Reviewerly/exHarmony huggingface dataset:
Erasable Pattern Mining with Quantitative Information

Erasable-itemset mining can assist users in scheduling production plans for product manufacturing. Previous methods focus on finding erasable itemsets without quantitative information. In the real world, however, a product usually consists of all kinds of materials with different quantities. In this paper, an interval-type representation is used to partition the quantity values of materials into several ranges. A quantitative erasable-itemset mining approach based on the representation is thus proposed to consider the quantities of the materials in products to find quantitative erasable patterns.
Location-Wise News Headlines Classification and Sentiment Analysis: A Deep Learning Approach

Text analysis has become the research interest for various researchers recently. Various text-related researches are being performed, of which text-based sentiment analysis is one. News headlines are the textual formatted data, which became the point of interest for various researchers for the purpose of text analysis. News headlines contain a long sequence of data so they can be used for various purposes. News headlines gives the information regarding what is currently going on but they give the overall situation. Our approach includes unique utilization of geospatial data-based technique to aggregate location-based news. This paper provides the novel approach of location-dependent news headlines classification and sentiment analysis over it using Deep Learning and clustering technique.
Automatic Text Generation in Slovak Language

Automatic text generation can significantly help to ease human effort in many every-day tasks. Recent advancements in neural networks supported further research in this area and also brought significant improvement in quality of text generation. Unfortunately, most of the research deals with English language and possibilities of text generation of Slavic languages was not fully explored yet. Our work is concerned with automatic text generation and language modeling for Slovak language. Since Slovak language has more complicated grammatical structure and morphology, the task of text generation is also more challenging. We experimented with the neural approaches in natural language generation and performed several experiments with text generation in both Slovak and English language for two different domains. Additionally, we performed an experiment with human annotators to assess the quality of generated texts. Our experiments showed promising results and we can consider using neural networks for text generation as sufficient also for text generation in Slovak language.
Blockchain Governance for Collaborative Manufacturing

The manufacturing industry is rapidly changing due to widespread adoption of information and communication technologies. This new landscape, described as the fourth industrial revolution, will be characterized by highly complex and interdependent systems. One particular aspect of this shift is horizontal integration, or the tight coupling of firms within a value chain. Highly interconnected and interdependent manufacturing systems will encounter new challenges associated with coordination and collaboration, specifically with regards to trust. This purpose of this chapter is to explore the potential of blockchain to address these challenges. Survey data collected from manufacturing professionals suggests that the perceived nature of trust and resource value can be bounded and controlled. Concepts from game theory, systems theory, and organizational economics are used to augment this research data and inform a collaborative manufacturing blockchain model and architecture.
Partial Discharge Spectrogram Data Augmentation based on Generative Adversarial Networks

High voltage insulators are critical elements in power distribution and transmission systems, since their failure may represent losses in power quality and system reliability, leading to faults which bring problems to both consumer and electric utilities. Insulators in operation are subjected to all kinds of pollution which may cause the formation of dry bands when in presence of moisture, which leads to the formation of partial discharges which, in turn, causes degradation and aging of the surface material. Partial discharges origin can be identified by the pattern of their pulses. Machine learning algorithms are one of the most useful and efficient solutions for pattern recognition and classification tasks. In this paper, a methodology is proposed for data augmentation of the training set for an artificial neural network by means of generative adversarial networks.
Using Label Information in a Genetic Programming Based Method for Acquiring Block Preserving Outerplanar Graph Patterns with Wildcards

Machine learning and data mining from graph structured data have gained much attention. Many chemical compounds can be expressed by outerplanar graphs. We propose a method for acquiring characteristic block preserving outerplanar graph patterns with wildcards for vertex and edge labels, from positive and negative outerplanar graph data, by Genetic Programming using label connecting information of positive examples. We report experimental results on real chemical compound data and synthetic data.
Book publishing and local government: the history of relations between publishers and regional administrations in Siberia and the Far East in the 90-ies of XX – 10-s of XXIst century

