Reviewerly/exHarmony huggingface dataset:
Two-photon Rabi–Stark model

The analytically exact solutions to the two-photon Rabi–Stark model are given by using the Bogoliubov operators approach. Transcendental functions responsible for the exact solutions are derived. The zeros of the transcendental functions reproduce completely the regular spectra. The first-order quantum phase transitions are also detected by the pole structure of the derived transcendental functions, similar to the one-photon Rabi–Stark model, different from the two-photon Rabi model where the energy levels for the ground-state and the first excited state do not cross. The spectra collapse characteristics resemble those in two-photon Rabi model. Some low lying levels can split off from the collapse energy, which is different from the one-photon Rabi–Stark model in which all levels collapse.
Empirical Analysis Тowards the Effect of Social Media on Cryptocurrency Price and Volume

Bitcoin’s value is highly dependent on the communities that use it. This network effect is true for all new technologies. Today’s online communities are so large in population that both the Facebook user and Youtuber populations have surpassed the Chinese population. We take a big data approach using millions of samples of posts from Twitter, Telegram, and Reddit to study how and if social media platforms, the epitome of online communities, affect Bitcoin’s price and volume as well as the price and volume of fifteen other top cryptocurrencies. We work in collaboration with Solume, a data centered fin-tech startup, as well as with Sentistrength, an opinion mining tool developed by researchers in the UK, to classify the sentiment of the millions of posts we study. We collected millions of posts related to 16 cryptocurrencies from November 2017 through August 2018 on an hourly basis and explore social media volume sentiment effect on these cryptocurrencies. Findings confirm that volumes of exchanged posts may predict the fluctuations of Bitcoin’s price but mainly, they predict volume. We also find that Reddit and Telegram posts have greater impact on Bitcoin volume than Twitter. Results indicate that information about the use of social media platforms can assist in tracking real world behavior and may even predict real financial market trends.
Integrasi N-gram, Information Gain, Particle Swarm Optimation di Naïve Bayes untuk Optimasi Sentimen Google Classroom

The use of Learning Management System (LMS) applications made by Google with name Google Classroom since 2015 in junior and senior high schools in Bekasi City helps the learning process become easier. However, its use can have positive and negative effects on students. Google Class Sentiment by integrating N-grams, Information Gain, Particle Swarm Optimization, and Naïve Bayes Classifiers that have never been done by researchers before. From the experiments carried out, N-gram can increase the accuracy of 6.7% and AUC 4%, while using PSO can increase the Accuracy of 9.9% and AUC of 10.4%.
The study of cryptographic hashing algorithms used in modern blockchain systems

Modern hashing algorithms that are or can be used in various blockchain systems are studied in this work. In particular, the most common and used cryptographic hashing algorithms are considered, which are standardized at the international and national levels, as well as algorithms, although not standardized, but used in most modern decentralized systems built on blockchain technology.
Investigation into Information Literacy and the Use of Web 2.0 Technologies in a Faculty of Architecture

Following a rapid development in technology, many information sources are now digitized. Consequently, individuals are expected to demonstrate the skills needed to critically evaluate, synthesize, and effectively use those sources. In this context, information literacy and information technology, especially skills with Web 2.0 tools, have become concepts that directly affect each other’s development. This study investigated the information literacy self-efficacy and use of Web 2.0 tools among architectural students in particular. A t-test and ANOVA were performed on the data collected through a questionnaire survey. The results revealed that there was no difference in information literacy self-efficacy according to the participants’ gender or duration of internet use but did vary with the level of their knowledge of foreign languages and Web 2.0 tools. In addition, instant messaging systems were the most preferred of the Web 2.0 tools to use for educational purposes. Ranked from most to least preferred, other Web 2.0 tools included Facebook–Twitter, video sharing platforms, blogs, wikis, and podcasts.
Impact Internal Control on Efficiency of the Organizations in Jaffna District

This study was undertaken with the objective of finding Impact of Internal control on efficiency of organizations in Jaffna District. The study is based on twenty five organizations and wo hundred and forty four respondent in Jaffna District, which are private and public organizations in the district. Internal control assessment is measured by control environment, risk assessment; information and communication, control activities, and monitoring and financial performance is measured by efficiency. Internal control and efficiency measured by correlation analysis and regression analysis. The study finds internal control and efficiency are statistically significant in determining efficiency. Further the study suggests that work performed by the organizations have been implemented in the past and the work schedules take into consideration the goals of internal control. The study further points out that keen attention should be paid on to adopt efficient management information system and training of the staff. All these enhance the staff productivity and reducing costs and also ensure the efficient operation of the firms and financial performance of their operation as well.
Analysis of Weight Product (WP) Algorithms in the best Go Car Driver Recommendations at PT. Maranatha Putri Bersaudara

