Reviewerly/exHarmony huggingface dataset:
VisioTM: A Tool for Visualizing Source Code Based on Topic Modeling

Latent Dirichlet Allocation (LDA) is a statistical topic modeling approach that has been used to support various software engineering activities. However, the main problem is the probabilistic distributions generated by LDA to represent topics and documents are not intuitive and easy to comprehend. In order to address this problem, in this paper, we present VisioTM, a language-independent platform to visualize software systems based on LDA. VisioTM provides several visualizations to represent the basic elements of LDA including words, topics, and documents. We discuss the key features and functionalities of the tool.
Analysis of P2P Online Lending Default Based on Random Forest

Abstract This article, based on Renren Loan website, will utilize SMOTE to process the debit and credit data in a balanced way. In addition, the importance for the variable-Random Forest and cross-validation conception will be applied for feature selection before the parameter optimization by grid searching, so that the model of basic Random Forest will be derived. Finally, featured modules of LDA will be added to further explore the reference value of loan description. The research findings show that the model has a satisfactory performance on test set in terms of predicting the results.
Genetic algorithms for affine transformations to existential <i>t</i> -restrictions

The subject of f-restrictions has garnered considerable interest recently as it encompasses many different types of combinatorial objects, all of which have unique and important applications. One of the most popular of these is an ingredient in the generation of covering arrays, which are used for discovering faulty interactions among software components. We focus on existential f-restrictions, which have a structure that can be exploited by genetic algorithms. In particular, recent work on such restrictions considers affine transformations while maximizing the corresponding "score" of the formed restriction. We propose to use genetic algorithms for existential f-restrictions by providing a general framework that can be applied to all such objects.
On the Use of Mobile Devices as Controllers for First-Person Navigation in Public Installations

User navigation in public installations displaying 3D content is mostly supported by mid-air interactions using motion sensors, such as Microsoft Kinect. On the other hand, smartphones have been used as external controllers of large-screen installations or game environments, and they may also be effective in supporting 3D navigations. This paper aims to examine whether a smartphone-based control is a reliable alternative to mid-air interaction for four degrees of freedom (4-DOF) fist-person navigation, and to discover suitable interaction techniques for a smartphone controller. For this purpose, we setup two studies: A comparative study between smartphone-based and Kinect-based navigation, and a gesture elicitation study to collect user preferences and intentions regarding 3D navigation methods using a smartphone. The results of the first study were encouraging, as users with smartphone input performed at least as good as with Kinect and most of them preferred it as a means of control, whilst the second study produced a number of noteworthy results regarding proposed user gestures and their stance towards using a mobile phone for 3D navigation.
Detecting light verb constructions across languages

Abstract Light verb constructions (LVCs) are verb and noun combinations in which the verb has lost its meaning to some degree and the noun is used in one of its original senses, typically denoting an event or an action. They exhibit special linguistic features, especially when regarded in a multilingual context. In this paper, we focus on the automatic detection of LVCs in raw text in four different languages, namely, English, German, Spanish, and Hungarian. First, we analyze the characteristics of LVCs from a linguistic point of view based on parallel corpus data. Then, we provide a standardized (i.e., language-independent) representation of LVCs that can be used in machine learning experiments. After, we experiment on identifying LVCs in different languages: we exploit language adaptation techniques which demonstrate that data from an additional language can be successfully employed in improving the performance of supervised LVC detection for a given language. As there are several annotated corpora from several domains in the case of English and Hungarian, we also investigate the effect of simple domain adaptation techniques to reduce the gap between domains. Furthermore, we combine domain adaptation techniques with language adaptation techniques for these two languages. Our results show that both out-domain and additional language data can improve performance. We believe that our language adaptation method may have practical implications in several fields of natural language processing, especially in machine translation.
Detect Chinese Cyber Bullying by Analyzing User Behaviors and Language Patterns

With the rapid growth of social media, people are more aware of cyber bullying on the internet. The most important aspect for preventing cyber bullying is to detect the abusive content. In this paper, we build a Long Short-Term MemoryNeural Network-Deterministic Finite Automaton (LND) model which considers not only the language content, but also the user's characteristics and historical speech on social network. Due to the lack of labeled content, we utilize Douban's reviewers data by analyzing speech patterns with polarized emotions. Then the learned model is applied to analyze Chinese cyber bully behaviors on Weibo. As a result, the accuracy of detecting cyber bullying increases from 89% (sensitive lexicon filtering method) to 95% by considering user's behavior features and language emotional polarity scores. Our model is capable of analyze real celebrities' Weibo webpages and assists prevention of cyber bullying on social media.
Choosing the Right Storage Solution for the Corpus Management System (Analytical Overview and Experiments)

