Reviewerly/exHarmony huggingface dataset:
Non-Invasive Monitoring of Vital Signs for the Elderly Using Low-Cost Wireless Sensor Networks: Exploring the Impact on Sleep and Home Security

Wireless sensor networks (WSN) are useful in medicine for monitoring the vital signs of elderly patients. These sensors allow for remote monitoring of a patient’s state of health, making it easier for elderly patients, and allowing to avoid or at least to extend the interval between visits to specialized health centers. The proposed system is a low-cost WSN deployed at the elderly patient’s home, monitoring the main areas of the house and sending daily recommendations to the patient. This study measures the impact of the proposed sensor network on nine vital sign metrics based on a person’s sleep patterns. These metrics were taken from 30 adults over a period of four weeks, the first two weeks without the sensor system while the remaining two weeks with continuous monitoring of the patients, providing security for their homes and a perception of well-being. This work aims to identify relationships between parameters impacted by the sensor system and predictive trends about the level of improvement in vital sign metrics. Moreover, this work focuses on adapting a reactive algorithm for energy and performance optimization for the sensor monitoring system. Results show that sleep metrics improved statistically based on the recommendations for use of the sensor network; the elderly adults slept more and more continuously, and the higher their heart rate, respiratory rate, and temperature, the greater the likelihood of the impact of the network on the sleep metrics. The proposed energy-saving algorithm for the WSN succeeded in reducing energy consumption and improving resilience of the network.
IMPACT OF ARTIFICIAL INTELLIGENCE TECHNOLOGY IN FINE ART: IN REFERENCE OF PRINTMAKING

Artificial intelligence (AI) is revolutionizing the art industry. Fine craftsmen are using man-made intelligence to make craftsmanship pieces that are tastefully satisfying as well as accomplish remarkable structures and make forward-thinking workmanship viewpoints. Man-made intelligence innovation is currently turning into a significant device in the inventive flow, permitting capable craftsmen to grow their imaginative capacities and open doors. However, AI has also sparked debates regarding whether or not it can substitute for human artists. Through my studies, I look at how AI affects fine artists and their work as well as how AI might affect the art industry in the future.
Implementing Backjumping by Means of Exception Handling

Abstract We discuss how to implement backjumping (or intelligent backtracking) in Prolog by using the built-ins throw/1 and catch/3. We show that it is impossible in a general case, contrary to a claim that “backjumping is exception handling." We provide two solutions. One works for binary programs; in a general case it imposes a restriction on where backjumping may originate. The other restricts the class of backjump targets. We also discuss implementing backjumping by using backtracking and the Prolog database. Additionally, we explain the semantics of Prolog exception handling in the presence of coroutining.
DELIZIA SELF SERVICE APPLICATION USING UX DESIGN AND ANDROID STUDIO

Delizia Self Service is a tart ordering service application using the concept of Self Service Technology (SST). In this application there are 2 kinds of prospective users, in terms of customers to order tarts who can choose directly the tarts they want and even customers can also customize tarts according to their tastes. And in terms of admin which is useful for managing products, from adding products, deleting products and modifying products. Based on its function, this application provides benefits such as providing alternative means of selling tarts, increasing sales value, increasing interest, user experience, satisfaction, and comfort of buyers when ordering tarts. This journal focuses on developing application recommendations by designing the UI/UX of the Delizia Self Service Application and realizing it using Android Studio, using the System Development Life Cycle (SDLC) method with the prototype model.
Dyadic Existential Rules

Abstract Existential rules form an expressive ${{\textsf{Datalog}}}$ -based language to specify ontological knowledge. The presence of existential quantification in rule-heads, however, makes the main reasoning tasks undecidable. To overcome this limitation, in the last two decades, a number of classes of existential rules guaranteeing the decidability of query answering have been proposed. Unfortunately, only some of these classes fully encompass ${{\textsf{Datalog}}}$ and, often, this comes at the price of higher computational complexity. Moreover, expressive classes are typically unable to exploit tools developed for classes exhibiting lower expressiveness. To mitigate these shortcomings, this paper introduces a novel general syntactic condition that allows us to define, systematically and in a uniform way, from any decidable class $\mathcal{C}$ of existential rules, a new class called ${{\textsf{Dyadic-}\mathcal{C}}}$ enjoying the following properties: ( i ) it is decidable; ( ii ) it generalizes ${{\textsf{Datalog}}}$ ; ( iii ) it generalizes $\mathcal{C}$ ; ( iv ) it can effectively exploit any reasoner for query answering over $\mathcal{C}$ ; and ( v ) its computational complexity does not exceed the highest between the one of $\mathcal{C}$ and the one of ${{\textsf{Datalog}}}$ .
A New Autonomous Navigation System of a Mobile Robot Using Supervised Learning

