Reviewerly/exHarmony huggingface dataset:
Classification of Cell Types on Histopathological Images Using Local Binary Patterns

In recent years, the use of computer aided diagnostic (CAD) systems has been increasing with a high acceleration in the field of digital pathology. Application and study areas are expanding over time include the detection, classification and segmentation of nuclei. In this study, various traditional machine learning methods (k-closest neighborhood, random forests and support vector machines) and deep learning (convolutional neural network) were used comparatively on CRC colorectal adenocarcinomas dataset. Since conventional machine learning algorithms do not receive a two-dimensional input such as convolutional neural network, local binary images are utilized. As a result, when the feature extraction for machine learning algorithms is performed, KNN and RF algorithms provide very successful results, whereas CNN algorithm gave better results without making any feature extraction.
Application rasch model using R program in analyze the characteristics of chemical items

Chemistry is one of the subjects taught in high school. To find out and assess students' understanding regarding chemistry subjects in one semester can be proven by a test. The tests used must have good quality. This study aims to provide information about the characteristics of chemical items test using the Rasch model. Descriptive explorative was used in this study. The subject of the study were tenth grade students in Xaverius Senior High School taken the final semester examination on chemistry subject. The object of this research were the form of item tests and student answer sheets. Data collection techniques used documentation. Student answer sheets were analyzed using the R program. The results showed that the reliability of the item tests was 0.3 to 0.54 or medium category. Subsequently acquired a good level of difficulty about which amounted to 28 items. In addition, the average student ability is 0.008 with a minimum ability of -2.309 and a maximum of 2.233. ICC and IIC obtained are very accurate in predicting students' abilities. Chemicals items used in the final semester examination can be used by teachers as a item bank for use in the evaluation of students' abilities. However, there are two items that need to be revised level of difficulty to produce a good question.
A Taxonomic Index for Retrieval of Digitized Whole Slide Images from an Electronic Database for Medical School and Pathology Residency Education

Since the advent of whole slide imaging, the utility of digitized slides for education in medical school and residency has been amply documented. Pathology departments at most major academic medical centers have made digitized slides available to pathology residents for study, even before the use of digitized slides for clinical purposes (i.e., primary diagnosis) has become commonplace. This article describes the experience of one academic medical center with the storage and indexing of large volumes of digitized slides. Our goal was to be able to retrieve scanned slides for a variety of educational applications and thereby maximize the heuristic value of the slides. This posed a formidable challenge in terms of development and deployment of an index system that would allow exemplary slides to be identified and retrieved irrespective of the purpose for which the slide was scanned. We used the structure inherent in Aperio's image management software (eSlide Manager) to build an educational database that allowed each image to be appended with a unique taxonomic identifier so that the individual files could be retrieved in a flexible and utilitarian manner.
Improved Collaborative Filtering Algorithm Based on Multi-dimensional Fusion Similarity

Collaborative filtering is the most commonly used technique in personalized recommendation system. In order to improve the accuracy of recommendation algorithm, the parameter is designed to improve the Pearson correlation coefficient after considering the user's interest differences in rating. In addition, the user fusion similarity is obtained by combining the user interest similarity calculated based on the user interest and the user trust similarity calculated based on the trust relationship between users. At last, the improved Pearson correlation coefficient and the user fusion similarity are weighted to obtain the multi-dimensional fusion similarity. Simulation which uses the MovieLens as dataset shows that the improved algorithm has better recommendation quality than traditional algorithms.
Unlikely intersections over finite fields: Polynomial orbits in small subgroups

We estimate the frequency of polynomial iterations which fall in a given multiplicative subgroup of a finite field of $ p $ elements. We also give a lower bound on the size of the subgroup which is multiplicatively generated by the first $ N $ elements in an orbit. We derive these from more general results about sequences of compositions on a fixed set of polynomials.
Decision support system for predicting increased data on objects of motor vehicle name transfer (BBNKB I) using trend moment method (Case study: wheels 2 and wheels 4 in Samarinda)

Abstract Transfer of Motor Vehicle Title Fee (BBNKB I) is first-person vehicle ownership. From this BBNKB I data, we can predict the number of vehicles that will enter Samarinda with the trend moment method. Historical data used are UPTB data from January 2015 to December 2018. To determine the prediction results, the existing historical data are used and use the MAPE value to calculate the error value or inaccuracy of the predicted results. Applications that are built using NetBeans with a MySQL database connection, where calculation formulas are included in the application. The end result is the prediction of the number of vehicles for 2-wheeled vehicles in Samarinda in 2017 with an average MAPE value of 0.55%, in 2018 with an average MAPE value of 1.92% and 2019 with an average MAPE value of 1.31 %. Then for 4-wheeled vehicles in Samarinda in 2017 with an average value of MAPE of 0.57%, in 2018 with an average value of MAPE of 0.77% and in 2019 with an average value of MAPE of 0.47%. MAPE criteria can be used to predict object data of BBNKB I wheels 2 and 4 wheels in Samarinda.
ON DISPERSION RELATIONS AND HADRONIC LIGHT-BY-LIGHT SCATTERING CONTRIBUTION TO THE MUON ANOMALOUS MAGNETIC MOMENT