This study aims to rank the best Go Car Driver. The problem arises because of the inaccuracy in giving value to the driver which results in the decision being given incorrectly so that the assessment tends to be subjective. This research was conducted at PT. Maranatha Putri Bersaudara. Sources of data obtained by observing, interviewing. The settlement method used is a decision support system with the Weight Producted method. The assessment criteria used are Performance (C1), Number of orders (C2), Rating (C3), Attitude (C4), Rating (C5) and Appearance (C6) where the alternatives used are 4 samples. The results obtained using the Weighted Product method are Alternative1 and Alternative4 which are recommended as the best go car driver with the assessment results of 0.0307 and 0.0272. It is expected that research results can be input to the relevant parties in recommending the best go car driver so as to minimize subjective judgment.
Annotation Guideline No. 4: Annotating Narrative Levels in Literature

Our participation in the Shared Task on the Analysis of Narrative Levels Through Annotation was motivated by a theoretical and practical interest in narratological phenomena of literary texts. We are a group of four literary scholars, three of whom are also working in the field of Digital Humanities. Combining these two scientific perspectives seems to be a fruitful research approach to formalize concepts of narratology with a focus on intersubjectivity. Therefore, a shared task dealing with narrative levels was particularly appealing to us, since narrative levels are both a delimited aspect of narratological categories and a complex concept of literary theory that can be of great importance for a formal text analysis and the following interpretation.
Penerapan metode fuzzy sugeno untuk prediksi persediaan bahan baku

PT. Cahaya Boxindo Prasetya is a company engaged in the manufacture of carton boxes or boxes. The company's activities also include cutting and printing services using machinery and human power. The problem faced in this company is the difficulty of predicting the amount of inventory of raw materials that will be included in the production. The remaining raw materials for production will be used as the final stock to get the minimum, the goal is to reduce excess stock Overcoming this problem, fuzzy logic is used to predict raw material inventories by focusing on the final stock. In this study using Fuzzy Sugeno, with three input variables, namely: initial inventory, purchase, production, while the output is the final stock. Determination of prediction results using defuzzification using the average concept of MAPE (Mean Absolute Percentage Error). The results obtained, using the Fuzzy Sugeno method can predict the inventory of raw materials with a MAPE value of 38%.
Modified Adaptive Implicit Shape Model for Object Detection

Automated threat object detection in X-ray images is needed urgently in baggage inspection at airports, railway stations and other public places. However, the works on object detection are still very limited to meet the needs of practical application. In this paper, we propose a modified adaptive implicit shape model (MAISM) to detect threat objects in X-ray images, in which the triangle patches are used to compute occurrence of the centroid of object instead of keypoints. This model is adaptive for object detection in images of variable scales through triangle patch matching. Experiments on three different threat objects images (razor blades, shuriken, handguns) of various scales demonstrate the effectiveness of the proposed method.
Symmetric- Based Steganography Technique Using Spiral-Searching Method for HSV Color Images

Steganography is defined as hiding confidential information in some other chosen media without leaving any clear evidence of changing the media's features. Most traditional hiding methods hide the message directly in the covered media like (text, image, audio, and video). Some hiding techniques leave a negative effect on the cover image, so sometimes the change in the carrier medium can be detected by human and machine. The purpose of suggesting hiding information is to make this change undetectable. The current research focuses on using complex method to prevent the detection of hiding information by human and machine based on spiral search method, the Structural Similarity Index Metrics measures are used to get the accuracy and quality of the retrieved image and to improve its perceived quality.&#x0D; The values of information measures are calculated through practical experiments of (perceptibility, robustness, capacity) by using interpolation technique and structural similarity measures. Experimental results show that the use of these measures (PSNR, MSE, and SSIM) has improved the image quality by 87% and has produced values of PSNR (38-41 dB), MSE = 0.6537 and SSIM= 0.8255. The results also demonstrate a remarkable progress in the field of hiding information and the increasing difficulty of detecting it by humans and machines.
Regularized Jacobi Wavelets Kernel for Support Vector Machines

A new family of regularized Jacobi wavelets is constructed. Based on this Jacobi wavelets, a new kernel for support vector machines is presented. Using kernel and frame theory, the Reproducing Kernel Hilbert Space of this kernel is identified. We show that without being a universal kernel, the proposed one possesses a good separation property and a big ability to extract more discriminative features. These theoretical results are confirmed and supported by numerical experiments.
A Fast Symmetric Alternating Direction Method of Multipliers

