Reviewerly/exHarmony huggingface dataset:
COMPARATIVE ANALYSIS OF PATHFINDING ARTIFICIAL INTELLIGENCE USING DIJKSTRA AND A* ALGORITHMS BASED ON RPG MAKER MV

In most games, an artificial pathfinding intelligence is required for traversing the fastest discovery. It is essential for many video games, particularly Role Playing Games (RPGs). The algorithm pathfindings implemented in this game are A* and Dijkstra Algorithms. This study aims to test an artificial intelligence system for discovering routes using the A* and Dijkstra algorithms based on RPG Maker MV. The result showed that from the time obtained, in the experiment on eight nodes using the Pathfinding mechanism of A* algorithm has faster result in discovering the nearest route with the time 08:15:23 with format (mm:ss: ms) whereas Dijkstra Algorithm has a 34:47:43 time result. The time record needed represents the distance between the search nodes. It indicates that the multiple weighting in the impassable nodes caused the cost calculation process becomes faster and more efficient.
Farmers Commerce

Abstract: This application basically aims at digitalizing and marketing agricultural products also, the application is constituted in such a way that it is profitable to the farmers. The home page of the website enables the user/farmer to register by using the user Id or email Id and enter the password as per the user’s choice. This allows the user to effortlessly login into the website. Once the login has been done, the farmer could upload the yielded vegetables, fruits and cereals. Further the price stipulated by the agricultural association is attached and uploaded with it. By doing so, the agricultural products are supplied directly to the consumers without the middle man service. Consumer can use their login information to purchase agricultural products right from the farmers, This is beneficial because the consumers could get chemical free vegetables from the farmers. The farmers commerce will be highly helpful by setting it up in the center of every districts, so that the consumers are benefit either directly or buy online shopping through this website.
Improving Sample Efficiency in Evolutionary RL Using Off-Policy Ranking

Evolution Strategy (ES) is a powerful black-box optimization technique based on the idea of natural evolution. In each of its iterations, a key step entails ranking candidate solutions based on some fitness score. For an ES method in Reinforcement Learning (RL), this ranking step requires evaluating multiple policies. This is presently done via on-policy approaches: each policy's score is estimated by interacting several times with the environment using that policy. This leads to a lot of wasteful interactions since, once the ranking is done, only the data associated with the top-ranked policies is used for subsequent learning. To improve sample efficiency, we propose a novel off-policy alternative for ranking, based on a local approximation for the fitness function. We demonstrate our idea in the context of a state-of-the-art ES method called the Augmented Random Search (ARS). Simulations in MuJoCo tasks show that, compared to the original ARS, our off-policy variant has similar running times for reaching reward thresholds but needs only around 70% as much data. It also outperforms the recent Trust Region ES. We believe our ideas should be extendable to other ES methods as well.
Optimizing Transformations for Contrastive Learning in a Differentiable Framework

Current contrastive learning methods use random transformations sampled from a large list of transformations, with fixed hyper-parameters, to learn invariance from an unannotated database. Following previous works that introduce a small amount of supervision, we propose a framework to find optimal transformations for contrastive learning using a differentiable transformation network. Our method increases performances at low annotated data regime both in supervision accuracy and in convergence speed. In contrast to previous work, no generative model is needed for transformation optimization. Transformed images keep relevant information to solve the supervised task, here classification. Experiments were performed on 34000 2D slices of brain Magnetic Resonance Images and 11200 chest X-ray images. On both datasets, with 10% of labeled data, our model achieves better performances than a fully supervised model with 100% labels.
Learning Driven Collaboration Management for Unstructured Service Processes

We conducted ethnographic studies of several service works, such as social welfare case work in a city and, customer support desk work for a computer maintenance company, etc. We found the unstructured nature of service to be one of the major problems in improving service performance and quality. We propose a collaboration management model for structuring and improving unstructured service processes. We conducted an experiment in social welfare work in a city. An expert and two teams of case workers participated. The case workers successfully elaborated the initial work template created by the expert by two learning cycles of the proposed collaboration management model.
Defining a Serviceability Architecture Framework

