Reviewerly/exHarmony huggingface dataset:
Embedding Taxonomical, Situational or Sequential Knowledge Graph Context for Recommendation Tasks

Learned latent vector representations are key to the success of many recommender systems in recent years. However, traditional approaches like matrix factorization produce vector representations that capture global distributions of a static recommendation scenario only. Such latent user or item representations do not capture background knowledge and are not customized to a concrete situational context and the sequential history of events leading up to it. This is a fundamentally limiting restriction for many tasks and applications, since the latent state can depend on a) abstract background information, b) the current situational context and c) the history of related observations. An illustrating example is a restaurant recommendation scenario, where a user’s assessment of the situation depends a) on taxonomical information regarding the type of cuisine, b) on situational factors like time of day, weather or location and c) on the subjective individual history and experience of this user in preceding situations. This situation-specific internal state of the user is not captured when using a traditional collaborative filtering approach, since background knowledge, the situational context and the sequential nature of an individual’s history cannot easily be represented in the matrix. In this paper, we investigate how well state-of-the-art approaches do exploit those different dimensions relevant to POI recommendation tasks. Naturally, we represent such a scenario as a temporal knowledge graph and compare plain knowledge graph, a taxonomy and a hypergraph embedding approach, as well as a recurrent neural network architecture to exploit the different context-dimensions of such rich information. Our empirical evidence indicates that the situational context is most crucial to the prediction performance, while the taxonomical and sequential information are harder to exploit. However, they still have their specific merits depending on the situation.
Digital Literacy: Implementasi Pelatihan English Speaking Performance pada Santriwati Pondok Pesantren Sahlan Rosyidi

The learning process and life activities during the Covid-19 pandemic proceed through online social media, so there is a need for technological literacy. This activity also happened at the Pondok Pesantren Putri Sahlan Rosyidi Semarang where the academic abilities of the female students, especially the English language, were very diverse but there was still a lack of English-speaking practice programs that were integrated with digital literacy. Initial findings of the daily English-speaking ability of female students had an average of 65 which had to be improved again. Based on this statement, our team focuses on the implementation and training of Digital Literacy through the practice of speaking English for female students at the Pondok Pesantren Putri Sahlan Rosyidi Semarang, consisting 60 female students. The method used is descriptive quantitative by applying brainstorming, group discussion, explanation, speaking practices, digital platform, and evaluation of students' digital understanding of literacy. The results show that the implementation of the practice of speaking English based on digital literacy with an increase in the average speaking English rising to 81. In addition, the female students gave a positive response to the implementation of digital literacy and the practice of speaking that was trained. They were also motivated to practice conversation or monologue by utilizing digital technology.
Faster Stochastic Second Order Method for Large-Scale Machine Learning Models

Stochastic optimization methods are becoming more and more popular in the big-data era.Among them, stochastic second order methods have attracted much attention since they can exploit the curvature information to improve the converging speed.However, the convergence rate of existing stochastic second order methods is not satisfied due to their heavy dependence on the condition number.Specifically, the first order oracle complexity is O((n + κκ H ) log( 1)) and the second order oracle complexity is O(κκ H log( 1)).In this paper, we propose a novel faster stochastic second order method which has a better dependence on the condition number.In particular, the first order oracle complexity is improved to O((n 1)) and the second order oracle complexity is improved to O( κκ H 1+ √ κκ H /n log( 1 )).Extensive experiments have verified the effectiveness of our proposed method.
Peran Repositori UIN Alauddin Makassar

Institutional Repositories (IR) have been owned by many higher education institutions as a medium for the preservation, storage, and dissemination of scientific works. IR is also known as a scientific communication medium for academics/scientists. This case study describes in general the role of UIN Alauddin Makassar’s IR since it was first launched at the end of 2016 until now. The data were obtained from the experience and empirical observations of the researchers, literature review, interviews, and other supporting documentation. The results showed that the UIN Alauddin Makassar’s IR has been playing roles in; digital preservation, building scientific communication, establishing synergy between libraries and faculty communities, supporting learning and research processes, and improving library skills and services. Due to space limitations, this study does not describe in more detail the roles mentioned. This research has implications for the management of IR at this campus in particular, and other IRs in other universities.
Development of ontology-based model to support learning process in LMS

