Reviewerly/exHarmony huggingface dataset:
On few-class Q-polynomial association schemes: feasible parameters and nonexistence results

We present the tables of feasible parameters of primitive 3 -class Q -polynomial association schemes and 4 - and 5 -class Q -bipartite association schemes (on up to 2800 , 10000 , and 50000 vertices, respectively), accompanied by a number of nonexistence results for such schemes obtained by analysing triple intersection numbers of putative open cases.
Ernst Denert Software Engineering Awards 2019

Abstract The need to improve software engineering practices is continuously rising and software development practitioners are highly interested in improving their software systems and the methods to build them. And well, software engineering research has numerous success stories. The Ernst Denert Software Engineering Award specifically rewards researchers that value the practical impact of their work and aim to improve current software engineering practices. This chapter summarizes the awards history as well as the current reward process and criteria.
Evolution of quality assessment in SPL: a systematic mapping

Software product line (SPL) is one of the most recent and effective reuse approaches. SPL derives several products from the core artefacts. SPL engineering includes two processes: domain engineering, which identifies the common and variable features to develop the core artefacts, and application engineering, which reuses the core artefacts to derive products. Once the artefacts are reused across multiple products, quality assessment is necessary to prevent inconsistencies from spreading across all SPL products. There are several frameworks and standards, as ISO/IEC 25010:2011, to evaluate quality characteristics. In this study, the authors provide an overview of the SPL quality assessment. Therefore, they perform a systematic mapping to compile and synthesise data regarding the quality characteristics assessed in studies from 2000 to 2019. The results include the identification of 346 metrics applied in 16 software properties to evaluate three quality characteristics of the ISO/IEC 25010:2011. Additionally, they find the domain engineering evaluation frequently occurs regarding the maintainability characteristic. Moreover, they provide analyses of the: (i) metrics used by programming paradigm, (ii) metrics used by software properties, (iii) software properties evaluated for each quality characteristic, (iv) tools used to extract metrics, (v) systems used as benchmarks, and (vi) datasets used for extracting metrics.
LEARNING GOALS IN 360° VIRTUAL EXCURSION - MEDIA CREATION GUIDELINE FOR MIXED-REALITY-BASED CLASSES

S. Khodaei1, J. Sieger2, A. Abdelrazeq1, I. Isenhardt1 1Information Management in Mechanical Engineering (IMA) - RWTH Aachen (GERMANY) 2Institute of Mineral Resources Engineering (MRE)- RWTH Aachen (GERMANY)
The State of the Science of Working Memory

The last five decades have seen a dramatic expansion of empirical and theoretical diversity in research on the concept of working memory. That diversity is reflected across the other 13 chapters in this book, written by prominent international scientists at the leading edge of that research. Authors were asked to respond to set of common questions regarding their research methods, theoretical assumptions, and how they address evidence that is not consistent with those assumptions. Each chapter starts with a summary of those responses. Chapters 2–6 describe contrasting theoretical perspectives and the evidence associated with each, including empirical, behavioural studies and computational modelling. Chapters 7–9 cover individual differences in working memory, including effects of brain damage and of expertise. Chapters 10–13 explore neural correlates and neurobiological models. Finally, Chapter 14 offers a possible means to integrate the seemingly diverse views in the other chapters by considering different levels of explanation and different participant strategies for performing working memory tasks.
Predicting Workplace Injuries Using Machine Learning Algorithms

Predicting workplace injury using automated techniques opens newer possibilities in evidence-based research. This paper presents our preliminary research in a PhD project in predicting workplace incidents using machine learning algorithms. The analysis on the model performance using several mainstream machine learning algorithms including random forest, k-nearest neighbor and decision tree indicated that the general performance of the decision tree model was found to be statistically higher than that of the other two algorithms.
Hacking Goals: A Goal-Centric Attack Classification Framework

Attack classification does represent a crucial activity in different security areas. During security assessment, it makes it easier to define which attacks must be performed. When conducting threat modeling activities, it simplifies the definition of attack graphs. Many works have addressed the attack taxonomy problem, by introducing different ways to classify attacks. However, these classifications are centered around vulnerabilities and have all been designed from the point of view of those defending a system. Nowadays, companies have a growing interest in Penetration Testing activities, as they have proven effective in detecting vulnerabilities. Penetration testers perform their activity by focusing on goals rather than attack types. In this paper we introduce a “goal-centric” methodology to classify attacks in terms of Hacking Goals.
Pattern-based access control in a decentralised collaboration environment

