Reviewerly/exHarmony huggingface dataset:
Constructing a Software Tool for Detecting Face Mask-wearing by Machine Learning

In the pandemic era of COVID19, software engineering and artificial intelligence tools played a major role in monitoring, managing, and predicting the spread of the virus. According to reports released by the World Health Organization, all attempts to prevent any form of infection are highly recommended among people. One side of avoiding infection is requiring people to wear face masks. The problem is that some people do not incline to wear a face mask, and guiding them manually by police is not easy especially in a large or public area to avoid this infection. The purpose of this paper is to construct a software tool called Face Mask Detection (FMD) to detect any face that does not wear a mask in a specific public area by using CCTV (closed-circuit television). The problem also occurs in case the software tool is inaccurate. The technique of this notion is to use large data of face images, some faces are wearing masks, and others are not wearing masks. The methodology is by using machine learning, which is characterized by a HOG (histogram orientation gradient) for extraction of features, then an SVM(support vector machine) for classification, as it can contribute to the literature and enhance mask detection accuracy. Several public datasets for masked and unmasked face images have been used in the experiments. The findings for accuracy are as follows: 97.00%, 100.0%, 97.50%, 95.0% for RWMFD (Real-world Masked Face Dataset)&amp; GENK14k, SMFDB (Simulated Masked Face Recognition Dataset), MFRD (Masked Face Recognition Dataset), and MAFA (MAsked FAces)&amp; GENK14k for databases, respectively. The results are promising as a comparison of this work has been made with the state-of-the-art. The workstation of this research used a webcam programmed by Matlab for real-time testing.
Rancang Dan Bangun Game Edukasi Anak-Anak Berbasis Android Dengan Unity Menggunakan Metode Game Development Life Cycle

Technology is growing with the times, relentlessly and constantly moving forward to make new breakthroughs in all aspects of life. The IEA report in the Study of Reading Literacy states that the ability of elementary school children in Indonesia is very low. Of the 31 countries studied, Indonesia was ranked 30th. Based on the explanation of the background above, the researchers formulated the problem in this study, namely how to design and build an Android-based educational game with unity using the Game development life cycle (GDLC). This research was conducted in August 2021 in 9/10 Ulu Village, Plaju District, Palembang with a sample of 10 children. The research method uses Action Research and application development methods use GDLC. The results of testing the game system with blackbox that can be operated smoothly and for the results of testing the feasibility of games with the results of 84.6% in the appropriate category, the results of development with Unity and GDLC succeeded in creating learning media based on android games quite well.
FACE RECOGNITION SYSTEM BY IMAGE PROCESSING

A wide variety of systems require reliable person recognition schemes to either confirm or determine the identity of an individual requesting their services. The purpose of such schemes is to ensure that only a legitimate user and no one else access the rendered services. Examples of such applications include secure access to buildings, computer systems, laptops, cellular phones, and ATMs. Face can be used as Biometrics for person verification. Face is a complex multidimensional structure and needs a good computing techniques for recognition. We treats face recognition as a two-dimensional recognition problem. A well-known technique of Principal Component Analysis (PCA) is used for face recognition. Face images are projected onto a face space that encodes best variation among known face images. The face space is defined by Eigen face which are eigenvectors of the set of faces, which may not correspond to general facial features such as eyes, nose, lips. The system performs by projecting pre extracted face image onto a set of face space that represent significant variations among known face images. The variable reducing theory of PCA accounts for the smaller face space than the training set of face. A Multire solution features based pattern recognition system used for face recognition based on the combination of Radon and wavelet transforms. As the Radon transform is in-variant to rotation and a Wavelet Transform provides the multiple resolution. This technique is robust for face recognition. The technique computes Radon projections in different orientations and captures the directional features of face images. Further, the wavelet transform applied on Radon space provides multire solution features of the facial images. Being the line integral, Radon transform improves the low-frequency components that are useful in face recognition
OWLOOP: A modular API to describe OWL axioms in OOP objects hierarchies

<h2>Abstract</h2> OWLOOP is an Application Programming Interface (API) for using the Ontology Web Language (OWL) by the means of Object-Oriented Programming (OOP). It is common to design software architectures using the OOP paradigm for increasing their modularity. If the components of an architecture also exploit OWL ontologies for knowledge representation and reasoning, they would require to be interfaced with OWL axioms. Since OWL does not adhere to the OOP paradigm, such an interface often leads to boilerplate code affecting modularity, and OWLOOP is designed to address this issue as well as the associated computational aspects. We present an extension of the OWL-API to provide a general-purpose interface between OWL axioms subject to reasoning and modular OOP objects hierarchies.
Digital technological model of heat exchange control in an air heat pump in a low-rise building

