Reviewerly/exHarmony huggingface dataset:
Integrating Ai and Machine Learning Technology for Educational Enhancement and Career Guidance System

Career and education guidance is undergoing a transformative phase, especially in the context of life-long learning. Modernizing career services to meet the diverse needs of students is imperative in the age of technology. This article navigates the considerations for career services professionals, exploring the characteristics and needs of today's students, available technologies, funding requirements, and confidentiality issues. The challenge lies in creating accessible services that seamlessly connect education with employment. This paper presents a pioneering study that explores the untapped potential of artificial intelligence (AI) in revolutionizing career and education guidance, not only for higher education but also for students above the 9th standard. Drawing insights from focus groups, scenario work, and practical trials, the research provides a comprehensive view of the requirements and possibilities of integrating AI into career guidance. Perspectives from students, guidance staff, and institutions shape the findings, highlighting potential values, functions, and the driving forces and obstacles in adopting AI for career guidance. The study introduces varied modes of agency and maturity levels, establishing a framework for AI involvement in guidance processes. Future research directions include a focus on agency in guidance interaction, developing a guidance data ecosystem, and addressing ethical concerns.
Democracy In The Dark: Internet Shutdowns In India As A Challenge to Freedom of Speech and Expression

The right to access the internet is intrinsically linked to fundamental rights such as the right to education and the right to privacy under Articles 21A and 21 of the Indian Constitution, respectively. The internet not only enhances the quality of education but also serves as a powerful tool for the free expression of knowledge and ideas. It has been recognized globally as a human right, and adherence to this right is imperative for countries signatory to the Universal Declaration of Human Rights. Freedom of speech and expression, enshrined in Article 19(1)(a) of the Indian Constitution, extends to the internet, with reasonable restrictions outlined in Article 19(2). In recent years, internet shutdowns in India have become increasingly common, particularly during periods of civil unrest and protests. While the government has the authority to impose reasonable restrictions on freedom of speech and expression to maintain public order, the arbitrary use of internet shutdowns has become a concerning trend. This paper explores the reasons behind and the consequences of these shutdowns on the right to freedom of speech and expression. The internet is a crucial medium for citizens to communicate, express dissent, and seek support, especially during times of crisis and unrest. Its importance is evident in regions like Jammu and Kashmir and the North Eastern states of India, where prolonged internet shutdowns have isolated affected communities from the rest of the country. The imposition of internet shutdowns as a means to restore peace raises questions about its reasonableness and adherence to constitutional provisions. The paper cites landmark cases, such as Anuradha Bhasin v. Union of India, where the Supreme Court emphasized the importance of necessity and proportionality in internet shutdown orders. The lack of accountability mechanisms, combined with normalized shutdowns, has allowed the government to stifle dissent without justifiable cause. To protect fundamental rights and uphold the principles of democracy, judicial activism and a critical evaluation of government actions are essential. The paper concludes that internet shutdowns, as a means of suppressing free speech and expression, often fail to meet the criteria of reasonable restrictions and are, therefore, a violation of the fundamental rights guaranteed under the Indian Constitution.
Improving Performance of Massive Text Real-Time Classification for Document Confidentiality Management

For classified and sensitive electronic documents within the scope of enterprises and organizations, in order to standardize and strengthen the confidentiality management of enterprises and meet the actual needs of secret text classification, a document automatic classification optimization method based on keyword retrieval and the kNN classification algorithm is proposed. The method supports keyword classification management, provides users with keywords of multiple risk levels, and then combines a matching scanning algorithm to label keywords of different levels. The text with labels is used as the training set of the kNN algorithm to classify the target text and realize the classification protection of text data. Aimed at solving the shortcomings of large feature vector dimension, low classification efficiency, and low accuracy in existing kNN text classification methods, an optimization method is proposed using a feature selection algorithm and a kNN algorithm based on an AVX instruction set to realize real-time classification of massive texts. By constructing a keyword dictionary and an optimized feature vector, parallel calculation of the feature vector weight and distance vector is realized, and the accuracy and efficiency of text classification are improved. The experimental results show that the multi-classification effect of the feature selection algorithm used in this paper, tf-DE, is better than that of the traditional tf-idf algorithm, and the classification effect of kNN is comparable to that of the support vector machine (SVM) algorithm. With the increase in feature vector dimensions, the classification effect of the text classification algorithm is improved and the classification time also increases linearly. The AVX-256 acceleration method takes about 55% of the time of the original version, thus verifying the effect of multi-classification of massive texts for document confidentiality management.
Dual view deep learning for enhanced breast cancer screening using mammography

