Reviewerly/exHarmony huggingface dataset:
Credit Card Fraud Detection Using Online Boosting with Extremely Fast Decision Tree

Nowadays, data stream mining is a very hot and high attention research field due to the real-time industrial applications from different sources are generating amount of data continuously as the streaming style. To process these growing and large data streams, data stream mining, classification algorithms have been proposed. These algorithms have to deal with high processing time and memory costs, class imbalance, overfitting and concept drift and so on. It is sure that ensembles of classifiers are being effectively used to make improvement in the accuracy of single classifiers in either data mining or data stream mining. Thus, to get higher performance in prediction with largely no increasing memory and time costs, this paper proposes an Online Boosting(OLBoost) Approach, which is firstly use the Extremely Fast Decision Tree (EFDT) as base (weak) learner, in order to ensemble them into a single online strong learner. The experiments of the proposed method were carried out for credit card fraud detection domain with the sample benchmark datasets.
Adaptive Succinctness

Representing a static set of integers S, $$|S| = n$$ from a finite universe $$U = [1{.}{.}u]$$ is a fundamental task in computer science. Our concern is to represent S in small space while supporting the operations of $$\mathsf {rank}$$ and $$\mathsf {select}$$ on S; if S is viewed as its characteristi c vector, the problem becomes that of representing a bit-vector, which is arguably the most fundamental building block of succinct data structures. Although there is an information-theoretic lower bound of $$\mathcal {B}(n, u)= \lg {u\atopwithdelims ()n}$$ bits on the space needed to represent S, this applies to worst-case (random) sets S, and sets found in practical applications are compressible. We focus on the case where elements of S contain non-trivial runs of consecutive elements, one that occurs in many practical situations. Let $$\mathcal {C}_n$$ denote the class of $${u\atopwithdelims ()n}$$ distinct sets of $$n$$ elements over the universe $$[1{.}{.}u]$$ . Let also $$\mathcal {C}^{n}_{g} \subset \mathcal {C}_{n}$$ contain the sets whose $$n$$ elements are arranged in $$g \le n$$ runs of $$\ell _i \ge 1$$ consecutive elements from U for $$i=1,\ldots , g$$ , and let $$\mathcal {C}^{n}_{g, r}\subset \mathcal {C}^{n}_{g}$$ contain all sets that consist of g runs, such that $$r \le g$$ of them have at least 2 elements.
Persona Writing

When preparing persona descriptions, you need to keep in mind that they must be usable for the many different professional competencies that are involved in a project. This could be: The management group where the descriptions are used for strategic decisions about present and potential users. The project group who uses the personas for general decisions about the direction of the project. Designers, both those who design product features and those who design the shaping or graphics, who must be able to identify with the descriptions and use them for decisions on details. Finally, test managers who need the persona descriptions to recruit users for product tests. The many and varied usages create a number of requirements for the descriptions as well as considerations of the relationship between the personal descriptions that create empathy and the technical descriptions that give insights.
The 4th International Workshop on Narrative Extraction from Texts: Text2Story 2021

Narrative extraction, understanding and visualization is currently a popular topic and an important tool for humans interested in achieving a deeper understanding of text. Information Retrieval (IR), Natural Language Processing (NLP) and Machine Learning (ML) already offer many instruments that aid the exploration of narrative elements in text and within unstructured data. Despite evident advances in the last couple of years the problem of automatically representing narratives in a structured form, beyond the conventional identification of common events, entities and their relationships, is yet to be solved. This workshop held virtually onApril 1st, 2021 co-located with the 43rd European Conference on Information Retrieval (ECIR'21) aims at presenting and discussing current and future directions for IR, NLP, ML and other computational fields capable of improving the automatic understanding of narratives. It includes a session devoted to regular, short and demo papers, keynote talks and space for an informal discussion of the methods, of the challenges and of the future of the area.
Romance Fraud

Romance fraud devastates the lives of millions of victims globally. Under the guise of a legitimate relationship, offenders seek to exploit an emotional connection with victims to fund their financial gain. Romance fraud is particularly insidious, with victims having to grieve not only any financial harm but also the loss of the relationship. Despite the magnitude of victims globally and the severity of the impact of these crimes, there is little research targeted at better understanding romance fraud. While there is an emerging body of work seeking to rectify this, there are still large gaps left to explore. The following chapter provides an overview of the current state of scholarship on romance fraud. This includes what is currently known about romance fraud, how it is perpetrated, the impact on victims, and how to prevent and support victimization. In doing this, the chapter highlights the need for further work in this area.
Making Agricultural Learning Accessible

