Reviewerly/exHarmony huggingface dataset:
The Backpack Quotient Filter: a dynamic and space-efficient data structure for querying<i>k</i>-mers with abundance.

Genomic data sequencing has become indispensable for elucidating the complexities of biological systems. As databases storing genomic information, such as the European Nucleotide Archive, continue to grow exponentially, efficient solutions for data manipulation are imperative. One fundamental operation that remains challenging is querying these databases to determine the presence or absence of specific sequences and their abundance within datasets. This paper introduces a novel data structure indexing k -mers (substrings of length k ), the Backpack Quotient Filter (BQF), which serves as an alternative to the Counting Quotient Filter (CQF). The BQF offers enhanced space efficiency compared to the CQF while retaining key properties, including abundance information and dynamicity, with a negligible false positive rate, below 10 -5 %. The approach involves a redefinition of how abundance information is handled within the structure, alongside with an independent strategy for space efficiency. We show that the BQF uses 4x less space than the CQF on some of the most complex data to index: sea-water metagenomics sequences. Furthermore, we show that space efficiency increases as the amount of data to be indexed increases, which is in line with the original objective of scaling to ever-larger datasets.
WEBINARS &amp; WORKSHOPS INTRODUCTION TO COMPUTER VISION: UNRAVELING THE WONDERS OF COMPUTER VISION

The computer vision workshop is a crucial platform addressing the escalating demands of this rapidly evolving field within computer science. In today's era, visual data processing is integral across diverse sectors such as healthcare, security, and industry, making the workshop a guiding beacon for participants navigating the complex terrain of computer vision. Central to its agenda is the dissemination of practical insights into training data for object recognition, a cornerstone for unleashing the full potential of computer vision applications. Participants delve into fundamental concepts, encompassing nuances of deep learning alongside the intricacies of data training and object detection. Employing Google Colab and Python programming language, the workshop emphasizes practical application, equipping participants with cutting-edge tools. Expected outcomes encompass participants fostering a profound grasp of underlying principles, refining proficiency in data training for nuanced object detection, and bolstering confidence through hands-on exercises. Designed holistically, the workshop integrates elements such as deep learning, computer vision, interactive Q&amp;A sessions, and practical exercises, ensuring participants acquire a robust skill set ready for real-world challenges in the dynamic realm of computer vision.
Studi Literatur: Strategi Reading Habit Sebagai Upaya Optimalisasi Literasi

This research study aims to determine the strategy of implementing "Reading Habit" in improving literacy skills in Madrasah Ibtidaiyah students seen from the role of parents and teachers, as well as the relationship between the application of "Reading Habit" to literacy skills in Madrasah Ibtidayah Students. The method taken uses a literature study approach, with reference to previous research relevant to the strategy of implementing reading habit programs in improving literacy skills in students. This research shows that education in today's era, the interest needed in students in supporting education is the interest in reading. Reading activities will affect insight into knowledge and literacy skills in students. But in fact, Indonesian students are less fond of reading, and cause Indonesia to be low in the reading category compared to other countries. So it needs to be applied to elementary school and Madrasah Ibtidaiyah students related to the reading habit program. The strategy in implementing this habituation can be assisted through the role of parents and teachers, so that the benefits of realizing the application of this reading habituation can improve reading skills by thinking critically in understanding problems, as well as literacy skills in elementary school and madrasah ibtidaiyah students
Segmented Acoustic Displays

For more than a century, segmented displays, such as the seven‐segment display, have been a popular and cost‐effective option. They can display various commonly used characters by controlling a few custom‐shaped binary light emitters. This paper introduces the acoustic equivalent of segmented displays, which uses heterogeneous sound modulators to generate a limited set of acoustic holographic images. Designing segmented acoustic displays is more challenging than optical ones due to the complex relationship between emitted sound and generated holographic images. To address this challenge, we propose a design methodology based on unsupervised learning techniques. Our approach balances the cost of acoustic displays and the quality of the images they generate, resulting in segmented displays that outperform existing general‐purpose ones when generating a finite set of acoustic images. Using simulations and physical fabrication of metamaterial‐based acoustic displays, we have proven that our approach can create segmented acoustic displays that produce high‐quality images at a lower cost. Additionally, we have applied our methodology to multi‐frequency acoustic displays and assessed its scalability as the number of images increases. The cost‐reduction through segmentation presented in this work is expected to democratise sound manipulators for acoustic displays and other applications like acoustic levitation and noise cancellation. This article is protected by copyright. All rights reserved.
Judge rules against university investigator

