Reviewerly/exHarmony huggingface dataset:
A Text-based Deep Reinforcement Learning Framework Using Self-supervised Graph Representation for Interactive Recommendation

Due to its nature of learning from dynamic interactions and planning for long-run performance, Reinforcement Learning (RL) has attracted much attention in Interactive Recommender Systems (IRSs). However, most of the existing RL-based IRSs usually face large discrete action space problem, which severely limits their efficiency. Moreover, data sparsity is another problem that most IRSs are confronted with. The utilization of recommendation-related textual knowledge can tackle this problem to some extent, but existing RL-based recommendation methods either neglect to combine textual information or are not suitable for incorporating it. To address these two problems, in this article, we propose a T ext-based deep R einforcement learning framework using self-supervised G raph representation for I nteractive R ecommendation (TRGIR). Specifically, we leverage textual information to map items and users into a same feature space by a self-supervised embedding method based on the graph convolutional network, which greatly alleviates data sparsity problem. Moreover, we design an effective method to construct an action candidate set, which reduces the scale of the action space directly. Two types of representative reinforcement learning algorithms have been applied to implement TRGIR. Since the action space of IRS is discrete, it is natural to implement TRGIR with Deep Q-learning Network (DQN). In the TRGIR implementation with Deep Deterministic Policy Gradient (DDPG), denoted as TRGIR-DDPG, we design a policy vector, which can represent user’s preferences, to generate discrete actions from the candidate set. Through extensive experiments on three public datasets, we demonstrate that TRGIR-DDPG achieves state-of-the-art performance over several baselines in a time-efficient manner.
Information Organization and Information Retrieval in the LIS Curriculum: An Analysis of Course Syllabi

Within LIS education, information organization (IO) and information retrieval (IR) are two well-established, foundational, and conceptually interlinked areas of study. They have long maintained a strong presence in the core curriculum, through either dedicated or combined courses. Recent trends in LIS education have seen a greater reliance on a smaller, more condensed core, raising questions about the presence, role, and format of these foundational topics. This study aims to provide a clearer picture of how IO and IR currently manifest in the LIS core, along with which particular IO and IR topics are most prevalent. Through content analysis, course syllabi for 58 IO and/or IR related courses from 38 different, ALA-accredited LIS programs were examined. Course descriptions and schedules of topics were assessed using the ISO 5127 controlled vocabulary as an analytical framework. Findings revealed three main course types: IO dedicated courses, IR-related courses typically in the context of reference services, and combined courses that often presented IO and IR alongside other fundamental topics. Though dedicated courses showed little leveraging of topics from the other areas, combined courses leveraged topics from both IO and IR. Among all courses, the prevalence of the topics of access, classification, databases, and metadata suggests a common area of crossover among IO and IR. Overall, findings point toward greater diffusion of IO and IR content throughout the core curriculum while demonstrating the continuing relevance of these areas.
e-Voting Protocol Modelling To Improve Verifiability Requirements

The ability of the voting system to protect voter votes until the end of the process can increase public confidence in the voting system. The verifiability aspect allows several parties to ensure that there is no change in the vote of the voters, thereby increasing trust in voting technology. To get to the concept of the proposed system of e-voting, an analysis e-voting needs has been carried out and the stage of the protocol model design analysis for verifiability needs. Some parties involved in meeting the needs of verifiability are Voters, Officers, Witnesses or KPU (Commission of General Election), where some parties can verify the votes of voters before, during, after, and after the vote count in election. In fulfilling the verifiability needs of this e-voting system, traditional simulation modeling and voting testing have been carried out as a comparison with modeling simulations and testing of e-voting protocols. Before modeling simulation and protocol testing, formal notation writing was carried out in the form of Communicating Sequential Processes (CSP) notation. Protocol testing will be carried out with formal verification, which proves that protocol specifications are in accordance with the integrity properties that have been defined previously. The verification tool used is based on reference modeling, which can analyze the specifications logical consistency, and verified properties reports, namely SPIN (Simple Promela Interpreter). The verified system used PROMELA language (MEta LAnguage process) which is translated from CSP formal notation.
Hardware Resource Reduction Using Winograd Approach on YOLOv3-Tiny