Worldwide women play an important role in agriculture but they still are marginalized in extension education programs. Traditionally, male-dominated extension services may contribute to gender bias in terms of access to information, participation, implementation and innovation. Limitations women face in accessing extension programs are related to social and cultural constructions. Recently, mobile phone technology has facilitated the work of extension agents and has provided participants new resources to improve their yields; nevertheless, farmer productivity in Mozambique varies between genders. This paper illustrates how agricultural messages designed as animations, and delivered via smartphones result in learning for farmers better than traditional extension presentations. This chapter focuses on the creation of educational materials by Scientific Animations Without Borders (SAWBO), and then illuminates the gaps and solutions related to gender agricultural learning. Lastly, a case study of Mozambican gender agricultural learning using animations is presented.
A Credit Rating Model in a Fuzzy Inference System Environment

One of the most important functions of an export credit agency (ECA) is to act as an intermediary between national governments and exporters. These organizations provide financing to reduce the political and commercial risks in international trade. The agents assess the buyers based on financial and non-financial indicators to determine whether it is advisable to grant them credit. Because many of these indicators are qualitative and inherently linguistically ambiguous, the agents must make decisions in uncertain environments. Therefore, to make the most accurate decision possible, they often utilize fuzzy inference systems. The purpose of this research was to design a credit rating model in an uncertain environment using the fuzzy inference system (FIS). In this research, we used suitable variables of agency ratings from previous studies and then screened them via the Delphi method. Finally, we created a credit rating model using these variables and FIS including related IF-THEN rules which can be applied in a practical setting.
Specification of the data warehouse for the decision-making dimension of the Bid Process Information System

In order to enhance the business process, the Bid Process Information System (BPIS) should be made more performant by ensuring greater flexibility and interoperability between devices. Moreover, the specication of this system has to deal with "three fit" problems. To this end, four dimensions have been identified in order to cope with potential failures: operational, organizational, decision-making, and cooperative dimensions. In this paper, we focus on the decision-making dimension of the BPIS and propose an approach for representing data warehouse schema based on an ontology that captures the multidimensional bid-knowledge.
Reservoir Computing with Dipole-Coupled Nanomagnets

An idea to use a magnetic nano-dots array for a reservoir computing is introduced. The mechanism of how the nonlinear calculation is carried out in the magnetic system is explained by showing the simplest case with three nano-dots system. The first trial to prove calculation ability and fabrication ability of the system is demonstrated. Since the proposed reservoir computing system may utilize integration technology of the magnetic random access memory (MRAM), it possesses a possibility to realize a large-scale reservoir computing system.
Preventing Fake Accounts on Social Media Using Face Recognition Based on Convolutional Neural Network

In today’s world, most people are intensely dependent on online social networks (OSN). People use social sites to find and make friends, to associate with people who share comparable intrigue, trade news, organize the event, exploring passion in an. According to a Facebook review, 5% of monthly active users had fake accounts, and in the last six months, Facebook has deleted 3 billion accounts. According to the Washington Post, Twitter has suspended over 1 billion suspect accounts over a day in recent months, Detection of a fake profile is one of the critical issues these days as people hold fake accounts to slander image, spread fake news, promotes sarcasm that has attracted cyber criminals. There are numerous machine learning methodologies such as supervised learning, SVM-NN, are produced for the effective detection of a fake profile. In this paper, convolution neural networks is proposed with many artificial neural network algorithms like face recognition, prediction, classification and clustering for the efficient identification of account being real or fake and elimination of fake profile account. Furthermore, the study is grounded on the fact of the face-recognizing of the user and performing feature detection and time series prediction. If the user account is detected fake it would not be created.
An Introduction of CNN: Models and Training on Neural Network Models

With the development of computer technology, practitioners of computer-related industries pay more attention to the neural network. The excellent predictive ability makes neural network models stand out and almost fill every corner of the network. The convolutional neural network is one of the neural network models. Compared with other models, the convolutional neural network has more advantages in image recognition and segmentation technology due to its unique layer types. Research on convolutional neural networks started in the 1980s and 1990s. In the 21st century, convolutional neural networks have developed rapidly. The theory of deep learning and the improvement of numerical computing equipment make great progress in computer vision, natural language processing and other fields.
Nonhypothesis-Driven Research: Data Mining and Knowledge Discovery

