Miracle-dz/UniFAR-FTUs huggingface dataset:
Accuracy Assessment Measures for Object-based Image Segmentation Goodness

To select an image segmentation from sets of segmentation results, measures for ranking the segmentations relative to a set of reference objects are needed. We review selected vector-based measures designed to compare the results of object-based image segmentation with sets of training objects extracted from the image of interest. We describe and compare area-based and location-based measures that measure the shape similarity between segments and training objects. By implementing the measures in two object-based image processing software packages, we illustrate their use in terms of automatically identifying parsimonious parameter combinations from arbitrarily large sets of segmentation results. The results show that the measures have divergent performance in terms of the identification of parameter combinations, Clustering of the results in measure space narrows the search. We illustrate combination schemes for the measures for generating rankings of segmentation results. The ranked segmentation results are illustrated and described.
Online Configuration in Continuous Decision Space

In this paper, we investigate the optimal online configuration of episodic Markov decision processes when the space of the possible configurations is continuous. Specifically, we study the interaction between a learner (referred to as the con-figurator ) and an agent with a fixed , unknown policy, when the learner aims to minimize her losses by choosing transition functions in online fashion. The losses may be unrelated to the agent’s rewards. This problem applies to many real-world scenarios where the learner seeks to manipulate the Markov decision process to her advantage. We study both deterministic and stochastic settings, where the losses are either fixed or sampled from an unknown probability distribution. We design two algorithms whose peculiarity is to rely on occupancy measures to explore with optimism the continuous space of transition functions, achieving constant regret in deterministic settings and ˜ O ( √ T ) regret in stochastic settings, respectively. Moreover, we prove that the regret bound is tight with respect to any constant factor in deterministic settings. Finally, we compare the empiric performance of our algorithms with a baseline in synthetic experiments.
Spectral Continuity and Subspace Change Detection for Recovery of Missing Harmonic Features in Power Quality

The disturbance monitoring, event sequence recording, and automated fault analysis in the power system require processing of power quality and digital fault recorder data. Through, reliable and granular data streaming/storage services, which inadvertently introduces unwanted data quality issues like data gaps or missing samples. The work proposes a data-driven, gap length, and spectral change independent missing harmonic recovery method using static and dynamic selectivity criteria of spectral continuity and rate of subspace affinity change. For static selectivity, a novel mean-shift cross-energy operator is proposed that quantifies the spectral similarity between the static snapshots of signals across the gap. For dynamic selectivity, a novel rate of subspace change method is proposed to detect the subspace change points in a dynamically changing data set. Based on the selectivity criterion, the missing harmonic parameters are estimated and filled using the rotational invariance technique. The proposed method could effectively reconstruct the power signals with longer data gaps, contiguous, and randomly gaped data sets under dynamic harmonic conditions. The proposition is tested with simulated data sets in Matlab/Simulink and real system data from the India grid.
FACTORS IN INFLUENCER MARKETING THAT AFFECT GENERATION Z'S IMPULSE BUYING BEHAVIOR ON INSTAGRAM

Influencer marketing is one of the strategies used to influence consumers in deciding on purchases through Instagram social media. However, along with the development of Instagram social media, the number of influencers on Instagram social media has also grown, which makes it difficult for brands to choose the right influencers to influence Generation Z to make purchase decisions. Generation Z, the majority of Indonesia's population, is the digital generation that most often makes online purchases. The phenomenon of impulse buying or unplanned purchases is a phenomenon that can be an opportunity for business actors to develop their marketing strategies to increase sales. This study aimed to analyze the factors that can influence the impulse buying behavior of Generation Z on Instagram. The survey was conducted based on a sample of 109 Instagram users in Indonesia who had made impulse purchases because they saw content uploaded by Influencers on Instagram. The results of this study state that factors in influencer marketing that can influence the impulse buying behavior of Generation Z on Instagram social media are source credibility moderated by the type of influencer. Source credibility has 3 components: expertise, attractiveness, and trustworthiness. At the same time, the type of influencer is divided into micro-influencers and macro-influencers, which are measured based on the number of followers they have. Furthermore, the findings from this study have beneficial implications for developing effective Instagram marketing communication strategies with the Generation Z target market. Keywords: Generation Z, Impulse Buying, Source Credibility, Influencer Marketing
Revisiting Inequality and Caste in State and Social Laws: Perspectives of Manu, Phule and Ambedkar

