Reviewerly/exHarmony huggingface dataset:
Analysis of Various Dehazing Algorithms for Underwater Images

The presence of sediments, lighting inconsistencies, colour variations and dissolved particles give hazy effect to the underwater images. To overcome this, enhancement techniques are required. In this paper, the model based dehazing algorithms are analysed. The effectiveness and limitations of various algorithms are analysed both in terms of subjective and objective measures. The Underwater Hazeline Prior (UHP) algorithm is contrast adjusted to form the Modified Colour Restoration (ColR <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</inf> ). The ColR <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</inf> achieves a metric improvement of 2.5% in Underwater Colour Image Quality Enhancement (UCIQE) than the UHP algorithm. Further contrast improvement strategies for underwater images are also discussed.
Filter re‐start strategy for angle‐only tracking of a highly manoeuvrable target considering the target's destination information

This study handles the three-dimensional (3D) angle-only tracking (AOT) problem, which is calculating the target's location and velocity by measuring the target's elevation and azimuth angle. Nowadays, a non-civilian (unmanned) aircraft can accelerate in an unpredictable manner. This study presents a stochastic filter re-start strategy for tracking a target which can abruptly change its velocity in any 3D directions. Moreover, this study assumes that the target's destination information is known a priori. Using the target destination information, the authors can improve the filter accuracy as well as the time efficiency. As far as they know, this study is novel in using the target's destination information for manoeuvring target tracking in 3D AOT problem. The performance of the proposed stochastic filter re-start strategy based on the target's destination information is verified under MATLAB simulations.
Ortho-Expert: A Fuzzy Rule-Based Medical Expert System for Diagnosing Inflammatory Diseases of the Knee

The proposed work is for the diagnosis of the inflammatory diseases of the knee joint. The main diseases which are discussed in this research under inflammatory diseases are osteoarthritis, rheumatoid arthritis and osteonecrosis of the knee joint. The software used for this research is MATLAB, and fuzzy logic method is employed in it. Mamdani inference engine is used. All the input parameters required are consulted with the expert of Orthopaedic during the phase of knowledge acquisition. Survey method is used for the data collection, and various defuzzification methods are used to check the accuracy of the proposed system.
Production of inflectional morphology in intercomprehension-based language teaching: the case of Slavic languages

The present paper investigates the acquisition of L3 Polish by L1 Italian university students of L2 Russian. The participants had never studied the L3 prior to the experiment, but took a meta-linguistically explicit course in Slavic Linguistics focussing on Polish/Russian contrastive grammar. The main research question is whether or not the similarity of the target structure in the two languages allows learners to transfer the L2 processing mechanisms to the L3. The conflicting predictions of the FT/FA hypothesis and of the DMTH are compared. The results show that alongside typical developmental errors, clear instances of L2 transfer can be identified. Although transfer does not always produce target-like results, it arguably constitutes evidence of the processing of a target structure which is not normally accessible in the early stages of acquisition.
Digital bio-inspired satisfiability solver leveraging fluctuations

As the end of Dennard scaling is almost approaching, a number of bio-inspired algorithms have been gaining attention these days to realize new types of computing models and architectures. AmoebaSAT is one of such algorithms that can efficiently solve satisfiability (SAT) problems and optimization problems that are transformed to SAT. It is regarded as a promising algorithm, especially when its hardware architecture is deployed on the Internet-of-Things (IoT) and embedded systems applications due to its inherent parallelism and excellent solution search performance. The key feature of this algorithm is to leverage fluctuations for efficiently searching a solution. However, no prior works sufficiently explored the suitable architecture structure nor the effective fluctuation parameters to realize the hardware solver. This paper intensively studies multiple architectures to implement the AmoebaSAT hardware solver and the effects of fluctuations, which are realized by pseudo random number generators on a digital device. By exhaustively examining the combinations of fluctuation parameters, we provide insightful findings and discussions on the efficient architecture and parameters from the algorithmic perspectives.
Modernization Updates to the Common Rule: Recommendations for Researchers Working With Human Participants