Corpus management systems are widely used to solve the problems of human-computer interaction. There are many developments associated with the management of language corpora, for example, Sketch Engine [1], Manatee [2], EXMARaLDA [3], etc. We developed the system which considers certain specific features of Turkic languages on the one hand and has new search functions and components from the other hand. The corpus management system "Tugan Tel" ( http://tugantel.tatar ) is specifically designed to work with the National Corpus of Tatar and can be used to work with both the linguistic corpora of Turkic languages and the corpora of other languages. The corpus management system developed by the authors allows searching of lexical units, morphological and lexical searching, searching of syntactic units, searching of the n-gram, named entity extraction and others. The semantic model of the Tatar language data representation is the core of the system. Storage and processing of corpus data, searching in corpus data are performed using open source tools (MariaDB DBMS, Redis data storage). There are three basic stages of corpus management search engine development: the data model development, the system architecture development, and the database architecture development. The issues of collecting and processing of corpus data should also be considered. The main task of our research is the identification and description of solutions for the corpus data storage, collection, and processing. The developed data model can be used for supervised and unsupervised document classification, as well as in corpus exploring. The proposed solutions have been implemented in the corpus management system which is currently used for data representation and processing for the National Corpus of Tatar "Tugan Tel".
JOint Shape Matching for Overlapping Cytoplasm Segmentation in Cervical Smear Images

We present a novel and effective approach to segmenting overlapping cytoplasm of cells in cervical smear images. Instead of simply combining individual cytoplasm shape information with the intensity or color information for the segmentation, our approach aims at simultaneously matching an accurate shape template for each cytoplasm in a whole clump. There are two main technical contributions. First, we present a novel shape similarity measure that supports shape template matching without clump splitting, allowing us to leverage more shape information, not only from the cytoplasm itself but also from the whole clump. Second, we propose an effective objective function for joint shape template matching based on our shape similarity measure; unlike individual matching, our method is able to exploit more shape constraints. We extensively evaluate our method on two typical cervical smear data sets. Experimental results show that our method outperforms the state-of-the-art methods in term of segmentation accuracy.
Deformation measurement of large buildings by holographical point replication

This manuscript gives an overview of the multipoint method and the development of an easy to use measurement system which measures the deformation of large buildings. The multipoint method has previously been tested1, 2 and used mainly under controlled laboratory conditions (e.g. indoors). Difficulties are introduced when this method is used outdoors, mainly because of the increased measurement scale and the uncontrolled environment. Differences in air pressure due to convection or wind, as well as fog or rain can cause severe perturbations to the light which propagates towards the sensors. Compared with existing systems, such as laser trackers, our system does not need to scan the building, which leads to much higher temporal resolution, which in turn can be used to achieve a reduced statistical measurement uncertainty (averaging in time).
ID-Based Strong Designated Verifier Signature over <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mi mathvariant="script">R</mml:mi></mml:mrow></mml:math>-SIS Assumption

In this paper, we propose an ID-based strong designated verifier signature (SDVS) over <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mi mathvariant="script">R</mml:mi><mml:mo>-</mml:mo></mml:math>SIS assumption in the random model. We remove pre-image sampling function and Bonsai trees such complex structures used in previous lattice-based SDVS schemes. We only utilize simple rejection sampling to protect the security of our scheme. Hence, we will show our design has the shortest signature size comparing with existing lattice-based ID-based SDVS schemes. In addition, our scheme satisfies anonymity (privacy of signer’s identity) proved in existing schemes rarely, and it can resist side-channel attacks with uniform sampling.
Laplacian filtering effect on digital image tuning via the decomposed eigen-filter

The Laplacian filter is widely used for acquiring the image edge in the fields of image processing and computer vision due to its excellent edge acquisition capability. However, little deep research on Laplacian filtering exists besides the conventional hierarchical handling technique. In this paper, the essence of Laplacian filter and filtering is investigated, the eigen-filters are extracted from the Laplacian filtering influence scale space, and the spectrum relevance eigen-filters are shown to represent different spectral filtering. Based on the extracted eigen-filters, a subtle filtering effect can be achieved by tuning a group of eigen-filter scalars. The experiments on the subtle filtering effect adjustment demonstrate that the given approach could extract the characteristic eigen-filter, which plays the essential role in Laplacian filtering.
Influence of Interactive Questions on the Sense of Presence and Anxiety in a Virtual-reality Job-interview Simulation

