Reviewerly/exHarmony huggingface dataset:
Algebraic Structure of Union of Fuzzy (Anti-Fuzzy) Normal Sub - Groups Fuzzy (Anti-Fuzzy) Normal Sub ch Bigroups And Soon Fuzzy (Anti-Fuzzy) Normal Sub - N Groups

In this paper, union of Fuzzy subsets, definition of Fuzzy normal subgroup, definition of Fuzzy normal sub bi-group, definition of Fuzzy normal sub tri-group, definition of Fuzzy normal sub &amp;ndash; Quadratic group, definition of fuzzy normal sub- Pentant group and definition of Fuzzy normal sub&amp;ndash;N groups are derived. Moreover, some properties and theorems of union in fuzzy normal based on these have been derived.
On profitability of block withholding strategies

We present recent developments in the understanding of the profitability block withholding strategies in Bitcoin mining and other Proof-of-Work based blockchains. Block withholding strategies (like selfish, stubborn, trailing or catch-up mining) are rogue mining strategies that violate the rules of the Bitcoin protocol. The authors found recently the exact model based on iterative games to evaluate the profitability per unit time. With a novel application of martingale techniques and Doob's Stopping Time Theorem we compute their profitability in close-form. We can then compare in parameter space these strategies and honest mining, and decide which one is more profitable.
Residual Stacked RNNs for Action Recognition

Action recognition pipelines that use Recurrent Neural Networks (RNN) are currently 5–10% less accurate than Convolutional Neural Networks (CNN). While most works that use RNNs employ a 2D CNN on each frame to extract descriptors for action recognition, we extract spatiotemporal features from a 3D CNN and then learn the temporal relationship of these descriptors through a stacked residual recurrent neural network (Res-RNN). We introduce for the first time residual learning to counter the degradation problem in multi-layer RNNs, which have been successful for temporal aggregation in two-stream action recognition pipelines. Finally, we use a late fusion strategy to combine RGB and optical flow data of the two-stream Res-RNN. Experimental results show that the proposed pipeline achieves competitive results on UCF-101 and state of-the-art results for RNN-like architectures on the challenging HMDB-51 dataset.
A Model-Driven and Generative Approach to Holistic Security

Functional and technical cyber-resilience gain increasing relevance for the health and integrity of connected and interoperating systems. In this chapter we demonstrate the power and flexibility of extreme model-driven design to provide holistic security to security-agnostic applications. Using C-IME, our integrated modelling environment for C/C++, we show how easily a modelled application can be enhanced with hardware security features fully automatically during code generation. We illustrate how to use this approach and design environment to make any modelled application ready to securely store its data in potentially insecure environments. The same approach can be used to secure communication over potentially insecure channels. In fact, our approach does not require any changes of the application model. Rather, our integrated modelling environment provides a dedicated modelling language for code generators which resorts to a Domain Specific Language for security. It is realized as a palette of security primitives whose implementation is based on underlying hardware security technology. The code generator injects security appropriately into the models of the applications under development. We illustrate the use of this security-injecting code generator on the case study of a to-do list management application. The code generator is generic and can be used to secure the file handling of any application modelled in the C-IME.
Improvement of similarity-diversity trade-off in recommender systems based on a facility location model

There is a growing interest in the offering of novel alternative choices to users of recommender systems. These recommendations should match the target query while at the same time they should be diverse with each other in order to provide useful alternatives to the user, i.e. novel recommendations. In this paper, the problem of extracting novel recommendations, under the similarity-diversity trade-off, is modeled as a facility location problem. The results from tests in the benchmark Travel Case Base were satisfactory when compared to well-known recommender techniques, in terms of both similarity and diversity. It is shown that the proposed method is flexible enough, since a parameter of the adopted facility location model constitutes a regulator for the trade-off between similarity and diversity. Also, our work can broaden the perspectives of the interaction and combination of different scientific fields in order to achieve the best possible results.
Image-Sensitive Language Modeling for Automatic Speech Recognition

Typically language models in a speech recognizer just use the previous words as a context. Thus they are insensitive to context from the real world. This paper explores the benefits of introducing the visual modality as context information to automatic speech recognition. We use neural multimodal language models to rescore the recognition results of utterances that describe visual scenes. We provide a comprehensive survey of how much the language model improves when adding the image to the conditioning set. The image was introduced to a purely text-based RNN-LM using three different composition methods. Our experiments show that using the visual modality helps the recognition process by a $$7.8\%$$ relative improvement, but can also hurt the results because of overfitting to the visual input.
ICT Integration in the Teaching/Learning Process of Natural Sciences for Seventh Grade Elementary Students’

