bluuebunny/crossref_metadata_2025 huggingface dataset:
Social Networks in Military Powers: Network and Sentiment Analysis during the COVID-19 Pandemic
Authors: Quilez-Robres Alberto , Acero-Ferrero Marian , Delgado-Bujedo Diego , Lozano-Blasco Raquel , Aiger-Valles Montserrat
Published: June 2023

The outbreak of the COVID-19 pandemic shifted socialization and information seeking to social media platforms. The armed forces of the major military powers initiated civil support operations to combat the invisible and common enemy. The aim of this study is to analyze the existence of differential behavior in the corporate profiles of the major military powers on Twitter, Instagram, and Facebook during the COVID-19 pandemic. The principles of social network analysis were followed, along with sentiment analysis, to study web positioning and the emotional content of the posts (N = 25,328). The principles of data mining were applied to process the KPIs (Fanpage Karma), and an artificial intelligence (meaning cloud) sentiment analysis was applied to study the emotionality of the publications. The analysis was carried out using the IBM SPSS Statistics 25 statistical software. Subsequently, a qualitative content analysis was carried out using frequency graphs or word clouds (the application “nubedepalabras” used in English). Significant differences were found between the behavior on social media and the organizational and communicative culture of the nations. It is highlighted that some nations present different preferences from the main communicative strategy developed by their armed forces. Corporate communication of the major military powers should consider the emotional nature of their posts to align with the preferences of their population.
Nitrogen-Doped Graphene Quantum Dot–Tin Dioxide Nanocomposite Ultrathin Films as Efficient Electron Transport Layers for Planar Perovskite Solar Cells
Authors: Le Ha Chi , Pham Nam Thang , Vu Duc Chinh , Pham Duy Long , Nguyen Si Hieu , Nguyen Thi Tu Oanh , Nguyen Chung Dong
Published: June 2023

Tin dioxide (SnO2) has recently been recognized as an excellent electron transport layer (ETL) for perovskite solar cells (PSCs) due to its advantageous properties, such as its high electron mobility, suitable energy band alignment, simple low-temperature process, and good chemical stability. In this work, nitrogen-doped graphene quantum dots (N-GQDs) were prepared using a hydrothermal method and then used to fabricate N-GQD:SnO2 nanocomposite ultrathin films. N-GQD:SnO2 nanocomposite ultrathin films were investigated and applied as electron transport layers in planar PSCs. The presence of N-GQDs with an average size of 6.2 nm in the nanocomposite improved its morphology and reduced surface defects. The excitation–emission contour map indicated that the N-GQDs exhibited a remarkably enhanced light-harvesting capability due to the possibility of absorbing UV light and producing emissions in the visible range. The quenching of photoluminescence spectra showed that the N-GQDs in nanocomposite ultrathin films improved electron extraction and reduced charge recombination. As a result, the power conversion efficiency (PCE) of our planar PSCs fabricated with the optimized N-GQD:SnO2 nanocomposite electron transport layer was improved by 20.4% over pristine SnO2-based devices.
Image Guided Radiotherapy (IGRT) and Delta (Δ) Radiomics—An Urgent Alliance for the Front Line of the War against Head and Neck Cancers
Authors: Mireștean Camil Ciprian , Iancu Roxana Irina , Iancu Dragoș Petru Teodor
Published: June 2023

The identification of a biomarker that is response predictive could offer a solution for the stratification of the treatment of head and neck cancers (HNC) in the context of high recurrence rates, especially those associated with loco-regional failure. Delta (Δ) radiomics, a concept based on the variation of parameters extracted from medical imaging using artificial intelligence (AI) algorithms, demonstrates its potential as a predictive biomarker of treatment response in HNC. The concept of image-guided radiotherapy (IGRT), including computer tomography simulation (CT) and position control imaging with cone-beam-computed tomography (CBCT), now offers new perspectives for radiomics applied in radiotherapy. The use of Δ features of texture, shape, and size, both from the primary tumor and from the tumor-involved lymph nodes, demonstrates the best predictive accuracy. If, in the case of treatment response, promising Δ radiomics results could be obtained, even after 24 h from the start of treatment, for radiation-induced xerostomia, the evaluation of Δ radiomics in the middle of treatment could be recommended. The fused models (clinical and Δ radiomics) seem to offer benefits, both in comparison to the clinical model and to the radiomic model. The selection of patients who benefit from induction chemotherapy is underestimated in Δ radiomic studies and may be an unexplored territory with major potential. The advantage offered by “in house” simulation CT and CBCT favors the rapid implementation of Δ radiomics studies in radiotherapy departments. Positron emission tomography (PET)-CT Δ radiomics could guide the new concepts of dose escalation on radio-resistant sub-volumes based on radiobiological criteria, but also guide the “next level” of HNC adaptive radiotherapy (ART).
Evaluation of SARS-CoV-2 Detection Systems Using Clinical Samples and Standard Material: A Comparative Study
Authors: Park Sunggyun , Kim Do-Hoon
Published: June 2023

