Reviewerly/exHarmony huggingface dataset:
What's in a Caption? Dataset-Specific Linguistic Diversity and Its Effect on Visual Description Models and Metrics

While there have been significant gains in the field of automated video description, the generalization performance of automated description models to novel domains remains a major barrier to using these systems in the real world. Most visual description methods are known to capture and exploit patterns in the training data leading to evaluation metric increases, but what are those patterns? In this work, we examine several popular visual description datasets, and capture, analyze, and understand the dataset-specific linguistic patterns that models exploit but do not generalize to new domains. At the token level, sample level, and dataset level, we find that caption diversity is a major driving factor behind the generation of generic and uninformative captions. We further show that state-of-the-art models even outperform held-out ground truth captions on modern metrics, and that this effect is an artifact of linguistic diversity in datasets. Understanding this linguistic diversity is key to building strong captioning models, we recommend several methods and approaches for maintaining diversity in the collection of new data, and dealing with the consequences of limited diversity when using current models and metrics.
Study on the Operational Status and Development Direction of a University Writing Center －Focusing on the Differences in Satisfaction according to Student Majors and the Analysis of Student Needs for Writing Instructional Content

In this paper, the operating system and operation process of Youngsan University's writing center were reviewed, and the operation status and performance were analyzed. Based on the research results, the direction for the development of the university writing center, along with implications that can be helpful in composing the content and methods of university writing education, were suggested. First, in order to understand the operating status of the writing center, detailed information such as the organizational chart, the training programs operated for one year at the writing center, the business plan, the publicity poster, the training program application form, and the online homepage screen were presented. Next, the level of satisfaction of the students who participated in the writing center education program was analyzed. The satisfaction of the students who participated in the writing camp was generally high. However, 'enhancement of competency' and 'improvement of motivation' showed a somewhat low level of satisfaction. Also, this research confirmed that the satisfaction level of science and engineering students was slightly lower than that of humanities and social science students according to the students' majors. In order to solve this problem, various themes of the camp were organized, and writing educational content for science and engineering students was proposed. As a result of analyzing the students' online correction application types, this study confirmed that the students had a high demand for guidance regarding paragraph composition. Accordingly, this study argued that the direction of writing instruction should change from instruction that is centered on getting results to instruction that is centered on the writing process itself.
FUNCTIONS OF RESEARCH WORK UNIVERSITY STUDENTS

The importance of research work of university students is considered, the functions of research work are listed and their characteristics are supplemented
The Design and Validation of A Tool to Measure Content Validity of A Computational thinking Game-Based Learning Module for Tertiary Educational Students

This study proved the design and content validity process of a computational thinking game-based learning module. The process involved a two-step method: instrument design and judgmental evidence. Content domain identification, item generation, and instrument construction were included in the former step while the latter involved seven experts to review and rate the essentiality, relevancy, and clarity of the generated 30 items in the first and 34 items in the second round. Suggestions and ratings by the panel of experts in the second step were used to examine the instrument content validity through content validity ratio (CVR), content validity index (CVI), and modified kappa statistic approach. The findings manifested the second round promised better results with the increment of totally essential items by 59.41 percent and the increment of total relevant, clear, and excellent items by 3.33 percent. It implies in the second round that 79.41 percent of overall items were significantly essential, and 100 percent of the overall items were significantly relevant, clear, and excellent. Overall, the instrument got significant content validity after the second round by s-CVI/UA=0.97 and s-CVI/Average=0.99. Hence, the instrument has a great potential to measure the content validity of a brand-new computational thinking game-based learning module. However, it was then recommended to involve more experts during content domain determination and item generation and to further explore the findings that support the content validity of 33 items on instrument reliability.
Inception generators for generative adversarial networks

Image-to-image translation aims at learning a mapping between two different picture styles. The style and texture of one image can be transferred to another. It is possible to do paired and unpaired image-to-image translations, both of which have a specified goal translation to aim for translating images. Using generative adversarial networks, we offer an overview of different generator designs for unpaired image-to-image translation and paired image-to-image translation. The Residual blocks or U-net architecture are often employed in generators of generative adversarial networks for image-to-image translation. In this research paper, we propose the use of an Inception module and their evaluation in unpaired image-to-image translation tasks by translating photos to artist images and also with unpaired data for converting Maps to aerial images.
Pemanfaatan k-Means Clustering dan Analytic Hierarchy Process terhadap Penilaian Prestasi Kerja Pegawai

