Reviewerly/exHarmony huggingface dataset:
Aggregated Deep Global Feature Representation for Breast Cancer Histopathology Image Classification

Convolutional neural networks (CNNs), successfully used in a great number of medical image analysis applications, have also achieved the state-of-the-art performance in breast cancer histopathology image (BCHI) classification problem recently. However, due to the large varieties among within-class images and insufficient data volume, it is still a challenge to obtain more competitive results by using deep CNN models alone. In this paper, we aim to explore the combination of CNN models with a milestone feature representation method in visual tasks, i.e., vector of locally aggregated descriptors (VLAD), for the BCHI classification, and further propose a novel aggregated deep global feature representation (ADGFR) for this problem. ADGFR adopts the deep features that are extracted from the fully connected layer to form an individual descriptor vector, and augments input images to generate different descriptors for achieving the final aggregated descriptor vector. The individual descriptor vector can effectively keep the global features of benign and malignant image, whose discriminability is further reinforced by the aggregate operation, leading to the more discriminant capability of ADGFR for BCHI. Extensive experiments on the public Break His dataset illuminate that our ADGFR can achieve the optimal classification accuracies of 95.05% at image level and 95.50% at patient level, respectively.
A critical analysis of the bat algorithm

Abstract This article presents an analysis of the bat algorithm (BA) based on elementary mathematical analysis and statistical comparisons of the first hitting time performance metric distributions obtained on a test set comprising five carefully selected objective functions. The findings show that the BA is not an original contribution to the metaheuristics literature and that it is not generally superior to the Particle Swarm Optimization algorithm when fair comparisons are made. It is also shown that some components of the BA can be either replaced by simpler alternatives or be removed entirely to increase performance. Finally, the results suggest that the best version of the BA is in fact a simple hybrid between Particle Swarm Optimization and Simulated Annealing. To encourage more transparency in metaheuristics research, the entirety of the MATLAB code used in this article is available in a GitHub repository for suggestions and/or corrections.
High-Speed Measurement of Shape and Vibration: Whole-Field Systems for Motion Capture and Vibration Modal Analysis by OPPA Method

In shape measurement systems using a grating projection method, the phase analysis of a projected grating provides accurate results. The most popular phase analysis method is the phase shifting method, which requires several images for one shape analysis. Therefore, the object must not move during the measurement. The authors previously proposed a new accurate and high-speed shape measurement method, i.e., the one-pitch phase analysis (OPPA) method, which can determine the phase at every point of a single image of an object with a grating projected onto it. In the OPPA optical system, regardless of the distance of the object from the camera, the one-pitch length (number of pixels) on the imaging surface of the camera sensor is always constant. Therefore, brightness data for one pitch at any point of the image can be easily analyzed to determine phase distribution, or shape. This technology will apply to the measurement of objects in motion, including automobiles, robot arms, products on a conveyor belt, and vibrating objects. This paper describes the principle of the OPPA method and example applications for real-time human motion capture and modal analysis of free vibration of a flat cantilever plate after hammering. The results show the usefulness of the OPPA method.
Simple and effective neural-free soft-cluster embeddings for item cold-start recommendations

Abstract Recommender systems are widely used in online platforms for easy exploration of personalized content. The best available recommendation algorithms are based on using the observed preference information among collaborating entities. A significant challenge in recommender system continues to be item cold-start recommendation: how to effectively recommend items with no observed or past preference information. Here we propose a two-stage algorithm based on soft clustering to provide an efficient solution to this problem. The crux of our approach lies in representing the items as soft-cluster embeddings in the space spanned by the side-information associated with the items. Though many item embedding approaches have been proposed for item cold-start recommendations in the past—and simple as they might appear—to the best of our knowledge, the approach based on soft-cluster embeddings has not been proposed in the research literature. Our experimental results on four benchmark datasets conclusively demonstrate that the proposed algorithm makes accurate recommendations in item cold-start settings compared to the state-of-the-art algorithms according to commonly used ranking metrics like Normalized Discounted Cumulative Gain (NDCG) and Mean Average Precision (MAP). The performance of our proposed algorithm on the MovieLens 20M dataset clearly demonstrates the scalability aspect of our algorithm compared to other popular algorithms. We also propose the metric Cold Items Precision (CIP) to quantify the ability of a system to recommend cold-start items. CIP can be used in conjunction with relevance ranking metrics like NDCG and MAP to measure the effectiveness of the cold-start recommendation algorithm.
Feature Space Augmentation for Long-Tailed Data

