bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessing the Role of Nrf2/GPX4-Mediated Oxidative Stress in Arsenic-Induced Liver Damage and the Potential Application Value of Rosa roxburghii Tratt [Rosaceae]
Authors: Xu Yuyan , Zeng Qibing , Sun Baofei , Wei Shaofeng , Wang Qingling , Zhang Aihua
Published: April 2022

Arsenic poisoning is a geochemical disease that seriously endangers human health. The liver is one of the important target organs for arsenic poisoning, several studies have shown that oxidative stress plays an important role in arsenic-induced liver damage. However, the specific mechanism of arsenic-induced oxidative stress has not yet been fully elucidated, and currently, there are no effective intervention measures for the prevention and treatment of arsenic-induced liver damage. In this study, the effect of the Nrf2/GPX4 signaling pathway and oxidative stress in the arsenic-induced liver damage was first evaluated. The results show that arsenic can activate the Nrf2/GPX4 signaling pathway and increase the oxidative stress, which in turn promotes arsenic-induced liver damage in MIHA cells. Moreover, when we applied the Nrf2 inhibitor, the promoting effect of arsenic on liver damage was alleviated by inhibiting the activation of the Nrf2/GPX4 signaling pathway. Subsequently, the Rosa roxburghii Tratt [Rosaceae] (RRT) intervention experiments in cells and arsenic poisoning population were designed. The results revealed that RRT can inhibit Nrf2/GPX4 signaling pathway to reduce oxidative stress, thereby alleviates arsenic-induced liver damage. This study provides some limited evidence that arsenite can activate Nrf2/GPX4 signaling pathway to induce oxidative stress, which in turn promotes arsenic-induced liver damage in MIHA cells. The second major finding was that Kaji-ichigoside F1 may be a potential bioactive compound of RRT, which can inhibit Nrf2/GPX4 signaling pathway to reduce oxidative stress, thereby alleviates arsenic-induced liver damage. Our study will contribute to a deeper understanding of the mechanisms in arsenic-induced liver damage, these findings will identify a possible natural medicinal food dual-purpose fruit, RRT, as a more effective prevention and control strategies for arsenic poisoning.
Influence of Cultural Differences on the Establishment of Consumer Trust in a Socialized Cross-Border E-Commerce
Authors: Huang Yuwen
Published: April 2022

This research paper examines the necessity of identifying cultural issues in online globalization and emphasizes that payment and logistic systems, as well as language, are the most important variables that any company should consider during their online globalization process. With the rapid development of the network and the continuous integration of the world economy, cross-border e-commerce has become more important and convenient. However, there are a number of electronic transaction problems that hinder the smooth functioning of this concept, thus creating distrust between the parties to the transaction, such as non-delivery, inaccurate quantities of goods, fraudulent transactions and low-quality goods, which are related to cultural differences, quality problems of the goods themselves and many other aspects. There is huge room for cross-border e-commerce development, and there are trust problems such as fraud and false transactions in all development processes. Based on the factors of cultural differences and consumer trust, this paper establishes an evaluation model of consumer trust in cross-border e-commerce, including honesty, goodwill, and ability. This article uses deep learning to establish a credit evaluation model, analyzes credit evaluation indicators, and obtains the factors that affect cross-border trade under different cultures through experimental testing and analysis.
Linking Genotype to Phenotype: Bench to Bedside
Authors: George Suzanne , Bertagnolli Monica M.
Published: April 2022

Over the past three decades, researchers in the NCI-funded cancer cooperative groups have routinely incorporated a collection of biospecimens, quality-of-life assessments, diet and physical activity data, and other health outcome variables from clinical trial participants to provide an expanding resource for correlative science in cancer clinical research.

See related articles by Nixon et al., p. 2771 and 2779
Peripheral Blood Monocyte Abundance Predicts Outcomes in Patients with Breast Cancer
Authors: Axelrod Margaret L. , Wang Yu , Xu Yaomin , Sun Xiaopeng , Bejan Cosmin A. , Gonzalez-Ericsson Paula I. , Nunnery Sara , Bergman Riley E. , Donaldson Joshua , Guerrero-Zotano Angel L. , Massa Chiara , Seliger Barbara , Sanders Melinda , Mayer Ingrid A. , Balko Justin M.
Published: April 2022

Biomarkers of response are needed in breast cancer to stratify patients to appropriate therapies and avoid unnecessary toxicity. We used peripheral blood gene expression and cell-type abundance to identify biomarkers of response and recurrence in neoadjuvant chemotherapy–treated patients with breast cancer. We identified a signature of IFN and complement response that was higher in the blood of patients with pathologic complete response. This signature was preferentially expressed by monocytes in single-cell RNA sequencing. Monocytes are routinely measured clinically, enabling examination of clinically measured monocytes in multiple independent cohorts. We found that peripheral monocytes were higher in patients with good outcomes in four cohorts of patients with breast cancer. Blood gene expression and cell type abundance biomarkers may be useful for prognostication in breast cancer.

