Reviewerly/exHarmony huggingface dataset:
Modeling The Dynamic Note-Taking Behavior of MOOC Video Learners via Representation Learning

Keeping notes is an important learning method for MOOC learners that can bring fun and new ideas to themselves and others. We identified the opportunity lied in the mass data of dynamic MOOC video notes, which can help us better understand the behavior and evaluate effectiveness of online learning. In this paper, we propose the task of modeling MOOC learners' dynamic video note-taking behavior and solving some subsequent down-stream tasks, which to the best of our knowledge is the first in literature. To model the video-commenting behavior, most existing methods consider singular factors of either video frames or surrounding texts. Moreover, they only consider the information `visible' to a learner, while in the scenario of MOOC what the lecturer speaks is sometimes more informative than what the camara records. To overcome these shortcomings, an extended neural model to jointly encode note of not only visual/text content but also the speech content is proposed. Evaluation on a large real-world dataset shows that our model has better performance than previous methods.
Time and Action Co-Training in Reinforcement Learning Agents

In formation control, a robot (or an agent) learns to align itself in a particular spatial alignment. However, in a few scenarios, it is also vital to learn temporal alignment along with spatial alignment. An effective control system encompasses flexibility, precision, and timeliness. Existing reinforcement learning algorithms excel at learning to select an action given a state. However, executing an optimal action at an appropriate time remains challenging. Building a reinforcement learning agent which can learn an optimal time to act along with an optimal action can address this challenge. Neural networks in which timing relies on dynamic changes in the activity of population neurons have been shown to be a more effective representation of time. In this work, we trained a reinforcement learning agent to create its representation of time using a neural network with a population of recurrently connected nonlinear firing rate neurons. Trained using a reward-based recursive least square algorithm, the agent learned to produce a neural trajectory that peaks at the “time-to-act”; thus, it learns “when” to act. A few control system applications also require the agent to temporally scale its action. We trained the agent so that it could temporally scale its action for different speed inputs. Furthermore, given one state, the agent could learn to plan multiple future actions, that is, multiple times to act without needing to observe a new state.
Region-wise loss for biomedical image segmentation

We propose Region-wise (RW) loss for biomedical image segmentation. Region-wise loss is versatile, can simultaneously account for class imbalance and pixel importance, and it can be easily implemented as the pixel-wise multiplication between the softmax output and a RW map. We show that, under the proposed RW loss framework, certain loss functions, such as Active Contour and Boundary loss, can be reformulated similarly with appropriate RW maps, thus revealing their underlying similarities and a new perspective to understand these loss functions. We investigate the observed optimization instability caused by certain RW maps, such as Boundary loss distance maps, and we introduce a mathematically-grounded principle to avoid such instability. This principle provides excellent adaptability to any dataset and practically ensures convergence without extra regularization terms or optimization tricks. Following this principle, we propose a simple version of boundary distance maps called rectified Region-wise (RRW) maps that, as we demonstrate in our experiments, achieve state-of-the-art performance with similar or better Dice coefficients and Hausdorff distances than Dice, Focal, weighted Cross entropy, and Boundary losses in three distinct segmentation tasks. We quantify the optimization instability provided by Boundary loss distance maps, and we empirically show that our RRW maps are stable to optimize. The code to run all our experiments is publicly available at: https://github.com/jmlipman/RegionWiseLoss.
Video captioning with stacked attention and semantic hard pull

Video captioning, i.e. , the task of generating captions from video sequences creates a bridge between the Natural Language Processing and Computer Vision domains of computer science. The task of generating a semantically accurate description of a video is quite complex. Considering the complexity, of the problem, the results obtained in recent research works are praiseworthy. However, there is plenty of scope for further investigation. This paper addresses this scope and proposes a novel solution. Most video captioning models comprise two sequential/recurrent layers—one as a video-to-context encoder and the other as a context-to-caption decoder. This paper proposes a novel architecture, namely Semantically Sensible Video Captioning (SSVC) which modifies the context generation mechanism by using two novel approaches—“stacked attention” and “spatial hard pull”. As there are no exclusive metrics for evaluating video captioning models, we emphasize both quantitative and qualitative analysis of our model. Hence, we have used the BLEU scoring metric for quantitative analysis and have proposed a human evaluation metric for qualitative analysis, namely the Semantic Sensibility (SS) scoring metric. SS Score overcomes the shortcomings of common automated scoring metrics. This paper reports that the use of the aforementioned novelties improves the performance of state-of-the-art architectures.
Implementation of Deep Learning Based Sentiment Classification and Product Aspect Analysis

