Reviewerly/exHarmony huggingface dataset:
Aspect Based Sentiment Analysis on Twitter Using Word2Vec Feature Expansion Method and Gradient Boosting Decision Tree Classification Method

Service success can be measured by customer satisfaction with the service. Twitter is one of the social media that Indonesian people often use to provide opinions on a topic, including opinions about internet services. Opinions of telecommunication service users, one of which is Telkomsel, can analyze evaluation materials for companies so that the satisfaction of Telkomsel product users increases and the company can compete with other companies. This study aims to determine the sentiment value of the signal and service aspects of the Telkomsel company. Results Based on the analysis and testing carried out on data taken from Twitter related to signal and service aspects with a total data of 16692 tweets. This study implements Gradient Boosting Decision Tree (GBDT) classification with the technique for imbalanced data handling, SMOTE, and Random Undersampling and compares it with the baseline model. The result is that SMOTE with GBDT consistently produces the best value compared to the Random Undersampling technique and without using an imbalanced data handling technique. SMOTE performs well in increasing both in the signal aspect and in the service aspect. F1-Score value in signal aspect gained 96,035%, an increase of 25,260% and for F1-Score in service aspect gained 90,256%, an increase of 27,918%.
Analysis and Redesign Web Navigation Telkom University Using Usability Testing and Trunk Test

A university web is a medium for delivering information and communication between universities and visitors. This kind of web does not show the user's position, what information is sought and how to find the desired information, then the user will find it difficult to use. The navigation system can provide this. This experiment conducted Usability Testing on Telkom University web navigation and Trunk Test analysis involving 21 participants. Navigation Usability Testing was carried out twice (before and after navigation redesign) and each participant was a different individual. The results of the comparison test show that by applying the trunk test, the total average of the Time on Task metric which was originally 36.77 seconds to 10.98 seconds, the Error rate metric which was originally 54.76% to 11.31%, and the Success metric the initial rate was 69.05% to 100.00%. From the test results and comparisons obtained, users can navigate well because there were general patterns in the redesigned navigation scheme. The general pattern was obtained by applying the Trunk Test strategy to determine the general pattern required for the tested web navigation scheme.
Few-shot Logo Recognition in the Wild

Brand logo recognition can be viewed as identification and classification task. It finds many usages such as market discovery, target advertising, etc. The number of logos growth every year and logo itself can appear in vast variety of contexts, therefore we propose a two-step few-shot framework. We describe a novel combination of universal logo detector and few-shot classifier. The logo detector is based on YOLOv5 and is used to find the areas on the image where logos are located. With this state-of-the-art single-stage object detector we achieved higher precision than similar double-stage solutions. To classify detected logos we propose few-shot classifier which consists of ensemble of pretrained feature extractors and fine-tuned head.
A Smooth and Time-Optimal Trajectory Planning Approach for Automated Systems

The discontinuity of jerk value of the traditional S-curve model seriously affects the accuracy and life of the equipment. To address such a problem, this paper proposes a smooth and time-optimal trajectory planning approach. Based on the piecewise trigonometric function, an infinitely differentiable jerk model and a parametric expression of the point-to-point programming algorithm based on this are proposed. The approach is simple in the calculation, can obtain analytical solutions, and can ensure the generated curve is smooth. The effectiveness of the proposed approach is verified through simulation.
Learn to climb: teaching a reinforcement learning agent the single rope ascending technique

Single rope ascending technique is used in industrial alpinism, forestry, or various leisure activities. This paper presents a multi-body model of this technique involving an actuated 3D model of a humanoid, the climbing gear, and the rope, modeled as a finite-element object. This model serves as a training ground for reinforcement learning agents trying to mimic humans in rope climbing. To demonstrate the environment, an agent with a state-of-the-art reinforcement learning algorithm (Soft Actor-Critic) was trained. Results suggest that the agent can learn how to ascend the rope with speed comparable to real humans. However, the learned technique is not human-like: the artificial agent uses its arms excessively to climb, which would be too tiring for a human. That is because the environment only rewards ascension and does not penalize the energy used. The presented learning environment is developed for humanoid robots in mind that can perform complex tasks while on the rope and can carry much heavier payloads compared to climbing robots in the literature.
Application of HATEOAS Principle in RESTful API Design