Case name: Dufort v. Liberty University , No. 6:21‐cv‐00054 (W.D. Va. 10/17/23).
Pengenalan Literasi Finansial Awal Pada Anak Usia Dini Di TK Aqidah Palangka Raya

This community service is motivated by the importance of introducing learning to children, especially in introducing edupreneur activities. Based on this, it is necessary to provide an introduction to edupreneur activities in terms of introduction to make children have an edupreneur spirit through activities to introduce children to early financial literacy. The aim of this community service is so that children can learn about educational activities that have been carried out, namely activities to introduce early financial literacy, and through these activities it can also teach children about transactions when selling, namely the existence of sellers and buyers using money. The target participants for this community service are children aged 5-6 years who are at the Palangka Raya Aqidah Kindergarten and students of the Early Childhood Islamic Education Study Program, Faculty of Tarbiyah and Teacher Training, IAIN Palangka Raya who are also actively involved in this activity. The results of this community service show that the children are very enthusiastic about participating in the initial financial literacy introduction activities that have been carried out at Bank Indonesia (BI) and the children look enthusiastic. The hope is that through the introduction of edupreneur activities carried out in early childhood, it will be able to stimulate children's cognitive abilities in recognizing early financial literacy, introduce children to the importance of edupreneurship for children, and increase children's experience because they have carried out edupreneur activities, namely through the introduction of early financial literacy. So that the aim of the introduction will be achieved optimally.
Penguatan Literasi Digital Dalam Pemanfaatan Website Pada Badan Usaha Milik Desa Cibogo

This community service activity aims to improve digital literacy in website utilisation at BUMDes in Subang Regency, namely BUMDes Cibogo. The scope of digital literacy in this activity is Digital Skill, which is the ability of individuals to know, understand, and use ICT hardware and software as well as digital operating systems in everyday life. The methodology used in this activity is Rapid Rural Appraisal (RRA), a strong approach to rural community development. RRA includes aspects of respecting the community, encouraging the community to express and share ideas or opinions, asking questions, listening attentively, and taking notes. After the community service activities, the management of BUMDes Cibogo understands digital literacy at the digital skill level and a perception that digital literacy is very important as an effort to optimise BUMDes performance.
SOSIALISASI LITERASI DIGITAL BAGI MASYARAKAT KECAMATAN ALAM BARAJO KOTA JAMBI JELANG PEMILU 2024

The development of technology which affects all lines of people's lives means that people have to adapt to these technological developments. In this phenomenon, people are able to utilize technology, such as gadgets, laptops and other technology as tools to help with daily activities. With gadgets, the process of exchanging and disseminating information becomes easier. One of the impacts is a large and massive flood of information, making it difficult for people to digest and verify the various news they receive. The method for implementing this community service activity consisted of observation, design and socialization of digital literacy, as well as monitoring and evaluation. Through this activity, it is hoped that the people of Alam Barajo District would have a better understanding of digital literacy. The results of digital literacy outreach showed an increase in public understanding of the concept of digital literacy. This can be seen from a significant increase in public knowledge of the importance of digital literacy in facing the dynamics of information in the digital era. Socialization of digital literacy for the community has had a significant positive impact. Increasing public understanding of digital literacy and changes in behavior in using social media show the success of the implementation methods that have been implemented. A positive response to outreach materials also shows an approach that is relevant to the local needs of the community and increases the effectiveness of message delivery.
Application of Mohand transform coupled with homotopy perturbation method to solve Newel-White-Segel equation

To solve the Newell-White-Segel equations (NWSE), this paper presents a novel analytical method that combines the Mohand Transform with the HPM in a unique way. Due to its intrinsic nonlinearity, NWSE, which is essential for simulating intricate events in mathematical biology and physics, poses analytical challenges. An efficient analytical framework for dealing with nonlinear systems is provided by extending the Mohand Transform in combination with the HPM. Comparative evaluations between the results of this study and those of previous research, together with proven analytical solutions, are carried out in the framework of three NWSE examples.
Likelihood-Free Estimation of IRT Models in Small Samples: A Neural Networks Approach

