Reviewerly/exHarmony huggingface dataset:
Low-Cost Real-Time Mental Load Adaptation for Augmented Reality Instructions - A Feasibility Study

Since the introduction of augmented reality (AR) technology, in-situ instructions for manual tasks have been a central use case for a large body of previous work. However, most implementations provide identical sets of instructions to each user disregarding the user's current mental load. This is a major issue since previous work has shown the importance and potential of an adapted instruction fidelity for manual tasks such as playing an instrument. To implement a low-cost mental load adaptation for AR instructions, we evaluated a mobile off-the-shelf electroencephalographic (EEG) device for its suitability and feasibility to measure mental load while wearing a video see-through AR head-mounted display (HMD). In a first user experiment (n=12), data of EEG power band values and proprietary performance metrics of the manufacturer were collected and analysed regarding their validity to estimate the user's mental load. Our results indicate that our setup successfully induced different levels of mental effort. The proprietary performance metrics, however, only partially reflected the participants' current mental effort and require further analysis.
Deep Morphological Gradient for Recognition of Handwritten Digits

The optical character recognition (OCR) is a technology that has evolved enormously in recent years. It is able to read characters with extreme precision. The recognition of handwritten numbers by the algorithms of deep learning is one of those places of research that has been remarkably popular. Recently, some studies are moving towards this side and give remarkable results for such recognition. We shed light on a deep morphological gradient for the problem of recognition of handwritten digits. We use a Multilayer Perceptron Network (MLP) preceded by the morphological gradient algorithm to detect the contours of the digits. This model is applied to the database MNIST of handwritten digits available on Kaggle, which consists of 70,000 images. The classification accuracy of the model was 100% with a very minimum loss of 10-5%.
Emotional Analysis of Sentences Based on Machine Learning

This paper introduces a new model for analysing the feelings sentences contained. At first, more than 100 experts in the field correctly labelled sentences and placed the same category of labels in a folder. Whereas smile was used to extract the acoustic features from the clips, it combines the global features and temporal features of utterances based on IS09_emotion and IS13_ComParE which are the baseline acoustic feature set of the 2009–2013 INTERSPEECH challenges in my Deep Neural Network model. Speech Recognition will be realized by CMUSphinx python and for text processing. It goes without saying that MonkeyLearn python will be used. Reading the text and corresponding label stored in the feature file (corresponding column), and uploading the <text, label> to MonkeyLearn. For all that, TensorFlow is used to do machine learning and the correct intention of the sentence will be output.
Improving scientific literacy through an interactive e-book: a literature review

Abstract Students’ scientific literacy in Indonesia still in low. Various attempts have been made by educators to improve student’s scientific literacy such as using different learning models; using varied facilities, media, or learning resources; and using scientific approach in the learning process. However, these efforts haven’t been able to helps students achieve the average standard of scientific literacy skills. Interactive e-book can be used as an alternative learning resources to improve scientific literacy. This literature study aims are to give overview how interactive ebook improving scientific literacy. Interactive e–book has 6 content that will help student to increase their ability on scientific literacy: Animation picture, videos, hyperlink, interactive question, student worksheet, and evaluation.
An Information Diffusion Path Construction Algorithm Based on User Characteristics and Text Characteristics

Social media platform is one of the main ways of information diffusion, and it's often filled with rumors, news and public opinions. Information diffusion path construction (IDPC) can reproduce the process of information diffusion in social media platform, which is widely used in monitoring public opinions and tracking information. To optimize IDPC, an IDPC algorithm based on user characteristics and text characteristics (IDPC-UCTC) was proposed. Information diffusion path in IDPC-UCTC is an optimization of explicit path and implicit path. The explicit path is constructed by the user reposting relationships. Implicit diffusion path is calculated by user interests' similarity, topic similarity, user interaction and text similarity. Previous studies on IDPC are only based on text characteristics such as reposting relationships and text similarity. But IDPC-UCTC also considers the user characteristics such as interests' similarity, topic similarity and interaction, which helps to find more potential diffusion relations. The experimental results show that the IDPC-UCTC can find more diffusion relations, and improved topic relevance by about 10%.
Imputation Methods Outperform Missing-Indicator for Data Missing Completely at Random

