Reviewerly/exHarmony huggingface dataset:
Python Strings

In the previous chapter we used strings several times, both as prompts to the user and as output from the print() function. We even had the user type in their name and store it in a variable that could be used to access this name at a later point in time. In this chapter we will explore what a string is and how you can work with and manipulate them.
Target Classifier Strategies

A target classifier receives image or signal data about a detection point. It infers the category of the object portrayed by the data. The classification decision can benefit from a host of other available information; the more information the better. ATR often involves a client–contractor relationship. The contractor is committed to providing a quality product to the customer. Yet, target classification is sometimes viewed in a naïve fashion. The customer throws data “over the fence.” The contractor is asked to classify the “targets.” Little thought is given to the breadth and scope of the problem. The usual “solution” involves showing that the contractor’s favorite classifier outperforms several alternatives. However, the true nature of the target classification problem is more complex. Ironically, choice of a classification paradigm may be the least important aspect of target classification. We will outline the issues involved in target classification. This will be followed by a review of a number of different types of classifiers.
Introduction to Object Orientation

This chapter introduces the core concepts in Object Orientation. It defines the terminology used and attempts to clarify issues associated with objects. It also discusses some of the perceived strengths and weaknesses of the object-oriented approach. It then offers some guidance on the approach to take in learning about objects.
Robust canonical correlation analysis based on L1-norm minimization for feature learning and image recognition (Erratum)

The <i>Journal of Electronic Imaging</i> (JEI), copublished bimonthly with the Society for Imaging Science and Technology, publishes peer-reviewed papers that cover research and applications in all areas of electronic imaging science and technology.
A Study of Polytechnic College ELLs’ Recognitions on Practical English Class with AR-embedded Contents

The purpose of this study is to conduct an Augmented Reality(AR) content-based English class for English language learners at Korean Polytechnic University and to explore the understanding of AR contents, affective factors, and class satisfaction. To this end, pre- and post-questionnaires were administered to 43 participants majoring in Power Plant Engineering and Industrial Diving. And the collected data were quantitatively analyzed using Jamovi, a statistical program. The results are as follows. First, AR content-based practical English learning has a positive effect on participants’ perceptions. Second, many factors in their affective fields have changed through the use of AR contents. Third, English learning based on AR content has a positive impact on its satisfaction over time. With these results, it is expected that English teaching and learning based on augmented reality would shed a light on boost English competence in workforce-based educational university. In the future, the derived research results can be used as a basic work to activate the application of AR content in English classes at the university level.
ARDUOUS 2020: 4th International Workshop on Annotation of useR Data for UbiquitOUs Systems – Program

Exploring design challenges in the development of annotation toolsApplying strategies drawn from HCI, user interaction and user centred design research to centre annotation tool design on the participant. S4: Poster session S5: Panel discussionHow could we build a community for sharing annotation activities, methods, and outcomes?Methods, tools, vocabularies, taxonomies, and ontologies for annotation of user data have been reported in many places, including prior editions of this workshop.How can we work together to increase the impact and visibility of these outcomes?Panel session: Building a community for sharing annotation activities, methods, and outcomes
Sentiment Analysis using Multiple Word Embedding for Words

Nowadays users express their opinions on different websites like e-commerce and special review websites. Analyzing customers' opinions and their responses is important for decision making. So the researchers worked on analyzing these reviews automatically using a classical machine learning approach like Support Vector Machine (SVM) and various modern deep neural networks. For these networks, words are represented by using vectors called word embeddings. The required word embeddings are taken from pre-trained Word2Vec or learned from a corpus of the given main task. But, each method has its demerits. In the case of pre-trained word embeddings, embeddings are learned from large general corpus so these embeddings are not task specific. While in the case of learning words from the corpus of the main task, it does not reflect the true semantics. To deal with these problems, we have proposed an embedding developer model. This model develops task specific word embedding which also reflects true semantics. Task specific word embeddings are generated from the given corpus using the embedding layer in Keras. It builds the embeddings by considering relationships between words in the window. While true semantics are taken from Word2Vec embeddings. The proposed model combines these two embeddings to generate true semantics and task specific word embeddings. Result analysis shows that the proposed system works better on many benchmark datasets.
Thought Interferometry

As human consciousness may have a physical field's nature, it must respond to signals coming into this field. The signals are undergoing interference, which could be detected by the interferometer. It is the way to verify a hypothetical quantum-wave model of the AI's cognitive semantics.
Penggunaan Kartu Huruf dalam Meningkatkan Kemampuan Menulis Ejaan yang Benar Kelas 2 Madrasah Ibtidaiyah

