Reviewerly/exHarmony huggingface dataset:
Methodology for the Development of Augmented Reality Applications: MeDARA. Drone Flight Case Study

Industry 4.0 involves various areas of engineering such as advanced robotics, Internet of Things, simulation, and augmented reality, which are focused on the development of smart factories. The present work presents the design and application of the methodology for the development of augmented reality applications (MeDARA) using a concrete, pictorial, and abstract approach with the intention of promoting the knowledge, skills, and attitudes of the students within the conceptual framework of educational mechatronics (EMCF). The flight of a drone is presented as a case study, where the concrete level involves the manipulation of the drone in a simulation; the graphic level requires the elaboration of an experiential storyboard that shows the scenes of the student’s interaction with the drone in the concrete level; and finally, the abstract level involves the planning of user stories and acceptance criteria, the computer design of the drone, the mock-ups of the application, the coding in Unity and Android Studio, and its integration to perform unit and acceptance tests. Finally, evidence of the tests is shown to demonstrate the results of the application of the MeDARA.
Emotion Recognition in Tweets using Optimized Ensemble Classifiers

The development of superior recognition/ classification techniques is essential for real-time Natural Language Processing (NLP) applications. Sentiment Analysis (SA) is a task of NLP that aims to extract the sentiments/ opinions of the people from texts. In addition, very recent SA techniques start to integrate the data from text, speech, and other modalities such as visual data. Effective computing techniques and SA techniques are the two keys to the development of Artificial Intelligence (AI) such as human computer interaction. This article presents the investigations on sentiment classification of political tweets about an individual on Twitter using optimized Ensemble classifier under different training rates. From the analysis done through simulation, it is noted that Ensemble Classifier (EC) provides better classification accuracy than that of traditional machine learning (ML) classifiers.
SGXDump: A Repeatable Code-Reuse Attack for Extracting SGX Enclave Memory

Intel SGX (Software Guard Extensions) is a hardware-based security solution that provides a trusted computing environment. SGX creates an isolated memory area called enclave and prevents any illegal access from outside of the enclave. SGX only allows executables already linked statically to the enclave when compiling executables to access its memory, so code injection attacks to SGX are not effective. However, as a previous study has demonstrated, Return-Oriented Programming (ROP) attacks can overcome this defense mechanism by injecting a series of addresses of executable codes inside the enclave. In this study, we propose a novel ROP attack, called SGXDump, which can repeat the attack payload. SGXDump consists only of gadgets in the enclave and unlike previous ROP attacks, the SGXDump attack can repeat the attack payload, communicate with other channels, and implement conditional statements. We successfully attacked two well-known SGX projects, mbedTLS-SGX and Graphene-SGX. Based on our attack experiences, it seems highly probable that an SGXDump attack can leak the entire enclave memory if there is an exploitable memory corruption vulnerability in the target SGX application.
Design and Research of VR Basketball Teaching System Based on Embedded Intelligent Sensor

Physical education pedagogy serves as a carrier of physical education theory and practical activities and plays an important role in physical education. The reform and development of education are always related closely to the growth of scientific and technological development, and people’s physical education training methods are also undergoing subtle changes. The traditional single and formulaic training methods in the past have gradually turned to modern training methods with better interaction and visibility. All of this prompts people to have new requirements on sports training concepts and training modes. On this basis, teachers can conduct virtual teaching in a more interactive, more involved, and orderly manner in the virtual classroom. This paper constructs the implementation framework of VR basketball teaching auxiliary training, divides students into an experimental group and a control group and tests the physical fitness of students before and after the experiment. The students’ basic basketball skills were tested by using embedded intelligent sensors to recognize the students’ movements. The experimental results showed that after the test, the four basic basketball skills scores of the students in the experimental group were 68.78, 74.02, 73.13, and 72.34, respectively. The virtual reality basketball teaching system designed in this paper can effectively improve the basketball level of students.
Semi-Supervised Semantic Annotator (S3A): Toward Efficient Semantic Labeling

Most semantic image annotation platforms suffer severe bottlenecks when handling large images, complex regions of interest, or numerous distinct foreground regions in a single image.We have developed the Semi-Supervised Semantic Annotator (S3A) to address each of these issues and facilitate rapid collection of ground truth pixel-level labeled data.Such a feat is accomplished through a robust and easy-to-extend integration of arbitrary python image processing functions into the semantic labeling process.Importantly, the framework devised for this application allows easy visualization and machine learning prediction of arbitrary formats and amounts of per-component metadata.To our knowledge, the ease and flexibility offered are unique to S3A among all opensource alternatives.
Deep learning for assessing the aesthetics of professional photographs

