Reviewerly/exHarmony huggingface dataset:
The Structure of Agile Development Under Scaled Planning and Coordination

Agile and the Scaled Agile Framework (SAFe) methodologies are increasingly being deployed, both for software development and also by teams in broader work settings. This is a proof-of-concept study to develop a DSM for a SAFe development environment. Key takeaways from this study are: (i) Agile and SAFe DSMs are characterized by a unique set of nested information dependencies that are absent in waterfall development; (ii) these dependencies are not readily evident in the conventional and layered rendering of SAFe process documentation. We discuss the organizational and analytical process improvement opportunities available based on such DSM analysis and conclude by identifying opportunities for follow-on research.
A dataset for automatic contrast enhancement of microscopic malaria infected blood RGB images

In this article we introduce a malaria infected microscopic images dataset for contrast enhancement which assist for malaria diagnosis more accurately. The dataset contains around two hundred malaria infected, normal, species and various stages of microscopic blood images. We propose and experimentally demonstrate a contrast enhancement technique for this dataset. This simple technique increases the contrast of an image and hence, reveals significant information about malaria infected cells. Experiments on the dataset show the superior performance of our proposed method for contrast enhancement of malaria microscopic imaging.
Distilling weighted finite automata from arbitrary probabilistic models

Weighted finite automata (WFA) are often used to represent probabilistic models, such as n-gram language models, since they are efficient for recognition tasks in time and space.The probabilistic source to be represented as a WFA, however, may come in many forms.Given a generic probabilistic model over sequences, we propose an algorithm to approximate it as a weighted finite automaton such that the Kullback-Leibler divergence between the source model and the WFA target model is minimized.The proposed algorithm involves a counting step and a difference of convex optimization, both of which can be performed efficiently.We demonstrate the usefulness of our approach on some tasks including distilling ngram models from neural models.
RBSEP: a reassignment and buffer based streaming edge partitioning approach

Abstract In recent years, the rapid growth of the Internet has led to creation of massively large graphs. Since databases have become very large nowadays, they cannot be processed by a simple machine at an acceptable time anymore; therefore, traditional graph partitioning methods, which are often based on having a complete image of the entire graph, are not applicable to large datasets. This challenge has led to the appearance of a new approach called streaming graph partitioning. In streaming graph partitioning, a stream of input data is received by a partitioner, and partitioner decides which computational machine the data should be transferred to. Often, streaming partitioner does not have any information about the whole graph, and usually distributes the vertices based on some greedy heuristics which may not be optimal for incoming vertices. Hence, partitioner’s decision can be significantly improved if more information about the graph is utilized. In this paper, we present a new vertex-cut streaming graph partitioning approach. The proposed method uses the idea of postponing the decision for some of the edges (by means of an intelligent buffering) and corrects some of the past decisions to improve the quality of the graph partitioning. The proposed approach is evaluated using from real-world graphs. The experimental results show that the performance of the proposed method is superior in comparison with the previous HDRF method.
Implementation of a HL7-CQL Engine Using the Graph Database Neo4J.

The Clinical Quality Language (CQL) is a useful tool for defining search requests for data stores containing FHIR data. Unfortunately, there are only few execution engines that are able to evaluate CQL queries. As FHIR data represents a graph structure, the authors pursue the approach of storing all data contained in a FHIR server in the graph database Neo4J and to translate CQL queries into Neo4J's query language Cypher. The query results returned by the graph database are retranslated into their FHIR representation and returned to the querying user. The approach has been positively tested on publicly available FHIR servers with a handcrafted set of example CQL queries.
Interdisciplinary approach principles’ implementation in the process of teaching professionally-oriented foreign language

The article is concerned with the identification of characteristic features inherent to the interdisciplinary approach in education and ways of its employment in the process of professionally-oriented foreign language teaching at the University level. The authorsubstantiates the necessity to employ the chosen set of interdisciplinary approach principles in the process of professionally-oriented language teaching; in the process of educational programmes' design; in the process of choosing the forms of interaction and modification of pedagogic technologies. On the example of the language manual written for the students mastering in "Industrial and Civil Engineering", the author demonstrated not only the possibility, but also the need to combine and take into account the highlighted principles of both interdisciplinary and competency-based approaches in the process of teaching professionally-oriented foreign language. Particular attention is paid to the effectiveness of the modular approach as a way to implement the principles of interdisciplinary approach, to design the subject and metasubject content of both educational programmes and academic syllabuses, and to structure students’ textbooks and manuals.
Conditional transition systems with upgrades