The Constitution, as a formal legal document, reflects a commitment to secure to all citizens, Equality, Justice, and Liberty, as a non-negotiable duty of the State. The nature and context of present society, however, is embedded in its socio-cultural development through civilisations. This study aims to engage with such a manifestation of state power as revealed in the text Manavdharmashastra, that marked the origin of codified social laws to derive legitimacy and establish a ‘divine’ authority to rule. Subsequently, the pioneers to critique the dysfunctions of Manu’s social laws became a subject of interrogation by social reformers like Jyotirao Phule and Dr. Bhimrao Ambedkar. Methodologically, our effort will be to weave together an intertextual analysis based on scientific observation of the case of caste subaltern, through three widely acknowledged texts—Manusmriti, Phule’s Slavery (Gulamgiri), and Ambedkar’s Annihilation of Caste, on ideals of society and governance, in order to present a historical legacy into the origins of social hierarchy as an institutional mechanism to perpetuate inequality among subjects. The aim is to develop an approach to evaluate the ancient political thought of Manusmriti, and probe contradictions and realism in actions, with explicit excerpts of relevant texts, to authenticate the credibility of facts and its alignment with the central thought. The article eventually attempts to suggest alternatives to secure the vision of an ideal Indian society that aims to disintegrate the institution of caste.
JAK3 Inhibition Regulates Stemness and Thereby Controls Glioblastoma Pathogenesis

Glioblastoma multiforme (GBM) is the most deadly brain tumor, effective treatment options for which still remain elusive. The current treatment procedure of maximal resection followed by chemotherapy has proved to be grossly insufficient to prevent disease progression and death. Despite best efforts, the maximum survival post-diagnosis is a mere 1.5 years. Therefore, there is a huge unmet clinical need to find effective therapeutic procedures to prevent the pathogenesis and relapse of GBM. Small-molecule inhibitors of signaling pathways are an attractive option to prevent various types of tumors. However, no effective small-molecule inhibitors have been successful against GBM in clinical trials. Various signaling pathways are altered and an array of signaling molecules, transcription factors (TFs), and epigenetic modifying factors have been implicated in the pathogenesis of GBM. JAK-STAT pathway alteration is an important contributor to GBM pathogenesis and relapse. Many small-molecule inhibitors of JAKs, or STAT TFs, especially JAK2 and STAT3, have been assessed for their anti-tumor activity in GBM. However, no definitive success so far has been achieved. Herein, by using two small-molecule inhibitors of JAK3, we show that they are quite effective in inhibiting GBM cell proliferation and neurosphere formation, downregulating their stemness character, and inducing differentiation into neuronal origin cells. The effect of a single treatment with the drugs, both in a serum-containing differentiation medium and in a proliferation medium containing EGF and FGF, was really strong in limiting GBM cell growth, suggesting a potential therapeutic application for these JAK inhibitors in GBM therapy.
Dataset construction and algorithm application based on hair follicle detection results

Hair disorders are currently highly prevalent and gradually becoming younger, directly damaging the image of the individual, so diagnosis and treatment of hair disorders is necessary and urgent. At this stage, hair follicle detection techniques have been highly imaged as a routine method of diagnosis. In response to the fact that there are currently no publicly available datasets that can apply the results of hair follicle detection to computer vision tasks, this paper, in cooperation with Sichuan Daisen Biotechnology Co. Ltd. obtains some of the hair follicle detection images accumulated during its medical consultations, and provides a method of constructing a dataset that can be applied to assisted diagnosis of hair follicle detection, and constructs two datasets that can be applied to the vast majority of deep learning algorithms. And the detection application is realized on some existing classical object detection algorithms. The feasibility of the dataset is verified by comparing and analyzing the detection results with the classical dataset PASCAL VOC2007, which effectively improves the diagnostic work of doctors for hair follicle detection.
Eyes can tell: Assessment of implicit attitudes toward AI art

