Reviewerly/exHarmony huggingface dataset:
Indonesia's News Topic Discussion about Covid-19 Outbreak using Latent Dirichlet Allocation

News related to Covid-19 or Corona dominates the news in several media.From various perspectives, a number of news have been produced regarding the corona outbreak.The purpose of this research is to look at various news topics related to corona, both in national online media and in local online media.This study uses the Latent Dirichlet Allocation (LDA) algorithm to find out news topics related to corona.Preprocessing was carried out on existing articles such as removing punctuation marks, numbers, and removing stopwords.This process is preceded by lowering the text to get unique words.The number of articles collected was 12.883 news titles from the national online media RMOL and the Aceh Tribunnews regional online media in the period January to May 2020.The news items came from various news categories such as politics, law, health, economy, sports and others.Based on this research, by using the LDA algorithm, evaluate using coherence value and visualize the topics, will have the best perspective of how many topics can be created.RMOL news articles have 12 topics discussion with the coherence value is 0.538795.Aceh Tribunnews has 8 topics with the coherence value of 0.522946.
A Framework Design for Information Management in Heritage Science Laboratories

The dataflow in any scientific research laboratory is continuous and considerable even in analytical niches such as heritage science laboratories. This article discusses advantages of using a Laboratory Information Management System (LIMS) for organising and systematising research in an interdisciplinary analytical laboratory. LIMS solutions are proven to be effective in managing laboratories, organising their daily management tasks, improving work conditions and increasing productivity, managing equipment and resources, and managing and safeguarding scientific data. LIMS software is commercially available since the 1990s with over a hundred software packages intended either for general or specific purposes. However, the ones currently available do not fulfil all the requirements for heritage science or are over-the-top, complex, one-fits-all standard solutions. The system here proposed was developed for a heritage science laboratory, considering its unique requirements, having as a case study the HERCULES Lab, from the University of Évora (Portugal). This article documents our approach based on the analysis of the unique requirements for the lab resulting in a proposal of a custom user-centred web-based system. For this, we discuss in detail the workflow, and the required system architecture. We conclude that although it is a small niche market for major manufacturers to dwell on, a custom LIMS is of the utmost importance for the current management of heritage science laboratories across the globe.
Utilization of Generative Adversarial Networks in Face Image Synthesis for Augmentation of Face Recognition Training Data

Face recognition has become a popular research field in computer vision and is widely applied in various sectors. The challenge with face recognition is that if the training data is limited, the face recognition rate will be less effective. Generative Adversarial Networks (GANs) is a deep learning method that can create synthesis images with high quality. This research aims to utilize GANs in synthesizing face images as a form of augmentation in face recognition training data. Initially, the latent space representation of the face image will be made using GANs, then adding styles to the face image using the latent direction method. In the experiment of making latent space representation, the loss value was able to reach 0.15. In the experiment of face recognition, the addition of face image synthesis was able to increase the accuracy of the face recognition classifier model from 0.74 to 0.89.
Development of an Interactive Educational Tool to Experience Machine Learning with Image Classification

In this study, we developed an interactive educational tool to experience machine learning with image classification. In the development of actual Artificial Intelligence (AI), it's important to think about what situations AI can be used in without coding. This system is based on machine learning for image classification and was developed as an iPad application. By using this system, beginners can feel the usefulness of AI. And It also enables users to experience AI development.
Visual recognition and performance prediction of athletes based on target tracking EIA algorithm

In the past, the research of target tracking was often to track problems in a static background, and the tracking scenes were often stable, and the targets were special. However, target tracking is often a tracking problem in the face of realistic complex scenes, and the target and scene are more complex. Therefore, the target tracking algorithm still faces many challenges in practical applications, especially in sports visual feature recognition. Based on the needs of sports feature recognition, this study combines the EIA algorithm to construct a feature recognition model. Moreover, for the shortcomings of the compressed sensing tracking algorithm that cannot accurately and comprehensively describe the target shape through a single target feature, the multi-feature adaptive fusion method is used to visualize the target appearance model, thus improving the accuracy of target tracking. In addition, this study design experiments to analyze the performance of the algorithm model. The research results show that the algorithm model of this study has certain recognition effects.
Regional forestry economic evaluation based on neural network and fuzzy model

