Reviewerly/exHarmony huggingface dataset:
Towards appropriate use of test phantoms in training deep learning models for mammographic image conversion

We are developing machine learning methods for converting images from a source system to a target image acquisition system. The generated images preserve the anatomical content of a source image and produce the physical characteristics of a target image acquisition system. Our previous results indicated that incorporating slanted edge images into the training dataset improved the performance of the conditional generative adversarial network (cGAN) used for the image conversion. In this study, we investigated the appropriate use of phantom images in training when similar phantoms are used to measure the physical characteristics of the model-generated images. We performed two experiments with different training and testing data settings to study the bias that might be introduced when measuring the high contrast edge preservation (HCEP) capability of the cGAN with edge phantoms used as part of the training dataset. HCEP was measured using techniques borrowed from the measurement of the modulation transfer function (MTF). Our results indicated that using similar edge images in training the cGAN and then in measuring HCEP resulted in a biased estimate of the network performance in reproducing the target system MTF. Augmenting the training phantom data so that the training images were diverse and did not match the edge angle of the test phantom images reduced the bias and improved cGAN performance in modeling the target acquisition system. Moreover, this technique led to an improvement in the multi-scale structural similarity (MS-SSIM) value between the generated and the target breast images. Our results indicated that caution must be exercised when including phantom images in the training of a deep learning network if similar phantom images are also planned to be used for obtaining physical measures of image quality such as the HCEP or MTF.
YOLOX-based framework for nuclei detection on whole-slide histopathological RGB and hyperspectral images

The current advances in Whole-Slide Imaging (WSI) scanners allow for more and better visualization of histological slides. However, the analysis of histological samples by visual inspection is subjective and could be challenging. State-of-the-art object detection algorithms can be trained for cell spotting in a WSI. In this work, a new framework for the detection of tumor cells in high-resolution and high-detail using both RGB and Hyperspectral (HS) imaging is proposed. The framework introduces techniques to be trained on partially labeled data, since labeling at the cellular level is a time and energy-consuming task. Furthermore, the framework has been developed for working with RGB and HS information reduced to 3 bands. Current results are promising, showcasing in RGB similar performance as reference works (F1-score = 66.2%) and high possibilities for the integration of reduced HS information into current state-of-art deep learning models, with current results improving the mean precision a 6.3% from synthetic RGB images.
Investigating the use of fuzzy logical data for evaluating the teaching quality of college teachers

The level of education in colleges is career and development-focused compared to that from high schools. Quality education relies on the teachers’ qualifications, knowledge, and experience over the years. However, the demand for technical and knowledge-based education is increasing with the world’s demands. Therefore, assessing the knowledge of teaching professionals to meet external demand becomes mandatory. This article introduces an Acceded Data Evaluation Method (ADEM) using Fuzzy Logic (FL) for teaching quality assessment. The proposed method inputs the teachers’ skills and students’ productivity for evaluation. The teachers’ knowledge and updated skills through training and self-learning are the key features for evaluating the independents’ performance. The impact of the above features on the student qualifying ratio and understandability (through examination) are analyzed periodically. Depending on the qualifications and performance, the teachers’ knowledge update is recommended with the new training programs. In this evaluation process, fuzzy logic is implied for balancing and identifying the maximum validation criteria that satisfy the quality requirements. The recommendations using partial and fulfilled quality constraints are identified using the logical truth over the varying assessments. The proposed method is analyzed using the metrics evaluation rate, quality detection, recommendations, evaluation time, and data balancing.
IMPLEMENTATION OF THE SIMPLE MULTI-ATTRIBUTE RATING TECHNIQUE METHOD IN DSS SELECTION OF EXTRACURRICULAR ACTIVITIES

Extracurricular activities at SMAN 1 Cibungbulang are a means of exploring student competencies. Currently, students can choose extracurriculars without considering their interests and talents. It creates problems for extracurricular coaches because it is challenging to explore student competencies. In this study, the Simple Multi-Attribute Rating Technique (SMART) method was implemented and implemented in a decision support system to overcome this problem. This study used four alternatives: Basketball, Volly, Aikido, and Futsal. At the same time, the criteria are interests, talents, schedules, and achievements. The type of criteria used is benefit criteria. The decision support system is designed using object-oriented design and built on a web-based basis. The decision support system is access by every user registered on the system.
SECURE DATA SHARING IN AWS CLOUD ENVIRONMENT