As the building industry is rapidly catching up with digital advancements, and Web technologies grow in both maturity and security, a data- and Web-based construction practice comes within reach. In such an environment, private project information and open online data can be combined to allow cross-domain interoperability at data level, using Semantic Web technologies. As construction projects often feature complex and temporary networks of stakeholder firms and their employees, a property-based access control mechanism is necessary to enable a flexible and automated management of distributed building projects. In this article, we propose a method to facilitate such mechanism using existing Web technologies: RDF, SHACL, WebIDs, nanopublications and the Linked Data Platform. The proposed method will be illustrated with an extension of a custom nodeJS Solid server. The potential of the Solid ecosystem has been put forward earlier as a basis for a Linked Data-based Common Data Environment: its decentralised setup, connection of both RDF and non-RDF resources and fine-grained access control mechanisms are considered an apt foundation to manage distributed building data.
CREMA: A Java Library for Credal Network Inference

We present CREMA (Credal Models Algorithms), a Java library for inference in credal networks. These models are analogous to Bayesian networks, but their local parameters are only constrained to vary in, so-called credal, sets. Inference in credal networks is intended as the computation of the bounds of a query with respect to those local variations. For credal networks the task is harder than in Bayesian networks, being NPPP-hard in general models. Yet, scalable approximate algorithms have been shown to provide good accuracies on large or dense models, while exact techniques can be designed to process small or sparse models. CREMA embeds these algorithms and also offers an API to build and query credal networks together with a specification format. This makes CREMA, whose features are discussed and described by a simple example, the most advanced tool for credal network modelling and inference developed so far.
Semi-supervised Named-Entity Recognition for Product Attribute Extraction in Book Domain

Products sold in today’s marketplace are very numerous and varied. One of them is the book product. Detail information about the book, such as the title of the book, author, and publisher, is often presented in unstructured format in the product title. In order to be useful for the commercial applications, for example catalogs, search functions, and recommendation systems, the attributes need to be extracted from the product title. In this study, we apply Named-Entity Recognition model in semi-supervised style to extract the attributes of e-commerce products in book domain. We experiment with the number of features extraction, i.e. lexical, position, word shape, and embedding features. We extract the book attributes from near to 30K product title data with F-1 measure 65%.
Detecting E-Commerce Phishing Website By Data Mining

We will use a data mining techniques and machine learning that will generate a classification model from a training dataset and then this model will be applied to testing dataset which will show the websites as malicious or legitimate. We will compare the accuracy and time taken of two models generated and conclude which is better among those two.
Image Captioning Using CNN Long Short-Term Memory Network

In recent years, various Deep Learning models have been used for deriving textual information from an image. Deep Learning has achieved success in the development and training and has applications in large (more layers) neural networks. The neural networks provide flexibility and have come with solutions which are useful and reliable. The real-world image captioning can be evaluated through the Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Neural Networks (DNNs) and Long Short-Term Memory (LSTM). Among these advanced models, the CNN’s and LSTMs have performed much better as compare to DNNs. The results show that CNN’s and LSTMs models have proved to more flexible and pragmatic in mapping the features. CNNs can reduce the frequency variations, therefore suited to models, which has spatial representation. CNNs successfully caters to the tasks relating to computer vision, image classification and object detection. LSTMs caters to temporal modeling, the tasks which has sequences and can perform the predictions on them. LSTMs have wide applications in the Natural Language Processing Tasks, as they do not change the entire information, but rather alter it slightly, or they can forget and remember the information selectively. The combination of CNN Long Short-Term Memory Network (CNN-LSTM) architecture are good to handle sequence prediction models with the spatial inputs like images. We observe that the merge model CNN-LSTM proves that machines can see very well as the images are correctly captioned.
An Improved Steganography Method Based on Least-Significant-Bit Substitution and Pixel-Value Differencing

This research was based on the study conducted by Khodaei et al. (2012), namely, the leastsignificant-bit (LSB) substitution combined with the pixel-value differencing (PVD) steganography, and presented an improved irreversible image steganography method.Such a method was developed through integrating the improved LSB substitution with the modulus function-based PVD steganography to increase steganographic capacity of the original technique while maintaining the quality of images.It partitions the cover image into nonoverlapped blocks, each of which consists of 3 consecutive pixels.The 2nd pixel represents the base, in which secret data are embedded by using the 3-bit LSB substitution.Each of the other 2 pixels is paired with the base respectively for embedding secret data by using an improved modulus PVD method.The experiment results showed that the method can greatly increase steganographic capacity in comparison with other PVD-based techniques (by a maximum amount of 135%), on the premise that the quality of images is maintained.Last but not least, 2 security analyses, the pixel difference histogram (PDH) and the contentselective residual (CSR) steganalysis were performed.The results indicated that the method is capable of preventing the detection of the 2 common techniques.
Scene Character Recognition with Morphological Filtering and HOG Features