As a government agency related to education and culture, the Department of Education and Culture of Bengkayang Regency needs qualified employees in their performance. Clustering can be used to determine whether the employee is performing well, moderately, or poorly. The clustering method used in this study is the k-Means method. The research wasconducted by studying and understanding the k-Means method and knowing employee performance data at the Education and Culture Office of Bengkayang Regency. The results of calculations using the k-Means method and the Analytic Hierarchy Process (AHP) method are as many as six employees have good performance (where two of them got the highest score in the AHP calculation), nine employees have moderate performance, and five employees have poor performance. These results can be used as a benchmark for employees in the cluster either to be promoted or rank, while employees in the cluster less are able to begiven employee performance training, in order to be better in the future. With this, employees can become more competitive and superior in the face of increasingly rapid developments.
AN EFFICIENT SOLUTION TO RAY TRACING PROBLEMS FOR HEMISPHERICAL REFRACTIVE INTERFACES

Abstract. Refraction effects, their description and modeling are important aspects of underwater and multimedia photogrammetry. For hemispherical interfaces, the usual approach to refraction is to rely on standard pinhole representations, e.g. by employing the Brown model. This is strictly only possible if entrance pupil of the lens and dome center coincide which is not trivial to achieve. However, simulations and other authors show that systematic residual errors occur with these approaches up to considerable margins if offsets of some millimeters are present. Hence, we propose a novel efficient, yet strict optimization algorithm to account for offsets between dome port centers and entrance pupil. It is about two orders of magnitude faster than standard ray tracing implementations that account for refraction while providing similar or equal results. The algorithm is employed for analysis on a simulation and two real data sets and performance of additionally estimating the dome center is investigated. Our method is capable of improving accuracy in one data set at a maximum of 30% but even so cannot provide improvements for the second data sets. An explicit calibration model is hence to be chosen carefully and most likely relies on the offset’s margins and each individual application.
The role of savings in achieving human capital development in resource-rich countries

This paper examined the impact of savings on human capital development in Nigeria (resource rich-country) from 1981- 2020. Using Autoregressive Moving Average (ARMA) model, the results indicate that the estimated parameters on gross savings (GSA), official development assistance (ODA), and democracy (DEM) are statistically significantly different from zero with the expected signs except that of GSA. The slope coefficient for GSA is surprisingly negative (despite Nigeria’s oil wealth) and implies that a percentage increase in the gross saving (GSA) will translate into a decrease in the level of human capital development. The possible reasons behind the negative impact of savings on human capital development could be that past governments did not effectively manage the country’s oil wealth, thus leading to low savings which culminated in under-investment in human capital development. As such, Nigeria still grades among the poorest and most miserable in the world in terms of the human development index. Keywords: Autoregressive Moving Average (ARMA) model; human capital development; Nigeria; resource rich-country.
Review on Intractability

In real life, the problems may be of infinite dimensions. Design of ingenious information structure, for minimizing complexity and redundancy of the problem space are versatile. This is unique in the sense: given an arbitrary ’n’ node graph the problem becomes countable infinite and in some cases it is uncountable infinite. Problems which can be mapped as graphs are normally simple in nature but we remember the adage “Simple things are mighty things”. What we mean that normally for example it is a practice to bring undue mathematics to make things complex but although the graph algorithms are of exponential complexity for large dimension.
On Identifying Assumptions Underlying Legal Arrangements

Legal arrangements rest on behavioural, cognitive, social, and other assumptions regarding their role and function in society and the legal system.The identification and subsequent evaluation of these assumptions is an important task for legal scholarship.In this article, we focus on the identification and categorisation of these assumptions, providing conceptual distinctions and methodological guidance.We distinguish between assumptions about the value(s), norm(s), or interest(s) underlying a legal arrangement, which can be legal or non-legal, and assumptions about the relationship between the legal arrangement and its underlying value(s), norm(s), or interest(s), which can be logical, causal, or contributory.Regarding the identification, we consider explicit references and inference to the best explanation and theory-driven evaluations as possible methods.Inference to the best explanation, we posit, functions as a manner of reconstructing the theory that the person(s) creating a legal arrangement had in mind regarding the place and function of that legal arrangement in society.Given this, we offer a step-by-step approach to reconstructing this theory in use, drawing from theory-driven evaluations and its sources in the social sciences.These distinctions and guidelines can contribute to understanding the context and untangling the complexities involved in identifying the assumptions that underlie legal arrangements.
Solving workflow scheduling problems with QUBO modeling