Convolutional neural network (CNN) is a very effective method in object detection. YOLOv3-Tiny, which uses a CNN base, has excellent performance for object detection. The Winograd algorithm can reduce the use of large multiplication operations on the convolution layer. In this paper, we explore the reduction of hardware resources in the convolution layer of the YOLOv3-Tiny model using the Winograd algorithm of various sizes. The results of the calculation of this algorithm can reduce the use of multiplication operations 49.76% - 74.09%, which on the other hand, increases the number of addition operations,
CLAHE Performance on Histogram-Based Features for Lymphoma Classification using KNN Algorithm

Immunohistochemistry or IHC is a method for gaining better diagnosis on lymphoma patients. Early diagnosis is needed to enhance their survival rate by giving the right treatment before the cancer spreads to other system organs. However, this method is not affordable in some developing countries, including Indonesia, since it requires advanced tools that are only available at certain high-class hospitals and top public hospitals. Therefore, a machine learning model using H&E (hematoxylin and eosin) stained tissue image was developed to reduce the needs of using IHC. Therefore, only samples classified as lymphoma will undergo the IHC analysis for validation. In this study, a weighted KNN (k-nearest neighbour) model was chosen since it has the performance in classifying datasets to three classes: BL (benign lesion), CA (carcinoma), and LY (lymphoma). Carcinoma was put into consideration because lymphoma can be in the organ as the carcinoma, especially for Non-Hodgkins Lymphoma (NHL). The developed model was analyzing the features on image's histogram in every RGB layer. Image preprocessing using CLAHE (contrast limited adaptive histogram equalization) was studied and succeeded to enhance the model performance with an average accuracy at 85.5% and specificity of LY at 90.3%.
How Can Knowledge Management Impact Organizational Performance? A Systematic Literature Review

The importance of Knowledge Management (KM) in organizations is well recognized. This is because the implementation of KM can have an impact on the organization at several levels, such as on people, processes, products, and organizational performance. However, the impact of implementing KM can differ from one another. Therefore, this study aims to identify the impact of implementing KM in organizations, especially on organizational performance. In addition, this study also identifies the model used in the application of KM to identify the factors that influence the impact of KM in the organization. This study also aims to help organizations that want to implement KM or for organizations that have implemented KM to better know the impact that can occur from implementing KM in organizations. This study uses a systematic literature review that will help gather literature from previous studies.
Visual interaction cues framework for recycling education using Augmented Reality

This paper proposes a visual interaction cues framework for recycling education using augmented reality (AR). These visual interaction cues are used to guide the users with what elements they can interact with, where to go with regards to the navigation, and what is the information for pertaining to the content in the proposed AR application called Recycl-AR. Four dimensions are proposed in the visual interaction cues framework: task, markedness, trigger, and characteristic. This framework will be used to specify interaction cues for the Recycl-AR. The framework has the potential to be used in producing new design concepts for visual interaction cues in other mobile augmented reality applications beyond Recycl- AR.
Systematic Literature Review: Knowledge Management Model in Private Organizations

With the help of knowledge management (KM), organizations can leverage their knowledge to organizations&#x2019; advantage. Because private organizations&#x2019; goal is profit and one of KM direct impacts is increasing organizations&#x2019; revenue and profit, an organization may choose the most suitable KM model as the KM initiative to achieve its strategic goals. This paper aims to help organizations that want to implement KM. This paper uses systematic literature review (SLR) methodology to identify KM models in private organizations. The results of SLR are 21 KM models which consists of 9 original models and 12 derived models. There were 14 out of 21 models that were suitable for 13 specific industries, while the other models didn&#x2019;t mention the industry. It is also found that 6 models were suitable for specific organization sizes and the other models didn&#x2019;t mention organization size.
User Interface Prototype Using User Centered System Design Method in Motorvice Information System

The development of technology has a very important role in everyday life. One of them is a motorvice service information system that can provide information easily and quickly. Motorvice is an information system that can make it easier for consumers to see the price of spare parts and also services at a workshop. With this motorvice, consumers can check the price of services and spare parts online. Without having consumers go to the workshop first. This paper uses a user centered system design method approach as the process of designing a motorvice application user interface prototype with two independent tests and two types of SEQ and SUS questionnaires to seek feedback from users so as to speed up the design process and determine the usability value. The user centered system design method is more suitable for motorvice applications in determining user and industrial needs because the usability test results of the SEQ questionnaire get easy to use results and the results of the SUS questionnaire test get acceptable results.
Android Application Design for Monitoring Weather Parameter and PM 2.5

