Reviewerly/exHarmony huggingface dataset:
Single Layer Recurrent Neural Network for detection of local swarm-like earthquakes—the application

SUMMARY We present results of applying a local event detector based on artificial neural networks (ANNs) to two seismically active regions. The concept of ANNs enables us to recognize earthquake-like signals in seismograms because well-trained neural networks are characterized by the ability to generalize to unseen examples. This means that once the ANN is trained, in our case by few tens to hundreds of examples of local event seismograms, the algorithm can then recognize similar features in unknown records. The detailed description of the single-station detection, design and training of the ANN has been described in our previous paper. Here we show the practical application of our ANN to the same seismoactive region we used for its training, West Bohemia/Vogtland (border area Czechia-Saxony, local seismic network WEBNET), and to different seismogenic area, Reykjanes Peninsula (South-West Iceland, local seismic network REYKJANET). The training process requires carefully prepared data set which is preferably achieved by manual processing. Such data were available for the West Bohemia/Vogtland earthquake-swarm region, so we used them to train the ANN and test its performance. Due to the absence of completely manually processed activity for the Reykjanes Peninsula, we use the trained ANN for swarm-like activity in such a different tectonic setting. The application of a coincidence of the single-station detections helps to reduce significantly the number of undetected events as well as the number of false alarms. Setting up the minimum number of stations which are required to confirm an event detection enables us to choose the balance between minimum magnitude threshold and a number of false alarms. The ANN detection results for the Reykjanes Peninsula are compared to manual readings on the stations of the REYKJANET network, manual processing from Icelandic regional network SIL (the SIL catalogues by the Icelandic Meteorological Office) and two tested automatic location algorithms. The neural network shows persuasively better detection results in terms of completeness than the SIL catalogues and automatic location algorithms. Subsequently, we show that our ANN is capable of detecting events from various focal zones in West Bohemia/Vogtland although mainly the focal zone of Nový Kostel was used for training. The performance of our detector is comparable to an expert manual processing and we can state that no important event is missed this way even in case of complicated multiple events during the earthquake swarms.
SPMgr: Dynamic workflow manager for sampling and filtering data streams over Apache Storm

In this article, we address dynamic workflow management for sampling and filtering data streams in Apache Storm. As many sensors generate data streams continuously, we often use sampling to choose some representative data or filtering to remove unnecessary data. Apache Storm is a real-time distributed processing platform suitable for handling large data streams. Storm, however, must stop the entire work when it changes the input data structure or processing algorithm as it needs to modify, redistribute, and restart the programs. In addition, for effective data processing, we often use Storm with Kafka and databases, but it is difficult to use these platforms in an integrated manner. In this article, we derive the problems when applying sampling and filtering algorithms to Storm and propose a dynamic workflow management model that solves these problems. First, we present the concept of a plan consisting of input, processing, and output modules of a data stream. Second, we propose Storm Plan Manager, which can operate Storm, Kafka, and database as a single integrated system. Storm Plan Manager is an integrated workflow manager that dynamically controls sampling and filtering of data streams through plans. Third, as a key feature, Storm Plan Manager provides a Web client interface to visually create, execute, and monitor plans. In this article, we show the usefulness of the proposed Storm Plan Manager by presenting its design, implementation, and experimental results in order.
Application of Improved A* algorithm in Mobile Robot Path Planning

A* algorithm has lots of extended nodes, which will raises the amount of calculation. This paper presents Improved A* algorithm, which can reduces the amount of calculation by reducing the number of extended nodes. Improved A* algorithm adds one parameter which is the cost from the previous point to the final point in the valuation function, which will greatly reduces the number of nodes and improved algorithm efficiency. For the sake of proving the effectiveness of improved algorithm, simulation software and wheeled mobile robot are used for experiment. The experiments result show that our algorithm can greatly reduce the number of nodes.
The Potential of User Experience (UX) as an Approach of Evaluation in Tangible User Interfaces (TUI)

The objective of this paper its presents the potential of using UX as the main evaluation approach in TUI, starting from results of a Systematic Review of Literature (SRL), in which it found other studies published in the last five years that evaluate TUI applications, analyzing the methods and tools used to perform them, relating these with an experiment of evaluation at AR Sandbox application, discuss in order to contribute of the proposal of new methodologies that aim to evaluate the applications of tangible interfaces, considering their due particularity.
Behind the Dystopian Sentiment: a Sentiment Analysis of George Orwell’s 1984