With the increase in E-Commerce businesses in the last decade,the sentiment analysis of product reviews has gained a lot of attention in linguistic research. In literature, the survey depicts the majority of the research done emphasizes on mere polarity identification of the reviews. The proposed system emphasized on classifying the sentiment polarity and the product aspect identification from the reviews. Proposed work experimented with traditional machine learning techniques as well as deep neural networks such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long Short Term Memory(LSTM) Networks. The proposed system gives a better understanding of these algorithms by comparing the outcomes. The Deep Learning approach in the proposed work successfully provides a mechanism which identifies the review polarity and intensity of the reviews and also analyses the short form words used by people in the reviews. The experimental results in this work, applied on amazon product dataset, shows that the LSTM model works the best for sentiment analysis and intensity of reviews with 93% accuracy. This research work also predicts polarity for short-form word reviews which is the common trend these days while writing the reviews.
Meta-heuristic Algorithm As Feature Selector For Convolutional Neural Networks

The huge popularity of heuristics contributes not only to the improvement and modeling of new solutions but also to their adaptation to selected goals. Recent years have shown the popularity of their use also in machine learning as a training algorithm or allowing for the selection of optimal architecture or hyper-parameters. In this paper, we propose an adaptation of a nature-inspired algorithm for preprocessing images in a parallel way for obtaining higher classification results. The proposed idea is based on analyzing images by heuristic representative which is Red Fox Optimization Algorithm and returning a specific value. These values are used in deciding to classify the entire image or trim it to eliminate unnecessary objects. We modeled this solution and evaluated using the learning transfer method for VOC 2007 dataset. The obtained results were compared on selected classes to show the advantages of a proposal.
Exploring the Effect of Augmented Reality on Verbal Communication and Code-editing in a Collaborative Programming Task

Augmented reality (AR) has been shown its prominence in immersive learning across various fields of education, especially in STEM curricula. Researchers have demonstrated that AR visualization can improve learning and cognitive outcomes by multimodal interaction. However, compared to passive modes of learning like reading and viewing, there is little research focused on the effects of AR on a collaborative problem-solving task like pair programming in computer science education, which is difficult to analyze due to the increased complexity in the task and user-user interaction. We designed an AR application, where two participants can work together to solve a programming task, and conducted a within-subject study to evaluate how the participants' code editing and vocal communication are different in the AR and non-AR visualizations. The result showed that participants using the AR visualization exhibited strategic programming behavior and had more interactive communication than using the non-AR visualization. Overall, this preliminary work contributes to the understanding of how AR can support collaborative problem-solving and engage users in verbal communication by multimodal analytics.
Research on infrared and visible image registration of substation equipment based on Multi-scale Retinex and ASIFT features

Infrared and visible image registration of substation equipment is of great significance for power equipment detection and fault diagnosis. The scene of substation is complex, and the background of equipment image is usually messy, and the feature points of visible image are easy to fall on the background. The metal has good thermal conductivity, and its temperature is close to the ambient temperature. The metal part in the infrared image with metal tower as the background can not be clearly displayed, which is easy to cause the image mismatch or even unable to match. The existing registration methods such as SIFT, SURF and ASIFT are difficult to effectively solve this kind of image registration problem of substation equipment with complex background. To solve this problem, this paper proposes an infrared and visible image registration algorithm based on Multi-scale Retinex and ASIFT features. Firstly, the Multi-scale Retinex algorithm is used to separate the components representing the properties of the object in the visible image, so as to weaken the influence of the clutter background. Then, the ASIFT algorithm is used to do affine transformation to simulate the affine deformation under all parallax, and the feature points are roughly matched Finally, the random sampling consistent algorithm is added to eliminate the mismatching points. Experimental results show that the algorithm can increase the number of matching points by at least 4 times, the average matching accuracy is improved by 13%, and the average matching time is shortened by 183ms.
On the role of Hermite-like polynomials in the Fock representations of Gaussian states

The expansion of quantum states and operators in terms of Fock states plays a fundamental role in the field of continuous-variable quantum mechanics. In particular, for general single-mode Gaussian operators and Gaussian noisy states, many different approaches have been used in the evaluation of their Fock representation. In this paper, a natural approach has been applied using exclusively the operational properties of the Hermite and Hermite-like polynomials and showing their fundamental role in this field. Closed-form results in terms of polynomials, exponentials, and simple algebraic functions are the major contribution of the paper.
A comprehensive approach to predict a rocket's impact with stochastic estimators and artificial neural networks

