Reviewerly/exHarmony huggingface dataset:
Machine Learning Tools for Digital Pathology - The Next Big Wave in MEDICAL SCIENCE

Digital pathology is a technology that enables the acquisition, management, and interpretation of pathological information generated from a digital slide. Glass slides are captured using digital pathology scanners and converted to digitized glass slides that can be viewed on the computer monitor. Digital pathology provides specific support for education and the practice of human pathology. With the recent advancements in digital pathology led to computer-aided diagnosis using machine learning approaches. So, machine learning tools assist physicians in diagnosing critical cases such as cancer, tumors, etc, and improve patient management. With an ever-increasing number of options, selecting a better machine learning tool for pathological data can be difficult. In this paper, huge potential efforts are made to study the complete background of digital pathology with the detail of how artificial intelligence made a contribution to digital pathology. This review also analyzes various machine learning tools by providing as much information as possible and quantifying what the tradeoffs will be. In the end, this paper provides the enhancements in available tools that will be required in the near future applications.
Learning by watching others learn: the use of videoed tutorials in undergraduate business education

The Fourth Industrial Age (4IA) is likely to be accompanied simultaneously by an increase in technology-mediated learning and an urgent need for people to learn rapidly, effectively and collaboratively. This study investigates the potential of vicarious learning from videoed tutorials as a pedagogical tool suitable for the challenges of 4IA. Undergraduate Business students observed videos of student tutees responding to tutor prompts as they tackled open-ended and conceptually challenging problems. The results revealed that student observers self-reported: gains in their conceptual understanding from watching videoed tutorials; preferences for watching tutorial dialogues over alternative learning methods; and that watching videoed tutorials had positive impacts on their affect and access to additional learning-related information. The study concludes that vicarious learning from videoed tutorials is an accessible technology-mediated pedagogy that is achievable by mainstream educators and is effective in developing conceptual understanding, engaging students and providing access to additional learning-related information.
High Performance Multicore SHA-256 Accelerator using Fully Parallel Computation and Local Memory

Integrity checking is indispensable in the current technological age. One of the most popular algorithms for integrity checking is SHA-256. To achieve high performance, many applications generally design SHA-256 in hardware. However, the processing rate of SHA-256 is often low due to a large number of computations. Besides, data must be repeated in many loops to generate a hash, which requires transferring data multiple times between accelerator and off-chip memory if not using local memory. In this paper, an ALU combining fully parallel computation and pipeline layers is proposed to increase the SHA-256 processing rate. Moreover, the local memory is attached near ALU for reducing off-chip memory access during the iterations of computing. In the high hash rate, we design a SoC-based multicore SHA-256 accelerator. As a result, our proposed accelerator enhances throughput by more than 40% and be 2x higher hardware efficiency compared with the state-of-the-art design.
A Knowledge-Informed Simplex Search Method Based on Historical Quasi-Gradient Estimations and Its Application on Quality Control of Medium Voltage Insulators

Quality control is of great significance for the economical manufacturing and reliable application of medium voltage insulators. With the increasingly stringent quality control requirement, traditional quality control methods in this field face a growing challenge on their efficiency. Therefore, this study aims to achieve quality specifications by optimizing process conditions with the least costs. Thus, a knowledge-informed simplex search method was proposed based on an idea of knowledge-informed optimization to enhance the optimization efficiency. Firstly, a new mathematical quantity, quasi-gradient estimation, was generated following a reconstruction of the simplex search from the essence and the development history of the method. Based on this quantity, the gradient-free method possessed the same gradient property and unified form as the gradient-based methods. Secondly, an implementation of the knowledge-informed simplex search method based on historical quasi-gradient estimations (short for GK-SS) was constructed. The GK-SS-based quality control method utilized the historical quasi-gradient estimations for each simplex generated during the optimization process to improve the method’s search directions’ accuracy in a statistical sense. Finally, this method was applied to the weight control of a kind of post insulator. The experimental simulation results showed that the method is effective and efficient in the quality control of medium voltage insulators.
An ensemble-based semi-supervised feature ranking for multi-target regression problems