Weather parameters and PM2.5 are important to become the object of observation. These two parameters have an influence on various fields. Weather parameters, for example, are very influential in the fields of agriculture, aviation, disasters and many more. PM2.5 parameter information is very much needed as disaster mitigation in the event of a widespread forest fire disaster. In this paper, we will discuss how to build an android application that functions to display weather and PM2.5 parameter data from sensors located at the observation station. The existence of this android application will make it easier for users to access information from anywhere using a mobile device via an internet connection. The design of the application is done by following the steps for making the application so that the application is more structured and can access data properly from sensors at the observation station that sends data to the database server. In general, the system consists of sensor devices and data senders, data base servers and android applications. To access the data base server, the Laravel framework is used to read data and generate an API so that it is more easily accessible by programs on android applications that use React Native. The test results show that the system as a whole is running well with the test parameters in the form of the success of the system in sending data, storing data in the database and displaying data on android applications and user friendly applications.
Reliable Batik Image Classification : Mulwin-LBP Algorithm and Deep Neural Network

This study proposed feature extraction using the Mulwin-LBP algorithm which can optimally supported batik image classification activities. In this classification activity, the Deep Neural Network algorithm was used to provide solutions to the Invariant dilemma. However, previously, the image segmentation process using the Watershed method would be carried out to support the feature extraction. Actually this feature extraction algorithm is done by combining 3 and 4 different types of windows. The window sizes were used 6x6 pixel, 9x9 pixel, 12x12 pixel, and 15x15 pixel or a combination of 3 or 4 windows to get optimal and maximum results. Some experiments included the Training Images number more than the test image number, but the accuracy and precision of classification could reach more than 72.88%. Meanwhile, if the test was carried out by adding image classes, including the number of training images more than test images, the resulting classification accuracy had reached more than 81.65%. Even in other experiments with the number training images more than test images, the image conditions have the same rotation but different scales, for classification accuracy had reached more than 93.68% with a total of 12 classes. Even if the drawing class studied is only 10 classes, the accuracy had reached more than 98%. The reliability of this algorithm in supporting batik image classification can continue to be improved in further research.
Interaction Design of Mathematics Learning Applications for Elementary School Students

Mathematics learning applications today are certainly very widely found, and we can easily download them from the app store or play store. Starting from the elementary, middle, and high school levels. Some of the current mathematics learning applications that we encounter only focus on their content, so the application's appearance is ignored. There are too many choices for the interface in the mathematics learning application before starting mathematics learning. This makes it difficult for users, mainly grade 6 elementary school students, when interact with the application. This research aims to develop an Android-based mathematics learning application for 6th-grade elementary school children with a User-Centered Design (UCD) approach. The ability to help 6th graders of the elementary school in learning mathematics independently will improve children's motor and cognitive skills.
Reshaped and Reduced Dimensionality Reduction Data Technique on Association Rule Mining

Online shops implement market basket analysis, where its association rule analysis helps people with their online shopping. Association rule's performance depends on the quality of the dataset. The association rule analysis depends on two values, namely the support value and the confidence value. There are two novelty techniques for preparing the dataset, reshape dataset technique and reduce dataset technique. The reshape dataset technique will decrease the dataset dimension on the number of rows; meanwhile, the reduced dataset technique will decrease the dataset by implementing the IST- EFP algorithm. This research compares 30%, 40%, 50%, 55% reshaped datasets against the reduced dataset. Both datasets can prune the original dataset dimension using the same confidence values on the strict rules. This research will analyze both techniques' performance by comparing the support value and confidence value against the original dataset. Both methods can reduce the dimensions of the dataset without changing the confidence value with a difference of less than 0.1% in the support value obtained; the reduced dataset has a better result.
A Sign Language Recognition System Using Ionic Liquid Strain Sensor