Real-world data often follow a long-tailed distribution as the frequency of each class is typically different. For example, a dataset can have a large number of under-represented classes and a few classes with more than sufficient data. However, a model to represent the dataset is usually expected to have reasonably homogeneous performances across classes. Introducing class-balanced loss and advanced methods on data re-sampling and augmentation are among the best practices to alleviate the data imbalance problem. However, the other part of the problem about the under-represented classes will have to rely on additional knowledge to recover the missing information. In this work, we present a novel approach to address the long-tailed problem by augmenting the under-represented classes in the feature space with the features learned from the classes with ample samples. In particular, we decompose the features of each class into a class-generic component and a class-specific component using class activation maps. Novel samples of under-represented classes are then generated on the fly during training stages by fusing the class-specific features from the under-represented classes with the class-generic features from confusing classes. Our results on different datasets such as iNaturalist, ImageNet-LT, Places-LT and a long-tailed version of CIFAR have shown the state of the art performances.
Modified election algorithm in hopfield neural network for optimal random<i>k</i>satisfiability representation

Election algorithm (EA) is a novel metaheuristics optimization model motivated by phenomena of the socio-political mechanism of presidential election conducted in many countries. The capability and robustness EA in finding an optimal solution to optimization has been proven by various researchers. In this paper, modified version of EA has been utilized in accelerating the searching capacity of Hopfield neural network (HNN) learning phase for optimal random- k SAT logical representation (HNN-R 2 SATEA). The utility of the proposed approach has been contrasted with the current standard exhaustive search algorithm (HNN-R2SATES) and the newly developed algorithm HNN-R2SATICA. From the analysis obtained, it has been clearly shown that the proposed hybrid computational model HNN-R 2 SATEA outperformed other existing model in terms of global minima ratio (Zm), mean absolute error (MAE), Bayesian information criterion (BIC) and execution time (ET). The finding portrays that the MEA algorithm surpassed the other two algorithms for optimal random- k SAT logical representation.
Penggunaan Aplikasi Zoom Cloud Meeting pada Proses Pembelajaran Online Sebagai Solusi di Masa Pandemi Covid 19

This article depicts a slightly different educational pattern due to the outbreak of the disease, which came into Indonesia since the beginning of last March. Impact on the whole order in Indonesia, such as economic, political, social, and even impact the education system. Since the circular letter issued by the government that all students/students to do distance learning and by using via online. So between teachers and students need a bridge to keep learning active even in the middle of pandemic. A cloud meeting's zoom application, as if to be a new class in the learning process and the application also becomes a solution for learning at school to run properly. Despite the many students who complained about this online learning. But this pattern of modern education should still be implemented until the return state is conducive.
PAAD: POLITICAL ARABIC ARTICLES DATASET FOR AUTOMATIC TEXT CATEGORIZATION

Now day’s text Classification and Sentiment analysis is considered as one of the popular Natural Language Processing (NLP) tasks. This kind of technique plays significant role in human activities and has impact on the daily behaviours. Each article in different fields such as politics and business represent different opinions according to the writer tendency. A huge amount of data will be acquired through that differentiation. The capability to manage the political orientation of an online article automatically. Therefore, there is no corpus for political categorization was directed towards this task in Arabic, due to the lack of rich representative resources for training an Arabic text classifier. However, we introduce political Arabic articles dataset (PAAD) of textual data collected from newspapers, social network, general forum and ideology website. The dataset is 206 articles distributed into three categories as (Reform, Conservative and Revolutionary) that we offer to the research community on Arabic computational linguistics. We anticipate that this dataset would make a great aid for a variety of NLP tasks on Modern Standard Arabic, political text classification purposes. We present the data in raw form and excel file. Excel file will be in four types such as V1 raw data, V2 preprocessing, V3 root stemming and V4 light stemming.
A Study of Students’ Perception: Identifying EFL Learners’ Problems in Speaking Skill

Language is a pivotal aspect of human being. By learning, language people can communicate with each other, get information from one to another and interact. Unfortunately, in the real condition, it is still difficult for Indonesian students to practice their English ability in daily conversation. This paper will discuss the student’s perception within implementation problems and its solutions. The aim of this study is to identify the speaking problems experienced by EFL learners in speaking English. The researcher conducted this study using the qualitative research method. To gather the results, the researcher uses a questionnaire centered on the Penny Tang (1997) on speaking problems in learning a foreign language. The study’s data is collected from thirty-five students from Tidar University in Magelang. The result of the study is that the students have problems in learning speaking English. The most problematic for the students is mother tongue use, 77% students use their other tongue in English class and 83% agree that mother tongue is easier than English.
Enhancing Border Gateway Protocol Security Using Public Blockchain