The article summarizes the historical experience on support of the Siberian and Far Eastern regional book publishing by regional and national republican authorities and management in the late twentieth – early twenty-first century. During 25 years regional administrations have passed a long way from unskillful attempts to copy the support measures practiced at the Federal center, from the single support of individual publications to an elaborate system of assistance for authors and publishers producing socially demanded books, mainly of local and regional content. The author considers the historical evolution of this process – development of management decisions on financing local publishing proposals aimed at strengthening the cultural, scientific and creative potential of the regions in the East of Russia. The classification, characteristics and evaluation of each of the forms for publishing support used in the regional practice are given. The author has come to conclusion that the best examples of publishing projects effective assistance are the activity of Omsk region and Krasnoyarsk Krai administrations. The former has the support model, in which distribution of funds is based on the publishing program of the regional Ministry of culture. In the latter the funds are allocated in the form of social grants to the regional winners of the annual contest “Book Krasnoyare”, that is fixed in its regional legislation as the protected item of expenditure. It is stated that not all administrations in the regions of Siberia and the Far East are concerned about the development of local publishing. In many regions the forms and level of support for the publishing environment do not meet the needs of society, remain unchanged since the end of the twentieth century.
Mobile Robot Control by Tangible programming for Developmenting of Computer Scientist Thinking skill in Elementary School

Generally, programming for controlling mobile robot is difficult for children in the elementary school. Such program is unsuitable for children or novices as they cannot understand how to use the program. This research seeks to present tangible programming for develop of computer scientist thinking skill for children. Normally, this idea taught at university or college level. This may be late as far as acquiring knowledge in the future. The program could be use in elementary schools. Using tangible programming control mobile robot without a computer or tablet which this encourages children to learn. Simple concept for idea open-loop control systems and close-loop control systems. In this research children can understand idea concrete. Another tangible programming for control mobile robot present idea programming 2 conditions: 1) sequential command 2) conditions for changing the status of the mobile robot. In the future tangible programming for control mobile robot will change learning and idea to children in development of computer scientist thinking.
Design of Deep Learning Controller for Vector Controlled Induction Motor Drive

This paper tends to the utilization of Deep Learning concept to structure the controller, to investigate achievability of using deep learning into control issues. Induction motors play an important role in industrial applications. If speed is not controlled properly, it is for all intents and purposes difficult to accomplish the preferred task for precise application. Induction motors are known for their simplicity and reliability. They are maintenance free and low-cost electric drives. Due to the lack of ability of traditional control techniques such as PI and PID controllers to serve under wide scope of activity, AI controllers are broadly utilized in industries such as fuzzy logic controller, neuro-fuzzy controller (NFC), genetic algorithm. By learning PI controller, which is mostly utilized in the industries, the suggested Deep Learning-based controller is planned. The input and output of PI controller are utilized as learning informational index for Deep Learning (DL) system. To stay away from computational weight, just speed error is given as the input to the proposed deep learning controller, not at all like a conventional controller which utilizes speed error and its derivative. Deep belief network (DBN) control process is utilized to design the DL controller. DLC gives incredible speed exactness. By using MATLAB Simulink, simulation is carried out. Results of suggested DLC and NFC are compared; finally, it was led to show the effectiveness and usefulness of the suggested procedure.
A Cost-Effective Cloud Event Archival for SIEMs

Security Information and Event Management (SIEM) systems have been adopted by organizations to enable holistic monitoring of malicious activities in their IT infrastructures. SIEMs receive events from diverse devices of the organization's IT infrastructure (e.g., servers, firewalls, IDS), correlate these events, and present reports for security analysts. Given the large number of events collected by SIEMs, it is costly to store such data for long periods. Since organizations store a relatively limited time-frame of events, the forensic analysis capabilities severely become reduced. This concern limits the organizations' ability to store important information about the past cybersecurity-related activity, limiting forensic analysis. A possible solution for this issue is to leverage public cloud storage services, exploiting their low cost and "infinite" scalability. We present SLiCER an archival system for long-term storage that makes use of a multi-cloud-based storage system to guarantee data security and ensures cost-effectiveness by grouping events in blocks and using indexing techniques to recover them. The system was evaluated using a real dataset and the results show that it is significantly more cost-efficient than competing alternatives.
Wireless Signal Identification in 230MHz Band Based on Interference Cleaning and Convolutional Neural Network

With the development of digital wireless communication technol-ogy, the wireless signal identification has been suffering from increasingly complex electromagnetic environment and higher spectrum utilization. In this paper, we propose a wireless signal identification method based on interference cleaning and convolutional neural network (CNN) in 230MHz Band. The method firstly analyzes the received signal in time domain, building feature data sets combined with amplitudes, phases, in-phase components and orthogonal components. The method then generalizes singular value decomposition(SVD) and subspace division to preserve signal subspace, eliminate noise subspace and interference compress subspace. Finally, it utilizes the data set to train the CNN and make the wireless signals' identification through the well-trained the CNN. The experimental results with different kinds of modulation show that this method can achieve high recognition accuracy and strong anti-noise ability.
High-Density Salt and Pepper Noise Filter using Anisotropic Diffusion