An intelligent mobile robot must have the ability to move autonomously in complex environments. Achieving this mission using classical paradigms requires a sophisticated perception of the environment and a high computational load. In this chapter, we present an autonomous navigation system based on supervised learning applied to a mobile robot equipped with ultrasonic sensors. The robot will be trained and tested in several environments of different complexity. The test results showed the effectiveness of the proposed navigation system.
Supporting and Shaping Human Decisions Through Internet of Behaviors (IoB): Perspectives and Implications

The rise of the Internet of Behaviors (IoB) has paved the way for new opportunities to support and shape human decision-making. IoB refers to the collection, analysis, and use of digital data generated by human activities to inform decision-making processes. This technology has the potential to significantly impact various aspects of our lives, including healthcare, education, finance, and transportation. This chapter explores the perspectives and implications of using IoB for supporting and shaping human decision-making. The chapter examines the extent to which IoB systems can influence human behavior and decision-making, and the ethical implications of such influence. It considers the benefits and challenges of using IoB in various domains, such as healthcare and education. The chapter also analyzes real-world examples of IoB systems in use, and their impact on human decision-making and behavior. The findings of this chapter will have important implications for both researchers and practitioners in the field of IoB. For researchers, the chapter provides a comprehensive overview of the current state of knowledge on the impact of IoB on human decision-making. For practitioners, the chapter offers insights into the ethical and practical considerations of using IoB for shaping human decisions and behavior, and offers recommendations for future research and development in this field.
Mathematical modelling, rule-making, and the COVID-19 pandemic

Abstract The COVID-19 pandemic brought to light the need to rethink the ways in which rules and decisions are made and what rule-making can achieve under conditions of uncertainty. Among the recommendations put forward by a recent Manifesto is to ‘pay attention to the unknowns’. This forces us to examine three interrelated questions: what is the nature of mathematical modelling? What kinds of uncertainty are there in the contested arena of science for rule-making? What ideas and approaches can help us better address the interaction between mathematical modelling and rule-making under conditions of uncertainty? This chapter addresses these three questions in detail.
Introduction to linear algebra for neural networks

Subject Cognition and Behavioural Neuroscience Collection: Oxford Scholarship Online
PERBANDINGAN LSTM DAN ELM DALAM MEMPREDIKSI HARGA PANGAN KOTA TASIKMALAYA

Humans have needs that must be met, one of which is the need for food, but food prices often change. Factors that affect price changes occur because the amount of demand is high while the supply is small. Making predictions about price changes will be very helpful to get an idea of the pattern of price changes. Therefore making predictions from price patterns is useful for providing information to the public. Predictions regarding price changes can be made using many methods. Long Short-Term Memory (LSTM) and Extreme Learning Machine (ELM) are several methods that can be used to predict time series data, these two methods can provide an overview of the predictions made. The results of the study show that both algorithms have good results in terms of the the evaluation value. The evaluation results showed no significant difference between the two algorithms. The evaluation value of the rice commodity showed that ELM tended to be better with MAE values of 6,721, MAPE 0.061%, MSE 115,281, RMSE 10,737 and CV 3,699%, while LSTM with MAE 31,707, MAPE 0.286%, MSE 1927.633, RMSE 43.905 and CV 3.655%. However, for other commodities, LSTM can produce a better evaluation value.
DIDACTIC POTENTIAL OF INFORMATION AND EDUCATIONAL ENVIRONMENT FOR THE ORGANIZATION OF MODERN LEARNING PROCESS

The article discusses the possibilities of the information and educational environment for organizing the learning process that meets the needs of the information society. The classification of environmental resources is presented, the structure of the electronic textbook as a means of mastering the content of education is determined, the problems arising during training in the information and educational environment are identified.The authors of the article describe the optimal didactic capabilities of modern information technologies to be used to improve the preparedness of a chemistry graduate. The aim of the article is to highlight the problem of finding the optimal didactic capabilities of modern information technologies used for improving the system of training specialists in the field of chemistry and to discuss the results of current studies in this direction
Analytical and Numerical Study of Information Retrieval Method Based on Single-Layer Neural Network with Optimization of Computing Algorithm Performance

This work presents a mathematical model of a fast-acting single-layer artificial neural network applied to the task of image reconstruction after noise. For research purposes, this algorithm was implemented in the Python and C++ programming languages. The numerical simulation of the recovery efficiency of the described neural network was performed for different values of the noise factor, the number of samples required to train elements in the sample and the dimensionality of the coupling coefficients, w. The study of the mathematical model of this neural network is presented; as a result, it is possible to identify its essence, to reduce the number of operations required to recover a single element and to increase recovery accuracy by changing the order of calculation of coupling coefficients, w.
Underwater Image Enhancement Based on Hybrid Enhanced Generative Adversarial Network