Hypermedia as the Engine of Application State (HATEOAS) is a principle that has been built upon the term hypermedia to refer to a constraint on the Representational State Transfer (REST) application architecture. This principle distinguishes REST from other application architectures, such as Simple Object Access Protocol in a way that client applications (e.g., a web app or a mobile app) need no prior knowledge for interacting with a REST-based Application Programming Interface (API) beyond a generic understanding of its hypermedia. Ion is an intuitive JavaScript Object Notation-based hypermedia type for REST, which can help software developers realize the principle of HATEOAS in their API design with ease. The goal of the work that is described in this paper has been to practically demonstrate the use of Ion in practice to have a self-documented REST API. For this purpose, a C# library has been implemented to demonstrate the use of Ion in having a REST API with self-documentation.
An optimized clustering model for heterogeneous cross-project defect prediction using Quantum Crow search

To improve the process of software development, the present technology pushes its boundary in software defect prediction by incorporating heterogenous cross projects, where the source and the target projects used for training and testing are with different metrics. Existing literature of Heterogeneous Cross Project Defect Prediction HCPDP focus on certainty theory where the case of inconsistency and hesitancy in categorizing unknown modules as defect or defect free is still challenging. The primary objective of this paper is to build an uncertainty model which has the ability to determine more precisely the inconsistent and vague unknown pattern of modules using unsupervised algorithm. The proposed model introduced uncertainty theory based unsupervised learning model to effectively handle the issue of hesitancy in determining software modules as defect or defect free. This paper contributed quantum crow search optimization algorithm to choose potential feature subset for feature reduction on target projects for mapping the source and target project to a projective matrix. The similarity among the instances are determined and clustered using intuitionistic fuzzy <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$C$</tex> means clustering. Each instance is represented in terms of belongingness, non-belongingness and hesitancy degree to handle vagueness and inconsistency avail in the HCPDP datasets. We conduct experiments on six different projects from five heterogenous datasets and results proved that the performance of the Quantum Crow Search Optimized Intuitionistic Fuzzy C Means Clustering (QCSO-IFCMC) achieves higher accuracy with its ability of avoiding local converge in software defect prediction compared to the existing state of arts clustering models.
Sentiment Analysis on Hotel Review in Bandung from Website Agoda Using KNN Algorithm

Hotel reviews are obtained from consumers' experiences after using hotel accommodation services as textual information. This information can help potential customers to choose services that suit their needs. Prospective customers are pretty difficult to judge the hotel from the overall reviews. To make consumers more accessible to determine hotels, this study builds a sentiment analysis model that can distinguish between positive and negative sentiments. This study created a sentiment analysis model using the Word2Vec extraction feature with the KNN classification algorithm. The final result in this study produces the best model using a stemming dataset. The vector dimensions of the Skip-gram model architecture are 300, and the use of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{K}=3$</tex> value in the KNN algorithm. The model can produce an accuracy value of 82.61% and an f1 score of 83.96%.
Cross-Platform Mobile Application Development for Smart Services

Current technological advances and the growth of smart applications and services require the development of multi-platform mobile applications. Writing software to run on multiple computing platforms can involve a large amount of duplicate effort. This duplicate effort can take the form of re-implementing the business logic in a different language, re-implementing the user interface or, in many cases, re-implementing both for each platform. There are many cross-platform frameworks to help reduce or eliminate this effort, but they make compromises on performance or user experience. Hence, this paper presents a cross-platform framework whose goal is to reduce this compromise by aiming to minimise platform-specific code, but not eliminate it. Using this method will allow all platforms to have a featurerich, native interface, with most of the code, including user interface code, contained in a cross-platform library used by all platforms. Hence, this work establishes a framework for developing cross-platform GUI applications, involving the design and development of a common library as well as translation libraries for iOS, Android, and Windows. This developed framework has been successfully applied to the implementation of a real-world application for smart lift services.
Multi-Aspect Sentiment Analysis on Tiktok Using Random Forest Classifier and Word2Vec

The number of people using the internet today is directly proportionate to the number of people using social media. Compared to other social media platforms, Tiktok is one of the most downloaded social media platforms on Google Play. However, not among Tiktok's reviews are positive. Based on reviews on Google Play, these reviews can be used as data in sentiment analysis to determine which aspects are reviewed by users and whether the sentiment is positive or negative. The aspects used in this study include features, business, and content. Word2Vec was used for data modeling, and Random Forest Classifier was used for classification. Using the Skip-gram model, without Stemming and CBOW model without stopwords the best parameter testing on Random Forest achieved the accuracy an average of all aspects of 78.33%.
An Automated Graph Construction Approach from Relational Databases to Neo4j

