Reviewerly/exHarmony huggingface dataset:
A Robust Ensemble Method for Classification in Imbalanced Datasets in the Presence of Noise

Noise and class imbalance are two common data characteristics related to the quality and nature of many real-world data sources, which usually negatively affect the performance of many machine learning classification algorithms. A wide range of studies have investigated the problem of class imbalance and noise in isolation, and very few of them have studied their combined effect. In this paper, we propose a robust bagging-based ensemble method that tries to pay attention to both problems combined. The proposed method is based on the idea of Balanced Bagging (BB) to balance the bootstraps, but with a different sampling process, in which the probability of selecting an instance will be based on its level of hardness, i.e. the probability of an instance being misclassified irrespective of the choice of the classifier. The approach of the proposed method is based on estimating the hardness of each instance in a training dataset, and ensuring that bootstraps are balanced, and at the same time have instances of varying degrees of hardness (Easy, Normal, and Hard). We evaluate the performance of the proposed method on 30 synthetic imbalanced datasets with different levels of noise and imbalance ratios and compare its performance against the BB method. We observe that the proposed method performs significantly better than BB regardless of the noise level or imbalance ratio. Furthermore, we calculate the Equalized Loss of Accuracy (ELA) to assess the robustness of both methods under different levels of noise. The results indicate that the proposed method is more robust (not affected by noise as much) compared to BB. The Wilcoxon signed rank statistical test shows that there is a significant difference in both, performance and robustness, between the proposed method and BB, suggesting that representing varying levels of hardness in bootstraps is a better bootstrapping approach that improves the performance of ensemble methods.
Research on the Performance of Character Convolutional Neural Network in Different Text Encoding Formats

Abstract With the continuous progress and development of society, text classification has become an important task in the field of text data mining and text value exploration. Compared with existing text classification technology, deep learning technology has many advantages, such as high accuracy and effective feature extraction. This paper is mainly based on the character convolutional neural network to study the classification performance of different texts, hoping to provide experience for related research work. Through research, it is found that character-level convolutional neural network deep learning technology can achieve Chinese text classification more effectively.
Self Configuring Wireless Sensor Network for Precision Irrigation

Precision Irrigation is a farm management strategy used to increase the water efficiency of a farm in which the right amount of water is fed to the soil at right time by sensing the real time soil conditions in the field. Wireless Sensor Network (WSN) Technology is widely used in farms to measure soil conditions at various locations of the farm and use the data for making decisions in the areas to be irrigated with the right amount of water. This project presents the design and development of a self organizing and fault tolerant ad-hoc multi-hop WSN for precision irrigation using low cost commercially available electronic components. This includes the design and development of hardware units for Sensor Nodes to be deployed in the field for the measurement of soil humidity and a centralized Base Station to collect the information from the nodes and make decisions on irrigating. A reliable multi-hop WSN system with ad-hoc routing algorithm for prolonged lifetime and fault tolerance has been developed and demonstrated using field trials and lab level simulations. The project also developed a user interface to monitor and archive the data acquired through the network.
The Vocabulary Ability of Senior High School Indonesian EFL’ Students

Vocabulary is a pivotal element in learning English. Learning English particularly vocabulary is very crucial for supporting the ability to speak, listening, writing and reading. This study intends to describe EFL pupils’ vocabulary ability of the eleventh-grade EFL pupils of SMAN 2 Martapura Academic year 2018/2019. This study is a descriptive quantitative study. The population is 7 classes which are consist of 203 EFL pupils who are taken by implementing cluster random sampling.  The instrument is a multiple-choice consist of 40 items, arranged based on the lesson plan, the syllabus, and material books currently implemented at the school. The test result is then calculated and categorized to ascertain EFL pupils’ English accomplishment level based on the school’s standard.  According to the result of this study, showcased that the EFL pupils’ vocabulary accomplishment in English, 27 EFL pupils did not accomplish the standard score of KKM. There are 6 EFL pupils accomplish who gain “good” category for the standard KKM in SMAN 2 Martapura, from a total of 33 EFL pupils. It can be inferred that the English vocabulary mastery is “poor” categories, which means that vocabulary mastery of SMAN 2 Martapura EFL pupils Grade Eleven in a low average level for accomplishment English lessons in mastering vocabulary that should be mastered by them. It is suggested for the tutor and EFL pupils to evaluate their way of English education to improve the EFL pupils’ vocabulary mastery.
Tracking, countermeasures and their effectiveness in targeted advertising

