Reviewerly/exHarmony huggingface dataset:
The Ethics of Realism as a New Media Language in Immersive Media

This work explores the ethical and moral limits of practicing realism in immersive nonfiction. To establish these practices, the nonfiction media ecosystem is analyzed from traditional to emerging immersive forms. Four significant forms of nonfiction works are discussed that reflect different ethics of realism: documentary, journalism, education, and cultural heritage. Through the description and presentation of each form, a provisional set of elements, variables, indicators and parameters that impact practices and ethics of realism are presented. These compositional elements can be implemented in the design, development and production of experiences for audiences that respect social, cultural, political, physical, and material realities.
Dancer Tracking Algorithm in Ethnic Areas Based on Multifeature Fusion Neural Network

Due to the complex posture changes in dance movements, accurate detection and tracking of human targets are carried out in order to improve the guidance ability of dancers in ethnic areas. A multifeature fusion-based tracking algorithm for dancers in ethnic areas is proposed. The edge contour model of video images of dancers in ethnic areas is detected, and the video tracking scanning imaging model of dancers in ethnic areas is constructed. The video images of dancers in ethnic areas are enhanced based on the initial contour distribution, and a visual perception model of dancers tracking images in ethnic areas is established. To improve the algorithm’s estimation of complex poses and finally complete the dance movement recognition, a feature pyramid network is used to extract the features of dance movements, and then, a multifeature fusion module is used to fuse multiple features. The tracking algorithm proposed in this paper has higher robustness than other algorithms and effectively reduces the error samples generated during the tracking process, thus improving the accuracy of long-term tracking.
Revisiting the Detection of Lateral Movement through Sysmon

This work attempts to answer in a clear way the following key questions regarding the optimal initialization of the Sysmon tool, towards the identification of Lateral Movement in the MS Windows ecosystem. First, from an expert&amp;rsquo;s standpoint and with reference to the relevant literature, what are the criteria of determining the possibly optimal initialization features of the Sysmon&amp;rsquo;s event monitoring tool, which are also applicable as custom rules within the config.xml configuration file? Second, based on the identified features, how can a functional configuration file, able to identify as many LM variants as possible, be generated? To answer these questions, we relied on the MITRE ATT&amp;amp;amp;CK knowledge base of adversary tactics and techniques, and focused on the execution of the nine commonest LM methods. The conducted experiments, performed on a properly configured testbed, suggested a great number of interrelated networking features, that were implemented as custom rules in the Sysmon&amp;rsquo;s config.xml file. Moreover, by capitalizing on the rich corpus of the 870K Sysmon logs collected, we create and evaluate in terms of TP and FP rates an extensible Python .evtx file analyzer, dubbed PeX, which can be used towards automatizing the parsing and scrutiny of such voluminous files. Both the .evtx logs dataset and the developed PeX tool are provided publicly for further propelling future research in this interesting and rapidly evolving field.
The EPOS Multi-Scale Laboratories: A FAIR Framework for stimulating Open Science practice across European Earth Sciences Laboratories

The Multi-scale Laboratories (MSL) are a network of European laboratories bringing together the scientific fields of analogue modeling, paleomagnetism, experimental rock and melt physics, geo- chemistry and microscopy. MSL is one of nine (see below) Thematic Core Services (TCS) of the European Plate Observing System (EPOS). The overarching goal of EPOS is to establish a compre- hensive multidisciplinary research platform for the Earth sciences in Europe. It aims at facilitating the integrated use of data, models, and facilities, from both existing and new distributed pan European Research Infrastructures, allowing open access and transparent use of data. The TCS MSL network allows researchers to collaborate with other labs and scientists. By becoming part of the rapidly growing TCS MSL network, new laboratories are offered a platform to showcase their research data output, laboratory equipment and information, and the opportunity to open laboratories to guest researchers through the Transnational Access (TNA) program. The EPOS Multi-scale laboratories offer researchers a fully operational data publication chain tailored to the specific needs of laboratory research, from a bespoke metadata editor, through dedi­ cated, (domain­specific) data repositories, to the MSL Portal showcasing these citable data publica- tions. During this process the data publications are assigned with digital object identidiers (DOI), published with open licenses (e.g. CC BY 4.0) and described with standardized and machine-read- able rich metadata (following the FAIR Principles to make research data Findable, Accessible, Interoperable and Reusable. The TCS MSL is currently working on linking these data publications to the EPOS Central Portal1, the main discovery and access point for European multi-disciplinary data, and on increasing the number of connected data repositories.
Impact of Leadership Styles on Job Satisfaction: Case Study