Due to the decreasing trends in daily confirmed COVID-19 cases and daily confirmed tests, there is a need for a new testing system capable of quickly and efficiently testing small amounts of samples. Therefore, we compared and evaluated the testing performance of the Aptima SARS-CoV-2 assay, an automated testing system that allows continuous loading of samples, and the Real-Q Direct SARS-CoV-2 detection kit that is currently being used in our laboratory. We compared the results of the two testing systems using 259 residual individual nasopharyngeal specimens and 91 residual pooled nasopharyngeal specimens that were submitted for COVID-19 testing in January and February 2023. The 95% limit of detection (LoD) for the Aptima SARS-CoV-2 assay determined using reference material for SARS-CoV-2 nucleic acid was confirmed to be 17.793 copies/mL, while the LoD for the Real-Q Direct SARS-CoV-2 detection kit was determined to be 131.842 copies/mL for the RdRP gene and 241.77 copies/mL for the E gene. The comparative study using clinical specimens showed almost perfect agreement. Our data showed that the Aptima SARS-CoV-2 assay has a very low LoD. In addition, the Aptima SARS-CoV-2 assay and Real-Q Direct detection kit have comparable clinical performance for SARS-CoV-2 for individual and pooled samples.
Outstanding Characteristics of Birt–Hogg–Dube Syndrome in Korea
Authors: Park Hye Jung , Choi Yong Jun , Park Chul Hwan , Kim Tae Hoon , Lee Sung Soo , Moon Duk Hwan , Lee Kyung-A , Lee Sang Eun , Park Moo Suk , Kim Song Yee , Chang Yoon Soo , Lee Seok Jeong , Jung Ji Ye , Lee Ji-Ho , Lee Su Hwan , Kim Taehee , Kim Sung-Ryeol , Kim Kangjoon , Byun Min Kwang
Published: June 2023

Birt–Hogg–Dube (BHD) is a rare genetic disorder characterized by multiple lung cysts, typical skin manifestations, and renal tumors. We prospectively enrolled thirty-one subjects from four South Korean institutions with typical lung cysts, and next-generation sequencing was conducted. We prospectively enrolled thirty-one subjects from four Korean institutions with typical lung cysts. Next-generation sequencing was performed to investigate mutations in the following genes: FLCN, TSC1, TSC2, CFTR, EFEMP2, ELN, FBLN5, LTBP4, and SERPINA1. BHD was diagnosed in 11 of the 31 enrolled subjects (35.5%; FLCN mutations). Notably, we identified three novel mutations (c.1098G>A, c.139G>T, and c.1335del) that have not been previously reported. In addition to FLCN mutations, we also observed mutations in CFTR (16.1%), LTBP4 (9.7%), TSC2 (9.7%), TSC1 (3.2%), ELN (3.2%), and SERPINA1 (3.2%). According to a systematic review of 45 South Korean patients with BHD, the prevalence of pneumothorax (72.7%) was greater in South Korea than in the rest of the world (50.9%; p = 0.003). The prevalence of skin manifestations (13.6%) and renal tumors (9.1%) was lower in Korea than in the rest of the world, at 47.9% [p < 0.001] and 22.5% [p = 0.027], respectively). This study confirmed a significant prediction model for BHD based on age, number of lung cysts (>40), and maximal diameter of lung cysts (>2 cm) regardless of skin manifestations and renal tumors. Importantly, three novel mutations (c.1098G>A, c.139G>T, and c.1335del) were identified. In conclusion, South Korean patients with BHD display characteristics that are different from those observed in patients of other nationalities. Detailed characterization of lung cysts is needed to define BHD, especially in South Korea, even if patients do not present with skin or renal lesions.
Dual Deep CNN for Tumor Brain Classification
Authors: Al-Zoghby Aya M. , Al-Awadly Esraa Mohamed K. , Moawad Ahmad , Yehia Noura , Ebada Ahmed Ismail
Published: June 2023

Brain tumor (BT) is a serious issue and potentially deadly disease that receives much attention. However, early detection and identification of tumor type and location are crucial for effective treatment and saving lives. Manual diagnoses are time-consuming and depend on radiologist experts; the increasing number of new cases of brain tumors makes it difficult to process massive and large amounts of data rapidly, as time is a critical factor in patients’ lives. Hence, artificial intelligence (AI) is vital for understanding disease and its various types. Several studies proposed different techniques for BT detection and classification. These studies are on machine learning (ML) and deep learning (DL). The ML-based method requires handcrafted or automatic feature extraction algorithms; however, DL becomes superior in self-learning and robust in classification and recognition tasks. This research focuses on classifying three types of tumors using MRI imaging: meningioma, glioma, and pituitary tumors. The proposed DCTN model depends on dual convolutional neural networks with VGG-16 architecture concatenated with custom CNN (convolutional neural networks) architecture. After conducting approximately 22 experiments with different architectures and models, our model reached 100% accuracy during training and 99% during testing. The proposed methodology obtained the highest possible improvement over existing research studies. The solution provides a revolution for healthcare providers that can be used as a different disease classification in the future and save human lives.
Integrative Interpretation of Cardiopulmonary Exercise Tests for Cardiovascular Outcome Prediction: A Machine Learning Approach
Authors: Cauwenberghs Nicholas , Sente Josephine , Van Criekinge Hanne , Sabovčik František , Ntalianis Evangelos , Haddad Francois , Claes Jomme , Claessen Guido , Budts Werner , Goetschalckx Kaatje , Cornelissen Véronique , Kuznetsova Tatiana
Published: June 2023