Introduction: This tutorial article explicates some of the 2017 updates to the 1991 Common Rule. Implemented in January 2019, these changes impact the evolving landscape of research with human participants in the US. The introduction provides context for the article as well as framing research questions. Key concepts: These include a brief definition of research for the purposes of the article; a discussion of the foundational ethical principles of autonomy (respect for persons), beneficence, and justice; and a brief overview of the recent policy revision process and its impact on scholarship in the field. After establishing this context, readers are provided five key lessons from the policy revisions. Key lessons: The lessons for researchers working with human participants include discussion of the impact of policy changes in the Common Rule associated with broad consent, single Institutional Review Board (IRB) mandate/cooperative review, updated exemptions, modified definitions of vulnerability, and international research. These are all contextualized within a specific example for professional communication researchers. Implications for practice: Although educative efforts are underway at both the federal and institutional levels, it is important for disciplines and researchers to understand how policy changes will impact the IRB review process. Attendance to the infrastructures that guide our research design and development warrants attention.
Model-driven process enactment for NFV systems with MAPLE

The network functions virtualization (NFV) advent is making way for the rapid deployment of network services (NS) for telecoms. Automation of network service management is one of the main challenges currently faced by the NFV community. Explicitly defining a process for the design, deployment, and management of network services and automating it is therefore highly desirable and beneficial for NFV systems. The use of model-driven orchestration means has been advocated in this context. As part of this effort to support automated process execution, we propose a process enactment approach with NFV systems as the target application domain. Our process enactment approach is megamodel-based. An integrated process modelling and enactment environment, MAPLE, has been built into Papyrus for this purpose. Process modelling is carried out with UML activity diagrams. The enactment environment transforms the process model to a model transformation chain, and then orchestrates it with the use of megamodels. In this paper, we present our approach and environment MAPLE, its recent extension with new features as well as application to an enriched case study consisting of NS design and onboarding process.
Efficient Microcontroller Implementation of BIKE

In the digital world, public-key cryptography is ubiquitous. Current public-key crypto schemes like RSA or Diffie-Hellmann are in widespread use and they represent an indispensable asset of our technological toolbox. However, the discovery of Shor's algorithm and the rapid progression in the field of quantum computers became a painful reminder of our alerting dependency on such technologies. At the same time, this realization started a demand for new cryptographic algorithms withstanding the power of quantum computers. The National Institute of Standards and Technology (NIST) aimed to satisfy this urge by initiating a standardization process in 2017 with a call for proposals of post-quantum key exchange mechanisms and signature algorithms. One of the submissions that made it to the second round is the key encapsulation mechanism BIKE. This work investigates various techniques to achieve an efficient and secure implementation of BIKE on embedded devices. We show that it is possible for BIKE to run on a Cortex-M4 microcontroller using reduced data representation and adequate decoding algorithms. Our implementation achieves a performance of 6 million cycles for key generation, 7 million cycles for encapsulation, and 89 million cycles for decapsulation for BIKE-1.
Didactic Complexity Evaluation of Theorems Proofs of the School Geometry Course

In the study of geometry, the student’s assimilation of the reasonings carried out in the theorems proofs has great importance. The diffi culty of understanding reasonings depends on the number of logical links, terms diversity and semantic complexity of mathematical statements. The article proposes the method for determining the didactic complexity of the theorems proofs and the results of its application. The essence of the method consists in “measuring” the amount of semantic information in theorem formulation, picture, reasonings and multiplication of the received volume with the diversity indicator of the terms used. For this, the theorem statement, the picture and the actual proof should be presented in text form, and the resulting file should be analyzed using a special computer program which calculates the number of diff erent terms in this text, takes into account their complexity, and fi nds the diversity indicator. The expert estimates complexity of terms by counting the words included in its defi nition and by the method of paired comparisons. An assessment of 12 frequently used theorems was carried out; this allowed them to be ordered by complexity. For each theorem the proof volume, the total amount of semantic information in it, the terms diversity indicator, the logical reasoning number, the information folding coeffi cient and the didactic complexity were determined.
R-CapsNet: An Improvement of Capsule Network for More Complex Data

Convolutional neural networks (CNNs) have achieved the best performance in some fields. However, they still have some defects. CNNs need a lot of images for training; they will lose much information in the pooling layer, which reduces the spatial resolution. Facing such problems, Hinton et al. proposed a capsule network (CapsNet). Although the CapsNet has achieved the best accuracy on MNIST dataset, it has not performed well on Fashion-MNIST, Cifar-10 and other datasets. Naturally, we established an improved version of capsule network (R-CapsNet). Results have shown that when using R-CapsNet model, the loss gets decreased and the accuracy gets improved on FashionMNIST. In the meanwhile, the training parameters are reduced by nearly half. Specifically, it reduces by 4.5M. Comparisons show that our proposed model reports improved accuracy of around 0.56% over the existing state-of-the-art systems in literature. The test accuracy of R-CapsNet model is 1.32% higher than that of the original model. Furthermore, better results have been achieved on Cifar-10 with R-CapsNet model and it has easily increased by 10% compared to CapsNet.
Study on Temperature Related Factors of Asphalt Pavement Based on APRIORI