We introduce a variant of transition systems, where activation of transitions depends on conditions of the environment and upgrades during runtime potentially create additional transitions. Using a cornerstone result in lattice theory, we show that such transition systems can be modelled in two ways: as conditional transition systems (CTS) with a partial order on conditions, or as lattice transition systems (LaTS), where transitions are labelled with the elements from a distributive lattice. We define equivalent notions of bisimilarity for both variants and characterise them via a bisimulation game. We explain how conditional transition systems are related to featured transition systems for the modelling of software product lines. Furthermore, we show how to compute bisimilarity symbolically via BDDs by defining an operation on BDDs that approximates an element of a Boolean algebra into a lattice. We have implemented our procedure and provide runtime results. This is an extended version of the TASE 2017 paper [1], including all proofs, additional examples, an extension of the formalism to account for deactivation of updates and detailed runtime results.
A Co-Prediction-Based Compression Scheme for Correlated Images

Deep learning has achieved a preliminary success in image compression due to the ability to learn the nonlinear spaces with compact features that training samples belong to. Unfortunately, it is not straightforward for the network based image compression methods to code multiple highly related images. In this paper, we propose a co-prediction based image compression (CPIC) which uses the multi-stream autoencoders to collaboratively code the multiple highly correlated images by enforcing the co-reference constraint on the multi-stream features. Patch samples fed into the multi-stream autoencoder, are generated through corresponding patch matching under permutation, which helps the autoencoder to learn the relationship among corresponding patches from the correlated images. Each stream network consists of encoder, decoder, importance map network and binarizer. In order to guide the allocation of local bit rate of the binary features, the important map network is employed to guarantee the compactness of learned features. A proxy function is used to make the binary operation for the code layer of the autoencoder differentiable. Finally, the network optimization is formulated as a rate distortion optimization. Experimental results prove that the proposed compression method outperforms JPEG2000 up to 1.5 dB in terms of PSNR.
Impact of Shape on Apparent Translucency Differences

Translucency is one of the major appearance attributes. Apparent translucency is impacted by various factors including object shape and geometry. Despite general proposals that object shape and geometry have a significant effect on apparent translucency, no quantification has been made so far. Quantifying and modeling the impact of geometry, as well as comprehensive understanding of the translucency perception process, are a point of not only academic, but also industrial interest with 3D printing as an example among many. We hypothesize that a presence of thin areas in the object facilitates material translucency estimation and changes in material properties have larger impact on apparent translucency of the objects with thin areas. Computergenerated images of objects with various geometry and thickness have been used for a psychophysical experiment in order to quantify apparent translucency difference between objects while varying material absorption and scattering properties. Finally, absorption and scattering difference thresholds where the human visual system starts perceiving translucency difference need to be identified and its consistency needs to be analyzed across different shapes and geometries.
KAdam: Using the Kalman Filter to Improve Adam algorithm

Nowadays, the Adam algorithm has become one of the most popular optimizers to train feed-forward neural networks because it takes the best features of other gradient-based optimizers, such as working well with sparse gradients, in online and non-stationary settings, and also it is very robust to the rescaling of the gradient. The above makes Adam the best choice to solve problems with non-stationary objectives, very noise gradients, and with large data inputs. In this work, we enhanced the Adam algorithm by using the Kalman filter, and the novel proposal is called KAdam. Instead of using the computed gradients directly from the cost function, we first apply the Kalman filter on them. As a result, the filtered gradients allow the algorithm to explore new (and potentially better) solutions on the cost function. The results obtained when applying our proposal and other state-of-the-art optimizers to solve classification problems show that KAdam is able to obtain better accuracies than its competitors in the same execution time.
Scientific Article Clustering Using String Similarity Concept

Scientific articles are one of the results of a study or research. Scientific articles from a study are disseminated through publications in a journal and a seminar. Journal or seminar committee classifies articles into several research topic clusters. To cluster articles, sometimes the journal committee reads the contents of the article to cluster it manually. The manual process is ineffective and inefficient in terms of processing time. From these problems, a system was created to help classify articles using a computer system based on the contents of the article. The application of article clustering was built using the word frequency method adopted from the term frequency of TF-IDF and using cosine similarity to cluster scientific articles according to the research topics. The processes in clustering scientific articles include calculating the number of words used, searching for words that often appear, eliminating stop words based on the Tala's stop word list, then from words that often appear are made into the new article's keywords. From the keywords obtained, then the level of similarity is calculated with the keywords used in the research topic using cosine similarity. The research topics used are based on the body of knowledge in computer science and information system based on ACM (Association for Computing Machinery) & IEEE (Institute of Electrical and Electronics Engineers). The system was built in the form of a web application using the PHP programming language and MySQL database with a responsive web design technique. The results of this research are that articles entered into the system could be generated into the new keywords by listing the words that appeared a lot. From the keywords obtained, the articles were successfully clustered using cosine similarity. The level of clarity from the descriptions of the research topics affected the results of clustering. The more clear and complete the description is given, the better the results of the clustering were obtained. From 102 article titles of KNS&I (Konferensi Nasional Sistem & Informatika) 2017 that were entered, 71.6% of articles were clustered correctly. From these data, the largest research publications were obtained in the "information management" cluster.
AUTOREGRESSIVE DISTRIBUTED LAG APPROACH TO THE INCOME INEQUALITY AND FINANCIAL LIBERALIZATION NEXUS: EMPIRICAL EVIDENCE FROM TURKEY

