Reviewerly/exHarmony huggingface dataset:
Breast Cancer Risk Analysis Using Fuzzy Inference System with the Mamdani Model: Literature Review

Breast cancer is one of the most common cancer causes of death in sufferers. Breast cancer is the second leading cause of death for Indonesian women. For this reason, a device that can identify the risk of breast cancer is needed. The fuzzy method with the Mamdani model is one method that has been widely used in software development to determine the level of breast cancer risk. Purpose: To know the software development of the Fuzzy Inference System Method with the Mamdani model to identify the level of breast cancer risk. Methods: This research is a literature study with the data collection process through the PubMed, Science Direct, ProQuest, and Google Scholar databases. The criteria for the articles used are 2010-2021 publications. Results: Based on a review of 10 journals using the Fuzzy Inference System (FIS) Mamdani model can provide effective results to identify risks for people who have the possibility of developing breast cancer. Conclusion: The fuzzy expert system using the Mamdani method can be applied to the problem domain of breast cancer diagnosis with a fairly good level of accuracy to be able to overcome the inequality of mammography results.
Use of Virtual Reality in Neurorehabilitation: How the Effects of Immersion and Presence May Contribute to Motor and Cognitive Recovery

This paper discusses the effects of virtual reality immersion in the functional recovery of neurological patients. To do so, we conducted a review of the literature after identifying relevant articles published between 2000 and 2013. The results of this review show that high levels of immersion can cause a greater sensation of presence, which can make some applications more effective. The most important level is the realistic experience that immersive VR prompts the user to have. By requiring to have a high level of sensory, visual, auditory and haptic fidelity, the immersive virtual environment enables the experience in the virtual world to correspond, as far as possible, to experiencing the real world being simulated.
Challenges and Issues in Blockchain-Based IoT Services

In this chapter, we present a couple of challenges and problems in Blockchain (BC)-based Internet-of-Things (IoT) applications, where considering the IoT into unique solution. IoT as distributed data manager, sensing solution, and advancements could be the right alternative. Among the customary categories of spread record improvements are present in the blockchain. This one uses a distributed way of thinking which passes on better capacity and takes out the single indication of thwarted expectation. In accumulation, blockchain passes on improved safety and information fairness concluded carefully arranged and constancy highlights. The wire of blockchain using IoT can sort out problems of IoT solidified design and then gives a decent procedure to upcoming movements. Hence, the goal of this chapter is to give an extensive conversation of intertwining the IoT framework with blockchain progression. In the wake of introducing the explanation of IoT and blockchain, the part gave a wide conversation of preparation IoT blockchain by featuring how blockchain settled issues of IoT. In addition, late assessments giving the blend of IoT blockchain are likewise introduced. At that point, blockchain by means of a help on behalf of IoT be there talked about to demonstration in what way different highlights of BC innovation can remain carried out, for instance, a help on behalf of different IoT solicitations. This was trailed through future exploration bearings of IoT with blockchain.newline.
Conditions of Educational Process Intensification

The paper aims to identify the conditions of intensification of educational process stages which allow increasing effectiveness, availability and reproducibility of intensive education. The author proposes to address the educational intensification process in the context of intensive realization of six main stages of educational process: learning goals setting, selection and structurization of educational content, operational and activity organization of education, implementation of stimulating and motivating aspect of educational process, the teacher’s control and management, evaluation of learning results. The scientific originality of the research lies in the fact that it uses the systemic approach to identify the interrelated conditions of intensification of the main educational process stages. As a result, the integral structure of educational process intensification which contains interrelated intensive stages and allows increasing effectiveness and reproducibility of education has been developed.
Research on Data Mining Algorithm Based on BP Neural Network

The current data mining algorithm has the problem of imperfect data mining function, which leads to the algorithm taking too long time. This paper designs a data mining algorithm based on BP neural network. Analyze the basic structure of the data mining algorithm, obtain the data characteristics of the multi-objective decision-making, adjust the convergence speed with the distributed computing technology to keep the inertia factor state unchanged, construct the local minimal discrete model, measure the interest of the model, calculate the optimal output value of the network using the BP (Back Propagation) neural network model, and complete the improved design of the data mining function. Experimental results: The average computational time consumption of the designed data mining algorithm is 559.827 seconds, which saves 145.975 seconds and 174.237 seconds respectively than other traditional algorithms. It is proved that the data mining algorithm based on BP neural network reduces the computational time consumption, improves the performance of data mining, and has high application value.
Cross‐domain person re‐identification based on background suppression and identity consistency