Significance:

Biomarkers are needed in breast cancer to identify patients at risk for recurrence. Blood is an attractive site for biomarker identification due to the relative ease of longitudinal sampling. Our study suggests that blood-based gene expression and cell-type abundance biomarkers may have clinical utility in breast cancer.
Literature Review of the Efficacy of High-Volume Hemofiltration in Critically Ill Pediatric Patients
Authors: Bhatt Girish Chandra , Sethi Sidharth Kumar , Mehta Ira , Nair Nikhil , Chakraborty Ronith , Sharma Bhavya , Singh Siddhartha , Kumar Nikhita , Gulati Kabir , Raina Rupesh
Published: April 2022

Background: Pediatric sepsis is a significant public health issue. This condition is exacerbated by rising serum creatinine and inflammatory cytokines that lead to deleterious effects upon the body. The current standard of care involves the use of continuous kidney replacement therapy to remove harmful cytokines until the body returns to homeostasis. In order to promote faster clearance and reduced stay in the ICU, high-volume hemofiltration (HVHF) has shown promise. However, there is a paucity of studies to fully elucidate its benefits. Methods: A literature search was done using PubMed/ MEDLINE and Embase. The literature was reviewed by two independent reviewers, who independently assessed the quality of randomized controlled trials by using the Cochrane risk of bias tool for RCTs and Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomized controlled trials. Data were combined from studies with a similar design. Results: The primary endpoint of all-cause mortality was found to be reduced by 40% across all of the pooled studies. For secondary endpoints, significant reductions of serum creatinine were found. Additionally, duration of ICU stays and treatment course was found to be significantly shorter in HVHF patients than the current standard of care. The rate of adverse effects was analyzed, and there was no difference in the proportion of patients developing hypokalemia, hyperkalemia, hypernatremia, or hyponatremia. The proportion of patients developing hyperglycemia was higher in patients undergoing HVHF, whereas the proportions of patients developing bleeding were significantly less in patients undergoing HVHF. One study reported a total number of adverse events between the two groups which were significantly lesser in patients undergoing HVHF. Conclusion: HVHF shows promise as a modality to treat pediatric patients with sepsis. In order to confirm the benefits of this modality, future studies need significantly more patients for analysis.
Senolytics Cocktail Dasatinib and Quercetin Alleviate Human Umbilical Vein Endothelial Cell Senescence via the TRAF6-MAPK-NF-κB Axis in a YTHDF2-Dependent Manner
Authors: Fan Ting , Du Yi , Zhang Mingwan , Zhu Austin Rui , Zhang Jianjun
Published: April 2022

Introduction: Senescent cells play a key role in the initiation and development of various age-related diseases. Human umbilical vein endothelial cells (HUVECs) senescence is closely associated with age-related cardiovascular diseases. Accumulating evidence has demonstrated that senolytics, the combination of dasatinib and quercetin (D+Q), could selectively eliminate senescent cells. N6-methyladenosine (m6A), the most abundant internal transcript modification, greatly influences RNA metabolism and modulates gene expression. We aimed to investigate whether RNA m6A functions in lipopolysaccharide (LPS)-induced HUVECs senescence and D+Q suppress HUVECs senescence by regulating RNA m6A. Methods: Senescence-associated β-galactosidase activity, western blot, and real-time quantitative polymerase chain reaction were performed to demonstrate that D+Q suppress HUVECs senescence. Methylated RNA immunoprecipitation (MeRIP)-qPCR assay and RIP-qPCR confirmed that RNA m6A plays a key role in the suppression of HUVECs senescence by D+Q. Chromatin immunoprecipitation and mRNA stability assay were carried out to prove that D+Q alleviate HUVECs senescence in a YTHDF2-dependent manner. Results: Here, we demonstrate that D+Q alleviate LPS-induced senescence in HUVECs via inhibiting autocrine and paracrine of the senescence-associated secretory phenotype (SASP). We further confirm that D+Q alleviate HUVECs senescence via the TNF receptor-associated factor 6 (TRAF6)-MAPK pathway. Mechanically, this study validates that D+Q suppress SASP by upregulating m6A reader YTHDF2. Besides, YTHDF2 regulates the stability of MAP2K4 and MAP4K4 mRNAs. Conclusion: Collectively, we first identified that D+Q alleviate LPS-induced senescence in HUVECs via the TRAF6-MAPK-NF-κB axis in a YTHDF2-dependent manner, providing novel ideas for clinical treatment of age-related cardiovascular diseases.
Ureteral Stenosis and Fistula after Kidney Transplantation
Authors: Santos Pérez de la Blanca Rocío , Medina-Polo Jose , Peña-Vallejo Helena , Juste-Álvarez Silvia , Pamplona-Casamayor Manuel , Duarte-Ojeda José Manuel , Miranda Utrera Natalia , García-González Lucía , Arrébola-Pajares Ana , Rodríguez Antolín Alfredo , Tejido-Sánchez Ángel
Published: April 2022