The main aim of this paper is to provide secure data sharing to authenticated users by the data owners by checking authorization. As it is effective and low-cost management, cloud-based data storage services have the interest in both academia and industry in recent years. Service providers must utilize secure data storage and sharing mechanisms to maintain data confidentiality and service user privacy because they deliver service through an open network. Encryption is the most widely used for protecting sensitive data from being hacked. However, simply encrypting data (for example, using AES) is insufficient to meet the actual need for data management. Also, effective access control over download requests must be considered to prevent Economic Denial of Service (EDoS) attacks from preventing users from using the service. In this work, we look at secure cloud-based storage, in the sense that we create a control mechanism that can handle both data access and download requests while holding security and efficiency using an attribute-based encryption algorithm. Keywords: Cloud, Economic Denial of Sustainability (EDOS), Advanced encrypted standard(AES), Denial of Service(DOS).
Designing Interactive Workbook via Liveworksheets for Reading Skill at Grade 10

This research aimed to design interactive workbook via Liveworksheets for reading skill at tenth grade. The subject of this research was a class of 10th grade students in a Senior High School in Tanjungpinang, which consisted of 16 students. This research was research and development with the ADDE model based on Richey and Klein (2014). This research focused on designing interactive workbook for the tenth grade second semester English materials. It consisted of announcement text, recount text, and narrative text. There were four phases in this research which are analysis phase, design phase, development phase, and evaluation phase. The result from the validation stage showed that there was minor revision in the media aspect. After the revision had been approved by the validator, the interactive workbook was decent to be used in the classroom. From the practical result, it can be seen that the interactive workbook got good responses from the students. The interactive workbook also met the students’ characteristics which were classified as generation Z.
A framework for testimony-infused automated adjudicative dynamic multi-agent reasoning in ethically charged scenarios

In "high stakes" multi-agent decision-making under uncertainty, testimonial evidence flows from "witness" agents to "adjudicator" agents, where the latter must rationally fix belief and knowledge, and act accordingly.The testimonies provided may be incomplete or even deceptive, and in many domains are offered in a context that includes other kinds of evidence, some of which may be incompatible with these testimonies.Therefore, before believing a testimony and on that basis moving forward, the adjudicator must systematically reason to suitable strength of belief, in a manner that takes account of said context, and globally judges the core issue at hand.To further complicate matters, since the relevant information perceived by the adjudicator changes over time, adjudication is a nonmonontonic/defeasible affair: adjudicators must dynamically strengthen, weaken, defeat, and reinstate belief and knowledge.Toward the engineering of artificial agents capable of handling these representation-and-reasoning demands arising from testimonial evidence in multi-agent decision-making, we explore herein extensions to one of our prior cognitive calculi: the Inductive Cognitive Event Calculus (IDCEC).We ground these extensions in a recent, tragic drone-strike scenario that unfolded in Kabul, Afghanistan, in the hope that use by humans of our brand of logic-based AI in future such scenarios will save human lives.
Organic Solid Waste Management by Producing Eco-Enzymes from Fruit Skin in Permata Tembalang

The Ministry of Environment and Forestry in 2020 estimates that waste dumps in Indonesia will be 67.8 million tons. However, organic waste management in Indonesia is still relatively low. The accumulation of organic waste in the landfill, which usually causes unpleasant odors and potentially cause an explosion due to the production of methane gas from natural decomposition processes, can be avoided by prioritizing waste management from the source. An alternative way to handle organic waste is make eco-enzymes because it is efficient, economical, and environmentally friendly. This is the focus of the community service team. The community service method is carried out in three stages, including the preparation stage, the implementation stage, and the final stage. The preparation stage consists of doing a module for sorting organic waste and its utilization (making eco-enzymes). The next phase consists of socialization, education, and training on sorting organic waste and making the right eco-enzymes. The final stage consists of evaluation and making a final report. The expected result after education, socialization, or training has been carried out by the community service team, the community members, especially RT 04 RW 05 Permata Tembalang, know better and understand the sorting and utilization of organic waste.
THE EFFECTIVNESS OF CANVA IN PROJECT BASED LEARNING TO INCREASE STUDENT WRITING SKILL

This study aims to determine the effectiveness of the usage of the Canva media utility to enhance students' writing abilties. Researchers used a quantitative method that's a pre-experimental research design by making use of pre-test and post-test. Written exams are used to gather scholar fulfillment as an tool of this studies. The researchers used two classes in the 9th grade of junior high school as research samples. Dedication of the sample of this have a look at the use of random sampling method. To investigate studies facts, researchers used the t-test. Primarily based at the studies evaluation, the common score of the students' writing take a look at for the remedy magnificence in the post-test changed into (85.25), and the pre-test turned into (65.50). For the non-treatment class (61.00) for the pre-test at consequences. For the post-test only got (61.25). The effects also show that the T-test fee (9.185) is better than desk (1.824), at a significance degree of 5%. If the t-test a look at consequences are better than the t-desk. It suggests that the alternative hypothesis (Ha) was accepted and (Ho) was rejected. Primarily based at the consequences of t-test calculations, it has a look at can be concluded that the Canva utility is an powerful medium for facilitating students in improving their writing performance of descriptive text. For similarly examine, English teacher can apply as the basic principle in this study as a attention for teaching writing capabilities. And will be a benefit later for the future.
Translation Error Taxonomies in Indonesian Tourism Guidebooks

