction in Uncontrolled Environments

With the increasing amount of data produced and collected, the use of artificial intelligence technologies has become inevitable. By using deep learning techniques from these technologies, high performance can be achieved in tasks such as classification and face analysis in the fields of image processing and computer vision. In this study, Convolutional Neural Networks (CNN), one of the deep learning algorithms, was used. The model created with this algorithm was trained with facial images and gender prediction was made. As a result of the experiments, 93.71% success rate was achieved on the VGGFace2 data set and 85.52% success rate on the Adience data set. The aim of the study is to classify low-resolution images with high accuracy.
Using i* and UML for Blockchain Oriented Software Engineering: Strengths,Weaknesses, Lacks and Complementarity

New blockchain-based projects do appear every day. The technology has indeed been popularized by cryptocurrencies but is now gaining interest in various domains and new types of applications are evaluated constantly. Understanding the impact of blockchain adoption on the organization and the internals of blockchain-related behavior nevertheless remains a challenge for managers but also for IT professionals. This article studies how two existing organizational and software modeling languages can be fit to document a blockchain development project in Supply Chain Management (SCM) at its earliest stages. These two frameworks are i* on the one side and the Unified Modeling Language (UML) use case and sequence diagrams on the other side. The real life project used as a case study in this application is ‘Farm-to-Fork’ where a blockchain solution for the Supply Chain (SC) of farm animals is developed. The application of the frameworks is intended to identify their strengths and weaknesses. An extension of i* is proposed to deal with blockchain privacy issues as well as laws and norms. We finally point to the complementarity of i* and UML use case and sequence diagrams in a Blockchain-Oriented Software Engineering (BOSE) context. The i* framework indeed supports early requirements to understand the impact of the project on stakeholders while UML use case and sequence diagrams support the late requirements and the design by depicting the use of blockchain and some of its behavioral mechanisms.
Last iterate convergence in no-regret learning: constrained min-max optimization for convex-concave landscapes

In a recent series of papers it has been established that variants of Gradient and Mirror Descent exhibit last iterate convergence in convex-concave zero-sum games. Specifically, \cite{DISZ17, LiangS18} show last iterate convergence of the so called Optimistic Gradient Descent/Ascent for the case of \textit{unconstrained} min-max optimization. Moreover, in \cite{Metal} the authors show that Mirror Descent with an extra gradient step displays last iterate convergence for convex-concave problems (both constrained and unconstrained), though their algorithm does not follow the online learning framework; it uses extra information rather than \textit{only} the history to compute the next iteration. In this work, we show that Optimistic Multiplicative-Weights Update (OMWU) which follows the no-regret online learning framework, exhibits last iterate convergence locally for convex-concave games, generalizing the results of \cite{DP19} where last iterate convergence of OMWU was shown only for the \textit{bilinear case}. We complement our results with experiments that indicate fast convergence of the method.
Effects of an Augmented Reality Library Orientation on Anxiety and Self-Efficacy: An Exploratory Study

Academic libraries use scavenger hunt library orientations to engage students and teach them about the library. Libraries are also starting to use augmented reality (AR) technology in orientations, instruction, and programming. Some libraries have tried augmented reality scavenger hunts for freshman library orientations, but studies have not been done to compare the traditional and AR orientations. This exploratory, quasi-experimental study investigates if the AR library orientation impacts freshman undergraduates' perceptions of the library as measured by students' anxiety and confidence levels. Two groups of first-time, first-year undergraduate students participated in a library orientation in Summer 2019. One received the traditional scavenger hunt model, and another experienced an augmented reality version of the scavenger hunt. Both groups took a pre-orientation survey to determine their comfort with the libraries and librarians in general, and then a post-orientation questionnaire to determine any change in perception of libraries and librarians as well as their satisfaction with the orientation experience. By comparing pre- and post-survey results from both sets of students, this study determined that while both orientations had a positive impact on students' perceptions of the library, the AR orientation had a significant impact on students' perceptions of librarians' desire to help them.
Analysis of Mobile Push Service Model of Smart Library Based on Big Data

Abstract [Purpose / Significance] With the development of big data, massive data resources appear. Smart library should provide more personalized services to better meet the needs of users. [Method/Process] This article analyzes the characteristics of smart library services. It includes precision, timeliness, interactivity and personalization. [Result / Conclusion] This paper analyzes the development opportunities of knowledge push service in Smart Library, and establishes the mobile push service model of Smart Library.
CLUE