Integrative interpretation of cardiopulmonary exercise tests (CPETs) may improve assessment of cardiovascular (CV) risk. Here, we identified patient phenogroups based on CPET summary metrics and evaluated their predictive value for CV events. We included 2280 patients with diverse CV risk who underwent maximal CPET by cycle ergometry. Key CPET indices and information on incident CV events (median follow-up time: 5.3 years) were derived. Next, we applied unsupervised clustering by Gaussian Mixture modeling to subdivide the cohort into four male and four female phenogroups solely based on differences in CPET metrics. Ten of 18 CPET metrics were used for clustering as eight were removed due to high collinearity. In males and females, the phenogroups differed significantly in age, BMI, blood pressure, disease prevalence, medication intake and spirometry. In males, phenogroups 3 and 4 presented a significantly higher risk for incident CV events than phenogroup 1 (multivariable-adjusted hazard ratio: 1.51 and 2.19; p ≤ 0.048). In females, differences in the risk for future CV events between the phenogroups were not significant after adjustment for clinical covariables. Integrative CPET-based phenogrouping, thus, adequately stratified male patients according to CV risk. CPET phenomapping may facilitate comprehensive evaluation of CPET results and steer CV risk stratification and management.
A Rare Complication of Ascariasis: A Case of Acute Interstitial Nephritis
Authors: Carullo Nazareno , Divenuto Francesca , Marascio Nadia , Adams Neill James , Giancotti Aida , Comi Nicolino , Faga Teresa , Bolignano Davide , Coppolino Giuseppe , Serapide Francesca , Costa Chiara , Torti Carlo , Matera Giovanni , Quirino Angela , Andreucci Michele
Published: June 2023

Acute interstitial nephritis (AIN) due to helminths is a rare cause of acute kidney injury (AKI). Helminthiases often progresses insidiously, making diagnosis difficult. This was the case of a 72-year-old man, who presented with renal failure, itching and diarrhoea. Urinalysis revealed leukocyturia, microhaematuria and mild proteinuria. A full blood count revealed leucocytosis with eosinophilia. A stool parasitological examination revealed fertilised eggs of Ascaris lumbricoides. Tubulointerstitial nephropathy secondary to A. lumbricoides infection was suspected. A percutaneous renal biopsy was not performed since the patient refused the anti-platelet therapy discontinuation. Mebendazole, albendazole and prednisone therapy was administered. After worm eradiation and discharge, recovery from the parasitosis, absence of pruritus and eosinophilia, and progressive improvement of renal function were observed, strongly suggesting a causal relationship between Ascaris infection and AIN. Parasite infection should be considered in the differential diagnosis of unexplained renal failure because early diagnosis and treatment are necessary to avoid irreversible complications.
Nailfold Videocapillaroscopy in Patients with Rheumatoid Arthritis and Psoriatic Arthropathy on ANTI-TNF-ALPHA Therapy
Authors: Anghel Daniela , Sîrbu Carmen Adella , Petrache Oana-Georgiana , Opriș-Belinski Daniela , Negru Maria Magdalena , Bojincă Violeta-Claudia , Pleșa Cristina Florentina , Ioniță Radu Florentina
Published: June 2023

Videocapillaroscopy is a simple, non-invasive investigation that allows the “in vivo” study of the nailfold capillaries. This method is inexpensive, easily accepted by patients and the results can be easily interpreted. It is mainly used in patients with Raynaud’s phenomenon and systemic sclerosis, but this examination can also be performed on patients who are suspected of having microcirculation alterations, such as rheumatoid arthritis and psoriatic arthritis. It may aid in the diagnosis, evaluation and prognosis of other rheumatic diseases, besides systemic sclerosis. The aim of this study is to identify the nailfold videocapillaroscopic abnormalities in rheumatoid arthritis and psoriatic arthritis patients and analyze the correlation between their evolution and 12 months of anti-TNF-α therapy. The abnormal capillaroscopic findings comprised widened, dilated or giant capillaries and the distortion of the normal nailfold architecture, avascular areas, hemorrhages and neoangiogenesis. Overall, capillary density, dilated capillaries, giant capillaries, elongated capillaries and angiogenesis significantly improved after 12 months. Moreover, no avascular areas were found after 12 months of anti-TNF treatment.
Epigenetic Regulation Interplays with Endometriosis Pathogenesis in Low-Birth-Weight Patients via the Progesterone Receptor B–VEGF-DNMT1 Axis
Authors: Setiawan Arief , Anwar Ruswana , Syamsunarno Mas Rizky Anggun Adipurna , Mose Johanes Cornelius , Santoso Budi , Maskoen Ani Melani , Permadi Wiryawan , Setiabudiawan Budi , Dhamayanti Meita , Hidayat Yudi Mulyana
Published: June 2023

