Reviewerly/exHarmony huggingface dataset:
AI-based pipelines for the automated recognition of hepatocellular carcinoma and the semantic segmentation of virtually stained liver biopsies

Nowadays, the pathologists' routine clinical practice is based on the visual observation, through a microscope, of Hematoxylin/Eosin (H&E)-stained tissue sections. In order to perform a tumor diagnosis, pathologists exploit only qualitative or semi-quantitative data, as morphological patterns or relevant biological structures. This approach is subjective, poorly informative, and based on expensive and time-consuming staining procedures, which lead to delays in obtaining a diagnosis. As a consequence, recently, algorithms able to assist the experts during the diagnosis procedure are attracting the interest of the histopathologists' community.
On publishing good work

American AnthropologistVolume 124, Issue 1 p. 7-7 FROM THE EDITOR On publishing good work Elizabeth Chin, Corresponding Author Elizabeth Chin Editor-in-Chief [email protected] ArtCenter College of Design, Pasadena, CA 91103, USA. [email protected]; she/herSearch for more papers by this author Elizabeth Chin, Corresponding Author Elizabeth Chin Editor-in-Chief [email protected] ArtCenter College of Design, Pasadena, CA 91103, USA. [email protected]; she/herSearch for more papers by this author First published: 10 February 2022 https://doi.org/10.1111/aman.13711Citations: 1Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article.Citing Literature Volume124, Issue1March 2022Pages 7-7 RelatedInformation
Class-attribute inconsistency learning for novelty detection

In this paper, we address the problem of novelty detection whose goal is to recognize instances from unseen classes during testing. Our key idea is to leverage the inconsistency between class similarity and (latent) attribute similarity. We are motivated by the observation that a novel class may holistically appear like a certain known class (class-level reference) but often exhibits unique properties similar to others (attribute-level references). That is, the related class- and attribute-level references are often inconsistent for a novel class. A new two-stage Class-Attribute Inconsistency Learning network (CAILNet) is proposed to explore class-attribute inconsistency for novelty detection. Stage one aims to learn both class and attribute features based on the class labels and fake attribute labels, and stage two aims to search for the corresponding references and make fine-grained comparisons for final novelty decision. Empirically we conduct comprehensive experiments on three benchmark datasets, and demonstrate state-of-the-art performance.
Revisiting occurrence typing

We revisit occurrence typing, a technique to refine the type of variables occurring in type-cases and, thus, capture some programming patterns used in untyped languages. Although occurrence typing was tied from its inception to set-theoretic types—union types, in particular—it never fully exploited the capabilities of these types. Here we show how, by using set-theoretic types, it is possible to develop a general typing framework that encompasses and generalizes several aspects of current occurrence typing proposals and that can be applied to tackle other problems such as the reconstruction of intersection types for unannotated or partially annotated functions and the optimization of the compilation of gradually typed languages.
Counting versus Detection in an FM Application that Deals with Rooms Reservation

With the evolution of the capabilities of large web applications as well as the IoT, the possibility of transmitting data read by sensors in real time in the application appeared. For FM applications, especially those that deal with Rooms Reservation, there is a need to determine the number of people in a room.
Comparative Analysis of Multiple Watermarking Medical Image Using Wavelet Transform

With the development of telecommunication, the transfer of medical data has become very easy which poses many problems in terms of confidentiality and security. The digital watermarking is a recent solution which has the aim to protect and to increase the security of medical images transferred in hospital networks. In this paper, we present a comparison of multiple watermarking medical image based on Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), Singular Value Decomposition (SVD), and Fast Walsh-Hadamard Transform (FWHT). The multiple watermarking is used to give a multiple protection, to increase robustness and to ensure high security of medical images. In our approach, we have compared two combinations DWT-DCT-SVD and DWT-FWHT-SVD. In these combinations, three watermark images of sizes 512 × 512 are inserted into a single medical image of diverse modalities such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT Scanner), and X-Ray imaging. The results show that DWT-FWHT-SVD combination gives a good compromise robustness-imperceptibility-capacity compared to DWT-DCT-SVD.
Variational Inference for a Recommendation System in IoT Networks Based on Stein’s Identity

