Reviewerly/exHarmony huggingface dataset:
Pemanfaatan Metode Fuzzy Logic Mamdani Dalam Menentukan Lokasi Terbaik Penjualan Coffee Shop Menggunakan Matlab

MSME business growth today continues to increase as stated on the www.indonesia.go.id website, MSME growth in 2021 reached 61.07 percent. So that it can help increase employment. One example of MSMEs is Coffee Shop which is increasingly mushrooming in certain areas. This contains business competition in the management of the coffee shop business must be the main calculation for business actors. Determining the right location will increase attractiveness for potential customers who will come, such as the location of crowds in the area such as close to lectures, in cities that have high mobility and so on, this needs to be carefully calculated in order to determine the right sales strategy. In determining it, a decision support system is needed that will help business actors in choosing a place of sale
How to Combine Expert Estimates? How to Estimate Probability in the Intersection of Two Populations?

In this paper, we consider two different practical problems that turned to be mathematically similar: (1) how to combine two expert-provided probabilities of some event and (2) how to estimate the frequency of a certain phenomenon (e.g., illness) in an intersection of two populations if we know the frequencies in each of these populations. In both cases, we use the maximum entropy approach to come up with a solution.
Computational Paradox of Deep Learning: A Qualitative Explanation

In general, the more unknowns in a problem, the more computational efforts is necessary to find all these unknowns. Interestingly, in state-of-the-art machine learning methods like deep learning, computations become easier when we increase the number of unknown parameters way beyond the number of equations. In this paper, we provide a qualitative explanation for this computational paradox.
Tracking missing persons using facial recognition

Finding missing people is a time-critical and labor-intensive task and the longer it takes to locate the person, the lower the likelihood of a successful outcome. To address this challenge, an integrated and centralized database of missing persons using Aadhar card details was developed. The approach incorporates facial recognition technology, specifically the deep face algorithm, which has shown high accuracy in identifying individuals. While facial recognition has been in use for several years, recent advancements have made it easier to identify individuals accurately. By leveraging Artificial Intelligence (AI) powered facial recognition technology, officials can enhance and streamline the process of finding, tracking, and retrieving missing persons. The system matches facial features with the data stored in Aadhar cards, providing a reliable means of identification. This research presents a system that centralizes data, improving the efficiency of locating missing individuals. By utilizing facial recognition and centralizing data, the system offers an efficient approach to find missing people. The integration of technology and data allows quick and more accurate identification, increasing the chances of locating missing persons promptly.
Development of anomaly detector for motor bearing condition monitoring using Fast Fourier Transform (FFT) and Long Short Term Memory (LSTM)-Autoencoder

Anomaly detection in motor bearings is a critical task for preventing downtime and ensuring efficient operation. This paper proposes a novel approach for anomaly detection using Fast Fourier Transform (FFT) and Long Short-Term Memory (LSTM)-Autoencoder (AE). A data processing approach based on FFT was developed to pre-process the raw sensor data. This helped to reduce noise and improve the Signal-to-Noise Ratio (SNR). Additionally, an anomaly detection model based on LSTM-Autoencoder was developed and trained on the pre-processed data. The proposed approach was able to detect anomalies at a low threshold and achieved a high accuracy score.
Development of a Decentralized Personal Indefinable Information (PII) Management Systems Using Blockchain dBFT Consensus Algorithm

Personal Identifiable Information (PII) management has been a major focus in data management due to the level of threat against it. Personally identifiable information is generally protected by an identity system whose roots can be traced to a centralized application whose data are saved in a database and can be retrieved through authentication using a username and password. Federated Identity management systems evolve because centralised systems get easily compromised and as a result, lots of PII data are exposed. Continuous breaches against these legacy systems have been the major concern of researchers, also the need for users to be in control of their PII data as stated in article 4 of GDPR. The aim of this research is to adopt a qualitative and design science research method to achieve the gap in the existing decentralized identity system which includes consensus algorithms, quantum-safe, compliance to GDPR policies, governance, development language, and scalability among others. This led to the use of Neo 3 blockchain in contrast to the common Ethereum and Hyperledger used by the existing system. This work has shown a new direction to safeguarding users’ data considering the technological factors in decentralized Identity management and has developed a framework that future work can lean on to develop a Decentralised Application for a dBFT PII management system.
Type-2 Fuzzy Relations: An Approach towards Representing Uncertainty in Associative Medical Relationships

