Reviewerly/exHarmony huggingface dataset:
Promoting Tail Item Recommendations in E-Commerce

The research area of recommender systems (RS) in e-commerce has become extremely popular in recent years. However, traditional RSs tend to recommend popular items, while niche (long-tail) items are often neglected, which is known as the long-tail problem. However, recent studies found that tail items are one of the key success factors in the e-commerce world. The availability of such items encompasses relatively high marginal profits and boosts the sales of popular short-head items. We suggest promoting long-tail items by leveraging the short-head items’ advantages and exposing the user to a tail item that may have not been considered otherwise. We use a classification model and statistical tools to generate personalized recommendations of a long-tail item considering a short-head item that has already been clicked. The uniqueness of our method lies in the combination of tail and head items to uplift the exposure of the latter and in using an applicable solution to deal with the extreme volume of tail items. We demonstrate the effectiveness of our method on real-world data from eBay and provide an analysis of the long-tail phenomenon and consumption behavior.
SMALT Software Package as a Tool for the Study of Graph-Theoretic Models of Texts

The SMALT software package (Statistical methods of analysis of literary texts) is implemented to conduct research in the field of attribution of literary texts. The article discusses new tools for storing, visualizing, comparing and search­ing data implemented in the system. They are implemented for text analysis using graph-theoretic models. Examples of philological studies performed using these tools are given.
Prototype Aplikasi Informasi Oleh-Oleh Di Purwokerto Dengan Menerapkan Metode User Experience Lifecycle

The Information Application for Souvenirs in Purwokerto is a mobile-based application that can be used to find information about souvenirs and gift shops in the city of Purwokerto. This application is intended for local tourists, immigrants and local residents to make it easier to find souvenirs in Purwokerto. Of course, when discussing travel or tourism, it cannot be separated from souvenirs, souvenirs, or souvenirs. This application is designed to provide information about souvenirs in the city of Purwokerto from products, shops, location of gift shops, prices for souvenirs and others. This study designed a prototype of information application for souvenirs in Purwokerto using the User Experience Lifecycle method, the method proposed by Hartson and Pyla to develop user experience with an emphasis on usability and user experience aspects. This method is divided into four stages, namely analysis, design, prototype, and evaluation. Testing in this study uses usability testing on the results of the medium fidelity level prototype with the System Usability Scale (SUS) method at the evaluation stage. The results of usability testing using the SUS method get a score of 74.17 or B (Good) where the score meets the minimum standard of the SUS score of 68. Usability measurement on the prototype application for souvenirs in Purwokerto has met the efficiency level with an overall relative efficiency value of 88 .5%, and the percentage effectiveness level is 91.3% of the results of task completion by all respondents. Thus the application prototype for souvenirs in Purwokerto has met the usability value in terms of efficiency, effectiveness and satisfaction. Based on this, this research has succeeded in applying the User Experience Lifecycle method to application design and usability testing with the System Usability Scale (SUS) which has exceeded the minimum standard. Therefore, the application design for souvenirs in Purwokerto based on user experience and needs can be carried out to the application development or production stage. Â Â Keywords : Gift, Application Prototype, User Experience Lifecycle, Usability, System Usability Scale (SUS)
Arabic vowels characterization and classification using the normalized energy relating to formants and pitch

Vowels are the primary units of a sound system of a language. The classification of these vowels is therefore very important for the recognition and synthesis of speech. In this paper, we propose a normalized energy-based approach in formants and pitch to characterize Arabic vowels (short vowels: / a /, / i /, / u /; long vowels: / a: /, / i: /, / u: /). The classification was performed using a developed algorithm on records extracted from an Arabic corpus after the extraction of the pitch and the first three formants and the computation of the normalized energy in these bands. The results showed that the algorithm distinguishes Arabic vowels by analyzing the normalized energy in the nucleus of F1, F2, and F3 formants and pitch F0 with a rate of 88.7% for long vowels and a rate of 90% for short vowels.
Promoting English Education Through Word-of-mouth: Case of Wall Street Bandung, Indonesia

There are many requests for English education as a result of the significance of raising the standard of English communication education in each location. Because it is believed that learning the English language can help one communicate better, it is necessary to have these skills in the workplace. This study examined the impact of word-of-mouth at an Indonesian English-language university. All of the Wall Street English Bandung students were employed as the population in this quantitative analysis. A questionnaire was used to gather information for this type of casual research from 100 students attending the "Wall Street English" institution. Word of mouth and brand image serve as the study's independent variables. The acquired results indicate that word-of-mouth has a large impact on decisions to use the study service when making purchases, and the outcomes of following regressions indicate that brand image also has a significant impact. In this study, recommendations and final suggestions are also covered.
PREVALENCE OF WORK-RELATED MENTAL DISORDERS IN BRAZIL: A PRE AND POST-PANDEMIC COMPARISON