&lt;p&gt;E-Learning is an important support mechanism for educational systems to increase the efficiency of the education process including students and teachers. The current e-learning systems typically lack the level of metacognitive awareness, adaptive tutoring, and time management skills and have not always met the expectations of the learners as required. In this study, we introduce a novel ontological model for the learning process in the e-learning domain. In the framework, we have built a domain ontology that represents knowledge of the learning, the outcome domain ontology covers the whole learning process. We focused on the learning process ontology model conceptualizing knowledge constructions, such as learning courses, and we present the created course and learning process ontology in detail. In this work, we considered three layers of learning process. The top layer defines a general framework of learning process, conceptual model layer, defines the framework of the actual process of the learning process and course ontology model contains the knowledge unit of the learning process. The prototype ontology is constructed in protégé and managed by Java web ontology language-application programming interface (OWL-API). As a result, our model can solve the problems of current e-tutor systems. Also, it can be used for different domain in e-tutor systems. It can reach the characteristics of standardization, reusability, flexibility, and open knowledge. By applying this model, we can avoid applying isolated databases. The constructed ontology can be used in the future to control adaptive intelligent e-tutor frameworks.&lt;/p&gt;
Automatic Sign Language Gesture Recognition using Prewitt &amp; Morphological Dilation

, Sign languages have their own linguistic structure, grammar and characteristics, and are independent of the rules that govern spoken languages. They are visual languages that rely on hand gestures as well as on bodily and facial expressions. Sign languages in different countries are vastly different from one another, so enabling easy communication is important: not just to break the barrier between hearing and deaf individuals, but also between people who do not sign in the same language. In India, sign language plays an important role in the field of communication among dumb and deaf people. There are different signs associated for communication in every country as per their convenient gestures. Automatic sign language gesture recognition is an approach for recognizing gestures and converts it to its actual meaning and convey either through speech or text as per requirements. Here the system is based on Prewitt Edge Detection that possesses the gestures of sign language and helps to recognize and assign their meanings. The Prewitt is second order derivative that has been used in image processing and computer vision, in the form of edge detection or extraction algorithms where it creates gradient of horizontal and vertical magnitude. System also uses certain pre-processing filtration technique such as morphological dilation for better feature extraction.
The digital didactics as a discipline of masters training program

Problem and goal. The problem lies in the effectiveness of the formation of motivation for the productive information activities among the subjects of the educational process. The goal is determined by the need to train specialists, taking into account changes in the field of education that have occurred due to the introduction of digital technologies in education and risks of digitalization generated by technogenic processes in the educational environment. Methodology. The author's programs and funds of evaluation tools were used, practical tasks and research project topics were developed to include them in the programs of pedagogical and industrial practices of undergraduates. The materials are based on the author's developments, which are reflected in the textbooks. In practice, the following technologies have found methodological justification for the implementation of the discipline: information and communication technologies for universal use, Big Data, distance learning, blended learning, organization of project activities of students. Results. The introduction of the discipline Digital Didactics allows undergraduates to form new competencies. Conclusion. The competencies formed during the training of the discipline Digital Didactics enable to take into account the existing changes in the field of education.
Online Enhanced Semantic Hashing: Towards Effective and Efficient Retrieval for Streaming Multi-Modal Data

With the vigorous development of multimedia equipments and applications, efficient retrieval of large-scale multi-modal data has become a trendy research topic. Thereinto, hashing has become a prevalent choice due to its retrieval efficiency and low storage cost. Although multi-modal hashing has drawn lots of attention in recent years, there still remain some problems. The first point is that existing methods are mainly designed in batch mode and not able to efficiently handle streaming multi-modal data. The second point is that all existing online multi-modal hashing methods fail to effectively handle unseen new classes which come continuously with streaming data chunks. In this paper, we propose a new model, termed Online enhAnced SemantIc haShing (OASIS). We design novel semantic-enhanced representation for data, which could help handle the new coming classes, and thereby construct the enhanced semantic objective function. An efficient and effective discrete online optimization algorithm is further proposed for OASIS. Extensive experiments show that our method can exceed the state-of-the-art models. For good reproducibility and benefiting the community, our code and data are already publicly available.
Prediksi Tingkat Produksi Buah Kelapa Sawit dengan Metode Single Moving Average

