Reviewerly/exHarmony huggingface dataset:
Early detection of Cancer using Mammograms with Advanced Artificial Intelligence (AI) Algorithms for Breast lesions

Background: Breast cancer is the second most common cancer and leading cause of cancer death among women worldwide. Objective: To early detect breast cancer by application of computerized methods on mammography images. Methods: This study is based on the computerized detection of lesions on mammograms which was conducted at the Radiation Therapy Unit, AIMC/Jinnah Hospital Lahore during 2021-22. A total of 322 cases from the Image Processing Archive (PEIPA) MIAS database were retrieved. The case can be described by seven attributes. The attributes include the MIAS database reference number, the type of background tissue (F-fatty, G-fatty-glandular, or D-Dense-glandular), and more. Results: Accuracy is calculated to check the performance of the method. Our algorithm produced good results with an accuracy of 98.12 % in the detection of cancer regions in images. The accuracy achieved for benign cases is 96.1% and for normal cases is 98.2% respectively. Conclusion: Based on this method, a tool for improved breast cancer detection can be created.
Introduction

Recent decades have seen a surge in the amount and diversity of data collected and processed by computing systems. Our online transactions, search queries, web browsing history, our movie preferences and videos we watch, and fitness goals are just a few examples of data that are being collected and processed on a daily basis. Several data privacy regulations such as General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and Health Insurance Portability and Accountability Act (HIPAA) restrict access to this personal and sensitive data.
Eye Blink—A Mode of Communication

The human eye blink has been the subject of much study in the field of human communication. This research paper investigates the potential use of eye blink as a mode of communication in individuals suffering from conditions such as motor neuron disease or amyotrophic lateral sclerosis (ALS), where verbal communication becomes limited. The study employs eye blink detection algorithms to convert the blink patterns into functionalities such as sending text messages and making calls. The results of the study demonstrate the feasibility of using eye blink as a means of communication in individuals with limited mobility and provide insights into the potential applications of this technology in the fields of assistive technology and rehabilitation.
Optimizing Cross-Domain Music Recommendation with Adaptive User Preference Transfer

The present study highlights the importance of technological progress in cross-domain recommendation systems, emphasizing the need to keep pace with innovation and adaptation. To address the limitations of previous approaches, the researchers propose a transfer learning approach that utilizes meta-networks and individualized preferences transfer to effectively overcome the challenge posed by limited data. The meta-learning paradigm enables the creation of a meta-network that can adapt to various target domains and establish personalized connections between them, providing an efficient and effective initialization of parameters for new tasks and users through task-oriented optimization. The methodology is thoroughly evaluated on a practical dataset, demonstrating superior performance in terms of recommendation precision compared with other leading cross-domain recommendation approaches. The study evaluates the feasibility of transferring individualized preferences using various models and train/test ratios, employing the MAE and RMSE performance evaluation metrics.
Video Encryption Using Optimization Lightweight Algorithm for Secure Internet of Things

Video encryption based on lightweight algorithm has grew in importance as a research problem in the areas of Internet of things (IoT). Traditional encryption techniques rely on long keys and more rounds, which are ineffective for small and low-power battery-operated devices. In this paper, we propose an effective approach for video encryption which is suggested in response to the popularity of lightweight encryption techniques created specifically for IoT environments. An effective video encryption technique based on optimization lightweight key generation method is proposed which uses two important approaches such as genetic algorithm for key generation and $$+_2$$ function for encryption and decryption. In this scenario, the most significant bit addition’s carry is mitigated by the binary addition, which presents as $$+_2$$ . Correlation, entropy, and the image histogram of video frames have all been used to evaluate the suggested work. Experimental evaluation on a standard video, named Xylophone, clearly demonstrates the outcomes of the proposed method.
TTSMSN: Design of an Efficient Temporal Trust-Based Sidechaining Model for Improving Storage Efficiency in IoT Networks

Due to the increase in use of IoT networks, there has been an exponential increase in data generation, which has increased storage requirements. Existing sidechaining methods have higher complexity, and most of them do not consider node-level trust, which increases number of verification computations while adding new blocks. To reduce this complexity, an efficient temporal trust-based sidechaining model for improving storage efficiency in IoT networks is discussed in this text. The proposed model evaluates a temporal trust level for identification of miner nodes. This trust level is calculated via estimation of miner’s temporal performance in terms of mining delay, energy consumed during mining, delay jitter, temporal throughput, and temporal block mining efficiency levels. Miners with higher trust levels are selected to add blocks to the chains. These chains are further segregated into small sidechains via progressive trust levels of selected miners, which assists in reducing mining delay, while improving security even under block-level attacks. Due to these operations, the proposed model is able to reduce the delay needed for mining by 8.3%, while reducing the energy consumption by 4.5%, and improving throughput by 2.3% when compared with existing sidechaining techniques under real-time scenarios.
Automatic Breast Mass Lesion Detection in Mammogram Image