The acquisition of precise values such as symptoms, signs, laboratory test results, and diseases/diagnoses for expressing meaningful associative relationships between medical entities has always been regarded as a critical part of developing medical knowledge-based systems. After the introduction of fuzzy sets, researchers became aware of the fact that a central problem in the use of fuzzy sets is constructing the membership function values. The complication arises from the uncertainty associated with assigning an exact membership grade for each element within the considered fuzzy set. Type-2 fuzzy set handles this problem by allocating a different fuzzy set to each element. This paper addresses the subject of medical knowledge acquisition and representation by proposing consistent interval type-2 fuzzy relations in the context of fuzzy inclusion as a measure of representing the degrees of association between medical entities. The concept of interval type-2 fuzzy relation will be introduced to represent the uncertainty and vagueness between medical entities.
Cooking and Dining at Home

The development of the kitchen has been greatly influenced by economic, technological, political, and historic circumstances. Parallel to the evolution of cooking and dining, their social importance in a dwelling also transformed. Green minded tendencies have simultaneously introduced design and construction aspects along sustainable principles through building materials, energy saving, recycling, composting, and growing food. This chapter discusses the latter as well as the rise of the social importance of the kitchen and key design concepts such as layout, modularity, storage, and adaptability for individuals with special needs.
Extracurricular reading lesson: «Who are we ?!» (based on Svetlana Talan’s novel «The Split Sky» 11th grade)

The author presents a model of an extracurricular reading lesson in Ukrainian literature – a sample of methodological technology of personal-oriented learning (OZON). The proposed structure and content of the lesson, its techniques and methods are aimed at forming the subjectivity of the reader, the formation of his national identity, and the actualization of historical memory while reading the novel «The Split Sky» by modern Ukrainian volunteer writer Svetlana Talan.
Self-Determination Efficacy and Career Barriers in University Students with After-school

본 연구는 충청권에 있는 대학교에서 2022년 1학기 빅데티이터활용 수업을 듣는 재학생 15명을 대상으로 진로자기결정효능감과 진로장벽의 효과분석을 검증하여 방과 후에 대학생의 빅데이터활용에 관한 활성화를 위한 시사점을 제공하는 데 그 목적이 있다. 연구결과, 첫째, 빅데이터활용 수업이 진로자기결정효능감의 구성요인인 결정효능감과 대처효능감에 통계적으로 유의하지 않지만, 평균점수 변화로 긍정적인 효과가 있는 것으로 조사되었다. 둘째, 빅데이터활용 수업이 통계적으로 유의미하지 않지만, 평균점수 변화로 진로장벽의 구성요인인 외적장애, 우유부단한 성격, 직업정보부족, 직업정보부족, 자기명확성부족, 필요성인식부족의 수준을 감소시키는 효과가 있었다. 따라서 방과 후에 동아리활동, 진로 관련 비교과 프로그램을 운영하거나 진로상담을 진행할 때, 빅데이터활용이 진로자기결정효능감에 긍정적 효과와 진로장벽에 부정적인 효과에 관한 정보를 활용하는 것이 필요하다.The purpose of this study is to verify the effect analysis of career self-determination efficacy and career barriers of 15 students attending the Big Data Utilization Class in the first semester of 2022 at a university in Chungcheong province, and to provide implications for the activation of university students' big data utilization after school. The results of this study are as follows: First, the Big Data-based instruction did not have statistically significant effects on decision efficacy and coping efficacy, which are the components of career self-determination efficacy, but had positive effects on average score change. Second, although Big Data-based instruction is not statistically significant, the change in average score has an effect of reducing the level of external disability, indecisive personality, lack of job information, lack of job information, lack of self-clearance, and lack of awareness of necessity, which are the components of career barriers. Therefore, when conducting club activities, career-related comparison programs or career counseling after school, it is necessary to utilize information on the positive effects of Big Data on career self-determination efficacy and the negative effects on career barriers.
A Study on the Recognition of the Out-of-School Care Management and Improvements in Schools

