Reviewerly/exHarmony huggingface dataset:
Implementation towards Blood Cancer Detection with Convolutional Neural Network

This Blood cancer is a life-threatening disease that requires early and accurate detection for effective treatment. In this project, we present a novel approach for blood cancer detection using a Convolutional Neural Network (CNN) model. The CNN model is trained on a dataset comprising cancer and normal blood cell images. Through extensive analysis and evaluation, we achieve a high level of accuracy in distinguishing between cancerous and normal blood cells. To evaluate the performance of our model, we conducted tests on a separate set of cancer and normal blood cell images. The accuracy of our model was determined by comparing the predicted labels with the ground truth labels. The results demonstrate that our CNN model achieves a commendable accuracy rate, making it a promising tool for blood cancer detection. Furthermore, we discuss the significance of our findings and their potential implications for early diagnosis and improved treatment outcomes. The robustness and reliability of our model contribute to its practical utility in clinical settings. By enabling early detection of blood cancer, our approach has the potential to positively impact patient outcomes and enhance the efficiency of treatment strategies. In conclusion, this project presents a novel approach for blood cancer detection using a CNN model. The results demonstrate the effectiveness of our model in accurately distinguishing between cancerous and normal blood cells. The proposed method holds promise for improving blood cancer diagnosis and ultimately contributing to better patient care.
Multispectral Imaging in Mapping and Utilization of Sensitive Bands for Characterization of Waste Rock Dumps: A Case Study of Dongri Buzurg Manganese Mine, Maharashtra, India

Waste rock dumping is a major problem for opencast mine. The key approach to waste rock dump management is its re-use in the form of aggregates, m-sand, etc. which is also the basis of circular economy. The application of multispectral satellite data is considered to be one of the best approaches to characterise the mineral content of the waste rock dumps. In this research, multispectral data from Landsat 9 (OLI) were used to determine the sensitive bands for muscovite schist and gneissic rocks present in the hanging wall and footwall faces of an open-cast manganese mine in India. The behaviour of the spectral signature curves and their association with the surface reflectance values were used to identify these sensitive bands. XRF and XRD were used to identify the minerals of the rocks. The spectral signature reflectance curves obtained from the multispectral data of the identified minerals were compared with the USGS spectral libraries to determine these sensitive bands (bands 4, 5, 6, and 7) for rocks. Applying the band ratio technique, formulae representing the optimal band ratios were developed for mineral mapping. Methods like object-oriented principal component (PC) and independent component (IC) analyses were applied to determine the lithology based on spectral characteristics. False colour composite images in the RGB band combinations (PC4-PC3-PC1) and (IC4-IC2-IC1) were utilised for precise mineral mapping of the area. The methods were also validated with the waste rock dumps of a neighbouring manganese mine having similar geological setting.
Development and Implementation of Blockchain Technology in Banking: A Case Study of Professionals in Banking Industry

The key objective of the study is to identify the opinion of banking professionals on the introduction of blockchain technology in the Banking sector. The primary data collection was conducted, and the write-up is derived from the analysis of the collected data and the researcher's subjective perspective. The study collected primary data from a sample of 147 respondents who are professionals working in the banking sector in Udaipur. The theme of research is very apt as there is no such related literature available on exactly the title, which makes it all the more unique, interesting and exclusive. The results revealed that the professionals are optimistic about the inception of blockchain technology in banking for several reasons, which they accepted through the Likert statement. However, the respondents of the young and middle age group are in favour of the technology, whereas others do not favour this change.
A Survey on College Administrative System using Java Methodology

The College Administrative System Project will work on the college area network. It will include all the departments in the college. It will have an admission system for class attendance, online notes, notice boards, and placement systems so that their students can get all the information by using their valid registration ID and password. Let us see how this College Administrative System Project will work. The College Administrative System Project will also help the college administrator. The administrator will be able to get all the information about any faculty members on a particular day and time. The admin panel will also be able to edit the login display panel of teachers, staff members, students, etc. Admin can add students, remove students, and can give special permission. Admin can schedule classes during their lecture time. Teachers can get information about any students in their style. Teachers can also query by using grades and percentages and see a list of students who come under the category of attendance shortage and many more under this one roof. For the student section, they can get any teacher's notes of a specific day and other information about their campus, such as placement session details, upcoming companies in a specific month, their criteria, their venue details, and many more. The student section will also include a learning section so that students can prepare for their exams.
The structuralist tradition meets empirical data: Corpus data enhancing the Czech Internet Language Reference Book