As the most popular blockchain that supports smart contracts, there are already more than 296 thousand kinds of cryptocurrencies built on Ethereum. However, not all cryptocurrencies can be controlled by users. For example, some money is permanently locked in wallets' accounts due to attacks. In this paper, we conduct the first systematic investigation on locked cryptocurrencies in Ethereum. In particular, we define three categories of accounts with locked cryptocurrencies and develop a novel tool named Clue to discover them. Results show that there are more than 216 million dollars value of cryptocurrencies locked in Ethereum. We also analyze the reasons (i.e., attacks/behaviors) why cryptocurrencies are locked. Because the locked cryptocurrencies can never be controlled by users, avoid interacting with the accounts discovered by Clue and repeating the same mistakes again can help users to save money.
Graph Attention Collaborative Similarity Embedding for Recommender System

We present Graph Attention Collaborative Similarity Embedding (GACSE), a new recommendation framework that exploits collaborative information in the user-item bipartite graph for representation learning. Our framework consists of two parts: the first part is to learn explicit graph collaborative filtering information such as user-item association through embedding propagation with attention mechanism, and the second part is to learn implicit graph collaborative information such as user-user similarities and item-item similarities through auxiliary loss. We design a new loss function that combines BPR loss with adaptive margin and similarity loss for the similarities learning. Extensive experiments on three benchmarks show that our model is consistently better than the latest state-of-the-art models.
Blackman–Tukey spectral estimation and electric network frequency matching from power mains and speech recordings

Forensic applications exploit electric network frequency (ENF) as a fingerprint to determine multimedia content authenticity, as well as the time and region of multimedia recording. ENF is present at a nominal frequency of 50/60 Hz and its harmonics. Strong interference due to speech content deteriorates ENF estimation accuracy. Herein, the authors propose a non-parametric approach for ENF estimation, which incorporates a customised lag window design into the Blackman–Tukey spectral estimation method. Leakage reduction is formulated as a problem of energy maximisation within the main lobe of the spectral window. The proposed approach is compared to state-of-the-art methods for ENF estimation. Maximum correlation coefficient and minimum standard deviation of errors are employed to measure ENF estimation accuracy. Hypothesis testing is performed to determine whether the improvements in ENF estimation accuracy of the proposed approach over the state-of-the-art methods are statistically significant. Experimental results and statistical tests indicate that the proposed approach improves ENF estimation against many state-of-the-art methods.
The influence of modern trends in digital art on the content of training in computer graphics and digital design

The development of digital technologies leads to a variety of pictorial arts. With the advent of computer technology and communication technologies, areas such as computer graphics, computer and digital design, and phenomena such as digital art have emerged. The article analyzes these concepts, which provided an opportunity for further study. Based on the analysis of Internet resources on digital art, the following main trends are identified: virtual art, 3D printing, open source software, art of artificial intelligence, a combination of 2D animation and modern technologies, 3D painting, UX / UI design, game design, concept art and character design. Examples of reflection these tendencies in modern art are given. Given the selected trends, it is shown how they affect the content of training in computer graphics and digital design for students of the educational program "Digital Design" (list of compulsory and optional educational components, the content of educational practice). It is noted that to successfully work with computer graphics, students need to master traditional knowledge of pictorial arts (the concept of composition, color, perspective, proportions, shadows) and the ability to use them to create digital products using computer programs (2D graphic editors and 3D graphics) and digital technology (graphics tablet, personal and personal computer, projection equipment, camera, devices for VR and AR reality, scanner and printer, including 3D scanner and 3D printer).
SMSIR: Spherical Measure Based Spherical Image Representation