Clinical information, stored over time, is a potentially rich source of data for clinical research. Knowledge discovery in databases (KDD), commonly known as data mining, is a process for pattern discovery and predictive modeling in large databases. KDD makes extensive use of data mining methods, automated processes, and algorithms that enable pattern recognition. Characteristically, data mining involves the use of machine learning methods developed in the domain of artificial intelligence. These methods have been applied to healthcare and biomedical data for a variety of purposes with good success and potential or realized clinical translation. Herein, the Fayyad model of knowledge discovery in databases is introduced. The steps of the process are described with select examples from clinical research informatics. These steps range from initial data selection to interpretation and evaluation. Commonly used data mining methods are surveyed: artificial neural networks, decision tree induction, support vector machines (kernel methods), association rule induction, and k-nearest neighbor. Methods for evaluating the models that result from the KDD process are closely linked to methods used in diagnostic medicine. These include the use of measures derived from a confusion matrix and receiver operating characteristic curve analysis. Data partitioning and model validation are critical aspects of evaluation. International efforts to develop and refine clinical data repositories are critically linked to the potential of these methods for developing new knowledge.
Modeling Co-evolution of Attributed and Structural Information in Graph Sequence

Most graph neural network models learn embeddings of nodes in static attributed graphs for predictive analysis. Recent attempts have been made to learn temporal proximity of the nodes. We find that real dynamic attributed graphs exhibit complex phenomenon of co-evolution between node attributes and graph structure. Learning node embeddings for forecasting change of node attributes and evolution of graph structure over time remains an open problem. In this work, we present a novel framework called CoEvoGNN for modeling dynamic attributed graph sequence. It preserves the impact of earlier graphs on the current graph by embedding generation through the sequence of attributed graphs. It has a temporal self-attention architecture to model long-range dependencies in the evolution. Moreover, CoEvoGNN optimizes model parameters jointly on two dynamic tasks, attribute inference and link prediction over time. So the model can capture the co-evolutionary patterns of attribute change and link formation. This framework can adapt to any graph neural algorithms so we implemented and investigated three methods based on it: CoEvoGCN, CoEvoGAT, and CoEvoSAGE. Experiments demonstrate the framework (and its methods) outperforms strong baseline methods on predicting an entire unseen graph snapshot of personal attributes and interpersonal links in dynamic social graphs and financial graphs.
Overcoming security limitations of Secret Share techniques: the Nested Secret Share

Secret Share (SS) is becoming a very hot topic within the scientific community. It allows us to split a secret into fragments and to share them among parties in such a way that a subset can recompose the original information. SS techniques assure a high redundancy degree, but the security level is fixed. Therefore, if a minimum number of peers collude then attackers can recompose the secret easily. A possible approach to improve the security of SS is designing nest fragment sharing techniques. In this paper, we propose the Nested Secret Share (NSS) as a more reliable and scalable strategy. In particular, we discuss the security of NSS considering the number of recomposition attempts that an attacker has to perform to retrieve the secret and then we deeply analyse the impact of the redundancy and the number of peers on the secret management against the percentage of compromised nodes. Experiments were promising and showed that the redundancy degree of SS can be highly improved by NSS.
The Implementation of Innovation in Educational Technology to Improve The Quality of Website Learning in Industrial Revolution Era 4.0 Using Waterfall Method

Abstract Implementation of learning technology is one of the necessary ways to demonstrate the competence of qualified graduates. This research aim to make model of website Implementation to improve student quality. The Current Study Provided only provides the existing General Implementation. The Participants of this study were lecturers, undergraduate and postgraduate students and education practitioners throughout Indonesia using online form. Data collection Methods used were direct interview, online survey and needs analysis using waterfall method. The results show that the implementation of existing learning does not lead to the needs in the industry era 4.0 almost no change. This research produces a complete learning implementation innovation as required by the Policy Makers.
Domain-specific Topic Model for Knowledge Discovery in Computational and Data-Intensive Scientific Communities

Shortened time to knowledge discovery and adapting prior domain knowledge is a challenge for computational and data-intensive communities such as e.g., bioinformatics and neuroscience. The challenge for a domain scientist lies in the actions to obtain guidance through query of massive information from diverse text corpus comprising of a wide-ranging set of topics when: investigating new methods, developing new tools, or integrating datasets. In this paper, we propose a novel domain-specific topic model (DSTM) to discover latent knowledge patterns about relationships among research topics, tools and datasets from exemplary scientific domains.Our DSTM is a generative model that extends the Latent Dirichlet Allocation (LDA) model and uses the Markov chain Monte Carlo(MCMC) algorithm to infer latent patterns within a specific domain in an unsupervised manner. We apply our DSTM to large collections of data from bioinformatics and neuroscience domains that include more than 25,000 of papers over the last ten years, featuring hundreds of tools and datasets that are commonly used in relevant studies. Through evaluation experiments with a perplexity metric, we show that our model has better generalization performance within a domain for discovering highly-specific latent topics. Lastly, we demonstrate applications that benefit from our DSTM to discover intra-domain, cross-domain and trend knowledge patterns.
Proof by experimentation?

