Reviewerly/exHarmony huggingface dataset:
Advanced text documents information retrieval system for search services

Information technology has explored the growth of text documents data in many organizations and the structural arrangement of voluminous data is a complex task. Handling the text document data is a challenging process involving not only the training of models but also numerous additional procedures, e.g., data pre-processing, transformation, and dimensionality reduction. In this paper, we describe the system’s architecture, the technical challenges, and the novel solution we have built. We propose a Recurrent Convolutional Neural network (RCNN), based text information retrieval system which efficiently retrieves the text documents and information for the user query. Pre-processing using tokenization and stemming, retrieval using TF-IDF (Term Frequency-Inverse Document Frequency), and RCNN classifier which captures the contextual information is implemented. A real-time advanced search system is developed on a huge set of MAHE University dataset. The performance of the proposed text document retrieval system is compared with other existing algorithms and the efficacy of the method is discussed. The proposed RCNN-based text document information retrieval model performs better in terms of precision, recall, and F-measure. A high-quality and high-performance text document retrieval search system is presented.
Variance Penalized On-Policy and Off-Policy Actor-Critic

Reinforcement learning algorithms are typically geared towards optimizing the expected return of an agent. However, in many practical applications, low variance in the return is desired to ensure the reliability of an algorithm. In this paper, we propose on-policy and off-policy actor-critic algorithms that optimize a performance criterion involving both mean and variance in the return. Previous work uses the second moment of return to estimate the variance indirectly. Instead, we use a much simpler recently proposed direct variance estimator which updates the estimates incrementally using temporal difference methods. Using the variance-penalized criterion, we guarantee the convergence of our algorithm to locally optimal policies for finite state action Markov decision processes. We demonstrate the utility of our algorithm in tabular and continuous MuJoCo domains. Our approach not only performs on par with actor-critic and prior variance-penalization baselines in terms of expected return, but also generates trajectories which have lower variance in the return.
Recognizing Activities of Daily Living using 1D Convolutional Neural Networks for Efficient Smart Homes

Human activity recognition is considered a challenging task in sensor-based monitoring systems.In ambient intelligent environments, such as smart homes, collecting data from ambient sensors is useful for recognizing activities of daily living, which can then be used to provide assistance to inhabitants.Activities of daily living are composed of complex multivariable time series data that has high dimensionality, is huge in size, and is updated constantly.Thus, developing methods for analyzing time series data to extract meaningful features and specific characteristics would help solve the problem of human activity recognition.Based on the noticeable success of deep learning in the field of time series classification, we developed a model called a deep one-dimensional convolutional neural network (Deep 1d-CNN) for recognizing activities of daily living in smart homes.Our model contains several onedimensional convolution layers coupled with max-pooling technique to learn the internal representation of time series data and automatically generate very deep features for recognizing different activity types.For the performance evaluation, we tested our deep model on the new real-life dataset, ContextAct@A4H, and the results showed that our model achieved a high F1 score (0.90).We also extended our study to show the potential energy saving in smart homes through recognizing activities of daily living.We built a recommendation system based on the activities recognized by our deep model to detect the devices that are wasting energy, and recommend the user to execute energy optimization actions.The experiment indicated that recognizing activities of daily living can result in energy savings of around 50%.
Particle swarm optimization for solving thesis defense timetabling problem

The thesis defense timetabling problem is a fascinating and original NP-hard optimization problem. The problem involves assigning the participants to defense sessions, composing the relevant committees, satisfying the constraints, and optimizing the objectives. This study defines the problem formulation that applies to Universitas Multimedia Nusantara (UMN) and use the particle swarm optimization (PSO) algorithm to solve it. As a demonstration of concept and viability, the proposed method is implemented in a web-based platform using Python and Flask. The implementation is tested and evaluated using real-world instances. The results show that the fastest timetable generation is 0.18 seconds, and the slowest is 21.88 minutes for 25 students and 18 department members, without any violation of the hard constraints. The overall score of the EUCS evaluation for the application is 4.3 out of 6.
The effect of electronic commerce on profitability of Jordanian commercial companies

The study aims to determine the effect of e-commerce on the profitability of commercial companies of Jordan’s economy and to study the magnitude of the effect of e-commerce (whether positive or negative). The research population includes the commercial companies that are listed on the Amman Financial Market for four years (2015-2018), which consisted of (10) Companies that meet the conditions. The results of the study show that e-commerce practices positively affected the profitability in the commercial companies, also e-commerce benefits on Return on Assets (ROA) and Return on Equity (ROE) for commercial companies in Jordan. The findings of this study indicate that there is an effect of e-commerce benefits on (ROA) for commercial companies, where (0.016). Findings also indicates that there is an effect of e-commerce benefits on ROE for commercial companies, where (0.012). The researcher suggests developing and improving electronic commerce operations in Jordanian companies to improve the relationship between the companies and its customers. Which will lead to improve Jordanian economy rapidly?
General Theoretical Issues of Improving Private Forensic Methods In The Field Of Combat Against Cybercrime

