Specific Categories Research Articles

Automated classification of nebulae using deep learning & machine learning for enhanced discovery

There are believed to be ~20,000 nebulae in the Milky Way Galaxy. However, humans have only cataloged ~1,800 of them even though we have gathered 1.3 million nebula images. Resources like the Hubble telescope can automatically explore space, discover new artifacts, and capture new images. Still, their classification is a human skill, which ultimately is interminable and subject to human error. Classification is important as it helps scientists understand the chemical composition of a nebula which in turn helps them understand the material of the original star. Our research on nebulae classification aims to make the process of classifying new nebulae faster and more accurate using a hybrid of deep learning and machine learning techniques. Our hypothesis is that the application of machine learning and deep learning methodologies effectively classifies nebulae based on images. Using a dataset primarily of images from the European Space Agency, we experimented with a range of artificial intelligence techniques such as featurization and color conversion and then determined the deep learning network/machine learning algorithms that produced the best results. The main conclusions reached from our research were that (a) the artificial intelligence (AI) was not dependent on color to classify nebulae, (b) dropping specific categories increased accuracy, and (c) featurization is the most effective technique to classify nebulae accurately. Automated classification of nebulae will help us discover, identify, and classify these marvels much faster and more accurately to expand our understanding of nebulae.

Spatial Structure Constraints for Weakly Supervised Semantic Segmentation.

The image-level label has prevailed in weakly supervised semantic segmentation tasks due to its easy availability. Since image-level labels can only indicate the existence or absence of specific categories of objects, visualization-based techniques have been widely adopted to provide object location clues. Considering class activation maps (CAMs) can only locate the most discriminative part of objects, recent approaches usually adopt an expansion strategy to enlarge the activation area for more integral object localization. However, without proper constraints, the expanded activation will easily intrude into the background region. In this paper, we propose spatial structure constraints (SSC) for weakly supervised semantic segmentation to alleviate the unwanted object over-activation of attention expansion. Specifically, we propose a CAM-driven reconstruction module to directly reconstruct the input image from deep CAM features, which constrains the diffusion of last-layer object attention by preserving the coarse spatial structure of the image content. Moreover, we propose an activation self-modulation module to refine CAMs with finer spatial structure details by enhancing regional consistency. Without external saliency models to provide background clues, our approach achieves 72.7% and 47.0% mIoU on the PASCAL VOC 2012 and COCO datasets, respectively, demonstrating the superiority of our proposed approach. The source codes and models have been made available at https://github.com/NUST-Machine-Intelligence-Laboratory/SSC.

EFFECT OF LUXURY BRAND PERCEIVED VALUE, BRAND ATTACHMENT ON PURCHASE INTENTION: STUDIES ON LUXURY BAG

(1) Background: The study adds to the literature on luxury brand perceived value dimensions as independent factors on the indirect influence on purchase intention via brand attachment. This study also became one of the few to research specific categories of fashion which is bag. (2) Methods: This research used a quantitative method approach and was distributed to 290 respondents in Indonesia via online platforms. This study used Partial Least Squares Structural Equation Model (PLS-SEM). (3) Results: We discovered that only experiential value has a significant and positive relationship with purchase intention. (4) Conclusions: Implications for the results are discussed based on the findings.

Assessing The Issues of Honour and Violence Against Women: A Human Rights Discourse Framework for The Detection of Violence Against Women

The term 'Honour Killing' is conventionally used to denote a specific category of extralegal killings where the act of murder is seen to have been motivated by pre-modern affiliations/sentiments of some sort. Acts of violence committed against people for marrying outside of caste/religion/ethnicity or other similar infringements where they are seen by their actions to have besmirched the honour of the family and the larger community come under the broad rubric of honour crime. Implicit in using the term are assumptions that mark it as different from other crimes. Honour is defined in terms of women assigned sexual and familial roles as dictated by traditional family ideology. Thus, adultery, premarital relationships (which may or may not include sexual relations), rape, and falling in love with an "inappropriate" person may constitute violations of family honour. 'Crimes of honour' as a form of violence against women does not imply that men also are not subjected to such crimes. In cases of forced marriage or interference with the right of choice and whom to marry, pressure from older family members over younger members will apply to men as well as to women. However, women remain the victims and survivors of 'crimes of honour' and have fewer available remedies.

Meta-Scaler: A Meta-Learning Framework for the Selection of Scaling Techniques.