Most IR experiments lack both internal and external validity. Top performance is mostly an illusion, given the lack of solid statistical evidence. Thus, reviewers should ignore performance numbers in their judgement, and pay more attention to proper methodology. Researchers need to focus more on performance analysis and prediction.
Quantum secrecy in thermal states II

In this paper we consider a scheme for cryptographic key distribution based on a variation of continuous variable quantum key distribution called central broadcast. In the continuous variable central broadcast scheme, security arises from discord present in the Hanbury Brown and Twiss effect from a thermal source. The benefit of this scheme is that it expands the range of frequencies into the microwave regime. Longer wavelengths—where the thermal photon number is higher and correlations remain robust over long distances—may even be preferable to optical wavelengths. Assuming that Alice controls the source but not the distribution of the light (e.g. satellite broadcasts), then we demonstrate that the central broadcast scheme is robust to an entangling cloner attack. We establish the security of the protocol both experimentally and theoretically.
Hyperspectral compression

Scientific and technological demands in spatial and spectral resolutions have drastically increased the data volume of hyperspectral images. For this reason, since the management, storage, and transmission of hyperspectral images can be extremely difficult, the compression of hyperspectral data is becoming more and more important. In the past decades, lossy compression approaches have been widely used for image compression because of the insensitiveness of the human visual system to some levels of distortion. In this way, an appropriate choice of loss of information can be used to significantly enhance the compression performance. However, in many cases, the distortion introduced during the compression process can get amplified by the processing and have a negative effect on the application. In general, there is not a preference of an approach over the other, but the choice of the type of compression mainly depends on the target application. Lossless compression is generally used for data archiving and in those applications where the data preservation is fundamental. On the other hand, as long as the radiometric resolution of the image increases, lossy compression approaches are preferred to lossless ones. Lossy compressions are then used to reduce data dimension for storage and for data transmission purposes.
Autonomous Path Planning Method of UUV in Complex Environment Based on Improved Ant Colony Optimization Algorithm

This paper uses a modified ant colony optimization (ACO) algorithm based on heuristic to simulate the three-dimensional (3D) underwater route planning of unmanned underwater vehicles (UUV). In view of the complex marine environment such as "favorable zone" and "unfavorable zone" caused by underwater ridge and marine environmental factors, this paper has carried out research on the improvement of ant colony algorithm for 3D route planning considering safety, concealment, rapidity and smoothness as multiple optimization objectives. The simulation results show that the improved ACO algorithm can well satisfy the requirements of fixed depth direct navigation, which can reduce the course change frequency of UUV. Furthermore, it can make better use of the attraction area and avoid the exclusion area, which provides technical support for the route planning of UUV in complex underwater combat environment, and has good engineering feasibility.
<i>X</i>

Abstract The grapheme and symbol x has been documented as relatively indeterminate and polysemic (e.g. Gale, 2015 ). Yet, various typographic, orthographic and other design choices make it particularly salient in the contemporary semiotic landscape. The paper starts by outlining briefly the history of the changing uses and associations of x in different areas of social life. This is followed by discussion of the typographic and orthographic salience of x , emphasizing its unique, unsettling, and ‘foreignizing’ effect on displayed language. The paper concludes by linking the salience of x with a global verbal-visual register that I have called ‘globalese’ ( Jaworski, 2015a ), and by briefly pointing to its origins in the typographic experiments of avant-garde art.
Introduction to the Design of Discrete Filters

Filtering changes the frequency content of an input signal. In the discrete-time domain, two types of filters designs are: the Infinite Impulse Response (IIR) filter, and the Finite Impulse Response (FIR) filter. Discrete filters are implemented using software or dedicated hardware and minimal direct, cascade and parallel forms. Filter design consists in specifying a desired magnitude or impulse response followed by rational or polynomial approximation to obtain a stable and realizable filter. For IIR filters, classical analog filter methods together with the bilinear transformation mapping the analog s-plane into the Z-plane are used to design low-pass filters. Frequency transformations allow for the design of other filters. Approximation procedures for FIR filters are unique to that domain. A basic design uses windowing. The effect of different windows and the linearity of the phase are discussed. Images are filtered using classical space filters and the conventional frequency domain filters. Edge detection, de-blurring, de-noising, and compression of images are possible using two-dimensional filters. Design of one and two-dimensional filters are illustrated using MATLAB.