Missing data is a ubiquitous cross-domain problem persistent in the context of big data analytics. Approaches to deal with missing data can be partitioned into methods that impute substitute values and methods that introduce missing-indicator variables. In this work, we demonstrate that the missing-indicator method underperforms compared to any of the other imputation methods. Most studies either focus on minimizing the squared error for the imputed values or use the missing-indicator in machine learning tasks as an assumed best practice. We study the difference between the missing-indicator method and various imputation methods on classifier learning performance when data are missing completely at random (MCAR). We compute the classifier performance over 22 complete classification datasets of varying sample size and dimensionality from an open data repository, simulating synthetic missingness at different percentages. We compare classifier performances yielded by applying mean, median, linear regression, and tree-based regression imputation methods with the corresponding performances yielded by applying the missing-indicator approach. The impact is measured with respect to three different classifiers, namely a tree-based ensemble classifier, radial basis function support vector machine classifier and k-nearest neighbours classifier. With these experiments, we conclude that given a classification problem with missing numerical data under MCAR, the missing-indicator method provides a performance decrease and should be, therefore, dismissed as a missing data-handling approach in the MCAR scenario.
Development of an IoT-Based Tourism Guide System

Tourism as an industry is multi-segmented in nature and is considered to be an economic bonanza. It not only helps in generation of national income but also contributes in the expansion of employment opportunities, improving level of tax revenue, foreign exchange and thereby transforming regional economy. The sector not only accords the economy directly but also has significant indirect and induced effects. Indian Tourism is an interesting journey of architectural and cultural escape from the reality into the glorious past and humungous variety in textiles, cuisines, art forms and folk tradition, to list a few. India attracts a lot of tourist every year. With the present scheme of Digitalization, Digital India initiative, coupling the tourism sector with Internet of things (IoT) and other technological advancements, will further the economic prospects of the sector in discussion. In this paper, a prototype named, Bharat Darshan, has been elaborated which described the integration of tourism sector with IoT. The paper gives an insight into the idea behind the prototype, its features and the architecture of the scheme implemented by the prototype for the Tourism management and support via RFID and IoT, thereby making “India Incredible.”
Selective Area Encryption using Machine Learning Technique

Detection of object and recognition of objects in real world computing environment is one of the challenging tasks in computer vision. To solve this task there are many challenges in designing algorithm, we have to introduce different and innovative techniques to detect objects in natural environments. Image security is of high importance in various applications including military, medical and many others. Security is one of the most challenging aspects in the internet and network applications. Encrypting entire image is very slow. Selective encryption is a scheme which intends to save computational power, network resources, and execution time. To encrypt object selectively object detection techniques are used. This technique is one of the most promising solutions to increase the speed of encryption as compared to the traditional encryption techniques. Selective encryption is helpful for the multimedia contents like image, video and audio. The compression ratio achieved is very high, since we store only the residues of extracted pixel of an image. So this paper discussed about Selective encryption with object detection technique.
Interactive and Immersive Image-Guided Control of Interventional Manipulators with a Prototype Holographic Interface

The emerging potential of augmented reality (AR) to improve 3D medical image visualization for diagnosis, by immersing the user into 3D morphology is further enhanced with the advent of wireless head-mounted displays (HMD). Such information-immersive capabilities may also enhance planning and visualization of interventional procedures. To this end, we introduce a computational platform to generate an augmented reality holographic scene that fuses pre-operative magnetic resonance imaging (MRI) sets, segmented anatomical structures, and an actuated model of an interventional robot for performing MRI-guided and robot-assisted interventions. The interface enables the operator to manipulate the presented images and rendered structures using voice and gestures, as well as to robot control. The software uses forbidden-region virtual fixtures that alerts the operator of collisions with vital structures. The platform was tested with a HoloLens HMD in silico. To address the limited computational power of the HMD, we deployed the platform on a desktop PC with two-way communication to the HMD. Operation studies demonstrated the functionality and underscored the importance of interface customization to fit a particular operator and/or procedure, as well as the need for on-site studies to assess its merit in the clinical realm.
Fast Dataset Collection Approach for Articulated Equipment Pose Estimation

Struck-by accidents are potential safety concerns on construction sites and require a robust machine pose estimation. The development of deep learning methods has enhanced the human pose estimation that can be adapted for articulated machines. These methods require abundant dataset for training, which is challenging and time-consuming to obtain on-site. This paper proposes a fast data collection approach to build the dataset for excavator pose estimation. It uses two industrial robot arms as the excavator and the camera monopod to collect different excavator pose data. The 3D annotation can be obtained from the robot's embedded encoders. The 2D pose is annotated manually. For evaluation, 2,500 pose images were collected and trained with the stacked hourglass network. The results showed that the dataset is suitable for the excavator pose estimation network training in a controlled environment, which leads to the potential of the dataset augmenting with real construction site images.
Estimation of greenhouse gas emissions from solid waste management and wastewater treatment in the Nizam Zachman Fishery Port, Jakarta, Indonesia