Introduction: Ureteral complications after kidney transplantation are frequent and may have a negative impact on morbidity and graft function. Treatment modalities include conservative, endourological, and surgical techniques, with variable outcomes. The purpose of this study was to report the incidence, characteristics, treatment, and outcomes of ureteral complications at our center. Methods: Retrospective study of kidney transplants performed at our unit between 2015 and 2020, analyzing incidence, characteristics, treatment, and outcomes of ureteral stenoses and fistulas. Results: Of 648 kidney transplants, we present 3.24% stenosis and 2.16% ureteral fistulas, with a mean time from transplantation of 101.4 and 24.4 days, respectively. Primary treatment was open surgical repair in 52.4% stenosis and 100% fistulas, with a success rate of 90.9% and 71.4%, respectively. Anterograde balloon dilatations were performed in 33.3% of stenosis with 40% success. Three patients required surgery as a secondary approach with 100% success. Major complications (Clavien-Dindo III) were observed in 18.5% following surgical repair. After a mean follow-up of 31.1 ± 20.9 months, we observe 88.6% of functioning grafts. We found no significant differences in graft survival between patients with or without ureteral complications (p 0.948). Conclusion: Surgical repair of ureteral complications offers satisfactory results with low associated morbidity. Endourological techniques are less effective and should be reserved for selected cases. With adequate management, there is no impact on graft survival.
Having a Good Time Together: The Role of Companionship in Older Couples’ Everyday Life
Authors: Lüscher Janina , Pauly Theresa , Gerstorf Denis , Stadler Gertraud , Ashe Maureen C. , Madden Kenneth M. , Hoppmann Christiane A.
Published: April 2022

Introduction: Companionship (i.e., enjoyable shared activities) is associated with higher emotional and relational well-being. However, the role of companionship for emotional well-being and relationship satisfaction in older couples’ everyday life is not well understood. This article studies time-varying associations of companionship with emotional and relational well-being as older couples engage in their everyday life. Methods: Participants provided three data points a day over 7 days using electronic surveys that were simultaneously completed by both partners. A total of 118 older heterosexual couples reported momentary companionship, positive and negative affect, and closeness. Data were analyzed using an intensive longitudinal dyadic score model. Results: Couples with higher average companionship showed lower overall negative affect, more overall positive affect, and higher overall closeness. During moments of elevated momentary companionship, partners reported more positive affect, less negative affect, and higher closeness. Regarding between-couple partner differences, i.e., when the female partner’s momentary companionship was higher on average than the male partner’s momentary companionship, the female partner also showed less negative affect, more positive affect, and higher closeness than the male partner. During moments in which the female partner’s momentary companionship was higher than the male partner’s momentary companionship, the female partner showed less negative affect, more positive affect, and higher closeness than the male partner. Discussion: Older couples show a consistent link between companionship and emotional well-being and closeness in everyday life emphasizing the importance of studying companionship in close relationships.
Sex-Specific Immune Responses in Stroke
Authors: Banerjee Anik , McCullough Louise D.
Published: April 2022

In both acute and chronic diseases, functional differences in host immune responses arise from a multitude of intrinsic and extrinsic factors. Two of the most important factors affecting the immune response are biological sex and aging. Ischemic stroke is a debilitating disease that predominately affects older individuals. Epidemiological studies have shown that older women have poorer functional outcomes compared with men, in part due to the older age at which they experience their first stroke and the increased comorbidities seen with aging. The immune response also differs in men and women, which could lead to altered inflammatory events that contribute to sex differences in poststroke recovery. Intrinsic factors including host genetics and chromosomal sex play a crucial role both in shaping the host immune system and in the neuroimmune response to brain injury. Ischemic stroke leads to altered intracellular communication between astrocytes, neurons, and resident immune cells in the central nervous system. Increased production of cytokines and chemokines orchestrate the infiltration of peripheral immune cells and promote neuroinflammation. To maintain immunosurveillance, the host immune and central nervous system are highly regulated by a diverse population of immune cells which are strategically distributed within the neurovascular unit and become activated with injury. In this review, we provide a comprehensive overview of sex-specific host immune responses in ischemic stroke.
Searching for Computer Vision North Stars
Authors: Fei-Fei Li , Krishna Ranjay
Published: April 2022