Recognition of scene text is an essential task in the extraction of meaningful information from camera captured images. It is a challenging problem due to heterogeneity in the nature of scene text and complexity in the surrounding background. In this paper, a new recognition method is proposed for recognizing scene characters extracted from scene images. At first, original scene character components are converted into binary character images and then, morphological operations are applied to reduce noises. Subsequently, we extract HOG features from these scene character components and apply multiple classifiers to classify those scene characters components. This method is evaluated on a dataset of 1905 camera captured scene character images and obtained character recognition rates are 56.83% (Naive Bayes), 77.59% (KNN), 75.23% (Random Forest), 78.06% (MLP) and 82.07% (SVM), which is reasonably satisfactory considering scene character complexity.
A Token-Wise CNN-Based Method for Sentence Compression

Sentence compression is a Natural Language Processing (NLP) task aimed at shortening original sentences and preserving their key information. Its applications can benefit many fields e.g., one can build tools for language education. However, current methods are largely based on Recurrent Neural Network (RNN) models which suffer from poor processing speed. To address this issue, in this paper, we propose a token-wiseConvolutional Neural Network, a CNN-based model along with pre-trained Bidirectional Encoder Representations from Transformers (BERT) features for deletion-based sentence compression. We also compare our model with RNN-based models and fine-tuned BERT. Although one of the RNN-based models outperforms marginally other models given the same input, our CNN-based model was ten times faster than the RNN-based approach.
“Chatty Devices” and edge-based activity classification

Abstract With increasing automation of manufacturing processes (focusing on technologies such as robotics and human-robot interaction), there is a realisation that the manufacturing process and the artefacts/products it produces can be better connected post-production. Built on this requirement, a “chatty" factory involves creating products which are able to send data back to the manufacturing/production environment as they are used, whilst still ensuring user privacy. The intended use of a product during design phase may different significantly from actual usage. Understanding how this data can be used to support continuous product refinement, and how the manufacturing process can be dynamically adapted based on the availability of this data provides a number of opportunities. We describe how data collected on product use can be used to: (i) classify product use; (ii) associate a label with product use using unsupervised learning—making use of edge-based analytics; (iii) transmission of this data to a cloud environment where labels can be compared across different products of the same type. Federated learning strategies are used on edge devices to ensure that any data captured from a product can be analysed locally (ensuring data privacy).
Systematic Engineering of Mutation Operators.

In the context of software engineering, mutation consists in injecting small changes in artefacts -like models, programs, or data -for purposes like (mutation) testing, test data generation, and all sorts of search-based methods.These tasks typically require defining sets of mutation operators, which are often built ad-hoc because there is currently poor support for their development and testing.To improve this situation, we propose a methodology and corresponding tool support for the proper engineering of mutation operators.Our proposal is model-based, representing the artefacts to be mutated as models.It includes a domain-specific language to describe the mutation operators, facilities to synthesize models that can be used to test the operators, different metrics to analyse operator coverage, and services to generate operators when the coverage is insufficient.We show automated support atop the WODEL tool, and illustrate its use by defining mutation operators for UML Class Diagrams.
A Bias and Correlation Free True Random Number Generator Based on Quantized Oscillator Phase under Sub-Harmonic Injection Locking

In this work, we demonstrated a novel oscillator phase based true random number generator (TRNG) design that can be high speed, and bias and correlation free. We are showing that: i) the arbitrary phase difference between the unsynchronized oscillator and injected synchronization signal is collapsed into two random and stable phases under subharmonic injection locking (SHIL); ii) the quantized oscillator phases under SHIL are fully symmetric and memoryless, generating bias and correlation free random bits; iii) the proposed oscillator phase TRNG is a generic design, independent of the oscillator platform. Thus, a CMOS ring oscillator based TRNG is also designed and evaluated. All of the generated random numbers pass the National Institute of Standards and Technology (NIST) tests and exhibit negligible bias and correlation from statistical analysis. Therefore, the proposed solution provides a competitive alternative to the existing on-chip TRNG design toolbox.
A Gentle Introduction to ML and NLP

This chapter will provide you a bird’s-eye view of machine learning (ML) and deep learning (DL). The history of these fields will be storified here in order to be more understandable. We will examine why they have emerged and what kind of applications they have. After gaining the principal knowledge, you will be introduced to natural language processing (NLP). You will learn how we make text data understandable for computers via NLP. Even if you have zero knowledge about these disciplines, you will gain the intuition behind after reading this chapter.
Automatic Content Extraction for Live Streaming Web Page Based on the Comparison Approach