This paper presents an approach to suppress salt- and-pepper noise. The approach that we proposed in this paper has two phases, noise interception and filtering. Firstly, pixels that have the maximum and minimum value are categorised as disturbed pixels and will be substituted with a value computed by the anisotropic diffusion approach in the next phase. Our experiments show that our approach can create better outcomes than several existing filtering approaches.
Dimensionality reduction of a phishing attack using decision tree classifier

Internet plays an important role in our day-to-day lives, and we are using many e-commerce websites. Cyber-criminals are using phishing attack technique to steal user's sensitive information like personal details, and bank details. It may lead to loss of both client and original seller. Our work involves the decision tree and best performing classifier to identify the significant dimension. The entropy features include SFH, pop up window, and URL of anchor. Through which, RF with 5 features at each split achieves 84.8% accuracy. In our work, RF with 6 features achieves 96.30% accuracy. However, the RF with all features performs only 93.20% accuracy.
The Dark Side of Interaction Design

Interaction designers have developed a number of techniques that are commonly used to nudge users, for example, to click on ads, stay on a site, keep playing a game and come back for more. In addition, a proliferation of books and guidelines have been published that explain how to change users' behaviour through designing persuasive interventions at the interface. Many of these are intended to be for personal improvement or social good, such as to help people get fitter, improve their work productivity, be more motivated, reduce energy consumption and so on. However, the same techniques can also be used for more nefarious ends. Sometimes, referred to as dark patterns, they have become essentially tricks that websites and apps use to make users do things they didn't intend doing, such as buying or signing up for something. At the same time, a backlash against interaction design is beginning to appear; criticizing us for our role in the rise in digital addiction, fake news, social media abuse, etc. Are we to blame for letting the genie out of the bottle?
Probabilistic nonlinear dimensionality reduction through gaussian process latent variable models: An overview

From an algorithmic complexity point of view, machine learning methods scale and generalize better using a few key features; using lots is computationally expensive, and overfitting can occur. High dimensional data is often counterintuitive to perceive and process, but unfortunately it is common for observed data to be in a representation of greater dimensionality than it requires. This gives rise to the notion of dimensionality reduction, a sub-field of machine learning that is motivated to find a descriptive low-dimensional representation of data. In this review, it is explored a way to perform dimensionality reduction, provided by a class of Latent Variable Models (LVMs). In particular, the aim is to establish the technical foundations required for understanding the Gaussian Process Latent Variable Model (GP-LVM), a probabilistic nonlinear dimensionality reduction model. The review is organized as follows: after an introduction to the problem of dimensionality reduction and LVMs, Principal Component Analysis (PCA) is recalled and it is reviewed its probabilistic equivalent that contributes to the derivation of GP-LVM. Then, GP-LVM is introduced, and briefly a remarkable extension of the latter, the Bayesian Gaussian Process Latent Variable Model (BGP-LVM) is described. Eventually, and the main advantages of using GP-LVM are summarized.
Parallel multi-scale CNN for image denoising

Deep learning has achieved outstanding performance in the field of image processing. Inspired by the inception model, we propose a deep neural network convolving simultaneously at two different scales. We utilize the convolution kernel of different sizes to carry out the convolution, which can obtain the features of the image at different scales. The features of the output are no longer uniformly distributed, but converge the features with strong correlation. Batch normalization (BN) and residual learning (RL) are utilized to speed up the training process as well as enhance the denoising ability of model. The experimental results of three salt-and-pepper noise levels based on the public dataset BSDS500 show that the newly proposed method can obtain significantly superior performance than current state-of-the-art approaches and higher peak signal to Noise Ratio (PSNR) in salt-and-pepper noise removal.
Cyberattack — Examples of Threats

Abstract The purpose of this article is to present selected threats that may affect the security of IT systems caused by cyberattacks coming from cyberspace, as well as to prescribe the results that could be caused by the failure to implement security measures to protect information system (IS).
Searching for Bugs Using Probabilistic Suspect Implications