The application of Serviceability as an integral part of the server and network product development process and the complete lifecycle of a product requires a methodology that can accurately and consistently identify and measure product serviceability issues and provide a means to measure change throughout. Cisco has developed a Serviceability Architecture and Design Framework (SADF) that can be used to conduct product serviceability audits and provide feedback into the product development and support process in a consistent, longitudinal manner. The SADF is composed of three main components; 1) Serviceability Architecture Framework, 2) Design Functions and Requirements, and 3) Process for Utilizing the SADF. The focus of this paper will be to introduce and describe the history of the Serviceability Architecture Framework (SAF) which is the foundation of the SADF.The Serviceability Architecture Framework is composed of 8 subsystems; Deployment Design, Installation, Configuration, Monitoring, Notification, Diagnostic, Troubleshooting, and Learning Engine. The subsystems are augmented by a set of services that are common across the subsystems (centralized management, documentation, logging, hardware, user experience, and application programming interface (API). Finally the architecture identifies the sources of information that provide input into the different architectural components and the desired outputs for the subsystems operating on the inputs.This paper will introduce a system process model that identifies the scope of serviceability areas in the context of customer product acquisition and system deployment and management. The architecture subsystems will be described in the context of this model and the details of the subsystems, their interdependencies, and their inputs and outputs will be introduced and discussed.
Introduction

The ARPANET is nearly 50 years old, and the World Wide Web is close to 3 decades old. Yet the internet technologies that emerged from these two remarkable milestones continue to transform industries and cultures today and show no signs of slowing down. The widespread use of such popular internet services as web-based email, search, social networks, online maps, and video streaming, plus the increased worldwide availability of high-speed connectivity, have accelerated a trend toward server-side or “cloud” computing. With such trends now embraced by mainline enterprise workloads, the cloud computing market is projected to reach close to half a trillion dollars in the next few years [Col17].
Finding Supporting and Objecting Statements

Detecting claims, the topic of the previous chapter, is obviously a core task of argumentation mining, but in order to find a complete argument, there is at least one more component to be identified: the statement(s) that the author introduced to support the claim. In the argumentation literature, this is often called the ‘premises’ of the argument; in other places it is ‘evidence’ or ‘justification’. In this chapter, we use these terms interchangeably with ‘support’.
Modeling Costs

As described in Chapter 1, one of the defining characteristics of WSCs is their emphasis on cost efficiency at scale. To better understand this, let us examine the total cost of ownership (TCO) of a data center. At the top level, costs split into capital expenses (Capex) and operational expenses (Opex). Capex refers to investments that must be made upfront and that are then depreciated over a certain timeframe. Examples are the construction cost of a data center or the purchase price of a server. Opex refers to the recurring monthly costs of actually running the equipment, excluding depreciation: electricity costs, repairs and maintenance, salaries of on-site personnel, and so on. Thus, we have:
Implementation of Model View Controller Architecture in Object Oriented Programming Learning

This study aims to provide an overview of the application of software design patterns, namely Model View Controller (MVC) in object-oriented programming learning. In the software development industry, most application development uses frameworks. MVC architecture is a design pattern that is widely used by various frameworks. Students as prospective programmers or software developers must master and be able to translate object-oriented programming concepts into programming languages. In this study, the Java programming language is used to apply the object-oriented concept and implement the MVC architecture. This research resulted in an increase in students' programming skills and abilities as well as being able to apply the MVC architecture in developing applications using Java.
The Design of Final Project Information System

The final project is an educational method to determine the competence of a student regarding the knowledge that has been obtained during lectures. The Final Project Information System is carried out in accordance with the General Guidelines for the Final Project of the Bangka Belitung Manufacturing Polytechnic, the final project data management process is managed by the final project commission. The implementation of the final project information is currently still being done manually. Making a website-based Final Project Information System aims to make the final project implementation process more efficient and effective. The actors involved in it are students, final project commissions, supervisors, and examiners. In this research, the method used is the development of the Software Development Lifecycle method.
Application Of Dynamic Segmentation In Stroke Detection Software With ANN

One way to find out whether there is a stroke is to do a CT scan . But the results of the examination with a new CT scan can be obtained in quite a long time. In addition, sometimes there are differences of opinion between doctors and radiologists regarding what is seen from the results of the examination. This research was conducted to produce a software that can later be integrated with the existing system on the CT Scan tool so that it can immediately be known whether or not stroke is present from the CT Scan results. In this study, a dynamic image segmentation method is implemented, namely the watershed transformation method which will later produce regions as a feature for the stroke detection process carried out with the backpropagation algorithm. From experiments conducted on CT scan images of the brain, this method can detect stroke well. The results obtained are 100% for training data and 90% for test data.
Analysis Of UMN Student Graduation Timeliness Using Supervised Learning Method

Education is one of the most important things in human life, and in the world of education. However, there are still many students who graduate not on time. The purpose of this study is to find out an overview of what factors influence, then data analysis, and visualization so that students can graduate on time or not on time for UMN student graduates in 2018-2020. The method or approach used to solve the problem is data collection, independent variable, dependent variable, CRISP-DM, with SQLYog tools, to store data, rapid miner for data cleaning, then calculate prediction accuracy with rapid miner using nave Bayes algorithm, and regression logistics, using the included 10-fold validation method, and visualizing the data with Tableau.
Bibliographic Computer Science Indexing Review with Disease Covid 19