In this paper we investigate the workflow scheduling problem, a known NP-hard class of scheduling problems. We derive problem instances from an industrial use case and compare against several quantum, classical, and hybrid quantum-classical algorithms. We develop a novel QUBO to represent our scheduling problem and show how the QUBO complexity depends on the input problem. We derive and present a decomposition method for this specific application to mitigate this complexity and demonstrate the effectiveness of the approach.
An E-commerce Personalized Recommendation Algorithm based on Fuzzy Clustering

The key to the performance of recommendation system lies in which recommendation technology to choose. Personalized recommendation technology has attracted the extensive attention of more and more merchants and researchers with its humanization and potential commercial value, and earliest and most widely used personalized recommendation technology. However, with the increasing number of users and commodities, the recommendation system is facing some problems, such as the difficulty of ensuring the real-time performance and the decline of recommendation quality. Many scholars put forward many solutions to the shortcomings of collaborative filtering. So as to obtain better recommendation effect. Based on different angles, this paper adopts fuzzy clustering recommendation algorithm. Using fuzzy clustering algorithm to increase population diversity can avoid the lack of diversity of adjacent users; The feature of flexible partition of fuzzy c-means clustering algorithm is used to reduce the dimension of data, so as to effectively solve the problem of recommendation quality degradation caused by data sparsity.
An Objective Method for Pedestrian Occlusion Level Classification

Pedestrian detection is among the most safety-critical features of driver assistance systems for autonomous vehicles. One of the most complex detection challenges is that of partial occlusion, where a target object is only partially available to the sensor due to obstruction by another foreground object. A number of current pedestrian detection benchmarks provide annotation for partial occlusion to assess algorithm performance in these scenarios, however each benchmark varies greatly in their definition of the occurrence and severity of occlusion. In addition, current occlusion level annotation methods contain a high degree of subjectivity by the human annotator. This can lead to inaccurate or inconsistent reporting of an algorithm's detection performance for partially occluded pedestrians, depending on which benchmark is used. This research presents a novel, objective method for pedestrian occlusion level classification for ground truth annotation. Occlusion level classification is achieved through the identification of visible pedestrian keypoints and through the use of a novel, effective method of 2D body surface area estimation. Experimental results demonstrate that the proposed method reflects the pixel-wise occlusion level of pedestrians in images and is effective for all forms of occlusion, including challenging edge cases such as self-occlusion, truncation and inter-occluding pedestrians.
Estimating Patch Propagation Times across (Blockchain) Forks

The wide success of Bitcoin has led to a huge surge of alternative cryptocurrencies (altcoins). Most altcoins essentially fork Bitcoin's code with minor modifications, such as the number of coins to be minted, the block size, and the block generation time. As such, they are often deemed identical to Bitcoin in terms of security, robustness, and maturity. In this paper, we show that this common conception is misleading. By mining data retrieved from the GitHub repositories of various altcoin projects, we estimate the time it took to propagate relevant patches from Bitcoin to the altcoins. We find that, while the Bitcoin development community is quite active in fixing security flaws of Bitcoin's code base, forked cryptocurrencies are not as rigorous in patching the same vulnerabilities (inherited from Bitcoin). In some cases, we observe that even critical vulnerabilities, discovered and fixed within the Bitcoin community, have been addressed by the altcoins tens of months after disclosure. Besides raising awareness of this problem, our work aims to motivate the need for a proper responsible disclosure of vulnerabilities to all forked chains prior to reporting them publicly.
Variants in managing supply chains on distributed ledgers

Smart contracts show a high potential for ensuring that Supply Chain Management strategies make a qualitative leap toward higher levels of optimality, not only in terms of efficiency and profitability but also in the aggregation of skills aimed at creating the best products and services to bring to the market. In this article, we illustrate an architecture that employs smart contracts to implement various algorithmic versions of the Income Sharing principle between companies participating in a supply chain. We implement our approach on Hyperledger Fabric, the most widespread platform for private and consortium distributed ledgers, and discuss its suitability to our purposes by comparing this design choice with the alternative given by public blockchains, with particular attention to Ethereum.
Three Lessons Learned from Teaching Gifted Learners During the COVID-19 Pandemic

To learn about the experiences of elementary gifted learners and teachers of the gifted during the first year of the pandemic and how instruction was adapted to meet the needs of gifted students, we surveyed 300 students in grade three through six and 312 elementary teachers of the gifted. We also held focus groups to explore what worked and how we might reimagine schools in a post-pandemic world. From the voices of teachers and gifted students, we discovered three practical lessons that can guide future instruction and support for gifted students in virtual and hybrid classrooms.
Face Mask and Social Distancing Detection System