This paper proposes an automatic scheme of web data extraction for live streaming platforms. Based on the similar desired blocks on web pages and corresponding HTML tag structures, the proposed scheme can automatically identify the desired blocks as well as desired content without any visual identification or computational training. The proposed scheme is a general data mining solution for diverse live streaming platforms. Furthermore, it adapts to a certain level of revision on web pages by means of updating its data-extracting parameters.
Robust Place Recognition using Convolutional Neural Networks

In this work we propose using Convolutional Neural Network for place visual recognition. The work focuses on the identification and automatic extraction of interest regions from a query image. These regions are used to build an image encoding through a vector of locally aggregated descriptors, which is turn used for image recovery. Unlike other methods, where the entire image is used to create the encoding, our approach only uses the most important image interest regions. This provides better invariance to changes at extreme view points of view, lighting and occlusions. Another contribution of the work consists in the integration of a totally convolutional spatial transformer according to the convolutional neural network architecture. This transformer is used for normalizing these interest regions, which allows achieving a greater robustness during coding. A loss function is also proposed that is used to train the artificial neural network to automatically identify regions. To measure the efficiency of the proposed model, a variety of experiments were carried out with challenging data sets. The reported results show that the proposed method produces superior results than other state of the art methods.
G-CNN and double-referenced thresholding for detecting time series anomalies

Anomaly detection based on time series data is of great importance in many fields. Time series data produced by man-made systems usually include two parts: monitored and exogenous data, which respectively are the detected object and the control/feedback information. In this paper, a so-called G-CNN architecture that combined the gated recurrent units (GRU) with a convolutional neural network (CNN) is proposed, which respectively focus on the monitored and exogenous data. The most important is the introduction of a complementary double-referenced thresholding approach that processes prediction errors and calculates threshold, achieving balance between the minimization of false positives and the false negatives. The outstanding performance and extensive applicability of our model is demonstrated by experiments on two public datasets from aerospace and a new server machine dataset from an Internet company. It is also found that the monitored data is close associated with the exogenous data if any, and the interpretability of the G-CNN is discussed by visualizing the intermediate output of neural networks.
Current and Future Intensity-Duration-Frequency Curves based on Weighted Ensemble GCMs and Temporal Dissagregation

Hydrological events are expected to increase in both magnitude and frequency in tropical areas due to climate variability.The Intensity -Duration -Frequency (IDF) curves are important means of evaluating the efficiency of irrigation and drainage systems.The necessity to update IDF curves arises from the need to gain better understanding of the impacts of climate change.This study explores an approach based on weighted Global Circulation Models (GCMs) and temporal disaggregation method to develop future IDFs under Representative Concentration Pathways (RCP) emission scenarios.The work consists of 20 ensemble GCMs, three RCPs (2.6, 4.5, and 8.5) and two projection periods (2050s and 2080s).The study compared three statistical distributions and selected Generalized Extreme Value (GEV) being the best fitting distribution with baseline rainfall series and therefore used for IDF projection.The result obtained shows that, the highest rainfall intensities of 19.32, 35.07 and 39.12 mm/hr occurred under 2-, 5-, and 20 years return periods, respectively.IDFs from the multi-model ensemble GCMs have shown increasing intensity in the future for all the return periods.This study indicated that the method could produce promising results which can be extended to other catchments.
Android Forensics Using Sleuth Kit Autopsy

Mobile device forensics has gained significance with the increase in the user base and the proliferation of smartphones. The digital shreds of evidence extracted from these devices may lead a forensic investigation to confidence. In this literature, we experimented with the Android Forensics process and have tried to explain the whole process in a detailed manner. We demonstrated the whole Android Forensics process in two stages. In the data acquisition stage, we have shown how we extracted the raw image of each memory location of the memory device from the android device and save it to a file with .dd extension. In order to achieve this imaging capability, we have also demonstrated the need for root access to the android file system along with the understanding of the android file system. Then in the second stage, called the artifact finding stage, we demonstrate the process of loading the raw disk image acquired by us in the data acquisition stage and analyzing it using the Sleuth Kit Autopsy tool to uncover the artifacts from the analysis of the raw data. We have demonstrated the usefulness of Sleuth Kit Autopsy which is an open-source forensic tool. We have recorded the level of detail about artifacts that are obtained using the Sleuth Kit Autopsy during our experiment. The report generated and the artifacts uncovered using Sleuth Kit Autopsy can be linked logically with a crime scene and may help lead a digital forensic investigation to confidence. The insight gained from this work is highly extensible in nature.