Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis of Tweets and Texts Using Python on Stocks and COVID-19

In a day and age where billions of users regularly use social media and express their opinions online, there is potentially a lot of data that can be harvested and utilized; therefore, it is crucial to develop a quick way to garner data.This study aimed to develop a program using Python to do the same, trying to understand the sentiments of the authors of the text and tweets as well as other additional information about the top tweets and retweets.The main objective of the Twitter Sentiment Analysis is a query-based analysis of tweets.In simple words, Twitter Sentiment Analysis focuses on analyzing the tweets of a specific/particular topic that the user wants to analyze.An extensive collection of such sentiments could leverage to provide a fair reflection of public sentiment towards a specific topic.There are thousands of tweets that can be quickly processed for the sentimental impact, compared to the amount of time it would take a large team of people to complete the same task manually.There are tons of text documents that can also be processed for sentiments in seconds, much faster than just a team of people manually skimming through the text.
Two-Level-Composite-Hashing Facilitating Highly Efficient Anonymous IoT and D2D Authentication

Resource limitation is quite popular in many Internet of Things (IoT) devices and eavesdropping on the identities of IoT devices could reveal the sensitive information; therefore, high efficiency (computation and communication) and anonymity protection are two desirable properties in IoT authentication and in device-to-device (D2D) authentication. Conventionally, dynamic pseudonyms are widely adopted to protect the device identity privacy in IoT authentication and in D2D communications; however, the conventional mechanisms of pseudonym-renewing and pseudonym-bound-public-keys updating could be very costly or be vulnerable to the desynchronization-based denial-of-service (DoS) attacks. In this paper, we propose a novel 2-level composite hashing (2LCH) mechanism to mitigate the problems, and propose the 2LCH-based anonymous IoT and D2D authentication schemes. The schemes simultaneously achieve high efficiency and strong anonymity for such environments; once two devices successfully complete one instance of the server-assist anonymous authentication, they can run several instances of the direct D2D anonymous authentication without the involvement of the server. The merits of the schemes include: (1) high efficiency in terms of computation and communication; (2) easy and efficient generation/synchronization of dynamic pseudonyms; (3) robustness to both desynchronization-based DoS attacks and the unreliable connections; (4) easy application to the existent IoT architectures and standards; and (5) formal security verification.
Human action recognition based on deep network and feature fusion

Feature representation is of vital importance for human action recognition. In recent few years, the application of deep learning in action recognition has become popular. However, for action recognition in videos, the advantage of single convolution feature over traditional methods is not so evident. In this paper, a novel feature representation that combines spatial and temporal feature with global motion information is proposed. Specifically, spatial and temporal feature from RGB images is extracted by convolutional neural network (CNN) and long short-term memory (LSTM) network. On the other hand, global motion information extracted from motion difference images using another separate CNN. Hereby, the motion difference images are binary video frames processed by exclusive or (XOR). Finally, support vector machine (SVM) is adopted as classifier. Experimental results on YouTube Action and UCF-50 show the superiority of the proposed method.
Dynamical system learning using extreme learning machines with safety and stability guarantees

Summary This article presents a continuous dynamical system model learning methodology that can be used to generate reference trajectories for the autonomous systems to follow, such that these trajectories are invariant to a given closed set and uniformly ultimately bounded with respect to an equilibrium point inside the closed set. The autonomous system dynamics are approximated using extreme learning machines (ELM), the parameters of which are learned subject to the safety constraints expressed using a reciprocal barrier function, and the stability constraints derived using a Lyapunov analysis in the presence of the ELM reconstruction error. This formulation leads to solving a constrained quadratic program (QP) that includes a finite number of decision variables with an infinite number of constraints. Theorems are developed to relax the QP with infinite number of constraints to a QP with a finite number of constraints which can be practically implemented using a QP solver. In addition, an active sampling methodology is developed that further reduced the number of required constraints for the QP by only evaluating the constraints at a smaller subset of points. The proposed method is validated using a motion reproduction task on a seven degree‐of‐freedom Baxter robot, where the task space position and velocity dynamics are learned using the presented methodology.
Genetic-algorithm-based Convolutional Neural Network for Robust Time Series Classification with Unreliable Data