Computer vision is one of the most fundamental areas of artificial intelligence research. It has contributed to the tremendous progress in the recent deep learning revolution in AI. In this essay, we provide a perspective of the recent evolution of object recognition in computer vision, a flagship research topic that led to the breakthrough data set of ImageNet and its ensuing algorithm developments. We argue that much of this progress is rooted in the pursuit of research “north stars,” wherein researchers focus on critical problems of a scientific discipline that can galvanize major efforts and groundbreaking progress. Following the success of ImageNet and object recognition, we observe a number of exciting areas of research and a growing list of north star problems to tackle. This essay recounts the brief history of ImageNet, its related work, and the follow-up progress. The goal is to inspire more north star work to advance the field, and AI at large.
Human Language Understanding & Reasoning
Authors: Manning Christopher D.
Published: April 2022

The last decade has yielded dramatic and quite surprising breakthroughs in natural language processing through the use of simple artificial neural network computations, replicated on a very large scale and trained over exceedingly large amounts of data. The resulting pretrained language models, such as BERT and GPT-3, have provided a powerful universal language understanding and generation base, which can easily be adapted to many understanding, writing, and reasoning tasks. These models show the first inklings of a more general form of artificial intelligence, which may lead to powerful foundation models in domains of sensory experience beyond just language.
The Curious Case of Commonsense Intelligence
Authors: Choi Yejin
Published: April 2022

Commonsense intelligence is a long-standing puzzle in AI. Despite considerable advances in deep learning, AI continues to be narrow and brittle due to its lack of common sense. Why is common sense so trivial for humans but so hard for machines? In this essay, I map the twists and turns in recent research adventures toward commonsense AI. As we will see, the latest advances on common sense are riddled with new, potentially counterintuitive perspectives and questions. In particular, I discuss the significance of language for modeling intuitive reasoning, the fundamental limitations of logic formalisms despite their intellectual appeal, the case for on-the-fly generative reasoning through language, the continuum between knowledge and reasoning, and the blend between symbolic and neural knowledge representations.
Toward a Theory of Justice for Artificial Intelligence
Authors: Gabriel Iason
Published: April 2022

This essay explores the relationship between artificial intelligence and principles of distributive justice. Drawing upon the political philosophy of John Rawls, it holds that the basic structure of society should be understood as a composite of sociotechnical systems, and that the operation of these systems is increasingly shaped and influenced by AI. Consequently, egalitarian norms of justice apply to the technology when it is deployed in these contexts. These norms entail that the relevant AI systems must meet a certain standard of public justification, support citizens' rights, and promote substantively fair outcomes, something that requires particular attention to the impact they have on the worst-off members of society.
Artificial Intelligence, Humanistic Ethics
Authors: Tasioulas John
Published: April 2022

Ethics is concerned with what it is to live a flourishing life and what it is we morally owe to others. The optimizing mindset prevalent among computer scientists and economists, among other powerful actors, has led to an approach focused on maximizing the fulfilment of human preferences, an approach that has acquired considerable influence in the ethics of AI. But this preference-based utilitarianism is open to serious objections. This essay sketches an alternative, “humanistic” ethics for AI that is sensitive to aspects of human engagement with the ethical often missed by the dominant approach. Three elements of this humanistic approach are outlined: its commitment to a plurality of values, its stress on the importance of the procedures we adopt, not just the outcomes they yield, and the centrality it accords to individual and collective participation in our understanding of human well-being and morality. The essay concludes with thoughts on how the prospect of artificial general intelligence bears on this humanistic outlook.
Distrust of Artificial Intelligence: Sources & Responses from Computer
Science & Law
Authors: Dwork Cynthia , Minow Martha
Published: April 2022

Social distrust of AI stems in part from incomplete and faulty data sources, inappropriate redeployment of data, and frequently exposed errors that reflect and amplify existing social cleavages and failures, such as racial and gender biases. Other sources of distrust include the lack of “ground truth” against which to measure the results of learned algorithms, divergence of interests between those affected and those designing the tools, invasion of individual privacy, and the inapplicability of measures such as transparency and participation that build trust in other institutions. Needed steps to increase trust in AI systems include involvement of broader and diverse stakeholders in decisions around selection of uses, data, and predictors; investment in methods of recourse for errors and bias commensurate with the risks of errors and bias; and regulation prompting competition for trust.
Democracy & Distrust in an Era of Artificial Intelligence
Authors: Katyal Sonia K.
Published: April 2022

Our legal system has historically operated under the general view that courts should defer to the legislature. There is one significant exception to this view: cases in which it appears that the political process has failed to recognize the rights or interests of minorities. This basic approach provides much of the foundational justifications for the role of judicial review in protecting minorities from discrimination by the legislature. Today, the rise of AI decision-making poses a similar challenge to democracy's basic framework. As I argue in this essay, the rise of three trends-privatization, prediction, and automation in AI - have combined to pose similar risks to minorities. In this essay, I outline what a theory of judicial review would look like in an era of artificial intelligence, analyzing both the limitations and the possibilities of judicial review of AI. Here, I draw on cases in which AI decision-making has been challenged in courts, to show how concepts of due process and equal protection can be recuperated in a modern AI era, and even integrated into AI, to provide for better oversight and accountability.
Rethinking AI for Good Governance
Authors: Margetts Helen
Published: April 2022