We discuss the use of dispersion relations for the evaluation of the pseudoscalar contributions to the muon anomalous magnetic moment. We point out that, in the absence of experimental data, reconstruction of light-by-light scattering amplitudes from their absorptive parts is ambiguous and requires additional theoretical input. The need for an additional input makes dispersive computations of the hadronic light-by-light scattering contribution to g-2 akin to phenomenological models, in spite of pretense to the contrary. In particular, we argue that the recent proposal [1], based on the dispersive approach, satisfies short distance constraints at the expense of unjustifiably large deviations from the chiral limit.
Motivación, barreras e incentivos para la compartición y reutilización de los datos de investigación. Visión de los investigadores

Research data sharing, data reuse and facilitating access to research data are not common researchers’ practices, except in certain disciplines, such as Meteorology, Genetics or Astronomy. Institutional, publishers’ and funders’ policies have made the culture of sharing and managing research data particularly relevant. However, the threat of being scooped, the fear to the misuse of their data, to lose control over their data, or the fear to infringe the legislation on intellectual property, are barriers that scientific community faces when trying to make their data findable, accessible, interoperable and reusable (FAIR data). In this work, we have analyzed the pros and cons of sharing and reusing data, based on previous works in the literature that have allowed us to perceive researchers’ habits and perceptions in this regard.
Least significant bit steganography method for the digital data protection in the barcode

In this paper, a computer program based on the least significant bit steganography method was developed. In the program, the message image (the original barcode) was inserted into cover image (false barcode) to produce the stego image barcode by this method. These two images were compared and validated by counting the Mean Square Error, the Root Means Square Error and the Peak Signal to Noise Ratio. The result shown that the program was proper because the Mean Square Error was 0.192314, Root Means Square Error was 0.438537 and the Peak Signal to Noise Ratio were 55.3247 dB. we can conclude that the 1 least significant bit gave the best stego image.
THE TASKS FOR THE ROBOTICS CHALLENGE "DRIVING SCHOOL"

The article presents some tasks to be done by a participant while taking part in a robotics challenge. The tasks can be used while teaching robotics at school. Schoolchildren may have different levels of training. The tasks are divided in three categories due to their difficulty: Beginner, Amateur and Master. The tasks have been successfully tested while the open regional robotics challenge "Driving school" being held. The challenge was held in 2018 by the Shadrinsk State Pedagogical University.
Identification of essential attributes in conclusions of student reports

Abstract This work seeks to help students in improving their first research reports, based on natural language processing techniques. We present a Conclusion model that includes three schemes: Goal Connectedness, Judgment and Speculation. These subsystems try to account for the main expected attributes in conclusions, specifically the Connectedness with the general objective of the research, the evidence of value Judgments, and the presence of Future work as a result of the student reflection after the inquiry. The article details the schemes, a validation of the approach in an annotated corpus, and a pilot test with undergraduate students. Results of a prior validation indicate that student writings indeed adhere to such attributes, especially at graduate level. Statistical results of the pilot test showed that undergraduate students in an experimental group achieved improved conclusion content when compared with the control group.
MODERNIZATION OF GEOGRAPHIC EDUCATION AT HIGH SCHOOL: GEOINFORMATION TRAINING MODELS

The paper deals with contemporary trends of modernization of higher education in the countries of Europe, emphasizes relation between the quality of education and the level of development of the countries' economy, confirms the importance of digital education and provision of new forms of educational services. The problem of mastering new technologies using geoinformation systems and technologies in the process of training future specialists-geographers and geography teachers is analyzed. It is proved that one of the ways of addressing the outlined problem is the implementation of geoinformation training models, and the place of such models among other types of geography training tools is determined. The paper explains the concepts "training model", "geoinformation training model" and outlines their didactic peculiarities. The system of graphic-sign geographic training models is presented, with geoinformation training models being their subtype. The trends of applying geoinformation systems and technologies in the process of teaching geographic disciplines at higher education institutions are characterized. Based on the experience of application of geoinformation training models, the paper emphasizes that they are tools of students’ independent cognitive activities. The paper distinguishes three groups of these models (general, special, combined) and characterizes their varieties. Considerable attention is paid to characterizing special geoinformation models, viz. rendering models, first of all digital elevation models, "draping" models, as well as models of kinematic animation or dynamic interactive visualization of 3D images, routing optimization and self-training models. A considerable didactic potential of electronic maps and atlases is emphasized, which make it possible to study geographic objects, processes and phenomena in the spatial aspect. The conclusion is made that the application of geoinformation training models, on the one hand, allows students to explore independently certain natural or socio-economic objects of the environment. On the other hand, tutors can plan students’ efficient training activities at a high level of complexity and in accordance with their interests.
Uniform mixing on abelian Cayley graphs