Advances in artificial intelligence (AI) have significantly improved the abilities of machines. Human-unique abilities, such as art creation, are now being challenged by AI. Recent studies have investigated and compared people's attitudes toward human-made and AI-generated artworks. These results suggest that a negative bias may exist toward the latter. However, none of these previous studies has examined the extent of this bias. In this study, we investigate whether a bias against AI art can be found at an implicit level. Viewers’ attitudes toward AI art were assessed using eye-tracking measures and subjective aesthetic evaluations. Visual attention and aesthetic judgments were compared between artworks categorized as human-made and AI-made. The results showed that although it was difficult for individuals to identify AI-generated artwork, they exhibited an implicit prejudice against AI art. Participants looked longer at paintings that they thought were made by humans. No significant effect of categorization of paintings was found in subjective evaluations. These findings suggest that although human and AI art may be perceived as having similar aesthetic values, an implicit negative bias toward AI art exists. Although AI can now perform creative tasks, artistic creativity is still considered a human prerogative.
Waveguide Measurements of Highly Anisotropic Graphene Augmented Inorganic Nanofibers

This work presents the preparation and the frequency domain measurements of graphene augmented inorganic nanofibers oriented across the longitudinal axis of the fibers, embedded in a hollow WR-10 waveguide cassette. A simple measurement setup is employed, allowing for rapid, broadband characterization of the material under test. Due to the orientation of the fibers, highly anisotropic behavior is revealed.
Evidence for the Existence of a North Hawaiian Ridge Current

Abstract Two different datasets are examined for information about the existence and characteristics of the North Hawaiian Ridge Current (NHRC). The pan-Pacific drifter dataset shows a mean NHRC to be confined to within about 100 km of the northern side of the Hawaiian Ridge. A close look at the distribution of the data shows that the current is mostly confined in time to a two-month period in the fall of 1991. MBT data from the Trade Wind Zone Oceanography Pilot Study conducted in the mid-1960s were also examined. Of the 14 cruises with sections north of the Hawaiian Ridge, 5 showed evidence of northwestward flow along the ridge. Of those 5, one was associated with northward meandering of the North Equatorial Current. It is thus suggested that the NHRC is highly intermittent. There are three related hypotheses for the formation of the NHRC, 1) propagation of Rossby waves westward into the ridge, 2) forking of the North Equatorial Current, and 3) as a western boundary current for the eastern Pacific north.
A re-engagement model to overcome the psychological distress of students

COVID-19 lockdowns and lack of social interaction increased academic disengagement among students in India. This study aims to identify the student engagement pitfalls and the prevalent psychological distress in higher education institutions (HEIs) in India prior to the pandemic and during the same. An exploratory research design is adopted by executing a purposive sampling technique. Researchers collected data from undergraduate students of the top five universities, verified 600 consistent responses, and tested the hypothesis using relevant research tools. Results display that psychological distress (PD) is inversely correlated to student engagement (SE) and negatively impacts it. Researchers introduced a novel re-engagement model called “CAIS”, which is the combination of four variables, creativity, physical activity (PA), internship, service learning (SL), and tested their relationship with PD and SE. Each variable under CAIS shows an inverse correlation with PD and positively impacts SE. This study suggests to include “CAIS” in the curriculum to improve the productivity of students and reduce their stress levels. This investigation is the first of its kind to explore the connection between CAIS, SE, and PD in Indian HEIs. The results contribute to the stakeholders in education to re-engage the students in an innovative pattern during post-pandemic times.
Mapping of the air–sea CO2 flux in the Arctic Ocean and its surrounding seas: Basin-wide distribution and seasonal to interannual variability

