In our previous work, we have proposed a method that links character string having a certain semantic meaning on papers in real world to the digital content in cyberspace, and call it "Ultimate Link." Ultima...
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ISBN:
(数字)9798350367331
ISBN:
(纸本)9798350367348
In our previous work, we have proposed a method that links character string having a certain semantic meaning on papers in real world to the digital content in cyberspace, and call it "Ultimate Link." Ultimate Link adds additional information to characters by drawing colored circles on top of the characters without changing the shape of the characters. A URL can be generated by connecting additional information of semantically organized character strings from an image taken with a digital device. The conventional Ultimate Link drew markers to normalize the drawn circles (markers for projective transformation), similar to the finder pattern in a QR code. In order to achieve a more realistic application, it is desirable it. We report on an improved Ultimate Link that can handle complex multiple character strings. Specifically, improvements were made to enable extraction of information by identifying character strings with embedded additional information from sheets of paper printed with multiple character strings. As a result of experiments, we confirmed that information could be successfully embedded and extracted for both character strings in the target image.
With the growing demands for Precision Agriculture (PA) in Indonesia, researchers have evaluated the utilization of Machine Learning for predicting oil palm yields and determining variables affecting them. Previous st...
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With the growing demands for Precision Agriculture (PA) in Indonesia, researchers have evaluated the utilization of Machine Learning for predicting oil palm yields and determining variables affecting them. Previous studies showed that meteorological variables, including rainfalls and wind speed, are associated with oil palm yields. In this research, the Extreme Gradient Boosting (XGBoost) model and the Shapley Additive exPlanations (SHAP) were deployed for analyzing the importance of 15 agrometeorological variables in predicting oil palm yield. The best model attained 1.911 RMSE and 0.855 R2. By analyzing the weights and gains of the XGBoost model along with the SHAP values, it was found that the yield in the previous year, the age and number of plants, the area of peat lands, and meteorological parameters such as rainfall rates and the number of rainy days in the previous three years were considered important. The previous year's yield in particular possesses the greatest weight and gain values according to the model, and the highest SHAP value among all input variables. However, the meteorological data used in this research are only limited to rainfall rates and the number of rainy days. In the future, more diverse variables can be analyzed.
This systematic literature review (SLR) study aims to analyze the role of New Media as a Tool to Improve Creative Thinking, with relevant articles from 2018 to 2023 taken from reputable international journals. It uses...
This systematic literature review (SLR) study aims to analyze the role of New Media as a Tool to Improve Creative Thinking, with relevant articles from 2018 to 2023 taken from reputable international journals. It uses three research questions (RQ) to explore New Media's relevance in stimulating creativity. The results indicate that interactive platforms such as social media and online collaboration tools positively influence creative thinking skills. Interaction through New Media allows individuals to share ideas, discuss, and be exposed to various points of view, all of which stimulate creative thinking. In addition, New Media also facilitates the creative process of solving problems and generating original ideas. The immersive experiences offered by this technology can enhance the exploration of ideas and increase the ability to think out-of-the-box. While New Media offers great potential to enhance creativity, there are also potential risks, such as false information, media addiction, and privacy concerns. Therefore, awareness of the wise use of New Media needs to be increased, especially in education and everyday life. Overall, this literature review provides in-depth insight into the role of New Media in enhancing creative thinking skills and driving innovation. With the right approach, New Media can be an effective tool in stimulating creative thinking and encouraging the creation of original ideas beneficial for future social and technological developments.
This paper proposes a method for detecting images generated by diffusion models using sparse coding. In the diffusion model, an image can be generated by removing noise from a noisy image. This different generation pr...
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ISBN:
(数字)9798350367331
ISBN:
(纸本)9798350367348
This paper proposes a method for detecting images generated by diffusion models using sparse coding. In the diffusion model, an image can be generated by removing noise from a noisy image. This different generation process from real images leads us to believe that there may be a statistical difference in pixels between the real and the generated images. Specifically, the image is divided into small patch regions, and all patch images are reconstructed using the basis image. In this process, sparse coefficients that contain many zeros are obtained using sparse coding, and features are calculated from the obtained coefficients. Then, a simple discriminator using the features as input is trained with a small number of data to discriminate the diffusion-generated images. In our experiments, we evaluated the proposed method on six datasets created using three diffusion models and two real image datasets. Experiments were also conducted to evaluate the robustness of the proposed method against JPEG compression. Experimental results show that our proposed method is sufficiently robust against JPEG compression with as few as 1800 training data.