Abstract Aesthetic quality assessment for photographs is an important research topic since it can be used for a number of applications, such as image database management or image browsing. In 2012, the aesthetic visual analysis (AVA) dataset has been proposed. It has since then been used to train the majority of computational models of aesthetics assessment. We observe that AVA is mainly composed of competitive photographs which notion of aesthetics differs from other kinds of photographs, such as professional photographs. In this paper, we evaluate whether or not recent aesthetics assessment models generalize well and perform well over professional photographs. We noticed that the different models we tested behave differently on both categories, and therefore do not generalize well. Besides, we fine‐tuned one of the tested model using professional photographs and the results show that this fine‐tuning is effectively improving the coverage of the methods.
General Depression Detection Analysis Using IndoBERT Method

Many of the tweets we discover on Twitter are concerning feelings of depression which will be caused by varied things. The amount of tweets additionally continues to increase. To be able to decide however depressed a user is, analysing tweets from users can facilitate with that. The method of analysing the detection of depression can help to supply applicable treatment for users who are detected to own depression. During this paper, the users to be analysed are users who have more than 1000 tweets and are Indonesian tweets. Then, crawling / retrieval of user tweet data is carried out. After that, data pre-processing is done. Once that done, using the IndoBERT method to classify the data obtained. In the end, this paper provides the accuracy value of this detection analysis using the IndoBERT method with an accuracy value of 51% and F1-Score of 31%.
Data Set Analysis Using Rapid Miner to Predict Cost Insurance Forecast with Data Mining Methods

The insurance protection program cannot be separated from everyday human life because there will always be risks in every human activity. Most people have entered into insurance agreements with state-owned and national private-owned insurance companies. The information system is one of the resources to increase competitive advantage. Information systems can be used to obtain, process, and disseminate information to support day-to-day operations and support strategic decision-making activities. The rapid growth of data accumulation has created data-rich but insufficient information conditions. Data mining is the mining or discovery of new information by looking for specific patterns or rules from large amounts of data expected to overcome these conditions. It is hoped that customer data can accurately produce information about insurance cost predictions. In this analysis, the authors use the RapidMiner Studio version 9.1 software. With the RapidMiner Studio app, authors can analyze the insurance data. A scientific novelty of this research is investigating data set cost insurance with data mining techniques consisting of classification, association, and clustering. Research goals for data mining techniques with classification, association, and clustering case studies implemented are to find all associative rules with high confidence, organize objects into groups whose members are similar, and collect objects between them. The following methods can be used: decision tree for data modeling, FP-Growth for determining which dataset occurs most frequently, and K-Means to classify the data attributes to facilitate the analysis.
Online Distributed ADMM Algorithm With RLS-Based Multitask Graph Filter Models

This article establishes a multitask graph filter model based on the recursive least square (RLS) method and proposes an online distributed alternating direction method of multipliers (ODADMM) algorithm. We are interested in the time-varying graph signal, i.e., the graph filter is estimated from streaming data. Considering that current popular graph shift operators' energy can not be preserved, which will lead to slow estimation speed, so the RLS method is adopted in graph filters (GFs) to improve the convergence rate. Besides, a multitask GFs model is proposed for node-variant GFs, where each vertex cooperates with neighbours to improve the estimation performance by utilizing the correlation of tasks. Then, according to our model, a distributed alternating direction method of multipliers (DADMM) algorithm is designed, while it has enormous computational complexity. To address this drawback, an ODADMM algorithm is further developed, and the algorithm can converge to an optimal point that is validated. Numerical results verify that the proposed algorithm is more competitive in convergence speed and performance than other related algorithms, and two real scenes are tested to verify the effectiveness of the algorithm.
Educating free citizens for the 21st century : On innovation in education

Everyone knows that education is essential, but even today millions of children still do not get primary education, and the quality of the education system in many countries is so poor that it does not guarantee basic skills. Even in the so-called rich countries there are problems, such as stress, depression and early school leaving. The current model of mass education, with a well-defined curriculum and the centrality of the teacher, is no longer able to respond to the complexity of the world in which we live. New educational paradigms are therefore urgently needed to accompany and foster the need for the future of young people. In this article, the author discuss a path of renewal that builds on the online learning experiences of schools around the world during the closure caused by the Covid-19 pandemic. It is based on six dimensions of innovation, which are as many ideas for school change.
Improved Weakly Private Information Retrieval Codes