Recent advanced methods address the cross-domain person re-identification (Re-ID) problem primarily by the pseudo label estimation. However, person images with similar backgrounds are usually incorrectly clustered due to the interference of background noise. Furthermore, it is the lack of filtering of incorrect pseudo labels that results in the poor quality of the final generated pseudo labels. Here, a progressive learning approach based on background suppression and identity consistency for cross-domain person Re-ID (BSIC-reID) is proposed. In the background suppression module, background mask attention and reverse attention are combined to effectively extract pedestrian features and suppress background noise, highlighting the foreground person information for person Re-ID. In addition, the BSIC-reID model is used to extract multi-scale person features and generate different perspective pseudo labels for the target domain images. The incorrect pseudo labels are filtered by comparing the potential similarity of multi-scale person features so that the higher quality pseudo labels for the target domain images could be generated. This method is performed on the Market-1501, DukeMTMC-reID, and MSMT17, evaluated by the Cumulated Matching Characteristics (CMC) and mean Average Precision (mAP). The experimental results demonstrate that this method also achieves state-of-the-art performance.
new multi-agent approach for generating feedbacks base on Multiple Choice Questions

Most intelligent tutorial systems promote the learning and resolution of exercises based on feedback in the form of advice, remarks, explanation…To always remain in self-assessment, our approach offers a new type of feedback in the form of multiple-choice questions applied in the field of algorithms (language c, java ...) dedicated for beginners in programming. The approach is based on the multi-agent model to have interaction between learners without the help of the teacher. There are three types of agents in our work, there is the learner agent who represents the learner, there is also the feedback agent who sends the MCQs to the learners who made mistakes and finally, the controller agent feeds the base of multiple-choice questions based on feedback from learners. The controlling agent compares the instructions of the learners with the instructions of the correct model based on the AST abstract syntax tree and detects errors which will be proposed as erroneous items (distractors) for the learners. We can say that this type of feedback is not direct like other work (advice, comments, explanation ...) but we always remain in the evaluation: MCQ, exercise ... As perspectives, we will focus mainly on the classification of LO learning objects in the form of an ontology to facilitate the use of data and the generation of multiple-choice questions at different levels. And also, we aim to develop a suitable platform which allows to define the agents and the messages to be exchanged between them to set up our system.
Nonclustered Rowstore Indexes on Columnstore Tables

Clustered columnstore indexes provide effective enough compression and data access speeds that most typical analytic workloads will not require any other indexes to provide adequate performance.
THE WORD SORT STRATEGY TO IMPROVE STUDENT’S ACHIEVEMENT IN WRITING DESCRIPTIVE TEXT

This study attempted to improve students’ writing achievement through The Word Sort Strategy. This study was conducted by using classroom action research. The subject was class X in SMA Amir Hamzah Medan that consisted of 40 students. The study was conducted in two cycles; cycle I consisted of three meetings (meeting I, II, and III) and cycle II consisted of three meetings (meeting IV, V, and VI). The instruments for collecting data were quantitative data (writing test) and qualitative data (interview sheet, observation sheet and diary notes). Based on writing tests’ scores, the mean of the students’ scores in the second cycle was 79.35 and in the first cycle as 71.95. Based on the interview sheet, observation sheet and diary notes, it was found that students interested in applying Word Sort Strategy because it helped them to improve their writing achievement. The result showed that Word Sort Strategy can improve students’ writing achievement.
Parallelized solution to the asymmetric travelling salesman problem using central processing unit acceleration

&lt;p&gt;&lt;span&gt;Travelling salesman problem is a well researched problem in computer science and has many practical applications. It is classified as a NP-hard problem as its exact solution can only be obtained in exponential time unless P = NP. There are different variants of the travelling salesman problem (TSP) and in this paper, asymmetric travelling salesman problem is addressed since this variant is quite often observed in real world scenarios. There are a number of heuristic approaches to this problem which provides approximate solutions in polynomial time, however this paper proposes an exact optimal solution which is accelerated with the help of multi-threading-based parallelization. In order to find the exact optimal solution, we have used the held-karp algorithm involving dynamic programming and to reduce the time taken to find the optimal path, we have used a multi-threaded approach to parallelize the processing of sub-problems by leveraging the central processing unit cores (CPUs). This method is an extension of a well researched solution to the TSP; however, this method shows that solutions to computationally intensive problems involving sub-problems such as the asymmetic travelling salesman problem (ATSP) can be accelerated with the help of modern CPUs.&lt;/span&gt;&lt;/p&gt;
Automatic Osteosarcoma Classification System Based Multilayer Grid XG Boost Architecture