This article critically examines the problems and gaps that arise in national legislation and law enforcement practice in the fight against cybercrime. Scientifically grounded ways and means of their overcoming are theoretically analyzed. In this regard, proposals and recommendations were developed for the development of private criminology methodologies for the development of the theory of forensic science.&#x0D; The article was prepared with the views of theorists and practitioners, as well as technical research which were based on scientific and practical research in the field of countering cybercrime.&#x0D; The study analyzes a number of proprietary forensic methods that serve to improve the effectiveness of investigative actions in the fight against cybercrime. National legislation, investigative and judicial practice, international prominent practices were studied, and their achievements and drawbacks were substantiated on the basis of the author's conclusions. Based on this, the most favorable directions for combating these crimes in Uzbekistan were selected.&#x0D; The article provides a systematic, legal, scientific and methodological analysis of problems in this area and the author's conclusions on this matter. At the same time, the role and importance of advanced foreign experience and international standards in improving national legislation and ensuring the implementation of the tasks set in the State Program are emphasized. In addition, the concepts of "electronic evidence" and "digital evidence" were scientifically analyzed. Their content and technical features are scientifically and theoretically substantiated.
Cyclic Task-Based Affordable Robot for Medicine-Intake Purpose of COVID-19 Patient

Robotic assistants become more efficient if it can automatically log information into the patient s electronic health records.To let the nurses and doctors utilize their time more for patient s treatment analysis purpose, cyclic task-based robotic assistance is needed for sure.This research has been carried out to make the medical science more efficient by reducing doctor s and nurse s inspection time and schedule maintaining error in patient s medicine intake criteria.Initially, this cyclic task-based robot has been designed and calibrated for COVID-19 patient s medicine intake purpose.Based on the demands and necessities, more related features will be added on the next phase.Line follower mechanism has been used in this research to maintain the communication between COVID-19 patient and the robot.Monitoring patient s necessities and alerting the nurses in emergencies can also be done flawlessly with this type of roboticmedical-assistants in future.
Cybersecurity in a Post-Quantum World: How Quantum Computing Will Forever Change the World of Cybersecurity

High Performance Computing (HPC) has recently been considerably improved, for instance quantum computing has been developed to achieve high performance computation in many areas, such as medical research, artificial intelligence, weather forecasting, etc.But it also poses a significant threat to cybersecurity, requiring changes to data encryption methods.Currently, the most widely used asymmetric algorithms are based on difficult mathematical problems, such as factoring large numbers, which can take thousands of years on today's most powerful supercomputers.The purpose of this paper is to dive into the field of cybersecurity and understand how modern practices will be affected by the advancements of quantum computing.In doing so, a fundamental understanding of modern-day computing, modern-day cybersecurity, and quantum computing will need to be established.This, in turn, will build the foundation to allow for a comprehensive analysis of how powerful quantum-based computing is in comparison to modern-day computing, and how this disruptive technology will ultimately change the field of cybersecurity on a global scale.In addition, current industry cybersecurity best practices will be presented to expose their projected vulnerabilities as well as what can be done in the immediate future to prepare for the ever-rapid advancements in computing.Finally, conclusions will be extrapolated on what is to come for future generations in the ongoing race between computing and cybersecurity.
High Accurate and a Variant of k-fold Cross Validation Technique for Predicting the Decision Tree Classifier Accuracy

In machine learning data usage is the most important criterion than the logic of the program. With very big and moderate sized datasets it is possible to obtain robust and high classification accuracies but not with small and very small sized datasets. In particular only large training datasets are potential datasets for producing robust decision tree classification results. The classification results obtained by using only one training and one testing dataset pair are not reliable. Cross validation technique uses many random folds of the same dataset for training and validation. In order to obtain reliable and statistically correct classification results there is a need to apply the same algorithm on different pairs of training and validation datasets. To overcome the problem of the usage of only a single training dataset and a single testing dataset the existing k-fold cross validation technique uses cross validation plan for obtaining increased decision tree classification accuracy results. In this paper a new cross validation technique called prime fold is proposed and it is experimentally tested thoroughly and then verified correctly using many bench mark UCI machine learning datasets. It is observed that the prime fold based decision tree classification accuracy results obtained after experimentation are far better than the existing techniques of finding decision tree classification accuracies.
Geographic Information System Mapping of Housing Locations Using Web-Based Breadth First Search Algorithm