In recent years, underwater image processing has played an essential role in ocean exploration. The complexity of seawater leads to the phenomena of light absorption and scattering, which in turn cause serious image degradation problems, making it difficult to capture high-quality underwater images. A novel underwater image enhancement model based on Hybrid Enhanced Generative Adversarial Network (HEGAN) is proposed in this paper. By designing a Hybrid Underwater Image Synthesis Model (HUISM) based on a physical model and a deep learning method, many richly varied paired underwater images are acquired to compensate for the missing problem of underwater image enhancement dataset training. Meanwhile, the Detection Perception Enhancement Model (DPEM) with Perceptual Loss is designed to transfer the coding knowledge in the form of the gradient to the enhancement model through the perceptual loss, which leads to the generation of visually better and detection-friendly underwater images. Then, the synthesized and enhanced models are integrated into an adversarial network to generate high-quality underwater clear images through game learning. Experiments show that the proposed method significantly outperforms several state-of-the-art methods both qualitatively and quantitatively. Furthermore, it is also demonstrated that the method can improve target detection performance in underwater environments, which has specific application value for subsequent image processing.
Mapping Prediction of Surface Solar Radiation with Linear Regression Models: Case Study over Reunion Island

This paper presents a novel mapping prediction method for surface solar radiation with linear regression models. The dataset for surface solar radiation prediction is the daily surface incoming shortwave radiation (SIS) product from CM SAF SARAH-E. The spatial resolution is 0.05° × 0.05° and the temporal coverage is from 2007 to 2016. The first five years (2007–2011) are used as training data, and the remaining five years (2012–2016) are used as test data in the prediction model. Datasets were detrended, de-seasonalized, and normalized before being applied to multiple linear regression (MLR), principal component regression (PCR), stepwise regression (SR), and partial least squares regression (PLSR), which are used to perform prediction mapping. The statistical analysis using MAE, MSE, and RMSE shows that the PCR model had the smallest MAE, MSE, and RMSE as compared to the other three models. The PCR model seems better for SSR mapping prediction over Reunion Island. Although the PCR model provides better prediction results, its MAE, MSE, and RMSE are quite large.
ACT2G

Recent increase of remote-work, online meeting and tele-operation task makes people find that gesture for avatars and communication robots is more important than we have thought. It is one of the key factors to achieve smooth and natural communication between humans and AI systems and has been intensively researched. Current gesture generation methods are mostly based on deep neural network using text, audio and other information as the input, however, they generate gestures mainly based on audio, which is called a beat gesture. Although the ratio of the beat gesture is more than 70% of actual human gestures, content based gestures sometimes play an important role to make avatars more realistic and human-like. In this paper, we propose a attention-based contrastive learning for text-to-gesture (ACT2G), where generated gestures represent content of the text by estimating attention weight for each word from the input text. In the method, since text and gesture features calculated by the attention weight are mapped to the same latent space by contrastive learning, once text is given as input, the network outputs a feature vector which can be used to generate gestures related to the content. User study confirmed that the gestures generated by ACT2G were better than existing methods. In addition, it was demonstrated that wide variation of gestures were generated from the same text by changing attention weights by creators.
Development and Validation of the Body Cognition Assessment System

Body awareness, which comprises the sense of body possession and action ownership, is essential for the adaptive movement of humans in response to external environments. However, existing body cognition assessments include many overt elements of cognitive functional activity, but no assessment captures the latent body cognition necessary for exercise and daily life activities. Therefore, this study aimed to devise a body cognition assessment system (BCAS) to examine the functional basis of body cognition in healthy participants and investigate its usefulness. The BCAS was used to assess body cognition on three occasions, and BCAS values were calculated from the results of the assessment. The intraclass correlation coefficient (ICC) was used to determine reproducibility. Neural activity in the brain during somatocognition assessment while conducting the BCAS was measured by electroencephalogram. Moreover, the functional basis for somatocognition with the BCAS was also investigated. The results demonstrated that the BCAS values varied across the three administrations (ICC (1.3) = 0.372), and changes in the state of neural activity in the brain were observed. The results suggest that assessment using the BCAS may be a new indicator of ever-changing body cognition.
Stepwise Soft Actor–Critic for UAV Autonomous Flight Control