From a Web of documents of uncertain commercial interest, driven by pioneers believing in knowledge sharing, the Web later evolved into a collaborative, real-time form that was made profitable by advertising. The latter has evolved towards targeted advertising, including behavioral advertising based on the massive collection of usage traces. These traces come from various tracking devices including IP addresses (IP tracking), the now known cookies or fingerprints (e.g. browser fingerprinting and canvas fingerprinting). While the collection was initially limited to the workstation (mainly through the browser), it was later extended to smartphones and connected objects. This led to the trace marketing and attention economy that digital natives were confronted with at an early stage. Various countermeasures were gradually deployed by users (parameterization, extensions, e.g. ad blockers), by anonymization services (e.g. VPN and proxy), by the publishers themselves or by the regulator (e.g. RGPD). This paper proposes, on the one hand, a presentation of the structuring of the online advertising sector followed by a state of the art on the tracking tools deployed there, on the other hand, an inventory and analysis of the countermeasures deployed as well as their effectiveness. We show in particular the rapid evolution of the techniques used and the heterogeneity of the coverage offered by a priori equivalent protective devices.
LibGuides: So you want to know more about Serial Killers?: Lady Misfortune

Are you interested in true crime, but don't know where to start? Are you a serial killer aficionado interested in what your library has to offer? You've come to the right place! Women Serial Killers
THE INTERRELATION BETWEEN SELF-REGULATION AND ACCEPTANCE OF TECHNOLOGIES BY THE HIGH SCHOOL STUDENTS

Education is increasingly using various technologies: training management systems, different platforms, mobile devices, social networks, etc. In the conditions of distance learning, the requirements for the independence of students are increasing, learning activity is carried out based on self-regulation. The authors carried out the study in April-May of 2020 in the context of switching of full-time students to distant learning. The paper aims at the study of the interrelation between self-regulation and the acceptance of technologies by high school students. The hypothesis of the research: The respondents, who have high performance of the self-regulation level, will demonstrate a higher level of technology acceptance. By the time of collecting the materials, the students have been studying online for two months using distance training technologies. The authors carried out the study of the selected group of 75 students of higher education institutions. In general, according to the results, the authors noted low values on the Modeling, Assessment of Results, and Flexibility scales and higher values on the Programming scale. The study revealed differences between male and female groups on the Modeling, Reliability, and General Level of Self-regulation scales (p≤0.05). The respondents, who subjectively assess the quality of Internet connection as low, in general, are more loyal to the technologies and, to a greater extent, show their intention to use technologies in their training. The study identified negative correlations between the Reliability, General Level of Self-regulation, and Personal Utility scales of two techniques. In this case, the hypothesis was not confirmed: it turned out that the respondents with a high level of self-regulation tended to believe that technologies were not so useful in their activity as they did not promote its efficiency.
Meta Adversarial Training

Recently demonstrated physical-world adversarial attacks have exposed vulnerabilities in perception systems that pose severe risks for safety-critical applications such as autonomous driving. These attacks place adversarial artifacts in the physical world that indirectly cause the addition of universal perturbations to inputs of a model that can fool it in a variety of contexts. Adversarial training is the most effective defense against image-dependent adversarial attacks. However, tailoring adversarial training to universal perturbations is computationally expensive since the optimal universal perturbations depend on the model weights which change during training. We propose meta adversarial training (MAT), a novel combination of adversarial training with meta-learning, which overcomes this challenge by meta-learning universal perturbations along with model training. MAT requires little extra computation while continuously adapting a large set of perturbations to the current model. We present results for universal patch and universal perturbation attacks on image classification and traffic-light detection. MAT considerably increases robustness against universal patch attacks compared to prior work.
Service robots: rising or falling stars?

The impact of service robots in hospitality, healthcare, and during the Covid-19 pandemic. This video shows a broad range of field studies in collaboration with industry partners currently done by an international research group at the School of Business and Economics at Maastricht University.
Taxonomias como objetos de fronteira

With the new characteristicsarising from interdisciplinary studies, itis necessary to investigate solutions forthe organization of knowledge in a waythat corresponds to the needs generated from the meeting of different areasand domains. This work is dedicatedto investigate the stages of elaboratingtaxonomies in the light of the characteristics of border objects, in order to contribute to the elaboration of taxonomiesthat place themselves as border objects,facilitating the conceptual understanding between areas and domains in interdisciplinary studies.
University Library Service Based on Big Data