This paper critically examines the validity of orthodox assumptions about the positive effects of financial liberalization on income inequality by employing the Autoregressive Distributed Lag (ARDL) Bound test by Pesaran et al. (2001) for the yearly data of Turkey over the 1987-2016 period. The benchmark results suggest that the income distribution worsens by the implementation of more liberalization in the financial sector for a long-run equilibrium relationship. Further, the study attempts to investigate an inverted U-shaped hypothesis of financial development. The findings indicate that income inequality improves at the initial stages of financial development but worsens over time. Therefore, it rejects the income-narrowing hypothesis for the latter stages of financial development. With the advantage that ARDL approach incorporates both I(0) and I(1) series, the study concludes that the positive relationship between financial liberalization and income inequality is prevalent both in the short- and the long-run in control of other variables.Keywords: Income Inequality, Financial Liberalization, Financial Development, Income Distribution, ARDL Bound TestJEL Classifications: F65, D31, C22DOI: https://doi.org/10.32479/ijefi.8626
Online P2P Internet Traffic Classification and Mitigation Based on Snort and ML

Peer to peer applications have modified the nature of internet traffic. It will consume high internet bandwidth and affect the performance of traditional traffic internet applications. Therefore, the management and monitoring activity of internet traffic is the important activities involved in the optimization. In order to detect and mitigate the P2P traffic, port, payload, and transport layer based methods were developed in the past. Nevertheless, the performances of these methods were not up to the expectation. Machine Learning (ML) is one of the promising methods to identify and mitigate the traffic of the Internet. However, the classification accuracy is inconsistent. The reason for the inconsistency is the relevant training datasets generation and feature selection. In this research, a technique based on signature-based and ML is proposed to develop a model for online P2P traffic detection and mitigation. The proposed work can be employed to evaluate the robustness of the online P2P machine learning classifier based on real network traffic traces containing flows labelled by SNORT tool and from special shared resources. Analysis and validation were carried out on traffic traces of University Technology Malaysia. The period of traffic was 2011 and 2013. The output of research is revealing that the proposed work has spent less computation time for classification. This method gives 99.7% accuracy which equals the classification performance attained for P2P using deep packet inspector. The findings show that classifying network traffic at the flow level can differentiate P2P over non-P2P (nP2P) with high confidence for online P2P mitigation.
Assessment of Association Rules based on Certainty Factor: an Application on Heart Data Set

Association rules mining is one of the uttermost applied techniques in data mining and artificial intelligence. Support and confidence are two basic measures employed in the evaluation of association rules. The rules obtained with these two values are often correct; however, they are not strong rules. Most of the rules, especially with a high support value, are misleading. For this reason, there are many interestingness measures proposed to achieve stronger rules. In this study it is aimed to establish strong association rules with variables in open sourced heart data set. In the current study, Apriori algorithm was used to obtain the rules. As a result of the analysis, only 55 confidence and support criteria were taken into consideration. For more powerful rules, certainty factor was used as one of the interestingness measure proposed in the literature, and it was concluded that only 26 of these rules were strong. As a result of the analysis of the findings obtained in the context of the research, it can be inferred that stronger rules can be obtained by using the certainty factor in association rules mining.
Cell Mechanics Based Computational Classification of Red Blood Cells Via Machine Intelligence Applied to Morpho-Rheological Markers