The teaching-learning process and all the technologies have evolved alongside, which has allowed new educative methods based on Information and Communications Technologies (ICT), and also has allow the fulfillment of the established educational institution’s goals in their curricular blocks by using computers, internet, interactive classrooms, social networks, audio, video, images, among others. In brief, this research shows ICT integration at the learning process of a Natural Science Class for Seventh Grade Elementary Students’ on “La Inmaculada” Franciscan Elementary School; in addition, it evaluates ICT contribution to validate the changes that have being made over students and teachers during years 2015–2016 and 2016–2017.
Barnacles Mating Optimizer Algorithm for Optimization

This article proposes a new technique to solve optimization problems based on barnacles’ mating behavior namely Barnacles Mating Optimizer (BMO). Barnacles are micro-organisms existed since Jurassic times and they attach themselves to objects in the water and grow shell. They are also known as hermaphroditic micro-organisms and one of the special characters of barnacles is they have long penises which is the longest in animal kingdom relative to their body size. The Hardy-Weinberg principle is adopted in BMO to generate new off-springs and the mating process is performed randomly based on the length of the barnacle’s penis. The effectiveness of BMO is tested through a set of 23 benchmark functions in finding the optimal solutions. Some impactful and reliable results are presented compared with other techniques in this paper.
A Novel Scalable Signature Based Subspace Clustering Approach for Big Data

“Big data” as the name suggests is a collection of large and complicated data sets which are usually hard to process with on-hand data management tools or other conventional processing applications. A scalable signature based subspace clustering approach is presented in this article that would avoid identification of redundant clusters. Various distance measures are utilized to perform experiments that validate the performance of the proposed algorithm. Also, for the same purpose of validation, the synthetic data sets that are chosen have different dimensions, and their size will be distributed when opened with Weka. The F1 quality measure and the runtime of these synthetic data sets are computed. The performance of the proposed algorithm is compared with other existing clustering algorithms such as CLIQUE.INSCY and SUNCLU.
Application of Partial Differential Equation in Brain Image Segmentation and Registration

Due to the problem that local binary fitting model is easy to fall into local extremum, based on the hypothesis that the brightness of the image on both sides of the target boundary has contrast, an improved method of adding contrast constraints to the fitting function is proposed in this paper. The contrast-constrained model can effectively overcome the problem that energy is easily trapped in local extremum, but there is still a problem that requires the initialization curve to be close to the edge of the target. Therefore, balloon force is added to the contrast-constrained model, which enlarges the scope of external force and enhances the robustness of the model to initialization. Meanwhile, the diffusion filtering method with adaptive image structure is introduced into image registration, and the anisotropic diffusion function with the ability of feature preservation and consistency enhancement is defined as the regularization term of the model. The improved model can effectively maintain image features and achieve accurate registration of complex images. In this paper, brain image (MRI) in Mcgill database is taken as an example to verify the simulation results. The simulation results verify the reliability and validity of the partial differential equation-based algorithm.
Statistical Infelicities in <i>The New Oxford Shakespeare Authorship Companion</i>

The New Oxford Shakespeare Authorship Companion contains, in its editors’ words, “an extensive sampling of new research, employing a range of new data and new methods in an effort to resolve proble...
Behaving socially with a virtual human role-player in a simulated counseling session

Visual attention is an essential social element in face-to-face conversations between people. The Army is now using virtual environments with virtual human role-players to provide for the development and practice of leader counseling skills. While literature indicates that virtual humans positively impact learning outcomes, this research contributes to the understanding of how people interact with virtual humans. Specifically, the research investigates a person’s social behavior toward a virtual human in a simulated counseling session. Utilizing a computer-based training environment, this research tracked visual attention toward and acceptance of a virtual human role-player by 120 military counselors. Behavior moderators included attentional influences on the counselor from the level of expression and full-body or close-up views of the virtual human. Results indicate trainees do practice appropriate social attention when the virtual human is speaking. Results also indicate counselors attended to an active more than a static full-body view indicating there is value in nonverbal communication channels. Switching from a full-body to a close-up view did not result in significant changes of the counselor’s visual attention. Further, the research indicates switching views may undermine the perception of a natural interaction. Counselors considered the experience useful. The research suggests study and design considerations to enhance future social skills training environments.
SEAKER: A Tool for Fast Digital Forensic Triage