Communication on the Internet consisting of a massive number of Autonomous Systems (AS) depends on routing based on Border Gateway Protocol (BGP). Routers generally trust the veracity of information in BGP updates from their neighbors, as with many other routing protocols. However, this trust leaves the whole system vulnerable to multiple attacks, such as BGP hijacking. Several solutions have been proposed to increase the security of BGP routing protocol, most based on centralized Public Key Infrastructure, but their adoption has been relatively slow. Additionally, these solutions are open to attack on this centralized system. Decentralized alternatives utilizing blockchain to validate BGP updates have recently been proposed. The distributed nature of blockchain and its trustless environment increase the overall system security and conform to the distributed character of the BGP. All of the techniques based on blockchain concentrate on inspecting incoming BGP updates only. In this paper, we improve on these by modifying an existing architecture for the management of network devices. The original architecture adopted a private blockchain implementation of HyperLedger. On the other hand, we use the public blockchain Ethereum, more specifically the Ropsten testing environment. Our solution provides a module design for the management of AS border routers. It enables verification of the prefixes even before any router sends BGP updates announcing them. Thus, we eliminate fraudulent BGP origin announcements from the AS deploying our solution. Furthermore, blockchain provides storage options for configurations of edge routers and keeps the irrefutable history of all changes. We can analyze router settings history to detect whether the router advertised incorrect information, when and for how long.
XML Data and Knowledge-Encoding Structure for a Web-Based and Mobile Antenatal Clinical Decision Support System: Development Study

Displeasure with the functionality of clinical decision support systems (CDSSs) is considered the primary challenge in CDSS development. A major difficulty in CDSS design is matching the functionality to the desired and actual clinical workflow. Computer-interpretable guidelines (CIGs) are used to formalize medical knowledge in clinical practice guidelines (CPGs) in a computable language. However, existing CIG frameworks require a specific interpreter for each CIG language, hindering the ease of implementation and interoperability.This paper aims to describe a different approach to the representation of clinical knowledge and data. We intended to change the clinician's perception of a CDSS with sufficient expressivity of the representation while maintaining a small communication and software footprint for both a web application and a mobile app. This approach was originally intended to create a readable and minimal syntax for a web CDSS and future mobile app for antenatal care guidelines with improved human-computer interaction and enhanced usability by aligning the system behavior with clinical workflow.We designed and implemented an architecture design for our CDSS, which uses the model-view-controller (MVC) architecture and a knowledge engine in the MVC architecture based on XML. The knowledge engine design also integrated the requirement of matching clinical care workflow that was desired in the CDSS. For this component of the design task, we used a work ontology analysis of the CPGs for antenatal care in our particular target clinical settings.In comparison to other common CIGs used for CDSSs, our XML approach can be used to take advantage of the flexible format of XML to facilitate the electronic sharing of structured data. More importantly, we can take advantage of its flexibility to standardize CIG structure design in a low-level specification language that is ubiquitous, universal, computationally efficient, integrable with web technologies, and human readable.Our knowledge representation framework incorporates fundamental elements of other CIGs used in CDSSs in medicine and proved adequate to encode a number of antenatal health care CPGs and their associated clinical workflows. The framework appears general enough to be used with other CPGs in medicine. XML proved to be a language expressive enough to describe planning problems in a computable form and restrictive and expressive enough to implement in a clinical system. It can also be effective for mobile apps, where intermittent communication requires a small footprint and an autonomous app. This approach can be used to incorporate overlapping capabilities of more specialized CIGs in medicine.
Two-Step Calibration Method for Inverse Finite Element with Small Sample Features

When the inverse finite element method (inverse FEM) is used to reconstruct the deformation field of a multi-element structure with strain measurements, strain measurement errors can lower the reconstruction accuracy of the deformation field. Furthermore, the calibration ability of a self-structuring fuzzy network (SSFN) is weak when few strain samples are used to train the SSFN. To solve this problem, a novel two-step calibration method for improving the reconstruction accuracy of the inverse FEM method is proposed in this paper. Initially, the errors derived from measured displacements and reconstructed displacements are distributed to the degrees of freedom (DOFs) of nodes. Then, the DOFs of nodes are used as knots, in order to produce non-uniform rational B-spline (NURBS) curves, such that the sample size employed to train the SSFN can be enriched. Next, the SSFN model is used to determine the relationship between the measured strain and the DOFs of the end nodes. A loading deformation experiment using a three-element structure demonstrates that the proposed algorithm can significantly improve the accuracy of reconstruction displacement.
An Empirical Analysis about the Generalization Performance of Data-driven Fault Diagnosis Methods