Abstract Nizam Zachman is a modern fishery port located in Muara Baru, Jakarta, Indonesia. Some of its facilities include a temporary collection of solid waste and wastewater treatment plants, emitting greenhouse gases that lead us to climate change by warming up the global temperature. In this study, greenhouse gas emissions are estimated from these two facilities by using the IPCC Tier 1 method and emission factor calculation. By estimating greenhouse gas in this area, it will give knowledge and convicing to make greenport as a consideration of the strategy to reduce greenhouse gas emission, starting from waste and wastewater management. Solid waste treatment comprises open dumping, recycling, and transportation of wastes to the temporary landfill in this area. With a total waste of 5, 411.4 tons/year, greenhouse gas emissions from open dumping and transportation are estimated to be 14, 340.2 and 22.3 tons CO2eq/year, respectively. Meanwhile, recycling activities contribute to 143 tons CO 2 eq/year of greenhouse gas emissions. In addition, the estimated greenhouse gas emissions include emissions from wastewater treatment, discharge of industrial wastewater to waterways via the drainage system, and domestic wastewater treatment by using a septic tank. In addition, greenhouse gas emissions from wastewater treatment, drainage system, and septic tanks are estimated to be 2, 830; 108.7; and 3.2 tons CO 2 eq/year, respectively. Based on the estimation, solid waste treatment generates 83% of the total greenhouse gas emissions, while wastewater treatment generates only 17% of the total emissions. Herein, composting, increase of recycling, and utilization of methane are recommended to reduce the greenhouse gas emissions from waste management.
Optimizing Power and Energy Efficiency in Cloud Computing

With the exponential growth in cloud computing, the steadily increasing amount of power consumption due to the use of physical and virtual machines is becoming a serious challenge. In this context, we report a study on optimizing the power and energy efficiency of physical and virtual machines in a cloud computing environment. The energy profile of different workloads is thoroughly investigated under different configurations. This paper presents the findings from our study which provides a good understanding of how different workloads affect power and energy efficiency of both physical and virtual machines.
Profile analysis of data literacy capability based on NGSS junior high school students in Takalar, South Sulawesi

Abstract This study aims to identify the data literacy ability of junior high school students associated with NGSS learning standards. This study uses a qualitative descriptive study to analyze the results of the test of students’ data literacy abilities in several junior high schools. The method used is a test with a sample determined by purposive sampling technique. The subjects of the study sample consisted of 74 junior high school students in class VIII 2019/2020. The research instrument is in the form of 10 essay data literacy questions. The results of the analysis of the ability of data literacy with the highest percentage of 34.93% in the indicators of decision making and the lowest percentage were in the indicators evaluating decisions of 22.83%. For the percentage of the dimensions of the NGSS, it showed a result of 24.31% in the indicators of analyzing and interpreting data and the dimension of cause and effect obtained 34.42%. The results of this study indicate that the ability of literacy based on NGSS grade VIII students in Junior High School Takalar Regency is in the low category. Thus, the data literacy ability of students in learning science needs to be developed.
Symbiotic Organisms Search Optimization for Multilevel Image Thresholding

Determination of optimum thresholds is the prime concern of any multilevel image thresholding technique. The traditional methods for multilevel thresholding are computationally expensive, time-consuming, and also suffer from lack of accuracy and stability. To address this issue, the authors propose a new methodology for multilevel image thresholding based on a recently developed meta-heuristic algorithm, Symbiotic Organisms Search (SOS). The SOS algorithm has been inspired by the symbiotic relationship among the organism in nature. This article has utilized the concept of the symbiotic relationship among the organisms to optimize three objective functions: Otsu's between class variance and Kapur's and Tsallis entropy for image segmentation. The performance of the SOS based image segmentation algorithm has been evaluated using a set of benchmark images and has been compared with four recent meta-heuristic algorithms. The algorithms are compared in terms of effectiveness and consistency. The quality of the algorithms has been estimated by some well-defined quality metrics such as peak signal-to-noise ratio (PSNR), structure similarity index (SSIM), and, feature similarity index (FSIM). The experimental results of the algorithms reveal that the balance of intensification and diversification of the SOS algorithm to achieve the global optima is better than others.
Multiresolution Wavelet Transform Based Anisotropic Diffusion for Removing Speckle Noise in a Real-Time Vision-Based Database