One of the current ways to continue space research is to launch ballistic rockets that carry scientific payloads. To improve the accuracy of the instantaneous evolution of the payload impact on the Earths surface, it is necessary to estimate indirect measures more efficiently. This study proposes a comprehensive approach to determine the impact point prediction of ballistic rocket payloads. This approach combines tracking algorithms that are based on stochastic estimators with artificial neural network (ANN) models. Four existing stochastic estimators, namely a recursive Kalman filter (RKF), an extended Kalman filter (EKF), an unscented Kalman filter (UKF), and a particle filter (PF) are compared with four proposed stochastic estimators. These include a recursive Kalman filter aided by an ANN (RKFN), an extended Kalman filter aided by an ANN (EKFN), an unscented Kalman filter aided by an ANN (UKFN), and a particle filter aided by an ANN (PFN). This study shows that the results that are obtained through the proposed tracking algorithms RKFN, EKFN, UKFN, and PFN are better than those of the existing tracking algorithms RKF, EKF, UKF, and PF. The proposed estimators can be efficient low-cost tools to mitigate inaccuracies during tracking up to the payload's impact.
The Prominent Barriers to Speaking in English: A Study Conducted Among Youngsters

This discourse analyses the prominent barriers to speaking in English while conducting online English Language classes during the pandemic, COVID-19. The study is conducted among business communication students in university colleges in India and takes five paradigms into consideration. They are: the motivational factor, the personality of the learner, attitude of the learner, the pedagogical management of English classes in online mode and the level of exposure to the English language. Data were collected by analyzing the survey questionnaire distributed among 150 business communication students. Data were analyzed with the help of SPSS in a descriptive mode. The result of the analysis shows that while dealing with online classes, teachers face several difficulties to manage the language subjects, especially the pedagogical management of the English subject. Another significant factor is the level of exposure to the English language. In this online system, ordinary students do not have an opportunity to communicate and practice English. They show some kind of hesitation to use English during the entire class time and give less attention to the words of the teacher. Most of them are distracted due to several factors. It contributes moderately to the predicaments of the learners. This study also helps to understand the crucial factors that act as language barriers in cross cultural business communication as the application level of language is more or less same all over the world.
Reservoir Computing for Forecasting Large Spatiotemporal Dynamical Systems

Forecasting of spatiotemporal chaotic dynamical systems is an important problem in several scientific fields. Crucial scientific applications such as weather forecasting and climate modeling depend on the ability to effectively model spatiotemporal chaotic geophysical systems such as the atmosphere and oceans. Recent advances in the field of machine learning have the potential to be an important tool for modeling such systems. In this chapter, we review several key ideas and discuss some reservoir-computing-based architectures for purely data-driven as well as hybrid data-assisted forecasting of chaotic systems with an emphasis on scalability to large, high-dimensional systems.
The Business Process of Good Manufacturing Practice Based on Blockchain Technology in the Pharmaceutical Industry

Blockchain technology is a way to improve data integrity and data traceability, and data manipulation. Blockchain technology can be used in the drug manufacturing business process. However, despite the existence of Good Manufacturing Practice (GMP) regulations, there are still many counterfeit drugs circulating in the community. It is a very public concern and can even cause death for people who consume fake drugs. Blockchain technology can be used as a solution to minimize the circulation of counterfeit drugs. The research was conducted using a qualitative approach, namely the User Center Design (UCD). The results of a business model created based on blockchain technology are then validated in one of the largest pharmaceutical industries in Indonesia, which consists of 5 experts from various fields. Validation is carried out by means of a Forum Group Discussion. The final result of the FGD states that the drug manufacturing process business model with blockchain technology is suitable and can be used by the drug industry. With this blockchain system, it provides excellent data integration for each transaction data on each party to be able to view and carry out transactions so that data can be traced properly and give industry owners a sense of confidence in the production section of the pharmaceutical industry.
Factors that influenced the purchase of gemstone among enthusiast in Kelantan