Existing estimators of parameters of item response theory (IRT) models exploit the likelihood function. In small samples, however, the IRT likelihood oftentimes contains little informative value, potentially resulting in biased and/or unstable parameter estimates and large standard errors. To facilitate small-sample IRT estimation, we introduce a novel approach for small-sample IRT estimation that does not rely on the likelihood. Our estimation approach capitalizes on item pool information and trains a neural network (NN) to interpolate the relationship between response patterns and item parameters. We describe and evaluate our approach for the three-parameter logistic (3PL) model; however, it is applicable to any model with an item characteristic curve (ICC). Three types of NNs are developed, supporting to obtain both point estimates and confidence intervals for IRT model parameters. The results of a simulation study demonstrated that for sample sizes of 300 and below, these NNs can perform at the level of Bayesian estimation using Markov chain Monte Carlo (MCMC) methods or even better in terms of quality of the point estimates and confidence intervals, but much faster. Among others, these properties facilitate (1) to pretest items in a real-time testing environment (e.g., CAT), (2) to pretest more items and, as a consequence, to replenish operational pools quicker and assemble more tests as well as (3) to pretest items only in a secured environment (e.g., in strictly proctored test centers) to eradicate possible compromise of new items in online testing.
A Web-Based Platform (AgileNudge+) to Facilitate Positive Behavioral Change Through Nudge Strategies: A Mixed Methods Feasibility Study (Preprint)

<sec> <title>BACKGROUND</title> In today’s digital age, web-based applications have become integral to daily life, driving transformative shifts in human behavior. "AgileNudge+” is a web-based solution to catalyze positive behavior change. By leveraging knowledge in behavioral economics, design, and technology, AgileNudge+ utilizes nudging interventions to facilitate long-lasting behavioral changes. </sec> <sec> <title>OBJECTIVE</title> This paper describes the design process, methodologies, and feasibility of "AgileNudge+," evaluated by testing usability, user-friendliness, and usefulness. </sec> <sec> <title>METHODS</title> Iterative processes incorporating principles from behavioral economics (nudges) and user experience design were used to design AgileNudge+. Usability testing was used to assess user experience, ease of use, and the overall usefulness of the web-based software. </sec> <sec> <title>RESULTS</title> Qualitative findings offer a multifaceted evaluation of AgileNudge+'s impact on facilitating positive behavior change. The combination of user feedback coupled with a uniquely Agile design process was able to streamline the development of various nudges to create demand for a new healthcare model within the healthcare delivery system. </sec> <sec> <title>CONCLUSIONS</title> This paper highlights the significance of web-based solutions in fostering positive behavior change. The usability testing validates the software's user-friendliness and usefulness, shedding light on its potential impact. </sec>
Topicality Boosts Popularity: A Comparative Analysis of NYT Articles and Reddit Memes

Abstract This study sheds light on interconnected topic dynamics across traditional news sources and social media platforms, emphasizing the influential role of topicality in shaping content popularity in social media. Using the Latent Dirichlet Allocation and BERTopic models, we define sets of 120 New York Times (NYT) topics to compare with 899,766 image-with-text memes from Reddit, showing that social media content aligns with many of the same topical patterns observed in news outlets. Topicality is formalized based on the temporal distributions of the topics over the past 5 years. Using these topicality features, the investigation reveals significant correlations between the rising popularity of NYT topics and increased average upvotes on Reddit, particularly evident in "innovator" memes posted during the early stages of a topic's prevalence in the NYT. Furthermore, topicality features show significant predictive power over other content-based control features in a CatBoost classifier prediction of viral Reddit memes.
Reading, knowledge systems and textual processing

The objective of this paper is to reflect on the importance of reading, as well as to reflect on the strategies used in the teaching-learning process of reading in Brazilian public schools. Thus, bibliographic studies were carried out with those on which the ideas of this article are based. From the 1970s onwards, Brazilian education went through a process of democratization. A school was created for all, however, the school grew in quantity, but the same did not happen in quality, because it was not prepared to give a quality education to all who had access to it. The present work is based on the studies of applied linguistics, based on authors such as: Isabel Solé, Angela Kleiman, Paulo Freire, and Ingedore Koch &amp; Vanda Maria Elias. The cited authors make a distinction between reading strategies, classifying them as "cognitive" and "metacognitive". Cognitive comprises the unconscious procedures that the reader undertakes to understand a text. Metacognitive strategies, on the other hand, are procedures in which the reader has defined goals in mind, thus having conscious control of the actions he performs, that is, they are action strategies that can be consciously controlled.
Inverse Firefly-Based Search Algorithms for Multi-Target Search Problem

Efficiently searching for multiple targets in complex environments with limited perception and computational capabilities is challenging for multiple robots, which can coordinate their actions indirectly through their environment. In this context, swarm intelligence has been a source of inspiration for addressing multi-target search problems in the literature. So far, several algorithms have been proposed for solving such a problem, and in this study, we propose two novel multi-target search algorithms inspired by the Firefly algorithm. Unlike the conventional Firefly algorithm, where light is an attractor, light represents a negative effect in our proposed algorithms. Upon discovering targets, robots emit light to repel other robots from that region. This repulsive behavior is intended to achieve several objectives: (1) partitioning the search space among different robots, (2) expanding the search region by avoiding areas already explored, and (3) preventing congestion among robots. The proposed algorithms, named Global Lawnmower Firefly Algorithm (GLFA) and Random Bounce Firefly Algorithm (RBFA), integrate inverse light-based behavior with two random walks: random bounce and global lawnmower. These algorithms were implemented and evaluated using the ArGOS simulator, demonstrating promising performance compared to existing approaches.
Cancer Diseases Prediction Using Multiple Transfer Learning based on CNN Algorithm