Introduction: Work-related mental disorders are frequent and affect not only the work activity, but also the individual’s quality of life. During the COVID-19 pandemic – which began in 2019, there were changes in terms of society’s behavior in relation to its work activity – such as social isolation and teleworking – which brought uncertainties in the work dynamics of most professionals and functioned as one of the factors that influenced the emergence of mental disorders in the Brazilian population.(1) In this context, the importance of assessing the prevalence of these disorders in a pre- and post-pandemic comparison is evident. Material and method: Descriptive, cross-sectional study through data collection using the DATASUS platform, using available data for the years 2018 to 2021. Objective: To describe the annual prevalence of work-related mental disorders in Brazil, from the comparison data for the years 2018 to 2021. In addition, expose whether there was an increase in prevalence in the year of the beginning of the pandemic (2019), in relation to the pre-pandemic period (2018), and if the upward trend persisted with regard to the pandemic period (2019 and 2020) and post-pandemic period (2021). Results: Between 2018 and 2021, there were a total of 7337 cases. Of these, 1812 (24.7%) were registered in 2018, 2378 (32.4%) in 2019, 1343 (18.3%) in 2020 and 1804 (24.6%) in 2021. Thus, comparing by periods: from 2018 to 2019, cases increased from 1812 to 2378, which represents a percentage increase of 31.2%. From 2019 to 2020, the registered numbers fell by 43.5%, and from 2020 to 2021, a new increase corresponding to 34.3% was observed. Discussion: This analysis showed that the highest percentage increase occurred from 2020 to 2021, a transition period between telecommuting and working in a physical environment. In addition, in the year of the greatest spread of COVID-19 (2019), there was also a considerable increase in the number of cases.Conclusion: of the 7,337 total cases, the year with the highest number of cases of work-related mental disorders in Brazil was 2019, when the pandemic began in the country, a factor that promoted changes in Brazilian labor dynamics. However, it was possible to conclude that the post-pandemic period (2021), in relation to the number of cases, had a similar prevalence to the pre-pandemic period (2018), with an increase of 34.3% compared to the year 2020. This conclusion was possible since there was also a decline of 43.5% of cases, when comparing the years 2019 and 2020. Keywords: mental disorders, teleworking, COVID-19.
Sistem Kontrol pada Automated Guided Vehicle Beroda Mekanum menggunakan Sliding Mode Controller

The production in industry, are involving distribution to transport the goods. Recently, distribution activities are using unmanned vehicle, that is Automated Guided Vehicle (AGV). In real condition, AGV are facing environment with complexity of high uncertainity and unlinearity. Because of this, robust control method could be considered to be used to improve the control performance. For instance, Sliding Mode Control has good robustness to the uncertainity of the system and disturbances. However, the chattering phenomenon is one of the major issues of the sliding mode control. This phenomenon could damage the motor. This research aim to reduce chattering and improve the control performance, with modifying signum function to saturation function. This research are using ROS, V-Rep and microcontroller. Microcontroller for processing algorithm and another function. Moreover, saturation function had succcessfully reducing rise time about 30%, overshoot 16% and RMSE 0.21%.
A Survey of English Translation Studies of Lu You’s Poetry Based on the Data Analysis of Domestic Academic Journals (2001-2022)

This article presents a comprehensive analysis of the current state of research on the English translation of Lu You's poetry, utilizing a data sample comprising research papers published in the CNKI Full-text Database from 2001 to 2022.Employing rigorous longitudinal statistical methods, the study examines the progress achieved over the past two decades.Notably, domestic researchers have displayed considerable interest in the study of Lu You's English translation works since 2001.The research on the English translation of Lu You's poetry reveals a diverse range of perspectives, indicating a rich body of scholarship.However, several challenges persist, including insufficient research, limited translation coverage, and a noticeable focus on specific poems such as "Phoenix Hairpin" in the realm of English translation research.Consequently, there is ample room for improvement in the quality of research output on the English translation of Lu You's poems, as well as its recognition within the academic community.Building on these findings, it is argued that future investigations pertaining to the English translation of Lu You's poetry should transcend the boundaries of textual analysis and encompass broader theoretical perspectives and research methodologies.By undertaking this shift, scholars will develop a more profound comprehension of Lu You's poetic works and make substantive contributions to the field of translation studies.Thus, this article aims to bridge the gap between past research endeavors and future possibilities, serving as a guide and inspiration for scholars to embark on a more nuanced and enriching exploration of Lu You's poetry as well as other Chinese literature classics.
Strategies in Teaching Academic Essay Writing, Level of Effectiveness, and Instructional Barriers: The Case of Filipino Learners