Job interviews are one of the most common methods of selecting employees. However, most people experience a certain amount of anxiety during a job interview. This study aims to help people overcome this anxiety by using a virtual reality job-interview simulation system. This system utilizes voice recognition, a dialogue knowledge base, and morphological analysis to generate new questions that include nouns from the user's answers.
Practical User and Entity Behavior Analytics Methods for Fraud Detection Systems in Online Banking: A Survey

User and entity behavior analytics (UEBA) methods in fraud detection and advertising are widely used cognitive science methods in modern online banking systems. But profusion of marketing papers complicates true situation. Most of academic papers contain a systematic error: “Correct sample choice error”. Fed to the input the real data of user behavior in online banking do not nothing benefit. The paper will be submitted to the criticism of several methods on “mouse track analysis” and “keystroke dynamics” algorithms. New type of algorithms will be present: “preference-behavioral chain” methods. One “preference-behavioral chain” algorithm for social engineering detection will be presented.
Bayesian Categorical Modeling of Multidimensional QoE in Haptic-Audiovisual Communications

This paper proposes a methodology for Bayesian modeling of multidimensional ordinal categorical data for QoE by means of the structural equation modeling (SEM) technique, focusing on the identifiability (indeterminacies) problem of latent variables. As an example material, we adopt a haptic-audiovisual interactive communication system along with previously collected data of 13 subjective QoE measures and a single objective QoE measure. To reduce the dimensionality of the model, we introduce two exogenous latent constructs, Audiovisual quality (AVQ) and Haptic quality (HQ), and an endogenous construct User experience quality (UXQ). For modeling of the categorical data of scores, we also employ continuous latent variables underlying the scores. We resort to the unit loading (UL) method and the unit variance (UV) method for the resolution of scale indeterminacies. Bayesian estimates of free parameters including the loading and regression coefficients by the two methods have different values, while estimates of the QoE measures have almost the same values, which are found to be satisfactory fit in terms of the overall posterior predictive p-values. A comparison of the two methods in terms of the p-value and DIC demonstrates that the model with UL constraints is somewhat better than the model with UV. We also learn that HQ has much stronger effects on UXQ than AVQ regardless of the identifiability constraint (UL or UV).
Supervised word sense disambiguation using new features based on word embeddings

Supervised Word Sense Disambiguation (WSD) systems use features of the target word and its context to learn about all possible samples in an annotated dataset. Recently, word embeddings have emerged as a powerful feature in many NLP tasks. In supervised WSD, word embeddings can be used as a high-q uality feature representing the context of an ambiguous word. In this paper, four improvements to existing state-of-the-art WSD methods are proposed. First, we propose a new model for assigning vector coefficients for a more precise context representation. Second, we apply a PCA dimensionality reduction process to find a better transformation of feature matrices and train a more informative model. Third, a new weighting scheme is suggested to tackle the problem of unbalanced data in standard WSD datasets and finally, a novel idea is presented to combine word embedding features extracted from different independent corpora, which uses a voting aggregator among available trained models. All of these proposals individually improve disambiguation performance on Standard English lexical sample tasks, and using the combination of all proposed ideas makes a significant improvement in the accuracy score.
HECTor: Homomorphic Encryption Enabled Onion Routing

Despite increasing popularity of anonymous communications, current anonymity networks are still vulnerable to traffic analysis attacks. We propose Homomorphic EnCrypTion enabled Onion Routing (HECTor) to defeat existing traffic analysis attacks. Instead of mixing traffic at the packet level as current anonymity networks, HECTor mixes traffic at the bit level with network coding techniques. In HECTor, homomorphic encryption enables onion-like layered encryption which has been proven successful in existing anonymity networks. We theoretically analyze the performance of HECTor. Our extensive experiments on HECTor show that HECTor can effectively mitigate existing traffic analysis attacks with high throughput.
Probabilistic grammar-based deep neuroevolution

Designing deep neural networks by human engineers can be challenging because there are various choices of deep neural network structures. We developed a deep neuroevolution system to automatically design deep neural network structures using deep neuroevolution. Our approach defines a set of structures using a probabilistic grammar and searches for best network structures using Probabilistic Model Building Genetic Programming. Our approach takes advantage of the probabilistic dependencies found among the structures of networks. The system was applied to tackle the problem of the physiological signal classification of abnormal heart rhythm. In the classification problem, our discovered model is more accurate than AlexNet. Our discovered model uses about 2% of the total amount of parameters of AlexNet.
Visualizing Salient Network Activations in Convolutional Neural Networks for Medical Image Modality Classification