Abstract Breast cancer has the highest incidence rate among women in Ethiopia compared to other types of cancer. Unfortunately, many cases are detected at a stage where a cure is delayed or not possible. To address this issue, mammography-based screening is widely accepted as an effective technique for early detection. However, the interpretation of mammography images requires experienced radiologists in breast imaging, a resource that is limited in Ethiopia. In this research, we have developed a model to assist radiologists in mass screening for breast abnormalities and prioritizing patients. Our approach combines an ensemble of EfficientNet-based classifiers with YOLOv5, a suspicious mass detection method, to identify abnormalities. The inclusion of YOLOv5 detection is crucial in providing explanations for classifier predictions and improving sensitivity, particularly when the classifier fails to detect abnormalities. To further enhance the screening process, we have also incorporated an abnormality detection model. The classifier model achieves an F1-score of 0.87 and a sensitivity of 0.82. With the addition of suspicious mass detection, sensitivity increases to 0.89, albeit at the expense of a slightly lower F1-score of 0.79.
The effects of tones and time pressure on the user experience of voice input on mobile devices for Mandarin Chinese: an experimental study

This study experimented with mobile devices to investigate the effects of tones and time pressure on the voice input experience for Mandarin Chinese. Voice input for Mandarin Chinese is gaining popularity, and its implementation on mobile devices has recently developed very rapidly. Previous studies have focused mainly on technical considerations for designing a voice input system and have not investigated the user experience of voice input. Two experiments were conducted to test single-character and two-character phrases. Four measurements, the number of errors, user satisfaction, mental workload, and skin conductance, were gathered from sixty-four participants for each experiment. The results indicated that Mandarin tones had a significant influence on the number of errors and user satisfaction, and time pressure significantly influenced all measurements. Specifically, the falling-rising tone produced the highest number of errors, and the rising tone led to the lowest satisfaction. A high level of time pressure resulted in low satisfaction, a high mental workload, and high skin conductance. The results indicated that (a) Mandarin tones significantly affected error rates and user satisfaction, and (b) time pressure had a significant effect on user experience. The findings have design implications for voice input systems in terms of user experience.
Disinformation and Hate Speech

chapter Share on Disinformation and Hate Speech Authors: Ronald M. Baecker University of Toronto (Computer Science Department) University of Toronto (Computer Science Department)Search about this author , Jonathan Grudin University of Washington (Information School) University of Washington (Information School)Search about this author Authors Info & Claims Digital Dreams Have Become Nightmares: What We Must DoFebruary 2024https://doi.org/10.1145/3640479.3640486Published:15 February 2024Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Strengths-based service solutions: mapping a way forward in marketplace vulnerabilities

Definitions of vulnerability are increasingly contested, engaging scholars across many disciplines. The many definitions and debates point to the multifaceted nature of the service challenges associated with the multiple dimensions of experienced vulnerability. Importantly, the deficit discourse that underpinned much of the work on vulnerability in different domains is increasingly changing to a strengths-based perspective. A strengths-based approach seeks opportunities for risk reduction, adaptation, and coping solutions for actors in service delivery and value creation. Thus, service managers and researchers must address both assets and adaptive capabilities of actors within the service system (process, design, and delivery) and within actors’ own contexts. This chapter therefore explores contemporary thinking to support actors experiencing vulnerability in the marketplace. We seek to promote debate on ways to enhance equitable service sustainability, particularly in light of the United Nations Sustainable Development Goals (SDGs). A lack of focus on sustainable transformative service as per the SDGs, limited strengths-based thinking, together with potentially unmet customer needs that stem from experienced vulnerability (or related to it), exacerbate existing vulnerabilities. These complex factors threaten progress on the SDGs’ agenda to reduce inequalities triggered by service systems. Service scholars and practitioners are therefore urged to consider sustainability and strengths-based approaches in their research and service design. In reviewing contemporary thinking, the chapter concludes with a new research agenda for considering strengths-based approaches to service delivery; service sustainability; and enhancing service inclusion for any actor in the system/network experiencing vulnerabilities.
Regulating the Animal Testing Industry