We produced 204 monthly maps of the air–sea CO2 flux in the Arctic north of 60°N, including the Arctic Ocean and its adjacent seas, from January 1997 to December 2013 by using a self-organizing map technique. The partial pressure of CO2 (pCO2) in surface water data were obtained by shipboard underway measurements or calculated from alkalinity and total inorganic carbon of surface water samples. Subsequently, we investigated the basin-wide distribution and seasonal to interannual variability of the CO2 fluxes. The 17-year annual mean CO2 flux shows that all areas of the Arctic Ocean and its adjacent seas were net CO2 sinks. The estimated annual CO2 uptake by the Arctic Ocean was 180 TgC yr−1. The CO2 influx was strongest in winter in the Greenland/Norwegian Seas (>15 mmol m−2 day−1) and the Barents Sea (>12 mmol m−2 day−1) because of strong winds, and strongest in summer in the Chukchi Sea (∼10 mmol m−2 day−1) because of the sea-ice retreat. In recent years, the CO2 uptake has increased in the Greenland/Norwegian Sea and decreased in the southern Barent
Semi‐adaptive control of convexly parametrized systems with application to temperature regulation of chemical reactors

In this paper, we are interested in the problem of adaptive control of non‐linearly parametrized systems. We investigate the viability of defining a stabilizing parameter update law for the case when the plant model is convex on the uncertain parameters. We show that, when the only prior knowledge is convexity, there does not exist an adaptation law—derivable from the standard separable Lyapunov function technique of Parks—applicable for all the state space. Therefore, we propose a semi‐adaptive state feedback controller where adaptation takes place only in the region of the state space where convexity can be used to reduce parameter uncertainty. In the remaining part of the state space we freeze the adaptation and switch to a robust controller. This scheme ensures semi‐global stability for convexly parametrized non‐linear systems with matched uncertainty. The proposed controller is then applied to the problem of temperature regulation of continuous stirred exothermic chemical reactors where reaction heat is convex in the uncertain parameters. Copyright © 2001 John Wiley & Sons, Ltd.
Clinical Decision Support System for Diabetes Classification with an Optimized CNN using PSO

Diabetes, a pervasive chronic metabolic disorder, affects a substantial portion of the global population. The timely and precise diagnosis of diabetes is pivotal for effective management and improved patient outcomes. In this study, we introduce an innovative approach aimed at augmenting the classification performance of Convolutional Neural Networks (CNNs) in diabetes diagnosis. Utilizing the Particle Swarm Optimization (PSO) algorithm, we fine-tune the CNN model to enhance the accuracy and efficiency of diabetes classification. Our comprehensive experiments, conducted on the Pima Indians Diabetes dataset, substantiate the effectiveness of our optimized CNN. These findings underscore the potential of integrating CNN and PSO optimization methodologies to significantly boost the accuracy of diabetes diagnosis, thereby facilitating more accurate assessments and tailored treatment strategies for patients. We evaluated the proposed model using standard metrics such as precision, recall, F1-score, and overall accuracy, with results demonstrating that the PSO-based optimized CNN model outperforms the custom CNN, achieving the highest precision, recall, F1-score, and overall accuracy.
STUDY OF THE APPLICATION OF NEURAL NETWORKS IN INTERNET TRAFFIC ENGINEERING

In this study, we showed various approachs implemented in Artificial Neural Networks for network resources management and Internet congestion control. Through a training process, Neural Networks can determine nonlinear relationships in a data set by associating the corresponding outputs to input patterns. Therefore, the application of these networks to Traffic Engineering can help achieve its general objective: "intelligent" agents or systems capable of adapting dataflow according to available resources. In this article, we analyze the opportunity and feasibility to apply Artificial Neural Networks to a number of tasks related to Traffic Engineering. In previous sections, we present the basics of each one of these disciplines, which are associated to Artificial Intelligence and Computer Networks respectively.
Exploring XGBoost as an Effective Machine Learning Algorithm for Stellar Spectral Data Classification in Astronomy