Abstract Asphalt is a typical temperature-sensitive material. In order to explore the correlation between surface temperature of asphalt pavements and meteorological factors, the association rules mining algorithm (Apriori) was applied to identify the key factors affecting the surface temperature of asphalt pavement. Firstly, errors and missing data in the meteorological dataset were cleaned. Then, Apriori was applied to identify the key factors affecting the asphalt pavement temperature. The results indicate that Apriori would perform an excellent ability to analyze the correlation rules between meteorological factors with the minimum confidence of 0.8 and the key meteorological factors which affect the temperature change of asphalt pavements are excavated including air temperature, air pressure, dew point temperature and relative humidity. The research would serve as a technical support for the machine learning algorithms applied in the field of the association rule analysis.
Measuring Peer Mentoring Effectiveness in Computing Courses: A Case Study in Data Analytics for Cybersecurity

Computing courses often suffer from lack of diversity. In this paper we evaluate an intervention method of peer mentoring to help increase interest in data analytics in cybersecurity. We present a text mining approach to analyze student assignments while they undergo a peer mentoring exercise. In our prior work, we have shown that the peer mentoring approach is effective at improving the students' interest in cybersecurity careers and contributes to an overall better knowledge gain throughout the semester. This was also reflected by an improvement in grades with two years of anonymous survey results. Across the years we also observed that peer mentoring is particularly effective in diverse groups. In this paper, we perform text mining of the written assignments for analyzing the group behavior of the control and experiment sections of a class while also documenting the effectiveness of intervention methods such as peer mentoring. We employ a few text mining techniques, namely Text Frequency Analysis, Lexical Diversity, Readability Analysis, Word Cloud Visualization, Hyperlink usage and Objectivity Analysis on the text assignments submitted by the students and show that students who receive peer mentoring are able to express more complex ideas with fewer words and thereby receive higher grades by the end of the semester. Based on these results, we also discuss how our methodology would be applicable in increasing reachability and diversity in other specialized computing courses such as Big Data and distributed systems.
A parameter adaptive differential evolution based on depth information

Differential Evolution (DE) was an easy-coding and efficient stochastic algorithm for global optimization, and the whole optimization process simulates biological evolution. Superior individuals of the population that were suitable for the environment were retained during the evolution, and conseq uently the tolerable solutions could be obtained in the end. Despite the excellent performance of DE algorithm, there were still some shortcomings. For example, the general performance of DE depended largely on mutation strategy and control parameters, how to design the appropriate control parameters and mutation strategy were difficult tasks. Here a novel DE variant was proposed to overcome these shortcomings. By incorporating the depth information of previous generations of populations, a better diversity of trial vector candidates could be secured during the evolution process. Moreover, the thought that successful parameters should be retained to guide the update of themselves during the evolution was also incorporated into the novel algorithm. The optimization performance of the new proposed DE variant was verified under CEC 2013 test suit containing 28 benchmarks, and the results showed its competitiveness with several state-of-the-art DE variants.
Hierarchical Multi-Stage Cyber Attack Scenario Modeling Based on G&E Model for Cyber Risk Simulation Analysis

With the advancement in cyber-defense capabilities, cyber attacks have continued to evolve like living creatures to breach security. Assuming the possibility of various enemy attacks, it is necessary to select an appropriate course of action by proactively analyzing and predicting the consequences of a particular security event. Cyber attacks, especially in large-scale military network environments, have a fatal effect on security; therefore, various experiments and analyses must be conducted to establish the necessary preparations. Herein, we propose a hierarchical multi-stage cyber attack scenario modeling based on the goal and effect (G&amp;E) model and analysis system, which enables expression of various goals of attack and damage effects without being limited to specific type. The proposed method is applicable to large-scale networks and can be utilized in various scenario-based cyber combat experiments.
Parsimonious Morpheme Segmentation with an Application to Enriching Word Embeddings

Traditionally, many text-mining tasks treat individual word-tokens as the finest meaningful semantic granularity. However, in many languages and specialized corpora, words are composed by concatenating semantically meaningful subword structures. Word-level analysis cannot leverage the semantic information present in such subword structures. With regard to word embedding techniques, this leads to not only poor embeddings for infrequent words in long-tailed text corpora but also weak capabilities for handling out-of-vocabulary words. In this paper we propose MorphMine for unsupervised morpheme segmentation. MorphMine applies a parsimony criterion to hierarchically segment words into the fewest number of morphemes at each level of the hierarchy. This leads to longer shared morphemes at each level of segmentation. Experiments show that MorphMine segments words in a variety of languages into human-verified morphemes. Additionally, we experimentally demonstrate that utilizing MorphMine morphemes to enrich word embeddings consistently improves embedding quality on a variety of of embedding evaluations and a downstream language modeling task.
Construction of Human Motivational Profiles by Observation for Risk Analysis