Palm oil is one of the largest agricultural products in Indonesia and has a high economic value and can improve the welfare of oil palm farmers. The amount of oil palm fruit production is not always stable or increasing, but increases up and down which is influenced by many factors. This study aims to estimate the average amount of oil palm fruit production every year and prepare anticipatory steps in the event of a decrease in oil palm fruit production. The image processed in this study was the production of palm fruit in a few years which was generated from the results of oil palm plantations. Furthermore, data is processed using the Single Moving Avarage method. This method is a method of forecasting or predictions using a number of actual data to generate predictive values ​​in the future. The results of testing on the single moving average method can be seen forecasts of oil palm fruit production in 2021 using Moving Averge 3 of 200.749 tons with Mean Absolute Deviation 19.604, Mean Squared Error 456.963.281 and Mean Absolute Percent Error 10,0%. Moving Averge 4 was 206.771 tons with the Mean Absolute Deviation 27.333, Mean Squared Error 752.202.579 and Mean Absolute Percent Error 14,2%. Moving Averge 5 was 210.908 tons with Mean Absolute Deviation 26.890, Mean Squared Error 723.072.100 and Mean Absolute Percent Error 14.1%. The test results using the Single Moving Average method can be concluded that forecasting using Moving Average 3 can be used because the relative error level is smaller than Moving Average 4 and 5, with the value of the Mean Absolute Percent error of 10.0% and Mean Absolute Deviation 19.604.
Analisis Sentimen Tweet Vaksin COVID-19 Menggunakan Recurrent Neural Network dan Naïve Bayes

COVID-19 has become a global pandemic including Indonesia, so the government is taking vaccinations as a preventive measure. The public's response to this continues to appear on social media platforms, one of which is Twitter. Tweets about the COVID-19 vaccine have generated various kinds of positive and negative opinions in the community. Therefore, it is very important to detect and filter it to prevent the spread of incorrect information. Sentiment analysis is a method used to determine the content of a dataset in the form of negative, positive or neutral text. The dataset in this study was obtained from 5000 COVID-19 vaccine tweets with the distribution of 3800 positive sentiment tweets, 800 negative sentiment tweets and 400 neutral sentiment tweets. The dataset obtained is then pre-processed data to optimize data processing. There are 4 stages of pre-processing, including remove punctuation, case folding, stemming and tokenizing. This study examines the performance of RNN and Naïve Bayes by adding the TF-IDF (Term Frequency-Inverse Document Frequency) technique which aims to give weight to the word relationship (term) of a document. The test results show that RNN (TF-IDF) has a greater accuracy of 97.77% compared to Naïve Bayes (TF-IDF) of 80%.&#x0D;
Positive Changes in Flood Mitigation Through Sand Dredging Works at Padas River and Tributary Based on HEC-RAS Hydrological Modelling

In the recent years, the impacts of floods have gained importance because of the increasing number of people who are affected by its adverse effects, especially in Beaufort area, Sabah, Malaysia. Flood destroyed critical infrastructures that are needed as shelter and also emergency relief for victim. This paper presents the findings of flood modelling undertaken to establish baseline and post mining flooding conditions during upstream storm and combination of upstream and downstream storm, respectively. A hydrologic model was established and calibrated based on 2014 flood. A structural approach by changing the physical dimension through dredging or sand mining between 2m to 3m is used for hydrology modelling is added into the existing floodgates and bunds. The outcome from sustainable sand is prevailing when it is able to reduce flood level for normal flow, upstream case, and both upstream and downstream case. Other findings are changes in velocity, shear and the significantly reduced power generated by the river during flooding.
Industry 4.0: Hand Recognition on Assembly Supervision Process

In the assembly industry, the process of assembling components is very important in order to produce a quality product. Assembly of components should be carried out sequentially based on the standards set by the company. For companies that still operate the assembly process manually by employee, sometimes errors occur in the assembly process, which can affect the quality of production. In order to be carried out the assembly process according to the procedure, a system is needed that can detect employee hands when carrying out the assembly process automatically. This study proposes an artificial intelligence-based real-time employee hand detection system. This system will be the basis for the development of an automatic industrial product assembly process to welcome the Industry 4.0. To verify system performance, several experiments were carried out, such as; detecting the right and left hands of employees and detecting hands when using accessories or not. From the experimental results it can be concluded that the system is able to detect the right and left hands of employees well with the resulting FPS average of 15.4.
A Statistical Analysis of Patrons’ In-Library Information Consumption Behaviors of Print Materials

Patrons’ information consumption behaviors of print materials are influenced by their content and tangible characteristics. This quantitative study incorporates several content-based factors widely adopted in collection management practices as well as two physical dimension–related factors, including book size and number of pages, and applies a series of regression analyses to evaluate the relative impact of these factors on patrons’ checkout versus in-library use decisions. The results suggest that shelf time and book size exhibit consistent and significant impact on patrons’ use of print materials. These findings can shed further light on collection management practices and policies in academic libraries striving to provide patrons more user-centered collections and space.
Dimensionality Reduction of SPD Data Based on Riemannian Manifold Tangent Spaces and Isometry