Recent daily political events returned the focus of the general public to a fictional work of art that may yet prove to be one of most influential through the use of its concepts and language - George Orwell's 1984. The language of Orwell's novel, specifically its terminology, has been proven by linguists and other researchers to be in common use in media speech and writing. The author's intent, in which he unanimously succeeded, was to create a dystopian world reminiscent of totalitarian regimes, through the careful use of style and language. Could this intent be somehow investigated and measured by using precise natural language processing methods? Basing this research on related work, the aim of this paper is to evaluate if it is possible to observe how the dystopian atmosphere of the Orwellian concept is created in the novel by applying sentiment analysis on the sentence level, and by exploiting a polarity lexicon as well. Moreover, can a computer by utilising a classification technique based on the Bayes' theorem demonstrate Orwell's intention of creating a dystopian atmosphere? In addition, this paper tries to highlight the possible applications of sentiment analysis of fictional work of art in academic curricula.
African Wordnet as a tool to identify semantic relatedness and semantic similarity

Semantic relatedness and semantic similarity play a vital role in information retrieval of words in natural language processing applications. The multilingual nature of South Africa affords people a superficial (as opposed to an expert) knowledge of many languages, resulting in them finding it difficult to distinguish between semantic relatedness and semantic similarity. Computing semantic relatedness and semantic similarity using African Wordnet could help in developing a proper understanding of the meaning of certain words in the various languages. This article explores how African Wordnet can be used to identify semantic relatedness and semantic similarity. The focus of this article will be on isiZulu as one of the selected languages used in the African Wordnet Project. The purpose of the African Wordnet Project is the development of aligned wordnets for African languages spoken in South Africa. This article further discusses and analyses the examples of semantic relatedness and semantic similarity in the chosen language with a view to giving accounts of the two terms by providing examples and the usages of the words.
From One-size-fits-all Teaching to Adaptive Learning: The Crisis and Solution of Education in The Era of AI

Abstract With the change of micro environmental variables such as internet technology, AI technology, the upgrade of education consumption, there has been a profound change in education, and then the value network created by undergraduates, teachers, employers and other stakeholders will be also changed. The crisis faced by education is the multiple trust crises among stakeholders, and the contradiction between the personal needs of undergraduates (personal desire for development, personal maturity cycle, personal growth rate, etc.) and the One-size-fits-all teaching is the main aspect of the multiple trust crises. Therefore, establishing the concept of adaptive learning and shifting the focus of reform from One-size-fits-all teaching to personalized education is the way for educational reform in the future. The reform should firmly establish the principles of cognitive science obeying, interest activating, diversity cultivating, and balance keeping. Evidence-based education, mastery learning, etc. are the core elements of design of adaptive learning environment. The theoretical base of this research is Piaget’s cognitive developmental psychology and adaptive learning theory. It applies educational theories and educational techniques, such as evidence-based education, OBE, mastery learning, etc., to the construction of the adaptive learning environment. It provides an overview of the main modules and algorithms of the environment, with the aim of using the top-level design to enable the hidden functions of education by the soft environment.
Marketing library and information resources and services using social media platforms: the security question

Purpose Information resources and services are considered tangible assets which are valuable in decision making. Information can fulfill this function only if it is delivered via effective marketing at the end users’ doorsteps. The evolution of ICT, social media in particular, has created changes in all productive sectors and service-oriented organizations such as libraries. As well as discussing the need and use of social media by libraries for marketing efforts, this paper focuses on understanding the myriad of security implications that may exist.
Deep Virtual Try-on with Clothes Transform

The goal of this work is to enable users to try on clothes by photos. When users providing their own photo and photo of intended clothes, we can generate the result photo of themselves wearing the clothes. Other virtual try-on methods are focused on the front-view of the person and the clothes. Meanwhile, our method can handle front and slightly turned-view directions. The details of the clothes are clearer. In the user study, about 90% of the cases, respondents chose our results over others.
UHTelPCC: A Dataset for Telugu Printed Character Recognition