Abstract The use of air source heat pumps together with other renewable energy devices and highly efficient heating equipment together with digital control systems is a promising European trend that continues to grow in the face of rising prices for traditional energy sources. The article presents a number of technical energy-saving solutions, such as a mixing chamber for HVH, built into the heat supply system and the use of a supply and exhaust ventilation system, in which the principle of recuperation is incorporated. A digital modular-functional-structural diagram of heat generation of a heat carrier from the environment is proposed. Relying on the energy efficiency of the circuit design with an electric boiler, a buffer tank and a patented mixing chamber, the authors achieve a rationally controlled heat and air exchange of a combined heat pump system for heating premises. Automation of the joint work of rationally selected elements of the heat pump air system is one of the Smart Home technologies that improve the efficiency of heat supply, create comfortable living conditions and safety through digitalization of the control algorithm for this system.
Sentiment Analysis on Social Media using Machine Learning Approach

Social media has been embraced by different people as a convenient and official medium of communication. People write messages and attach images and videos on Twitter, Facebook and other social media which they share. Social media therefore generates a lot of data that is rich in sentiments from these updates. Sentiment analysis has been used to determine opinions of clients, for instance, relating to a particular product or company. Knowledge based approach and Machine learning approach are among the strategies that have been used to analyze these sentiments. The performance of sentiment analysis is however distorted by noise, the curse of dimensionality, the data domains and size of data used for training and testing. This research aims at developing a model for sentiment analysis in which dimensionality reduction and the use of different parts of speech improves sentiment analysis performance. It uses natural language processing for filtering, storing and performing sentiment analysis on the data from social media. The model is tested using Naïve Bayes, Support Vector Machines and K-Nearest neighbor machine learning algorithms and its performance compared with that of two other Sentiment Analysis models. Experimental results show that the model improves sentiment analysis performance using machine learning techniques.
Approximate Ontology Reasoning for Domain-Specific Knowledge Graph based on Deep Learning

Ontology reasoning has great potential in application of domain-specific knowledge graph. However, the traditional ontology reasoner is difficult to perform rapidly reasoning on domain-specific knowledge graph as its complex logical expression and large-scale assertion set. On the other hand, the knowledge graph reasoning method based on presentation learning has better scalability, but it is hard to incorporate expert knowledge in the schema-level information effectively, so it can't implement complex reasoning. The above restricts the application of reasoning in domain-specific knowledge graph. In order to solve the problem, researchers try to combine ontology reasoning with deep learning to achieve approximate ontology reasoning. However, the state-of-the-art method has shortcomings of model design and evaluation which make it lose generalization ability. This paper proposes a novel method to transform ontology reasoning into a graph-to-graph relational mapping learning model, and realizes approximate ontology reasoning based on deep learning. Besides, this paper also defines new knowledge graph reasoning evaluation criterion, and synthesizes new test data. On this basis, relevant comparative experiments are carried out. The results show that the proposed method achieves good performance on LUBM synthetic dataset, even though the data mode that not appear in the training can also be inferred effectively, which can make the model applicable to a wider range of application scenarios.
Tuning Suitable Features Selection using Mixed Waste Classification Accuracy

A B S T R A C TClassification accuracy can be used as method to tune suitable features.Some features can be mistakenly selected hence derailed the classification accuracy.Currently, feature optimization has gained many interests among researchers.Hence, this paper aims to demonstrate the effects of features reduction and optimization for higher classification results of mixed waste.The most relevant features with respect to mix waste characteristic were observed with respect to classification accuracy.There are four stages of features selection.The first stage, 40 features were selected with training accuracy 79.59%.Then, for second stage, better accuracy was obtained when redundant features were removed which accounted for 20 features with training accuracy of 81.42%.As for the third stage 17 features were maintained at 90.69% training accuracy.Finally, for the fourth stage, additional two more features were removed, however the classification accuracy was decreased to less than 80%.The experiments results showed that by observing the classification rate, certain features gave higher accuracy, while the others were redundant.Therefore, in this study, suitable features gave higher accuracy, on contrary, as the number of features increased, the accuracy rate were not necessarily higher.
Research on Image Enhancement Algorithm Based on Artificial Intelligence