이 연구는 학교안과 학교밖 돌봄운영실태에 관한 인식비교와 그에 따른 개선점을 살펴봄으로써 시사점을 제시하는 것에 목적이 있다. 본 연구대상은 학교안은 초등돌봄교실의 돌봄전담사, 학교밖은 다함께돌봄센터의 센터장과 돌봄선생님으로 한정하였다. 이에 따른 연구결과는 다음과 같다. 첫째, 학교 안과 밖 돌봄운영실태에 관한 인식의 차이는 ‘돌봄교실은 학생의 특기&#8228;적성 개발에 도움, 돌봄교실은 학생의 학습활동에 도움, 학부모는 돌봄교실 운영 전반에 만족, 돌봄교실 활동의 기본교육 매뉴얼이 필요, 아동돌봄교사 1인당 담당하는 돌봄 아동 수는 적당, 돌봄교실은 사교육비를 줄이는 데 도움’ 문항이고, 나머지 문항에서는 인식의 차이가 없었다. 둘째, 돌봄운영 개선점에서는 학교 안은 돌봄의 법적근거 마련, 학교 밖은 돌봄교실 업무환경 개선이, 프로그램의 개선점에서는 학교 안과 밖 모두 보호·안전 프로그램이, 아동돌봄교사 업무 개선점에서는 학교 안과 밖 모두 급여 등 처우개선이, 아동돌봄교사의 역량강화 개선점에서는 학교안은 학부모 상담 및 문제아동에 대한 행동 이해, 학교 밖은 방과 후에 이루어지는 돌봄 활동에 대한 전문성이, 돌봄학생 서비스 향상 개선점에서는 학교 안과 밖 모두 돌봄서비스 내용 및 질 개선이 가장 높았다. 본 연구결과에 따른 시사점은 다음과 같다. 첫째, 돌봄기관 간의 협력적 거버넌스 체제 구축 방안 마련 둘째, 언제, 어디서나 원하는 돌봄서비스를 이용하기 쉬운 돌봄환경 셋째, 돌봄 부처 간의 긴밀한 협력적 네트워크 구축 및 강화 넷째, 양적확대의 돌봄정책과 질적확대의 돌봄정책 균형이 요구된다.The purpose of this study is to suggest implications by examining the recognition comparison and improvements in the school s internal and external care management. The subjects of this study were limited to the teachers in charge of care in elementary care classes, the head of the center and the teachers in care centers outside the school. The results of this study are as follows. First, as for the difference in perception of the actual condition of caring operation inside and outside the school, ‘the caring classroom helps the development of students’ speciality and aptitude, the caring classroom helps the students’ learning activities, the parents are satisfied with the overall operation of the caring classroom, the basic education manual of the caring classroom activities is needed, the number of caring children per child care teacher is appropriate, and the care classroom is helpful in reducing the private education expenses’. There was no difference in perception. Second, in terms of improvement of the operation of the care classroom, the legal basis of the care in the school, the improvement of the work environment of the care classroom outside the school, the protection and safety program in both inside and outside the school, the improvement of the work of the child care teacher, the improvement of the treatment such as the salary in both inside and outside the school, In terms of professionalism and improvement of care service for students, both inside and outside the school showed the highest improvement in care service contents and quality. The implications of the study are as follows. First, the plan to establish a cooperative governance system among caring institutions is required; second, it is necessary to use caring environment that is easy to use caring services anytime, anywhere; third, to build and strengthen a close cooperative network between caring ministries; and fourth, to balance caring policy and qualitative expansion policy.
Data collection and information security analysis in sports teaching system based on intelligent sensor