This study focuses on semi-supervised feature ranking (FR) applications for multi-target regression problems (MTR). As MTRs require prediction of several targets, we use a learning model that includes target interrelations via multi-objective trees. In processing the features for a semi-supervised learning model, transformation or scaling operations are usually required. To resolve this issue, we create a dissimilarity matrix via totally randomized trees to process the unsupervised information. Besides, we treat the split score function as a vector to make it suitable for considering each criterion regardless of their scales. We propose a semi-supervised FR scheme embedded to multi-objective trees that takes into account target and feature contributions simultaneously. Proposed FR score is compared with the state-of-the-art multi-target FR strategies via statistical analyses. Experimental studies show that proposed score significantly improves the performance of a recent tree-based and competitive multi-target learning model, i.e. predictive clustering trees. In addition, proposed approach outperforms its benchmarks when the available labelled data increase.
Experience of formation of readyness for professional career growth of Postgraduates of H. S. Skovoroda Kharkiv National Pedagogical University

In the article, the concept of readiness for professional career growth of postgraduate students majoring in 011 Educational, Pedagogical Sciences has been defined. The principles of training postgraduate students of the third (educational and scientific) level of higher education have been revealed. They are: "flexibility"; the relationship between labour and vocational training; the unity of professional competences, interest and practical orientation; the integrity of education. A typical career trajectory was presented in three variants. The article proved that the analysis of motives for professional growth begins at the stage of admission to postgraduate courses. An appropriate place in the content of postgraduates’ training is given to the disciplines ensuring the development of universal basic skills (soft skills): "Academically and professionally oriented communication (“English / German / French / Ukrainian / Russian)", "Grant and Project Activities", "World Development and Domestic Pedagogical Thought". The development of universal skills of the researcher is provided by disciplines of applicants’ free choice ("Theoretical Foundations of Didactic Research", "Current Issues of Comparative Research", "History of Pedagogy as a Science and Direction of Pedagogical Research", etc.). The training for further career growth is closely related to the introduction of innovative technologies within general, active and interactive teaching methods (modelling of professional situations, role-playing games, storytelling, case study method, elements of cooperative learning) (group practical tasks), discussion, master class, etc.). A proper place in the educational process is given to research and teaching (pedagogical) practice. The university is working to form a "feedback" system with graduates and leading employers in the field of higher education. In particular, the Association of Alumni, a network of clubs alongside creative and sports teams were created. The membership in the Council of Young Scientists, which operates at the university, is an incentive for career growth in scientific activity.
Pedagogy by proxy: teachers’ digital competence with crowd-sourced lesson resources

This study explores how teachers of Information and Communications Technology (ICT) faced with a major National Curriculum change were able to teach the new elements of the programmes of study. A multiple case study involving nine experienced teachers was carried out, using thematic analysis to explore audio-visual and documentary data from lesson planning sessions captured mostly via video calling and desktop sharing. The process captured the various ways that teachers located, modified and re-used digital materials and accessed online communities of practice to develop crowd-sourced curricula. The results reveal that the alignment of teachers’ digital competence with their need to assimilate unfamiliar but necessary concepts into the pedagogical reasoning process facilitated the teachers in developing sufficient subject knowledge and pedagogical content knowledge (PCK). Knowledge deficits slowed down the fluency of teachers’ lesson-planning processes, but the location and use of crowd-sourced resources helped them to develop PCK. The teachers’ digital competences in sourcing suitable teaching resources from their communities of practice allowed the development of pedagogy by proxy.
Class Imbalanced Data: Open Issues and Future Research Directions

Since last two decades, imbalanced data is becoming a hot topic to do research or to determine meaningful results. One of the problems of machine learning and data mining areas is class imbalance. Data sets with imbalances have hindered the efficiency of algorithms for data mining and machine learning (in terms of overall accuracy, decision making). In the big data era, the expansion of data mining and machine learning has raised new challenges with the nature of data. In class imbalanced data, majority class lead to problem, i.e., having an imbalance between minority and majority class samples created several problems for researchers. In result, researchers are unable to learn much from systems or they are unable to find or determine prediction or take decision for respective applications like fraud detection, rare diseases identification/ prediction, approval of credit card, software defect prediction, etc. A survey for class imbalance problem is proposed in this paper with discussing several applications (where this problem getting attention). For solving this famous problem or balance this imbalanced data, three methods like Data-level, algorithm-level and hybrid methods are being considered/ used. Among these methods, a hybrid method is receiving much popularity. This paper also discusses several open issues and challenges (which are required to be developed in near future for efficient/ imbalanced learning). Also, in last several (essential) future research directions have been also discussed in this work, which makes this work as important one for research community.
Recursive joint Cramér‐Rao lower bound for parametric systems with two‐adjacent‐states dependent measurements