Abstract - Researchers in conducting their research use the search using the homepage of the publication, according to expertise, collaboration in research, and research interests. And at this time the Covid 19 pandemic, became a trending topic for researchers, in various scientific fields. This study classifies based on publications located on the homepage source namely Scopus and Google Scholar, by analyzing the following topics, namely Natural Language Processing, Text Mining, Remote Sensing, and Sentiment Analysis using Name Entity Recognition to detect and classify named entities in text and using occurrence and link strength methods. The results showed science index literature about diseases Covid 19, obtained that Scopus has the most equitable percentage, has a good occurrence and link strength among the five scientific fields, namely Natural Language Processing 23.81%.33%, Text Mining 19.05%%, Remote Sensing 0 %, Sentiment Analysis 57.14 % then Google Scholar Natural Language Processing 51.35%, Text Mining 0 %, Remote Sensing 48.65 %, Sentiment Analysis 0 %&#x0D; &#x0D; Index Terms : Information Extraction; Bibliographic indexing; Disease Covid 19
Rucio: Open-source Scientific Data Curation [Poster]

backends include an ASIC workflow. Taken together, these and continued efforts in hls4ml will arm a new generation of domain scientists with accessible, efficient, and powerful tools for machine-learning-accelerated discovery.
Intrinsically Secure Information Barrier for Arms Control Verification.

including data validation, sources and attributes, verification, and security. Using information analytics and data visualization capabilities available at Pacific Northwest National Laboratory (PNNL), we provide graphical examples of some social media "signatures" of potential relevance for nonproliferation and arms control purposes. We conclude by describing a proposed case study and offering recommendations both for further research and next steps by the policy community.
Korelasi Antara Penguasaan Kosakata Siswa Dengan dan Pencapaiannya Dalam Pemahaman Membaca

This research deals with the correlation between students vocabulary mastery and their achievement in their reading comprehension. The data were taken from 33 students from class X-TKJ of SMK As Sakienah in Sliyeg The writer used quantitative method by Pearson Product Moment. From the calculation by using product moment formula, it was found out that the coefficient correlation from this research is 0,8409.. It can be concluded that there is positive and high correlation level between students’ vocabulary mastery and their achievement in reading comprehension. This research used significance level 5%. From the hypothesis testing result, it was found that r_count is 0,8409 &gt;r _table 0,3494. Based on the fact, it can be concluded that Alternative Hypothesis (Ha) is accepted and the Null Hypothesis (Ho) is rejected. So, there is significance correlation between students’ vocabulary mastery and their achievement in reading comprehension.
Analytic Assessment On Students’ Ability In Writing Descriptive Text

This study is aimed at describing students’ ability in writing descriptive texts viewed from analytic assessment presented by O’Malley and Pierce, involving composing, style, sentence formation, usage and mechanics. The study was conducted by applying a descriptive quantitative-qualitative method. The data were taken from 15 descriptive texts made by the second semester students of English Department, Pancasakti University Tegal 2018/2019. The result of the study shows that there are 33.4% of the students are “good”, 40% of the students are “fair”, and 26.6% of the students are “bad”. The students mean score is 65 (fair), and the scores of each components are as follows: composing is 71.6, style is 66.2, sentence formation is 60, usage is 55.7, and mechanics is 55. The result also shows that the texts are developed trough correct generic structure involving identification and descriptions. The writer suggested that the students need to study harder and practice more frequently to increase their writing ability, specifically in mechanics as their weakness in writing.
Aspect-Based Sentiment Analysis on Application Review using Convolutional Neural Network

As an obligatory application during the COVID-19 pandemic by Indonesians, PeduliLindungi must have provided outstanding quality services to its users. However, as of December 2021, users’ sentiment toward the quality and service of the PeduliLindungi application was still low, with an application rating of 3.6 out of 5 on the Google Play Store. This study uses text mining techniques for the Aspect-Based Sentiment Analysis (ABSA) task in the PeduliLindungi application review, a sentiment analysis task based on the aspect category of the application. This study aims to classify the users’ sentiment on aspects of the application and provide insight and knowledge to improve the quality of the PeduliLindungi application. The ABSA method used in this study is the classification of aspects and sentiments using the Convolutional Neural Network (CNN) algorithm. The results showed that the CNN model could produce such good performance with an f1 score of 92.23% in the aspect classification and 95.13% in the sentiment classification. The results of user sentiment modelling showed the dominance of negative sentiment in the eight aspects of the application, namely Visual Experience, Scan – Check-in/Out, Vaccine Certificate, eHac, COVID Test, Register/Login, Performance and Stability, and Privacy, Data, and Security.&#x0D; Index Terms—Aspect-Based Sentiment Analysis, Convolution Neural Network, PeduliLindungi, Text Classification, Text Mining.
Learning Attention Maps for Unsupervised Depth Estimation in Monocular Videos