There are still few research methods proposed to convert relational databases to graph databases. Although a graph database has been equipped with a scripting language to use for querying and converting the data, it still requires time-consuming efforts by the domain expert to analyze the various constraints present in the source database. This paper proposes a novel technique to help automate the conversion by extracting relational database metadata and then sorting the entity relationships before converting them into graphs. To validate the conversion results, the total number of records in the source database with the number of nodes and edges created in the graph database are compared, and the node properties are validated for consistency using a probabilistic data structure. Based on our test results, their completeness can be checked accurately and efficiently with test parameters that can be adjusted according to the size of the source database.
Knowledge Retrieval and Relation Mining from Tolkien’s History of Middle Earth

In this paper, we focus on the problem of automatic analysis of literature via its characters’ interaction networks. We demonstrate the ability of the well-known knowledge retrieval algorithms on the text series of books by John Ronald Reuel Tolkien about Middle-earth. We study the identified relationships of characters, visualizing the results using the network science approaches via introducing the general pipeline for named entities recognition, relation retrieval, and analysis of their clustering via racial division by using the Adjusted Rand Index.
Efficient Skeleton-Based Human Assembly Action Recognition Optimized by Data Augmentation

As a new assembly model, human-robot collaborative assembly has attracted wide attention due to the diverse requirements of assembly tasks, complex shapes of assembly parts, and limited space in the current manufacturing industry. In the human-robot collaborative assembly environment of complex products, how to recognize human actions has become a key issue to better promote human-robot interaction. Firstly, considering the requirements of safety, efficiency, and accuracy in the human-robot collaborative assembly environment, we realize skeleton-based assembly action recognition. The channel-wise topology refinement graph convolution network (CTR-GCN) is utilized to extract the 3D skeletal features of the operator. In addition, considering the complexity of the assembly action dataset collection, we improve the recognition accuracy by data augmentation and find an appropriate training dataset size. Finally, in this paper, we focus on three assembly actions of the reducer and make an assembly action dataset in NTU RGB+D format. The optimization model based on data augmentation achieved high accuracy on the self-made assembly action dataset.
Mothed and System of Visibility Analysis Based on Human Eye Imaging Process

The human eye is the most important organ to perceive the external environment, and visibility analysis is the basis of ergonomic design and evaluation. The current mainstream visual simulation tools mainly focus on the qualitative analysis within the visual range, lacking the optical modeling process in the complex human-machine-environment system, and cannot reflect the visual features of the human eyes under different working conditions. According to the principle of the rendering pipeline of the current manifold graphics simulation engine, a human vision simulation method based on physical rendering theory is proposed. A real-time rendering engine is designed and implemented. Experiments are carried out on the components of the visual system (camera, lighting and materials), and the results are evaluated using a unified glare evaluation model. The experiments show that the proposed method is helpful to determine the appropriate position and posture of virtual human. Aiming at CATIA platform, CAA/C++technology is adopted to realize the visual analysis component, which further broadens the visibility analysis of CATIA.
Improved garment detection algorithm based on YOLOv5

An improved target detection algorithm based on YOLOv5 is proposed to address the problems of high similarity of existing clothing data, complex backgrounds, and low detection accuracy due to the difficult extraction of inter-class features. Based on the characteristics of the DeepFashion2 dataset, a new feature extraction layer with 64-times downsampling is designed, and a four-feature layer is used to detect larger clothing images; a lightweight GhostNet network is used instead of the backbone extraction network to reduce the network parameters and enhance the feature representation; Position information is incorporated into feature extraction to enhance the sensitivity of the model to position information;The experimental results show that compared with the original YOLOv5 network, the improved network models map50 and map50:95 are improved by 3.36% and 4. 7%, respectively, which can meet the practical application requirements of garment detection.
Intelligent monitoring system for motor equipment based on Internet of Things

In enterprise production activities, motor equipment that relies on traditional manual operation and maintenance is not only inefficient, but also increasingly expensive to operate and maintain. How to efficiently and cost-effectively detect, predict and solve equipment failures has become one of the important factors affecting the survival and development of manufacturing enterprises. The intelligent monitoring system of motor equipment based on the Internet of Things sends and saves the data related to the operation of the equipment to the cloud platform by adding sensors and data collection gateways to the motor equipment, and the cloud platform contains relevant intelligent algorithms that can quickly and accurately find faults and predict faults. The operation status and fault warning information of each motor equipment can be displayed in real time at the monitoring terminal for the operation and maintenance personnel to check and make equipment maintenance plan in time. The intelligent monitoring system can improve the efficiency of motor equipment operation and maintenance, reduce operation and maintenance costs, support the development of automation, informatization, intelligence and security of motor equipment operation and maintenance of enterprises, and enhance the core competitiveness of enterprises in today's manufacturing intelligence era.
Selection of Heat Dissipation Devices for Photovoltaic Power Generation Module based on Cluster Analysis