Skin cancer's increasing incidence rates necessitate advanced diagnostic tools. This research uses MobileNet architecture to develop an enhanced system for skin cancer detection. MobileNet's efficient CNN arch...
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ISBN:
(数字)9798350378511
ISBN:
(纸本)9798350378528
Skin cancer's increasing incidence rates necessitate advanced diagnostic tools. This research uses MobileNet architecture to develop an enhanced system for skin cancer detection. MobileNet's efficient CNN architecture, combined with Keras for model lifecycle management and TensorFlow for real-time inference, forms the foundation of this study. Using 10,015 dermatoscopic pictures from seven different classifications of skin cancer in the HAM10000 dataset, the methodology includes several key stages: image preprocessing to correct illumination and adjust resolution, data augmentation to balance the dataset, and model training using Transfer Learning. The MobileNet model was trained over 50 epochs with a comprehensive architecture incorporating multiple specialized layers. By combining large-scale data analysis and adaptive learning, this methodology demonstrates The revolutionary possibilities of AI and ML in improving skin cancer diagnostics and public health outcomes. Keywords Skin Cancer Detection, Dermatoscopic Images, Machine Learning, Deep Learning
Software-defined networking(SDN)is a new paradigm that promises to change by breaking vertical integration,decoupling network control logic from the underlying routers and switches,promoting(logical)network control ce...
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Software-defined networking(SDN)is a new paradigm that promises to change by breaking vertical integration,decoupling network control logic from the underlying routers and switches,promoting(logical)network control centralization,and introducing network ***,the controller is similarly vulnerable to a“single point of failure”,an attacker can execute a distributed denial of service(DDoS)attack that invalidates the controller and compromises the network security in *** address the problem of DDoS traffic detection in SDN,a novel detection approach based on information entropy and deep neural network(DNN)is *** approach contains a DNN-based DDoS traffic detection module and an information-based entropy initial inspection *** initial inspection module detects the suspicious network traffic by computing the information entropy value of the data packet’s source and destination Internet Protocol(IP)addresses,and then identifies it using the DDoS detection module based on *** assaults were found when suspected irregular traffic was *** reveal that the algorithm recognizes DDoS activity at a rate of more than 99%,with a much better accuracy *** false alarm rate(FAR)is much lower than that of the information entropy-based detection ***,the proposed framework can shorten the detection time and improve the resource utilization efficiency.
Convolutional Neural Networks (CNNs) have received substantial attention as a highly effective tool for analyzing medical images, notably in interpreting endoscopic images, due to their capacity to provide results equ...
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The current decade has been marked as the Decade of Healthy Ageing, where increased participation, social and digital inclusion for the silver population within and for the Silver Economy has become imperative. This p...
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This paper presents a radar-based, non-contact Blood Pressure (BP) estimation model based on accurate detection of cardiac activities, which enables BP monitoring to be performed in a touch-free and continuous manner....
This paper presents a radar-based, non-contact Blood Pressure (BP) estimation model based on accurate detection of cardiac activities, which enables BP monitoring to be performed in a touch-free and continuous manner. Cardiac movements have been regarded as essential factors for BP estimation. However, accurately obtaining these movements by a radar system remains a challenging problem because these movements are too inconspicuous and could be easily hindered by respiration and random noise. In this paper, we propose a method that mainly focuses on cardiac feature extraction in radar-based BP monitoring. First, we employ an integrated-spectrum waveform. It is derived from short-time Fourier transform (STFT) and is capable of recording and preserving minor cardiac activities. Compared with the pulse-wave signals used in previous works, the integrated-spectrum focuses on energy changes introduced by short and high-frequency vibrations. It can eliminate the interference of respiration and random noise, and cardiac contractile movement can be reserved accurately. Second, we propose a cardiac features estimation method in which a hidden semi-Markov model (HSMM) is applied to the integrated-spectrum for feature extraction. Compared to the pulse wave signal, the Root-Mean-Square Error (RMSE) of the estimated interbeat intervals (IBI), Systolic time, and Diastolic time is reduced by 44.2%, 73.3%, and 76.7% respectively. The estimated accurate cardiac features are further used as inputs for a Random Forest model for BP prediction. Though previous work required the subject to hold one's breath, we achieved a comparable prediction accuracy even when our subject is breathing normally. The Diastolic BP (DBP) error of our model is $4.27\pm 5.84$ mmHg (Mean Absolute Difference ± Standard Deviation), and the Systolic BP (SBP) error is $6.63\pm 8.95$ mmHg.
There are increasing concerns about malicious attacks on autonomous vehicles. In particular, inaudible voice command attacks pose a significant threat as voice commands become available in autonomous driving systems. ...
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