This paper demonstrates how the corpus grammar tool GramatiKat can be used to improve and refine morphological information in the Internet Language Reference Book (ILRB), which presents complete declension paradigms for 45,632 standard Czech nouns. The paradigm tables are based mainly on morphological types, following structuralist conceptions of language as a fully articulated system. The paper discusses how to update the ILRB and provide users with empirically based grammatical information for individual word forms in each cell of the paradigm. All noun lemmas have been investigated using the GramatiKat tool for research into grammatical categories in Czech. The tool observes the distribution of word forms of a particular lexeme in comparison with the standard distribution across the whole word class. It is capable of identifying nouns that have an unusually high occurrence of a certain word form, as well as nouns with unattested word forms. GramatiKat is based on the data from two corpora of Czech written texts, SYN2015 and SYN2020 (200 million word tokens). The paper investigates the relationship between defectiveness and overabundance on one side and language variability and potentiality on the other. Based on the unique combination of data from the ILRB and GramatiKat, the paper suggests how information about unusually frequent or overabundant word forms as well as unattested ones should be pointed out, so that ILRB provides the user with accurate, empirically based data.
EXCHANGE RATE AND ECONOMIC STABILITY DURING CRISES: CASE STUDY SYRIA

The aim of this article is to measure the impact of the fluctuations in the real exchange rate of the Syrian pound on the key macroeconomic indicators of the Syrian economy during the period 1961-2020 to analyze the role of the real exchange rate in achieving economic stability. Since 2011, the Syrian economy suffered from a devastating war that effected all economic and social aspects of business activities leading to high fluctuations and instability of the macroeconomic ecosystem. During the first 40 years of study, the political stability in Syria has not been translated into economic stability on at the macroeconomic level. Moreover, during the last 11 years, the political instability has worsened the economic situation. To achieve the objective of this study, we applied autoregressive model and simple regression model on a various macroeconomic variables including the real exchange rate. The results show that the real exchange rate has a significant positive impact on the balance of payment, inflation, GDP gap, and negative impact on Real GDP, unemployment rate, monetary supply (M2).&#x0D;
THE INFLUENCE OF LEADERSHIP STYLE ON PERFORMANCE AT UNIVERSITIES OF HO CHI MINH CITY

This paper derived from the study of the theoretical basis of leadership styles and the impact of leadership styles on the performance and empirical research in organizations. The author uses qualitative and quantitative research methods to analyze the nature of leadership styles in the universities of Ho Chi Minh City. Testing research hypotheses of the impact of leadership style on the performance of universities based on data collected from 466 responses from leaders, faculty and officials from 22 universities in Ho Chi Minh City. As a result, 7 elements were found: (1) Idealized Influence (IDI), (2) Inspirational Motivation (IM), (3) Intellectual Stimulation (IS), (4) Personalization (CP), (5) Contingent Reward (CR) (6) Management by Exception (ME) and (7) Liberal leadership (LL).
Expanding Methodological Approaches in <scp>DDL</scp> Research

ABSTRACT This paper analyses the methodologies in 148 empirical data‐driven learning studies for L2 English in prestige journals to examine best practice. Manual coding and corpus analysis of key words and n‐grams from the past 5 years (2018–2022) explore the field as a whole and how methodologies have evolved, suggesting improvements and future avenues for research.
Psychometric Properties of the Instructional Satisfaction Questionnaire in Face-to-Face and Emergency Remote Education Settings

The psychometric properties of a questionnaire to measure and compare student satisfaction with instruction in Face-to-Face Education (FFE) and Emergency Remote Education (ERE) scenarios were analyzed. The sample consisted of 393 students from a Mexican public university. An Exploratory Factor Analysis (EFA) was performed for the application of the questionnaire in the FFE scenario revealed a four-factor/dimensional solution with an explained variance of 66.45%; the Cronbach’s alpha internal consistency coefficient for the application in the EP scenario was .904. The EFA results for the questionnaire application in the ERE scenario revealed a four-factor/dimensional solution that explained 76.30% of the variance; Cronbach’s alpha internal consistency coefficient for this scenario was .951. The results revealed solutions congruent with the theory and structure of the questionnaire.
‘Um, so like, is this how I speak?’: design implications for automated visual feedback systems on speech