In the process of people’s development, many resources in natural resources provide convenience and help for people’s survival and development, and forest is an important part of ecological balanced development. In the process of economic development, forest resources can also bring people a lot of economic benefits. People develop regional forestry economy by planting trees with economic value. Asset assessment of forests can better promote the development of forestry economy and help people better use and manage resources. During the process of forestry economic development, the technology of asset evaluation established on the basis of BP neural network enriches the theoretical content of forestry economic development to a certain extent and provides a more solid theoretical basis for development. At the same time, the qualitative and quantitative methods are combined to ensure that the data are more accurate and the key influencing factors can be better analyzed. Based on the analysis of the relevant research results, this paper takes some trees of the same growth year as the object of study. Using BP neural network to analyze the factors that will change the overall value of forest resources, a special BP neural network model belongs to forestry and economic development is established.
Report Search Function Using TF-IDF for PBL Education

The 21st century works need both `hard' skills (traditional domains, such as, history, mathematics, science) as well as `soft' skills (teamwork, disciplined thinking, creativity, social skills, computer literacy and analyze information). KIT and ICT have committed to on-line PBL education to educate students on the skills they need. Accurately grasping the learning situation of each student and promoting his growth by giving appropriate advice is effective for student's growth, and AI can be utilized to achieve this. We propose the on-line PBL education system using AI. Especially, a past activity case presentation system using AI is explained in detail.
Exploring the Impact of Similarity Model to Identify the Most Similar Image from a Large Image Database

Identifying the most similar image from a given large image database is becoming more and more popular, and many companies have brought out related products. However, there are some incomplete functions and shortcoming of previous work, the database was disorganized and does not have any regulation, and the similarity method used in the image searching system can be improved. In this study, we have implemented an improved similar image searching system that can filter the dataset based on the requirement of users. Besides, an experiment about testing different similarity models has been done. The result is an improved similarity model which can be subsequently applied to the searching system.
Specifying Key-properties to Improve the Recognition Skills of Neural Networks

Software engineers are increasingly asked to build datasets for engineering neural network-based software systems. These datasets are used to train neural networks to recognise data. Traditionally, data scientists build datasets consisting of random collected or generated data. Their approaches are often costly, inefficient and time-consuming. Software engineers rely on these traditional approaches that do not support precise data selection criteria based on customer's requirements. We introduced a software engineering method for dataset augmentation to improve neural networks by satisfying the customer's requirements. In this paper, we introduce the notion of key-properties to describe the neural network's recognition skills. Key-properties are used all along the engineering process for developing the neural network in cooperation with the customer. We propose a rigorous process for augmenting datasets based on the analysis and specification of the keyproperties. We conducted an experimentation on a case study on the recognition of the state of a digital meter counter. We demonstrate an informal specification of the neural network's key-properties and a successful improvement of a neural network's recognition of the meter counter state.
Point-Based Value Iteration for VAR-POMDPs

Partially observable Markov decision processes have been widely adopted in the automatic planning literature since it elegantly captures both execution and observation uncertainties. In our previous paper, we proposed a model called vector autoregressive partially observable Markov decision process (VAR-POMDP) which extends the traditional POMDP by considering the temporal correlation among continuous observations. However, it is a non-trivial problem to develop a tractable planning algorithm for the VAR-POMDP model with performance guarantees as most existing algorithms need to explicitly enumerate all possible observation histories, which is in an unbounded continuous space. In this letter, we extend the famous point-based value iteration algorithm to a double point-based value iteration and show that the VAR-POMDP model can be solved by dynamic programming through approximating the exact value function by a class of piece-wise linear functions. Meanwhile, we prove that the approximation error is bounded. The effectiveness of the proposed planning algorithm is illustrated by an example.
Flexible, non-parametric modeling using regularized neural networks

Abstract Non-parametric, additive models are able to capture complex data dependencies in a flexible, yet interpretable way. However, choosing the format of the additive components often requires non-trivial data exploration. Here, as an alternative, we propose PrAda-net, a one-hidden-layer neural network, trained with proximal gradient descent and adaptive lasso. PrAda-net automatically adjusts the size and architecture of the neural network to reflect the complexity and structure of the data. The compact network obtained by PrAda-net can be translated to additive model components, making it suitable for non-parametric statistical modelling with automatic model selection. We demonstrate PrAda-net on simulated data, where we compare the test error performance, variable importance and variable subset identification properties of PrAda-net to other lasso-based regularization approaches for neural networks. We also apply PrAda-net to the massive U.K. black smoke data set, to demonstrate how PrAda-net can be used to model complex and heterogeneous data with spatial and temporal components. In contrast to classical, statistical non-parametric approaches, PrAda-net requires no preliminary modeling to select the functional forms of the additive components, yet still results in an interpretable model representation.
Heuristic Approach to Distributed Server Allocation with Preventive Start-Time Optimization against Server Failure