The recommendation services are critical for IoT since they provide interconnection between various devices and services. In order to make Internet searching convenient and useful, algorithms must be developed that overcome the shortcomings of existing online recommendation systems. Therefore, a novel Stein Variational Recommendation System algorithm (SVRS) is proposed, developed, implemented and tested in this paper in order to address the long-standing recommendation problem. With Stein’s identity, SVRS is able to calculate the feature vectors of users and ratings it has generated, as well as infer the preference for users who have not rated certain items. It has the advantages of low complexity, scalability, as well as providing insights into the formation of ratings. A set of experimental results revealed that SVRS performed better than other types of recommendation methods in root mean square error (RMSE) and mean absolute error (MAE).
Word Reading Accuracy as a Mediator for Fluency

This paper aims to test the predictive role of nonword reading abilities (accuracy and fluency) and rapid naming (RAN) over word reading fluency. Letter knowledge (LK) and phonological awareness (PA) for literacy were accounted for as control factors due to their ceiling effects. We test the direct impact of the main predictors and their indirect outcomes by controlling for word reading accuracy. To this end, 206 children participated and completed measures for nonword reading abilities, rapid naming, word reading accuracy, fluency, and letter knowledge, and phonological awareness. The results show that RAN and nonword reading accuracy are direct predictors of word reading accuracy. Word reading fluency is significantly associated with rapid naming. The effect of RAN, nonword reading accuracy, nonword reading fluency on word reading fluency is partially mediated by word reading accuracy. We discuss the practical implications.
Interactive Design Psychology and Artificial Intelligence-Based Innovative Exploration of Anglo-American Traumatic Narrative Literature

The advent of the intelligence age has injected new elements into the development of literature. The synergic modification of Anglo-American (AAL) traumatic narrative (TN) literature by artificial intelligence (AI) technology and interactive design (ID) psychology will produce new possibilities in literary creation. First, by studying natural language processing (NLP) technology, this study proposes a modification language model (LM) based on the double-layered recurrent neural network (RNN) algorithm and constructs an intelligent language modification system based on the improved LM model. The results show that the performance of the proposed model is excellent; only about 30% of the respondents like AAL literature; the lack of common cultural background, appreciation difficulties, and language barriers have become the main reasons for the decline of reading willingness of AAL literature. Finally, AI technology and ID psychology are used to modify a famous TN work respectively and synergically, and the modified work is appreciated by respondents to collect their comments. The results corroborate that 62% of the respondents like original articles, but their likability scores have decreased for individually modified work by AI or ID psychology. In comparison, under the synergic modification efforts of AI and ID psychology, the popularity of the modified work has increased slightly, with 65% of the respondents showing a likability to read. Therefore, it is concluded that literary modification by single ID psychology or AI technology will reduce the reading threshold by trading off the literary value of the original work. The core of literary creation depends on human intelligence, and AI might still not be able to generate high-standard literary works independently because human minds and thoughts cannot be controlled and predicted by machines. The research results provide new ideas and improvement directions for the field of AI-assisted writing.
Feature Selection Method Based on Classification Performance Score and P-value

Learning from high dimensionality data has gained increasing attention in recent years due to the massive growth of skewed data across many scientific fields. Some researches show that feature selection does play an essential role in selecting the significant features, also reducing the number of dimensions in the process. In this study, we propose a feature selection approach based on P-value and a performance score. In fact, our solution contains two main steps that work in parallel. The first one measured the performance score of each feature applying support vector machine (SVM). The second estimated the P-value of each feature, then fixed the threshold. Combining both steps, we obtain the feature kth and the range features that will be used to create the subsets. We tested the classification performances of the different selected features subsets using performance score with three different classifiers, and we choose NB that showed the highest performance to continue the discussion section based on it. Besides, we assessed the performance score of the proposed approach with four different datasets from Kaggle. According to the results of the study, and comparing two cases for each selected subset, we concluded that the higher performance could be reached by including the Kth, Kth + 1 in the selected subset of features.
Food and nutrition education improves knowledge about the treatment of type 2 diabetes mellitus: a qualitative study