Faced with a preponderance of high capacity digital media devices, forensic investigators must be able to review them quickly, and establish which devices merit further attention. This early stage of an investigation is called triage and it is a chief part of evidence assessment; see [1, Chap. 2]. In this paper we present a digital forensic device, which we named SEAKER (Storage Evaluator and Knowledge Extraction Reader), which enables forensic investigators to perform triage on many digital devices very quickly. Instead of imaging the drives, which takes hours, SEAKER does a search for files with names that conform to pre-established patterns. The search is done by mounting the devices in read-only mode (to preserve evidence) and listing the contents of the device. Unlike imaging, this approach takes minutes rather than hours. Also, SEAKER’s hardware consists principally of a Raspberry Pi (RP) and so it is very inexpensive—this is crucial in this era of budgetary constraints; see [2]. Once SEAKER has identified media devices of interest, those can be confiscated for further investigation in a lab. But devices that do not have hits can be left at the scene. This has two principal benefits: forensic examiners can concentrate on those devices that are promising in terms of evidence for the given investigation, and devices without hits are not confiscated from legitimate users.
Mixtures of general location model with factor analyzer covariance structure for clustering mixed type data

Cluster analysis is one of the most widely used method in statistical analyses, in which homogeneous subgroups are identified in a heterogeneous population. Due to the existence of the continuous and discrete mixed data in many applications, so far, some ordinary clustering methods such as, hierarchical methods, k-means and model-based methods have been extended for analysis of mixed data. However, in the available model-based clustering methods, by increasing the number of continuous variables, the number of parameters increases and identifying as well as fitting an appropriate model may be difficult. In this paper, to reduce the number of the parameters, for the model-based clustering mixed data of continuous (normal) and nominal data, a set of parsimonious models is introduced. Models in this set are extended, using the general location model approach, for modeling distribution of mixed variables and applying factor analyzer structure for covariance matrices. The ECM algorithm is used for estimating the parameters of these models. In order to show the performance of the proposed models for clustering, results from some simulation studies and analyzing two real data sets are presented.
Emerging Auditing Perspectives in the Age of the Fourth Industrial Revolution

The purpose of this chapter is to investigate the role of audit activities and auditors in Industry 4.0. The preferred methodological approach in the study is a general analysis of auditing in Industry 4.0 in the form of a literature review. According to the purpose of the study, the effect and role of auditing big data, the internet of things, the cloud, artificial intelligence, and other components in Industry 4.0 are investigated. Furthermore, auditing activities that can be implemented in Industry 4.0 are presented as suggestions in the study. The study explains the role of auditing as a whole in Industry 4.0 as a consequence of examining audit activities for each component in Industry 4.0.
Safeguarding against bad luck when attempting to discredit a state-trace model

A state-trace model for a natural phenomenon proposes that there is a causal “bottleneck” that makes the joint causal effects of two independent variables look like the effect of a single independent variable. Imagine that a state-trace model has been formulated, but the model seems implausible and we wish to find empirical evidence that it is wrong. How can we do that? Given reasonable monotonicity assumptions, a state-trace model is wrong if a point on one state trace is delta-discordant with a point on another state trace. [We say that two points (x,y) and (x′,y′) in the plane are delta-discordant if Δx=x′−x and Δy=y′−y are nonzero and have opposite signs.] So, to show the model wrong, we need to find a pair of delta-discordant points lying on different state traces. One method for finding two such points is to observe multiple points on each of two state traces with the hope that, among those points, there will be a delta-discordant pair. But this approach can have bad luck and fail to find delta-discordant pairs even though they exist. A new approach to “locating” a delta-discordant pair of points, an approach that is much less dependent on luck, is formulated in this paper. In addition, the paper describes a statistical test for evaluating whether the “located” points are really delta-discordant.
Items2Data: Generating Synthetic Boolean Datasets from Itemsets

Boolean data is a core data type in machine learning. It is used to represent categorical and transactional data. Unlike real valued data, it is notoriously difficult to efficiently design boolean datasets that satisfy particular constraints. Inverse Frequent Itemset Mining (IFM) is the problem of constructing a boolean dataset, satisfying given support constraints for some itemsets. Previous work mainly focuses on the theoretical complexity of IFM and practical solutions scale poorly or do not satisfy all the constraints. We propose Items2Data, a practical algorithm for generating boolean datasets which is efficient under specific conditions. We introduce global closure to describe the condition which a dataset can be efficiently constructed. We evaluate Items2Data and its use in designing synthetic datasets and to analyze its accuracy, scalability and speed on real world datasets. The results indicate Items2Data is practical and efficient for generating synthetic boolean data when pre-defined itemsets are globally closed.
Siamese Tracking from Single Point Initialization