Finding robust solutions to time series classification problems using deep neural networks has received wide attention.However, unreliable data makes classification very difficult.Traditional deep neural networks cannot effectively solve problems with strong noise.In this paper, we propose a hybrid convolutional neural network (CNN) model combined with a genetic algorithm (GA) for time series classification (TSC) with unreliable data.To obtain a robust CNN structure, even though network structural optimization is an NP-hard problem, we design a GA for network structure optimization.Several benchmarks and actual datasets are adopted, and tests are carried out to prove the effectiveness of the proposed GA-based CNN.The numerical results show that our approach has better performance than other state-of-the-art deep neural networks.
Resource-constrained project scheduling with ant colony optimization algorithm

Resource allocation commonly becomes one of the critical problems in project scheduling. This issue usually occurs because project managers estimate the schedule of activities and network time without considering resource availability. Resource-Constrained Project Scheduling Problem (RCPSP) links to the allocation of resource or set of resources into certain activities in order to accomplish particular objectives. Various approaches have been performed to overcome RCPSP, including the heuristic approach. In this research, we used the Ant Colony Algorithm in solving RCPSP. We used 11 examples of projects with dissimilarity in-network and several activities. The implementation of the Ant Colony Algorithm resulted in the percentage of a near-optimal solution of 63.64%. Besides, the duration obtained from the algorithm above the manual scheduling (assumed optimal) was only 4.29%. Sensitivity analysis was performed to understand how substantial the changes of ACO parameters influenced the result obtained from the algorithm. Based on the result, we could conclude that the parameters of ACO have no significant effect to project duration.
Exploration and Exploitation of Mobile Apps for English Language Teaching: A Critical Review

With the progression of various mobile technologies, mobile applications have tremendously increased, especially during the Covid-19 pandemic, and such applications have been exploited much in teaching and learning. This study explores the educational potential of using mobile applications in English language teaching (ELT) or Mobile Assisted Language Teaching (MALT). A critical review of the research in mobile applications in English language teaching is explored in this study, specifically from the published papers since 2015. Initially 131 articles were selected from ScienceDirect, SAGE, IEEEXplore, and Google Scholar. However, only 13 articles matched the inclusion criteria. These articles were analyzed and reviewed using the following categories: the role of mobile technology, pedagogical practices, research methodologies, the context of usage, and outcomes. The research found that mobile technologies in teaching language are increasing, and it is expected to rise in the future. In addition, teachers use different technologies to enhance English language teaching in the settings of inside and outside classrooms. During the COVID-19 pandemic, schools have closed indefinitely. This unexpected situation has forced students to stay at home, and online learning seems to grow exponentially. Thus, through this research review, significant educational outcomes are identified for future investigation practices.
Cluster Analysis of Mixed and Missing Chronic Kidney Disease Data in KwaZulu-Natal Province, South Africa

Real-world datasets, particularly Electronic Health Records, are routinely found to be mixed (comprised of both categorical and continuous variables) and/or missing in nature. Such datasets present peculiar challenges related both to their clustering and the evaluation of the clusterings obtained. In this paper, we discuss these challenges in detail, as well as the solution approaches applied to them in the literature. We then apply some of these approaches to a multi-racial Chronic Kidney Disease (CKD) dataset comprising of 20 continuous and 12 categorical variables with an over 30% missingness ratio, evaluating our results through external and internal validation as well as cluster stability testing. From the results of our study, the Ahmad-Dey distance measure consistently outperformed Gower's distance on our mixed and missing dataset. In addition, our results show that advanced imputation methods like multiple imputation, which take into consideration the uncertainty inherent in imputation, should be explored when clustering missing datasets. Three clusters were identified from our dataset which were significantly differentiated by age, sex, estimated Glomerular Filtration Rate (eGFR), creatinine, urea, and hemoglobin, but not by race or blood pressure. The fact that, through proper cluster analysis, we were unable to identify five clusters corresponding to the five CKD stages usually used to classify CKD patients indicates that datasets with more than the usual four/six variables used for computing eGFR may contain a latent structure different from this five-group structure, the identification of which will provide valuable insights peculiar to each cohort for medical practitioners.
The impact of mobile money, remittances, and financial development on innovative growth in sub-Saharan Africa

Mobile money has become a mode of banking for the unbanked residents and the system has been gaining patronage among citizens of developing countries. This trend especially refers to sub-Saharan Africa, where the level of financial inclusion is low. Thus, the expansion of the mobile money as well as easy access to it promotes the development of the financial sector in the region. To define the role of the financial elements in innovation growth in sub-Saharan African countries, we examined the relationship between mobile money activities, remittance, financial development, and innovation growth in sub-Saharan Africa (SSA). Using partial least squares (PLS), we conducted a comprehensive analysis to econometrically establish the nexus between innovation development and financial activities in sub-Saharan African region. The results show that significant positive relationship exists between all the independent variables and innovation growth (the dependent variable). Thus, this study indicates that mobile money services, financial development and remittances have significant impact on economic growth. However, mobile money services are the most influential variable. Hence, these results can be used by policymakers to encourage and improve mobile money payment and banking system as this could facilitate the pooling of resources and their effective allocation to productive sectors, thus leading to the promotion of innovative growth in the region.
Implementation of Institution's E-Learning Platform in Teaching Online at ITB AAS Indonesia

