Reviewerly/exHarmony huggingface dataset:
Implementation of Text Mining for Sentiment Analysis of Online Lectures During the Covid-19 Pandemic

Strategy against the spread of the Covid-19 virus in Indonesia by enacting Large-Scale Social Restrictions. The implementation of the Scale Social Restrictions forced all universities in Indonesia to close their institutes and conduct lectures online. Online lectures are considered as a solution to continue the teaching process during a pandemic. However, the lack of adaptation and sudden changes caused various responses and public opinions on social media. For this reason, this study aims to conduct text mining on Twitter in order to analyze public sentiment on the topic of "online lectures" the data obtained are classified into 2 classes (positive and negative). The results of the accuracy of the nave Bayes test with the cross validation technique obtained a result of 81.57%. For class precision, positive predictions are 100%, while for negative predictions the results are 73.06% and recall from true positive is 63.13% for true negative is 100%. And for the accuracy of K-Nearest Neighbor 62.10%, for class precision positive prediction is 62.06% while for negative prediction results are 62.13% and recall from true positive is 62.24% for true negative is 61.95%
A Risk-Bounded Cooperative Path Planning Model for Multi-UAVs Based on Chance Constrained Optimization

How to improve flight efficiency and reduce crashes or collisions risk are the challenges encountered by multi-UAVs cooperative path planning technologies. However, the existing methods cannot guarantee rigorous safety performance. This paper proposed a risk-bounded cooperative path planning model for multi-UAVs based on chance constrained optimization. The solution to path planning model via two phases: producing paths to meet the curvature constraints-the flyable paths, and then tuning the flyable safety paths to meet the safety level on demand. By employing the deterministic equivalence method to solve the formulated chance-constrained optimization, the optimal paths of multi-UA Vs are achieved and the probability of collision is limited to a prefixed confidence level. The numerical simulations reveal the effectiveness of the proposed model, and demonstrate its risk-aware capabilities are better than that of without using the chance constrained optimization.
User Identification in Online Social Networks using Graph Transformer Networks

The problem of user recognition in online social networks is driven by the need for higher security. Previous recognition systems have extensively employed content-based features and temporal patterns to identify and represent distinctive characteristics within user profiles. This work reveals that semantic textual analysis and a graph representation of the user’s social network can be utilized to develop a user identification system. A graph transformer network architecture is proposed for the closed-set node identification task, leveraging the weighted social network graph as input. Users retweeting, mentioning, or replying to a target user’s tweet are considered neighbors in the social network graph and connected to the target user. The proposed user identification system outperforms all state-of-the-art systems. Moreover, we validate its performance on three publicly available datasets.
Analysis of Threats to a University Network Using Open Source Technologies

Cybersecurity is part in one of the complicated wars the world has ever known. Battles are fought daily against nation states, organized crime, thieves, terrorists and bored but smart children. Academic institutions are no exception. The article presents an analysis of information collected from a university network and shows what and how computer network administrators face.
Imprecise Discrete-Time Markov Chains

Abstract I present a short and easy introduction to a number of basic definitions and important results from the theory of imprecise Markov chains in discrete time, with a finite state space. The approach is intuitive and graphical.
Design and Implementation of a New Serverless Conversational Survey System

Conversational agents or chatbots have great potentials in improving survey responses and accessibility. However, it is still a challenge for researchers without programming skills to create voice-enabled chatbots to conduct customizable surveys. This paper presents a new easy-to-use serverless survey chatbot system based on the existing TigerAware mobile survey platform, called TigerAware chatbot. This chatbot system enables non-technical persons to build and deploy customized surveys on mobile devices as text or voice-based conversations. The system is based on Dialogflow and Firebase Realtime database, supports voice input and social dialog, and provides visual responses for users to select answers among several options. The chatbots can be deployed on any iOS and Android mobile device and platform that support Google Assistant. Survey question types that have been implemented include yes/no, multiple-choice, free response, numeric entry, date/time picker, and scale. This system is more efficient and effective than existing survey chatbot systems.
Development and Effectiveness of Drone as a Learning Media in Islamic Boarding School