The development of reading as one of the basic abilities that a protégé must possess, consists of several stages according to his age and developmental characteristics. This research is based on the low ability of the letters of ibitidaiyah madrassa students. In general, this research aims to improve the ability to recognize children with aspect 1) know and understand the letter 2) know and understand the sound of letters 3) write letters correctly. The specific purpose of this research is to improve the correct spelling through the use of letter cards for 2nd graders MI Ma'arif NU 2 Pancasan as many as 29 students. The step used in this study is by means of playing letter cards. The subject is ibtidaiyah madrassa students. A research procedure is research conducted through cycles of action as Classroom Action Research (CAR). Each step starts from: (1) planning, (2) the implementation of actions, (3) observations, (4) analysis and reflection results. The results prove that the use of letter cards can improve the ability to write the correct spelling for students of madrasah ibtidaiyah. Proven students' learning performance in the average initial condition when compared to the final condition of the child rose by 0.9 or 16.07%. From the results of this study is expected to be used as a consideration material for madrasah ibtidaiyah teachers in teaching the spelling of Bahasa Indonesia, especially in low classes in order to use letter cards as a learning aid.
High utility differential privacy based on smooth sensitivity and individual ranking

Differential privacy can provide provable privacy security protection. In recent years, a great improvement has been made, however, in practical applications, the utility of original data is highly susceptible to noise, and thus, it limits its application and extension. To address the above problem, a new differential privacy method based on smooth sensitivity has been proposed in this paper. Using this method, the dataset's utility is improved greatly by reducing the amount of noise that is added, and this was validated by experiments.
Designing E-learning Application to Enhance Education Effectiveness for PELNI Medical Center

PELNI Medical Center is one of the oldest Medical Centers in Indonesia which in 2018 is already 100 years old. In providing good health services to the community, PELNI Medical Center also conducts education and training processes for its employees. The current training is conventional training, where the time and place of training can change because it adjusts to the conditions of existing services and patients. Besides, the costs required are quite a lot to meet the needs of the training that is held. Therefore, PELNI Medical Center wants an e-Learning system as a learning medium to overcome these problems and can facilitate the education and training process. The design of the e-Learning system is built using the method of designing Object-Oriented Analysis and Design with the Unified Process. As for the data collection techniques used, namely interviews and questionnaires. The expected results of the design of this e-Learning system are that it can be used as an educational medium in the education and training process at Pelni Medical Center, increase the ability of knowledge and independent learning in each of its users, and can facilitate the improvement and storage of learning materials applied at the PELNI Medical Center.
Phish webpage classification using hybrid algorithm of machine learning and statistical induction ratios

Although the conventional machine learning-based anti-phishing techniques outperform their competitors in phishing detection, they are still targeted by zero-hour phish webpages due to their constraints of phishing induction. Therefore, phishing induction must be boosted up with the extraction of new features, the selection of robust subsets of decisive features, the active learning of classifiers on a big webpage stream. In this paper, we propose a hybrid feature-based classification algorithm (HFBC) for decisive phish webpage classification. HFBC hybridises two statistical criteria optimised feature occurrence (OFC) and phishing induction ratio (PIR) with the induction settings of the most salient machine learning algorithms, Naïve bays and decision tree. Additionally, we propose two constituent algorithms of features extraction and features selection for holistic phish webpage characterisation. The superiority of our proposed approach is justified and proven throughout chronological, real-time, and comparative analyses against existing machines learning-based anti-phishing techniques.
IEEE ISORC 2019 Keynote: “Quantum Computing: A Systems Perspective”

Provides an abstract of the keynote presentation and may include a brief professional biography of the presenter. The complete presentation was not made available for publication as part of the conference proceedings.
Enhancing QOSQOE in MANETs using OLSR Protocol