This study about academic essay writing strategies was conducted in order to propose teachers' lesson guide based on the effective strategies that were ascertained after the investigation. The study used the descriptive-quantitative method of research. The University-approved questionnaire was used to identify the frequency of use of the strategies utilized in students' essay writing activities. There were 126 students and 20 English teachers in Cebu City, Philippines, used as the respondents of the study. It was discovered that the three academic essay writing strategies investigated in the study were always used in both argumentative and informative essay writing, as perceived by the respondents. The first two strategies, traffic light color coding, and planning using informal outline, were found to be very effective in both writing the argumentative and informative essays, while the third strategy, framed paragraph, was also effective to use in both writing the two types of essays. The strategies used, and the students' performance showed a significant relationship. The top barriers in teaching academic essay writing were as follows: teaching essay writing to second language learners, lack of time for explicit instruction, no strategies in place for the part of the students, lack of parental support, and lack of essay structures on the part of the teachers. It was concluded that there were various effective pedagogical strategies that teachers could utilize in teaching academic essay writing. Based on the findings, this study further presents its recommendations.
Phrase-level attention network for few-shot inverse relation classification in knowledge graph

Relation classification aims to recognize semantic relation between two given entities mentioned in the given text. Existing models have performed well on the inverse relation classification with large-scale datasets, but their performance drops significantly for few-shot learning. In this paper, we propose a Phrase-level Attention Network, function words adaptively enhanced attention framework (FAEA+), to attend class-related function words by the designed hybrid attention for few-shot inverse relation classification in Knowledge Graph. Then, an instance-aware prototype network is present to adaptively capture relation information associated with query instances and eliminate intra-class redundancy due to function words introduced. We theoretically prove that the introduction of function words will increase intra-class differences, and the designed instance-aware prototype network is competent for reducing redundancy. Experimental results show that FAEA+ significantly improved over strong baselines on two few-shot relation classification datasets. Moreover, our model has a distinct advantage in solving inverse relations, which outperforms state-of-the-art results by 16.82% under a 1-shot setting in FewRel1.0.
Analisis Konservasi Kawasan Banjir Di Kota Bandung: Studi Kasus Kecamatan Sukasari

This research aims to analyze the conservation or solutions to flood disaster problems as a community need with a healthy environment. The research was conducted in Sukasari District, Bandung City, West Java Province, with an area of 6.27 km2. The method used in this paper is descriptive analysis and the data used is secondary data. Based on the analysis of the flood hazard map for Bandung City, the research results show that the entire area of Sukasari District is highly prone to flooding. Based on the findings, the overflow of the Cidurian, Cipamokolan, and Cibeureum rivers poses a threat of flooding in Sukasari sub-district. Green Open Spaces in Sukasari District consist of 16 location points spread across 3 sub-districts. The utilization of Green Open Spaces serves to maintain ecosystem balance and increase the availability of clean air and water catchment areas. Solutions to reduce flood disasters include reducing the level of risk, decreasing vulnerability, and enhancing community capacity.
TEACHING METHODS USED BY LECTURERS IN TEACHING LISTENING IN THE SECOND SEMESTER AT ENGLISH LANGUAGE EDUCATION DEPARTMENT

The purpose of this study are to know the appropriate teaching methods used by the lecturers in teaching listening II at an English Language Education Department. This study is conducted due to the reality that students are not interested in listening classes because they face challenging times understanding the audio. This research used descriptive qualitative research. In this case, teaching listening II was investigated by the researcher to explore the teaching methods used by the lecturers. The data of the study were analyzed to report the teaching methods used by the lecturers. The instruments to collect the data were observation, questionnaire, and interview. The result of the analysis of teaching methods showed that the lecturers used different methods in teaching listening II. The methods used by the lecturers are: Direct Method, Audio Lingual Method, Grammar Translation Method, and Task-Based Learning. Those methods are appropriate for teaching listening because there are some characteristics and goals in those methods that make the students interested in listening class.
Machine Learning-Based Technique for Phishing URLs Detection from TLS 1.2 and TLS 1.3 Traffic Without Decryption