Food and nutrition education is a determining factor for the prevention and control of chronic non-communicable diseases such as type 2 diabetes mellitus. Thus, the aim of this study was to evaluate the effect of food and nutrition education actions on the perception and knowledge about the disease, its treatment, and the difficulties faced by people with type 2 diabetes mellitus. Longitudinal quantitative-qualitative study, carried out with 10 adults with type 2 diabetes mellitus, assisted by the Academic Diabetes Nutrition League of the Federal University of Sergipe. Individuals were invited to participate of the food and nutrition education activities carried out weekly for six weeks. Participants were interviewed before (T0) and after (T1) the activities in order to investigate the knowledge obtained. A semi-structured interview with two questions was used to obtain their responses, the first was related to the perception about the disease and treatment, and the second concerned the difficulties faced in the search for healthy eating. The analysis was performed using the Content Analysis technique. For the first question, responses were categorized into emotional, physiological meanings and treatment, while for the second, external, internal aspects and those without difficulties emerged. After the activities, the responses were positive about the adoption of healthy eating in the treatment. The intervention allowed individuals to gain more knowledge of the disease and aspects related to treatment.
Effectiveness of learning using application zoom theon student learning outcomes during COVID-19 at STKIP Muhammadiyah Sungai Penuh

Online learning and working from home for educators are changes that must be made by lecturers to continue teaching students. Education with distance has the aim of improving the quality of education and relevance education as well as increasing equitable access and expansion of education. Distance education which is held with good quality assurance and in accordance with the stakeholder needs is one of the mechanisms for expanding access higher education. The purpose of this study was to determine the effect of learning Statistics the basics of using the application Zoom on student learning outcomes during the period COVID-19 in Indonesia STKIP Muhammadiyah Sungai Penuh. The method and type of this research is Ex-Post Factor post-event and descriptive correlational measurements. Based on the results of the analysis and discussion of the data that has been stated previously, it can be concluded: there is no effect of learning Basic Statistics using the application zoom on student learning outcomes during the period COVID-19 at STKIP Muhammadiyah Sungai Penuh. This matter it can be seen from rcount &gt;rtable that is 0.472 &gt; 0.444. Because the price of rcount&gt;rtable then H1 accept, the coefficient of determination = 0.2314.
Non-Linear Relationship Between Inflation and Growth: The Case of Transition Economies

This study examines the non-linear relationship between inflation and economic growth using a Panel Threshold Regression on a dataset over the period 1998-2019 for selected transition economies. &#x0D; The findings of the study show that there exists a threshold effect of inflation on economic growth. Furthermore, our findings provide evidence that the population growth, workers’ remittances, and domestic investments are statistically significant in explaining economic growth. Findings obtained from this study can be useful for policymakers of the countries under consideration. Since inflation above the threshold negatively affects growth, central banks in the countries considered may contribute to an increase in economic growth by reducing the inflation rate to the estimated threshold value.
A Novel Bayesian Approach for Construction of Random Forest

Decision tree is one of the commonly used machine learning algorithm. Random Forest (RF) is an ensemble of such decision trees. The construction of optimal Decision Tree and hence Random Forest is NP Hard when data is large. The Bayesian statistics have been used in the past for various machine learning and pattern recognition problems. The Bayesian statistics provide a tool to construct Random Forest when no prior information for data is available. Here a forest is generated based on Bayesian statistics where numerous trees are sampled given the prior distribution without the use of training data, and after that weighted ensemble is performed. In the past, it has been used for classification problems. In this paper, we are proposing construction of RF under Bayesian framework using Tree Strength concept. Also, we extend our proposal to regression problems. The proposal is evaluated on UCI data sets for both classification and regression task and found satisfactory results.
DEVELOPING INTERACTIVE MULTIMEDIA TO TEACH READING NARRATIVE TEXT USING ADOBE FLASH CS6

This research aims to find out the target needs, learning needs and developed theappropriate reading interactive multimedia for the tenth-grade students of MAN2 Pontianak. This is a Research and Development (R&amp;D) using ADDIEprocedures. ADDIE stands for Analysis, Design, Develop, Implement, andEvaluate. The subjects of this research were the English teacher and the tenth-grade students of MAN 2 Pontianak. The data were collected through interviews,questionnaires, and expert judgments. The researcher used Adobe Flash CS6 todevelop interactive multimedia. The result of the needs analysis showed that thestudents needed interesting materials, fun activities, and animation videos tosupport their learning English, especially in reading skills. Therefore, this studydeveloped the topic of narrative text, which was implemented in interactivemultimedia. This interactive multimedia consists of materials of the narrativetext, narrative story, animation videos with subtitles, and quiz with four activities.This interactive multimedia focused on learning the correct pronunciation,vocabulary, grammar (past tense), and the text types of narrative text. Based onthe result both from the teacher and the multimedia expert, it said that theinteractive multimedia quality belongs to the “very good” category with 4.21 &lt;X ≤ 5. It can be concluded that the interactive multimedia is effective andappropriate to implement in the class by the English teacher.Keywords: Interactive Media, Narrative Text, Reading
Information support of students in scientific activity in higher education institutions system