There are many tourism guidebooks spread all around Indonesia yet they are not of good quality. This study embarks on the idea that the quality of tourism guidebook is still far from perfect. This study intends to explore the type of error taxonomies found in the tourism guidebooks by employing qualitative method. Six tourism guidebooks were collected and used as data and the theory being used is proposed by Dastjerdi and Abdolmaleki (2012). They modify the classification of errors in translation based on Keshavarz and ATA and proposed the name of error taxonomy. This error taxonomy has four major error types of syntactic error with three error patterns, semantic errors with 12 error patterns, pragmatic errors with 4 patterns, and translation-specific errors with 4 patterns. The selected guidebooks are then analyzed sentence by sentence using the error taxonomy in order to find out the error in the translation. Total data gathered are 519 with 292 are recognized as errors. These are then broken down into four types of errors: semantic errors having 149 errors (51%), syntactic errors having 133 errors (45%), and the least occurrence of errors is in the type of both pragmatic and translation-specific errors with only 5 errors (2%). This result will provide recommendation for the tourism boards or the local government who published these guidebooks to take prompt action to review and revise their edition of tourism guidebooks.
Microfacies and depositional environment of Shiranish Formation in kirkuk Oil fields north Iraq

This study is reveal the characteristics of Shiranish Formation to elucidate the sedimentological properties of Kirkuk Oil fields north of Iraq from wells k-331, k-334, k-411. The studied rocks are consist of limestone, shaley limestone, marly limestone and thin bedded of marl. Facies analysis showed that the exact composition of Shiranish sequences composed of three facies succession of President rich Balmikarait and shells Alforamenavera floating. These facies and depending on popularity are Lime Wackestone Microfacies and Lime Mudstone Microfacies, these facies are divided in turn into secondary facies which Planktonic Foraminiferal Lime Wackestone Submicrofacies (W1), and Bioclastic Lime Wackestone Submicrofacies (W4). Depending on Facies and environmental analysis shows that the composition of sequences have deposited within the basin foreland achieved on the inactive edge Arabian plate and deposited in deep bathyal and out shelf environment
Adaptive multi-predictor based reversible data hiding with superpixel irregular block sorting and optimization

Reversible data hiding (RDH) is a special class of steganography that is capable of recovering the original cover image upon the extraction of the secret data. The main goal of this paper is to develop different adaptive predictors based on superpixel irregular block sorting. Firstly, a superpixel irregular block and sorting strategy is proposed which is applied to histogram shifting for the first time. Then, a multi-directional edge classification method is proposed, which divides pixels into strong edge pixels, normal edge pixels, and weak edge pixels. Moreover, strong edge pixels and normal edge pixels are further divided into four directions. According to edge classification, the most appropriate adaptive multi-predictor is proposed. Finally, an optimization-based data hiding strategy is proposed. The proposed scheme focuses on constructing a sharp enough histogram. The investigational results demonstrate that the proposed scheme achieves large capacity, high image quality, and low complexity.
ANALISIS PERANCANGAN SISTEM INFORMASI PADA APLIKASI VIDIO.COM

Streaming is currently being favored by millennials. Streaming is a multimedia service that is able to offer a service that is quite adequate for users to enjoy multimedia services in real-time and maintain a stable level of stability. Streaming can be applied to wireless networks or LAN networks. With the streaming on a LAN network, the costs required to provide streaming services are even lower. The protocol used for this media server is Real Time Protocol (RTP). One of the uses of streaming technology that is widely used in daily activities is Streaming Video. One of the most popular streaming media in Indonesia today is Netflix, Disney and Vidio.com. From the research we made, we concluded that watching streaming online is something that is being popular with many people. Therefore, we chose the object, namely Vidio.com, present as one of the platforms that provide film and series shows at this time that we can watch streaming throughout Indonesia. The purpose of this report is to find out the analysis and design of the subscription system in Vidio.com. begins by looking for information on the method that will be used in our object, namely Data Structured Oriented Methodologies because this methodology emphasizes the structure of the input and output systems. After that we made a DFD or Data Flow Diagram of our object, namely Vidio.com, there are several descriptions of the process from Vidio.com's Data Flow Diagram from level 0 regarding Login to level 1.3 Watching. After creating a DFD we created a BPMN or what can be called a Business Process Modeling Notation, by making this BPMN the aim is to provide a notation that is easy to use and understandable by everyone involved in the business. after that we explain in detail about the business process of the Vidio.com object.
Introduction to <i>topiCS</i> Volume 15, Issue 2