For many years, the most common types of cancers that causes death has been lung and brain cancer. In addition, they are very hard to treat once they have been spread in the late stages. Early cancer detection and diagnosis have become crucial for providing immediate and efficient care to those who are afflicted with the disease. To predict cancer, this process needs cancer image classification which is a difficult task in the recent improvement of computer-based diagnostics. The main objective of this research is to classify Computed-Tomography (CT) images of the lungs and brain as cancerous or not. Therefore, this research shows the possibility of using a convolutional-neural-network (CNN) for lung and brain cancer detection through transfer learning (TL) without the difficulty of specifying features for image-classification. The proposed TCNN is trained and tested from scratch using two public and one private dataset using the TL technique. The suggested transfer learning-based model attained an accuracy of 92.81%, 97.21%, and 99.40% for Scratch TCNN Model, Pre-Trained TCNN Model, and Transfer Pre-Pre-Trained Model in that order. Hence, the proposed method gives high accuracy and decreases the time of diagnosis and treatment.
Uncertainty Quantification of Neural Reflectance Fields for Underwater Scenes

Neural radiance fields and neural reflectance fields are novel deep learning methods for generating novel views of 3D scenes from 2D images. To extend the neural scene representation techniques to complex underwater environments, beyond neural reflectance fields underwater (BNU) was proposed, which considers the relighting conditions of on-aboard light sources by using neural reflectance fields, and approximates the attenuation and backscatter effects of water with an additional constant. Because the quality of the neural representation of underwater scenes is critical to downstream tasks such as marine surveying and mapping, the model reliability should be considered and evaluated. However, current neural reflectance models lack the ability of quantifying the uncertainty of underwater scenes that are not directly observed during training, which hinders their widespread use in the field of underwater unmanned autonomous navigation. To address this issue, we introduce an ensemble strategy to BNU that quantifies cognitive uncertainty in color space and unobserved regions with the expectation and variance of RGB values and termination probabilities along the ray. We also employ a regularization method to smooth the density of the underwater neural reflectance model. The effectiveness of the present method is demonstrated in numerical experiments.
A visually secure image encryption scheme based on parallel compressive sensing

Visually secure image encryption has become a hot topic in the information security field. However, the embedding capacity of the existing visually secure image encryption schemes is too limited. To solve this problem, a visually secure image encryption scheme using parallel compressive sensing (PCS) is proposed in this paper. Firstly, the secret image is preprocessed by using global random scrambling. Secondly, the scrambled secret image is compressed by PCS. Thirdly, the compressed bitstream is encrypted through a XOR encryption operation. Finally, the encrypted bit stream is embedded into the low bit-plane of the carrier image. The experiment results demonstrate that the proposed scheme significantly increases the embedding capacity in comparison with the current visually secure image encryption schemes.
An Effectiveness Evaluation on Critical Thinking Exercises in Chinese College English Textbooks

Cultivating students’ critical thinking has long been the consensus reached by the international education circle and a common goal of international higher education reform. However, the training of critical thinking of college students in China started late, and no mature talent training system has been formed in higher education. As a compulsory public basic course for college students in China, College English is one of the most involved courses in universities. As an important material and tool for teaching and learning, the design quality of college English textbook exercises will directly affect the quality of teaching and talent training. Therefore, this study analyzes the effectiveness of textbook exercise design, with a view to contributing to college English textbooks and teaching reform, so as to help students develop critical thinking. In this study, exercises in New Horizons College English (Reading and Writing Course 1), one of the most widely used textbooks, were selected for discourse analysis, and the Delphi Panel’s Dualistic Model was used as a comparison for internal evaluation. Moreover, taking a Chinese university as an example, questionnaire survey are used to conduct external evaluation of textbook exercise design, found that: (a) Exercises in Chinese college English textbooks correspond to the cognitive skills mentioned in the Dualistic Model, which is helpful to the training of students' critical thinking; (b) There are also some problems, such as the imbalance of skills training and the homogenization of questioning methods; (c) Students of College English generally have a high recognition of the importance of critical thinking and textbook design, but believe that textbook design, course teaching and assessment need to be reformed.