The spread of COVID-19 has been taken on pandemic magnitudes and has already spread over 200 countries in a few months. In this time of emergency of COVID-19, especially when there is still a need to follow the precautions and developed vaccines are not available to all the developing countries in the first phase of vaccine distribution, the virus is spreading rapidly through direct and indirect contacts. The World Health Organization (WHO) provides the standard recommendations on preventing the spread of COVID-19 and the importance of face masks for protection from the virus. The excessive use of manual disinfection systems has also become a source of infection. That is why this research aims to design and develop a low-cost, rapid, scalable, and effective virus spread control and screening system to minimize the chances and risk of spread of COVID-19.We proposed an IoT-based Smart Screening and Disinfection Walk-through Gate (SSDWG) for all public places entrance. The SSDWG is designed to do rapid screening, including temperature measuring using a contact-free sensor and storing the record of the suspected individual for further control and monitoring. Our proposed IoT-based screening system also implemented real-time deep learning models for face mask detection and classification. This module classified individuals who wear the face mask properly, improperly, and without a face mask using VGG-16, MobileNetV2, Inception v3, ResNet-50, and CNN using a transfer learning approach. We achieved the highest accuracy of 99.81mask detection and classification module. We also implemented classification to classify the types of face masks worn by the individuals, either N-95 or surgical masks. We also compared the results of our proposed system with state-of-the-art methods, and we highly suggested that our system could be used to prevent the spread of local transmission.
Optimal Adaptive Prediction Intervals for Electricity Load Forecasting in Distribution Systems via Reinforcement Learning

Prediction intervals (PIs) offer an effective tool for quantifying uncertainty of loads in distribution systems. The traditional central PIs cannot adapt well to skewed distributions, and their offline training fashion is vulnerable to the unforeseen change in future load patterns. Therefore, we propose an optimal PI estimation approach, which is online and adaptive to different data distributions by adaptively determining symmetric or asymmetric probability proportion pairs for quantiles of PIs’ bounds. It relies on the online learning ability of reinforcement learning (RL) to integrate the two online tasks, i.e., the adaptive selection of probability proportion pairs and quantile predictions, both of which are modeled by neural networks. As such, the quality of quantiles-formed PI can guide the selection process of optimal probability proportion pairs, which forms a closed loop to improve PIs’ quality. Furthermore, to improve the learning efficiency of quantile forecasts, a prioritized experience replay (PER) strategy is proposed for online quantile regression processes. Case studies on both load and net load demonstrate that the proposed method can better adapt to data distribution compared with online central PIs method. Compared with offline-trained methods, it obtains PIs with better quality and is more robust against concept drift.
Health Ratio Optimization of Group Detection-Based Data Network Using Genetic Algorithm

A physical region can have multiple parts, each part is monitored with the help of a Special DDN (SDDN). In the existing methods, namely, LEACH, the Fuzzy method has a larger path between the initiator DDN to destination DDN. Non-healthy DDNs can occur in the Group-based Detection Data Network (GDDN) when the battery level of the DDN reaches below the threshold. The possibility of more Non-healthy DDNs can be of multiple reasons (i) when the link path is of larger length (ii) Same DDN is used multiple times as an SDDN and (iii) repeated communication between base station to DDNs causes the DDN to lose more battery. If a mechanism is created to recover the DDNs or recharge them, then the number of Non-healthy DDNs can be reduced and DDN performance can be improved a lot. The Proposed Genetic (PGENETIC) method will find the SDDN in a battery-aware manner and also at path will be of minimum length along with regular interval trigger to identify DDNs which are non-healthy and replace or recharge them. PGENETIC is compared with LEACH, Fuzzy method, and Proposed CHEF (PCHEF) and proved that PGENETIC exhibits better performance.
The Relationship of Indonesian Learning with the Environment

The environment is an aspect that has something to do with many things, one of which is related to learning. The environment can affect the quality of learning for students, especially when studying Indonesian. In Indonesian learning can be called environmentally friendly learning. Environmentally based Indonesian learning is known to reduce student boredom during learning activities. Learning Indonesian is the basis for other subjects. This is a great responsibility for teachers as the spearhead of the learning process. In line with the development of science and technology today, students are required to have good language skills. A person who has adequate language skills will more easily absorb and convey information both orally and in writing. Language skills themselves consist of four aspects, namely listening or listening, speaking, reading, and writing. Students must master these four aspects in an integrated manner so that they are skilled in language (Syofiani et al., 2019).