The spring issue of Topics in Cognitive Science hosts contributions from three ongoing topics, two of which are devoted to excellent and award-winning research, and the third hosting a debate that has engaged many in and beyond cognitive science. The first contribution was invited as part of the topic aimed at introducing the Fellows of the Cognitive Science Society (outlined in Bender, 2022), so far featuring articles by Cleotilde “Coty” Gonzalez (Gonzalez, 2022), Steven Sloman (Sloman, 2022), and Jenny R. Saffran (Ruba, Pollak, & Saffran, 2022). This distinguished and well-cited line-up now includes Michael J. Spivey from the University of California at Merced, who was elected Fellow of the Cognitive Science Society in 2022. In his paper, “Cognitive science progresses toward interactive frameworks,” Spivey chose to give an overview of a development in the field that may turn out to be a paradigm shift—away from the computer metaphor of the mind toward nonlinear accounts of cognition that focus on interactions and relations rather than on components—illustrating this tour de force with numerous examples, many of which come from his own groundbreaking research program. Because he frames this account also in response to the debate on the status of interdisciplinary integration in cognitive science (Núñez et al., 2019; and see the commentaries in topiCS 11:4; Contreras Kallens, Dale, & Christiansen, 2022; Núñez et al., 2020), we have assigned his manuscript to an additional topic, the Commentaries on Rafael Núñez's article, “What happened to cognitive science?” (Gray, 2019). This makes Spivey's paper the first in the history of our journal that will be part of two topics. The topiCS team wishes to take this opportunity to congratulate all recipients of CSS prizes for their awards and for the outstanding work that earned them these awards—kudos! topiCS encourages letters and commentaries on all topics, as well as proposals for new topics. Letters are not longer than two published pages (ca. 400–1000 words). Commentaries (between 1000 and 2000 words) are often solicited by Topic Editors prior to the publication of their topic, but they may also be considered after publication. Letters and commentaries typically come without abstract and with few references, if any. The Executive Editor and the Senior Editorial Board (SEB) are constantly searching for new and exciting topics for topiCS. Feel free to open communications with a short note to the Executive Editor ([email protected]) or a member of the SEB (for a list, see the publisher's homepage for topiCS: http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1756-8765/homepage/EditorialBoard.html).
Web Random Token Generation Technology Based on Asymmetric Encryption Technology

With the progress and development of the world, the improvement of various hardware speeds and the rapid development of networks, especially distributed applications, the current encryption algorithms are very likely to be cracked one day in the future. At that time, more advanced algorithms should be sought to ensure the security of data. Therefore, the encryption algorithm of asymmetric encryption technology (AET) is only safe at this stage, and needs to be improved continuously to provide faster speed and stronger security. With the background of enterprise after-sale service project, this paper makes a detailed and in-depth study on the construction of web-based enterprise information network system, and attempts to develop a web-based enterprise information network system using AET on the new generation of WEB random token generation technology (RTGT) to realize the networked management of enterprise information. First of all, based on the review and summary of the basic technology research of the Web information network system, this paper deeply studies the DES and AET data encryption technology used in the data security design of the enterprise information network, proposes the WEB RTGT based on AET, and introduces its implementation process in detail.
UTILIZING DEEP LEARNING NLP IN AUTO CLASSIFICATION OF OCRED DOCUMENTS

Paper content was and still is predominant over electronic content in some scenarios, such as official contracts and printed articles.Hence, the importance of building efficient systems that can recognize printed materials and convert them into digital ones is of utmost importance.The domain of Optical Character Recognition (OCR) has been richly accelerated during the past decade, with deep learning becoming widely available and computationally feasible.However, the process of archiving and transforming documents into digital content has been a widely researched area with more innovations in mixing multiple deep learning technologies into a single OCR pipeline.This paper aims to introduce a classification pipeline to OCR documents using deep learning techniques in the natural language processing domain to help classify scanned documents efficiently and with accurate results.
Application of the GMDH Neural Network in Monitoring Soil Salinity of Saveh Plain using Spectral and Topographic Indicators

Application of the GMDH Neural Network in Monitoring Soil Salinity of Saveh Plain using Spectral and Topographic Indicators