The rapid development of big data technology has brought huge challenges to the service innovation of university libraries, but at the same time it has also brought strong technical support, which plays a huge role in improving the service quality of university libraries.The big data analysis technology is a product of the information age widely used in the service system of university libraries, and it effectively improves the service mode and data information management method of university libraries.This article clarifies the new ways of service for university libraries in the era of big data as the basis of research, and innovates the current function transformation and service innovation, and fully researches and analyzes the new modes of university libraries.This thesis develops more personalized services and proposes effective innovation paths through the types of innovative services, creating a quality sharing platform, improving the skill level of librarians, and establishing a cloud service model for big data analysis.The experimental research results show that the big data analysis technology has raised the service form of the library to a new height, and the current situation of students who usually like to go to the library to read, the design can reflect the promotion style of the library, and let the current big data The digital reading method in the era can have a better space for development, and better play its analytical role to guide the whole school students to actively study in the library, and the sharing platform of the university library can effectively integrate big data.Analyze technology and reasonably distribute it with artificial intelligence technology, Internet technology and various front-end development software.The purpose is to enable the platform to get more student support and meet the diverse needs of students themselves, and establish a sharing Another advantage of the platform is that it can improve the current library management methods and improve the service system.
LibGuides: HACC Testing: CLEP Testing

HACC's Testing Center serves students, faculty, and community members by providing a secure and confidential testing environment for academic, personal, and career advancement.
The Economics of Cryptocurrencies -- Bitcoin and Beyond

How well can a cryptocurrency serve as a means of payment? We study the optimal design of cryptocurrencies and assess quantitatively how well such currencies can support bilateral trade. The challenge for cryptocurrencies is to overcome double-spending by relying on competition to update the blockchain (costly mining) and by delaying settlement. We estimate that the current Bitcoin scheme generates a large welfare loss of 1.4% of consumption. This welfare loss can be lowered substantially to 0.08% by adopting an optimal design that reduces mining and relies exclusively on money growth rather than transaction fees to finance mining rewards. We also point out that cryptocurrencies can potentially challenge retail payment systems provided scaling limitations can be addressed.
Federated Learning with Decoupled Probabilistic-Weighted Gradient Aggregation

In the federated learning paradigm, multiple mobile clients train local models independently based on datasets generated by edge devices, and the server aggregates parameters/gradients from local models to form a global model. However, existing model aggregation approaches suffer from high bias on both data distribution and parameter distribution for non-IID datasets, which result in severe accuracy drop for increasing number of heterogeneous clients. In this paper, we proposed a novel decoupled probabilistic-weighted gradient aggregation approach called FeDEC for federated learning. The key idea is to optimize gradient parameters and statistical parameters in a decoupled way, and aggregate the parameters from local models with probabilistic weights to deal with the heterogeneity of clients. Since the overall dataset is unaccessible by the central server, we introduce a variational inference method to derive the optimal probabilistic weights to minimize statistical bias. We further prove the convergence bound of the proposed approach. Extensive experiments using mainstream convolutional neural network models based on three federated datasets show that FeDEC significantly outperforms the state-of-the-arts in terms of model accuracy and training efficiency.
Distributional Reinforcement Learning for Risk-Sensitive Policies

We address the problem of learning a risk-sensitive policy based on the CVaR risk measure using distributional reinforcement learning. In particular, we show that applying the distributional Bellman optimality operator with respect to a risk-based action-selection strategy overestimates the dynamic, Markovian CVaR. The resulting policies can however still be overly conservative and one often prefers to learn an optimal policy based on the static, non-Markovian CVaR. To this end, we propose a modification to the existing algorithm and show that it can indeed learn a proper CVaR-optimized policy. Our proposed approach is a simple extension of standard distributional RL algorithms and can therefore take advantage of many of the recent advances in deep RL. On both synthetic and real data, we empirically show that our proposed algorithm is able to produce a family of risk-averse policies that achieves a better tradeoff between risk and the expected return.
LibGuides: Computer and Device Handouts: Home

Trying to figure out how to do something at the Library and can't figure it out? Try one of these computer and device handouts created by one of our librarians!
The Use of Business Intelligence and Predictive Analytics in Detecting and Managing Occupational Fraud in Nigerian Banks