This paper presents a spherical measure based spherical image representation(SMSIR) and sphere-based resampling methods for generating our representation. On this basis, a spherical wavelet transform is also proposed. We first propose a formal recursive definition of the spherical triangle elements of SMSIR and a dyadic index scheme. The index scheme, which supports global random access and needs not to be pre-computed and stored, can efficiently index the elements of SMSIR like planar images. Two resampling methods to generate SMSIR from the most commonly used ERP(Equirectangular Projection) representation are presented. Notably, the spherical measure based resampling, which exploits the mapping between the spherical and the parameter domain, achieves higher computational efficiency than the spherical RBF(Radial Basis Function) based resampling. Finally, we design high-pass and low-pass filters with lifting schemes based on the dyadic index to further verify the efficiency of our index and deal with the spherical isotropy. It provides novel Multi-Resolution Analysis(MRA) for spherical images. Experiments on continuous synthetic spherical images indicate that our representation can recover the original image signals with higher accuracy than the ERP and CMP(Cubemap) representations at the same sampling rate. Besides, the resampling experiments on natural spherical images show that our resampling methods outperform the bilinear and bicubic interpolations concerning the subjective and objective quality. Particularly, as high as 2dB gain in terms of S-PSNR is achieved. Experiments also show that our spherical image transform can capture more geometric features of spherical images than traditional wavelet transform.
Openness and Government Size in Sub-Saharan African countries

Using a panel dataset of 40 Sub-Saharan African (SSA) countries covering the period 1990-2015, through several panel data analysis, this study provide an empirical assessment of the relationship between openness and government size. To measure openness, trade openness, financial openness and globalization are used. Results of fixed effects model reveal that while trade openness confirms the evidence of the compensation hypothesis, financial openness and globalization are relevant to government size and confirm the efficiency hypothesis. The robustness of initial findings by the System Generalized Method of Moments (SGMM) estimations shows that trade openness has a significant positive effect on government size - supporting the compensation hypothesis-, while financial openness is negatively and significantly related to government size - supporting the efficiency hypothesis. In addition, globalization does not appear to have a significant effect on government size.
Segments Interpolation Extractor for Finding the Best Fit Line in Arabic Offline Handwriting Recognition Words

In the last few years, deep learning-based models have made significant inroads into the field of handwriting recognition. However, deep learning requires the availability of massive labelled data and considerable computation for training or automatic feature extraction. The role of handcrafted features and their significance is still crucial for a specific language type because it is a unique way of writing the characters. These are primitive segments that describe the letter horizontally or vertically distinguish an Arabic letter. This article develops a new type of feature for handwriting using Segments Interpolation (SI) to find the best fitting line in each of the windows and build a model for finding the best operating point window size for SI features. The experimental design was done on two subsets of the Institute for Communications Technology/Ecole Nationale d'Ingénieurs de Tunis (IFN/ENIT) database. The first one contains 10 classes (C10), and the second one has 22 classes (C22). The extracted features were trained with Support Vector Machine (SVM) and Extreme Learning Machine (ELM) with different kernels and activation functions. The evaluation metrics from a classification perspective (Accuracy, Precision, Recall and F-measure) were applied. As a result, SI shows significant results with SVM 90.10% accuracy for C10 and 88.53% accuracy for C22.
Partially Overlapped Inference for Long-Form Speech Recognition

While the end-to-end speech recognition models show impressive performance on many domains, they have difficulties in decoding long-form utterances. The overlapped inference algorithm with tie-breaking between two parallel hypotheses has been proposed for long-form speech recognition and shows dramatic performance improvements at the expense of double computational costs. In this paper, we propose a more effective way of overlapped inference by aligning partially matched hypotheses. Through the experiment on LibriSpeech dataset, the proposed algorithm showed improved performance with less computational cost compared to the conventional overlapped inference.
GitHub-OSS Fixit: Fixing Bugs at Scale in a Software Engineering Course

Many studies have shown the benefits of introducing open-source projects into teaching Software Engineering (SE) courses. However, there are several limitations of existing studies that limit the wide adaptation of open-source projects in a classroom setting, including (1) the selected project is limited to one particular project, (2) most studies only investigated on its effect on teaching a specific SE concept, and (3) students may make mistakes in their contribution which leads to poor quality code. Meanwhile, software companies have successfully launched programs like Google Summer of Code (GSoC) and FindBugs "fixit" to contribute to open-source projects. Inspired by the success of these programs, we propose GitHub-OSS Fixit, a team-based course project where students are taught to contribute to open-source Java projects by fixing bugs reported in GitHub. We described our course outline to teach students SE concepts by encouraging the usages of several automated program analysis tools. We also included the carefully designed instructions that we gave to students for participating in GitHub-OSS Fixit. As all lectures and labs are conducted online, we think that our course design could help in guiding future online SE courses. Overall, our survey results show that students think that GitHub-OSS Fixit could help them to improve many skills and apply the knowledge taught in class. In total, 154 students have submitted 214 pull requests to 24 different Java projects, in which 93 of them have been merged, and 46 have been closed by developers.
Ergowear: an ambulatory, non-intrusive, and interoperable system towards a Human-aware Human-robot Collaborative framework