E-learning is one of impact of the development of information and communication technology (ICT) technology applied in education using the accessible website. The purpose of this research is to know the implementation of the Institution’s E-Learning Platform and to know the strengths and weaknesses of the implementation of the Institution’s E-Learning Platform of ITB AAS Indonesia. This research applies descriptive qualitative research. The result of the study shows that ITB AAS Indonesia began implementing e-learning to support face-to-face classrooms in the academic year 2020/2021. E-learning’s platform was made based on Learning Management System (LMS) that can be accessed at the website https://elearning.itbaas.ac.id/ Based on the implementation of the e-learning, there are several menus found such as “Home, Message, Teaching Schedule, Material, Task, My Comment, Lecturer’s Filter, Student’s Filter, Video Conference, and Logout”. But, during the observation of the implementation of the e-learning platform, there are several strengths and weaknesses. In strengths are 1) Multi Users, 2) Online Application. 3) Several menus available. In the weaknesses, 1. This application contains a lot of data, 2. This application’s design will only look good when opened by using a laptop or a computer, 3. In making the material, it can only be shared into the Study Program category- Semester, and it cannot be shared with each class. 4. There may be the possibility of sending the material incorrectly. 5. This application does not have the facilities for importing and exporting user data. 6. In making multiple-choice question assignments, there is still a manual import feature, 7. E-learning does not have a Video Conference feature, 8. In printing grades into a file pdf, an additional application is required. 9. E-learning cannot record learning and participants in a video. 10. This application does not have an installer version on the android/smartphone.
JomMachineLearning

When a user intends to custom make a name card, the very first step that the printing service provider would ask for is their name card design artwork, yet users do not have their name card design artwork. The client would feel frustrated with the designer to create artwork that does not meet their requirements. The designer would also feel the same as the artwork has been rejected regularly by the client. From the primary data survey result, most respondents agreed that they have a clear idea in mind but have difficulty expressing their idea to the designer. The opinions concluded from the interviews would there be another choice for the user to design an artwork besides hiring a designer. This study has proposed a designer matching platform to match the client's preference and designer's talent and an online artwork design editor as an additional option for the user.
A Novel Density-based Technique for Outlier Detection of High Dimensional Data Utilizing Full Feature Space

Recently, anomaly detection has acquired a realistic response from data mining scientists as a graph of its reputation has increased smoothly in various practical domains like product marketing, fraud detection, medical diagnosis, fault detection and so many other fields. High dimensional data subjected to outlier detection poses exceptional challenges for data mining experts and it is because of natural problems of the curse of dimensionality and resemblance of distant and adjoining points. Traditional algorithms and techniques were experimented on full feature space regarding outlier detection. Customary methodologies concentrate largely on low dimensional data and hence show ineffectiveness while discovering anomalies in a data set comprised of a high number of dimensions. It becomes a very difficult and tiresome job to dig out anomalies present in high dimensional data set when all subsets of projections need to be explored. All data points in high dimensional data behave like similar observations because of its intrinsic feature i.e., the distance between observations approaches to zero as the number of dimensions extends towards infinity. This research work proposes a novel technique that explores deviation among all data points and embeds its findings inside well established density-based techniques. This is a state of art technique as it gives a new breadth of research towards resolving inherent problems of high dimensional data where outliers reside within clusters having different densities. A high dimensional dataset from UCI Machine Learning Repository is chosen to test the proposed technique and then its results are compared with that of density-based techniques to evaluate its efficiency.
Image Statistics Preserving Encrypt-then-Compress Scheme Dedicated for JPEG Compression Standard