This essay examines what AI can dofor government, specifically through three generic tools at the heart of governance: detection, prediction, and data-driven decision-making. Public sector functions, such as resource allocation and the protection of rights, are more normatively loaded than those of firms, and AI poses greater ethical challenges than earlier generations of digital technology, threatening transparency, fairness, and accountability. The essay discusses how AI might be developed specifically for government, with a public digital ethos to protect these values. Three moves that could maximize the transformative possibilities for a distinctively public sector AI are the development of government capacity to foster innovation through AI; the building of integrated and generalized models for policy-making; and the detection and tackling of structural inequalities. Combined, these developments could offer a model of data-intensive government that is more efficient, ethical, fair, prescient, and resilient than ever before in administrative history.
Luminance Contrast Drives Interactions between Perception and Working Memory
Authors: Kosilo Maciej , Martinovic Jasna , Haenschel Corinna
Published: April 2022

Visual working memory (WM) enables the use of past sensory experience in guiding behavior. Yet, laboratory tasks commonly evaluate WM in a way that separates it from its sensory bottleneck. To understand how perception interacts with visual memory, we used a delayed shape recognition task to probe how WM may differ for stimuli that bias processing toward different visual pathways. Luminance compared with chromatic signals are more efficient in driving the processing of shapes and may thus also lead to better WM encoding, maintenance, and memory recognition. To evaluate this prediction, we conducted two experiments. In the first psychophysical experiment, we measured contrast thresholds for different WM loads. Luminance contrast was encoded into WM more efficiently than chromatic contrast, even when both sets of stimuli were equated for discriminability. In the second experiment, which also equated stimuli for discriminability, early sensory responses in the EEG that are specific to luminance pathways were modulated by WM load and thus likely reflect the neural substrate of the increased efficiency. Our results cannot be accounted for by simple saliency differences between luminance and color. Rather, they provide evidence for a direct connection between low-level perceptual mechanisms and WM by showing a crucial role of luminance for forming WM representations of shape.
Design of AI Digital Signage System Through User Hierarchy Analysis Based on Deep Learning
Authors: Kim Hyeong-Gyun
Published: April 2022

Existing digital signage advertisements send advertisements to unspecified people, so there is a limit to the advertisement exposure effect, and it is very difficult to analyze advertisement effects because several advertisements are repeatedly exposed for a certain period of time.
This paper proposes an AI digital signage system that automatically analyzes the user hierarchy based on deep learning and displays customized advertisements to users. For this, a system that learns the face of an ad viewer is built using a convolutional neural network. For preliminary training,
we used face images from Korean face image data, and layered them into six using the gender and age information provided. The facial features of these pictures were quantified using a convolutional neural network, and these values were stored in a database for learning. Real-time automatic
hierarchy was tested using the data set produced in this way, and the success rate was between 84% and 95% for each hierarchy. It is thought that a system with a higher success rate can be created if the user hierarchy learning model learns images of various face poses and collects and learns
data used in the automatic classification process through tracking.
Evaluation of the Usefulness of Intensity Modulated Radiation Therapy Using Jaw-Tracking Method
Authors: Kim Gha-Jung
Published: April 2022

This study aimed to compare the changes in the radiation dose to the normal tissue and tumor in prostate cancer patients between the jaw tracking and fixed-jaw method through radiation treatment planning. To compare the volume dose in the IMRT (Intensity Modulated Radiation Therapy)
plan according to the use of Jaw Tracking (JT), the HI (homogeneity index) and the CI (conformity index) for prostate cancer were compared based on the respective DVH (Dose Volume Histogram), and the volume doses of the bladder and rectum, as the normal organs in the vicinity of the prostate,
were analyzed. The Delivery Time (DT) and Monitor Unit (MU) values were analyzed to compare the radiotherapy efficiency of the IMRT plan. The mean HI and CI with Fixed Jaw (FJ) were 0.9849 and 1.0863, respectively, while those with Jaw tracking (JT) were 0.9847 and 1.0802, respectively. The
mean difference between the FJ and JT methods was 0.0002(CI) and 0.0061(HI). The dose evaluation result for the normal organs showed that the Dmean was 301.8 cGy in the rectum for JT, while the mean V20 and V30 were lower, at 2.8% and 1.1%, respectively. Also the dose evaluation result for
the normal organs showed that the Dmean was 46.9 cGy in the bladder for JT, while the mean V20 and V30 were lower, at 2.6% and 0.9%, respectively. The MU and delivery time were also shown to be lower, at 0.1 MU and 0.1 min, respectively, upon the use of the JT. Based on our findings, the use
of JT could reduce the mean dose and volume dose in normal organs, as well as the MU and delivery time, thereby improving the treatment efficiency.
A New Development of the Medical lnformation Management for the Treatment of Retinal Vascular Occlusions
Authors: Lee Seong-Ran
Published: April 2022

