Reviewerly/exHarmony huggingface dataset:
An Efficient Autism Detection Using Structural Magnetic Resonance Imaging Based on Selective Binary Coded Genetic Algorithm

In this work, an efficient machine learning technique for autism diagnosis using structural magnetic resonance imaging (MRI) is proposed. The proposed technique employs the voxel-based morphometry (VBM) approach to extract a set of 989 relevant features from MRI. These features are used to train an efficient extreme learning machine (ELM) classifier to identify autism spectrum disorder (ASD) and healthy controls. The proposed selective binary coded genetic algorithm (sBCGA) found a subset of significant VBM features. The selected subset of features was used to build a final ELM classifier with maximum overall accuracy. The proposed sBCGA uses a selective sample-balanced crossover designed to improve the classification of ASD and healthy controls. The proposed sBCGA has been extensively tested, and the experimental results clearly indicated better accuracy than existing methods.
A heterogeneous signcryption scheme for smart grid with trusted multi-ciphertext equality test

Energy utilization rates have been largely improved thanks to the wide application of smart grids, thereby realizing the reliable, economic and efficient operation of the grids. However, such an application is also accompanied by many security issues. In response to the many problems within existing security schemes, such as not supporting the communication between heterogeneous cryptosystems, low security levels and a low data retrieval efficiency, a heterogeneous signcryption (HSC) scheme that supports a trusted multi-ciphertext equality test (MET) is proposed. The adoption of the HSC helps to identify secure communications from identity-based cryptosystems to certificateless cryptosystem, eliminates the certificate management problems in the traditional public key cryptography scheme, and ensures the confidentiality and authentication of power data. The introduction of the MET technology can avoid the high cost of equality test calculations after grouping ciphertexts in pairs. Using blockchain and smart contract technologies ensure the credibility of test results and eliminates the reliance on trusted cloud servers. Under the random oracle model, on the basis of the bilinear Diffie-Hellman, the computational Diffie-Hellman and the q-strong Diffie-Hellman problems, this paper proves that the scheme proposed herein meets the requirements of indistinguishability and one-way security under adaptive choice ciphertext attacks, and the unforgeability under the adaptive choice message attack. From the findings of the analysis, it has been shown that the proposed scheme satisfies more security attributes and requires lower computational overhead compared to similar schemes.
Research on the Development Trend and Application of VIX Index

This research paper focuses on providing a comprehensive understanding and introduction to the VIX index. The history of the VIX index, its applications, and related products are systematically analyzed. It also derives the formula for the VIX Index (the volatility index introduced by the CBOE) using the standard deviation formula. A detailed explanation includes example data using Excel to calculate the VIX index for a particular day, an analysis of the error scenarios generated, and ways to improve it. Studying the VIX index helps us better understand today's financial investment situation and analyze investments in a case with VIX index criteria.
An Information Content and Set of Common Superconcepts-Based Algorithm to Estimate Similarity between Concepts of Ontologies

Ontologies have been used for several years in life sciences to formally represent concepts and reason about knowledge bases in domains such as the semantic web, information retrieval and artificial intelligence. The exploration of these domains for the correspondence of semantic content requires calculation of the measure of semantic similarity between concepts. Semantic similarity is a measure on a set of documents, based on the similarity of their meanings, which refers to the similarity between two concepts belonging to one or more ontologies. The similarity between concepts is also a quantitative measure of information, calculated based on the properties of concepts and their relationships. This study proposes a method for finding similarity between concepts in two different ontologies based on feature, information content and structure. More specifically, this means proposing a hybrid method using two existing measures to find the similarity between two concepts from different ontologies based on information content and the set of common superconcepts, which represents the set of common parent concepts. We simulated our method on datasets. The results show that our measure provides similarity values that are better than those reported in the literature.
VoxelNextFusion: A Simple, Unified, and Effective Voxel Fusion Framework for Multimodal 3-D Object Detection

LiDAR-camera fusion can enhance the performance of 3D object detection by utilizing complementary information between depth-aware LiDAR points and semantically rich images. Existing voxel-based methods face significant challenges when fusing sparse voxel features with dense image features in a one-to-one manner, resulting in the loss of the advantages of images, including semantic and continuity information, leading to sub-optimal detection performance, especially at long distances. In this paper, we present VoxelNextFusion, a multi-modal 3D object detection framework specifically designed for voxel-based methods, which effectively bridges the gap between sparse point clouds and dense images. In particular, we propose a voxel-based image pipeline that involves projecting point clouds onto images to obtain both pixel- and patch-level features. These features are then fused using a self-attention to obtain a combined representation. Moreover, to address the issue of background features present in patches, we propose a feature importance module that effectively distinguishes between foreground and background features, thus minimizing the impact of the background features. Extensive experiments were conducted on the widely used KITTI and nuScenes 3D object detection benchmarks. Notably, our VoxelNextFusion achieved around +3.20% in AP@0.7 improvement for car detection in hard level compared to the Voxel R-CNN baseline on the KITTI test dataset
Hybrid-Input Convolutional Neural Network-Based Underwater Image Quality Assessment