This study aimed to investigate and compare the impact of transformational leadership and the impact of transactional leadership, on job satisfaction among workers in at Istiklal Private Hospital in Amman-Jordan.To fulfill this objective, the researcher developed two hypotheses to be tested using multiple regression analysis as a tool for data analysis.Data for this research was collected using a questionnaire developed for this purpose and distributed to the sample and a response rate of around 80.6% was realized.The data was entered and analyzed using the SPSS version 25 statistical program.Findings indicated that the transformational and transactional leadership styles both have significant impact on the employees' job satisfaction.But the effect of transactional style was more significant than transformational style.The descriptive statistics results showed that the level of job satisfaction has high mean value, which means that most of employees in the private hospital in Amman-Jordan were satisfied with their job.The current study gives some direction to carry out future research for studying different leadership styles, in different context and culture.The results indicated that the leadership style does not alone affect employee satisfaction, and the researcher would like to recommend a further research studies on other factors such as wages, training, job growth, the environment, and others to understand the extent of the impact of these factors on employee satisfaction.
The Energy Pairs Theory

The acceptable notion today is that Electromagnetic waves from separate sources cannot consolidate [4]. However, the author of this article published several articles [1], [2], [3], [5] which argue that Electromagnetic (EM) waves from separate sources do consolidate. In addition to the scenarios described in articles [1], [2], [3], Article [5] adds further support to the statement that EM waves from separate sources can consolidate, support that everybody can experience, from everyday experience, every day, which can also be recorded on photos. This scenario is presented again, also in this article. However, if EM waves from separate sources do consolidate, this seems to embed paradoxes, which must be addressed, because the Energy Conservation Principle seems to be violated. The Energy Pairs Theory, proposed initially by the author of this article in article [1], and presented again briefly in this article, proposes a framework which resolves and explains the paradoxes embedded in consolidations of EM waves from separate sources, and other paradoxes and unanswered and open questions, as presented already in article [1], and presented briefly again in this article.
Media literacy as a strategy to counter fake news

This study was conducted with the objective of integrating media literacy in the teaching-learning process to develop students' critical thinking in the face of fake news. For its execution, a non-experimental design of descriptive transectional type was applied, using quantitative methodology, with the implementation of a survey to 596 high school students of Loja-Ecuador. The results obtained showed that students mainly access or receive information from social networks and that they are aware that these are not a reliable source; that although they try to look for more sources of information they are not able to discern between real information and fake news and, therefore, they limit themselves to observe, which means that they are not making an adequate and efficient use of the media available in today's knowledge society.
An Multi-Sensors 3D Detection Network Using Guidance-Point-Based Feature Fusion

An accurate and efficient 3D object detection system is crucial to for the autonomous vehicle. However, due to the complexity of the environment, a single sensor, such as LIDAR or camera, cannot meet the safety requirements of autonomous driving. In this paper, a two stage 3D detection network using Guidance-Point-Based feature fusion is proposed. For the first stage network, firstly, the features in the image space are converted to BEV(bird's-eye-view) through the Guidance-Point-Based feature mapping module designed in this paper. Secondly, the LIDAR feature and the camera feature in BEV are fused through the adaptive fusion module, and finally a Cneter-Based strategy is used for detection. In the second stage, keypointed features are used to further refine the objects output by the first stage network. Evaluation on the nuScenes dataset shows that the network we proposed achieves higher accuracy with less additional time.
A Comparison of Automatic Labelling Approaches for Sentiment Analysis

Labelling a large quantity of social media data for the task of supervised machine learning is not only time-consuming but also difficult and expensive. On the other hand, the accuracy of supervised machine learning models is strongly related to the quality of the labelled data on which they train, and automatic sentiment labelling techniques could reduce the time and cost of human labelling. We have compared three automatic sentiment labelling techniques: TextBlob, Vader, and Afinn to assign sentiments to tweets without any human assistance. We compare three scenarios: one uses training and testing datasets with existing ground truth labels; the second experiment uses automatic labels as training and testing datasets; and the third experiment uses three automatic labelling techniques to label the training dataset and uses the ground truth labels for testing. The experiments were evaluated on two Twitter datasets: SemEval-2013 (DS-1) and SemEval-2016 (DS-2). Results show that the Afinn labelling technique obtains the highest accuracy of 80.17% (DS-1) and 80.05% (DS-2) using a BiLSTM deep learning model. These findings imply that automatic text labelling could provide significant benefits, and suggest a feasible alternative to the time and cost of human labelling efforts.
Stacked Ensemble Model for Enhancing the DL based SCA