This paper proposes a heuristic approach for distributed server allocation with preventive start-time optimization against a single server failure. Server allocation is decided beforehand under each failure pattern to minimize the largest maximum delay among all failure patterns. The Previous work formulated the model as an an integer linear programming (ILP) problem. As the number of users and that of severs increase, the size of ILP problem increases; the computation time to solve the ILP problem becomes large. The proposed heuristic approach adopts both simulated annealing and ILP approach in a hybrid manner. The numerical results reveal that the proposed approach reduces computation time by 26% compared with the ILP approach with increasing the largest maximum delay by 3.4% in average. It reduces the largest maximum delay compared with the start-time optimization model; it avoids instability caused by the unnecessary disconnection, which occurs in the run-time optimization model.
The Comfort Benefits of Gaze-Directed Steering

Spatial steering is a common virtual reality (VR) travel metaphor that affords virtual locomotion and spatial understanding. Variations of spatial steering include Gaze-, Hand-, and Torso-directed steering. We present a study that employed a dual-task methodology to investigate the user performance characteristics and cognitive loads of the three spatial steering techniques, in addition to several subjective measures. Using the two one-sided tests (TOST) procedure for dependent means, we have found that Gaze- and Hand-directed steering were statistically equivalent for travel performance and cognitive load. However, we found that Gaze-directed steering induced significantly less simulator sickness than Hand-directed steering.
Improving Dynamic Recommendation using Network Embedding for Context Inference

Network embedding, which is a method to learn low-dimensional latent representations of nodes in networks, can be effectively utilized to infer contexts in the context-aware recommender domain. One of the fundamental challenges of network embedding is how to effectively and efficiently learn embeddings from dynamic networks, whose nodes and edges change over time. Network embedding approaches designed for static networks are infeasible to use with dynamic networks for reasons of scalability. The use of network embedding for inferring contexts in the incremental recommender task poses two fundamental challenges: (1) efficiently inferring contextual information that changes over time; and (2) integrating learned contextual features with a recommender technique that can be updated incrementally. To address these challenges, we present a neural recommender approach that models user interactions in the dynamic setting. Furthermore, we introduce a novel dynamic network embedding method based on an efficient neighborhood sampling technique, which employs a temporally biased form of random walk. We have successfully applied our approach to Point-Of-Interest recommendation domain by improving efficiency in context inference and quality of recommendations.
Features and possibilities of remodeling nonlinear systems based on Takagi-Sugeno fuzzy models

Remodeling involves the transition from existing models of the given system to the models of some other class that reveals possibilities for overcoming research difficulties. Takagi- Sugeno (T-S) fuzzy models provide an established approach to description, analysis and synthesis of nonlinear systems. However the potential of transition to fuzzy description on the basis of T- S models is not fully understood and implemented in analysis and synthesis of nonlinear systems. The paper uncovers the peculiarities of the approach to nonlinear system remodeling based on T-S fuzzy models. It demonstrates the scheme of fuzzy remodeling that allows replacing the initial model of the nonlinear system by the T-S fuzzy model equivalent to it in a bounded area of the phase space. The examples illustrating the range of approach applications are given. It is shown that the transition from a nonlinear model to a T-S fuzzy one enables us to solve various analysis and control tasks via the implementation of the apparatus of linear matrix inequalities. The sources of conservatism of fuzzy remodeling, that lie in peculiarities of T-S fuzzy models are emphasized.
Quantum Machine Learning: A Patent Review

One of the central problems bottlenecking machine learning research is classical computational power limits. Quantum computing provides a solution, offering more processing power for less electric cost. Quantum Machine Learning (QML) is a research field at the intersection of quantum computing and machine learning technologies, driving the cutting edge in technological innovation. While the legal literature on software patents is rapidly scaling, the research focused on QML patents is noticeably nascent. As such, this Article contributes the first empirical patent survey for QML technologies.
Symmetrical feature extraction via novel Mirror PCA