Joint Cramér-Rao lower bound (JCRLB) is very useful for the performance evaluation of joint state and parameter estimation (JSPE) of non-linear systems, in which the current measurement only depends on the current state. However, in reality, the non-linear systems with two-adjacent-states dependent (TASD) measurements, that is, the current measurement is dependent on the current state as well as the most recent previous state, are also common. First, the recursive JCRLB for the general form of such non-linear systems with unknown deterministic parameters is developed. Its relationships with the posterior CRLB for systems with TASD measurements and the hybrid CRLB for regular parametric systems are also provided. Then, the recursive JCRLBs for two special forms of parametric systems with TASD measurements, in which the measurement noises are autocorrelated or cross-correlated with the process noises at one time step apart, are presented, respectively. Illustrative examples in radar target tracking show the effectiveness of the JCRLB for the performance evaluation of parametric TASD systems.
From granulation-degranulation mechanisms to fuzzy rule-based models: Augmentation of granular-based models with a double fuzzy clustering

In this study, we develop a novel clustering with double fuzzy factors to enhance the performance of the granulation-degranulation mechanism, with which a fuzzy rule-based model is designed and demonstrated to be an enhanced one. The essence of the developed scheme is to optimize the construction of the information granules so as to eventually improve the performance of the fuzzy rule-based models. In the design process, a prototype matrix is defined to express the Fuzzy C-Means based granulation-degranulation mechanism in a clear manner. We assume that the dataset degranulated from the formed information granules is equal to the original numerical dataset. Then, a clustering method with double fuzzy factors is derived. We also present a detailed mathematical proof for the proposed approach. Subsequently, on the basis of the enhanced version of the granulation-degranulation mechanism, we design a granular fuzzy model. The whole design is mainly focused on an efficient application of the fuzzy clustering to build information granules used in fuzzy rule-based models. Comprehensive experimental studies demonstrate the performance of the proposed scheme.
Kim-type APN functions are affine equivalent to Gold functions

The problem of finding APN permutations of ${\mathbb {F}}_{2^{n}}$ where n is even and n > 6 has been called the Big APN Problem. Li, Li, Helleseth and Qu recently characterized APN functions defined on ${\mathbb {F}}_{q^{2}}$ of the form f(x) = x3q + a1x2q+ 1 + a2xq+ 2 + a3x3, where q = 2m and m ≥ 4. We will call functions of this form Kim-type functions because they generalize the form of the Kim function that was used to construct an APN permutation of ${\mathbb {F}}_{2^{6}}$ . We prove that Kim-type APN functions with m ≥ 4 (previously characterized by Li, Li, Helleseth, and Qu) are affine equivalent to one of two Gold functions G1(x) = x3 or $G_{2}(x)=x^{2^{m-1}+1}$ . Combined with the recent result of Göloğlu and Langevin who proved that, for even n, Gold APN functions are never CCZ equivalent to permutations, it follows that for m ≥ 4 Kim-type APN functions on ${\mathbb {F}}_{2^{2m}}$ are never CCZ equivalent to permutations.
Plain Language Summaries Explained in Plain Language

Find out what a Plain Language Summary is, how to write an effective one, and how it benefits publications.
Organizational Conflict Management: The All Important Public Universities Performance Strategy Under Neglect in Kenya

Workplace conflicts are inevitable in any work-organization. However, there is contestation as to whether management of organizational conflicts enhances or deteriorates employees’ performance. This study sought to determine the effect of organizational conflict management techniques, namely negotiation, mediation, collaboration and avoidance, on employees’ performance in selected public universities in Kenya. The study was based on the human relations, human capital and contingency theories. The study adopted a positivist quantitative approach - a methodological approach that seeks to quantify data and generalize results from a sample of a target population in an objective manner using statistical means. The study adopted descriptive research design. The study population was employees of the selected public universities in Kenya. A sample of 160 participants was chosen using stratified and simple random sampling methods. Gathered data was analyzed using descriptive and inferential statistics with the help of the Statistical Package for Social Sciences. Both ANOVA and regression analysis were utilized in analysis of the data. The study established that there was a strong positive and significant relationship between negotiation, mediation as well as collaboration and employees’ performance in the selected public universities in Kenya as the three variables had positive beta coefficients and p-values less than the set significance level threshold. However, avoidance was found to have a negative and significant relationship with employees’ performance in the selected public universities in Kenya as denoted by its negative beta coefficient value and a p value less than the set significance level threshold. The study concluded that negotiation, mediation and collaboration positively influenced employees’ performance while avoidance adversely affected employees’ performance. Consequently, the study recommends that the managements of public universities in Kenya may apply a mix of these organizational conflict management techniques to achieve optimal outcomes in resolution of organizational conflicts.
Identifying Key Players in Violent Extremist Networks: Using Socio-Semantic Network Analysis as Part of a Program of Content Moderation