Retinal vascular occlusions are an emergency disease of ophthalmology that causes blood vessels in the eye to block or burst, causing vision loss or blindness. Therefore, this study attempts to develop new medical information management to treat retinal vascular occlusion. The data
were surveyed on 122 people who visited the ophthalmologist from May 6 to July 10, 2019. The survey was conducted by classifying 61 test respondents who mediated the information management and 61 comparative respondents who did not mediated. The basic information of participants was provided
using the X2 test. The approach for preventing retinal vascular occlusion before and after information management application was analyzed as t-test. A comparative analysis of overall eye conditions was performed with t-test. The participants’ practice
of eye health was analyzed with t-test. Findings: The derived data present the following. (1) The blinking eyes inner canthus were significantly higher after the attempt than before management (t = −5.83, p = .000). (2) The acupressure around the eyes was
significantly higher 43.62% after the management attempt, compared to the 14.93% before the attempt (t = −3.47, p = .000). (3) The eye movement was significantly higher after the attempt than before the attempt (t = −1.08, p = .000). (4) Renal vessel
has been confirmed that the retinal vessel is also improving significantly after care than before (p < .05). (5) In particular, retinal blood vessels have improved significantly since the 18th. However, since the 22nd, retinal blood vessels have shown a slight decrease. The above
findings pointed out that retinal vascular occlusion requires routine eye care. Therefore, this study found that medical information management applied to the experiment was effective in preventing retinal vascular occlusion.
Internet of Things Sensors and Data Analytics Based Decision Support System for Smart Greenhouse
Authors: Song Mi-Hwa
Published: April 2022

A decision support model and system is proposed based on Internet of Things (IoT) sensors and data analytic for smart greenhouse. The smart greenhouse system monitors multiple environmental factors around it, analyzes climatic and soil condition data, and automates the irrigation process.
We also create dashboards and applications that can monitor the sensed data and help users make decisions. The proposed system architecture model studies a practical method that can operate in a microprocessor, IoT sensor and wireless network environment to control a greenhouse. The mobile
application and service prototype are implemented and evaluated. The test was conducted in a general indoor space. Data measured in real time by various sensors notifies the smartphone or smart watch as needed. Automatic indoor gardening was implemented by operating the fan when indoor air
quality was deteriorated or the humidity was above the appropriate value. It was also confirmed that water was supplied through the automatic water pump according to the measured value of the soil sensor. Data monitoring of the system has been developed successfully. This system provides real-time
data and status of the output. The sensor will monitor the system and the controller will manage the all the system’s decision and activities automatically. This research was successfully conducted. However, some improvements can be done for future works. First, dissemination of green
computing techniques where IoT devices shall consume less amount of energy thus increase their life expectancy and increase the productivity. Second, Interoperability between IoT devices where devices should be capable to communicate with others from different genre so as the overall system.
Software Education Model for Artificial Intelligence Learning by Beginning Learners
Authors: Lee Ae-Ri
Published: April 2022

Recently, artificial intelligence technology is taking roots in various sectors as a core technology that will lead the knowledge and information society. Interest in Artificial Intelligence education is growing as it is time to prepare for Artificial Intelligence education in addition
to existing information literacy education. However, students who are not computer majors tend to perceive Artificial Intelligence technology as a difficult field, which is why a more efficient Artificial Intelligence education is needed. Therefore, this study reviewed educational programming
languages and Artificial Intelligence education methods and proposed an Artificial Intelligence education program that offers Artificial Intelligence learning based on the supervised learning theory of machine learning. As for learning suitability test of the program developed in this study,
the program was found to be valid as a result of expert validity analysis and it produced positive results when applied to learners. Based on these findings, the program is expected to be the basis of research on Artificial Intelligence education implementable for non-computer majors who are
beginning to learn Artificial Intelligence.
Line-Segment Feature Extraction Algorithm for Input Dimensionality Reduction in Multi-Layer Perceptron Model
Authors: Kim Chan-Min , Park Roy C. , Moon Mi-Kyung , Cho Dae-Soo , Kim Dong-Hyun
Published: April 2022