Abstract：In recent years, data-driven fault diagnosis methods have been widely researched.A prerequisite of ensuring those methods' effectiveness is that the data for training diagnosis models and data to be tested should be collected from the same machine and the working environment.However, it is very difficult to satisfy this prerequisite in the actual diagnosis problem, and only the historical fault data collected from other same-type machines or different operating conditions are available for training the diagnosis model.The validity of conventional data-driven fault diagnosis methods for the actual diagnosis scenarios between datasets with potential discrepancy has rarely been discussed yet.The possible factors that dominate the generalization performance of fault diagnosis methods are first analyzed theoretically, and then multiple cross-dataset diagnosis tasks are organized.Based on them the empirical analysis about the generalization performance of several data-driven fault diagnosis methods is conducted.It is found that the distribution discrepancy between datasets is a major factor to influence the generalization performance.Meanwhile, the further fundamental reason for generalization performance declines is also explained from the perspective of signal characteristics under both model difference and operating condition difference.The discussion is conducive to inspiring the studies of data-driven diagnosis methods that can handle actual diagnosis scenarios.
InstanceMotSeg: Real-time Instance Motion Segmentation for Autonomous Driving

Moving object segmentation is a crucial task for autonomous vehicles as it can be used to segment objects in a class agnostic manner based on its motion cues. It will enable the detection of objects unseen during training (e.g., moose or a construction truck) generically based on their motion. Although pixel-wise motion segmentation has been studied in the literature, it is not dealt with at instance level, which would help separate connected segments of moving objects leading to better trajectory planning. In this paper, we proposed a motion-based instance segmentation task and created a new annotated dataset based on KITTI, which will be released publicly. We make use of the YOLACT model to solve the instance motion segmentation network by feeding inflow and image as input and instance motion masks as output. We extend it to a multi-task model that learns semantic and motion instance segmentation in a computationally efficient manner. Our model is based on sharing a prototype generation network between the two tasks and learning separate prototype coefficients per task. To obtain real-time performance, we study different efficient encoders and obtain 39 fps on a Titan Xp GPU using MobileNetV2 with an improvement of 10% mAP relative to the baseline. A video demonstration of our work is available in this https URL.
Whitening and second order optimization both destroy information about the dataset, and can make generalization impossible

Machine learning is predicated on the concept of generalization: a model achieving low error on a sufficiently large training set should also perform well on novel samples from the same distribution. We show that both data whitening and second order optimization can harm or entirely prevent generalization. In general, model training harnesses information contained in the sample-sample second moment matrix of a dataset. For a general class of models, namely models with a fully connected first layer, we prove that the information contained in this matrix is the only information which can be used to generalize. Models trained using whitened data, or with certain second order optimization schemes, have less access to this information; in the high dimensional regime they have no access at all, resulting in poor or nonexistent generalization ability. We experimentally verify these predictions for several architectures, and further demonstrate that generalization continues to be harmed even when theoretical requirements are relaxed. However, we also show experimentally that regularized second order optimization can provide a practical tradeoff, where training is accelerated but less information is lost, and generalization can in some circumstances even improve.
Status of the data sharing policies of scholarly journals published in Brazil, France, and Korea and listed in both the 2018 Scimago Journal and Country Ranking and the Web of Science

Purpose: The present study analyzed the current status of the data sharing policies of journals published in Brazil, France, and Korea that were listed in the 2018 Scimago Journal and Country Ranking and Web of Science Core Collection.Methods: Web of Science journals were selected from the 2018 Scimago Journal and Country Ranking. The homepages of all target journals were searched for the presence of statements on data sharing policies, including clinical trial data sharing policies, the level of the policies, and actual statements of data availability in articles.Results: Out of 565 journals from these three countries, 118 (20.9%) had an optional data sharing policy, and one had a mandatory data sharing policy. Harvard Dataverse was the repository of one journal. The number of journals that had adopted a data sharing policy was 11 (6.7%) for Brazil, 64 (27.6%) for France, and 44 (25.9%) for Korea. One journal from Brazil and 20 journals from Korea had adopted clinical trial data sharing policies in accordance with the International Committee of Medical Journal Editors. Statements of data sharing were found in articles from two journals.Conclusion: Journals from France and Korea adopted data sharing policies more actively than those from Brazil. However, the actual implementation of these policies through descriptions of data availability in articles remains rare. In many journals that appear to have data sharing policies, those policies may just reflect a standard description by the publisher, especially in France. Actual data sharing was not found to be frequent.