Motivated by industry 5.0 paradigm and Human-robot collaboration (HRC) technology, this paper presents the first steps of the design and development of Ergowear, a wearable, non-intrusive, interoperable upper body inertial Motion Capture system. This system was developed aiming to be used as a sensing technology to make the robot aware of the human's intentions and states and, therefore, achieving human-awareness. Consequently, it was developed to run on ROS2, to ease its integration in a HRC framework. This work depicts the design process and development of the prototype. Firstly, the system requirements are presented along with the system components and architecture. To verify interoperability, a protocol was designed to test the Ergowear's hardware, namely its autonomy, storing capacity, wireless communication's performance, and overall mechanical robustness. Overall, the achieved results are within the specified technical requirements, presenting the Ergowear as a promising sensing technology to be integrated within a Human-aware HRC framework.
Facial Emotion Recognition Using Asymmetric Pyramidal Networks With Gradient Centralization

Facial expression recognition (FER) is a promising but challenging area of Computer Vision (CV).Many researchers have devoted significant resources to exploring FER in recent years, but an impediment remains: classifiers perform well on fine resolution images but have difficulty recognizing inthe-wild human emotional states.In order to solve the aforementioned issue, we introduced three novel designs and implemented them in neural networks.More specifically, we utilized an asymmetric pyramidal network (APNet) and employed multi-scale kernels instead of identical size kernels.In addition, square kernels were replaced by a sequence of square, horizontal, and vertical convolutions.This structure can increase the description ability of convolutional neural networks (CNN) and transfer multi-scale features between different layers.Additionally, when training CNN, we adopted stochastic gradient descent with gradient centralization (SGDGC) where it centralizes gradients to have zero mean and makes the training process more efficient and stable.To verify the effectiveness of APNet with SGDGC, we used three of the most popular in-the-wild emotion datasets, FER-2013, CK+, and JAFFE, for our experiments.The results of our experiment and comparisons with state-of-the-art designs from others demonstrate that our method outperforms all the single model methods and has comparable performance with model fusion methods.
Active Learning Based 3D Semantic Labeling From Images and Videos

3D semantic segmentation is one of the most fundamental problems for 3D scene understanding and has attracted much attention in the field of computer vision. In this paper, we propose an active learning based 3D semantic labeling method for large-scale 3D mesh model generated from images or videos. Taking as input a 3D mesh model reconstructed from the image based 3D modeling system, coupled with the calibrated images, our method outputs a fine 3D semantic mesh model in which each facet is assigned a semantic label. There are three major steps in our framework: 2D semantic segmentation, 2D-3D semantic fusion, and batch image selection. A limited annotation image set is first used to fine-tune a pre-trained semantic segmentation network for obtaining the pixel-wise semantic probability maps. Then all these maps are back-projected into 3D space and fused on the 3D mesh model using Markov Random Field optimization, thus yield a preliminary 3D semantic mesh model and a heat model showing each facet’s confidence. This 3D semantic model is used as a reliable supervisor to select the parts that are not well segmented for manual annotation to boost the performance of the 2D semantic segmentation network, as well as the 3D mesh labeling, in the next iteration. This Training-Fusion-Selection process continues until the label assignment of the 3D mesh model becomes steady. By this means, we significantly reduce the amount for annotation but not the labeling quality of 3D semantic models. Extensive experiments demonstrate the effectiveness and generalization ability of our method on a wide variety of datasets.
Virtual Marker Technique to Enhance User Interactions in a Marker-Based AR System

In marker-based augmented reality (AR) systems, markers are usually relatively independent and predefined by the system creator in advance. Users can only use these predefined markers to complete the construction of certain specified content. Such systems usually lack flexibility and cannot allow users to create content freely. In this paper, we propose a virtual marker technique to build a marker-based AR system framework, where multiple AR markers including virtual and physical markers work together. Information from multiple markers can be merged, and virtual markers are used to provide user-defined information. We conducted a pilot study to understand the multi-marker cooperation framework based on virtual markers. The pilot study shows that the virtual marker technique will not significantly increase the user’s time and operational burdens, while actively improving the user’s cognitive experience.