Mammography imaging is one of the most successful techniques for breast cancer screening and detecting breast lesions. Detection of the Region of Interest (ROI) (where the possible abnormalities could be present) is the backbone for the success of any Computer-Aided Detection or Diagnosis (CADx) system. In this paper, to assist the CADx system, one computational model is proposed to detect breast mass lesions from mammogram images. At the beginning of the process, pectoral muscles from the mammograms are removed as a pre-processing step. Then by applying an automatic thresholding scheme with the required image processing techniques, different regions of breast tissues are ranked to detect the possible suspected region to refine the further segmentation task. One seeded region growing approach is proposed with an automatic seed selection criterion to detect the suspected region to segment the ROI. The proposed model has very less user intervention as maximum of the parameters are computed automatically. To evaluate the performance of the proposed model, it is compared with four different methods with six different evaluation metrics viz., Jaccard &amp; Dice co-efficient, relative error, segmentation accuracy, error and Fowlkes–Mallows index (FMI). On the proposed model, 57 mammogram images are tested, consisting of four different cases that are collected from the publicly available benchmark database. The qualitative and quantitative analyses are performed to evaluate the proposed model. The best dice co-efficient, Jaccard co-efficient, accuracy, error and FMI values observed are 0.9506, 0.9471, 95.62%, 4.38% and 0.932, respectively. The superiority of the model over six state-of-the-art compared methods is well evident from the experimental results.
Analysis of Learners' Performance for Fostering the Attainment of Programme Outcomes

In any learning process, it is quite common that teachers to come across slow, moderate, and fast learners. In the Outcomes Based Education (OBE) system, it is essential to analyze the learners for types, and engagement in learning and offer them remedial inputs to ensure that each learner attains COs at the targeted level. Further, analysis of learners leads to reinforcement of teaching and learning processes (TLP). Hence remedial inputs for slow learners play a major role in fostering PO attainments. In this research work a framework showcasing remedial inputs, enriching assignments, reinforcement of TLP, and format to document the processes is proposed.
THE EMPLOYABILITY SKILLS FOR ACCOUNTING GRADUATES IN DIGITAL ERA

Graduate employability (GE) is a major concern of all academic institutions globally as well as in Malaysia. After the COVID-19 pandemic breakout in 2020, the issue of GE had become a serious concern. Further investigation by the Department of Statistics of Malaysia (DOSM), highlighted a concerning fact that the rate of graduates with skill-related underemployment is continuing to rise, reaching 33.9% in 2021 as compared to 31.2% in 2020. Graduates who experience skill-related underemployment are classified as having the wrong career or profession for them given their educational background. The rapid development of information and communication technology (ICT) may make occupation mismatch worse, and accounting graduates are not an exception. The accounting profession has been warned that it is also quite likely to become automated and digital in the near future. Accounting graduates with great academic qualifications may nevertheless struggle to find employment because employers may think that the standards for certificates and approvals are insufficient. Instead, they demand more basic advantages like teamwork abilities, soft skills, and communication abilities. Even before to graduating, it is important that students have a comprehensive understanding of the abilities that employers are looking for in candidates. To address this issue, the objectives of this conceptual paper is to summarize the existing research studies regarding the appropriate knowledge, technical and other skills that should be equipped by accounting graduates to ensure they will be employed in accounting related fields. This conceptual paper will provide the valuable insight to help teachers, lecturers, other academician, and education institutions in producing qualified accounting graduates.
Dark Side Case: Decisive Moment for Uber’s Chief Security Officer

In November 2016 Uber’s Chief Security Officer, Joseph Sullivan, was faced with making a critical decision. He had just learned that hackers were claiming they had gained access to the data 57 million users. They were also requesting $100k for their silence and the deleting of the private information they acquired. What should Sullivan do? Uber already has a negative reputation with the media related to a list of incidents including a large data breach in 2014. Uber did not need more negative publicity. Moreover, Uber’s CEO, Travis Kalanaik, and the organization’s culture would probably prefer hiding the breach and paying the hackers. Sullivan has a family and was fairly new in the job so he probably did not want to make any missteps and risk loss of his job. The case also raises the issue of whether to use a (retroactive) Bug Bounty Program – one in which he could hire (reward) the hackers to reveal how the breach was done and keep quiet about the breach. All in all, Sullivan had to decide if he should: pay or not pay the hackers ransom; hire the hackers on a Bug Bounty contract; publically disclose the breach or hide it; or even simplhy just ignore the whole thing and see what happens. Whatever decision Sullivan makes will have consequences for Uber, various stakeholders and him personally.
Power BI Admin Portal and Tenant Settings