Since precisely sensing the underwater environment is a challenging prerequisite for safe and reliable underwater operation, interest in underwater image processing is growing at a rapid pace. In engineering applications, there are redundant underwater images addressed in real-time on the remotely operated vehicle (ROV). It puts the equipment or operators under great pressure. To relieve this pressure by transmitting images selectively according to the degradation degree, we propose an end-to-end hybrid-input convolutional neural network (HI-CNN) to predict the degradation of underwater images. First, we propose a feature extraction module to extract the features of original underwater images and saliency maps concurrently, which is composed of two branches with the same structure and shared parameters. Second, we design an end-to-end model to predict the quality scores of original images, which consists of a feature extraction module and a prediction module. Finally, we establish a real-world dataset to make the proposed model be duplicated in the practical underwater environment. Through several experiments, we demonstrate that the proposed model outperforms existing models in predicting underwater image quality.
Going Beyond Formalisms

Computer-based interactive simulations that model the processes of sampling from a population are increasingly being used in data literacy education. However, these simulations are often summarized by graphs designed from the point of view of experts which makes them difficult for novices to grasp. In our ongoing design-based research project, we build and test alternative sampling simulations to the standard ones. Based on a grounded and embodied learning perspective, the core to our design position is that difficult and abstract sampling concepts and processes should: be grounded in familiar objects that are intuitive to interpret, incorporate concrete animations that spontaneously activate learners’ gestures, and be accompanied by verbal instruction for a deeply integrated learning. Here, we report the results from the initial two phases of our project. In the first iteration, through an online experiment (N=126), we show that superficial perceptual elements in a standard simulation can lead to misinterpretation of concepts. In the second iteration, we pilot test a new grounded simulation with think-aloud interviews (N=9). We reflect on the complementary affordances of visual models, verbal instruction, and learners’ gestures in fostering integrated and deep understanding of concepts.
GROWING STUDENT INTEREST IN READING AND READINESS IN LEARNING THROUGH HABIT ACTIVITIES IN CLASS X IPS SMA NEGERI 3 RANGKASBITUNG

Sociology subjects are seen by a number of students as boring subjects. The content of sociology material which presents many theories and concepts such as having consequences for students to demand that everything be memorized well. Boring learning models increasingly make this subject less attractive to students Innovative strategies have been carried out, but in practice the operationalization of the learning model is less effective so that many educators return to using conventional learning models. The main problem found in the students of SMA Negeri 3 Rangkasbitung is the low interest and reading behavior that affects students' readiness to participate in the learning process. In general, the problem-solving strategy used is to direct students to certain behaviors, namely fond of reading through habituation activities. The development of interest and good reading habits should begin as early as possible in childhood. Parents, especially mothers, and teachers, especially Playgroup, Kindergarten, and Elementary School teachers, have a very decisive role in these development efforts. The development of interest and reading habits should start at home. Meanwhile, schools are obliged to foster reading interests and habits that have been developed at home. Support the compulsory reading program at a certain time outside school activities, because reading is a very basic activity in a series of learning activities. Interest and good reading habits are impossible to have in a short time. Its development takes a relatively long time, and must be in line with educational developments that occur in society. The habituation process is expected to have a positive impact on students during the learning process. The learning process can run optimally, if students have readiness to learn, one of which is that students first have reference material through reading activities. The literacy movement is more developed towards subjects, meaning that books read by students are according to the subjects studied by students. Each subject requires students to finish reading several reference books within a certain period of time. DOI: http://dx.doi.org/10.23960/jlg.v4.i1.27245
TinyML but by No Means a Tiny Feat!

In this article, the EIC introduces the Special Issue on TinyML and bids farewell to the readers, editors, and staff. A collage with the cover pages of IEEE Micro for the last five years is included.
Comparative performance analysis of binary variants of FOX optimization algorithm with half-quadratic ensemble ranking method for thyroid cancer detection