Dataset scaling, a.k.a. normalization, is an essential preprocessing step in a machine learning (ML) pipeline. It aims to adjust the scale of attributes in a way that they all vary within the same range. This transformation is known to improve the performance of classification models. Still, there are several scaling techniques (STs) to choose from, and no ST is guaranteed to be the best for a dataset regardless of the classifier chosen. It is thus a problem-and classifier-dependent decision. Furthermore, there can be a huge difference in performance when selecting the wrong technique; hence, it should not be neglected. That said, the trial-and-error process of finding the most suitable technique for a particular dataset can be unfeasible. As an alternative, we propose the Meta-scaler, which uses meta-learning (MtL) to build meta-models to automatically select the best ST for a given dataset and classification algorithm. The meta-models learn to represent the relationship between meta-features extracted from the datasets and the performance of specific classification algorithms on these datasets when scaled with different techniques. Our experiments using 12 base classifiers, 300 datasets, and five STs demonstrate the feasibility and effectiveness of the approach. When using the ST selected by the Meta-scaler for each dataset, 10 of 12 base models tested achieved statistically significantly better classification performance than any fixed choice of a single ST. The Meta-scaler also outperforms state-of-the-art MtL approaches for ST selection. The source code, data, and results from the experiments in this article are available at a GitHub repository (http://github.com/amorimlb/meta_scaler).

Hydra: Computer Vision for Online Data Quality Monitoring

Hydra is a system utilizing computer vision for near real-time data quality monitoring. Currently operational across all of Jefferson Lab’s experimental halls, it reduces the workload of shift takers by autonomously monitoring diagnostic plots during experiments. Hydra uses "off-the-shelf" supervised learning technologies and is supported by a comprehensive MySQL database. To simplify access, web apps have been developed to facilitate both labeling and monitoring of Hydra’s inferences. Hydra can connect with the alarm system and incorporates complete historical tracking, enabling it to identify issues that shift takers could miss. When issues are detected, a natural first question is: "Why does Hydra think there is a problem?" To answer, Hydra employs Gradient-weighted Class Activation Maps (GradCAM) to identify regions of the image that are important for the specific classification. This interpretive layer enhances transparency and trustworthiness, which is essential for integration with experiment workflows and operation. The Hydra system, results, and sociological considerations for deployment will be discussed.

Solving a classification problem using Transfer learning on small image datasets

In this study, we explore the efficiency of transfer learning in training convolutional neural networks (CNNs) for image classification tasks, focusing on a dataset with limited size. Utilizing the VGG16 model, we investigate the impact of varying the number of trainable layers on classification accuracy. Our specific classification challenge involves distinguishing between images of cars with open and closed hoods. The study demonstrates that employing transfer learning enables the pretrained VGG16 model to achieve over 97% accuracy in this binary classification task, even with a training set of just 1000 image samples. This research was conducted solely by the undersigned, who also represents the sole author of this paper.

Array-based specific classification of bacterial species via ligands with dimethylamino/amino groups.

The early detection of bacterial species plays a crucial role in patient prognosis and the development of effective therapeutic regimens. This study introduces an accessible and promising colorimetric sensor array designed to classify gram-positive (G+) and gram-negative (G-) bacterial species. The classification relies on 6 chemical ligands with dimethylamino/amino groups as sensing elements and silver nanotriangles as colorimetric probes. Using these specific sensor arrays, we successfully differentiated G- and G+ bacterial species and discriminated individual bacterial strains, and the sensors exhibited remarkable reproducibility and high sensitivity. Moreover, the sensor array can identify bacterial mixtures and bacteria at varying concentrations, underscoring its versatility. In summary, this sensor array offers an effective tool for bacterial analysis with promising applications in the field of biomedical diagnostics.

Array-based specific classification of bacterial species via hydrophilic/hydrophobic biosensors

The early detection of bacterial species plays a crucial role in patient prognosis and the development of effective therapy regimens. This study introduces an accessible and promising fluorescent sensor array...

Assesment of the Development of European Air Forces in the Context of US-China Competition

The air force plays an important role in armed conflicts. It was no different in the Odyssey Dawn and Unified Protector operations in Libya in 2011. The European dependence on the US became fully apparent during those operations. After 2011, US attention has been shifted to Asia. This article focuses on the evaluation of the development of European air forces from 2011 to 2021 in the context of the shift of US attention to Asia. The article uses a dataset to compare the number of aircraft in specific categories in 2011 and 2021. The research shows that European states maintain a fleet of around 2,000 fighter jets, while compared to 2011, the number of ISR aircraft decreased from 183 to 122 and the number of air-to-air refuelling aircraft from 70 to 67. In contrast, the number of MALE unmanned vehicles has doubled from 45 to 91. However, in the context of the US shift Asia, those numbers are insufficient, and European states are still dependent on US support.