Background/Objectives: Today, the deep neural network has more intermediate layers along with learning data and has gradually become larger to draw a lower result due to the bound conventional neural network. Methods/Statistical analysis: Therefore, in this paper, we proposed
the line-segment feature extraction for input dimensionality reduction in pattern mining. The proposed algorithm extracts the line segment information, constituting the image of input data, and assigns a unique value to each segment using predefined filters. Using such unique values to identify
the number of line segments, create a one-dimensional vector, in the size of 256 or 512. Findings: This vector is used as the input data for a multi-layer perceptron (MLP). For performance evaluation, LFA was compared with principal component analysis (PCA). As a result, LFA-256 (at
96.81% accuracy) had −0.1% lower performance but a faster speed than PCA (96.91% accuracy), and LFA-512 (at 97.25% accuracy) had 0.35% higher performance than PCA. Improvements/Applications: We will study the recognition service possible to use in industry (mobile devices, PC,
edge computing, etc.) and real-life through this algorithm.
Algorithm for Handling Incomplete Data by Finding the Probability of Compensating the Missing Value with the C4.5 Classifier
Authors: Lee Jong Chan
Published: April 2022

Background/Objectives: This paper deals with the handling method of incomplete training data that is partially lost. It is unavoidable in a ubiquitous environment that deals with data collected from multiple devices or from a long distance, but it is difficult to expect good
results due to loss of information if the lost part is discarded. Methods/Statistical analysis: Various algorithms have been proposed to solve this problem. Among them, the algorithm for learning by changing the format of the training data to fit incomplete data has been applied to
various problems and has shown good results. This data format conversion method is called a data expansion technique, and has two characteristics: the importance can be adjusted differently for each event, and a probability value can be assigned to each cardinality of each variable. Findings:
The second feature, the ability to assign a probability value, was used to assign a compensation value for loss data. The first attempt to do this was to assign equal probability values to the loss values. In the classification algorithm using the entropy function, the variable containing
more loss values is not selected in the upper node. However, the method of allocating the equal value had the point that the original information is ignored. Therefore, a method of obtaining the entropy probability with complete information excluding the loss value and filling it in the loss
value was also proposed. This paper starts with the basic idea that this method is further developed and the lost information can be found in the area classified by the classification algorithm. In terms of implementation, the training data is divided into two parts: lost and non-lost events,
and then classified using the C4.5 classification algorithm with the data that is not lost, and each classification area is obtained. Then, by using the information remaining in the event of the loss data, the classification area is sequentially traversed according to a predetermined algorithm
to find the area closest to the loss event. And after expressing the value of this area as a probability, the loss value is replaced with this probability value, which means compensation for the loss value. Improvements/Applications: After compensating for the total loss values, the
recovered training data is learned with one of the SVM algorithms in order to evaluate the performance of how much information leakage has been preserved, and its performance is compared. As a result of the experiment with different degrees of loss for each variable, it was confirmed that
the loss of information can be minimized and used as a method of meaningful compensation.
Cybersecurity Policy and Risk Management for Non-Face Service in Coronavirus Disease 2019 (COVID-19) Environment
Authors: Hong Jin-Keun , Han Jung-Soo
Published: April 2022

What’s most important in the infrastructure environment of Corona virus (COVID)-19 is cybersecurity. Therefore, in this paper, we describe cybersecurity insights in the current situation of COVID-19 environment. We then focused on the cybersecurity management elements that must
be considered when supporting to non-face services in the COVID-19 environment. First of all, in this paper, we research about the related issues, including COVID-19 epidemic spread. This study research about these real-COVID-19 issues and focused on security management that should be checked
in terms of cybersecurity required for these environments. The factors derived as management points are the measures derived from the analysis of COVID-19 infection diseases situations and cases of cyber security attacks. For the purpose of research analysis, the characteristics of existing
COVID-19 statistical studies on the spread of infections, or studies on visualizing them, or tracking solutions, were analyzed. From this, the approach was chosen to derive and purpose the necessary security elements in terms of cybersecurity management. Various related issues, including the
spread of COVID-19 infectious diseases, include the study of predictive solutions to infection cases, the study of data collection and analysis related to infectious diseases, the study of human and robot interfaces and platforms to support non-face services, and the study of visual methods
for identifying the spread of COVID-19 infectious diseases. Therefore, this study approaches at these real COVID-19 issues and focuses on security management insights that should be checked in terms of cybersecurity required for these environments. Key security management points to be found
to support non-face-to-face services in the context of infection diseases of COVID-19 are cybercrime problems, security management, human or edge assets, and remote operations and home security management enhancements, enhanced monitoring in a safe operating environment, enhanced security
in the supply chain, enhanced security in cyber physical system, and enhanced security management in privacy. In addition, the need and urgency of practical risk management were recognized by focusing on the case, importance and characteristics of attacks in terms of risk management of cyber
security responding to the spread environment of COVID-19 infections. In this paper, security management elements were analyzed in COVID-19 environment. The managed items, that can help security managers check the main elements of what should be checked in the COVID-19 environment, were analyzed.
Focusing on realistic issues, the elements of security management and risk management can be derived and provided in the related security awareness. These factors are key elements that should be considered in security measures when implementing to non-face services.
Patent Litigation Research Trends and Trend Analysis
Authors: Kim Young-Ki , Park Seong-Taek
Published: April 2022