In this work, we propose an approach to learn attention maps based on the features learned by a convolutional network. This network is trained to estimate dense depth maps using a view synthesis approach, in which sequences of frames are used as a supervised signal. The attention maps are used to increase the error contribution of the meaningful regions on the error computation between the synthesized and the real views. The attention maps are learned simultaneously with the depth network in an end-to-end manner. We evaluate our method on the depth estimation and odometry tasks of the KITTI benchmark [2]. The inclusion of the attention maps shows an improvement in both tasks when compared to a competitive baseline method.
Developing a scale for Assessing the Quality of E-learning Service in Saudi Universities: An Applied Study at King Khalid University – Abha

The aim of this study was to build a scale for assessing the quality of electronic learning service in Saudi universities, which has been named “E-L-SQUAL”. The study was carried out during five stages using the scientific methodology for developing scales. The sample of the study consisted of 729 male and female students of King Khalid University. The data were subjected to statistical analysis using the SPSS program. A number of statistical methods were used for checking the validity, consistency and, reliability, as well as exploratory factor analysis (EFA) to determine the quality of the e-learning service. In addition, the confirmatory factor analysis (CFA) conducted by using AMOS statistical program. Finally, all the study results confirmed the validity of the (E-L-SQUAL) scale, which consisted of (43) items, divided into six dimensions of quality: content, technology, support, user interface, assessments, security and communication.&#x0D; Keywords: scale, evaluation, service quality, e-learning, E-L-SQUAL .
Penguatan Sikap Peduli Lingkungan Peserta Didik melalui Pendekatan Jelajah Alam Sekitar (JAS) Berbasis Model Problem Base Learning (PBL)

This study aims to determine the effect of the “Jelajah Alam Sekitar”/ Approach of Natural Around (JAS) Based on the Problem Base Learning Model (PBL) Through Observation of Pollution Indicators Towards Learners' Environmental Care Attitudes. The data analysis of this study was divided into two, namely the initial data analysis in the form of a validity test using product moment correlation, reliability test and final data analysis in the form of a final data test covering descriptive analysis, normality test, homogeneity test, t test (paired sample t test) , descriptive attitude care about the environment. The results showed that the learning of science based on the JAS approach with the PBL method on environmental attitudes of students on the theme of pollution is influential because by using the t test (paired sample t test) the sig (2-tailed) value of 0,000 &lt;0.05 was obtained. Learning by using a Natural Approach to explore around the PBL Model based can be applied in the eyes of natural science students, especially in environmental pollution material because according to this research Approach to the Natural Approach Around the PBL model based can improve students' environmental care attitudes
Conclusions and Future Challenges

These days, Semantic Web technologies are a well-established standard for publishing data on the Web. More than a decade of publishing and interlinking has made available RDF datasets that exhibit very large sizes (>1 billion triples) and semantically bridge together information from very different domains. Linked Data, the core technology behind Semantic Web, is not a research prototype anymore; it is used in real-world systems and large-scale companies, under various different names (e.g., Knowledge Graphs).
Active Gaze Control for Foveal Scene Exploration

Active perception and foveal vision are the foundations of the human visual system. While foveal vision reduces the amount of information to process during a gaze fixation, active perception will change the gaze direction to the most promising parts of the visual field. We propose a methodology to emulate how humans and robots with foveal cameras would explore a scene, identifying the objects present in their surroundings with in least number of gaze shifts. Our approach is based on three key methods. First, we take an off-the-shelf deep object detector, pre-trained on a large dataset of regular images, and calibrate the classification outputs to the case of foveated images. Second, a body-centered semantic map, encoding the objects classifications and corresponding uncertainties, is sequentially updated with the calibrated detections, considering several data fusion techniques. Third, the next best gaze fixation point is determined based on information-theoretic metrics that aim at minimizing the overall expected uncertainty of the semantic map. When compared to the random selection of next gaze shifts, the proposed method achieves an increase in detection F1-score of 2-3 percentage points for the same number of gaze shifts and reduces to one third the number of required gaze shifts to attain similar performance.