Abstract Osteosarcoma is the most frequent primary malignant bone tumour. Computer-aided detection (CAD) and diagnosis are being used to enhance osteosarcoma detection and diagnosis . The use of machine learning and deep learning algorithms may save up surgeons' time while also improving patient outcomes. An enormous quantity of data must be fed into the classifier for it to become more accurate. Adapted to a public dataset of osteosarcoma histology pictures, a mix of machine and deep learning is used in this work to distinguish between necrotic and healthy tissue images. First, the dataset was preprocessed, and contour based threshold segmentation techniques are applied. Then, Stochastic linear embedding based Feature extraction is used for extracting the abnormal features. Finally, the proposed multilayer grid XG Boost classifier is trained on stained images in order to increase the output accuracy. The experimental findings indicate that the proposed classifier has the greatest accuracy of any illness classification approach currently available. Our finetuned model showed superior performance in identifying osteosarcoma malignancy using H and E stained pictures.
Don’t miss the mismatch: investigating the objective function mismatch for unsupervised representation learning

Finding general evaluation metrics for unsupervised representation learning techniques is a challenging open research question, which recently has become more and more necessary due to the increasing interest in unsupervised methods. Even though these methods promise beneficial representation characteristics, most approaches currently suffer from the objective function mismatch. This mismatch states that the performance on a desired target task can decrease when the unsupervised pretext task is learned too long - especially when both tasks are ill-posed. In this work, we build upon the widely used linear evaluation protocol and define new general evaluation metrics to quantitatively capture the objective function mismatch and the more generic metrics mismatch. We discuss the usability and stability of our protocols on a variety of pretext and target tasks and study mismatches in a wide range of experiments. Thereby we disclose dependencies of the objective function mismatch across several pretext and target tasks with respect to the pretext model's representation size, target model complexity, pretext and target augmentations as well as pretext and target task types. In our experiments, we find that the objective function mismatch reduces performance by ~0.1-5.0% for Cifar10, Cifar100 and PCam in many setups, and up to ~25-59% in extreme cases for the 3dshapes dataset.
Linguistic Markers of Communicative-Cultural Memory of Generations in the Reflection of the Social Network

This chapter presents a new approach to the sociolinguistic analysis of text data from the VKontakte social network. As new sources of big data for linguistic analysis, social networks are a reasonable basis for interdisciplinary research. On the one hand, the communication represented in social networks is predominantly verbal. On the other hand, any account contains a user’s personal information, such as age, gender, place of residence and education. It should be noted that the analysis of social media data has some bias since not all users honestly specify their personal information. However, despite some “noise” affecting the quality of the sociolinguistic data, we assume that most of the data is valid considering its quantity and allows correlating text and social characteristics. In this chapter we would like to make an emphasis on the age characteristics, implement data analytics and describe typical features for identified age groups.
Perbandingan Tingkat Akurasi Metode Average Based Fuzzy Time Series Markov Chain dan Algoritma Novel Fuzzy Time Series

Fuzzy time series method can be applied in predicting the situation in food price development data such as rice. The position of rice as a staple food has resulted in this commodity being one of the indicators of economic growth. The importance of suppressing rice prices so that they are stable can be done by forecasting rice prices in Indonesia in the future. The research method used for forecasting is average based fuzzy time series Markov chain and novel algorithms fuzzy time series. Researchers will compare the two methods in the case of rice prices by looking at the level of accuracy that is better. The data used in this study is the average monthly rice price at the wholesale trade level from January 2015 to March 2021 in units of Rp/Kg as much as 75 data. The results of the comparison of the level of accuracy using the value of Mean Absolute Percentage Error (MAPE), obtained the forecast of the average price of rice at the Indonesian wholesale trade level for average based fuzzy time series Markov chain which is 0.36%, while the MAPE value for novel algorithm fuzzy time series is 0.19%. Based on the MAPE results, it can be concluded that the novel algorithm method fuzzy time series produces a better level of accuracy compared to the method average based fuzzy time series Markov chain.
A Day-in-the Life of a Systems/Software Engineer 3-5 Years Ahead

Key technology shifts such as big data, social technologies, internet-of-things, and mobile are reshaping the landscape in which engineers operate. This paper will present a vision of software and systems engineering of the future, specifically considering development of complex large-scale real-time embedded systems. We will discuss the future of software/systems craftsmanship, and how this impacts the day-in-the-life of an engineer. In order to address challenges related to speed, quality, and cost we see organizations responding by driving initiatives in the following areas:Agile – deliver more often with focus on business outcomeLean – take out non-value adding tasksCollaboration – a culture of open information sharingInnovation – create room for risk-takingDevOps – breaking down organizational silosWe also see development practices evolving to respond to the changing landscape in the areas of architecture, steering/governance, people/teams as well as how to manage development platforms.
Path Optimization for Mobile Robots using Genetic Algorithms