Abstract Nowadays, drone technology is not only be used in commercial businesses but can also be utilized in a teaching-learning process, including in Islamic boarding school. This article will be discussed the development, performance, feasibility, and effectiveness of drone technology to be utilized as a learning media at Pesantren Hidayatullah Yogyakarta that is one of Islamic boarding school. The developed drone was the multicopter with a multi-rotor type using four rotors. This study was conducted with the development research approach developed by Alessi and Trollip and the descriptive research approach. This drone was applied in teaching-learning process at Pesantren Hidayatullah Yogyakarta. Data were obtained through drone’s testing and observation, interviews, questionnaires for the drone’s affectivity in teaching-learning process. The results of this study can be known that the drone performance is categorized as “Very Good” in terms of functionality and usability aspects. The drone feasibility in the term of material quality and its usefulness is included in the “Very Feasible” category. The drone as learning media has had significantly impact when it’s applied in learning process in extracurricular activities.
High-speed embedded optical flow measurement system for real-time use

In recent years, there are more and more opportunities for robots to be active in society. It is necessary to develop a system that processes information that enters the robot&#x0027;s vision faster and more accurately. In the development of visual sensors, the calculation speed and accuracy of the measurement are issues that should be improved. Parallel trade off of the processing was used as a means of speeding up the calculation. In this research, we have developed a system that can measure optical flow with an embedded microprocessor, to detect obstacles in real time and accurately detect its own movement speed on a small robot. We have conducted two experiments, one in which the camera was fixed and a moving subject was photographed, and the other in which the camera was moved and photographed. Experimented results show that was possible to reduce the calculation time by about one-third for real-time processing, and it was possible to detect the difference in the speed of the camera itself.
Track correlation uncertainty evaluation for Multi-sensor Data Fusion System and its application

An evaluation method of track correlation uncertainty was put forward in this paper. Firstly, the measure of track correlation uncertainty was defined by Shannon entropy measure and Hartley measure of information uncertainty. Then, an evaluation model for track correlation uncertainty was proposed. Lastly, the usage of online evaluation of track correlation uncertainty in data fusion system was briefly illustrated. Simulation results show that the track correlation uncertainty can be quantitatively evaluated and can be used to improve the level of automation for data fusion system.
Proposed QoE Models Associated with Delay and Jitter Using Subjective Approach and Applications for 4G and 5G Networks

This study aims to propose the Quality of Experience (QoE) models with the assumption that users’ perception may vary according to the technology changes. Therefore, 24 subjects participated by using an online questionnaire form. The QoE models for delay and jitter were created. The obtained models were applied to evaluate 4G and 5G networks in Thailand and found that 5G networks in Bangkok have excellent performance while 4G networks have fair to poor performance. The average Mean Opinion Score (MOS) results referring to delay and jitter in this study are 4.48 and 4.54 respectively, whereas the results from 4G networks referring to delay and jitter is 3.80 and 3.22 maximum respectively. The MOS results from this study can be understood by general users easily. Thus, the concept to create models as shown in this paper can be extended to the other QoS parameters.
ICT in Indian Agriculture: Opportunities and Constraints

Information and communications technology (ICT) has brought revolutionary changes in the lives of millions of Indian populace. Due to progress in fields of computer science and telecommunication, large amount of information can be acquired, processed, and delivered at any place and time to a person who needs it. Recent technological developments have made handling of enormous amount of information relatively easy. This information can be accessed by a huge number of users concurrently. These technological developments have provided vast prospects in improving utility and productivity of occupational technical areas like agriculture, extension, and educations. It has become a challenge for researchers to harness the ICT developments for improvising the quality of life of rural populace. In this paper, an attempt has been made to evaluate opportunities and constraints of associated with use of ICT to enhance productivity of agriculture and associated sectors in India.
CNN for Breast Cancer Metastases Classification

We present a Deep Convolutional Neural Network based on VGG16 for the classification of patch images from the benchmark of PatchCamelyon 2016. This dataset consists of 327680 color images from histopathologic scans of lymph node sections. The images were originally acquired at 2 different hospital centers with a 40x objective.The main way to optimize the construction of our model was using a train, validation and test set, which is the standard approach to do it. We made changes to the architecture and its parameters one by one, observing its performance in the validation set in terms of accuracy and average loss.Finally, we could obtain a optimized CNN architecture that performed better than the VGG16 model by itself. Also, the AUĆs obtained in the valid and test set were 0.91 and 0.90 respectively, which shows the neural network was trained in a optimal way so that it did not perform overfitting. The final accuracy for valid was 89% and 86% for test set. This shows that our model is between those in the state of the art in classification and can be used for further harder problems such as semantic segmentation in the actual Camelyon dataset.
Research data stewardship at the University of Hong Kong