EXPLORING THE WELL-BEING OF SPECIAL NEEDS CHILDREN IN MALAYSIA: A STUDY AT SEKOLAH KEBANGSAAN PENDIDIKAN KHAS KUANTAN

This study investigates the well-being of Malaysian Muslim children with special needs, focusing on various aspects such as social, physical, emotional, intellectual, and spiritual well-being. The research was conducted at Sekolah Kebangsaan Pendidikan Khas Kuantan in Indera Mahkota 2, involving 24 students with Developmental Disabilities (DD) and Learning Disabilities (LD), aged 8-12, and 20 teachers. The children's well-being was assessed through self-report surveys and teacher evaluations, complemented by direct observations. Methodologically, the study combined quantitative and qualitative approaches, using self-report questionnaires and informal interviews. The sample size comprised 41 teachers and 107 students, with a subset of 30 participants for detailed study. Analysis of Variance (ANOVA) and independent sample t-tests were employed to analyse the data. The results showed no significant differences in well-being between LD and DD children. However, disparities were observed in self and teacher ratings, particularly in social and physical well-being, with children rating themselves lower. Emotional, intellectual, and spiritual well-being showed alignment between children's and teachers' perceptions. The study highlights the unique challenges faced by special needs children, including social integration difficulties, emotional stress, and the need for tailored educational strategies. Despite its insights, the study faced limitations, including a small sample size and focus on specific disability categories, which may limit generalizability. Future research should consider larger, more diverse samples and alternative data collection methods. In conclusion, the study underscores the need for comprehensive support for special needs children in Malaysia, encompassing various dimensions of well-being. It calls for the involvement of families, educators, and the broader community in ensuring the holistic development and integration of these children into society..

Upotreba foto-elicitacije u sociologiji

In sociological research, photography is most commonly used to supplement presented data and less frequently as a data collection technique. This paper focuses on using photography to examine the gender aspect of poverty in rural areas of Serbia. Through the application of photo-elicitation, data were collected in field research during 2015 in Serbia. The data include photographs taken by girls and women beneficiaries of social services. Additionally, they incorporate the notes on the motives and interpretation of the photographs taken, along with transcripts of interviews conducted with the photographers a month after the photography sessions. The research has shown that photography, as a medium, connects the emotional experiences of poverty among research participants with the factual conditions of their lives in material deprivation and isolation. Consequently, it offers the possibility of articulating diverse meanings of poverty. The diversity of meanings of poverty is evident in the specific social categories to which the interviewees in this study belong, such as persons with disabilities, for example. Based on the findings of this research, the use of photo-elicitation emerges as particularly significant in sociological studies that involve the exploration of subjective meanings and experiences of specific population groups, such as girls and women living in poverty in rural areas of Serbia.

사법부 경호보안 담당 공무원의 감정노동이 직무태도에 미치는 영향

The purpose of the study is to investigate the effect of emotional labor of public officials in charge of security and security at the judiciary on job attitudes. A total of 336 final study subjects conducted a survey of 20 research variables using the self-evaluation entry method. Exploratory factor analysis, reliability analysis, frequency analysis, correlation analysis, independent t-test, one-way variance analysis, and multiple regression analysis were conducted as research analysis methods.
 Depending on the results of the study, conclusions can be presented as follows. First of all, it can be interpreted that the difference in superficial behavior depending on age, education, work experience, and salary is due to the difference in knowledge and information ownership about the job and the difference in response and response based on experience. However, it is analyzed that the perception of emotional labor of public officials in charge of security and security at the judiciary shows no structural difference in job attitude depending on the demographic group classification, and the problem of emotional labor is recognized as a common problem for all members. Changes and improvements in the work environment due to emotional labor do not need to be made according to a specific sociological classification, and welfare and benefits for workers who feel tired by emotional labor need to be strengthened in general. In addition, job satisfaction represents the emotional state of the affectionate response that occurs when evaluating the job, so it may have a high relationship with inner behavior. Accordingly, it is judged that the individual employee's mood or emotional control is directly related to job satisfaction.
 The implications of this study are that the duties of public officials in charge of security and security at the judiciary fall within the category of emotional labor, which can environmentally cause or function as a medium among stakeholders and various job groups. Therefore, efforts will be needed to improve the negative perception of emotional labor of public officials in charge of security and security at the judiciary.

Using In Death: Unchained as case to investigate embodiment-related elements in VR game design

The research focuses on the concept of embodiment in VR game design, which is a trending topic in VR theories and applications. Currently, other investigations focus on a more broad term of VR products or a specific category of VR games, which ignores the importance of embodiment in generic VR game design. This research focuses on various approaches towards embodiment and uses the VR game In Death: Unchained as an example to demonstrate those approaches' application, effect, and limitations. To begin with, the virtual environment on embodiment in VR games like In Death will be examined, the same as the usage of players’ actions and haptic feedback designed to increase the sense of presence. Next, the limitation of VR games will be listed after comparing with other VR projects from Meta or Google, which undermine the embodiment of VR games. finally, this research is looking forward to the future and trend of development in VR games' embodiment.