In the world of Power BI, there are some configurations in the Desktop tool and in the service. One of the more critical configurations is the Tenant Settings of the Power BI administrator panel. Tenant Settings include a list of highly important configurations across your Power BI tenant. If you don't configure the settings properly, it might result in leaking data, authorizing people who should not be authorized to see reports, and many other catastrophic scenarios. In this chapter, you learn about the configurations available in Tenant Settings and the recommended options for each.
Signals for Governance Token Performance: Role of DAOs’ Governance Token Design and Distribution

Decentralized autonomous organizations (DAOs) are a new blockchain-enabled organization form. DAOs give out governance tokens to jointly (with their token holders) shape the DAO’s course. Governance tokens represent the venture’s value and a way for token holders to participate in the venture’s governance. They are unique compared to utility tokens due to their associated governance rights. We can use signaling theory to enhance our understanding of governance tokens. DAOs disclose signals regarding technical token design, allocation to investors, and distribution mechanism. Analyzing a unique dataset of 218 DAOs, we observe the importance of the tokenomics’ underlying influence on DAO’s governance. We find that having a token cap ingrained in the token’s code relates positively to token performance. Furthermore, we challenge established theory by revealing that having a larger share of governance tokens allocated to institutional investors is negatively related to token performance
Limitations of self-imitation. Effects of self-imitation practice on L2 pronunciation with the use of a Golden Speaker Builder – an interactive tool for pronunciation training

<p style="margin-bottom: 0px; font-stretch: normal; font-size: 16px; line-height: normal; font-family: &quot;Times New Roman&quot;; color: rgb(38, 38, 38);"><span style="font-size:18px;font-kerning: none">One increasingly popular method of L2 pronunciation improvement is self-imitation practice, which involves mirroring one's own voice synthesised with that of a native speaker. The Golden Speaker Builder (Ding et al. 2019) is a tool that allows users to generate a personalised model voice, which mirrors the learner's voice but with a native accent. Thirty-five participants of the study performed a&nbsp; three-week self-imitation task by repeating some of the sentences they had previously recorded. The participants took a pre-test, a post-test after three weeks of practice, and a delayed post test. Each participant completed two qualitative questionnaires: before and after the exercises, to find out their opinion about the tool used. The results show a significant improvement in pronunciation in terms of fluency and comprehensibility, but the feedback from the questionnaire indicates that GSB cannot replace the personalised comments received directly from a teacher during pronunciation training.</span>
Unveiling cultural intelligence: A comparative study of Japanese and Indonesian idiomatic expressions

This paper delves into the realm of cultural intelligence inherent in Japanese and Indonesian idiomatic expressions. In the context of intercultural communication, Indonesian speakers utilize diverse cultural intelligence frameworks to express various facets of Japanese culture, with language serving as a prominent component. While a plethora of studies have examined idioms from semantic and semiotic perspectives, a notable gap exists in the literature regarding the exploration of cultural intelligence within idiomatic expressions in both languages, encompassing both structural and semantic analyses. Filling this research void, the present study aims to elucidate the concept of cultural intelligence, specifically focusing on the comprehension of Japanese and Indonesian idioms, particularly those related to the notion of "face". Employing a descriptive research approach, data comprising 16 Japanese idioms and 13 Indonesian idioms were meticulously examined to unveil the cultural significance within each group. The idioms were sourced from various dictionaries and specifically focused on expressions related to body parts, especially the face, which are commonly employed in everyday life. These idiomatic expressions were systematically classified into three categories and subjected to comprehensive analysis. The findings reveal that the majority of the idioms convey emotions, personal characteristics, and concepts of honor, thereby reflecting both cultural similarities and differences between Indonesian and Japanese cultures through idiomatic expressions. This study sheds further light on the intersection between cultural intelligence and idiomatic language, enhancing our understanding of how culture influences language use and interpretation.
How Fuzzy Set Theory Informs Strategic Representations Under Knightian Uncertainty

Recent studies on small-world representations and entrepreneurs-as-scientists reinforce the thesis that managers and entrepreneurs’ decisions rely on incomplete information in strategic situations of Knightian uncertainty. However, the bounded rationality framework of strategic decision-making that currently prevails implicitly makes unrealistic assumptions about the nature of the categories that underlie decision criteria, i.e., as if they were conceptually sharp or well-defined. We draw on fuzzy set theory to explain that the liminal boundaries between perceptual categories that shape partial knowledge and judgment are typically ‘fuzzy’ and have unsharp frontiers. We take a step toward strengthening the analysis of the strategic representations that rule managerial and entrepreneurial thinking. We proffer fuzzy logic as a more approximate representation of the actual reasoning that underlines the remarkable ability of the human brain to solve ill-defined and abstract problems without crisp or quantifiable measurements, such as those faced in business. These fuzzy representations are captured with natural language (rather than bivalent logic) to better exploit uncertainty and convey more meaningful decisions. Finally, we revisit heuristics, small-world representations, and the processes of belief formation, testing, and feedback as capturing nuance and gradation in the elements of judgment. We discuss several implications for strategic heuristics and entrepreneurship theory.
Analisis Bibliometrik pada Jurnal Al-Maktabah, Pustakaloka, Unilib &amp; Libraria Periode 2015-2021