The article presents the results of the study of information support of students in research activities. The relevance of the study is due to the fact that the development of information support is one of the important indicators of the university's effectiveness. The problem is that the degree of student involvement in scientific activity in most universities is not high enough. The purpose of the study is to identify the degree of interest of students in scientific activities. The novelty of the research is that an attempt made to construct a typology of students in terms of their attitude to scientific activity. In the course of the research, the percentage of how students receive information on scientific events and who organized the work to attract students to NID (research activity) was determined. The study conducted among 1-4-year students of the Kazan Federal University (KFU) and the Kazan National Research Technical University named after A.N. Tupolev-KAI (KNITU-KAI). An online questionnaire survey used as a research method. Based on the results of an online survey, it is obvious that 87,3% of students are ready to engage in scientific activities in the framework of the chosen specialty. At the same time, interest in scientific activity is high, this is confirmed by 88,1% of students with scientific achievements. The results of the study make it possible to assess in reality the perception of scientific activity by students and to show the problems of an objective nature that exist in the organization of the university.
Experiences with Constructing and Evolving aSoftware Product Line with Delta-Oriented Programming

A Software Product Line (SPL) captures families of closely related software variants. The configuration options of an SPL are represented by features. Typically, SPLs are developed in a feature-centric manner and, thus, require different development methods and technologies from developing software products individually. For developers of single systems, this means a shift in paradigm and technology. Especially with invasive variability realization mechanisms, such as Delta-Oriented Programming (DOP), centering development around configurable features realized via source code transformation is commonly expected to pose an obstacle, but concrete experience reports are lacking. In this paper, we investigate how DOP and cutting-edge SPL development tools are picked up by non-expert developers. To this end, we report on our experiences from a student capstone SPL development project. Our results show that participants find easy access to SPL development concepts and tools. Based on our observations and the participants' practices, we define guidelines for developers using DOP.
Person following control for a mobile robot based on color invariance corresponding to varying illumination

In this paper, we present a method of person following control for a mobile robot using visual information. Color information is often used for object tracking. Color information of objects varies greatly under illumination changing environment. In such conditions , the robot controlled by visu al information may lose sight of a person. In this paper, we consider a robust person following method by color invariance and image - based control. Color invariance show s robust features of colored objects in terms of changing illumination conditions . At f irst, we estimate the lowest positions of both feet of a tracked person through particle filters based on color invariances . Then, we control the velocity of the robot to track the person by using the image - based controller. Experimental results using a n a ctual robot demonstrate the effectiveness of the proposed method.
Contradictions in Learning to Teach Digital Literacy Practices in an EFL Public Setting: An Activity Theory Analysis

Teaching reforms, methods for teaching efl and institutional requirements present a significant number of contradictions in the activity system of efl pre-service teachers (pst). As novice practitioners, efl pst are exposed to these con­tradictions while engaging in new activity systems, particularly when learning to put theory into practice. Hence, this case study examines the contradictions that an efl pst, from a university in Córdoba, Colombia, faced when incorporating digital literacy practices (dlp) in her planning as a result of her interaction with the activity theory (at) ele­ments. It also describes the shifts in agency that occurred when she was exposed to contradictory dynamics of teaching. The study involved the pst and a group of 35 students in 10th grade. Class observations and semi-structured inter­views with the pst revealed contradictions in terms of mediating artifacts, objects, division of labor, and rules. They also indicated how the identified contradictions could form the basis for a shift in her teaching activity system. These results suggest teaching programs should not only concentrate on how much our students learn new strat­egies but also on how much they have acquired transferable skills for transforming their contexts. Keywords: Activity theory; contradictions; digital literacy practices; pre-service teachers; efl; teacher education.