We study the problem of weakly private information retrieval (W-PIR), where a user wishes to retrieve a desired message from N non-colluding servers in a way that the privacy leakage regarding the desired message’s identity is less than or equal to a threshold. We propose a new code construction which significantly improves upon the best known result in the literature, based on the following critical observation. In previous constructions, for the extreme case of minimum download, the retrieval pattern is to download the message directly from N−1 servers; however this causes leakage to all these N−1 servers, and a better retrieval pattern for this extreme case is to download the message directly from a single server. The proposed code construction allows a natural transition to such a pattern, and for both the maximal leakage metric and the mutual information leakage metric, significant improvements can be obtained. We provide explicit solutions, in contrast to a previous work by Lin et al., where only numerical solutions were obtained.
Adaptive Gap Entangled Polynomial Coding for Multi-Party Computation at the Edge

Multi-party computation (MPC) is promising for designing privacy-preserving machine learning algorithms at edge networks. An emerging approach is coded-MPC (CMPC), which advocates the use of coded computation to improve the performance of MPC in terms of the required number of workers involved in computations. The current approach for designing CMPC algorithms is to merely combine efficient coded computation constructions with MPC. Instead, we propose a new construction; Adaptive Gap Entangled polynomial (AGE) codes, where the degrees of polynomials used in computations are optimized for MPC. We show that MPC with AGE codes (AGE-CMPC) perform better than existing CMPC algorithms in terms of the required number of workers as well as storage, communication and computation load.
Stochastic Chaining and Strengthened Information-Theoretic Generalization Bounds

We propose a new approach to apply the chaining technique in conjunction with information-theoretic measures to bound the generalization error of machine learning algorithms. Different from the deterministic approach previously proposed by Asadi et al., which is based on hierarchical partitions of a bounded metric space, we propose a stochastic approach that replaces the hierarchical partitions with an abstract Markov model inspired by successive refinement source coding in information theory. Our approach has three main benefits over the deterministic approach: 1) applicability to unbounded metric space, 2) feasibility of subsequent analysis to yield explicit bounds, and 3) increased flexibility for optimization. We illustrate these benefits through the problems of estimating Gaussian mean and phase retrieval. For the problem of estimating Gaussian mean, we derive a chaining bound that provides an order-wise improvement over previous results; for the problem of phase retrieval, we construct a stochastic chain that allows optimization over the chaining parameter.
Aligning MNEs with SDGs: peace, justice, and strong institutions

This chapter examines specific targets within UN Sustainable Development Goal (SDG) 16 for promoting "peace, justice, and strong institutions." The focus in this chapter is on 16.4 (organized crime), 16.5 (corruption), 16.10 (strengthening institutions in support of 16.4 and 16.5), and 16a (human rights institutions). The methodology is an integrative review of literature, empirical studies, and case studies. The general purpose is to assess knowledge about determinants of MNE corruption and MNE anti-corruption efforts and relative efficacy of alternative approaches for influencing MNEs. The basic thesis of the chapter is that four interacting dimensions affect implementation and effectiveness of SDG 16 targets: organized crime and terrorism, money laundering, government and private corruption, and citizen access to information and channels of influence. There are four levers for action: international intergovernmental cooperation, domestic government action, domestic and multinational business action, and domestic and international civil society mobilization. MNEs play two very different roles in corruption. One role is to be the subject of extortion by public officials. The other role is to be the willing instigator of, or at least participant in, bribery of public officials. There is also commercial (business-to-business) bribery. MNEs have five strategic options: avoidance through not entering or by exiting countries; not engage in corruption and money laundering; actively engage in such misconduct; actively combat corruption and money laundering; and actively foster constitutional and democratic institutions. MNEs are key players in reducing corruption. However, a constellation of varying circumstances makes the task both difficult and country specific.
Improved POS Tagging Model for Malay Twitter Data based on Machine Learning Algorithm

Twitter is a popular social media platform in Malaysia that allows for 280-character microblogging.Almost everything that happens in a single day is tweeted by users.Because of the popularity of Twitter, most Malaysians use it daily, providing researchers and developers with a wealth of data on Malaysian users.This paper explains why and how this study chose to create a new Malay Twitter corpus, Malay Part-of-Speech (POS) tags, and a Malay POS tagger model.The goal of this paper is to improve existing Malay POS tags so that they are more compatible with the newly created Malay Twitter corpus, as well as to build a POS tagging model specifically tailored for Malay Twitter data using various machine learning algorithms.For instance, Support Vector Machine (SVM), Naïve Bayes (NB), Decision Tree (DT), and K-Nearest Neighbor (KNN) classifiers.This study's data was gathered by using Twitter's Advanced Search function and relevant and related keywords associated with informal Malay.The data was fed into machine learning algorithms after several stages of processing to serve as the training and testing corpus.The evaluation and analysis of the developed Malay POS tagger model show that the SVM classifier, as well as the newly proposed Malay POS tags, is the best machine learning algorithm for Malay Twitter data.Furthermore, the prediction accuracy and POS tagging results show that this research outperformed a comparable previous study, indicating that the Malay POS tagger model and its POS were successfully improved.
Use of visual-diagnostic color parameters of soils and optical reflectometry for determination of organic carbon content