Deep learning (DL) has proven to be very effective for image recognition tasks, with a large body of research on various models for object classification.The application of DL to side-channel analysis (SCA) has already shown promising results, with experimentation on open-source variable key datasets showing that secret keys for block ciphers like Advanced Encryption Standard (AES)-128 can be revealed with 40 traces even in the presence of countermeasures.This paper aims to further improve the application of DL in SCA, by enhancing the power of DL when targeting the secret key of cryptographic algorithms when protected with SCA countermeasures.We propose a stacked ensemble model, which trains the output probabilities and Maximum likelihood score of multiple traces and/or sub-models to improve the performance of Convolutional Neural Network (CNN)-based models.Our model generates state-of-the art results when attacking the ASCAD variable-key database, which has a restricted number of training traces per key, recovering the key within 20 attack traces in comparison to 40 traces as required by the state-of-the-art CNN-based model with Plaintext feature extension (CNNP)-based model.During the profiling stage an attacker needs no additional knowledge of the implementation, such as the masking scheme or random mask values, only the ability to record the power consumption or electromagnetic field traces, plaintext/ciphertext and the key is needed.However, a two step training procedure is required.Additionally, no heuristic pre-processing is required in order to break the multiple masking countermeasures of the target implementation.
A Lightweight Machine Learning Pipeline for LiDAR-simulation

Virtual testing is a crucial task to ensure safety in autonomous driving, and sensor simulation is an important task in this domain. Most current LiDAR simulations are very simplistic and are mainly used to perform initial tests, while the majority of insights are gathered on the road. In this paper, we propose a lightweight approach for more realistic LiDAR simulation that learns a real sensor's behavior from test drive data and transforms this to the virtual domain. The central idea is to cast the simulation into an image-to-image translation problem. We train our pix2pix based architecture on two real world data sets, namely the popular KITTI data set and the Audi Autonomous Driving Dataset which provide both, RGB and LiDAR images. We apply this network on synthetic renderings and show that it generalizes sufficiently from real images to simulated images. This strategy enables to skip the sensor-specific, expensive and complex LiDAR physics simulation in our synthetic world and avoids oversimplification and a large domain-gap through the clean synthetic environment.
Analisis Hubungan Antara Makro Ekonomi dan Indeks Sektoral di Bursa Efek Indonesia

This study aims to determine the relationship between the rupiah exchange rate, world oil prices, inflation and the selected sectoral indices, namely the mining sector, the financial sector, as well as the basic and chemical industry sectors. This study uses a stationary test, cointegration test, and Granger causality test with Eviews10 software. The population of this research is sectoral index, rupiah exchange rate, inflation and world oil prices. While the samples of this research are the rupiah exchange rate, world oil prices, inflation, the mining sector, the financial sector, and the basic and chemical industry sector for the period January 2017 – March 2021. The data used in this study is secondary data in the form of time series data. Data collection techniques used the documentation method taken in the form of monthly data through the official websites www.idx.co.id, www.bi.go.id, and www.investing.com. The results of this study indicate that the rupiah exchange rate, world oil prices, inflation, the mining sector, the financial sector, as well as the basic and chemical industry sectors are stationary at the first difference level. The results of the Johansen Test show that there is no cointegration between variables in this study. There is a causality between inflation and the mining sector, the basic and chemical industry sector and the rupiah exchange rate as well as a causality between the financial sector, the mining sector, and the basic and chemical industrial sector and world oil prices. Furthermore, the rupiah exchange rate, world oil prices, and inflation have an impact on the mining sector, the financial sector, as well as the basic and chemical industry sectors.
Cycle Self-Training for Semi-Supervised Object Detection with Distribution Consistency Reweighting

Recently, many semi-supervised object detection (SSOD) methods adopt teacher-student framework and have achieved state-of-the-art results. However, the teacher network is tightly coupled with the student network since the teacher is an exponential moving average (EMA) of the student, which causes a performance bottleneck. To address the coupling problem, we propose a Cycle Self-Training (CST) framework for SSOD, which consists of two teachers T1 and T2, two students S1 and S2. Based on these networks, a cycle self-training mechanism is built, i.e., S1$\rightarrow $T1$\rightarrow $S2$\rightarrow $T2$\rightarrow $S1. For S$\rightarrow $T, we also utilize the EMA weights of the students to update the teachers. For T$\rightarrow $S, instead of providing supervision for its own student S1(S2) directly, the teacher T1(T2) generates pseudo-labels for the student S2(S1), which looses the coupling effect. Moreover, owing to the property of EMA, the teacher is most likely to accumulate the biases from the student and make the mistakes irreversible. To mitigate the problem, we also propose a distribution consistency reweighting strategy, where pseudo-labels are reweighted based on distribution consistency across the teachers T1 and T2. With the strategy, the two students S2 and S1 can be trained robustly with noisy pseudo labels to avoid confirmation biases. Extensive experiments prove the superiority of CST by consistently improving the AP over the baseline and outperforming state-of-the-art methods by 2.1% absolute AP improvements with scarce labeled data.
A novel conservative chaos driven dynamic DNA coding for image encryption