Due to the excessive cost associated with manual RTL design debugging, automated tools are often employed to identify a set of suspect bug locations. To further accelerate the process, one observes that the anytime behavior of these tools allows partial results to be analyzed before the suspect search is complete. Thus, it is preferable for the tool to maximize the number of suspects that are found in the early stages of its search. Toward this end, this article proposes a new SAT-based debugging algorithm which predicts where solutions are most likely to be found and prioritize examining these locations. Two techniques are proposed to predict solution locations by learning from historical debug data. The first technique does so using belief propagation on a probabilistic graph, while the second trains a neural network to classify candidate suspects as solutions or nonsolutions. Intensive empirical evaluation demonstrates that these techniques can predict suspect sets with accuracies of 81% and 87%, respectively, but the second method requires more training data and careful hyperparameter tuning in order to do so. Furthermore, when guided by these suspect prediction models, the proposed debugging algorithm finds an average of 83% more suspects within a given amount of time.
An Effective Many-Objective Extension of Genetic Programming and Its Benchmark

This paper describes many-objective extension (MaO extension) of GP problem. The extension reduces a bloat phenomena what often happens in GPs. MaO extension of GP consists of addition of objective functions, a partial sampling (PS) operator, a structural distance, elimination of identical trees and subset size scheduling. In this paper, MaO extension of GP are applied to NSGA-II and SPEA2. Because few researchers have considered effective benchmark problems for many-objective genetic programming (MaOGP), MaOGP is less developed than the many-objective evolutionary algorithm (MaOEA). This paper proposes a new benchmark problem for MaOGP. The benchmark problem is based on the many-objective knapsack problem (MaOKSP). In the benchmark, a function given by GP returns a binary vector for MaOKSP. A research on such benchmark problems contributes to the future development of MaOGPs.
FML-based Intelligent Agent for Robotic e-Learning and Entertainment Application

This paper proposes a fuzzy markup language (FML)-based intelligent agent for robotic e-learning and entertainment application. We develop a robotic partner system with a robot teacher iPhonoid to interact with students in the classroom to collect students' feedback on the developed iOS app. In this way, the human teachers can make sure that students in their classes whether understand the lesson or not. In addition, we future embed the developed feedback app function to the novel FML-based intelligent agent with the robot Zenbo for music listening application and edutainment. The developed agent converts human brainwaves into physiological indices and infers subjects' feeling while they are listening to music based on the constructed knowledge base (KB) and rule base (RB) of FML. Then the human sends real-time feedback to the FML-based intelligent agent. Furthermore, the genetic algorithm is utilized for personalized learning the KB and RB of FML tool. The experimental results show that the proposed approach is feasible for e-learning and music application. In the future, we will introduce the robot partner system into the teaching field to have more interaction with children to increase their learning simulations.
Correction to: Scaling constructs with semantic networks

For reason beyond the control of the authors or the editors, the article titled “Scaling constructs with semantic networks” by James A. Danowski and Kenneth Riopelle (https://doi.org/10.1007/s11135-019-00879-5) was published.
A hybrid codebook model for object categorization using two-way clustering based codebook generation method

Both the visual codebook and the codebook model are considered as two main parts of most object classification frameworks. In the original codebook model, each image descriptor is encoded using a single codebook obtained usually using a clustering approach. In this paper, we propose a hybrid codebook model for an object classification task. A simultaneous clustering approach is applied to image descriptors to generate two variant codebooks and used them separately to encode and represent an image through a patch-based codebook model and a feature-based codebook model respectively. The proposed codebook model has been tested on the Caltech-101 dataset. Experimental results demonstrate state-of-the-art performance compared to typical clustering-based codebook model.
Exploiting Multi-Document Term Extraction to Improve Named Entity Recognition for Major Concept Detection

With worldwide events happening everyday, it's useful to discover the most discussed topics for intelligence and trend analysis. However, existing news media only provide manually annotated tags related to each article. Given huge amount of Web data with new or unknown terms, it's critical if these topic-related keywords can be automatically extracted in an efficient way. In this paper, we propose a multi-document term extraction approach to major concept detection. First, major keywords occurring in multiple documents are extracted by statistical term extraction methods. These are then utilized as the dictionary for word segmentation to improve named entity recognition, by which the most relevant concepts in each single document can be extracted. Finally, the extracted concepts are ranked by their importance score based on their popularity and average position in documents. In the experimental results for major online news sources in Chinese, exploiting multi-document term extraction helps to improve the effectiveness of named entity recognition, with the best F1-measure of 0.61, a significant 24% improvement over the baseline. By combining the popularity with average position of terms as the importance score, we able to obtain the best precision of 68% when keeping the top-25% content in documents. Keyword extraction results for selected major events further validated the effectiveness of our proposed approach using topic-related news articles. Further investigation is needed to evaluate the performance of the proposed approach with various data sources.