&#x0D; &#x0D; &#x0D; To get additional tools for the assessment of carbon sequestration, along with the visual assessment of soil coloration with the applying of A. H. Munsell’s atlas, the analysis of color and spectral characteristics of soil using portable colorimeter NixPro and reflectometer Our Sci Reflectometer was carried out in this study. Elemental analysis of soil samples using X-ray fluorescence analysis was performed and the content of organic carbon was estimated. The spectral range of reflected light, which correlates most with the content of organic soil substance, was singled out. Based on the data, received by methods of reflectometry and colorimetry, prognostic regression models were constructed. A multiple linear regression equation with a statistically authentic luminosity predictor (L*) (R2=0.61) was obtained. It allows describing the link between the content of the organic substance in the studied soils and the parameters of the color setting system CIELab, as well as the equation describing 69 % of the data link dispersion between the integrated reflection coefficient and the organic carbon content of the soil. The link between the integral reflection coefficient and the total organic substance content was found. The most correlated spectral range with the content of organic substance – 500–632 nm was singled out. Regression models, which were based exclusively on the spectral data of pre-treated H2O2 soils, increased their predictability by 8–10 %. Approaches that can complement the tools for rapid determination of the organic carbon content in the soil were presented in the work. Researchers are expanding their arsenal of technical support for estimation of color or spectral coefficients of light reflection, based on which it is possible to conduct geospatial analysis and determine the content of the organic substance in low-humus soils with a probability of 69 %.&#x0D; &#x0D; &#x0D;
Application of Intelligent Analysis Technology of Football Video Based on Online Target Tracking Algorithm of Motion Characteristics in Football Training

Because the scene of football video is fixed and simple, the events in football video, such as shooting and offside , also have clear semantics. At the same time, they have sufficient domain knowledge and have broad application prospects. The research framework of sports video intelligence analysis is usually regarded as a three-level framework, namely, the low-level feature layer, the middle-level key primitive generation layer, and the high-level event analysis layer. A MF_O2T (Moving Feature Online Target Tracking) algorithm is proposed. First, based on the marked first frame image, this algorithm extracts a set of standardized local images from the target areas of visible and infrared images as target convolution filters, removes the main color of the stadium by using HSV color space nonuniform quantization algorithm, and extracts the histogram of the main color of players in upper and lower blocks. Experimental results show that the algorithm designed in this paper has strong robustness, can better adapt to player tracking in different scenes of football video, and meets the real-time requirements of football training.
User-tag Profile Modeling in Recommendation System via Contrast Weighted Tag Masking

User-tag profile modeling has become one of the novel and significant trends for the future development of industrial recommendation systems, which can be divided into two fundamental tasks: User Preferred Tag (UPT) and Tag Preferred User (TPU) in practical scenarios. In most existing deep learning models for user-tag profiling, the network inputs all the combined tags of the item with the user features when training but inputs only one tag with the user feature to evaluate the user's preference on a single tag when testing. This leads to data discrepancy between the training and testing samples. To address such an issue, we attempt a novel Random Masking Model (RMM) to remain only one tag at the training time by masking. However, it causes two other serious downsides. First, not all tags attached to the same item are equally predictive. Irrelevant tags may introduce noisy signals and thus cause performance degradation. Second, it neglects the impact of combined tags aggregated together, which may be an essential factor leading to user clicks. Therefore, we further propose a framework called Contrast Weighted Tag Masking (CWTM) in this work, which tackles these two issues with two modules: (i) Weighted Masking Module (WMM) introduces the importance network to compute a score for each tag attached to the item and then samples from these tags weightedly according to the score; (ii) Contrast Module (CM) makes use of a contrastive learning architecture to inherit and distill some understanding about the effect of aggregated tags. Offline experiments on four datasets (three public datasets and one proprietary industrial dataset) demonstrate the superiority and effectiveness of CWTM over the state-of-the-art baselines. Moreover, CWTM has been deployed on the training platform of Alibaba advertising systems and achieved substantial improvements of ROI and CVR by 16.8% and 9.6%, respectively.