Abstract With the continuous development of social science and technology, people have higher and higher requirements for image quality. This paper integrates artificial intelligence technology and proposes a low-illuminance panoramic image enhancement algorithm based on simulated multi-exposure fusion. First, the image information content is used as a metric to estimate the optimal exposure rate, and the brightness mapping function is used to enhance the V component, and the low-illuminance. The image and the overexposed image are input, the medium exposure image is synthesized by the exposure interpolation method, and the low illumination image, the medium exposure image and the overexposure image are merged using a multi-scale fusion strategy to obtain the fused image, which is corrected by a multi-scale detail enhancement algorithm. After the fusion, the details are enhanced to obtain the final enhanced image. Practice has proved that the algorithm can effectively improve the image quality.
UNDERSTANDING BIG DATA-FAIR VALUE MEASUREMENT MODEL OF BIOLOGICAL ASSETS

This research establishes a Big Data-Fair Value Measurement Model of Biological Assets using a Smart Farming Approach. The aim of the study is to gather literature knowledge about the judgment of biological assets. The use of big data and innovative farming in the agricultural sector is relatively new in Malaysia, making people wonder about its effectiveness, whether positive or negative. However, determining fair value can be a daunting task due to the existence of personal biological properties and the variety of specimens (offspring), classes, and conditions. Agricultural sectors need to be more emphasised by many parties as the increases in cost production is not a trivial matter that can be ignored. There have been pressures from foreign countries such as Thailand, Vietnam, and Indonesia in the agricultural sector. Malaysia faces one major challenge: the cost of production, including wages and inputs, is relatively higher. (Rozhan 2019). Agricultural accounting is a minor standard, but it has a broad scope and a significant impact on agricultural businesses based on the accounting perspective, the MFRS141/IAS 41. The standard improves the transparency of the cost to replace capital (by allowing for depreciation and amortisation) and better reflects the productive lifespan of assets in agricultural operations. However, the application of MFRS 141 Agriculture is still relatively new in Malaysia, and a thorough examination of the literature indicates several gaps and deficiencies. This literature review is vital to support the study on Big Data-Fair Value Measurement Model of Biological Assets as the agricultural sector's involvement was recently popular. It might also offer some good ideas for handling problems involving the fair value measurement. Future studies will help the Ministry of Agriculture and Food Industries, agriculture sector workers, in terms of practical perspective. Therefore, the future result of the study suggests an improvement within the agriculture sector related to the treatment in their fair values, issues, and strategies.
Universal Dependencies for Tweets in Brazilian Portuguese: Tokenization and Part of Speech Tagging

Automatically dealing with Natural Language User-Generated Content (UGC) is a challenging task of utmost importance, given the amount of information available over the web. We present in this paper an effort on building tokenization and Part of Speech (PoS) tagging systems for tweets in Brazilian Portuguese, following the guidelines of the Universal Dependencies (UD) project. We propose a rule-based tokenizer and the customization of current state-of-the-art UD-based tagging strategies for Portuguese, achieving a 98% f-score for tokenization, and a 95% f-score for PoS tagging. We also introduce DANTEStocks, the corpus of stock market tweets on which we base our work, presenting preliminary evidence of the multi-genre capacity of our PoS tagger.
A Joint Imitation-Reinforcement Learning Framework for Reduced Baseline Regret

In various control task domains, existing controllers provide a baseline level of performance that—though possibly suboptimal—should be maintained. Reinforcement learning (RL) algorithms that rely on extensive exploration of the state and action space can be used to optimize a control policy. However, fully exploratory RL algorithms may decrease performance below a baseline level during training. In this paper, we address the issue of online optimization of a control policy while minimizing regret with respect to a baseline policy performance. We present a joint imitation-reinforcement learning framework, denoted JIRL. The learning process in JIRL assumes the availability of a baseline policy and is designed with two objectives in mind (a) training while leveraging demonstrations from the baseline policy to minimize regret with respect to the baseline policy, and (b) eventually surpassing the baseline performance. JIRL addresses these objectives by initially learning to imitate the baseline policy and gradually shifting control from the baseline to an RL agent. Experimental results show that JIRL effectively accomplishes the aforementioned objectives in several, continuous action-space domains. The results demonstrate that JIRL is comparable to a state-of-the-art algorithm in its final performance while incurring significantly lower baseline regret during training. Moreover, the results show a reduction factor of up to 21 in baseline regret over a trust-region-based approach that guarantees monotonic policy improvement.
State representations of convolutional codes over a finite ring