The discipline of astronomy has experienced a substantial metamorphosis due to the advent of contemporary telescopes, which facilitate the accumulation of extensive spectral information. The processing of data through traditional manual means has become unfeasible owing to its vast volume, thereby necessitating the adoption of automated classification. The advent of machine learning algorithms and artificial intelligence has enabled the efficient and accurate processing of spectral data, thereby facilitating a more comprehensive analysis of celestial objects. Prior research has investigated the efficacy of diverse machine learning algorithms in the context of stellar classification endeavors, as well as the significance of pre-processing spectral data. The present investigation centers on the utilization of XGBoost, a machine learning algorithm that exhibits the ability to handle heterogeneous data types and address imbalanced datasets, thus rendering it a viable option for classification endeavors. In this research, we found that Random Forest and XGBoost gave good result compared to other classifier. In contrast, reduction dimension using PCA are lowering the achieved result.
A machine learning-based prediction model of pelvic lymph node metastasis in women with early-stage cervical cancer

Objective To develop a novel machine learning-based preoperative prediction model for pelvic lymph node metastasis (PLNM) in early-stage cervical cancer by combining the clinical findings and preoperative computerized tomography (CT) of the whole abdomen and pelvis. Methods Patients diagnosed with International Federation of Gynecology and Obstetrics stage IA2-IIA1 squamous cell carcinoma, adenocarcinoma, and adenosquamous carcinoma of the cervix who had primary radical surgery with bilateral pelvic lymphadenectomy from January 1, 2003 to December 31, 2020, were included. Seven supervised machine learning algorithms, including logistic regression, random forest, support vector machine, adaptive boosting, gradient boosting, extreme gradient boosting, and category boosting, were used to evaluate the risk of PLNM. Results PLNM was found in 199 (23.9%) of 832 patients included. Younger age, larger tumor size, higher stage, no prior conization, tumor appearance, adenosquamous histology, and vaginal metastasis as well as the CT findings of larger tumor size, parametrial metastasis, pelvic lymph node enlargement, and vaginal metastasis, were significantly associated with PLNM. The models’ predictive performance, including accuracy (89.1%–90.6%), area under the receiver operating characteristics curve (86.9%–91.0%), sensitivity (77.4%–82.4%), specificity (92.1%–94.3%), positive predictive value (77.0%–81.7%), and negative predictive value (93.0%–94.4%), appeared satisfactory and comparable among all the algorithms. After optimizing the model’s decision threshold to enhance the sensitivity to at least 95%, the ‘highly sensitive’ model was obtained with a 2.5%–4.4% false-negative rate of PLNM prediction. Conclusion We developed prediction models for PLNM in early-stage cervical cancer with promising prediction performance in our setting. Further external validation in other populations is needed with potential clinical applications.
Detection of Lysteria monocytogenes in Frozen Meatballs Using Real-Time PCR

Detection of pathogenic bacteria Listeria monocytogenes in processed food products of frozen meatballs is one of the critical test parameters in product quality testing. The challenge in testing product quality is how to find a reliable and effective test method to generate test results data. With this research, it is hoped that it can provide information or references for similar research. The method used in this research is the enrichment method with a direct isolation technique and amplification using real-time PCR. Data analysis was carried out based on 2 main criteria which included: (1) Ct analysis (Cycle threshold) and (2) melting temperature (Tm) analysis. From the results of the analysis using real-time PCR, the results of the tested samples were detected at Ct 11.60 with a Tm value of 81.50. For NTC control not detected, this indicates that the master mix used is in good condition and no contamination occurred when testing the sample. The use of this NTC control plays an important role in monitoring the quality of the master mic and test run. For the LOD control and positive control, the results were detected, whereas in the LOD control, the Ct value was detected at 10.08 and the Tm value was at 81.30. for positive control, the value of Ct was detected at 10.85 and the value of Tm was at 82.0. Based on the results of the study, it can be concluded that the test method used can detect Listeria monocytogenes in processed meat products in the form of frozen meatballs.