Despite the growing demand for unmanned aerial vehicles (UAVs), the use of conventional UAVs is limited, as most of them require being remotely operated by a person who is not within the vehicle’s field of view. Recently, many studies have introduced reinforcement learning (RL) to address hurdles for the autonomous flight of UAVs. However, most previous studies have assumed overly simplified environments, and thus, they cannot be applied to real-world UAV operation scenarios. To address the limitations of previous studies, we propose a stepwise soft actor–critic (SeSAC) algorithm for efficient learning in a continuous state and action space environment. SeSAC aims to overcome the inefficiency of learning caused by attempting challenging tasks from the beginning. Instead, it starts with easier missions and gradually increases the difficulty level during training, ultimately achieving the final goal. We also control a learning hyperparameter of the soft actor–critic algorithm and implement a positive buffer mechanism during training to enhance learning effectiveness. Our proposed algorithm was verified in a six-degree-of-freedom (DOF) flight environment with high-dimensional state and action spaces. The experimental results demonstrate that the proposed algorithm successfully completed missions in two challenging scenarios, one for disaster management and another for counter-terrorism missions, while surpassing the performance of other baseline approaches.
Knowledge Graph Dataset for Semantic Enrichment of Picture Description in NAPS Database

This data description introduces a comprehensive knowledge graph (KG) dataset with detailed information about the relevant high-level semantics of visual stimuli used to induce emotional states stored in the Nencki Affective Picture System (NAPS) repository. The dataset contains 6808 systematically manually assigned annotations for 1356 NAPS pictures in 5 categories, linked to WordNet synsets and Suggested Upper Merged Ontology (SUMO) concepts presented in a tabular format. Both knowledge databases provide an extensive and supervised taxonomy glossary suitable for describing picture semantics. The annotation glossary consists of 935 WordNet and 513 SUMO entities. A description of the dataset and the specific processes used to collect, process, review, and publish the dataset as open data are also provided. This dataset is unique in that it captures complex objects, scenes, actions, and the overall context of emotional stimuli with knowledge taxonomies at a high level of quality. It provides a valuable resource for a variety of projects investigating emotion, attention, and related phenomena. In addition, researchers can use this dataset to explore the relationship between emotions and high-level semantics or to develop data-retrieval tools to generate personalized stimuli sequences. The dataset is freely available in common formats (Excel and CSV).
SJBCD: A Java Code Clone Detection Method Based on Bytecode Using Siamese Neural Network

Code clone detection is an important research topic in the field of software engineering. It is significant in developing software and solving software infringement disputes to discover code clone phenomenon effectively in and between software systems. In practical engineering applications, clone detection can usually only be performed on the compiled code due to the unavailability of the source code. Additionally, there is room for improvement in the detection effect of existing methods based on bytecode. Based on the above reasons, this paper proposes a novel code clone detection method for Java bytecode: SJBCD. SJBCD extracts opcode sequences from byte code files, use GloVe to vectorize opcodes, and builds a Siamese neural network based on GRU to perform supervised training. Then the trained network is used to detect code clones. In order to prove the effectiveness of SJBCD, this paper conducts validation experiments using the BigCloneBench dataset and provides a comparative analysis with four other methods. Experimental results show the effectiveness of the SJBCD method.
Fuzzy Adaptive PSO-ELM Algorithm Applied to Vehicle Sound Quality Prediction

When dealing with specific tasks, the hidden layer output matrix of an extreme learning machine (ELM) may change, largely due to the random assigned weight matrix of the input layer and the threshold matrix of the hidden layer, which sequentially leads to the corresponding change to output weights. The unstable fluctuations of the output weights increase the structural risk and the empirical risk of ELM. This paper proposed a fuzzy adaptive particle swarm optimization (PSO) algorithm to solve this problem, which could nonlinearly control the inertia factor during the iteration by fuzzy control. Based on the fuzzy adaptive PSO-ELM algorithm, a sound quality prediction model was developed. The prediction results of this model were compared with the other three sound quality prediction models. The results showed that the fuzzy adaptive PSO-ELM model was more precise. In addition, in comparison with two other adaptive inertia factor algorithms, the fuzzy adaptive PSO-ELM model was the fastest model to reach goal accuracy.
Implementasi Model Autoregressive Integrated Moving Average pada Proyeksi Komoditas Ekspor Timah

The Bangka Belitung Archipelago is a potential area in the mining sector because many soils contain tin minerals and minerals that are spread evenly. Based on this phenomenon, this study uses the ARIMA model to analyze the prediction of the number of tin export commodities in the Bangka Belitung Islands Province. The time series data used in this study begins in January 2020 and ends in September 2022, with projected results ending in June 2023. Based on the analysis results, it is found that the ARIMA model (1,1,0) is the best model that can be used to project the value of tin export commodities in the Bangka Belitung Islands Province. This model was selected through the results of the correlogram test, which shows that the data is cut off at the second lag for the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots. This projection analysis was carried out after passing the stationarity test first through the Augmented Dicky Fuller (ADF) test. Through this test, it is found that the data is stationary at the first difference, and the prob value is 0.0003 with the projected result that there will be an increase in the number of exports of tin commodities with a total increase of 0.03%. The results of this analysis can certainly be part of preventive actions for the government to be able to assist the country in increasing the country’s foreign exchange through increasing export commodities.