In this paper, we propose a novel conservative chaotic standard map-driven dynamic DNA coding (encoding, addition, subtraction and decoding) for the image encryption. The proposed image encryption algorithm is a dynamic DNA coding algorithm i.e., for the encryption of each pixel different rules for encoding, addition/subtraction, decoding etc. are randomly selected based on the pseudorandom sequences generated with the help of the conservative chaotic standard map. We propose a novel way to generate pseudo-random sequences through the conservative chaotic standard map and also test them rigorously through the most stringent test suite of pseudo-randomness, the NIST test suite, before using them in the proposed image encryption algorithm. Our image encryption algorithm incorporates a unique feed-forward and feedback mechanisms to generate and modify the dynamic one-time pixels that are further used for the encryption of each pixel of the plain image, therefore, bringing in the desired sensitivity on plaintext as well as ciphertext. All the controlling pseudorandom sequences used in the algorithm are generated for a different value of the parameter (part of the secret key) with inter-dependency through the iterates of the chaotic map (in the generation process) and therefore possess extreme key sensitivity too. The performance and security analysis has been executed extensively through histogram analysis, correlation analysis, information entropy analysis, DNA sequence-based analysis, perceptual quality analysis, key sensitivity analysis, plaintext sensitivity analysis, etc., The results are promising and prove the robustness of the algorithm against various common cryptanalytic attacks.
Secure Storage and Sharing of COVID-19 Data in Health Facilities using AES-Cryptography and Audio Steganography

Well over two years after the breakout and subsequent declaration of the dreaded COrona VIrus Disease 2019 as a pandemic by the World Health Organisation (WHO), the nations and continents of the world are still suffering from the incredible damage the disease has done to human life and the global economy at large.As the world still grapples with all these, most of the strategies that have been proposed and adopted for eliminating or ameliorating the effect of COVID-19 involves real time data collation, storage (in hospital databases) and even sharing (among hospitals) across untrusted/unprotected open channels.A very significant concern that needs serious and urgent attention than it is currently getting is the security of the COVID-19 data while in store or while being exchanged, as well as the privacy of persons involved.Existing solutions still suffer susceptibility to fraudulent security/privacy breaches.This study thus presents an efficient/robust audio steganography system for secure storage and sharing of COVID-19 related data.In the developed system, sensitive/private COVID-19 patients' data is first encrypted using the Advanced Encryption Standard (AES) crypto-scheme, then the resultant cipher-text is compressed using the Lempel-Ziv Welch and Huffman's Compression Algorithms.Thereafter, the compressed file is embedded in a suitable cover (audio-file) to obtain an output which is not distinguishable from the original audio cover file.This output can then be safely stored or shared as necessary.The Signal-to-Ratio (SNR) obtained from our experiments reveal that, the new system has little or no distortion in its outputs.
Oversampling Techniques in Machine Learning Detection of Credit Card Fraud

More than ever before, the trend of doing things online has been explored and successfully implemented in many areas, including online shopping, online learning, working online, to name but a few.However, it has brought with it challenges, including the fraudulent use of credit cards in online purchases, the challenge of academic integrity in online learning, especially in doing exams online, and how to keep people in engaged in meetings, when working and studying online, and still give them adequate privacy.This paper deals with the attempt to detect the fraudulent use of credit cards in a timely manner, to avoid as much negative effects in the world of E-commerce and help maintain consumer confidence.Thus, in the current study, machine learning algorithm LightGBM has been used to detect fraudulent credit card transactions from a real-life dataset containing credit card transactions of the customers.The performance of this classifier is compared with two state-of-the-art classifiers -Decision Tree, and Random Forests, which are extensively used for solving such problems.Since there is data imbalance between fraudulent and nonfraudulent class, the data sampling technique used is the Synthetic Minority Oversampling Technique (SMOTE).SMOTE Oversampling performed best on all classifiers and LightGBM obtained precision value of 1 for both fraudulent and non-fraudulent class.
Markerless Augmented Reality (AR) pada Media Pembelajaran Pengenalan Komponen Transmisi Manual Mobil

Nowdays technology has developed very rapidly. Technological development that are currently being developed by many game developers and other applications, is Augmented Reality (AR). AR can be used as an alternative learning media, such as for the introduction of manual transmission components in cars. The study was aimed to develop a learning media application for the introduction of manual transmission components in cars and to test the feasibility of applications to support learning based on Markerless Augmented Reality. This application development method used SDLC or System Development Life Cycle software. The SDLC model that used in this study was the Waterfall model. SDLC consists of the planning, analysis, design, implementation, testing and maintenance. This reaction produced an AR application of Manual Car Transmission which contains material on the introduction of car manual transmission components. Testing applications included blackbox, media and material feasibility, and user response testing. The blackbox testing result got 100 %. The media feasibility analysis show that the assessment of media experts was 87,5 %, material expert 94,4 %, and user response was 85,47 %. From that the AR learning media application manual transmission of cars is categorized as “feasible” to be used as learning media applications.