This study aimed at analyzing the extent to which publicly observable pieces of information representing stakeholders' past and current choices can be utilized for the construction of motivational profiles. Motivation is operationalized by the theory of Basic Human Values, which organizes 10 values capturing distinct aspects of human motivation into a hierarchical order. The construction of motivational profiles for individual stakeholders is motivated by the need to enhance the existing decision-maker model in the Conflicting Incentives Risk Analysis (CIRA) method. This study utilized an online questionnaire to collect responses from participants (n = 331) about a wide range of habits and personal items that are easily observable in various contexts by an analyst. The validity of the set of observables as surrogate predictors of the motivational profiles is evaluated by various methods (i.e., comparison to previous results, cross-validation of models, comparison to test-retest reliability of the psychometric instrument) and techniques (calculation of prediction interval for individual profile scores). The assessment of the uncertainties associated with predicting motivational profiles is explored in detail. Additionally, an example illustrates how the profiles can be utilized for the assessment of action desirability (i.e., prediction of behavior) based on the utility calculations established in CIRA. The results contribute to an improved understanding about the accuracy with which human stakeholder motivation can be inferred from public observables and utilized within the context of information security risk analysis.
Experimentation with Prolate Spheroidal Wave Function Pulses for Physical-Layer Security

Prolate spheroidal wave functions (PSWF) are a temporally orthogonal set of waveforms in which energy is concentrated in a finite time window and finite bandwidth. In wireless communications, where binary information is encoded onto a sequence of pulses, individual symbols can conceivably be represented by different orders of PSWF pulse shapes for transmission. Because these pulses have approximately constant bandwidth and pulsewidth across all orders, they are an interesting alternative to conventional modulation formats. Although they are certainly capable of improving spectral efficiency and data capacity, in this study we investigate their feasibility in achieving physical-layer security in wireless RF transmissions.
Investigating the Performance of Machine Learning Algorithms for Improving Fault Tolerance for Large Scale Workflow Applications in Cloud Computing

Cloud platform is emerging distributed system mainly used to host applications from the client side. Maintenance of user’s data and execution of client’s application with the help of hardware, software and network resources are allowed in cloud distributed environment. In this era, modern cloud data centers are used for hosting many non-commercial applications used in scientific field. Cloud being a distributed platform, many errors and fault can occur. In such a distributed environment it becomes difficult to identify errors and faults. Hence there is a high requirement for implementation of fault tolerance mechanism in cloud platform. This mechanism ensures that although the failure occurs in cloud the client’s data are not affected in any manner. Cloud’s performance can be improved by ensuring users their on-demand services as required with the help of fault tolerance mechanisms. In this research work the comparative analysis of three machine learning algorithms K-Means, Decision Tree and KNN (K Nearest Neighbors) algorithm for different structures of scientific workflow applications such as Pipeline, Merge, Split, Diamond using parameters like Sensitivity and Specificity are discussed.
Evaluating Essential and Accidental Code Complexity Triggers by Practitioners&amp;rsquo; Perception

Code complexity determines the difficulty of understanding code. Survey results show that many elements influence complexity, most of which are accidental and can be removed. Meanwhile, several elements captured by the traditional complexity metrics have a small influence on complexity.
Differential Huffman Coding Approach for Lossless Compression of Medical Images

Medical images form a vital part of a patient’s record in medical centers. Medical imaging devices generate data with huge memory requirements. Medical image compression is mandatory for storage and communication of medical data for the purpose of diagnosis. Compression removes the extraneous and redundant data in an image to reduce the storage cost as well as data transmission cost. Compression involves removing coding, interpixel or psychovisual redundancy in an image and, at the same time, retaining the integrity of the information required for the diagnosis in medical images. Lossless compression assures exact reconstruction of the original image after decompressing it and provides greater quality but lesser compression ratio. This paper presents two approaches for lossless compression of medical images. In the first approach, Huffman coding is implemented directly on medical images, whereas in the second approach, differential coding is applied on medical images before implementing Huffman coding. Experimental results show that differential Huffman coding improves the compression ratio.