This research has been conducted to identify three main factors i) the level of knowledge on gemstone, ii) the choices of the types of gemstone among enthusiast, iii) factors that influenced the purchase of gemstone. A quantitative research questions were used to collect information and answered the aforementioned main factors. The total of 302 respondents includes Malay male from all over Kelantan. The focus of the research is on the knowledge of gemstone only in the category of precious gemstone which are diamond, ruby, sapphire and emerald. The outcome of the analysis shows over 86.1% of respondent can identify the characteristics of natural or synthetic gemstone. However, over 66.6% of respondents are not confident with the naturalness of the purchased gemstone and are not familiar with the term of the treated gemstone and gemstone certificate. The knowledge about gemstone colour, gemstone-producing countries and the price of gemstone are from the range of 17% to 84%. Ruby was the most popular choice among the respondents and the price that respondents willing to pay for a gemstone are between RM 500 – RM 1000. Moreover, most of the respondents agreed that personal factors (age, profession, economy, life style, personality) influenced individual in purchasing the gemstone. Overall, it can be included that the level of knowledge of gemstone among enthusiast in Kelantan is at alarming level based on the findings that 76.8% of the enthusiast are not familiar with the terms of treated gemstone and gemstone certificate.
Energy efficient VM scheduling in Reservation supported Cloud Data Centers under availability constraints

The exponential surge in digital usage is resulting in a significant need for computational resources. This has led to businesses migrating to the cloud for benefits like usage of resources as a service, scalability, etc. Infrastructures supporting these businesses will need to conserve energy in the context of the ever-increasing size of the data centers and provide a highly available environment with mechanisms for business continuity, disaster recovery, etc. These would need to be supported at minimal costs while meeting the expected performance along with conformance to the SLAs. This paper considers a cost-efficient resource reservation based environment and evolves an approach to perform energy-efficient VM scheduling with migrations, while factoring in the network Overheads, having Availability as a constraint, and orchestrating the devices in terms of Dynamic Voltage Frequency Scaling, for optimal energy usage. Two novel algorithms are proposed: the Power and Availability Aware Best Fit Decreasing (PAABFD) allocation policy and the Power Availability Network DVFS Aware (PANDA) migration policy, when combined achieving up to 82.35% improvement in data center energy while offering an availability of 99.999% when compared with other scheduling policies.
Random Forest For Hijab Style Selection Based on Face Shape Using Morphological Facial Index

Face shape identification is needed to reference the hijab style selection. Many people don’t know their face shape. The mistake of choosing a hijab style makes the face look disproportionate. The morphological facial index model is used to identify the face shape by calculating the length and width of the face. Face Landmark point is used for facial feature extraction which is used as an attribute in the study, namely face height, face width, upper jaw width and lower jaw width. Various kinds of hijab styles and the absence of a hijab style guide make it difficult to choose a hijab style that suits your face shape. Machine learning is needed to create a hijab style selection system. Random Forest was chosen as the method for choosing the hijab style. The purpose of this study is the implementation of a random forest for the selection of hijab styles based on face shape using morphological facial index. This study used 129 images of female facial data which was split into 89% training data and 11% test data. The prediction system produces an accuracy value of 93% with 30 trees for pashmina, scarf and instant hijab styles. The use of the random forest method is proven to be able to predict the selection of hijab styles based on face shape using morphological facial index. Scikit-learn tool used to random forest modelling and evaluated the efficiency of our proposed method.
SpeakQL Natural Language to SQL

Incorporating SQL questions from normal language is a long-standing open issue and has been drawing in extensive intrigue as of late. Natural Language Interface (NLI) is the confluence of Natural Language Processing (NLP) and Human-Computer Interaction, which allows interaction between humans and computers through the utilization of Natural Language. Here we are gonna deal with the problem of automatic generation of Structured Query Language (SQL) queries. SQL is a database language for querying and manipulating relational databases. Despite the spectacular rise in the acceptance of relational databases, there is a fundamental limitaion to the ability to fetch data from those databases. One of the major reasons for this is the fact that the users of these relational databases need to comprehend convoluted structured query languages. In this body of work, we present an interface that allows users to interact with the databases using Natural Lanaguage as opposted to the conventional structure query languages.
Blockchain Based Trusted Identity Authentication in Ubiquitous Power Internet of Things

To solve the problem of data loss and malicious tampering caused by IoT devices without security verification during the implementation of smart grid projects, this paper utilizes the distributed ledger and non-tampering characteristics of blockchain technology to ensure the security of the stored data and to solve the problem of data reliability before winding. We design a hardware module based on the Ed25519 signature protocol combined with identity authentication technology to provide the identity key for IoT devices. By combining the two solutions, we can effectively solve the data security problems in the implementation of smart grid projects.
Inter-Operator Machine Learning Model Trading over Acumos AI Federated Marketplace

We demonstrate the development of a QoT classifier over an autonomous machine-learning pipeline, the trading of the classifier over a federated marketplace, and eventually its deployment in the customer’s network as a cloud-native micro-service.