This paper describes how UHTelPCC, a dataset for Telugu printed character recognition, is created and its characteristics. The dataset is created from characters extracted from images of printed Telugu texts from the period 1950–1990. Thus, it is hoped that the dataset provides the basis for developing practical Telugu OCR systems. UHTelPCC is to provide a standard benchmark for comparing different algorithms for Telugu OCR and helps in research and development of Telugu OCR systems. UHTelPCC contains 70K samples of 325 classes, and these samples are divided into 50K, 10K, 10K training, validation, and test sets respectively. It is hoped that UHTelPCC serves like MNIST, a dataset for handwritten digit recognition, for Telugu printed character recognition. The baseline performances on the test set using KNN, MLP, and CNN are 98.85%, 99.52%, and 99.68% respectively. UHTelPCC is available at http://scis.uohyd.ac.in/~chakcs/UHTelPCC.html .
NorJADE

Normative multi-agent systems are multi-agent systems where agents are governed by norms. This recent research domain is now in full expansion. Despite the progress made in this area, various challenges remain the subject of research studies. In particular, there is a need for innovative solutions to support the implementation of this kind of multi-agent systems in order to improve the development process and consequently to simplify the developers' task. In the literature, most proposed works in this area are either closely related to specific problems or require complicated theoretical frameworks. In this article, a new framework baptized NorJADE is proposed to support the implementation of normative multi-agent systems. The proposed framework is open source, simple, modular and extensible. It is based on the popular open source JADE platform and uses the two well-known software technologies: ontology and aspect-oriented programming. The framework and the associated tool are illustrated using a concrete case study.
A Decentralized Private Data Transaction Pricing and Quality Control Method

In the past few years, it has become increasingly popular to analyze the information obtained to develop services by conducting a decentralized survey of private data for specific populations. Privacy security requirements for data providers force operators to implement reasonable privacy protections. But increasing the investment in privacy protection will also lead to a decline in operator revenue. In this case, operators need to ensure the privacy and security requirements of users while ensuring the sustainability of customized services. To this end, We study the relationship between collecting data quality and operator strategy, quantifying the price of private data, and building a model to maximize operator profitability. Specifically, closed-form solutions for best privacy data prices and subscription fees are designed to maximize the gross profit of service providers. Also includes the collection of data quality factors to ensure that the user perceived quality of service can be guaranteed to a certain extent. Finally, we explored the relationship between spending, subscription fees, and maximum gross profit of carriers during the data collection phase, based on the distribution of different user groups' privacy attitudes. In particular, we also explored the relationship between adding additional noise and collecting data utility in a decentralized privacy protection scenario. The simulation results show that compared with the existing methods, the algorithm can maximize the collected data quality while ensuring the provider's privacy security requirements. In addition, we demonstrate the benefits of our dynamic pricing approach and its applicability to other private data pricing algorithms.
A Comprehensive Experimental and Reproducible Study on Selfie Biometrics in Multistream and Heterogeneous Settings

This contribution presents a new database to address current challenges in face recognition. It contains face video sequences of 75 individuals acquired either through a laptop webcam or when mimicking the front-facing camera of a smartphone. Sequences have been acquired with a device allowing to record visual, near-infrared, and depth data at the same time. Recordings have been made across three sessions with different, challenging illumination conditions and variations in pose. Together with the database, several experimental protocols are provided and correspond to real world scenarios, when a mismatch in conditions between enrollment and probe images occurs. A comprehensive set of baseline experiments using publicly available baseline algorithms show that extreme illumination conditions and pose variations are remaining issues. However, the usage of different data domains-and their fusion-allows to mitigate such variation. Finally, experiments on heterogeneous face recognition are also presented using a state-of-the-art model based on deep neural networks, and showed better performance. When applied to other tasks, this model proved to surpass all existing baselines as well. The data, as well as the code to reproduce all experiments are made publicly available to help foster research in selfie biometrics using latest imaging devices.
A Novel Machine Learning Algorithm to Reduce Prediction Error and Accelerate Learning Curve for Very Large Datasets

This paper presents a novel machine learning algorithm with an improved accuracy and a faster learning curve, for very large datasets. Previously, an algorithm using lr-partitions was designed to improve upon C4.5. However, this algorithm has a relatively high percentage of undefined combinations of attribute values in its final results, increasing the learning error. In this paper, a new type of clustering algorithm was proposed to generate output values for those undefined combinations, thus accelerating the learning curve and reducing the prediction error by several percentage points on various popular datasets from the UCI Machine Learning Database.
Heuristic-Q: A Privacy Data Pricing Method Based on Heuristic Reinforcement Learning