With the rapid development of intelligent sensor technology and the application of scenarios, the video images of moving targets collected by intelligent terminal devices can be obtained in sports teaching scenarios, and then through preprocessing, target tracking and behavior analysis, the analysis report is finally generated to achieve the effect of sports training. However, in the process of processing, the edge information of the video image is damaged, the color deviation exists, and when tracking the moving target, the final result will be affected by the deformation and partial occlusion of the target. In view of the above problems, the original bilateral filtering algorithm is changed to the multi-scale Retinex algorithm in the preprocessing to ensure that the video image is clearer and more complete and can be effectively protected. At the same time, for the target tracking algorithm, the Camshift algorithm which takes the color histogram as the tracking target and records the distribution probability is used. So that the results will not be affected by the deformation and partial occlusion of the target. The final experimental results show that the improved pre-processing algorithm and target tracking algorithm have smaller errors in operation, better protection of the edge information of video images, reduced the time consumption of the target tracking unit to be detected, and also improved its anti-jamming stability. In the future development, the image processing algorithm should have a better operation path, further reduce the running time, improve the value of UCIQE and peak signal to noise ratio, and track multiple targets at the same time with the smallest error, capture the moment to reach the single frame level, and also become better for teaching. In the system design, information security performance is compared with vulnerability identification to evaluate the security level of physical education teaching system.
Enhancing human sight perceptions to optimize machine vision: Untangling object recognition using deep learning techniques

The goal of machine vision is to develop human-like visual abilities; however, it is unclear whether understanding human vision will advance machines. Here, it exemplifies two key conceptual advancements: It first shows that the majority of computer vision models consistently differ from the way that individuals perceive objects. To do this, a significant dataset of human perceptions of the separations of isolated things was acquired, and it was then examined to see if a well-known machine vision algorithm can predict these perceptions. The best algorithms can account for the majority of the volatility in the intuitive data, but every algorithm we verified repeatedly misjudged several different object types. Second, it shows that removing these systemic biases can considerably increase classification accuracy. For instance, machine techniques overestimated detachments between symmetric objects in comparison to human vision. These results illustration that methodical differences between human and machine vision can be identified and improved.In order to improve the machine vision, employing a deep learning algorithm Visual Geometry Group (VGG 16) with planar reflection symmetry (PRS-Net) technique. VGG 16 is a convolutional neural network with 16 deep layers. VGG pre-trained architecture can point out visual features present in the image. The planar reflection symmetry concept is appended with VGG to create a hybrid environment that can improve machine vision significantly by 90%.
WITHDRAWN: Visual analytics of multivariate networks with representation learning and composite variable construction

Multivariate networks are commonly found in real-world data-driven applications. Uncovering and understanding the relations of interest in multivariate networks is not a trivial task. This paper presents a visual analytics workflow for studying multivariate networks to extract associations between different structural and semantic characteristics of the networks (e.g., what are the combinations of attributes largely relating to the density of a social network?). The workflow consists of a neural-network-based learning phase to classify the data based on the chosen input and output attributes, a dimensionality reduction and optimization phase to produce a simplified set of results for examination, and finally an interpreting phase conducted by the user through an interactive visualization interface. A key part of our design is a composite variable construction step that remodels nonlinear features obtained by neural networks into linear features that are intuitive to interpret. We demonstrate the capabilities of this workflow with multiple case studies on networks derived from social media usage and also evaluate the workflow through an expert interview.
Development of an offline OOH advertising recommendation system using negative sampling and deep interest network

Abstract The out-of-home (OOH) advertising market has been operated exclusively following the know-how of salespeople. Thus, it is difficult to make scientific decisions and systematically provide various options to advertisers. In this regard, this study develops an OOH advertising recommendation system by analyzing past OOH history data. The OOH advertising allocation problem has the characteristics that the training data are implicit feedback, and only one advertisement can be posted per offline billboard. This study proposes a recommendation system suitable for OOH history data using negative sampling and Deep Interest Network. The proposed recommendation system showed a higher performance than excisting models used for comparison purposes, and the findings of this study present implications for solving similar recommendation problems.
HDR video synthesis by a nonlocal regularization variational model