At a time when diverse forms of automated visual feedback systems on speech are introduced to the Human–Computer Interaction (HCI) community, our research investigates the possible design spaces and design implications to consider when creating them. We utilised a Wizard of Oz prototype that recognises fillers during speech and sends out real-time feedback in the form of a flickering screen and a post-report with graphic charts. We let 27 participants use this prototype and interviewed them about their general experience of using such systems. Based on thematic analysis, we propose eight design implications to consider when designing an automated visual feedback system on speech. Additionally, we discuss the possible future for automated visual feedback systems on speech where they collaborate with fields such as personal informatics (quantified self), self-determination theory (SDT) and motivation, and other socio-ethnic areas
Stopy metryczne jako jednostki kształtujące przebieg utworu fortepianowego

Metric Feet as Units Shaping the Course of a Piano Piece: In this paper, I make an attempt to present the analytical possibilities offered by using metric feet as units that shape the course of a (piano) piece. This approach results from cognitive studies that clearly show that the perception of auditory events is based on grouping them into larger units on the basic strong–weak axis. In this approach, the foot is a scheme of one strong sound and one or two weak sounds (in different combinations) that repeats regularly in the course of the work. In this article, I present the most common ways of distinguishing strong sounds: using duration, dynamics, and pitch (features derived analogously from language, distinguishing strong syllables from the speech stream) and characterising the most common feet.
Language Programs at Rochester Institute of Technology: A Successful Recent Initiative (2018-Present)

RIT offers undergraduate immersions and minors in 10 languages including Arabic, ASL, Chinese, French, German, Italian, Japanese, Portuguese, Russian, and Spanish. The university has no language requirement or graduate programs in spoken languages; students are encouraged to study abroad with no foreign language preparation. Recent changes in enrollment patterns and consideration of RIT’s strengths led the Department of Modern Languages and Cultures to develop a new option for students: a Bachelor of Science degree in Applied Modern Language and Culture. This is a 4-year undergraduate degree that incorporates foreign language, intercultural understanding, and career preparation. The degree program pairs language and culture with a professional, scientific, or other field to enable students to internationalize their education through sustained engagement with language and culture. The degree is available in four tracks: Chinese, French, Japanese, and Spanish. Japanese is the most popular and highly enrolled. Students complete a professional or technical core as well as specialized courses in their language track. These courses include language for professional use and language for science and technology. International experience is a key component of the degree and students have benefited greatly from the international travel, virtual exchanges, internships, and other innovative hybrid opportunities currently available.
Higher Level Models

This chapter brings to completion our game theoretic analysis. We introduce the “instrumental” approach to game theory advocated by Larry Samuelson as the methodological stance that shapes our discussion. We do not aim at an exhaustive review of the extant literature but rather at a selective review of relevant models of mining competition concerning the vertical structure of the industry, in which the game form features two stages, one of which coincides with (or closely resembles) the basic game discussed in the previous chapter. The exhaustive analytical treatment of the previous chapter is not replicated for evident reasons of limited space. We consider risk-neutral miners engaged in acquiring specialized hardware. Then, we consider risk-averse miners and argue that mining pools emerge for risk-sharing purposes. The degrees of freedom at play and the resulting concentration of mining differentiate from those depicted by Arnosti and Weinberg for stylized miners. The interaction of miners and users is further addressed. Backward induction is given a thorough discussion.
Expanding Access Through Online Asynchronous Language Courses

Since 2019 the German program at the University of Wisconsin-Milwaukee has been developing and implementing fully online, asynchronous elementary German-language courses. This contribution highlights the guiding principles behind this ongoing project, namely the prioritization of accessibility for our students as well as the fostering of a collaborative support structure for instructors. To date, our asynchronous courses have significantly increased our enrollments and attracted new groups of students (those with full-time jobs and/or caregiving responsibilities). Challenges remain, however, such as the uneven development of language skills like pronunciation across in-person and online sections and the increased workload for instructors well beyond the initial design phase. Our contribution will help language instructors, programs, and administrators who are considering the introduction of partially or fully asynchronous offerings at their institutions.
Smart Contracts for Mobile Network Operator Bandwidth Sharing

We propose a new way to share licensed spectrum bandwidth capacity in mobile networks between operators, service providers, and end users using blockchain-based smart contracts. We discuss the foundational building blocks in the contract as well as various extensions to support more advanced features such as bulk purchases and future reservations. Furthermore, we demonstrate how the system can be implemented with an open source, permissioned enterprise blockchain, Hyperledger Sawtooth. We show that our smart contract implementation can improve blockchain transaction performance, by approximately four orders of magnitude compared to serial transactions and one order of magnitude compared to parallel transactions, by using public key infrastructure driven bulk purchases of mobile access grants, paving the way for fully automated, efficient, and fine-grained roaming agreements. We conclude with a discussion of lessons learned from two end-to-end use cases we implemented to validate our distributed ledger design.
Decoupled Progressive Distillation for Sequential Prediction with Interaction Dynamics

