Hasil untuk "Cybernetics"

Ya-Han Hu, Ting-Hsuan Liu, Chih-Fong Tsai et al.

The rise of social media has amplified online sharing, necessitating businesses to comprehend public sentiment. Traditional sentiment analysis struggles with sarcasm detection and class imbalance. To address this, we introduce Synthetic Ensemble Oversampling methods (SEO) that effectively leverage the strengths of various oversampling algorithms. By incorporating ensemble learning principles into oversampling techniques, our proposed methods offer distinct strategies for selecting newly generated sarcastic data. In this study, we employ five oversampling algorithms: Synthetic Minority Oversampling TEchnique (SMOTE), Adaptive Synthetic Sampling (ADASYN), polynom-fit-SMOTE, Proximity Weighted Synthetic Sampling (ProWSyn), and SMOTE with Instance Prioritization and Filtering (SMOTE_IPF). We work with two imbalanced sarcasm detection datasets, iSarcasmEval and SARC-reduced, collected from Twitter and Reddit. After extracting features from using Word2Vec, Global Vectors (GloVe), and FastText, we apply oversampling and ensemble techniques. Evaluated across six classifiers – Support Vector Machine, Decision Tree, Random Forest, Extreme Gradient Boosting, Logistic Regression, and BERT – the results demonstrate that the SEO2 framework consistently enhances classifier performance compared to single oversampling techniques. Notably, the Cluster Uncentered method frequently provides the best improvements across datasets, achieving significant gains in both AUC and F1 scores. These findings highlight the potential of ensemble-based oversampling in addressing class imbalance for sarcasm detection.

Haifeng Sima, Meng Gao, Lanlan Liu

Real-time semantic segmentation of images requires both rich contextual and accurate spatial information. However, Multiple downsampling in deep convolutional neural networks often lead to loss of such information, resulting in reduced segmentation accuracy. To address the above problems, we propose SPCONet, a lightweight real-time semantic segmentation network that integrates spatial and contextual features. The network incorporates three key modules: (1) a Spatial Feature Aggregation Module (SFAM) that captures fine spatial details from shallow layers using spatially separable convolutions with multiple kernel sizes; (2) a Contextual Information Retrieval Module (CIRM) that extracts semantic context from deeper layers using dynamic convolution; (3) an Attention Fusion Module (AFM) that combines spatial and contextual features via local and global attention mechanisms. Quantitative experiments show that SPCONet achieves 77.5% and 75.3% mIoU at 74 FPS and 82 FPS on the Cityscapes and CamVid datasets, respectively. These results suggest that SPCONet provides an effective balance between segmentation accuracy and real-time inference capability.

Marius Lyng Danielsson, Roya Doshmanziari, Berit Brurok et al.

Abstract Background The Apple Watch (AW) was the first smartwatch to provide wheelchair user (WCU) specific information on energy expenditure (EE), but was found to be inaccurate (i.e., it underestimated) and imprecise (i.e., the underestimation was variable). Insight is therefore needed into where these inaccuracies/imprecisions originate. Accordingly, the aim of this study was to investigate how much of the variation in AW EE is explained by heart rate (HR), in addition to other factors such as body mass and height, sex, age, physical activity level and disability. Methods Forty participants (20 WCU, 20 non-disabled) performed three 4-min treadmill wheelchair propulsion stages at different speed-incline combinations, on three separate days, while wearing an AW series 4 (setting: “outdoor push walking pace”). Linear mixed model analyses investigated how much of the variation in AW EE (kcal·min−1) is explained by the fixed effects AW HR (beats·min−1), body mass and height, sex, age, physical activity level and disability. Participant-ID was included as random-intercept effect. The same mixed model analyses were conducted for criterion EE and HR. Marginal R2 (R2m; fixed effects only) and conditional R2 (R2c; fixed and random effects) values were computed. An R2m close to zero indicates that the fixed effects alone do not explain much variation. Results Although criterion HR explained a significant amount of variation in criterion EE (R2m: 0.44, R2c: 0.92, p < 0.001), AW HR explained little variation in AW EE (R2m: 0.06, R2c: 0.86, p < 0.001). In contrast, body mass and sex explained a significant amount of variation in AW EE (R2m: 0.74, R2c: 0.79, p < 0.001). No further improvements in fit were achieved by adding body height, age, physical activity level or disability to the AW EE model (R2m: 0.75, R2c: 0.79, p = 0.659). Conclusion Our results remain inconclusive on whether AW heart rate is used as factor to adjust for exercise intensity in the black box AW EE estimation algorithms. In contrast, body mass explained much of the variation in AW EE, indicating that the AW EE estimation algorithm is very reliant on this factor. Future investigations should explore better individualization of EE estimation algorithms.