Background: Low birth weight (LBW) is a risk factor associated with endometriosis. Our study aimed to analyze the risk of endometriosis in women with a LBW history and the relationships of progesterone receptor B (PR-B) gene promoter methylation, DNA methyltransferase-1 (DNMT1) expression, PR-B expression, and vascular endothelial growth factors (VEGF) with endometriosis. Methods: This study was conducted in two stages, a retrospective case-control design and a cross-sectional design, with 52 cases of endometriosis and 30 controls, which were further subdivided into LBW and non-LBW groups, at Hasan Sadikin General Hospital and its hospital networks from October 2017 to August 2021. Menstrual blood was taken from subjects and analyzed using pyrosequencing techniques to assess DNA methylation, while q-RT PCR was used to assess gene expression. Results: There were significant differences in PR-B methylation, DNMT1 expression, PR-B expression, and VEGF expression (p < 0.001) between the case and control groups. There was a significant negative correlation between PR-B methylation and PR-B expression (r = −0.558; p = 0.047). Based on a multiple logistic analysis, the most dominant factor affecting endometriosis incidence is PR-B (OR 10.40, 95% CI 3.24–33.4, R2 = 45.8). We found that patients with a low birth weight history had a 1.41-times-higher risk of developing endometriosis (95% CI 0.57–3.49, p = 0.113), although the relationship was not statistically significant. Conclusion: Endometriosis is associated with PR-B gene promoter hypermethylation, decreased PR-B expression, and increased DNMT1 and VEGF expression. The methylation of PR-B is the most dominant factor affecting endometriosis incidence.
A Comparison of Bone Marrow Morphology and Peripheral Blood Findings in Low and High Level JAK2 V617F Allele Burden
Authors: Babarović Emina , Marijić Blažen , Vranić Luka , Ban Josipa , Valković Toni , Hadžisejdić Ita
Published: June 2023

Cases with low level JAK2 V617F mutations are increasingly detected; however, the clinical interpretation of the low allele JAK2 burden may be challenging. The aim of this study is to analyze and compare the bone marrow morphology and peripheral blood findings in the low level JAK2 V617F allele burden (≤15% of JAK2) and high JAK2 V617F mutation burden patients (>15% JAK2). In total, 122 JAK2 V617F positive cases with concomitant bone marrow biopsies and peripheral blood findings were re-evaluated (62 low and 60 high level JAK2 V617F positive). Within the low burden group, normal looking megakaryocytes (p = 0.0005) were more frequently found, compared with those with no atypia (p = 0.0003), their number was more frequently not increased (p = 0.009), and they did not form clusters (p = 0.001). We found statistically significant difference in the number of platelet (p = 0.0003) and hematocrit levels (p = 0.032) when comparing the JAK2 V617F <3% and ≥3% mutation burden. In the high-level burden, the megakaryocytes were more frequently atypical (p = 0.054), and more frequently formed clusters (p = 0.053) with nuclei with maturation defects (p ≤ 0.0001). In conclusion, the JAK2 V617F mutation burden is reflected by morphological changes in the bone marrow and careful follow up of each and every patient with a low JAK2 V617F positivity is mandatory.
Clinical Implication and Prognostic Value of Artificial-Intelligence-Based Results of Chest Radiographs for Assessing Clinical Outcomes of COVID-19 Patients
Authors: Shin Hyun Joo , Kim Min Hyung , Son Nak-Hoon , Han Kyunghwa , Kim Eun-Kyung , Kim Yong Chan , Park Yoon Soo , Lee Eun Hye , Kyong Taeyoung
Published: June 2023

This study aimed to investigate the clinical implications and prognostic value of artificial intelligence (AI)-based results for chest radiographs (CXR) in coronavirus disease 2019 (COVID-19) patients. Patients who were admitted due to COVID-19 from September 2021 to March 2022 were retrospectively included. A commercial AI-based software was used to assess CXR data for consolidation and pleural effusion scores. Clinical data, including laboratory results, were analyzed for possible prognostic factors. Total O2 supply period, the last SpO2 result, and deterioration were evaluated as prognostic indicators of treatment outcome. Generalized linear mixed model and regression tests were used to examine the prognostic value of CXR results. Among a total of 228 patients (mean 59.9 ± 18.8 years old), consolidation scores had a significant association with erythrocyte sedimentation rate and C-reactive protein changes, and initial consolidation scores were associated with the last SpO2 result (estimate −0.018, p = 0.024). All consolidation scores during admission showed significant association with the total O2 supply period and the last SpO2 result. Early changing degree of consolidation score showed an association with deterioration (odds ratio 1.017, 95% confidence interval 1.005–1.03). In conclusion, AI-based CXR results for consolidation have potential prognostic value for predicting treatment outcomes in COVID-19 patients.
Attention-Enabled Ensemble Deep Learning Models and Their Validation for Depression Detection: A Domain Adoption Paradigm
Authors: Singh Jaskaran , Singh Narpinder , Fouda Mostafa M. , Saba Luca , Suri Jasjit S.
Published: June 2023