Abstract Thyroid cancer is a life-threatening condition that arises from the cells of the thyroid gland located in the neck’s frontal region just below the adam’s apple. While it is not as prevalent as other types of cancer, it ranks prominently among the commonly observed cancers affecting the endocrine system. Machine learning has emerged as a valuable medical diagnostics tool specifically for detecting thyroid abnormalities. Feature selection is of vital importance in the field of machine learning as it serves to decrease the data dimensionality and concentrate on the most pertinent features. This process improves model performance, reduces training time, and enhances interpretability. This study examined binary variants of FOX-optimization algorithms for feature selection. The study employed eight transfer functions (S and V shape) to convert the FOX-optimization algorithms into their binary versions. The vision transformer-based pre-trained models (DeiT and Swin Transformer) are used for feature extraction. The extracted features are transformed using locally linear embedding, and binary FOX-optimization algorithms are applied for feature selection in conjunction with the Naïve Bayes classifier. The study utilized two datasets (ultrasound and histopathological) related to thyroid cancer images. The benchmarking is performed using the half-quadratic theory-based ensemble ranking technique. Two TOPSIS-based methods (H-TOPSIS and A-TOPSIS) are employed for initial model ranking, followed by an ensemble technique for final ranking. The problem is treated as multi-objective optimization task with accuracy, F2-score, AUC-ROC and feature space size as optimization goals. The binary FOX-optimization algorithm based on the $$V_1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>V</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> transfer function achieved superior performance compared to other variants using both datasets as well as feature extraction techniques. The proposed framework comprised a Swin transformer to extract features, a Fox optimization algorithm with a V1 transfer function for feature selection, and a Naïve Bayes classifier and obtained the best performance for both datasets. The best model achieved an accuracy of 94.75%, an AUC-ROC value of 0.9848, an F2-Score of 0.9365, an inference time of 0.0353 seconds, and selected 5 features for the ultrasound dataset. For the histopathological dataset, the diagnosis model achieved an overall accuracy of 89.71%, an AUC-ROC score of 0.9329, an F2-Score of 0.8760, an inference time of 0.05141 seconds, and selected 12 features. The proposed model achieved results comparable to existing research with small features space.
UX-Driven Methodology to Design Usable Augmented Reality Applications for Maintenance

In recent decades industrial development has led to increasingly sophisticated machinery and systems, which require complex maintenance routines. Consequently, maintenance operators may not have the sufficient skills to perform recovery procedures properly and quickly, so that the need of assistance from the manufacturer’s after-sales service or companies specialized in maintenance services. Such actions usually lead to very long recovery times, high maintenance costs, and a temporary drop in production. In this scenario, we should consider that Industry 4.0 is making available innovative technologies, such as Augmented Reality (AR), suitable for improving the skills and competencies of operators without burdening their cognitive load, and consequently wellbeing. However, technologies must be selected, designed, and used according to the users’ needs to be effective and useful. The paper presents a user experience (UX)-driven methodology for designing user-centric AR applications for complex maintenance procedures. The methodology was applied to a real industrial case concerning the management of CNC machines in a plant producing tractors components, where a smartphone-based AR application was designed and tested with users. The satisfactory results highlighted the potential benefits of AR in industry and specifically in maintenance.
Pengaruh Model Pembelajaran Kooperatif Tipe STAD Berbantuan Media Big Book Terhadap Hasil Belajar IPA Kelas IV SD

Learning is an activity carried out by educators in a programmed manner which can create a process of interaction between students and other students, educators and students and students and learning resources. In this study, researchers used Probability Sampling with a simple random sampling type, which means it is the simplest sample determining technique with a sample of 30 students. Data collection techniques use pretest and posttest test methods. The type of design used is One Group Pretest – Posttest Design and uses a quantitative approach. The population used was 30 students in class IV at SD Negeri Candiharjo. In the N-Gain test, the average result from the pretest was 46.667, while the posttest mean was 81.967 with a total of N or samples of 30, 16 male students and 14 female students. Based on the test data for the validity of the question items, normal data was also obtained with a total of 0.158, then entered into the Eta Square Test formula to get a final calculation result of 0.875, which indicates an increase in the use of the STAD type cooperative learning model assisted by Big Book media. It can be concluded that the use of the STAD type cooperative learning model assisted by Big Book media at Candiharjo State Elementary School has a great influence on students' learning outcomes.
Improving Student Digital Literacy through Google Classroom-Based Lectures