A bibliometric analysiss was conducted to determine 1) the number of articles published annualy from 2015-2021;2) the number of authors who contributed to the four journals for tha 2015-2021 peroid;30 the productivity of writers in the four journals for the 2015 – 2021 period, for the journals in the field of library, documentation and information, respectively are ALMAKTABAH, PUSTAKALOKA, UNILIB &amp; LIBRARIA. Data sources are accessed from each of the journal sites, https://journal.iainkudus.ac.id/index.php/Libraria, https://journal.uii.ac.id/unilib, https://jurnal.iainponorogo.ac.id/index.php/pustakaloka/index, dan https://journal.uinjkt.ac.id/index.php/al-maktabah/index. The result of this research is the total number of articles published in ALMAKTABAH, PUSTAKALOKA, UNILIB &amp; LIBRARIA, 377 titles, consisting of 69, 115, 72 and 121 titles, respectively The total authors are 529 people consisting of . Almaktabah 104 authors, Pustakaloka 161 authors, Unilib 87 authors and Libraria 177 authors. The most prolific authors of the AlMaktabah journal are U Undayani (7 articles) from UIN Syarif Hidayatullah Jakarta, L Lolytasari (5 articles) from UIN Syarif Hidayatullah Jakarta, R Rodin (4 articles) IAIN Curup-Rejang Lebang Bengkulu, then A Alfida and L Istiqoriyah (3 articles) each from UIN Syarif Hidayatullah Jakarta, and 2 other authors from UIN Imam Bonjol and UIN Sunan Kalijaga. The pattern of authorship shows that from a total of 377 articles, single authors (259 articles 0.69%) and collaborating authors (118 articles 0.31%) with an average collaboration degree of 0.31. The conclusion of the study is that during 2015-2021, 377 articles have been written from four journals (ALMAKTABAH, PUSTAKALOKA, UNILIB &amp; LIBRARIA), written by 529 authors mostly written individually, and the most productive authors are U Undayani (7 articles), writes at AlMaktabah UIN Syarif Hidyatullah
Infrastructures for Institutional Change: Inscription and the Role of Technological Prefiguration

Actors working to bring about institutional change increasingly leverage digital infrastructures while doing so. Ample scholarship has examined how change-oriented actors use infrastructures like social media to organize and communicate their change vision. More recently, scholars’ and practitioners’ attention has turned to new phenomena such as Blockchain and open data platforms, where institutional change efforts and technology intersect in a different way. In these cases, change-oriented actors attempt to kick-start large-scale change by inscribing their envisioned institutional logics directly into IT artefacts serving a pivotal role in institutional arrangements. While scholars have long recognized the validity of such technological inscription as an approach to institutional change, a systematic understanding of how inscription of digital infrastructures proceeds at the organizational level is currently lacking. In this conceptual paper, I synthesize literature from information systems and organizational research to develop a theory of “technological prefiguration” that illuminates this topic. The concept and associated propositions highlight a crucial but hitherto disregarded organizational challenge that institutional change efforts proceeding via inscription of digital infrastructures face. I illustrate these arguments with examples from the Blockchain space and discuss how an understanding of technological prefiguration can inform research and practice.
Diving Deeper into Digital Mindset – Development and Validation of a Scale

An individual’s digital mindset has become essential for successfully coping with digital trans-formation. Despite growing interest in digital mindset, research has lagged due to a lack of con-ceptual clarity of digital mindset and a measure of the construct. To address these issues, we provide an overview of previous digital mindset definitions, conceptualizations, and literature and conclude with a valid and suitable definition. Building on this definition, we develop the digital-mindset-scale and validate it through five phases across four samples. First, we generat-ed items for the digital mindset dimensions. Second, exploratory and confirmatory factor anal-yses supported a three-dimensional structure of digital mindset (digital consciousness, digital expertise, and digital entrepreneurship). Third, based on the results of confirmatory factor anal-yses in the second phase, we generated additional items and assessed the psychometric proper-ties and factor structure. Fourth, we established convergent and discriminant validity of the digi-tal-mindset-scale with relevant related constructs. Finally, in phase 5, we examined relations with well-established outcomes (resistance to change, innovative behavior, digital strategy per-formance, and entrepreneurial behavior). The digital-mindset-scale initiates empirical studies on the construct in research and contributes to a more nuanced approach to research and practice relating to digital mindset.