Depression is increasingly prevalent, leading to higher suicide risk. Depression detection and sentimental analysis of text inputs in cross-domain frameworks are challenging. Solo deep learning (SDL) and ensemble deep learning (EDL) models are not robust enough. Recently, attention mechanisms have been introduced in SDL. We hypothesize that attention-enabled EDL (aeEDL) architectures are superior compared to attention-not-enabled SDL (aneSDL) or aeSDL models. We designed EDL-based architectures with attention blocks to build eleven kinds of SDL model and five kinds of EDL model on four domain-specific datasets. We scientifically validated our models by comparing “seen” and “unseen” paradigms (SUP). We benchmarked our results against the SemEval (2016) sentimental dataset and established reliability tests. The mean increase in accuracy for EDL over their corresponding SDL components was 4.49%. Regarding the effect of attention block, the increase in the mean accuracy (AUC) of aeSDL over aneSDL was 2.58% (1.73%), and the increase in the mean accuracy (AUC) of aeEDL over aneEDL was 2.76% (2.80%). When comparing EDL vs. SDL for non-attention and attention, the mean aneEDL was greater than aneSDL by 4.82% (3.71%), and the mean aeEDL was greater than aeSDL by 5.06% (4.81%). For the benchmarking dataset (SemEval), the best-performing aeEDL model (ALBERT+BERT-BiLSTM) was superior to the best aeSDL (BERT-BiLSTM) model by 3.86%. Our scientific validation and robust design showed a difference of only 2.7% in SUP, thereby meeting the regulatory constraints. We validated all our hypotheses and further demonstrated that aeEDL is a very effective and generalized method for detecting symptoms of depression in cross-domain settings.
Topic Discovery and Hotspot Analysis of Sentiment Analysis of Chinese Text Using Information-Theoretic Method
Authors: Zhang Changlu , Fan Haojie , Zhang Jian , Yang Qiong , Tang Liqian
Published: June 2023

Currently, sentiment analysis is a research hotspot in many fields such as computer science and statistical science. Topic discovery of the literature in the field of text sentiment analysis aims to provide scholars with a quick and effective understanding of its research trends. In this paper, we propose a new model for the topic discovery analysis of literature. Firstly, the FastText model is applied to calculate the word vector of literature keywords, based on which cosine similarity is applied to calculate keyword similarity, to carry out the merging of synonymous keywords. Secondly, the hierarchical clustering method based on the Jaccard coefficient is used to cluster the domain literature and count the literature volume of each topic. Thirdly, the information gain method is applied to extract the high information gain characteristic words of various topics, based on which the connotation of each topic is condensed. Finally, by conducting a time series analysis of the literature, a four-quadrant matrix of topic distribution is constructed to compare the research trends of each topic within different stages. The 1186 articles in the field of text sentiment analysis from 2012 to 2022 can be divided into 12 categories. By comparing and analyzing the topic distribution matrices of the two phases of 2012 to 2016 and 2017 to 2022, it is found that the various categories of topics have obvious research development changes in different phases. The results show that: ① Among the 12 categories, online opinion analysis of social media comments represented by microblogs is one of the current hot topics. ② The integration and application of methods such as sentiment lexicon, traditional machine learning and deep learning should be enhanced. ③ Semantic disambiguation of aspect-level sentiment analysis is one of the current difficult problems this field faces. ④ Research on multimodal sentiment analysis and cross-modal sentiment analysis should be promoted.
Comment on Dinesen et al. Diphoterine for Chemical Burns of the Skin: A Systematic Review. Eur. Burn J. 2023, 4, 55–68
Authors: Hall Alan , Bouraoui Amal , Padois Karine , Blomet Joel , Jacquemin Denise , Burgher François , Bodson Lucien , Fortin Jean-Luc , Maibach Howard
Published: June 2023

We read with interest the recent publication of Dinesen et al. [...]
Editorial for Special Issue on Research on Supporting Remote Teaching and Learning during the COVID-19 Pandemic
Authors: Lo Chung Kwan , Hew Khe Foon
Published: June 2023

The global outbreak of COVID-19 profoundly altered our lives and teaching practices [...]
Professionalization Pathways for School Leaders Examined: The Influence of Organizational and Didactic Factors and Their Interplay on Triggering Concrete Actions in School Development
Authors: Tanghe Els , Schelfhout Wouter
Published: June 2023

It is believed that school leadership contributes to efficiency and equity in school performance. Therefore, it is essential that professionalization initiatives for school leaders foster learning and development processes towards effective leadership. Based on a literature review, several factors appear to facilitate the influence of professionalization programs on learning outcomes of school leaders but empirical research on real effects and on explanatory processes is limited. This research gap forms the basis for this mixed methods study, in which we design and implement a longitudinal professionalization program as the research setting. We distinguish an organizational dimension focusing on structural choices and an intertwined didactic dimension. We examine which specific interaction between both contributes most to concrete learning-driven actions at the school of the participant. The results indicate that by participating in the program with such a design, school leaders prepare action plans for their own school and start up school development. The interaction between actively providing theoretical frameworks, further deepening insights through peer learning in professional learning communities, the conversion of insights into concrete action plans and supporting this with school-specific coaching leads to the strongest results, analyses show.
Study on a Low-Illumination Enhancement Method for Online Monitoring Images Considering Multiple-Exposure Image Sequence Fusion
Authors: Zhao Wenlong , Jiang Chengwei , An Yunzhu , Yan Xiaopeng , Dai Chaofeng
Published: June 2023

In order to improve the problem of low image quality caused by insufficient illumination, a low-light image enhancement method with robustness is proposed, which can effectively handle extremely dark images while achieving good results for scenes with insufficient local illumination. First, we expose the images to different degrees to form a multi-exposure image sequence; then, we introduce global-based luminance weights and contrast-based gradient weights to fuse the multi-exposure image sequence; finally, we use a bootstrap filter to suppress the noise that may occur during the image processing. We employ pertinent assessment criteria, such as the Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM), the Average Gradient (AG), and the Figure Definition (FD), to assess how well the method enhances. Experimental results show that PSNR (31.32) and SSIM (0.74) are the highest in pretty dark scenes compared to most conventional algorithms such as MF, BIMEF, LECARM, etc. Similarly, in processing uneven illumination such as “moonlit night” images, the AG (10.21) and the FD (14.54) are at maximum. In addition, other evaluation metrics such as Shannon (SH) are optimal in the above scenarios. In addition, we apply the algorithm in this paper to the online monitoring images of electric power equipment, which can improve the image lightness while recovering the detail information. The algorithm has strong robustness in extremely dark images and natural low-light images, and the enhanced images have minimal distortion and best appearance in different low-light scenes.
Medical Image Fusion Using SKWGF and SWF in Framelet Transform Domain
Authors: Kong Weiwei , Li Yiwen , Lei Yang
Published: June 2023