This paper studies the selection of a heat dissipation device based on a photovoltaic power generation module. All countries are currently carrying out photovoltaic power generation projects to solve the problem of heat dissipation in photovoltaic power generation, which is very important. In this paper, the k clustering algorithm of artificial intelligence is established. Then the selection of appropriate materials is analyzed through cluster analysis so that the decision is more optimized, and the specific structure and material selection of photovoltaic devices are designed. We consider the fin structure and study the influence of different fin structures in different environments. Finally, the simulation results show that the heat dissipation coefficient of the sawtooth fin is significantly higher than that of the other fin structures in the high Reynolds number region.
Multi-party Secure Privacy Computation Based on Secrecy Coding

In this work, we propose a secrecy coding enabled multi-party secure privacy computation method, and the data of the multi participants can be used without harming the data privacy. By coding the secrecy data into secrecy vectors, the secrecy comparison between two parties can be converted to the partial scalar product problem of the secrecy vectors. Based on this design, we formulate the Millionaire and Relative Prime problems using the proposed secrecy coding. The security of the proposed method is guaranteed by selecting a random number locally at the multi users. Analysis shows that the proposed secrecy coding enabled multi-party secure privacy computation is lightweight and efficient.
A Lightweight Network for Real-Time Target Semantic Segmentation Based on Dual Paths

High-precision extraction of target features is a challenge in real-time image analysis. Contemporary real-time semantic segmentation methods sacrifice segmentation accuracy to meet the high inference speed, resulting in poor model performance. Therefore., in order to achieve the best segmentation accuracy and reasoning speed as possible., based on the biseNet network structure., this paper uses the pre training model mobilenet to preliminarily extract image features., and replaces the spatial path with ASPP(Atrous spatial pyramid pooling)., increasing the receptive field and improving the ability to obtain global information. The experimental results show that the method has good performance on the data set made by collecting the images of butt welds in the process of argon arc welding of thin-walled stainless steel pipes., and the detection speed reaches 55 FPS., which fully meets the requirements of real-time segmentation.
Research on Facial Expression Recognition Algorithm Based on Deep Learning

Expression is an important non-verbal means for human to express cognition and emotion. Facial expression recognition can not only be used to study human behavior and psychological characteristics, but also can be applied to many fields such as intelligent safe driving, medical treatment, distance education and so on. In this paper, the Convolutional Neural Network (CNN), which is widely used, is studied and applied to facial expression recognition. In this study, a Network with small convolution kernel convolutional layer is built to make it applicable to facial expression recognition, and compare the expression recognition accuracy with a network structure built using a depth-separable convolutional layer, Batch Normalization and residual module, ResNet18 network and VGG19 network, borrowed from Xception Network. Based on Fer2013 and CK+ datasets, the network structure in this paper is used to compare the impact of whether to add the pooling layer, Batch Normalization, and different parameters such as epoch, optimizer, and whether to perform data enhancement on the accuracy of facial expression recognition.
EYL's PERCEPTIONS ABOUT THE USE OF READING ALOUD TECHNIQUE FOR STUDENTS' COGNITIVE AND AFFECTIVE IN READING SKILL

One of the skills used to obtain information is reading skills. Reading is the first step and very important if you want to get a knowledge or information. Reading culture or literacy habits in Indonesia have a very low percentage compared to other countries. It will be more difficult if the student is learning a new language and has no interest whatsoever in reading. To overcome this, a technique is known to improve students' reading skills, especially in learning English. This study aims to determine the perception of EYL students in the use of reading aloud techniques for their reading skills in terms of cognitive and affective aspects. In addition, this study used narrative inquiry as a research design and data collection is carried out through interviews. The data were analyzed by categorizing and interpreting the data using Braun and Clarke’s theory. The results indicated that the students had positive responses based on their experiences using reading aloud technique in reading skill. For cognitive aspect, reading aloud technique improved students’s reading comprehension. Furthermore the data showed that for affective aspects, reading aloud technique increased students’ reading interest, improve students’ self-confidence and motivation to learn English better.Keywords: English Young learners (EYL), Reading Aloud Technique, Cognitive aspects, Affective aspects.