Phishing is one of the major leading cyberattack leading to huge financial loss and sensitive information loss such as account information, card details, password, login credentials. Existing techniques for phishing URL detection are unable to efficiently classify them. The use of TLS 1.2 and TLS 1.3 for client/server applications to communicate over the Internet securely has also contributed to the increase in these attacks. TLS 1.2 and TLS 1.3 traffic is encrypted, so detecting phishing URLs from encrypted traffic without decryption is a challenging task. In this paper, a machine learning (ML)-based technique is proposed for the detection of phishing URLs from encrypted traffic. The features are extracted from TLS 1.2 and TLS 1.3 traffic and based on the extracted features URLs are classified using ML algorithms. The dataset has been prepared for legitimate and phishing sites based on the features extracted from TLS 1.2 and TLS 1.3 traffic. Based on the experimental results, it is observed that the proposed model achieved promising results in the detection of phishing URLs from the encrypted traffic with an accuracy of 89.6%.
The Classification of Breast Cancer Based on Hyper-Tuned AdaBoost Ensemble Model

This paper simply presents an automated approach based on hyperparameter tuned AdaBoost ensemble model for the classification of cancer of breast into benign and malignant. The AdaBoost ensemble model is tuned properly with the aid of techniques like Grid search and Parameter grid. This hyperparameter tuning is done in order to avoid the problem of over fitting and hence delivers accurate results. The famous global dataset, i.e., the Wisconsin diagnose breast cancer dataset is used for the evaluation of this automated approach based on the AdaBoost ensemble model. The proposed automated approach offers an encouraging accuracy of 98.24%. In order to prove the efficiency and accuracy of the proposed model, it is compared with the other conventional machine learning and ensemble learning classifiers like SVM, KNN, Random Forest, Bagging, and XGBoost ensemble model.
Detection of Malaria by Using a CNN Model

Malaria is a dangerous and potentially fatal disease caused by Plasmodium parasites that are transferred to humans through the bites of infected Anopheles mosquitos. The most common and accepted technique of diagnosing malaria is for a competent technician to visually analyze a blood smear under a microscope for parasitized red blood cells. The individual conducting the inspection’s experience and knowledge is used to make a diagnosis. It’s also time-consuming and, in rare situations, prone to human mistakes. The research work has been processed into two stages: Stage one deals with dataset summarization, whereas Stage two deals with the proposed algorithm. We present a completely automated CNN-based model for identifying malaria in blood smear micrographs in this study. Our proposed algorithm has a ninety-six percent accuracy rate in detecting malarial parasites from microscopic images, which can be improved further when trained and tested on larger datasets.
CCL-Net: Complete Comprehensive Learning and Modality Preserving-Based RGBD Complex Salient Object Detection

Recently, a significant advancement in salient object detection has been achieved by utilizing depth modality and cross-complementary features fusion in a convolution neural network. The existing models usually compute RGB and depth features separately and fuse them to find saliency. The recent complementary-based features fusion model only focused on cross-complementary features and ignored modality-dependent non-complementary features. The complementary and non-complementary features are essential to predict the correct salient object. The proposed model targets the above limitations and achieves them by proposing three-stream networks. The color and depth streams are used to produce modalities-dependent non-complementary features. The fused stream utilized the salient features from both streams to explore complementary features. The global deep guidance loss function is formulated to purify the deep localized saliencies features used to start the fusion process. The output of this fused stream combines with the other two streams to preserve the non-complementary features to generate final saliency. The experimental analysis using recent metrics across the four publicly available challenging RGBD datasets is thoroughly evaluated. The performance is compared with thirteen RGBD state-of-the-art saliencies, which shows better performance in comparison with the closely related state-of-the-art methods.
Denotators over General Categories

We recapitulate the successful concept architecture of forms and denotators, which was also essential in software developments, in particular for the software RUBATO®. However, with regard to the newer concepts of musical gestures, we now extend forms and denotators from module categories to include directed graph topoi as well as topological categories.
Formal Representation of Musical Structures

We discuss what classification (the determination of isomorphism classes) means for understanding and creating music. We analyze the scientific versus artistic perspective of classification, in particular its often subconscious presence while musical creativity is operated. A basis of any serious investigation of musical objects/structures is the project of their reliable and precise conceptual description. And also being useful for the software management of music. Such a representation should include not only the objects, but also their possible relationships.