Accurately localizing and describing patients’ lesions has long been considered a crucial aspect of clinical diagnosis. The fusion of multimodal medical images provides a feasible solution to the above problem. Unfortunately, the trade-off between the fusion performance and heavy computation overhead remains a challenge. In this paper, a novel and effective fusion method for multimodal medical images is proposed. Firstly, framelet transform (FT) is introduced to decompose the source images into a series of low and high frequency sub-images. Next, we utilize the benefits of both steering kernel weighted guided filtering and side window filtering to successfully fuse sub-images. Finally, the inverse FT is employed to reconstruct the final fused image. To verify the effectiveness of the proposed fusion method, we fused several pairs of medical images covering different modalities in simulation experiments. The experimental results demonstrate that the proposed method yields better performance than current representative ones in terms of both visual quality and quantitative evaluation.
Unified Scaling-Based Pure-Integer Quantization for Low-Power Accelerator of Complex CNNs
Authors: Al-Hamid Ali A. , Kim HyungWon
Published: June 2023

Although optimizing deep neural networks is becoming crucial for deploying the networks on edge AI devices, it faces increasing challenges due to scarce hardware resources in modern IoT and mobile devices. This study proposes a quantization method that can quantize all internal computations and parameters in the memory modification. Unlike most previous methods that primarily focused on relatively simple CNN models for image classification, the proposed method, Unified Scaling-Based Pure-Integer Quantization (USPIQ), can handle more complex CNN models for object detection. USPIQ aims to provide a systematic approach to convert all floating-point operations to pure-integer operations in every model layer. It can significantly reduce the computational overhead and make it more suitable for low-power neural network accelerator hardware consisting of pure-integer datapaths and small memory aimed at low-power consumption and small chip size. The proposed method optimally calibrates the scale parameters for each layer using a subset of unlabeled representative images. Furthermore, we introduce a notion of the Unified Scale Factor (USF), which combines the conventional two-step scaling processes (quantization and dequantization) into a single process for each layer. As a result, it improves the inference speed and the accuracy of the resulting quantized model. Our experiment on YOLOv5 models demonstrates that USPIQ can significantly reduce the on-chip memory for parameters and activation data by ~75% and 43.68%, respectively, compared with the floating-point model. These reductions have been achieved with a minimal loss in mAP@0.5—at most 0.61%. In addition, our proposed USPIQ exhibits a significant improvement in the inference speed compared to ONNX Run-Time quantization, achieving a speedup of 1.64 to 2.84 times. We also demonstrate that USPIQ outperforms the previous methods in terms of accuracy and hardware reduction for 8-bit quantization of all YOLOv5 versions.
Recommender System Metaheuristic for Optimizing Decision-Making Computation
Authors: Bajenaru Victor , Lavoie Steven , Benyo Brett , Riker Christopher , Colby Mitchell , Vaccaro James
Published: June 2023

We implement a novel recommender system (RS) metaheuristic framework within a nonlinear NP-hard decision-making problem, for reducing the solution search space before high-burden computational steps are performed. Our RS-based metaheuristic supports consideration of comprehensive evaluation criteria, including estimations of the potential solution set’s optimality, diversity, and feedback/preference of the end-user, while also being fully compatible with additional established RS evaluation metrics. Compared to prior Operations Research metaheuristics, our RS-based metaheuristic allows for (1) achieving near-optimal solution scores through comprehensive deep learning training, (2) fast metaheuristic parameter inference during solution instantiation trials, and (3) the ability to reuse this trained RS module for traditional RS ranking of final solution options for the end-user. When implementing this RS metaheuristic within an experimental high-dimensionality simulation environment, we see an average 91.7% reduction in computation time against a baseline approach, and solution scores within 9.1% of theoretical optimal scores. A simplified RS metaheuristic technique was also developed in a more realistic decision-making environment dealing with multidomain command and control scenarios, where a significant computation time reduction of 87.5% is also achieved compared with a baseline approach, while maintaining solution scores within 9.5% of theoretical optimal scores.
Explainable Feature Extraction and Prediction Framework for 3D Image Recognition Applied to Pneumonia Detection
Authors: Pintelas Emmanuel , Livieris Ioannis E. , Pintelas Panagiotis
Published: June 2023