Animal testing has been a customary practice in numerous civilizations throughout human history. Humanity tends to want to understand the true nature of our physiology as a whole, and animals have been beneficial to help satisfy this curiosity. Yet ethically speaking, scientists argue for the protection of the rights of humans, while many fail to do so for our natural counterparts, animals. Current standards of the industry bring up concern for the ethical treatment of animals; additionally, many of the standards that exist are outdated and not systematized throughout the scientific world. Furthermore, new scientific developments can possibly allow for the phasing out entirely of animal testing, with new computer modeling software. It can render many of the existing tests obsolete as this new software allows scientists to bypass some of the animal testing in drug trials. Due to many of the shortcomings in the industry, this has caused an inefficiency. This inefficiency can be described as an overall loss of time and money, which could instead be put towards developing new drugs or other studies. Although it is unlikely that animal testing will be removed entirely, it is necessary that new standards be put in place for the ethical treatment of animals. Keywords: animal testing, ethics, regulation, drug trials, computer modeling
Automatic label curation from large-scale text corpus

Abstract The topic modeling technique extracts themes based on their probabilistic measurements from any large-scale text collection. Even though topic modeling pulls out the most important phrases that describe latent themes in text collections, a suitable label has yet to be found. Interpreting the topics extracted from a text corpus and identifying a suitable label automatically reduces the cognitive load for the analyst. Extractive methods are used typically to select a label from a given candidate set, based on probability metrics for each candidate set. Some of the existing approaches use phrases, words, and images to generate labels using frequency counts of different words in the text. The paper proposes a method to generate labels automatically to represent each topic based on a labeling strategy to filter candidate labels and then apply sequence-to-sequence labelers. The objective of the method is to get a meaningful label for the result of the Latent Dirichlet Allocation algorithm. The BERTScore metric is used to evaluate the effectiveness of the proposed method. The proposed method generates good interpretative labels as compared to baseline models for topic words or terms automatically. The comparison with the label generated through ChatGPT API shows the quality of the generated label with the experiment performed on Four Datasets NIPS, Kindle, PUBMED, and CORD-19.
A Multi-Attention Feature Distillation Neural Network for Lightweight Single Image Super-Resolution

In recent years, remarkable performance improvements have been produced by deep convolutional neural networks (CNN) for single image super-resolution (SISR). Nevertheless, a high proportion of CNN-based SISR models are with quite a few network parameters and high computational complexity for deep or wide architectures. How to more fully utilize deep features to make a balance between model complexity and reconstruction performance is one of the main challenges in this field. To address this problem, on the basis of the well-known information multi-distillation model, a multi-attention feature distillation network termed as MAFDN is developed for lightweight and accurate SISR. Specifically, an effective multi-attention feature distillation block (MAFDB) is designed and used as the basic feature extraction unit in MAFDN. With the help of multi-attention layers including pixel attention, spatial attention, and channel attention, MAFDB uses multiple information distillation branches to learn more discriminative and representative features. Furthermore, MAFDB introduces the depthwise over-parameterized convolutional layer (DO-Conv)-based residual block (OPCRB) to enhance its ability without incurring any parameter and computation increase in the inference stage. The results on commonly used datasets demonstrate that our MAFDN outperforms existing representative lightweight SISR models when taking both reconstruction performance and model complexity into consideration. For example, for <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"><mo>×</mo></math> 4 SR on Set5, MAFDN (597K/33.79G) obtains 0.21 dB/0.0037 and 0.10 dB/0.0015 PSNR/SSIM gains over the attention-based SR model AFAN (692K/50.90G) and the feature distillation-based SR model DDistill-SR (675K/32.83G), respectively.
Sentiment Analysis of Society Towards the Child-free Phenomenon (Life Without Children) on Twitter Using Naïve Bayes Algorithm

The difference in societal perspective regarding personal well-being and understanding life choices is genuinely diverse. Lately, there is a prevalent thought where individuals believe that personal well-being can be achieved by choosing to live without children. Most of them prefer to prioritize their careers, education, or other activities that they believe can bring greater happiness and well-being to their lives. This topic has become a frequently discussed subject in almost every region of Indonesia, especially in urban areas. Not only facing negative stigma, the choice to live a life without children in Indonesia also carries positive connotations. Views on child-free in Indonesia are highly diverse, considering the many differences in social environments and each individual’s personal experiences. In this research, the Naïve Bayes algorithm is used as a sentiment classifier in the form of textual data collected through Twitter using the Rapid Miner. The data collection period spanned from May 3rd to May 10th, 2023. The research aims to analyze and present data regarding public sentiment towards the child-free phenomenon in Indonesia. The results of this research reveal the presence of 320 positive sentiments and 180 negative sentiments, with the accuracy value of the Naïve Bayes algorithm in conducting sentiment analysis on the child-free phenomenon reached 95.00%.
Theory of infinite sequences and series by Ludmila Bourchtein and Andrei Bourchtein , pp. 377, £54.99, (paper), ISBN 978-3-03079-430-9, Springer Verlag (2022)