Sidoarjo Regency as one of the cities with an increasingly dense population and the needs of people who want to find information about housing quickly, makes a Geographical Information System indispensable, especially in terms of finding housing locations. Therefore, the authors create a web-based housing geographic information system in Sidoarjo Regency using Breadth First Search to design the system interface and logic, MySQL for database system design and Bing Api for mapping. Information presented in the form of housing names, addresses, photos of housing, pictures of house types, house plans, availability and prices of each type of house. With the GIS (Geographical Information System), it is hoped that the public can more easily and quickly get housing information in Sidoarjo Regency. The results of this study show the details of the housing location from the user's location according to the radius to be selected and know the approximate distance of travel time to the residential location
A comparative study of audio latency feature of Motorola and Samsung mobile phones in forensic identification

Background:In forensic science the process of proving authenticity of audio recording plays an important role.In recent times, Forensic experts mostly receives digital recording for authentication as compared to analog recording.A digitally altered audio signal, leaves no visual indications of being tampered, and it will be indistinguishable from an original audio signal.Objective: To highlight the significance of latency feature of mobile phone handsets in forensic science via comparing input audio latency feature of Samsung and Motorola mobile phone in two audio formats.Methods: In this work two wellestablished and most used brands of mobile phones were considered for comparison: SAMSUNG and MOTOROLA.In the present paper, the digital audio samples have been recorded using 20 mobile phones of various models from two different makes i.e.SAMSUNG and MOTOROLA, in two audio formats i.e.WAV and 3GP.Audio samples were then analysed using Adobe Audition 3.0 software for the input audio latency feature of mobile phones and compared.Findings: Input audio latency value of digital audio recordings can be helpful in forensic identification of make and model of source mobile phone.Novelty: A new technique in digital forensics, to classify the given audio samples on the basis of input audio latency feature and identifying the make of source mobile handsets.
Strategi Kesantunan Bertutur Mahasiswa Kepada Dosen Melalui Komunikasi WhatsApp

ABSTRACT This article reveals the politeness strategy for students speaking to lecturers through WhatsApp communication. There are the four purposes of this research. First, describe the student’s speaking strategy to lecturers via WhatsApp communication. Second, describe the principle of politeness to lecturers through WhatsApp. Third, describe the scale of student language politeness. Fourth, describe the politeness of student language to lecturers. The design of this study is qualitative with descriptive method. This research was conducted at the Indonesian Language Education Study Program, Padang State university. The data was collected from two semesters, namely the odd semester of July―Desember 2019 and the even semester of Januari―June 2020. Basedon the results of the study, it can be concluded that the most widely used strategy is the strategy of speaking using negative politeness and student speech can be said to be polite. Kata kunci: Strategi, Kesantunan Bertutur, Mahasiswa kepada Dosen, Komunikasi WhatsApp.
PENGARUH MODEL PROJECT BASED LEARNING BERBANTUAN MEDIA GAMBAR BERSERI TERHADAP KETERAMPILAN MENULIS TEKS PROSEDUR SISWA KELAS VII SMP NEGERI 20 PADANG

ABSTRACT This purpose of this study is three. First, describing the procedure of writing text for students of class VII in Junior High School 20 Padang before using the project based learning model assisted by the media of serial images. Secondly, describing the procedure of writing text for students of class VII in Junior High School 20 Padang after using the project based learning model assisted by the media of serial images. Third, analyzing the effect of the use of project based learning models assisted by the media of serial images on the skills of writing procedure text for students of class VII in Junior High School 20 Padang. This type of research is quantitative research with one group pretest and posttest design experimental methods. There are three results of this study. First, the procedure of writing text in the seventh grade of Junior High School 20 Padang before using the project based learning model assisted by the media of serial images is of sufficient qualification (C) with an average value of 62,76. Second, the procedure of writing text in the seventh grade of Junior High School 20 Padang after using the project based learning model assisted by the media of serial images is of good qualification (B) with an average grade of 78,52. Third, based on the t-test, H1 is accepted at the 95% confidence level and dk = (n-1) because tcount&gt;ttable (6,25&gt;1,82). Kata Kunci: Pengaruh, Model Project Based Learning, Media Gambar Berseri, Keterampilan Menulis Teks Prosedur
Development of Physics Learning Media Based-Mobile Learning Using Adobe Flash CS6 at Muhammadiyah University of Makassar