Explainable machine learning is an emerging new domain fundamental for trustworthy real-world applications. A lack of trust and understanding are the main drawbacks of deep learning models when applied to real-world decision systems and prediction tasks. Such models are considered as black boxes because they are unable to explain the reasons for their predictions in human terms; thus, they cannot be universally trusted. In critical real-world applications, such as in medical, legal, and financial ones, an explanation of machine learning (ML) model decisions is considered crucially significant and mandatory in order to acquire trust and avoid fatal ML bugs, which could disturb human safety, rights, and health. Nevertheless, explainable models are more than often less accurate; thus, it is essential to invent new methodologies for creating interpretable predictors that are almost as accurate as black-box ones. In this work, we propose a novel explainable feature extraction and prediction framework applied to 3D image recognition. In particular, we propose a new set of explainable features based on mathematical and geometric concepts, such as lines, vertices, contours, and the area size of objects. These features are calculated based on the extracted contours of every 3D input image slice. In order to validate the efficiency of the proposed approach, we apply it to a critical real-world application: pneumonia detection based on CT 3D images. In our experimental results, the proposed white-box prediction framework manages to achieve a performance similar to or marginally better than state-of-the-art 3D-CNN black-box models. Considering the fact that the proposed approach is explainable, such a performance is particularly significant.
The Reality of Internet Infrastructure and Services Defacement: A Second Look at Characterizing Web-Based Vulnerabilities
Authors: Albalawi Neaimh , Alamrani Norah , Aloufi Rasha , Albalawi Mariam , Aljaedi Amer , Alharbi Adel R.
Published: June 2023

In recent years, the number of people using the Internet has increased worldwide, and the use of web applications in many areas of daily life, such as education, healthcare, finance, and entertainment, has also increased. On the other hand, there has been an increase in the number of web application security issues that directly compromise the confidentiality, availability, and integrity of data. One of the most widespread web problems is defacement. In this research, we focus on the vulnerabilities detected on the websites previously exploited and distorted by attackers, and we show the vulnerabilities discovered by the most popular scanning tools, such as OWASP ZAP, Burp Suite, and Nikto, depending on the risk from the highest to the lowest. First, we scan 1000 URLs of defaced websites by using three web application assessment tools (OWASP ZAP, Burp Suite, and Nikto) to detect vulnerabilities which should be taken care of and avoided when building and structuring websites. Then, we compare these tools based on their performance, scanning time, the names and number of vulnerabilities, and the severity of their impact (high, medium, low). Our results show that Burp Suite Professional has the highest number of vulnerabilities, while Nikto has the highest scanning speed. Additionally, the OWASP ZAP tool is shown to have medium- and low-level alerts, but no high-level alerts. Moreover, we detail the best and worst uses of these tools. Furthermore, we discuss the concept of Domain Name System (DNS), how it can be attacked in the most common ways, such as poisoning, DDOS, and DOS, and link it to our topic on the basis of the importance of its infrastructure and how it can be the cause of hacking and distorting sites. Moreover, we introduce the tools used for DNS monitoring. Finally, we give recommendations about the importance of security in the community and for programmers and application developers. Some of them do not have enough knowledge about security, which allow vulnerabilities to occur.
An Improved Unscented Kalman Filtering Combined with Feature Triangle for Head Position Tracking
Authors: Yu Xiaoyu , Zhang Yan , Wu Haibin , Wang Aili
Published: June 2023

Aiming at the problem of feature point tracking loss caused by large head rotation and facial occlusion in doctors, this paper designs a head-position-tracking system based on geometric triangles and unscented Kalman filtering. By interconnecting the three feature points of the left and right pupil centers and the tip of the nose, they form a coplanar triangle. When the posture of the doctor’s head changes due to rotation, the shape of the corresponding geometric triangle will also deform. Using the inherent laws therein, the head posture can be estimated based on changes in the geometric model. Due to the inaccurate positioning of feature points caused by the deflection of the human head wearing a mask, traditional linear Kalman filtering algorithms are difficult to accurately track feature points. This paper combines geometric triangles with an unscented Kalman Filter (UKF) to obtain head posture, which has been fully tested in different environments, such as different faces, wearing/not wearing masks, and dark/bright light via public and measured datasets. The final experimental results show that compared to the linear Kalman filtering algorithm with a single feature point, the traceless Kalman filtering algorithm combined with geometric triangles in this paper not only improves the robustness of nonlinear angle of view tracking but also can provide more accurate estimates than traditional Kalman filters.
Study of Ground Plane Effects on Monopole Antenna Performance
Authors: Gao Pengjian , Li Jia , Wang Weibing
Published: June 2023

With the continuous development of the IoT, compact wireless communication modules have become indispensable components, and their antennas are gradually being developed from external devices into onboard integrated devices. The serpentine antenna, a variant of the monopole antenna known for its small size and easy integration, is often applied to engineering practices. However, its performance has always been closely affected by the size of the surrounding grounding plane. By conducting a characteristic mode analysis (CMA), this study explored the variation patterns in the ground plane size and the resonant frequency. Based on the simulation results, it was clear that when the ground plane size is less than a quarter of the working wavelength, the ground plane will have a significant effect on the antenna’s resonant frequency. Thus, this study further analyzed a serpentine antenna with a grounding branch, and through analysis of the basic law of the influence of grounding structure on the antenna’s performance, we found that by adjusting the branch length, the matching performance of the antenna can be effectively improved. Furthermore, by changing the size of the ground plate, the antenna’s resonant frequency can be adjusted. Such a conclusion will hopefully provide a reference for future designs of integrated antennas in engineering applications.
Ship Detection in PolSAR Images Based on a Modified Polarimetric Notch Filter
Authors: Zhou Xiangyu , Li Tao
Published: June 2023