The purpose of this study is to provide useful points to companies by performing an analysis about patent litigation (dispute) trends that is recently coming to the fore as an important issue and also the solution measures. In this study, network analysis method will be used. For this,
data was collected from dissertation database and Twitter, and data preprocessing was conducted before performing the analysis. The data analysis was performed by applying a data analysis tool called R and a network analysis tool. First of all, the analysis results of Twitter showed that patent
and dispute, Nokia, litigation and patent, coronavirus, legal, and developments appeared as important factors because of the high weight value compared to other factors. The analysis results of Scopus showed that patent and litigation, infringement, system, payment, and settlement appeared
as important factors because of the high weight value compared to other factors. The results of this study can provide a practical guideline to the researchers that intend to carry out research about patent dispute and also to the persons in charge of company that need prior knowledge about
patent dispute.
E-Passport Advanced Security Technique Using Biometric Information Watermarking
Authors: Lee Yong-Joon , Na Won-Shik
Published: April 2022

There has been significant research in security of e-passport. E-passport combine the security technologies such as smartcard, PKI, and biometric, so that these technologies can prevent unauthorized copies and counterfeits. We propose an e-passport security technology in which biometric
watermarking is used to prevent the copy of biometric information in the e-passport. The biometric watermarking embeds the invisible date of acquisition into the original data so that the human visual system cannot perceive its invisibly watermarked information. Then the biometric sample,
having its unauthorized copy, is retrieved at the moment of reading the e-passport from the issuing database. The previous e-passport security technology placed an emphasis on both access control readers and anti-cloning chip features, and it is expected that the proposed feature, copy protection
of biometric information, will be demanded as the cases of biometric recognition to verify personal identity information has increased. This study proposed test results through issuing and deciphering of watermarks for e-passports, and it is expected that it can be used as standards for protecting
biometric information.
Sentimental Analysis of Multimedia Contents Using Convolutional Neural Networks-Based Adaptive Emotion Prediction
Authors: Chung Kyungyong , Kim Jin-Su
Published: April 2022

Sentimental analysis is a technique of analyzing subjective text data, such as people’s opinions, dispositions, and views, through natural language processing. Recently with the development of internet and mobile, users’ dispositions of various themes tend to be predicted
much. Users express their sentiments by using sentiment words, newly coined words, acronyms, and emoji, in order to present their dispositions. To predict users’ dispositions on the basis of their short texts, this study tries to analyze their described sentiments and judge whether their
sentiment words are positive or negative. In this study, a sentiment tries to be predicted in the way of collecting content opinions and extracting sentiment keywords through morphological analysis and the consideration of newly coined words including emoji and acronyms. In this case, learning
is processed in consideration of relevant content information, and an efficient sentiment prediction system is established with the application of weighting of the rating score to a representative keyword and with the use of deep learning. Through word embedding, a similarity between sentences
and whether a sentiment word is positive or negative is judged in sentimental analysis. Based on the sentiment information on multimedia content, it is possible to judge the polarity of a multimedia content opinion and thereby to create a bond of empathy.
The Taxi Information System Based on the Path Selection
Authors: Cho Hye Swong , Kim Dong Hyun
Published: April 2022

A Taxi is the favorite one of the public transmits most people like to ride. On the legacy system to call a taxi, the taxi responding the call request first picks up a passenger. However, since the passenger does not know any information about the driver or the taxi, it is possible
to occur disputes, such as fare, route to the destination, time, etc., between the passenger and the driver. To solve this problem, we propose the taxi information system to provide the data of the driver. In the presented system, a passenger locates taxis inside the specified distance from
the passenger before calling a taxi. After the passenger explores the searched results and decides the path to the destination, he sends the request to the specific driver he selects. During the service of the taxi, the off course alarm is notified to the passenger when the locations of the
passenger are off the path. We hope that the proposed system will improve the quality of service calling a taxi and provide the more opportunity deciding a taxi to the passenger.
Design and Implementation of a Summary Note System for Educational Video Contents
Authors: Kim Seon-Min , Cho Dae-Soo
Published: April 2022

Nowadays, video content consumption through video platforms is growing exponentially. In the education field, although video content is used more often than text content, but there are certain challenges. In this paper, we propose a system that can easily and efficiently create summary
notes for the video while playing educational content videos. The summary notes will include keyword-based text information, image information at specific points in the video, and video bookmark information based on video frame and play time. When playing a video, a user can repeat two kinds
of events at the moment a specific scene appears, capture a particular video frame in the video, and generate a keyword. The system provides functions for text extraction and to input of user comments that help organize the video information so that summary notes can be created more conveniently.
It can collectively organize the video information to lessen the user’s time summarizing video information. it is also possible to quickly search the contents of the summary note using keyword.