Purpose The purpose of this paper is to look at the journey and experience of the University of Hong Kong (HKU) Research Data Management (RDM) practice to respond to the needs of researchers in an academic library. Design/methodology/approach The research data services (RDS) practice is based on the FAIR data principle. And the authors designed the RDM Stewardship framework to implement the RDS step by step. Findings The HKU Libraries developed and implemented a set of RDS under a research data stewardship framework in response to the recent evolving research needs for RDM amongst the academic communities. The services cover policy and procedure settings for research data planning, research data infrastructure establishment, data curation services and provision of online resources and instructional guidelines. Originality/value This study provides an example of an approach to respond to the needs of the academic libraries about how to start the RDS including the data policy, data repository, data librarianship and data curation.
The Variability of the Educational Needs of college Teachers as a Condition for Their Professional and Personal Development

The article presents the results of an empirical study focused on identifying the features of the attitude of college teachers to their additional professional education, continuous self-development and self-improvement. The article examines the ideas of 214 college teachers about the ideal teacher of the secondary vocational education system, the peculiarities of their perception of themselves as subjects of pedagogical activity, their attitude to the existing requirements of periodic training in advanced training courses, the degree of interest in them and the content of their educational needs. The analysis of the survey results showed the need to take into account the peculiarities of their educational needs and the variability of their professional and personal development when organizing advanced training courses for college teachers.
Block Diagonal Sparse Subspace Clustering

Sparse subspace clustering (SSC) is a spectral-type clustering-based method, which deals with high dimensional data via sparse representation. When the subspaces are independent of each other, the coefficient matrix obtained by SSC satisfies the block diagonal structure, which can better reveal the subspace attributes of data. In the actual environment, due to noise data and dependent subspaces, the obtained block diagonal structure is easy to be destroyed. To address the problem, we proposed the BDSSC method, which directly imposes the k-block diagonal regularizer on the coefficient matrix to purse the block diagonal structure. With the help of sparse prior and k-block diagonal regularizer, the coefficient matrix has a better block diagonal structure, and then the clustering performance is improved. Experiments on several actual datasets indicate that the proposed BDSSC method is superior to other state-of-the-art methods.
Efficiency of short text classifiers for payment classification

Traditionally, the Central Bank of Russia used regular expressions for the payment classification as part of its supervisory activities. Regular expressions often spanned multiple pages to cover varied relevant keywords and their forms. We compare this approach to two modern short text classification approaches: fastText and BERT-based transformer in terms of speed, accuracy and flexibility, including few-shot learning.
Automated Employee Objective Matching Using Pre-trained Word Embeddings

Employee performance objectives are recurrent short-term goals established between an organization and its employees. These objectives are typically aligned with the strategic goals of the company. Human Resources professionals are responsible for supporting employees in achieving their objectives, which in turn mediates enterprise success. One form of such support is grouping employees who have similar objectives in teams so that they can cooperate with one another. This process becomes difficult in large organizations with thousands of employees.In this paper, we present a system to assist Human Resources professionals in finding employees having similar objectives. The system automates the process of matching employees based on their common objectives using pre-trained word embeddings and semantic similarity metrics. Even though word embeddings are used to solve a variety of business problems, their effectiveness in searching for similar employee objectives is not sufficiently studied. In accordance with the feedback received from the Human Resources department of a partner company, the system proved to be effective in reducing both the time and effort consumed in the matching process.
Collective AHU Anomaly Detection for Building Energy Optimization

As for the national agenda, carbon neutral 2050, the efficient use of energy is very important along with the development of new and renewable energy. In the case of building energy, energy efficiency largely depends on the operating efficiency of the HVAC system. The AHU is a main part of the HVAC system. Therefore, the normal operation of the AHU device is very important for energy efficiency. This paper summarizes the study results about AHU device anomaly detection. It is very difficult to obtain AHU device fault data at the actual site, so we generated simulation data by using domain knowledge and a simulation tool. The building energy simulator energyPlus™ was used, and the data that was the basis for the simulator was measured from the actual building in KIER. Ensemble algorithms were used for multiclass classification and obtained over 90% accuracy for a cooling season and 99% accuracy for a heating season.
A cooperative overcurrent protection method for permanent magnet synchronous motor driver

with the improvement of power electronics technology, motor drive technology has also developed rapidly. A reliable overcurrent protection method is a prerequisite for ensuring the performance of the drive system. Aiming at the respective shortcomings of traditional methods for overcurrent protection, overcurrent protection method of permanent magnet synchronous motor driver. A software-hardware overcurrent protection method for permanent magnet synchronous motor driver is proposed. The article content covers the scheme design and principle analysis. Finally, experimental verification is completed on a certain motor platform. The results show that when the overcurrent situation occurs due to abnormal working conditions, the softwarehardware cooperative overcurrent protection method can achieve the protection and stop the system in time, providing support for improving the safety and stability of electronic control systems.
Quantum circuit design using neural networks assisted by entanglement