Abstract:Mathematics teacher candidates need to have good digital literacy so that they will be able to guide their students to use digital media wisely. Therefore this study aims to describe the increase in students' digital literacy after attending microteaching lectures (Introduction to Schooling Field 1) using Google Classroom. This type of research is quasi-experimental research with a quantitative approach. The research sample was 30 students in the 6th semester of the 2021/2022 academic year. Methods of collecting data using performance tests. Data analysis of students' digital literacy improvement used the Wilcoxon Signed-Rank Test. Digital literacy indicators used include aspects of technical skills, critical understanding, and communicative abilities. Based on the analysis of research data, it is concluded that there is an increase in student digital literacy after attending lectures using Google Classroom. Student digital literacy is included in the advanced category.Abstrak:Mahasiswa calon guru matematika perlu memiliki literasi digital yang baik agar nantinya mampu membimbing peserta didiknya menggunakan media digital dengan bijak. Oleh karena itu penelitian ini bertujuan mendeskripsikan peningkatan literasi digital mahasiswa sesudah mengikuti perkuliahan microteaching (Pengenalan Lapangan Persekolahan 1) menggunakan Google Classroom. Jenis penelitian adalah penelitian eksperimen semu dengan pendekatan kuantitatif. Sampel penelitian adalah 30 mahasiswa semester 6 tahun akademik 2021/2022. Metode pengumpul data menggunakan tes unjuk kerja. Analisis data peningkatan literasi digital mahasiswa menggunakan uji Wilcoxon Signed-Rank Test. Indikator literasi digital yang digunakan meliputi aspek technical skill, critical understanding dan communicative abilities. Berdasarkan analisis data penelitian diperoleh simpulan bahwa terdapat peningkatan literasi digital mahasiswa setelah mengikuti perkuliahan menggunakan Google Classroom, Literasi digital mahasiswa termasuk dalam kategori advance.
Texture-Based Data Augmentation for Small Datasets

This paper proposes a texture-based domain-specific data augmentation technique applicable when training on small datasets for deep learning classification tasks. Our method focuses on label-preservation to improve generalization and optimization robustness over data-dependent augmentation methods using textures. We generate a small perturbation in an image based on a randomly sampled texture image. The textures we use are naturally occurring and domain-independent of the training dataset: regular, near regular, irregular, near stochastic and stochastic classes. Our method uses the textures to apply sparse, patterned occlusion to images and a penalty regularization term during training to help ensure label preservation. We evaluate our method against the competitive soft-label Mixup and RICAP data augmentation methods with the ResNet-50 architecture using the unambiguous “Bird or Bicyle” and Oxford-IIT-Pet datasets, as well as a random sampling of the Open Images dataset. We experimentally validate the importance of label-preservation and improved generalization by using out-of-distribution examples and show that our method improves over competitive methods.
Study of multistep Dense U‐Net‐based automatic segmentation for head MRI scans

Despite extensive efforts to obtain accurate segmentation of magnetic resonance imaging (MRI) scans of a head, it remains challenging primarily due to variations in intensity distribution, which depend on the equipment and parameters used.The goal of this study is to evaluate the effectiveness of an automatic segmentation method for head MRI scans using a multistep Dense U-Net (MDU-Net) architecture.The MDU-Net-based method comprises two steps. The first step is to segment the scalp, skull, and whole brain from head MRI scans using a convolutional neural network (CNN). In the first step, a hybrid network is used to combine 2.5D Dense U-Net and 3D Dense U-Net structure. This hybrid network acquires logits in three orthogonal planes (axial, coronal, and sagittal) using 2.5D Dense U-Nets and fuses them by averaging. The resultant fused probability map with head MRI scans then serves as the input to a 3D Dense U-Net. In this process, different ratios of active contour loss and focal loss are applied. The second step is to segment the cerebrospinal fluid (CSF), white matter, and gray matter from extracted brain MRI scans using CNNs. In the second step, the histogram of the extracted brain MRI scans is standardized and then a 2.5D Dense U-Net is used to further segment the brain's specific tissues using the focal loss. A dataset of 100 head MRI scans from an OASIS-3 dataset was used for training, internal validation, and testing, with ratios of 80%, 10%, and 10%, respectively. Using the proposed approach, we segmented the head MRI scans into five areas (scalp, skull, CSF, white matter, and gray matter) and evaluated the segmentation results using the Dice similarity coefficient (DSC) score, Hausdorff distance (HD), and the average symmetric surface distance (ASSD) as evaluation metrics. We compared these results with those obtained using the Res-U-Net, Dense U-Net, U-Net++, Swin-Unet, and H-Dense U-Net models.The MDU-Net model showed DSC values of 0.933, 0.830, 0.833, 0.953, and 0.917 in the scalp, skull, CSF, white matter, and gray matter, respectively. The corresponding HD values were 2.37, 2.89, 2.13, 1.52, and 1.53 mm, respectively. The ASSD values were 0.50, 1.63, 1.28, 0.26, and 0.27 mm, respectively. Comparing these results with other models revealed that the MDU-Net model demonstrated the best performance in terms of the DSC values for the scalp, CSF, white matter, and gray matter. When compared with the H-Dense U-Net model, which showed the highest performance among the other models, the MDU-Net model showed substantial improvements in the HD view, particularly in the gray matter region, with a difference of approximately 9%. In addition, in terms of the ASSD, the MDU-Net model outperformed the H-Dense U-Net model, showing an approximately 7% improvements in the white matter and approximately 9% improvements in the gray matter.Compared with existing models in terms of DSC, HD, and ASSD, the proposed MDU-Net model demonstrated the best performance on average and showed its potential to enhance the accuracy of automatic segmentation for head MRI scans.