In this report, we develop a system converting sign language into voices and text. The system includes a glove attached self-developed ionic liquid-based strain sensors on each finger to detect the movement of them. When the hand move to express a letter lead to the resistance of the strain sensors on each finger change. Therefore, through the information from set of sensors, the system can recognize the letter that user want to convey. A measuring and data acquisition circuit also are developed to receive and analyze data from the sensors. Furthermore, a data digitizing method is proposed as a solution for decoding purpose. The system is tested with 10 distinguished basic letters in American Sign Language table (ASL). The results show the accuracy of system is 98%. The system operates stably with the low power of 0.81W at rest state and 1.71W when working. Besides, applications on smartphone and PC are also built to translate the data into text and speech. These apps make it easier to communicate with deaf people. With achieved results, the system is fully extensible to detect more letters in ALS table in the future.
Face Recognition Based on Viola-Jones Algorithm as Dataset for Image Classification

Face detection is the first step in the face recognition system to identify the entire image area to find facial areas and non-face areas. The face recognition process can be known through the position and distance of the face from the camera. The Viola-Jones and HOG algorithms have good accuracy in facial recognition. This is evident from the experimental results obtained that face recognition using this algorithm has a recognition ability of 98.9%. This algorithm can also be used to create a dataset from an image. In this study, image classification was performed using three models, that is Naive Bayes, random forest, and logistic regression. The dataset used comes from facial recognition performed with Viola-Jones and HOG. The result obtained from this face classification is that the logistic regression model has the highest level of accuracy because it can classify all images perfectly. The Naive Bayes model has one error in image classification, and the random forest model has three errors in image classification.
Developing Human Movement Monitoring System using HAAR Cascade Classifier Algorithm

Walking speed is widely used as the material for research studies in the health sector, especially for early detection of someone who is healthy or sick. The existence of a monitoring system will greatly help a company to detect the health of its workers early. In this paper, an algorithm for calculating a person’s walking speed is developed. This study uses HAAR Cascade Classifier as pedestrian detection and the process of image processing is performed with the help of the OpenCV library. The test results show that the system successfully detects humans both indoors and outdoors in dim lighting conditions (in the afternoon) and bright (during the day). Based on the test results, the system has successfully detected both single-object and multi-object human objects with accuracy in calculating running speed in the range of 86%.
The history of the introduction of digital competencies among students studying in the direction of “State and Municipal Management”

The large-scale introduction of modern information and communications technologies in various spheres of the Company&#x2019;s vital activity has made relevant new requirements for state civil servants. Since it is the civil servant who has to interact with the population. All this encourages developing new approaches to the preparation of civil servants on the basis of a competence approach, which makes it possible to formulate the necessary skills, including digital. The article provides an analysis of how the transition to the distance form of training influenced the formation of digital competencies in students learning in the direction of &#x201C;State and Municipal Management&#x201D; and what difficulties arose at this transition.
Content-based Traditional Clothes Pattern Retrieval Using Convolutional Neural Network

Indonesia is a country with an inclusive culture spread all over the country. Usually, cultural objects can have different meanings based on the details. Traditional cloth is an example of Indonesia's culture. Each province in Indonesia has various colors, patterns, textures, characteristics of traditional fabrics. However, each cloth's patterns and textures have different meanings or meanings. The various features and characteristics of traditional clothing can be extracted using the Convolutional Neural Network. This experiment uses 74 types of traditional patterns collected from the internet. Pre-processing creates 4144 new images with seven different rotation variations, four different overlap settings, and also two crop sides for the image database. CNN type for VGG19 is used as feature extraction of fabric pattern and Euclidean Distance for feature vector distance calculation. Based on the experimental results, the average output using this method is 86%. Besides that, about 69% of the results of the query process for pattern and texture images have an accuracy greater than 80%. Even some patterns can produce accuracy up to 100%. This study makes an excellent image retrieval accuracy but also needs a lot of improvement.
Early Prediction on Electrical Energy Consumption in Households by using Machine Learning

Nowadays Machine Learning is widely used in various fields to perform a smart learning or predictions of a data, one of the methods in Machine Learning is Linear Regression. This research aims to examine the application of Machine Learning with the Linear Regression method programmed with Python Programming to predict electrical energy consumption in the next 30 days for prepaid and postpaid household electricity. Predictions were made based on data collected for 30 days, read through the PZEM-004T V3.0 sensor to measure electrical energy. The comparison experimental results sensor with measurement equipment have an average error of 2% energy reading or with a difference of 0.01 kWh every day for postpaid, and electrical energy consumption with a prediction range of 30 days on prepaid electricity types had an average error value of 1.21% or a difference of 3.68 kWh.