In this paper, the authors analyze in more details an image encryption scheme, proposed by the authors in their earlier work, which preserves input image statistics and can be used in connection with the JPEG compression standard. The image encryption process takes advantage of fast linear transforms parametrized with private keys and is carried out prior to the compression stage in a way that does not alter those statistical characteristics of the input image that are crucial from the point of view of the subsequent compression. This feature makes the encryption process transparent to the compression stage and enables the JPEG algorithm to maintain its full compression capabilities even though it operates on the encrypted image data. The main advantage of the considered approach is the fact that the JPEG algorithm can be used without any modifications as a part of the encrypt-then-compress image processing framework. The paper includes a detailed mathematical model of the examined scheme allowing for theoretical analysis of the impact of the image encryption step on the effectiveness of the compression process. The combinatorial and statistical analysis of the encryption process is also included and it allows to evaluate its cryptographic strength. In addition, the paper considers several practical use-case scenarios with different characteristics of the compression and encryption stages. The final part of the paper contains the additional results of the experimental studies regarding general effectiveness of the presented scheme. The results show that for a wide range of compression ratios the considered scheme performs comparably to the JPEG algorithm alone, that is, without the encryption stage, in terms of the quality measures of reconstructed images. Moreover, the results of statistical analysis as well as those obtained with generally approved quality measures of image cryptographic systems, prove high strength and efficiency of the scheme’s encryption stage.
Investigating the Potential of Network Optimization for a Constrained Object Detection Problem

Object detection models are usually trained and evaluated on highly complicated, challenging academic datasets, which results in deep networks requiring lots of computations. However, a lot of operational use-cases consist of more constrained situations: they have a limited number of classes to be detected, less intra-class variance, less lighting and background variance, constrained or even fixed camera viewpoints, etc. In these cases, we hypothesize that smaller networks could be used without deteriorating the accuracy. However, there are multiple reasons why this does not happen in practice. Firstly, overparameterized networks tend to learn better, and secondly, transfer learning is usually used to reduce the necessary amount of training data. In this paper, we investigate how much we can reduce the computational complexity of a standard object detection network in such constrained object detection problems. As a case study, we focus on a well-known single-shot object detector, YoloV2, and combine three different techniques to reduce the computational complexity of the model without reducing its accuracy on our target dataset. To investigate the influence of the problem complexity, we compare two datasets: a prototypical academic (Pascal VOC) and a real-life operational (LWIR person detection) dataset. The three optimization steps we exploited are: swapping all the convolutions for depth-wise separable convolutions, perform pruning and use weight quantization. The results of our case study indeed substantiate our hypothesis that the more constrained a problem is, the more the network can be optimized. On the constrained operational dataset, combining these optimization techniques allowed us to reduce the computational complexity with a factor of 349, as compared to only a factor 9.8 on the academic dataset. When running a benchmark on an Nvidia Jetson AGX Xavier, our fastest model runs more than 15 times faster than the original YoloV2 model, whilst increasing the accuracy by 5% Average Precision (AP).
Acoustics of the banjo: theoretical and numerical modelling

A previous paper [Woodhouse et al., Acta Acustica 5, 15 (2021) https://doi.org/10.1051/aacus/2021009 ] showed acoustical measurements of an American 5-string banjo alongside similar measurements on a guitar, revealing a strong contrast in bridge admittance. Theoretical and numerical modelling is now presented to probe the physics behind this contrast. Without the bridge and strings, the banjo membrane has a rising trend of admittance associated with its modal density, and it has a distinctive pattern of sound radiation because an ideal membrane has no critical frequency. When the bridge and strings are added to the banjo, three formants shape the amplitude envelope of the admittance. One is associated with local effects of mass and stiffness near the bridge, and is sensitive to bridge mass and the break angle of the strings over the bridge. The other two formants are associated with dynamical behaviour of the bridge, analogous to the “bridge hill” in the violin.
Robust Recognition of Chinese Text from Cellphone-acquired Low-quality Identity Card Images Using Convolutional Recurrent Neural Network

An automatic reading of text from an identity (ID) card image has a wide range of social uses.In this paper, we propose a novel method for Chinese text recognition from ID card images taken by cellphone cameras.The paper has two main contributions: (1) A synthetic data engine based on a conditional adversarial generative network is designed to generate million-level synthetic ID card text line images, which can not only retain the inherent template pattern of ID card images but also preserve the diversity of synthetic data.(2) An improved convolutional recurrent neural network (CRNN) is presented to increase Chinese text recognition accuracy, in which DenseNet substitutes VGGNet architecture to extract more sophisticated spatial features.The proposed method is evaluated with more than 7000 real ID card text line images.The experimental results demonstrate that the improved CRNN model trained only on the synthetic dataset can increase the recognition accuracy of Chinese text in cellphone-acquired low-quality images.Specifically, compared with the original CRNN, the average character recognition accuracy (CRA) is increased from 96.87 to 98.57% and the line recognition accuracy (LRA) is increased from 65.92 to 90.10%.