This article proposes a path planning strategy for mobile robots based on image processing, the visibility graphs technique, and genetic algorithms as searching/optimization tool.This proposal pretends to improve the overall execution time of the path planning strategy against other ones that use visibility graphs with other searching algorithms.The global algorithm starts from a binary image of the robot environment, where the obstacles are represented in white over a black background.After that four keypoints are calculated for each obstacle by applying some image processing algorithms and geometric measurements.Based on the obtained keypoints, a visibility graph is generated, connecting all of these along with the starting point and the ending point, as well as avoiding collisions with the obstacles taking into account a safety distance calculated by means of using an image dilation operation.Finally, a genetic algorithm is used to optimize a valid path from the start to the end passing through the navigation network created by the visibility graph.This implementation was developed using Python programming language and some modules for working with image processing ang genetic algorithms.After several tests, the proposed strategy shows execution times similar to other tested algorithms, which validates its use on applications with a limited number of obstacles presented in the environment and low-medium resolution images.
Image Rain Removal Using Conditional Generative Networks Incorporating

The research of removing rain from pictures or videos has always been an important topic in the field of computer vision and image processing.Most noise reduction methods more or less remove texture details in rain-free areas, resulting in an over-smoothing effect in the restored background.The research on image noise removal is very meaningful.We exploit the powerful generative power of a modified generative adversarial network (CGAN) by enforcing an additional condition that makes the derained image indistinguishable from its corresponding ground-truth clean image.An efficient and lightweight attention machine mechanism NAM is introduced in the generator, and an IDN-CGAN model is proposed to capture image salient features through attention operations.Taking advantage of the mutual information in different dimensions of the features to further suppress insignificant channels or pixels to ensure better visual quality, we also introduce a new fine-grained loss function in the generator-discriminator pair, predicting and real data degree of disparity to achieve improved results.
Features of Organising Educational Communication in the Setting of Using the Virtual Reality Technology

The study aims to determine the features of organising educational communication in the setting of using the virtual reality technology. The author identifies the key issues that a teacher may face in the process of organising students’ educational activities using the virtual reality technology. Scientific novelty lies in describing the components of educational communication in the setting of using the virtual reality technology. As a result, the features of organising the educational process using VR learning tools have been identified, the importance of educational communication has been substantiated, the conditions and features of its organisation have been determined.
THE DEVELOPMENT OF AUDIOVISUAL TEACHING MEDIA BASED ON GRAPHIC ORGANIZER TO IMPROVE STUDENTS’ ABILITY IN FINDING THE MAIN IDEA OF A PARAGRAPH

&lt;p&gt;This study aims to determine the validity, practicality, and effectiveness of the development of audio-visual teaching media based on Graphic Organizer to improve students’ ability to find the main idea of a paragraph at the fifth grade of MIN 2 in Labuhanbatu Selatan. This study follows the Research and Development (R &amp;amp; D) developed by Borg and Gall and has been modified by Sugiyono. This includes nine steps, namely; Potential and Problems, Gathering Information, Product Design, Design Validation, Design Revision, Product Trial, Revision Product, Trial of Use, and Revision of Final Products. The data were collected from interviews, observation, and questionnaires. There were three instruments used, namely preliminary instruments, product development instruments, and product testing instruments. The data were analyzed using descriptive qualitative and quantitative analysis to describe the results of product development. The findings of this study indicate that 1) the validity of assessment made by the material experts of the product media was found to be 80%. The media expert’s assessment of product media obtained a percentage of 86.6%; 2) In terms of the practicality of the product, the percentage of responses given by small-scale students shows 97.6% and the percentage of responses given by large-scale students is 98.4%. Meanwhile, the results of the questionnaires filled by the teacher is 100%, which is adjusted to the practicality criteria table; 3) the average score for the effectiveness before using the product is 76.87%. Meanwhile, the average score for the effects after using the product is 94.37&lt;/p&gt;
More on Convening Technology: Blockchain, Fashion, and the Right to Know

For many years mired in its cryptocurrency history, blockchain held little interest to those working outside the financial world.It now offers the fashion industry and its diverse publics the enticing prospect of a transparent value chain for ethical and sustainable fashion, catering to public demands for a right to know data on authenticity and provenance.Whether this is a feasible prospect remains to be seen.Nevertheless, in staking out its position, blockchain appears to be moving into an interesting phase of its short tumultuous existence.In short, it is taking on the character of a "convening technology" -becoming "the focus of a conversation that can [potentially] address issues far beyond what it may ultimately be able to address itself", and marshalling "resources, institutions and other forms of power".A difficulty is reconciling this beneficial function with the need for the so-called "technology of trust" to be trustworthy in practice, requiring at least a minimal governance model.