Some moderation strategies of online content have targeted the individuals believed to be the most influential in the diffusion of such material, while others have focused on censorship of the content itself. Few approaches consider these two aspects simultaneously. The present study addresses this gap by showing how a socio-semantic network analysis can help identify individuals and subgroups who are strategically positioned in radical networks and whose comments encourage the use of violence. It also made it possible to identify the individuals and subgroups who act as intermediaries and whose statements are often the most violent.
Blockchain and IOT integrated Smart City Architecture

Recently, the concept of Cities has been greatly improved with the growth and development of the Internet of Things as a new form of sustainable development. Smart cities rely on independent and distributed infrastructure that includes information processing and control systems, diverse network infrastructure, and ubiquitous sensitivity that includes millions of information sources. Due to the continued growth of data volume and the number of connected IoT devices, however, issues such as high delays, bandwidth barriers, security and privacy, and scalability emerge from the current smart city network construction. Designing an efficient, secure and scalable distributed architecture by bringing computer and storage resources closer to endpoint is needed to address the limitations of today’s smart city network. In this paper, we propose a hybrid network design for an intelligent city using the emerging technologies of Software Defined Networking and blockchain technology. To achieve efficiency and address current constraints, our paper is divided into two parts: the main network and the edge network. In this we proposed hybrid architecture which inherits the capability of both centralized and decentralized network integrated with proof of work for the security and privacy. To evaluate the feasibility and performance of our proposed model, we simulate our model and evaluate it based on various performance metrics. The result of the evaluation shows the effectiveness of our proposed model.
Online Performance Modeling and Prediction for Single-VM Applications in Multi-Tenant Clouds

Clouds have been adopted widely by many organizations for their supports of flexible resource demands and low cost, which is normally achieved through sharing the underlying hardware among multiple cloud tenants. However, such sharing with the changes in resource contentions in virtual machines (VMs) can result in large variations for the performance of cloud applications, which makes it difficult for ordinary cloud users to estimate the run-time performance of their applications. In this article, we propose online learning methodologies for performance modeling and prediction of applications that run repetitively on multi-tenant clouds (such as on-line data analytic tasks). Here, a few micro-benchmarks are utilized to probe the in-situ perceivable performance of CPU, memory and I/O components of the target VM. Then, based on such profiling information and in-place measured application’s performance, the predictive models can be derived with either Regression or Neural-Network techniques. In particular, to address the changes in the intensity of resource contentions of a VM over time and its effects on the target application, we proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">periodic model retraining</i> where the sliding-window technique was exploited to control the frequency and historical data used for model retraining. Moreover, a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">progressive modeling</i> approach has been devised where the Regression and Neural-Network models are gradually updated for better adaptation to recent changes in resource contention. With 17 representative applications from PARSEC, NAS Parallel and CloudSuite benchmarks being considered, we have extensively evaluated the proposed online schemes for the prediction accuracy of the resulting models and associated overheads on both a private and public clouds. The evaluation results show that, even on the private cloud with high and radically changed resource contention, the average prediction errors of the considered models can be less than 20 percent with periodic retraining. The prediction errors generally decrease with higher retraining frequencies and more historical data points but incurring higher run-time overheads. Furthermore, with the neural-network progressive models, the average prediction errors can be reduced by about 7 percent with much reduced run-time overheads (up to 265 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">X</i> ) on the private cloud. For public clouds with less resource contentions, the average prediction errors can be less than 4 percent for the considered models with our proposed online schemes.
Viewpoint-Aware Action Recognition Using Skeleton-Based Features from Still Images

In this paper, we propose a viewpoint-aware action recognition method using skeleton-based features from static images. Our method consists of three main steps. First, we categorize the viewpoint from an input static image. Second, we extract 2D/3D joints using state-of-the-art convolutional neural networks and analyze the geometric relationships of the joints for computing 2D and 3D skeleton features. Finally, we perform view-specific action classification per person, based on viewpoint categorization and the extracted 2D and 3D skeleton features. We implement two multi-view data acquisition systems and create a new action recognition dataset containing the viewpoint labels, in order to train and validate our method. The robustness of the proposed method to viewpoint changes was quantitatively confirmed using two multi-view datasets. A real-world application for recognizing various actions was also qualitatively demonstrated.