Skin Cancer Detection Using CNN

Abstract: Skin cancer disease is recognized as one of the most perilous types of cancer & an increasing rate of mortality is attributed to insufficient awareness of symptoms and preventive measures. Consequently, it is of very importance to detect skincancer at the beginning of stage to prevent its spread. Various types of skin cancer exist, with some posing significant risks. Detecting malignant skin lesions, particularly those with pigmentation, necessitates advanced image detection techniques and computer classification capabilities. In the proposed model, we have used the HAM10000 dataset, comprising 10,015 images, for enhancing the accuracy of skin cancer detection. We have carefully selected a subset of this dataset and implemented augmentation techniques to improve the model's precision. Our analysis focuses on the CNN- based model. Our proposed system achieved an outstanding validation accuracy of 97.92 % with the CNN model. This research contributes to the early identification of specific categories of skin diseases, empowering medical practitioners to validate and administer appropriate treatments

Editorial

Editorial

Pengembangan Instrumen Kecakapan Kerja Mahasiswa (Rasch Model Analisis)

Employability Skills for College Students in University are an asset that can bring students closer to being ready to enter the world of work. Employability Skill can be interpreted as a series of characteristics including knowledge, attitudes, and skills that make a person capable of being trained. To get a general trend or a long work profile students need an instrument to reveal these variables. This study aims to develop employability skill instruments that are psychometrically tested both in terms of validity testing and instrument reliability testing using the RASCH model analysis method. The research instrument has been tested on 728 people. The results showed that from testing the instrument's validity using the RASCH model analysis on Outfit MNSQ, Outfit ZSTD, and Pt Measure Corr, of the 54 items developed, a total of 48 items met psychometric standards. The instrument's reliability on Cronbach's alpha value is in very good criteria, even in terms of reliability a person is in the very good category, and item reliability is in the special category. Therefore, the student employability skills instrument meets psychometric requirements and can be used as an instrument to measure student employability skills.

How Do Halal Component and Marketing Drive Muslim Consumers?

This research addresses a specific issue within halal marketing, emphasizing its novelty. Despite widespread recognition in marketing literature of the roles played by various halal components and strategies in influencing consumer purchase intentions, there is a distinct research gap in the lack of studies focusing on specific categories. This study aims to fill that gap by examining the relationship between halal awareness, certification, and other marketing elements in shaping the halal purchase intentions of Mixue consumers in Indonesia. Structural equation model analysis of data from 156 Mixue consumers revealed positive correlations between halal awareness, certification, and brand with purchase intentions, while food quality and promotion showed negative and non-significant relationships. Theoretically, the research strives to advance a conceptual model of halal purchase intentions by integrating halal components with marketing elements, contributing to marketing literature with a specific focus on Mixue and its consumers in Indonesia. The practical implications involve a deeper understanding of factors influencing halal product purchase intentions, aiding marketing decision-making and business strategies in this rapidly growing market.

CIVILIAN HOSTAGE LEGAL PROTECTION IN TIMES OF WAR: PURSUING EFFECTIVE TOOLS TO ENSURE THEIR CONSTITUTIONAL RIGHTS AND FREEDOMS

This article explores the issue of legal protection for civilian hostages in times of war. The authors of the article set out to answer the question of whether effective instruments for safeguarding the rights and freedoms of this specific category of citizens can exist. The study delves into the procedural differences and challenges inherent in the legal protection of civilian hostages, which deviates significantly from the conventional mechanisms of citizen legal protection. In the article, the authors present arguments suggesting that the establishment of specialized legal clinics is one of the effective instruments to ensure the constitutional rights and freedoms of civilian hostages. However, such initiatives should be undertaken in collaboration with civil organizations specializing in matters related to this category. Furthermore, another crucial tool for their protection lies in seeking assistance from international human rights institutions and actively working towards their release from captivity. The authors conclude that this comprehensive approach constitutes an effective system for the legal protection of civilian hostages in times of war.

Identifying Relationship between Regional Centrality and POI Facilities: A Case Study of Seoul Metropolitan Area

While understanding the dynamic urban network through the concept of regional centrality has provided various implications on the structure and hierarchy of cities, the macroscopic focus of previous studies has largely overlooked the small-scale physical and social urban entities in central places. Meanwhile, recent advances in real-time Point-of-Interest (POI) data have quickly replaced much of traditional urban facility data, emerging as a new representation of urban activities and demands. Therefore, this study proposes a method to identify the relationship between regional centrality and the distribution of POI facilities, particularly focused on the Seoul metropolitan area of South Korea. To this end, this study conducts a correlation analysis between regional centrality results derived from social network analysis and POI indices obtained from POI distribution analysis. The results indicate that a statistically significant relationship exists between regional centrality and the distribution of urban facilities, with a particularly strong correlation exhibited in specific POI categories. The results also demonstrate the effectiveness of the method in capturing disparities in the provision of facilities concerning growing commuting centers. The findings of the study provide pragmatic implications for prioritization and planning of facility development, as well as making informed decisions in real estate and facility investment.