Video streaming over Mobile Ad-hoc Networks (MANETs) has been on the fore as one of the most chiefly solicited web services. Within this context, the enhancement of the Quality of Experience (QoE) on such platforms, whose signature characteristic is the real-time fluctuation of their Mobile Nodes (MNs), remains a high-stakes challenge for high-fidelity transmission via the extant MANET routing protocols. Indeed, the free mobility of MNs (i.e. their real-time physical reallocation, viz. intra-network movement), renders network topology often subject to unpredictable fluctuations. It is this margin of relative unpredictability which lends itself to such instances where QoE, as perceived by the Customer/User, may be subject to varying degrees of depreciation.In this perspective, our contribution in this paper aims to optimize the MANETs' mobility system, through the re-adaptation of some extant MANET routing protocols, so as to afford safer routing courses (lower mobility thresholds equate to lower chances of noise and/or data corruption/loss); all for the capital purpose of improving subjective quality (i.e. the anticipated end-user's personal assessment of the service).For the implementation of our MANET network, our two-fold choice consisted of the NS2 version 2.9 (a powerful platform with highly reliable protocol support), supplemented by the Evalvid Framework (a field-proven tool according to many expert ratings, ideal for close monitoring of QoE metrics).We have considered various video transmission scenarios through the OLSR protocol, one of the most well-known and reliable proactive MANET routing protocols .As for QoE prediction, we expect the mean opinion scores (MOS) to provide a metric template for a rough estimation of the end-users' anticipated appraisal .The results are to demonstrate, as we shall see, that the modified heuristic algorithm of OLSR, leaning on the underlying criterion of mobility, can lead to a significant performance boost in MANETs and, by the same token, to higher returns of QoS and QoE.
Creating Subscription Agent/Client Relationships in the Emerging Serials Environment

Working with a service vendor is different from buying a physical product-the client and the vendor are investing in a relationship that has to be developed over time. Following the workshop leaders’ discussion of issues of concern during a transition, attendees explored the practical aspect of this evolution, using scenarios that look at the human perspective as well as technological considerations. [Article copies available for a fee from The Haworth Document Delivery Service: 1-800-342-9678. E-mail address: getinfo@haworth.com]
IMPLEMENTASI AKUNTANSI FORENSIK DAN AUDIT INVESTIGASI DALAM MENDETEKSI FRAUD DI LINGKUNGAN DIGITAL

The purpose of this study to find out how forensic accounting and investigative audits applied in detecting fraud in the environment Digita. The research method with qualitative methods, the primary type of data used, the results of interviews with internal auditors and investigation to strengthen research, conducted interviews with 3 internal auditors and investigations. Data analysis technique used is qualitative data analysis techniques with descriptive approach.The results of this study indicate that the forensic accounting in detecting fraud in the digital environment can be made by Computer Forensic and investigation should be done is to make Copies of all log data, create Fingerprint of the data mathematically, making Fingerprint of Copies, making Hashes Masterlist and documentation of data has worked. Reasons forensic accounting and investigative audits applied to secure and analyze digital evidence. Another reason is to eradicate fraud in the digital environment
Neuro fuzzy cognitive connection functional point for enterprise network management

Software effort estimation (SEE) is one of the vital roles in enterprise management. This created SEE process used to minimise the incomplete data involvements. It may require large amount of data, which increases the efficiency of the effort estimation system. So, in this paper introduces the enterprise data analytics process by using the size and judgmental software effort estimation process. This method analyses the effort in terms of using expert's opinions, use case, functional points and software size unit information. This method evaluates the neuro fuzzy cognitive connection-based functional points are used to estimates the effort with effective manner. This method examines the connectivity between one requirement to another requirements and it constructs the relationship graph that eliminates the incomplete requirements and details successfully. It reduces the time for examining the software effort. Then the performance of the system is calculated with the support of experimental results such as MRE and VAF.
The performance and QoE analysis of B&lt;SUP align="right"&gt;2&lt;/SUP&gt;-DASH algorithm

Dynamic adaptive streaming over HTTP (DASH) protocol provides several video bit rates for each video segment depending on the current network conditions and it can be used for several platforms. In this paper, bandwidth and buffer-based (B2)-DASH algorithm is proposed to enhance DASH algorithm using both buffer and bandwidth-based approaches to provide high average video bit rate in fluctuated network conditions. B2-DASH is compared with FDASH algorithm, which is the best adaptive algorithm among others. This work compares the performance in low switching rate, fast switching rate, and Wi-Fi network scenarios. The performance metrics are average video bit rates, number of interruptions, and resolution changes. B2-DASH parameters and Quality of Experience (QoE) are analysed for the best performance for each scenario. From the analysis, B2-DASH outperformed FDASH in all scenarios. In Wi-Fi network scenario, the number of resolution changes was enhanced 22%, and QoE was improved 13% over FDASH.
At the Edge of Mind