Ship detection based on synthetic aperture radar (SAR) imagery is one of the key applications for maritime security. Compared with single-channel SAR images, polarimetric SAR (PolSAR) data contains the fully-polarized information, which better facilitates better discriminating between targets, sea clutter, and interference. Therefore, many ship detection methods based on the polarimetric scattering mechanism have been studied. To deal with the false alarms caused by the existence of ghost targets, resulting from azimuth ambiguities and interference from side lobes, a modified polarimetric notch filter (PNF) is proposed for PolSAR ship detection. In the proposed method, the third eigenvalue obtained by the eigenvalue–eigenvector decomposition of the polarimetric covariance matrix is utilized to construct a new feature vector. Then, the target power can be computed to construct the modified PNF detector. On the one hand, the detection rate of ship targets can be enhanced by target-to-clutter contrast. On the other hand, false alarms resulting from azimuth ambiguities and side lobes can be reduced to an extent. Experimental results based on three C-band AIRSAR PolSAR datasets demonstrated the capability of the proposed PNF detector to improve detection performance while reducing false alarms. To be specific, the figure of merit (FoM) of the proposed method is the highest among comparative approaches with results of 80%, 100%, and 100% for the tested datasets, respectively.
Fast Mode Decision Method of Multiple Weighted Bi-Predictions Using Lightweight Multilayer Perceptron in Versatile Video Coding
Authors: Lee Taesik , Jun Dongsan
Published: June 2023

Versatile Video Coding (VVC), the state-of-the-art video coding standard, was developed by the Joint Video Experts Team (JVET) of ISO/IEC Moving Picture Experts Group (MPEG) and ITU-T Video Coding Experts Group (VCEG) in 2020. Although VVC can provide powerful coding performance, it requires tremendous computational complexity to determine the optimal mode decision during the encoding process. In particular, VVC adopted the bi-prediction with CU-level weight (BCW) as one of the new tools, which enhanced the coding efficiency of conventional bi-prediction by assigning different weights to the two prediction blocks in the process of inter prediction. In this study, we investigate the statistical characteristics of input features that exhibit a correlation with the BCW and define four useful types of categories to facilitate the inter prediction of VVC. With the investigated input features, a lightweight neural network with multilayer perceptron (MLP) architecture is designed to provide high accuracy and low complexity. We propose a fast BCW mode decision method with a lightweight MLP to reduce the computational complexity of the weighted multiple bi-prediction in the VVC encoder. The experimental results show that the proposed method significantly reduced the BCW encoding complexity by up to 33% with unnoticeable coding loss, compared to the VVC test model (VTM) under the random-access (RA) configuration.
The Implementation of Interactive VR Application and Caching Strategy Design on Mobile Edge Computing (MEC)
Authors: Chuang Shu-Min , Chen Chia-Sheng , Wu Eric Hsiao-Kuang
Published: June 2023

Virtual reality (VR) and augmented reality (AR) have been proposed as revolutionary applications for the next generation, especially in education. Many VR applications have been designed to promote learning via virtual environments and 360° video. However, due to the strict requirements of end-to-end latency and network bandwidth, numerous VR applications using 360° video streaming may not achieve a high-quality experience. To address this issue, we propose relying on tile-based 360° video streaming and the caching capacity in Mobile Edge Computing (MEC) to predict the field of view (FoV) in the head-mounted device, then deliver the required tiles. Prefetching tiles in MEC can save the bandwidth of the backend link and support multiple users. Smart caching decisions may reduce the memory at the edge and compensate for the FoV prediction error. For instance, caching whole tiles at each small cell has a higher storage cost compared to caching one small cell that covers multiple users. In this paper, we define a tile selection, caching, and FoV coverage model as the Tile Selection and Caching Problem and propose a heuristic algorithm to solve it. Using a dataset of real users’ head movements, we compare our algorithm to the Least Recently Used (LRU) and Least Frequently Used (LFU) caching policies. The results show that our proposed approach improves FoV coverage by 30% and reduces caching costs by 25% compared to LFU and LRU.
Scalable Communication-Induced Checkpointing Protocol with Little Overhead for Distributed Computing Environments
Authors: Ahn Jinho
Published: June 2023

The existing communication-induced checkpointing protocols may not scale well due to their slow acquisition of the most recent timestamps of the next checkpoints of other processes. Accurate situation awareness with diversified information conveyance paths is needed to reduce the number of unnecessary forced checkpoints taken as few as possible. In this paper, a scalable communication-induced checkpointing protocol is proposed to considerably cut down the possibility of performing unnecessary forced checkpointing by exploiting the beneficial features of reliable communication channels. The protocol enables the sender of an application message to swiftly attain the most recent timestamp-related information of the next checkpoint of its receiver and accelerate the spread of the information to others, with little overhead. This behavioral feature may significantly elevate the accuracy of the awareness of the situations in which forced checkpointing is actually needed for useless checkpoint-free recovery. In addition, it generates no extra control message and no message logging overhead while significantly lessening the latency of message sending. Moreover, the protocol can always be operated under the non-deterministic execution model. The evaluation results indicate that the proposed protocol outperforms the existing ones at the reduced forced checkpointing overheads from 12.5% to 84.2%, and at the reduced total execution times from 2.5% to 11.5%.