Symmetric positive definite (SPD) data have become a hot topic in machine learning. Instead of a linear Euclidean space, SPD data generally lie on a nonlinear Riemannian manifold. To get over the problems caused by the high data dimensionality, dimensionality reduction (DR) is a key subject for SPD data, where bilinear transformation plays a vital role. Because linear operations are not supported in nonlinear spaces such as Riemannian manifolds, directly performing Euclidean DR methods on SPD matrices is inadequate and difficult in complex models and optimization. An SPD data DR method based on Riemannian manifold tangent spaces and global isometry (RMTSISOM-SPDDR) is proposed in this research. The main contributions are listed: (1) Any Riemannian manifold tangent space is a Hilbert space isomorphic to a Euclidean space. Particularly for SPD manifolds, tangent spaces consist of symmetric matrices, which can greatly preserve the form and attributes of original SPD data. For this reason, RMTSISOM-SPDDR transfers the bilinear transformation from manifolds to tangent spaces. (2) By log transformation, original SPD data are mapped to the tangent space at the identity matrix under the affine invariant Riemannian metric (AIRM). In this way, the geodesic distance between original data and the identity matrix is equal to the Euclidean distance between corresponding tangent vector and the origin. (3) The bilinear transformation is further determined by the isometric criterion guaranteeing the geodesic distance on high-dimensional SPD manifold as close as possible to the Euclidean distance in the tangent space of low-dimensional SPD manifold. Then, we use it for the DR of original SPD data. Experiments on five commonly used datasets show that RMTSISOM-SPDDR is superior to five advanced SPD data DR algorithms.
The collective subject in complex human-dimensional systems: intelligence or the sum of technologies?

The relevance of addressing the problem of the collective subject is due to the need to study the laws of complex self-developing human-dimensional systems. In these systems, the subjects of knowledge and activity are organically connected with the means of knowledge and activity and objects. Self-development is realized in these systems through the information and communication technology environment. Self-development is carried out with the help of the reflexive activity of the collective subject. The purpose of the article is to identify the features of the collective subject in complex human-dimensional systems. It is shown that the characteristics of the collective subject are influenced by such trends as the intellectualization of the technosphere and the technologization of human cognition and activity. The functions of a collective subject in the information society are beginning to be performed by information management systems. The collective subject under the conditions of the co-evolution of man and technology evolves in the direction of a hybrid collective subject.
Integrating Artificial Neural Network and Smartbot on the Development of an E-learning Platform

An e-learning is overtaking conventional classroom teaching methods. It is a teaching-learning approach that entails to maximize learning opportunities of learners through the integration of current technology in the classroom and to practice the principle of Learning Management System (LMS). Through this approach, knowledge could propagate possibly and absorbed irrespective of the teaching personnel and learners. This study is specifically addressed the following objectives: (1) utilize an e-learning platform with the integration of Artificial Neural Network focused on the predictive readiness of Science, Technology, Engineering and Mathematics (STEM) learners for Work Immersion; (2)To evaluate the developed learning platform (SMARTBOT) using ISO 25010 in  terms of functional suitability, performance efficiency, usability, security, reliability and portability. In this study, applied and developmental research will utilize; it is a type of research design that deals on the formulation or development of such research output. The study came out with the following salient findings: 1) the ANN which is highly optimized system has a data accuracy of 92.80% based on WEKA results; and 2) the developed android application meets the software quality standards in terms of functional suitability, performance efficiency, usability, security, reliability, and portability.
Converting Access Microbiology to an open research platform: community survey results

Following the Microbiology Society’s successful bid for a Learned Society Curation Award from the Wellcome Trust and Howard Hughes Medical Institute, the Society is converting our sound science, open access journal, Access Microbiology , to an open research platform. As part of this, we conducted a survey of our community to gauge current attitudes towards the platform and here we present some of these results. The majority of respondents (57 %) said they would always or sometimes want to remain anonymous on their peer review report, whilst 75 % of respondents said that as an author they would be happy to make the data underlying their research open. There was a clear desire for a range of research types that are often seen with sound science publications and rigorous research. An encouraging 94 % of respondents stated that the platform is somewhere they would consider publishing, demonstrating the enthusiasm in these respondents for a new publishing platform for their community. Given this data and that from our previous focus group research, the platform will launch as outlined in the original project proposal and adopt a transparent peer review model with an open data policy.