Recently, we have been concerned with locating and tracking vehicles in aerial videos. Vehicles in aerial videos usually have small sizes due to use of cameras from a remote distance. However, most of the current methods use a fixed bounding box region as the input of tracking. For the purpose of target locating and tracking in our system, detecting the contour of the target is utilized and can help with improving the accuracy of target tracking, because a shape-adaptive template segmented by object contour contains the most useful information and the least background for object tracking. In this paper, we propose a new start-up of tracking by clicking on the target, and implement the whole tracking process by modifying and combining a contour detection network and a fully convolutional Siamese tracking network. The experimental results show that our algorithm has significantly improved tracking accuracy compared to the state-of-the-art regarding vehicle images in both OTB100 and DARPA datasets. We propose utilizing our method in real time tracking and guidance systems.
Normalization of Java Source Codes

Security issues can be leveraged when input parameters are not checked. These missing checks can lead an application to an unexpected state where an attacker can get access to assets. The tool Chucky-ng aims at detecting such missing checks in source code. Such source codes are the only input required for ChuckyJava. Since it is sensible to the identifier names used in these source codes, we want to normalize them in order to improve its efficiency. To achieve this, we propose an algorithm which works in four steps. It renames constant, parameter, variable and method names. We evaluate the impact of this renaming on two different experiments. Since our results are concluding, we show the benefits of using our tool. Moreover, we suggest another new way to improve Chucky-ng.
Multiwavelet density estimation for biased data

In this article, we present our method for multiwavelet estimation for a density function based on random samples from a weighted distribution. First, we propose linear and nonlinear multiwavelet estimators for the biased data. Then, we provide and prove the upper bound of Lp risk for the proposed nonlinear estimator over a large class of densities in the Besov space, Br1,qs. Finally, we provide simulation and real data experiments to illustrate the effectiveness and behavior of the proposed method and estimator.
A Statistical Salt-and-Pepper Noise Removal Algorithm

This paper proposes a novel approach to remove salt-and-pepper noise from a given noisy image. The proposed algorithm is based on statistical quantities such as mean and standard deviation. It determines the intensity to be placed on the impulse point by calculating the eligibility of the nearby points in a very simple way. This method works iteratively and removes all the impulse points restoring the edges and minute details. The proposed algorithm is very efficient and gives better results than various existing algorithms. The performance of the proposed method are compared with other existing methods with images of noise density as high as 99% and is found to perform better.
Squeezing-enhanced superposition of coherent states and their nonclassical properties

We study the squeezing-enhanced superposition of coherent states, which are generated by operating a squeezing operator with two parameters on the superposition of coherent states with a different relative phase. The aim of this study is to discuss the nonclassical properties of these novel states by their quantum statistical and squeezing properties. The results show that the squeezing parameter r and relative phase θ play key roles in these states. It is clear to see that these new quantum states have stronger squeezing, antibunching effect, sub-Poissonian distributions behavior when compared to the normal squeezing superposition of coherent states.
Mobile App Risk Ranking via Exclusive Sparse Coding

To improve mobile application (App for short) user experience, it is very important to inform the users about the apps' privacy risk levels. To address the challenge of incorporating the heterogeneous feature indicators (such as app permissions, user review, developers' description and ads library) into the risk ranking model, we formalize the app risk ranking problem as an exclusive sparse coding optimization problem by taking advantage of features from different modalities via the maximization of the feature consistency and enhancement of feature diversity. We propose an efficient iterative re-weighted method to solve the resultant optimization problem, the convergence of which can be rigorously proved. The extensive experiments demonstrate the consistent performance improvement using the real-world mobile application datasets (totally 13786 apps, 37966 descriptions, 10557681 user reviews and 200 ad libraries).
Decision of Neural Networks Hyperparameters with a Population-Based Algorithm

This paper proposes a method named Population-based Algorithm (PBA) to decide the best hyperparameters for a neural network (NN). The study focuses on which type of hyperparameters achieve better results in neural network problems. Population-based algorithm inspired from evolutionary algorithms and uses basic steps of genetic algorithms. The distinctive feature of our algorithm from genetic algorithms is fitness evaluation of individuals. To test our approach, we implemented our algorithm to a handwritten digits recognition problem to find the best hyperparameters for a simple neural network and we reached 98.66 accuracy score. Finally, we conclude, how PBA used in neural networks for the best way.