In recent years, alternating direction method of multipliers (ADMM) and its variants are popular for the extensive use in image processing and statistical learning.A variant of ADMM: symmetric ADMM, which updates the Lagrange multiplier twice in one iteration, is always faster whenever it converges.In this paper, combined with Nesterov's accelerating strategy, an accelerated symmetric ADMM is proposed.We prove its O( 1k 2 ) convergence rate under strongly convex condition.For the general situation, an accelerated method with a restart rule is proposed.Some preliminary numerical experiments show the efficiency of our algorithms.
Teaching Vocabulary through Fly Swatter Game

The study aimed to find out whether fly swatter game can give influence toward students’ vocabulary mastery. The subject of this study was grade VII-2 of SMPN 9 Tangerang Selatan in the academic year of 2018/2019, which consisted of 42 students. The method used in this study is a quantitative method with the design of the pre-experimental study. The data from pre-test and post-test were analyzed statistically using spss 16. The result of this study showed; the students’ pre-test mean score was 45.4 and the students’ post-test mean score was 86.6. The result of t-test was 21.55 and significant (2-tailed) was 0.00 &lt; p (0.05). The hypothesis (H1) was accepted, which means there is a significant difference between pre-test and post-test scores of the experimental group. It can be concluded that applying fly swatter game in teaching vocabulary to 7th grade students of SMPN 9 Tangerang Selatan is effective.
Intuition, Accuracy, and Immersiveness Analysis of 3D Visualization Methods for Haptic Virtual Reality

The purpose of this study is to analyze the usefulness of 3D immersive stereoscopic virtual reality technology in applications that provide tactile sensations.Diverse experiments show that the haptic 3D visualization method presented in a 3D stereoscopic space using headsets is more intuitive, accurate and immersive than the haptic 3D visualization method displayed in 3D flat displays.In particular, the intuitiveness has been significantly improved in a stereoscopic 3D visualization rather than flat 3D visualization.In spite of the general superiority of the stereoscopic 3D visualization, it is mentionable that the precise operation performance has not been greatly improved in the elaborate object movements.It means that users can recognize and manipulate the position of objects more quickly in 3D stereoscopic immersive VR environments, however, the precise operation does not benefit greatly in the 3D stereoscopic visualization.Note that the degree of game immersion is remarkably augmented in the case of using 3D stereoscopic visualization.
Attention-Guided Spatial Transformer Networks for Fine-Grained Visual Recognition

The aim of this paper is to propose effective attentional regions for fine-grained visual recognition. Based on the Spatial Transformers' capability of spatial manipulation within networks, we propose an extension model, the Attention-Guided Spatial Transformer Networks (AG-STNs). This model can guide the Spatial Transformers with hard-coded attentional regions at first. Then such guidance can be turned off, and the network model will adjust the region learning in terms of the location and scale. Such adjustment is conditioned to the classification loss so that it is actually optimized for better recognition results. With this model, we are able to successfully capture detailed attentional information. Also, the AG-STNs are able to capture attentional information in multiple levels, and different levels of attentional information are complementary to each other in our experiments. A fusion of them brings better results.
Vision-based system model for detecting violence against children

We present in this paper a machine learning model for detecting violence against children. This model, which uses skeletal data acquired by depth sensors achieved a high accuracy violence detection rate of 99.03 %. In sum, this research method presents: •First ML-based method for detecting most common child abuses, which keeps the privacy of people by using only skeleton joints data.•The model has only two classes (violent action, non-violent action).•The model can be a base for other researches and implementations in schools by school psychologists and counselors.
Automatic Semantic Categorization of News Headlines using Ensemble Machine Learning: A Comparative Study

Due to widespread availability of Internet there are a huge of sources that produce massive amounts of daily news.Moreover, the need for information by users has been increasing unprecedently, so it is critical that the news is automatically classified to permit users to access the required news instantly and effectively.One of the major problems with online news sets is the categorization of the vast number news and articles.In order to solve this problem, the machine learning model along with the Natural Language Processing (NLP) is widely used for automatic news classification to categorize topics of untracked news and individual opinion based on the user's prior interests.However, the existing studies mostly rely on NLP but uses huge documents to train the prediction model, thus it is hard to classify a short text without using semantics.Few studies focus on exploring classifying the news headlines using the semantics.Therefore, this paper attempts to use semantics and ensemble learning to improve the short text classification.The proposed methodology starts with preprocessing stage then applying feature engineering using word2vec with TF-IDF vectorizer.Afterwards, the classification model was developed with different classifier KNN, SVM, Naïve Bayes and Gradient boosting.The experimental results verify that Multinomial Naïve Bayes shows the best performance with an accuracy of 90.12% and recall 90%.
Assessing the Influence Integration of Customer Relationship Management (CRM) on Financial Performance: An Empirical Study on Commercial Jordanian Banks in Amman