Convolutional neural network (CNN) has become the architecture of choice for visual recognition tasks. However, these models are perceived as black boxes since there is a lack of understanding of their learned behavior from the underlying task of interest. This lack of transparency is a drawback since poorly understood model behavior could adversely impact subsequent decision-making. Researchers use novel machine learning (ML) tools to classify the medical imaging modalities. However, it is poorly understood how these algorithms discriminate the modalities and if there are implicit opportunities for improving visual information access applications in computational biomedicine. In this study, we visualize the learned weights and salient network activations in a CNN based Deep Learning (DL) model to determine the image characteristics that lend themselves for improved classification with a goal of developing informed clinical question-answering systems. To support our analysis we cross-validate model performance to reduce bias and generalization errors and perform statistical analyses to assess performance differences.
Mobile Phone-Based Device for Personalised Tutorials of 3D Printer Assembly

There are a number of studies exploring materials and mechanisms of 3D printers that can help product designers develop and evaluate interactive systems efficiently. As 3D printers are increasingly adopted, designers are more likely to encounter difficulties in assembling 3D printers on their own, as the assembly process involves specialised skills and knowledge of fitting components in right positions. Conventional solutions use text and video manuals but still requires high understandings of the assembly. We designed and evaluated the mobile phone-based device for personalised tutorials of 3D printer assembly. The device consists of a modified dongle and mobile phone application. The former detects electromagnetic signals upon physical contacts with the components and the latter displays tutorials accordingly. The contributions include the device design with electromagnetic signal-based object detection and importantly, the approach to integrating component touching with component detection for personalised interactions. Generalising implications for the approach are discussed.
Exploring Methods to Promote Interoperability in Adaptive Instructional Systems

This paper explores design principles and methods to promote interoperability and reuse in a class of instructional technologies called adaptive instructional systems (AISs) which include intelligent tutoring systems (ITSs), intelligent mentors or recommender systems, and intelligent instructional media. AISs are artificially-intelligent, computer-based systems that guide learning experiences by tailoring instruction and recommendations based on the goals, needs, and preferences of each individual learner or team in the context of domain learning objectives. IEEE is exploring standards and best practices for AIS modeling, interoperability, and evaluation under its Project 2247 and affiliated working group. This paper was composed to document recommendations related to interoperability standards for AISs. The after school market for AISs is large and on the rise in China, the US, and in Europe. The desired level of interoperability for AISs is at the lowest possible level to allow component reuse without impinging upon the intellectual property of the vendor.
Optimization Algorithms Study and Implementation on Graph Drawing Based on XML Document

In the process of engineering applications, we often use a large number of vertices and edges to represent the complex system model, but sometimes the model is so complex that it is impossible to draw the graph neatly and beautifully by hand. So how to draw the graph automatically using the computer and how to make the graph visual become an important research topic. Based on the study on the genetic algorithm, SHLA algorithm and FR algorithm, we implement these algorithms by analyzing the pseudo-codes. Especially we focus on the study of the source programming about FR-boost and then implement an improved FR algorithm that selects XML document as the input and output graph files. It can effectively reduce the complexity of algorithm and increase the point-line graph’s visualization.
Supporting Decision Makers in Search-Based Product Line Architecture Design using Clustering

The Product Line Architecture (PLA) is one of the most important artifacts of a Software Product Line (SPL). PLA design can be formulated as an optimization problem with many factors. In this context, the MOA4PLA approach was proposed to optimize PLA design using search algorithms and metrics specific to the context. MOA4PLA treats the PLA design as a multi-objective optimization problem. At the end of the search process several PLA design alternatives are presented to the decision maker, difficulting the decision about which PLA alternative best fits with his/her needs. In this sense, this work proposes the usage of clustering algorithms to group PLAs design alternatives, according to their characteristics and assist the decision maker in the choice of one PLA design for the SPL. For this purpose, an empirical study was carried out, involving quantitative and qualitative experiments. Such an study integrated the K-Means++ and DBSCAN clustering algorithms in the MOA4PLA approach. The results of the experiments were promising, since an appropriate grouping of the solutions can be quantitatively observed, and also, qualitatively, the suitability of the solutions to the decision makers needs was verified.
An Intrusion Detection and Response Scheme for CP-ABE-Encrypted IoT Networks

This paper introduces a new method of applying both an Intrusion Detection System (IDS) and an Intrusion Response System (IRS) to communications protected using Ciphertext-Policy Attribute-based Encryption (CP-ABE) in the context of the Internet of Things. This method leverages features specific to CP-ABE in order to improve the detection capabilities of the IDS and the response ability of the network. It also enables improved privacy towards the users through group encryption rather than one-to-one shared key encryption as the policies used in the CP-ABE can easily include the IDS as an authorized reader. More importantly, it enables different levels of detection and response to intrusions, which can be crucial when using anomaly-based detection engines.