In this research article, a novel algorithm is introduced to identify the noisy pixels in video frames and correct them to enhance video quality. The technique consists of three stages: fragmentation of the video sequences to respective 2D frames, noisy pixel identification in the 2D frames, and denoising the pixels to obtain original pixels. Due to the complexity in the background and the change in appearance of the body in motion, noise variation occurs. Various researchers discuss that in order to denoise the video sequences, spatio-temporal filtering is required which identifies noise and preserves the edges. In the first stage, the video sequences are analyzed for the removal of redundant frames. This is done by using the video fragmentation process in the MATLAB toolbox. In the next stage, color smoothing is applied to the target frames for processing the flat regions and identifying all the noisy pixels. In the final stage, an improvised multiresolution wavelet transform based anisotropic diffusion filtering is applied which enhances the denoising process in horizontal, vertical, and diagonal sub bands of the video frame signal. The proposed technique can remove the speckle noise and estimate the motion by preserving the minute details of the processed video frames.
E-learning as Part of Self-Regulated Foreign Languages Acquisition

The paper considers a case study whether students of Bauman Moscow State Technical University (BMSTU) master foreign languages due to their autonomous different aspect activities via both traditional and computer employed language activities by the use of Internet and other digital technology services. The current research explores how students apply self-independent studies in spare time through the immersion into digital media (computer learning software, pocket edition software, e-books, magazines, e-mail messages, WhatsApp and social net conversations, videos, podcasts, You Tube documentaries and movies, news broadcasts, texts, graphics, drawings the current data attachments and more). The purpose of the study is assessing the students current language expertise and its enlarging by means of additional learning matter via asynchronous e-learning. Methods used for the research included a theoretical study of the e-learning issue as well as the empirical data instruments (google. form questionnaire, English for General Purposes test compiled in accordance with the International standards, pedagogical monitoring, experiment, results calculation in Microsoft Excel 2010 program). Students majoring in Engineering and economics (n=155) participated in the experiment on comparing their linguistics outcomes obtained from the comprehensive lifespan learning both by teacher-management and self-independent immersion to e-learning. The research results show that self-regulated engagement in language both by traditional didactic materials and authentic e-materials help develop students' collaborative and communicative skills. The motivation for e-learning can be an efficient tool for further holistic and professional development of students.
Anonymized Credit Card Transaction Using Machine Learning Techniques

In the last few years, anonymized credit card transactions have grown more threats that have caused serious consequences in the finance and banking sectors. Due to the dramatical growth of the online payment system, now many banks and financial sectors are implementing various types of automatic fraud detection system to analyze the fraud transactions; machine learning (ML) is one of the promising approaches to find out the fraud transactions. Machine learning methodologies have proved the most promising solution for anonymized transactions. This paper comparatively analyzes the basic machine learning algorithms which include SVM, LDA, QDA, DT, and RF for fraud detection. At the same time, some of the modern open-sourced boosting machine learning algorithms which include XGBoost, LGBoost, and CatBoost are also implemented.
Development and Application of VR Support System for Medical Students

It is a must-have content for medical students to attend school at the surgery site. It enables students to deepen their study and understanding of the surgical content by observing the surgical procedure. But usually a long-time Da Vinci Surgical System observation can only see the in-vivo surgery shown on the screen, lack of detailed explanations makes it difficult for students to concentrate on the details of the operation. The use of the VR support system to simultaneously demonstrate and explain the surgical status makes it easier for students to understand and mitigate the fatigue and distraction of long-term observation.
Face Recognition based on Linear Projective Non-negative Matrix Factorization with Kullback-Leibler Divergence

Abstract In order to solve the problem of feature extraction and classification of Non-negative Matrix Factorization (NMF) in face recognition, a Linear Projective Non-negative Matrix Factorization method based on Kullback-Leibler divergence (LP-NMF-DIV) is proposed. In LP-NMF-DIV, an objective function of Kullback-Leibler divergence is considered. The Taylor series expansion and the Newton iteration formula are used to find the root formula. The iterative algorithm of basis matrix and linear transformation matrix are derived, and the convergence of the algorithm is proved. The results of experimental show that the algorithm is convergent. In face recognition, compared with some typical NMF methods, the algorithm has high recognition accuracy; when the ranks of the basis matrices are set to different values, the algorithm is stable. The method for LP-NMF-DIV is effective.