In the past few decades, quantum algorithms have become a popular researcharea of both mathematicians and engineers. In 2003, Childs et al. found a graphin which the continuous-time quantum walk spreads exponentially faster than anyclassical algorithm for a certain black-box problem. Since then, there areextensive explorations on quantum walks on graphs. Among them, uniform mixingprovides a uniform probability distribution of information over time whichattracts a special attention. However, there are only a few known examples ofgraphs which admit uniform mixing. In this paper, we give a characterization ofabelian Cayley graphs having uniform mixing. Some concrete constructions ofsuch graphs are provided. Particularly, for cubelike graphs, it is shown thatthe Cayley graph ${\rm Cay}(\mathbb{F}_2^{2k};S)$ having uniform mixing if andonly if $f$ is a bent function, where $f$ is the characteristic function of$S$.
Black-Box Accountable Authority Identity-Based Revocation System

Abstract Identity-based revocation system (IBRS) generates the ciphertext with a revoked identity list such that only the non-revoked identities can use their private keys to decrypt this ciphertext. IBRS can be efficiently applied in some practical applications, such as the pay-TV systems when the number of revoked identities are much less than the non-revoked ones. However, since IBRS is based on identity-based cryptography, it also suffers from the inherent key escrow problem where the private key generator (PKG) has full control of each user’s private key. As a consequence, it is hard to judge whether a pirated private key is generated by the PKG or the suspected user. There is no study on IBRS fulfilling accountability in literature to date. In this paper, we introduce the notion of accountable authority IBRS (A-IBRS), which provides accountability in IBRS schemes. In an A-IBRS, the aforementioned problem can be alleviated and resolved. Furthermore, a full black-box A-IBRS can distinguish the creator of a black box between the PKG and the associated user and the dishonest PKG is allowed to access the decryption results of the user private key. We formalize the definition and security models of the full black-box A-IBRS schemes. Then, we present a concrete full black-box A-IBRS scheme with constant-size master public key and private key. Finally, we prove the security of our scheme under the defined security models without random oracle.
REAPS: Towards Better Recognition of Fine-Grained Images by Region Attending and Part Sequencing

Fine-grained image recognition has been a hot research topic in computer vision due to its various applications. The-state-of-the-art is the part/region-based approaches that first localize discriminative parts/regions, and then learn their fine-grained features. However, these approaches have some inherent drawbacks: (1) the discriminative feature representation of an object is prone to be disturbed by complicated background; (2) it is unreasonable and inflexible to fix the number of salient parts, because the intended parts may be unavailable under certain circumstances due to occlusion or incompleteness, and (3) the spatial correlation among different salient parts has not been thoroughly exploited (if not completely neglected). To overcome these drawbacks, in this paper we propose a new, simple yet robust method by building part sequence model on the attended object region. Concretely, we first try to alleviate the background effect by using a region attention mechanism to generate the attended region from the original image. Then, instead of localizing different salient parts and extracting their features separately, we learn the part representation implicitly by applying a mapping function on the serialized features of the object. Finally, we combine the region attending network and the part sequence learning network into a unified framework that can be trained end-to-end with only image-level labels. Our extensive experiments on three fine-grained benchmarks show that the proposed method achieves the state of the art performance.
Hand Gesture Recognition from RGB-D Data using 2D and 3D Convolutional Neural Networks: a comparative study

Despite considerable enhances in recognizing hand gestures from still images, there are still many challenges in the classification of hand gestures in videos. The latter comes with more challenges, including higher computational complexity and arduous task of representing temporal features. Hand movement dynamics, represented by temporal features, have to be extracted by analyzing the total frames of a video. So far, both 2D and 3D convolutional neural networks have been used to manipulate the temporal dynamics of the video frames. 3D CNNs can extract the changes in the consecutive frames and tend to be more suitable for the video classification task, however, they usually need more time. On the other hand, by using techniques like tiling it is possible to aggregate all the frames in a single matrix and preserve the temporal and spatial features. This way, using 2D CNNs, which are inherently simpler than 3D CNNs can be used to classify the video instances. In this paper, we compared the application of 2D and 3D CNNs for representing temporal features and classifying hand gesture sequences. Additionally, providing a two-stage two-stream architecture, we efficiently combined color and depth modalities and 2D and 3D CNN predictions. The effect of different types of augmentation techniques is also investigated. Our results confirm that appropriate usage of 2D CNNs outperforms a 3D CNN implementation in this task.
Evaluating Anti-Graft Agencies Governance Practices in Nigeria