High dynamic range (HDR) video synthesis is a very challenging task. Consecutive frames are acquired with alternate expositions, generally two or three different exposure times. Classical methods aim at registering neighbouring frames and fuse them using image HDR techniques. However, the registration often fails to obtain accurate results and the fusion produces ghosting artifacts. Deep learning techniques have recently appeared imitating the structure of existing classical methods. The neural network is intended to estimate the registration function and choose the fusion weights. In this paper, we propose a new method for HDR video synthesis using a variational model. The proposed model uses a nonlocal regularization term to combine pixel information from neighbouring frames. The obtained results are competitive with state-of-the-art. Moreover, the proposed method gives a more reliable and understandable solution than deep-learning based ones.
Fleet Profile: Using visual analytics to prospect logistic solutions in industrial vehicles fleet

Fleet planning and management activities are essential to establish the quantity and type of vehicles needed to pursue production plans and reduce costs. In steel companies, logistic analysts are responsible for these fleet activities. However, analysts still face challenges assessing fleet utilization due to the volume and form in which the data is found. Normally, this type of problem is out of the scope of fleet planning models in the literature. For information extraction from massive and complex databases, visual analytics is an alternative that employs data analysis and visualization techniques. This research investigates visual analytics to support industrial vehicle fleet management. As a result, two artifacts were developed: a fleet measurement model and Fleet Profile, a visual analytics solution. Two research cycles, each one producing a Fleet Profile version that was evaluated with real cases after the development. Findings from the analysis of the evaluation contents indicated that the Fleet Profile’s visual description could help to evaluate fleet utilization and identify gaps for fleet optimization.
Detecting API-Misuse Based on Pattern Mining via API Usage Graph with Parameters

API misuse is a common issue that can trigger software crashes, bugs, and vulnerabilities. To address this problem, researchers have proposed pattern-based violation detectors that automatically extract patterns from code. However, these detectors have demonstrated low precision in detecting API misuses. In this paper, we propose a novel API misuse detector. Our proposed detector initially extracts API usages from the code and represents them as API Usage Graphs with Parameters (AUGPs). Utilizing the association rule algorithm, it then mines the binary rules, which are subsequently employed to detect the possible violations. The experimental results show that, comparing against five state-of-the-art detectors on the public dataset MuBench, our detector achieves the highest precision (1x more precise than the second-best one) and the highest F1-score ( $$50\%$$ higher than the second-best one).
THE ADJUSTED HISTOGRAM-BASED OUTLIER SCORE - AHBOS

Histogram is a commonly used tool for visualizing data distribution. It has also been used in semi-supervised and unsupervised anomaly detection tasks. The histogram-based outlier score is a fast unsupervised anomaly detection method that has become more popular because of the rapid increase in the amount of data collected in recent decades. Histogram-based outlier score can be computed using either static or dynamic bin-width histograms. When a histogram contains large gaps, the dynamic bin-width approach is preferred over the static bin-width approach. These gaps in a histogram usually occur as a result of various distributions in real data. When working with a static bin-width histogram, gaps can be utilized to acquire better distinction between outliers and inliers. In this study, we propose an adjusted version of the histogram-based outlier score named adjusted histogram-based outlier score, which considers neighboring bins prior to density estimation. Results from a simulation study and real data application indicate that the adjusted histogram-based outlier score yields a better performance not only in the simulated data but also for various types of real data.
A Hierarchical Spatial Logic for Knowledge Sharing and Fusion in Intelligent Connected Vehicle Cooperation

Intelligent connected vehicles have attracted great concerns from researchers in intelligent transportation filed for recent years. The safety of a scenario involving multiple intelligent vehicles and infrastructures depends on delicate design of their collaboration. A unified and efficient representation for knowledge sharing and fusion is essential for such collaboration. However, the existing logical representation of spatial knowledge seldom considers integrating the knowledge from distributed agents with different granularities of views and data models. This paper designs a novel spatial logic based on a hierarchical spatial model, and provides a knowledge fusion method based on view and grained conversion between distributed agents. The spatial logic can be used to specify and share the key spatial knowledge in vehicle infrastructure cooperation scenarios. The converted knowledge shared by other agents is beneficial for an intelligent vehicle to improve its cognition and decision-making process. The effectiveness of our approach is illustrated with the case studies in path planning and cooperative cross-traffic turning.