Sequential prediction has great value for resource allocation due to its capability in analyzing intents for next prediction. A fundamental challenge arises from real-world interaction dynamics where similar sequences involving multiple intents may exhibit different next items. More importantly, the character of volume candidate items in sequential prediction may amplify such dynamics, making deep networks hard to capture comprehensive intents. This article presents a sequential prediction framework with Decoupled Progressive Distillation (DePoD), drawing on the progressive nature of human cognition. We redefine target and non-target item distillation according to their different effects in the decoupled formulation. This can be achieved through two aspects: (1) Regarding how to learn, our target item distillation with progressive difficulty increases the contribution of low-confidence samples in the later training phase while keeping high-confidence samples in the earlier phase. And, the non-target item distillation starts from a small subset of non-target items from which size increases according to the item frequency. (2) Regarding whom to learn from, a difference evaluator is utilized to progressively select an expert that provides informative knowledge among items from the cohort of peers. Extensive experiments on four public datasets show DePoD outperforms state-of-the-art methods in terms of accuracy-based metrics.
The Insp-AIR-ation (Art + Science Project)

For many emerging into the realisation that the climate crisis is here and present and that we will all be affected, there is a feeling of being overwhelmed and the sense that we are standing on the edge of a precipice. In these times of uncertainty and fear, artist interventions have a capacity to engage with these embodied experiences and bring a sense of hope to the conversation through creative reflective engagement. The act of making can reduce anxiety and is a way for people to express themselves as they enter this phase of climate adaptation. As UK activist group, Culture Declares Emergency, puts it, ‘creativity is the antidote of despair’ (2019). Through the Insp-AIR-ation ArtScience community project, artist facilitators focused on perceptions of air quality. The ways in which we organise our collective lives are very influential on weather cycles and climate rhythms. Through the kaupapa (guiding process) in this emergent project, we sought to give voice to the concerns of community groups, their hopes, and aspirations. This arts project provided people with a space and platform to shape their feelings and express values. While science has been pivotal in highlighting the precariousness of our current way of living, the arts have never been more important than now in expressing who we are and shaping a positive response towards a liveable and just future for all. Commenting on the Climate Crisis, former US advisor, Gus Speth has called for a social response and culture as a necessary agent for bringing about transformational change. Basarab Nicolescu, in La transdisciplinarité: Manifeste (1996), talks about building bridges between science and our ways of being through symbolic language which is enriched by the originating values of the community. This project is one such response in building bridges.
Semi-supervised instance segmentation algorithm based on transfer learning

Semi-supervised instance segmentation algorithms are mainly divided into algorithms based on pseudo-label generation and algorithms based on transfer learning. The algorithms based on pseudo-label generation need to design a specific pseudo-label generation process, but the process is not scalable for different types of source tasks. The algorithms based on transfer learning that started late have relatively high scalability, but the algorithm research ideas are relatively simple. To expand the research on semi-supervised instance segmentation based on transfer learning, this paper proposes a feature transfer-based semi-supervised instance segmentation algorithm Feature Transfer Mask R-CNN (FT-Mask). The FT-Mask algorithm is more scalable than algorithms based on pseudo-label generation and can be used to transfer knowledge from different types of source tasks. Compared with other semi-supervised instance segmentation algorithms based on transfer learning, FT-Mask uses the feature transfer method to achieve semi-supervised instance segmentation for the first time. The experimental results show that the FT-Mask model improves the semi-supervised instance segmentation accuracy of the Mask R-CNN benchmark model through the semi-supervised learning process, and can achieve effective transfer learning.
The Influence of Noise on Multi-Parent Crossover for an Island Model Genetic Algorithm

Many optimization problems tackled by evolutionary algorithms are not only computationally expensive, but also complicated with one or more sources of noise. One technique to deal with high computational overhead is parallelization. However, though the existing literature gives good insights about the expected behavior of parallelized evolutionary algorithms, we still lack an understanding of their performance in the presence of noise. This paper considers how parallelization might be leveraged together with multi-parent crossover in order to handle noisy problems. We present a rigorous running time analysis of an island model with weakly connected topology tasked with hill climbing in the presence of general additive noise (i.e., noisy OneMax ). Our proofs yield insights into the relationship between the noise intensity and number of required parents. We translate this into positive and negative results for two kinds of multi-parent crossover operators. We then empirically analyze and extend this framework to investigate the trade-offs between noise impact, optimization time, and limits of computation power to deal with noise.