The Nigerian state has witnessed exponential increase in corruption and various anti-graft agencies have been established by government to curb this malaise which has branded the Nigerian state and its citizens all over the world as corrupt. The agencies have over the years been criticized as not being effective and a militia of government in power in the way they prosecute anti-corruption wars. The study evaluated the anti-graft agencies and their governance practices, their effectiveness in addressing the cankerworm in the country by employing the purposive sampling technique where 400 copies of questionnaires were distributed to professional accountants, bankers, journalist and lawyers. The data collected were analyzed using both descriptive and inferential statistics. The study revealed inadequate capacity of the workforce, non-commitment to integrity, ethical values and the rule of law, lack of openness, lack of transparency and accountability. The study recommends amendment of the extant laws establishing the anti-graft agencies for operational efficiency in prosecution, responsiveness to the constitution and adequate and targeted training for officials.
Improving Learning Outcomes of Vocational High School Students through Application of Circular Motion Learning Media Using Microcontrollers

Abstract The purpose of this study was to determine the effect of application circular motion practicum media using microcontrollers-based toward the learning outcomes of students. The object of this research is the students of State Vocational High School and Private Vocational High Schools in Madiun. The research design was Nonequivalent control group pretest-posttest. Data collection technique used 5 questions of cognitive essays tests. Data analyzed using qualitative descriptive techniques. The findings found out that the application of circular motion learning media using microcontroller has a significant influence on improving the students’ cognitive learning outcomes.
Algoritma K-Medoids untuk Mengelompokkan Desa yang Memiliki Fasilitas Sekolah di Indonesia

School facilities are learning facilities and infrastructure. Study rooms, study rooms, sports halls, prayer rooms, arts rooms and sports equipment. Means of learning to read textbooks, reading books, school laboratory tools and facilities and various other learning media. This study discusses the application of the K-Medoids method in grouping villages that have school facilities based on the province and education level. Data sources used from the National Statistics Agency (BPS). This study uses data mining techniques in data processing using the k-medoids clustering method. The k-medoid method is part of a fairly efficient grouping of partitions in small datasets and looks for the most representative points. The advantages of this method can overcome the shortcomings of the k-means method that is sensitive to outliers. Another advantage of this method is that the results of the grouping process do not match the entry sequence of the dataset. Grouping k-medoid method can be applied to the percentage of facilities based on the province, so that provincial grouping can be determined based on the data. From the grouping data, 3 clusters were obtained, namely a low cluster of 15 provinces, a moderate cluster of 16 provinces and a high cluster of 3 provinces from the percentage of school facilities in each province. It is hoped that this research can provide information to the government about data collection of school facilities in Indonesia which discusses examiners in the provision of school facilities in Indonesia.
Automating Multi-Label Classification Extending ML-Plan

Existing tools for automated machine learning, such as Auto-WEKA, TPOT, auto-sklearn, and more recently ML-Plan, have shown impressive results for the tasks of single-label classification and regression. Yet, there is only little work on other types of machine learning problems so far. In particular, there is almost no work on automating the engineering of machine learning solutions for multi-label classification (MLC). We show how the scope of ML-Plan, an AutoML-tool for multi-class classification, can be extended towards MLC using MEKA, which is a multi-label extension of the well-known Java library WEKA. The resulting approach recursively refines MEKA's multi-label classifiers, nesting other multi-label classifiers for meta algorithms and single-label classifiers provided by WEKA as base learners. In our evaluation, we find that the proposed approach yields strong results and performs significantly better than a set of baselines we compare with.
Consumers Trust, Privacy and Security Issues on Mobile Commerce Websites

The proliferation of mobile devices and the increased adoption of the internet across the globe has led to the rise of m-commerce. reports highlight that in spite of the different advancements in the technology, trust in the platform is still a significant hindrance to its adoption. Consequently, the current study seeks to identify privacy and security issues affecting m-commerce users of three shopping sites: Amazon, Alibaba and eBay. The aim here is to develop recommendations that mitigate these challenges. The expected output of the study is an anticipation for insights regarding user perspectives on trust in m-commerce and as a result, contribute to existent knowledge in the area benefitting regulatory bodies and online vendors.