Symmetry is an important property of human faces. Many researchers exploit symmetry to improve face recognition. In this paper, we explore the symmetry of faces under the framework of PCA (Principal Component Analysis). Unlike previous studies that manipulate facial symmetry by averaging the two halves, we argue that well-estimated symmetrical faces lie in a low-dimensional subspace. Inspired by this, a PCA-like model, referred to as Mirror PCA, is proposed to extract low-rank symmetrical features of faces, as well as their principal directions. Although the optimization problem is non-convex, a suboptimal solution could be found under existing optimization frameworks such as ADMM, PGD, etc. A number of experiments are conducted to verify the effectiveness and robustness of our method.
Visualization of Team Cohesion in Hybrid Intelligent Multi-Agent Systems

The paper is presented within the framework of the large project. The main aim of this project is a new class of intelligent information systems, namely cohesive hybrid intelligent multi-agent systems that simulate the process of rallying a team of experts when solving problems at the "round table" by coordinating the goals and ontologies of agents, as well as forming a cohesive interaction protocol by agents to solve the problem. The paper presents the results of the development of the visual way of describing cohesion in hybrid intelligent multiagent systems.
Implementation of ontology-based on Word2Vec and DBSCAN for part-of-speech

POS tagging is a process of marking text into an appropriate word-class based on word definitions and word relationships. In general, several POS tagging approaches have been applied in Bahasa Indonesia namely rule-based, stochastic, and neural. Besides, there is another approach to POS tagging which has been applied to English, namely the approach using ontology. This approach has not yet been applied to Bahasa Indonesia so we will implement an ontology to conduct POS tagging in Bahasa Indonesia. In this study, the ontology was constructed using the Word2Vec and the DBSCAN clustering method. The Word2Vec model is implemented to extract each word in vector form based on its context and the DBSCAN clustering method is implemented for the classification process of word classes based on word vectors modeled by Word2Vec. The process of POS tagging with ontology is carried out in several stages, namely: data collection using web scraping techniques from Kompas.com and Detik.com online news articles, text preprocessing, Word2Vec feature building, clustering with DBSCAN, ontology construction and evaluation. The experiments carried out in this study were to choose the optimal parameter values from DBSCAN in forming word clusters for ontology construction. Overall, the implementation of ontology with Word2Vec and DBSCAN can do POS tagging with the highest accuracy value of 0.62, the highest precision value of 0.79, the highest recall value of 0.62, and the highest f1-score of 0.67.
Neural Transduction for Multilingual Lexical Translation

We present a method for completing multilingual translation dictionaries.Our probabilistic approach can synthesize new word forms, allowing it to operate in settings where correct translations have not been observed in text (cf.cross-lingual embeddings).In addition, we propose an approximate Maximum Mutual Information (MMI) decoding objective to further improve performance in both many-to-one and one-to-one word level translation tasks where we use either multiple input languages for a single target language or more typical single language pair translation.The model is trained in a many-to-many setting, where it can leverage information from related languages to predict words in each of its many target languages.We focus on 6 languages: French, Spanish, Italian, Portuguese, Romanian, and Turkish.When indirect multilingual information is available, ensembling with mixture-of-experts as well as incorporating related languages leads to a 27% relative improvement in whole-word accuracy of predictions over a single-source baseline.To seed the completion when multilingual data is unavailable, it is better to decode with an MMI objective.
A Virtual Play Environment and Game Strategy Analysis System Using Imitation Learning Agents

By using machine learning to learn the behavior of human players in a logical board game, It is possible to create an agent that mimics the strategy of a human player. These imitation learning agents can be used for various educational purposes. In this paper, we describe a virtual play environment and game strategy analysis system that can be constructed using imitation learning agents in a logical board game.
The Crowdsourcing Method to Normalize “Bahasa Alay”, a Case of Indonesian Corpus

In verbal communication, people use sentences that can be classified into two categories, namely formal and non- formal. The former meets the grammatical standard as prescribed by linguistic rules of the language, while the latter deviates it. In daily communication, however, non-formal sentences are more intensively used because they are more practical and easier to understand. With this deviation, nonformal sentences cause problems in linguistic computation because most linguistic computations use formal languages that already have standard rules. This research aims to develop an Indonesian closed corpus related to airline ticket reservations. The data used to develop the corpus are taken from conversations between customer service staff and consumers in airline ticket reservations. This is a preliminary study to propose and develop a chatbot in airline ticket reservations. The result of this study is the Indonesian closed corpus related to airline ticket reservations to determine the right response for consumers.