Purpose : The purpose of this paper is to investigate the relationship between Integration of CRM and various aspects of organization performance (i.e. financial, customer, internal process, and learning and growth) in Jordan banks. Design/methodology/approach: This is a quantitative study, the response came from the managers of 155 Commercial banks and data collected was subjected to correlation and regression analysis in pursuance of the study's stated objectives. Findings: The results of this study suggest that tow dimension of CRM integration (Data Integration and CRM Functionality). CRM Functionality has a positive and significant impact on different perspectives of bank performance. However, Data integration failed to show a significant relationship with perspective of financial performance. Research limitations/implications: An emphasis has been placed on the direct relationship between CRM Functionality and organization performance perspectives as well as, the study concentrated only on bank in Amman. Practical implications: Meaningful implications are made that building an extensive and effective CRM integration in bank firms is crucial to face a high competition and improve performance in Jordan bank sector. Originality/value: The paper addresses CRM integration issues specifically for bank in Jordan. Keywords: CRM, CRM integration, CRM Functionality, Bank performance. DOI : 10.7176/JRDM/60-06 Publication date :October 31 st 2019
Classification of Online Grooming on Chat Logs Using Two Term Weighting Schemes

Due to the growth of Internet, it has not only become the medium for getting information, it has also become a platform for communicating. Social Network Service (SNS) is one of the main platform where Internet users can communicate by distributing, sharing of information and knowledge. Chatting has become a popular communication medium for Internet users whereby users can communicate directly and privately with each other. However, due to the privacy of chat rooms or chatting mediums, the content of chat logs is not monitored and not filtered. Thus, easing cyber predators preying on their preys. Cyber groomers are one of cyber predators who prey on children or minors to satisfy their sexual desire. Workforce expertise that involve in intelligence gathering always deals with difficulty as the complexity of crime increases, human errors and time constraints. Hence, it is difficult to prevent undesired content, such as grooming conversation, in chat logs. An investigation on two term weighting schemes on two datasets are used to improve the content-based classification techniques. This study aims to improve the content-based classification accuracy on chat logs by comparing two term weighting schemes in classifying grooming contents. Two term weighting schemes namely Term Frequency – Inverse Document Frequency – Inverse Class Space Density Frequency (TF.IDF.ICSdF) and Fuzzy Rough Feature Selection (FRFS) are used as feature selection process in filtering chat logs. The performance of these techniques were examined via datasets, and the accuracy of their result was measured by Support Vector Machine (SVM). TF.IDF.ICSdF and FRFS are judged based on accuracy, precision, recall and F score measurement.
An Empirical Study on the Performance Evaluation of Introducing Artificial Intelligence Medical Service System into Medical Ecological Environment

Under the advance of time, development of science and technology, and popularity of network, the combination of medical ecological environment and information & communication technology provides diverse medical care services for the public. Having the elderly receive diverse medical services in neighboring communities, under constantly increasing medical expenses becomes the common objective of various countries. An artificial intelligence medical service system might be the ideal tool to actualize local aging. Taking prefecture-level cities in Shanghai as samples, total 16 DMUs are studied. With the operation of Modified Delphi Method, the geometric mean is used as the consensus of experts’ input/output evaluation; and, DEA is applied to evaluate the performance on the introduction of artificial intelligence medical service system into medical ecological environment. The research results conclude that one DMU presents strong efficiency on the introduction of artificial intelligence medical service system into medical ecological environment, 6 DMUs reveal the efficiency in 0.9-1, and 9 DMUs appear the efficiency lower than 0.9. Slack Variable Analysis is further applied to improve excess and short inputs of prefecture-level cities. According to the results, suggestions are proposed, expecting that medical staff could real-time grasp the physiological state and offer timely assistance to reduce medical costs, promote the quality of life of people in communities, and improve doctor-patient interactive relationship.
Students’ Experiences of Using Vlog to Learn English

There has been a change in the process of learning from a conventional way into the technologically-based way these days. From this change, it is necessary to find out the significance of utilizing technology in learning English from students’ perspectives. This study aims to investigate the EFL students’ experiences of using vlogs, as one of the technology products to learn English. In conducting this research, the researchers adopted exploratory study as the approach and employed the hermeneutic phenomenology as the research design. These designs have relations to description and interpretation of the meaning of EFL students’ experience. One student participated in this study. The researchers employed the student’s reflection as part of the assignment after making vlogs as well as a one-on-one in-depth interview to obtain the data. At this point, two research questions are underlying this study, namely: (1) How are the students’ experiences in learning English using vlogs?; (2) What does the student learn from it? From the data analysis, the researchers found several findings in this study. For the first research question, there are three findings found, namely, losing words to say, conveying English language expression, expressing his identity, looking for additional information in English. For the second research question, there are four findings revealed. Those were detailed guideline making, better editing, confidence gaining, and vocabulary improvement.