The Yale ReviewVolume 108, Issue 2 p. 172-172 POETRY At the Edge of Mind Adriana Socoski, Poet, teacher, and musician.Search for more papers by this author Adriana Socoski, Poet, teacher, and musician.Search for more papers by this author First published: 07 July 2020 https://doi.org/10.1111/yrev.13645AboutPDF ToolsExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume108, Issue2Summer 2020Pages 172-172 RelatedInformation
SELECTION OF THE NUMBER OF CLUSTERS IN K-MEAN ALGORITHM USING CLUSTER SOLUTION ENTROPY

The article discusses the problem of choosing the number of clusters in popular k-means clustering algorithm. It is noted that an unsuccessful choice of this hyper parameter can lead to the creation of a cluster structure the meaningful interpretation of which in the process of data mining leads to false conclusions and making incorrect management decisions based on them. The aim of the work is to develop a method for automatic selection of the number of clusters for k-means algorithm. The article provides an analytical review of the known methods for determining the number of clusters, their advantages and disadvantages being noted. The proposed approach is based on the elbow method, which uses the entropy of cluster solutions instead of the mean squares of clustering error. A practical example shows that the use of cluster solution entropy makes it possible to choose the number of clusters even in the case when the approach based on clustering error turns out to be untenable.
Full Issue

Africa Research Bulletin: Economic, Financial and Technical SeriesVolume 58, Issue 6 p. I-I Full Issue Full Issue First published: 17 August 2021 https://doi.org/10.1111/j.1467-6346.2021.10095.xAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Volume58, Issue6August 2021Pages I-I RelatedInformation
Qualitative content analysis in visual question answering-based datasets and algorithms

One of the most overwhelming and exhilarating advancements in the field of intelligent systems has been the introduction of visual question answering (VQA), a new and exciting problem that makes use of natural language processing and computer vision for measuring the ability of a system for image understanding and generating inferences beyond object recognition, segmentation and image captioning. One of the primary goals of research in visual-based artificial intelligence is to design systems that can understand and reply to questions about visual data. The first part of the study very vividly presents numerous diversified as well as diagnostic datasets testing different visual reasoning abilities, while the second part of the study details a plethora of the recently developed approaches for VQA. Finally, a qualitative comparison of the diversity present in the various approaches has been done that can serve as an important benchmark for analysing and comparing the different characteristics and applications of these algorithms and datasets that can prove to be helpful to someone new to the field of VQA and its community.
Sequences

The first abstract data structure we will implement is sequences. We already know this data type because we have it built into Python as lists, but we will make our own implementations now. You are unlikely to need your own implementations of sequences, although there are alternative implementations where there are different trade-offs to the operations. Python’s implementation is efficient and easy to use, and there is no need to implement something you already have readily available. There is, however, good reason to learn how to construct your own types for simple structures like these, so you are equipped to construct more complex data structures when you need to in the future.
Identification of the leading research domains and grouping of articles on the smart city using text mining

Purpose:The objective of the paper is to use text mining to identify leading research domains concerning the smart city following an analysis of research articles with a high citation index according to the Web of Science.Design/methodology/approach: An original method is proposed for analysing academic texts using the R language, tokenisation, lemmatisation, n-grams and correspondence analysis.The author analysed fifty of the most cited articles indexed in the Web of Science from 2014 to 2019.Findings: The paper presents the advantages and drawbacks of the proposed method of analysing research publications.The assets include automation and repeatability of the analysis of a large number of documents and improved knowledge about links among the articles in terms of research domains.The disadvantage is the loss of information from diagrams and figures.The method identified two leading research domains related to the notion of the smart city, technologies and systems.The analysed publications were categorised by selected keywords.Research limitations/implications: Future work should include further refinement of the assumptions for the method, analyses of a more significant number of research texts and a narrowing down of the domain of the smart city.It is desirable to consider other functional domains of the city, such as energy, public health, environmental protection or transport.Practical implications: The proposed method can complement a standard literature analysis regarding the smart city.The leading research domains related to the smart city in the analysed articles were systems and technologies employed to improve how the city operates.Social implications: Text mining can be employed by various experts focusing on the smart city and constitutes a refreshing complement for other research methods, such as questionnaire surveys, interviews or observations.Originality/value The publication can be useful for researchers from various fields and managers seeking to create and use simple, useful methods and tools for analysing unstructured text documents for decision-making.The paper proposes a separate text mining analysis of abstracts and whole documents using n-grams.This yielded a more precise list of areas relevant to the smart city.The grouping was done using correspondence analysis of the fifty most cited articles indexed in the Web of Science from 2014 to 2019.