This research is motivated by the fact that physics learning content based on mobile learning can be accessed widely and most students have cellphones, but it has not been widely used to increase student enthusiasm in learning physics. Physics Learning Media Based on Mobile Learning is an effort to solve this problem. One of them is by Development of Physics Learning Media-Based Mobile Learning Using Adobe Flash CS6. This study aimed to determine the development of learning media products based on mobile learning using adobe flash CS6 and to examine the the feasibility of learning media based on mobile learning using Adobe Flash CS6 at Muhammadiyah University of Makassar. This type of research was a research and development starting from the defining stage (define), the design stage (design), and the development stage (develop). This mobile learning-based physics learning media was tested for validity by 2 experts of media, and tested for its effectiveness by 33 students. The results achieved were the m-learning media products that used the adobe flash CS6 application with validity criteria that were relevant, consistent and have practical usability, as well as effective implementation. These instructional media products can be used by teaching staff and students in higher education. as well as effective implementation. These instructional media products can be used by teaching staff and students in higher education. as well as effective implementation. These instructional media products can be used by teaching staff and students in higher education.
A Framework For Differentiable Discovery Of Graph Algorithms

Recently there is a surge of interests in using graph neural networks (GNNs) to learn algorithms. However, these works focus more on imitating existing algorithms, and are limited in two important aspects: the search space for algorithms is too small and the learned GNN models are not interpretable. To address these issues, we propose a novel framework which enlarge the search space using cheap global information from tree decomposition of the graphs, and can explain the structures of the graph leading to the decision of learned algorithms. We apply our framework to three NP-complete problems on graphs and show that the framework is able to discover effective and explainable algorithms.
A Dynamic ACT-R Model of Simple Games

A model of humans playing the simple game of Paper Rock Scissors based on the ACT-R architecture (Anderson, 1993; Anderson &amp; Lebiere, 1998) is presented. This model stores in long-term memory sequences of moves and attempts to anticipate the opponent’s moves by retrieving from memory the most active sequence. This results in a tightly linked dynamical system in which each player drives the play of its opponent. The performance of this model as a function of the length of the sequences stored and the amount of noise in the system is investigated, and is compared to the performance of human subjects.
Content-based recommender system architecture for similar e-commerce products

Recommendation systems are quite famous and are increasingly being used on e-commerce platforms for a variety of purposes. The recommendation system technique used also varies greatly depending on the scope and Item of recommendation. Content-based filtering, for example, is used to recommend related product items based on user preferences. However, how the recommendation system architecture should be built starts by creating a data model for bringing up related product items. This paper offers a system architecture by considering the initial problem usually faced by recommendation systems, namely the cold start problem. The problem of lack of user preferences data is trying to be overcome by utilizing product item documents. Product item documents are processed using the TF-IDF algorithm and Vector Space Model to generate a data model. Then a query can be applied to find similarities to items that the user has seen. In the end, the recommendation system architecture that was built produced excellent Precision using Recall and Precision testing. Tests are carried out for data using the weighting of product names and product labels. The result obtained 0.84 for the average value of Recall and 0.78 for the average value of Precision.
THE REALIZATION OF THEMATIC PROGRESSION IN ONLINE IELTS WRITING TASK 2 SAMPLES IN AN ENGLISH-LEARNING WEBSITE: A CASE STUDY

This research aims at investigating the realization of thematic progression (TP) patterns, which is defined as “the way in which the theme of a clause may pick up, or repeat a meaning from a preceding theme or rheme” (Paltrigde, 2006, p. 148), and their effects on text quality of online IELTS Writing Task 2 samples. Investigated in the light of systemic functional approach, TP is classified into five patterns, namely Simple Linear Progression (SLP), Constant Progression (CP), Split Rheme Progression (SRP), Split Theme Progression (STP) and Derived Hypertheme Progression (DHP) (McCabe, 1999). In this study, the corpus consisting of 24 sample essays derived from an English-learning website was analyzed using both quantitative and qualitative methods. The results of this study highlight that SLP is the most frequently employed pattern in the corpus, followed by CP, SRP, STP and DHP patterns respectively. The analysis also shows that proper employment of TP overall is a contributing factor to the coherence and cohesion of the text, with each pattern exerting different effects. SLP, with its cross-referential links, yields a constantly developing and cohesive text whilst CP keeps a strong thematic focus. Meanwhile, SRP and STP patterns are both found to offer a planned text development and DHP creates cohesion with hypernym-hyponym relation. Problems related to the lack of TP, inappropriate theme positioning and the overuse of the theme “It” and “There” are also reported in the corpus.