This work introduces a novel structure based on universal quantum circuits which uses classical machine learning techniques in order to solve a quantum problem: the decomposition of a generic quantum operator into a sequence of elementary unitary matrices (universal basic quantum gates). Even though the postulates of quantum mechanics guarantee any unitary operation as a feasible operation over a quantum system, there is no simple method to implement an arbitrary algorithm. By means of a multilayer hybrid neural network in which the basic cell is made up of CNOTs and universal one-qubit unitary gates, this work offers a solution to the given problem. These specific gates were chosen since they are the gates available in real quantum computers such as IBMQ's quantum processors. The network learns the unitary gates classically using the method of the steepest descent and is aided in learning the entangling gates by the use of two types of quantum correlations: Mutual Information (MI) and Cumulative Correlation Measure (CCM). The algorithm implemented in this case is a type of supervised learning. The results show that the model fits the data gracefully and correctly predicts a wide range of algorithms.
High-level Image Classification by Synergizing Image Captioning with BERT

Conventional image classification methods mostly aim to classify a single object in an image in which an object often occupies a large area. However, images in social network services (SNS) are more complicated. They usually include multiple objects that have much information, such as people, environments, and actions. In this work, we aim at understanding images from SNS and classifying them to categories such as fashion, traveling, education, beauty, and animals. To improve the classification accuracy in such complicated scenario, in this paper, we propose a new framework for high-level image classification by synergizing the image captioning and the Natural Language Processing (NLP) model. First, we use an image captioning model to understand images, which generates text descriptions about the images. Second, we use a natural language processing model to classify the generated text descriptions from the images. In this way, we can classify the images according to the classification results of the generated text descriptions. Our framework includes two models; one is image captioning model, which we use a TensorFlow based visual attention model with the inception V3 model for pre-processing and extracting the image features. The other model is the NLP model, Bidirectional Encoder Representations from Transformers (BERT). We have built a labeled image dataset from Instagram, a popular SNS platform, to test our framework. Our results show that our proposed method has a promising performance in terms of classification accuracy.
Representing academic identities in email: content and structure of Automatic Signatures

This paper analyses the automatic email signatures (ASs) of 200 academics. Qualitative and quantitative analyses of the ASs reveal that they are often written in one language, and only occasionally in two, English being a frequent choice. The ASs contain information with a primarily identificatory function, and occasionally with a promotional and socialising function. Despite the absence of clearly compulsory components, a typical structure can be identified in the ASs, which includes a specification of the writers’ identity, reference to their affiliation, mention of their achievements, and an indication on how they can be reached. Like other academic texts, the ASs examined are places of self-categorisation and self-identification, tools for presenting one’s professional identity, which are developing as sites of self-promotion.
Count Regression Models for Analyzing Crime Rates in The East Java Province

Abstract Crime rate is the number of reported crimes divided by total population. Several factors could contribute the variability of crime rates among areas. This study aims to model the relationship between crime rates among regencies and cities in the East Java Province (Indonesia) and some potentially explanatory variables based on Statistics Indonesia publication in 2020. The crime rate in the East Java Province was consistently at the top three after DKI Jakarta and North Sumatra during 2017 to 2019. Therefore, it is interesting for us to study further about the crime rate in the East Java. Our preliminary analysis indicates that there is an overdispersion in our sample data. To overcome the overdispersion, we fit Generalized Poisson and Negative Binomial regression. The ratio of deviance and degree of freedom based on Negative Binomial is slightly smaller (1.38) than Generalized Poisson (1.99). The results indicate that Negative Binomial and Generalized Poisson regression, compared to standard Poisson regression, are relatively fit to model our crime rate data. Some factors which contribute significantly (α=0.05) for the crime rate in the East Java Province under Negative Binomial as well as Generalized Poisson regression are percentage of poor people, number of households, unemployment rate, and percentage of expenditure.
Workload Management System for Cricketers

Cricketers are dynamic players in the field and hence more vulnerable to injuries. The injury rate of Sri Lankan cricketers is very high, resulting in their careers being shortened. Therefore, we established a workload management system for cricketers to resolve this issue with wearable Inertial Measurement Unit (IMU) sensors mounted on their bodies. In order to mitigate their accidents, we evaluated kinds of the activities performed by an athlete using Convolutional Neural Network (CNN) and computed the workload parameters after the session. The expected results of our project were to develop a system to collect and analyze the critical workload parameters of cricketers and showcase results in a user-friendly manner.