The problem of occupational fraud is one of the most wide-reaching operational risk event types in the Nigerian banking system. This event type spans many departments, roles, processes and systems and causes significant financial and reputational damage to banks. As a result, fraud presents banks with a real challenge in terms of knowing where to start. One of the main aims of this paper is to use stochastic probability models to predict aggregate fraud severity and fraud frequency within the Nigerian banking sector using historical data. Another objective is to describe how banks can develop and deploy business intelligence (BI) outlier-based detection models to recognize internal fraudulent activities. As the volume of transaction data grows and the industry focuses more closely on fraud detection, BI has evolved to provide proactive, real-time insights into fraudulent behaviors and activities. We discuss the fraud analytic development process, since it is a central issue in real application domains.
Best Computer For Internet Surfing

Web browsing powers depend largely on a computer’s memory. This is so essential to allow the computer to work on multiple tabs and perform other ...
Development Semantic Analysis System for Recruitment Information through Recruitment Portal

The 4th Industrial Revolution is eddying out the boundaries between existing industries with technological advances and changes in the economic and social environment, making to mutual convergences and new value creations. With the development of Artificial Intelligence, the roles of humans and machines are changing, leading to changes in future work environments. Beyond simple repetitive tasks throughout the industries, it has a wide range of impacts on the lives of modern people. With the proliferation of Artificial Intelligence jobs, newly graduated candidates are experiencing double difficulties such as job changes and competition with Artificial Intelligence.<BR> This study aims to solve the employment problem through the analysis of employment information in accordance with the changes in the 4th Industrial Revolution era. Latent Semantic Analysis was applied using the Singular Value Decomposition process and cosine similarity to the recruitment information registered in the recruitment portal. As a result, the priority of specifications required for employment by industry was determined and visualized in a hierarchical structure using a sunburst chart. In addition, it was possible to confirm the relationship between a specific industry and job, and provided a web page as a dashboard through the field research process of the vast employment information analyzed for potential meaning. This article can be supportive for the later study of analyzing industrial competency and developing job recommendation system.
THE CHARACTER BUILDING IN ESSAY WRITING PROJECT: A QUALITATIVE INQUIRY ON STUDENTS’ EXPERIENCE

This research paper shares the inquiry on the character making detected from students’ experience inthe process of completing their collaborative essay assignment in English as Foreign Language Class(EFL class). As a qualitative work, this research gained the data by carrying out in-depth interviewswith respondents are advanced students in writing class at English department. This research wasconducted before the covid-19 pandemic. Before doing an interview, students are given assignmentwith one semester writing project particularly collaborative essay writing on particular given topics.The data analysis method of this research applied the grounded theory prescribing the open, axial andselective coding. The results reveal that students as foreign learners stated that collaborative essaywriting assignment in English engaged them with several characters, such as leadership values, learningresponsibility, self-initiative values, self-regulated learning, and trust. These findings highlight thecontribution of essay writing assignment in a foreign language toward character education in Indonesia
R.A.F.T (ROLE, AUDIENCE, FORMAT, AND TOPIC) AS WRITING STRATEGY TO TEACH WRITING FORMAL LETTERS (APPLICATION LETTER)

Teaching English writing to students of senior high school level needs high creativity and need to be improved from time to time through creative ways.  Additionally, the students themselves face many problems: the lack of vocabulary and poor knowledge of grammar. RAFT is writing strategy focuses on students’ point of view as a writer, the addressed audience, unique format of writing and stated topic. The researcher conducted this study as a descriptive qualitative research. The subject of the study is twelve grade students of MAN 2 Bojonegoro. The students come from XII IPA 2. The data of this study are the result of field notes, observation checklist, students writing task, and interview.  The process of teaching writing formal letters which are focused on application letter using RAFT strategy was successfully implemented. Students show a positive attitude toward the strategy and English subject.
PERCEPTION OF COMPULSORY SOCIAL ISOLATION BEHAVIOR IN YOUNG UNIVERSITY STUDENTS BY COVID-19

Due to the closure of university institutions and the cancellation of events, many adolescents and young people assume that they are missing some of the moments of their youth, such as chatting with friends, participating in class and socializing appropriate to their age Empathy, stress, self-control and anxiety are the most recurrent behaviors that young people manifest due to this confinement, which is the reason for much research The objective of the research was to determine the manifestation of behavior that young university students present in the face of compulsory social isolation by Covid-19 A survey was applied online, the vast majority of respondents belong to public universities The methodology used was a descriptive, exploratory, non-experimental study According to the results found, we can conclude that the behavior of young university students is mostly assertive
Mobile Application Development : Framework with key criteria for choosing native or cross-platform application development

To develop mobile applications, developing companies can use different platforms. One approach is to use a native development environment targeting a specific operating system that gives access to ...