Deyi Li

In the first half of the 20th century, 5 classic articles were written by 3 outstanding scholars, namely, Wiener (1894 to 1964), the father of cybernetics, Schrödinger (1887 to 1961), the father of quantum mechanics, and Turing (1912 to 1954), the father of artificial intelligence. The articles discuss the concepts such as computability, life, machine, control, and artificial intelligence, establishing a solid foundation for the intelligence of machines (how can machines recognize as humans do?) and its future development.

P. Musa, I. Purwanto, D.A. Christie et al.

Topography is the study of an area on the earth's surface. This term relates to the land's slope or contour, which is the interval of elevation differences between two adjacent and parallel contour lines. Topography generally presents a three-dimensional model of object surface relief and an identification of land or hilly areas based on horizontal coordinates such as latitude and longitude, and vertical position, namely elevation. The topography is essential information that must be provided in the execution of building or road construction based on the ground contour. The problem which is the ground contour which can provide visualization topography as a three-dimensional (3D) condition of the ground contour is not normal (non-linear). Another problem is that the traditional measurement techniques with wheel rotation only measure distances and cannot represent the trajectory of the ground contour in 3D. The proposed in-depth evaluation of orientation estimation results in the topography accuracy level. This methodology consists of several processes; Inertia and orientation of an object, Distance measurement, Terrestrial topocentric – Euclidean transformation, and Topography visualization. This research designed a prototype and proposed a new visualization method of the ground contours to reconstruct a topography map between three algorithms; Direct Cosine Matrix-3D Coordinate, Madgwick-3D Coordinate, and Complementary Filter. The methodology was tested and evaluated intensively by direct observation at three measurement locations with different difficulty levels. As a result, the Direct Cosine Matrix-3D Coordinate is able to visualize the ground contours by reconstructing a topography map much better than other methods.

Martina Mariki, Elizabeth Mkoba, Neema Mduma

Presumptive treatment and self-medication for malaria have been used in limited-resource countries. However, these approaches have been considered unreliable due to the unnecessary use of malaria medication. This study aims to demonstrate supervised machine learning models in diagnosing malaria using patient symptoms and demographic features. Malaria diagnosis dataset extracted in two regions of Tanzania: Morogoro and Kilimanjaro. Important features were selected to improve model performance and reduce processing time. Machine learning classifiers with the k-fold cross-validation method were used to train and validate the model. The dataset developed a machine learning model for malaria diagnosis using patient symptoms and demographic features. A malaria diagnosis dataset of 2556 patients’ records with 36 features was used. It was observed that the ranking of features differs among regions and when combined dataset. Significant features were selected, residence area, fever, age, general body malaise, visit date, and headache. Random Forest was the best classifier with an accuracy of 95% in Kilimanjaro, 87% in Morogoro and 82% in the combined dataset. Based on clinical symptoms and demographic features, a regional-specific malaria predictive model was developed to demonstrate relevant machine learning classifiers. Important features are useful in making the disease prediction.

Zulfiqar Haider (Pakistan), Dr. Hamidreza Rezania (Iran)