With the development of big data applications in recent years, the value of personal data has received more and more attention. How to balance the conflict between data value development and personal privacy protection is an urgent problem to be solved. In response to the above contradictions, the market proposed a private data transaction mechanism to realize the commercial use of private data. However, existing private data transaction mechanisms still have deficiencies in some respects. In order to seek pricing strategies that maximize total revenue, this paper designs and constructs a privacy data dynamic pricing model from the perspective of data collectors, introduces heuristic reinforcement learning ideas, and proposes a pricing strategy algorithm based on heuristic functions. Finally, the simulation experiment of the proposed pricing model and strategy learning algorithm is carried out, and its performance is analyzed. The simulation results confirm that the algorithm can help data collectors get higher returns in the process of limited private data transactions.
On Online Banking Authentication for All: A Comparison of BankID Login Efficiency Using Smartphones Versus Code Generators

Authentication is an essential component of any digital service. In Norway, such authentication is often relying on BankID using either mobile BankID or a code generator. This study set out to explore the efficiency of these authentication user interfaces. A mixed experiment involving 20 users was conducted. The results show that the code generator is faster, but for individuals with a preference for mobile BankID the difference is insignificant. Individuals with a preference for code generators take significantly longer time to use BankID.
Transparent Access and Integration of Heterogeneous Encrypted Database in Hybrid Cloud Environment

Many cloud encryption methods have been proposed to achieve the encryption and decryption of users' data stored on public cloud database, and there are also some studies on heterogeneous database connection access methods, but none of them can implement the users's transparent (cross-language and cross-platform) or heterogeneous access to encrypted database on public cloud. To solve these problems, we modularly comply a middleware system in a private cloud by using the Mysql protocol and heterogeneous database data integration technology, which is applied in a hybrid cloud environment to achieve the users' transparent access to heterogeneous encrypted database. We talk about the preliminary assessment on the system security. Besides, the experimental evaluation testifies the system performance through separately testing time costs for the users to execute transparent access to individual and heterogeneous encrypted database.
A stable graph layout algorithm for processes

Abstract Process mining enables organizations to analyze data about their (business) processes. Visualization is key to gaining insight into these processes and the associated data. Process visualization requires a high‐quality graph layout that intuitively represents the semantics of the process. Process analysis additionally requires interactive filtering to explore the process data and process graph. The ideal process visualization therefore provides a high‐quality, intuitive layout and preserves the mental map of the user during the visual exploration. The current industry standard used for process visualization does not satisfy either of these requirements. In this paper, we propose a novel layout algorithm for processes based on the Sugiyama framework. Our approach consists of novel ranking and order constraint algorithms and a novel crossing minimization algorithm. These algorithms make use of the process data to compute stable, high‐quality layouts. In addition, we use phased animation to further improve mental map preservation. Quantitative and qualitative evaluations show that our approach computes layouts of higher quality and preserves the mental map better than the industry standard. Additionally, our approach is substantially faster, especially for graphs with more than 250 edges.
Limits in the data for detecting criminals on social media

Social media represent one of the most popular online tools to spread and exchange formal and informal information based on specific human interests, habits and purposes. As they are free, easy to use and widely adopted, criminals commonly exploit them to quickly disseminate information and propaganda, to recruit people and so on. Due to the vast usage and breadth of topics discussed on them, it is not trivial to identify criminals abusing of social media for their purposes. Machine learning techniques have already shown benefits in classification problems in different application domains. As a consequence, a trained classifier for the identification of potential malicious users on these platforms would represent a desirable solution. In this perspective, this work explores the possibility of using data which are extracted from public personal features in order to identify potential terrorists on social media, by showing current limits. To this aim, a public dataset on known terrorist's details is combined with a manually collected dataset of public Facebook profiles. The adopted approach is presented and then the data-related issues, which emerged from this experience, are discussed1.
A generator for dynamically constrained optimization problems

Dynamic constrained optimization problems (DCOPs) provide larger complexity for an optimization algorithm by changing the problem landscape throughout the optimization process. Introducing constraints to an already changing dynamic environment increases the observed complexity of the problem space. Allowing such constraints to have irregular shapes which change along with the problem space itself provides an even greater level of complexity for an optimization algorithm. This paper proposes a function generator capable of creating dynamically constrained dynamic environments by extending the moving peaks benchmark (MPB) function generator. An analysis of the resulting environments produced by the generator is performed using fitness landscape analysis (FLA). A visual inspection of the resulting generated environments is also included.