Despite fluorescent cell-labelling being widely employed in biomedical studies, some of its drawbacks are inevitable, with unsuitable fluorescent probes or probes inducing a functional change being the main limitations. Consequently, the demand for and development of label-free methodologies to classify cells is strong and its impact on precision medicine is relevant. Towards this end, high-throughput techniques for cell mechanical phenotyping have been proposed to get a multidimensional biophysical characterization of single cells. With this motivation, our goal here is to investigate the extent to which an unsupervised machine learning methodology, which is applied exclusively on morpho-rheological markers obtained by real-time deformability and fluorescence cytometry (RT-FDC), can address the difficult task of providing label-free discrimination of reticulocytes from mature red blood cells. We focused on this problem, since the characterization of reticulocytes (their percentage and cellular features) in the blood is vital in multiple human disease conditions, especially bone-marrow disorders such as anemia and leukemia. Our approach reports promising label-free results in the classification of reticulocytes from mature red blood cells, and it represents a step forward in the development of high-throughput morpho-rheological-based methodologies for the computational categorization of single cells. Besides, our methodology can be an alternative but also a complementary method to integrate with existing cell-labelling techniques.
The robustness-fidelity trade-off in Grow When Required neural networks performing continuous novelty detection

Novelty detection allows robots to recognise unexpected data in their sensory field and can thus be utilised in applications such as reconnaissance, surveillance, self-monitoring, etc. We assess the suitability of Grow When Required Neural Networks (GWRNNs) for detecting novel features in a robot’s visual input in the context of randomised physics-based simulation environments. We compare, for the first time, several GWRNN architectures, including new Plastic architectures in which the number of activated input connections for individual neurons is adjusted dynamically as the robot senses a varying number of salient environmental features. The networks are studied in both one-shot and continuous novelty reporting tasks and we demonstrate that there is a trade-off, not unique to this type of novelty detector, between robustness and fidelity. Robustness is achieved through generalisation over the input space which minimises the impact of network parameters on performance, whereas high fidelity results from learning detailed models of the input space and is especially important when a robot encounters multiple novelties consecutively or must detect that previously encountered objects have disappeared from the environment. We propose a number of improvements that could mitigate the robustness-fidelity trade-off and demonstrate one of them, where localisation information is added to the input data stream being monitored.
Convergence guarantees for a class of non-convex and non-smooth optimization problems

We consider the problem of finding critical points of functions that arenon-convex and non-smooth. Studying a fairly broad class of such problems, weanalyze the behavior of three gradient-based methods (gradient descent,proximal update, and Frank-Wolfe update). For each of these methods, weestablish rates of convergence for general problems, and also prove fasterrates for continuous sub-analytic functions. We also show that our algorithmscan escape strict saddle points for a class of non-smooth functions, therebygeneralizing known results for smooth functions. Our analysis leads to asimplification of the popular CCCP algorithm, used for optimizing functionsthat can be written as a difference of two convex functions. Our simplifiedalgorithm retains all the convergence properties of CCCP, along with asignificantly lower cost per iteration. We illustrate our methods and theoryvia applications to the problems of best subset selection, robust estimation,mixture density estimation, and shape-from-shading reconstruction.
About the Unification Type of Modal Logics Between $$\mathbf {KB}$$ and $$\mathbf {KTB}$$

The unification problem in a normal modal logic is to determine, given a formula $$\varphi $$ , whether there exists a substitution $$\sigma $$ such that $$\sigma (\varphi )$$ is in that logic. In that case, $$\sigma $$ is a unifier of $$\varphi $$ . We shall say that a set of unifiers of a unifiable formula $$\varphi $$ is minimal complete if for all unifiers $$\sigma $$ of $$\varphi $$ , there exists a unifier $$\tau $$ of $$\varphi $$ in that set such that $$\tau $$ is more general than $$\sigma $$ and for all $$\sigma ,\tau $$ in that set, $$\sigma \not =\tau $$ , neither $$\sigma $$ is more general than $$\tau $$ , nor $$\tau $$ is more general than $$\sigma $$ . When a unifiable formula has no minimal complete set of unifiers, the formula is nullary. We usually distinguish between elementary unification and unification with parameters. In elementary unification, all variables are likely to be replaced by formulas when one applies a substitution. In unification with parameters, some variables—called parameters—remain unchanged. In this paper, we prove that normal modal logics $$\mathbf {KB}$$ , $$\mathbf {KDB}$$ and $$\mathbf {KTB}$$ as well as infinitely many normal modal logics between $$\mathbf {KDB}$$ and $$\mathbf {KTB}$$ possess nullary formulas for unification with parameters.
Training and Development of Professional Skills: An Analysis of activity in Professional Skills

A body of research has shed light on the question of skills development in term of acquisition and training.Research in skills development has become of essence especially in the field of adult education.Most researches have focused on the development of professional skills and their implementation.In other words, in order to understand skills development phenomena, approach the question from the point of view of doing the action, an analysis of the strategies adopted to achieve such development is of utmost importance.