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An exploratory study on the readability of authentic English chapter books for Croatian primary school students

The paper explores chapter books, explaining their nature and providing information on their usefulness and possible uses in the context of teaching English as a foreign language. It outlines their properties, layout and subject matter. The goal of the exploratory study was to investigate whether young Croatian pupils could read English chapter books written for native speakers and whether illustrations helped them to comprehend the narrative. Furthermore, it aimed to examine pupils’ response to the format of chapter books. The study about the suitability of chapter books was conducted among pupils in the third, fourth and fifth grade in two Croatian schools, and its results show that young learners of English as a foreign language can grasp the essence of a story and can reflect on what they read, identify points of difficulty and the strategies applied. The findings suggest that carefully chosen chapter books can be valuable authentic materials in aiding language acquisition, fostering discussions, and engaging higher-order thinking skills in young EFL learners in Croatia.
Hubungan Kepercayaan Diri dengan Interaksi Sosial pada Remaja Kecanduan Game Online di SMP Negeri 1 Asembagus

Confidence is one of the things social interaction well.But for the students had been addicted to online games of course would be questionable, people were addicted to online games would be hard to interact with others because they already feel comfortable with their activities.This study bertujuhan identify the effects confidence on social interaction in adolescents are addicted to online games in junior high schools the sample of 1 asembagus 168 teenagers. Using a scale addicted to online games, confidence and social interaction that will impact analysis to know. Hence analysis techniques the data used was pearson correlation.The result of the research is 200 &gt;0,05 which means there is a confidence with social interaction in adolescents addicted to online games.
On constrained Kaczmarz algorithm with momentum for image reconstruction

Summary The Kaczmarz algorithm is widely used in image reconstruction. In this paper, we propose an enhanced version of the Kaczmarz algorithm by integrating Nesterov momentum into the Kaczmarz algorithm, denoted as the Kaczmarz algorithm with momentum. Our innovation offers a novel approach to accelerate the convergence rate of the Kaczmarz algorithm significantly. Based on greedy rules for selecting work row, we prove that the Kaczmarz method with momentum can achieve global linear convergence rates. Furthermore, we extend a constrained version of the Kaczmarz algorithm with Nesterov momentum for improved image reconstruction and prove its convergence. Numerical experiments show that the proposed algorithm outperforms the original Kaczmarz algorithm in terms of efficiency for image reconstruction.
Analisis Kesulitan Mahasiswa Santri Fakultas Tarbiyah Universitas Ibrahimy Sukorejo Situbondo dalam Menyusun dan Menyelesaikan Tugas Akhir Skripsi (TAS) pada Tahun 2023

This research reveals the difficulties Santri students face at the Tarbiyah Faculty of UNIB Situbondo in compiling and completing their final thesis assignment (TAS) in 2023. The research method used combines two approaches, descriptive quantitative and qualitative. The descriptive approach is the preparation and presentation of data into images or graphs. Meanwhile, the quantitative approach analyzes quantitative data obtained through questionnaire instruments regarding the average percentage of difficulties faced by Islamic boarding school students. Quantitative data was analyzed using descriptive and inferential statistics with the help of the Excel/SPSS 23 program. The qualitative approach was to carry out an analysis of qualitative data obtained through interview instruments. The population in this study was 256 Tarbiyah Faculty students who graduated in 2022-2023. Meanwhile, the samples taken as research objects/subjects were 64 students. The sample size used was 25% of the total population. The results of the research show that three indications are very dominant faced by students, namely: 1) students have difficulty in determining statistical formulas that suit the data (percentage 82.26%), 2) students have difficulty in using statistical program applications such as SPSS, Eviews, Smart PLS and others are often used in processing/analyzing research data (percentage 91.94%), and 3) students feel anxious while waiting for the thesis exam, worried or afraid when facing the thesis exam (percentage 87.10%).
Quantum tomography for arbitrary single-photon polarization-path states

Quantum state tomography (QST), the process through which the density matrix of a quantum system is characterized from measurements of specific observables, is a fundamental pillar in the fields of quantum information and computation. In this work, we propose a simple QST method to reconstruct the density matrix of two qubits encoded in the polarization and path degrees of freedom of a single photon, which can be realized with a single linear-optical setup. We demonstrate that the density matrix can be fully described in terms of the Stokes parameters related to the two possible paths of the photon, together with a quantum version of the two-point Stokes parameters introduced here. Our findings put forward photonic circuits for the investigation of the dynamics of open quantum systems.