SUBJECT AND OBJECTIVES: The study of the human soul was considered by philosophers since ancient times and eastern philosophers especially Indian had believed that the human soul is eternal and for thousands of years transmigrates into various bodies and eventually it would disappear in Brahma’s essence. The most important school of Upanishadic Indian philosophy that is called Vidanta has this theory about the soul. Another school under study is the transcendent wisdom of Sadr al-Muta'allehin. With this description, Are the theories of both philosophical schools about the soul, the same or different? If there are differences, how should the theories of the Upanishads be criticized on the basis of the Transcendent Wisdom?METHOD AND FINDING: This article is a comparative one and is a new work in this ground, and the main sources of this research are the four journeys of the Transcenddent Wisdom of Mulla Sadra and The Sirri-Akbar of Dara Shikoh. The Upanishads is the first and ancient book of Indian philosophy explains; the soul is present in all parts of the human body and all body powers are manifestations of the soul, and the soul has unity with its all powers. The same claim is made by Transcendent Wisdom of Sadr al-Mute'allihin in Islamic philosophy. He says: The soul in its unity, is the whole of the powers and is present in all body.CONCLUSION: This article will compare the theories of two schools and the major ideas which will be discussed are; The Definition of the Soul, its Proofs, The Immateriality of the Soul, The Relationship between the Soul and the Body and its Powers, The Eternity and Creation of the Soul, The Immortality and the Reincarnation of the Soul.

Tayou Djamegni Clémentin, Tabueu Fotso Laurent Cabrel, Kenmogne Edith Belise

The extraction of frequent gradual pattern is an important problem in computer science and largely studied by the scientist’s community of research in data mining. A frequent gradual pattern translates a recurrent co-variation between the attributes of a database. Many applications issues from many domains, such as economy, health, education, market, bio-informatics, astronomy or web mining, are based on the extraction of frequent gradual patterns. Algorithms to extract frequent gradual patterns in the large databases are greedy in CPU time and memory space. This raises the problem of improving the performances of these algorithms. This paper presents a technique for improving the performance of frequent gradual pattern extraction algorithms. The exploitation of this technique leads to a new, more efficient algorithm called SGrite. The experiments carried out confirm the interest of the proposed technique.

Andrii Podorozhniak, Nataliia Liubchenko, Mykyta Kvochka et al.

The subject of study in the article is artificial intelligence methods that can be used for recognition of specific areas of the earth's surface in multispectral images provided by Earth remote sensing systems (ERS). The goal is to automate data analysis for recognizing areas affected by fire on multispectral remote sensing images. The task is to study and formulate a method for processing multispectral data, which makes it possible to automate the process of operational recognition of areas of burned-out areas in images, for the development of an eco-monitoring software system using artificial intelligence tools such as deep learning and neural networks. As a result of the analysis of modern methods of processing multispectral data, an investigation of the supervised learning strategy was chosen. The choice of the described method for solving an applied problem is based on the high flexibility of these method, as well as, provided that there is a sufficient amount of used training input data and correct training strategies, the possibility of analyzing heterogeneous multispectral data with ensuring high accuracy of results for each individual sample. Conclusions: the application of methodologies for intelligent processing of multispectral images has been investigated and substantiated. The theoretical foundations of the construction of neural networks are considered, the applied area of ​​application is selected. An architectural model of a software product is analyzed and proposed, taking into account its scalability, the model of software system is developed and the results of its work are shown. The obtained results show the efficiency of proposed system and prospects of the proposed algorithms, which is a reason for further research and improvement of the used algorithms, with their possible use in industrial and enterprise eco-monitoring systems.

Martin Čertický, Michal Čertický, Peter Sinčák et al.

Analyses of user experience in the electronic entertainment industry currently rely on self-reporting methods, such as surveys, ratings, focus group interviews, etc. We argue that self-reporting alone carries inherent problems&#8212;mainly the misinterpretation and temporal delay during longer experiments&#8212;and therefore, should not be used as a sole metric. To tackle this problem, we propose the possibility of modeling consumer experience using psychophysiological measures and demonstrate how such models can be trained using machine learning methods. We use a machine learning approach to model user experience using real-time data produced by the autonomic nervous system and involuntary psychophysiological responses. Multiple psychophysiological measures, such as heart rate, electrodermal activity, and respiratory activity, have been used in combination with self-reporting to prepare training sets for machine learning algorithms. The training data was collected from 31 participants during hour-long experiment sessions, where they played multiple video-games. Afterwards, we trained and compared the results of four different machine learning models, out of which the best one produced &#8764;96% accuracy. The results suggest that psychophysiological measures can indeed be used to assess the enjoyment of digital entertainment consumers.