In this paper we study finite support convolutional codes over Zpr by means of an input-state-output representation. We show that the set of finite weight input-state-output trajectories associated to this type of representations has the structure of a Zpr-submodule of Zprn and therefore is a (finite support) convolutional code. Fundamental system-theoretical properties such as observability, reachability or minimality, are investigated in this context.
Novelty detection for unsupervised continual learning in image sequences

Recent works in the domain of deep learning for object recognition on common image classification benchmarks often address the representation learning problem under the assumption of i.i.d. input data. Although achieving satisfying results, this assumption seems not realistic when agents have to learn autonomously. An autonomous agent receives a continual visual flow of objects which is far from an i.i.d. distribution of objects. Moreover, agents have to construct their representations of the world and adapt to unknown environments, without relying on external sources of information such as labels that would be provided post-classification and are unavoidable when an over-segmentation is done. Then, in order to exploit the learned representation effectively for object recognition, a clear and meaningful relationship w.r.t. real object categories is required, which has been largely neglected in existing unsupervised algorithms.In this paper, we propose a novelty detection method for continual and unsupervised object recognition, as an extension for the recent CURL model, which allows to moderate over-segmentation while preserving accuracy, in order to meet the requirements for autonomy. We experimentally validated our approach on two standard image classification benchmarks, MNIST and Fashion-MNIST, in this unsupervised and continual learning setting and improve the state of the art in terms of cluster purity, which is crucial for subsequent object recognition, since it facilitates clustering when information on ground truth labels is not available for free.
Flash Memory and Its Manufacturing Technology for Sustainable World

In today’s data-driven society, memory is an unavoidable component. IoT and 5G are accelerating data generation exponentially, and the demands of high-capacity memory are increasing. Flash memory is a crucial infrastructure component and inevitable for Data Center. The advantages of a Solid-State Drive over a Hard Disk Drive are power consumption and volume per giga-byte. Low power consumption of storage devices, which are components of the data center itself, is critical in reducing power consumption, which has become a primary social concern. This paper describes current efforts and future trends in storage devices and their manufacturing technologies for realizing a sustainable society. New device structure which increases storage capacity with significant reduction of both materials and process steps, environmentally friendly and efficient manufacturing technologies and future technologies under consideration for even higher capacity will be shown. These technological innovations will make it possible to realize a society that enriches people’s lives.
Exploring social media use of Filipino learners

Profusely widespread use of social media opened multiple reading opportunities for learners. However, educators have growing concern that excessive use of social media compromises learners’ reading competence and, eventually, academic performance. The current work aimed to examine the extent to which social media use may affect the university students’ (N=101) reading attitudes and competence, which are the backbone of scholastic performance. A mixed-methods approach was applied in the study. The data collected were analyzed using version 26 of the Statistical Package for Social Sciences (SPSS) software. Interview answers were qualitatively analyzed. Results indicate that social media platforms (SMPs) were least used for educational purposes. Students showed positive reading attitudes emphasizing their accorded importance to reading and finding happiness to learn new things. However, as they managed to read posts daily, lesser inclination was accorded to productive reading attitudes such as finding time to read during vacant hours and having enthusiasm towards advanced reading and reading with friends. Overall, reading competence indicated ineptitude. This study provides insights into the context of social media use in shaping learners’ reading attitudes and affecting their level of reading competence.
Using MDP to Find the Best Path in Multi-floor World

This paper discusses the stochastic path (SSP) problem in an uncertain grid world. In a chaotic environment where lots of factors, like people, can disturb performances of an agent, like an indoor service robot, the agent needs to consider the uncertainty to make sequential decisions so that accomplishes tasks. Markov Decision Process (MDP) is a widely used mathematic model as well as reinforce learning model for dynamic algorithms solving problems in an environment with uncertainty. Based in MDP, an algorithm was proposed to find the best path in a multi-floor grid world where the agent can move upstairs or downstairs by lifts and stairs. The results show that, given the grid map, the algorithm can find the path with the lowest energy or time cost considering in such environment. The algorithm can be applied to mobile agents in stochastic environment like service robots in shopping malls.
A Data Integration Architecture for Smart Cities

The data generated by smart cities have low integration, as the systems that produce them are usually closed and developed for specific needs. Moreover, the large volume of data, and the semantic and structural changes in datasets over time make the use of data to support decision-making even more difficult. In this work, we identify the main requirements of a data integration system to support decision-making in cities, focusing on its challenges. We analyze some existing data integration solutions, to uncover their features and limitations. Based on these results, we propose a new microservice architecture to support the development of software platforms for integrating smart cities’ heterogeneous data and a guideline to assess their performance.