Lorayne Robertson

Definitions of digital literacies can often be located in the literature, but much of the focus has been on the technological advances of online learning tools and the ubiquity of access to information. As a result, less attention has been directed toward aspects of the ethos associated with new literacies and how learning can be impacted and improved. Some examples of ethos issues include the personalization of education, the design of more open, collaborative learning spaces, and the need for student assignments to have high degrees of authenticity and connection to applied settings. This paper explores digital literacy and provides a brief case study that is an example of digital literacy skills applied across disciplines. The author concludes that today's higher education students need to be strong communicators who can navigate in spaces that are characterized by interdisciplinary discourses and digital literacy skills.

Sergey Andrieiev, Volodymyr Zhilin, Alina Melnyk

Предметом дослідження є методика побудови площадкових, лінійних та об’ємних анаморфозних картографічних моделей для аналізу геоданих. Об’єктом дослідження є процес створювання різноманітних типів анаморфозних картографічних моделей. Метою роботи є підвищення якості сприйняття геоданих в картографічних моделях на підставі нового підходу до їх візуалізації, що забезпечується незвичним представленням структури геоданих при одночасному врахуванні великої кількості параметрів, які вони містять. Висновки. Розроблено методики побудови площадкових, лінійних та об’ємних анаморфозних картографічних моделей, отримано математичну модель та алгоритм процесу створення  анаморфозних картографічних моделей з використанням програмного пакетуArcGIS. Методика побудови площадкових анаморфозних картографічних моделей, зокрема, передбачає, завдяки використанню додаткового модуля Cartogram, можливість реалізації в середовищі ArcMap пакету ArcGIS анаморфозних картографічних моделей демографічного стану в Україні на підставі даних про кількість населення. При цьому забезпечується можливість отримання також і об’ємних анаморфозних картографічних моделей демографічного стану. Крім того, на підставі аналізу діяльності авіакомпанії Wizz Аir (у якості прикладу сучасних lowcost-авіаперевізників) розроблено методику побудови в середовищі ArcMap лінійних анаморфозних картографічних моделей перельотів, що виконуються даною компанією з України. При цьому обов’язковими складовими таких картографічних моделей є спеціально впроваджені реляційні бази геоданих, що містять вичерпні характеристики авіарейсів. Методика є універсальною і може застосовуватись для будь-яких авіакомпаній і геолокацій перельотів. Запропоновані методики побудови анаморфозних картографічних моделей дозволяють отримувати їх найрізноманітніші типи, що за рахунок специфічної візуалізації забезпечують можливість з різних боків поглянути на досліджувані геодані та отримати нові знання щодо довколишніх природних і техногенних явищ. Саме анаморфозні картографічні моделі дають унікальну можливість візуально представити неочевидні факти з можливістю висвітлення прихованих природно-географічних закономірностей та проведення аналізу взаємозв’язків між явищами довкілля на тлі їх визначальних характеристик.

Long Chen, Yanlai Zhang, Chao Zhou et al.

Modern designs of micro air vehicles (MAVs) are mostly inspired by nature's flyers, such as hummingbirds and flying insects, which results in the birth of bio-inspired MAVs. The history and recent progress of the aerodynamic mechanisms in bio-inspired MAVs are reviewed in this study, especially focused on those compound layouts using bio-inspired unsteady aerodynamic mechanisms. Several successful bio-mimicking MAVs and the unsteady high lift mechanisms in insect flight are briefly revisited. Four types of the compound layouts, i.e. the fixed/flapping-wing MAV, the flapping rotary wing MAV, the multiple-pair flapping-wing MAV, and the cycloidal rotor MAV are introduced in terms of recent findings on their aerodynamic mechanisms. In the end, future interests in the field of MAVs are suggested. The authors' review can provide solid background knowledge for both future studies on the aerodynamic mechanisms in bio-inspired MAVs and the practical design of a bio-inspired MAV.

Andrei Pavelev, Vitalii Semin

In this paper, we investigate non-markovian dynamics of a system of two interacting qubits. With the help of stochastic calculus we derive the non-Markovian non-linear stochastic Schrödinger equation. This equation is solved by the direct computer simulation. The simulation is used to obtain some dynamic properties of the system.