Reviewerly/exHarmony huggingface dataset:
CEAFFOD: Cross-Ensemble Attention-based Feature Fusion Architecture Towards a Robust and Real-time UAV-based Object Detection in Complex Scenarios

Deploying object detectors in embedded devices such as unmanned aerial vehicles (UAVs) comes with many challenges. This is due to both the UAV itself having low embedded resources in terms of computation and memory, and also due to the nature of the captured visual data with the variations in objects' scale, orientation, density, viewpoint, distribution, shape, context and others. It is crucial for the object detector to be robust with high accuracy, real-time with fast inference and light-weight to be applicable. Inspired by YOLO architecture, we propose a novel single-stage detection architecture. Our contributions are, first, feature fusion spatial pyramid pooling (FFSPP) block that applies attention-based feature fusion across both time and space utilizing the information of subsequent frames and scales in an efficient manner. Secondly, we introduce a multi-dilated attention-based cross-stage partial connection (MDACSP) block that helps in increasing the receptive field and producing per-channel modulation weights after aggregating the feature maps across their spatial domain. Third, scaled feature fusion head (SFFH) fuses both the FFSPP block features and the connected MDACSP block features specific for this head. For a more robust result across different scenarios, we perform cross-ensembling with three of the top UAV/traffic surveillance datasets: UAVDT, UA-DETRAC and VisDrone. Our ablation study shows how every contribution improves over the baseline. Our approach yielded the state-of-the-art results in all the aforementioned datasets achieving 89.3% mAP, 93.5% mAP, and 42.9% mAP respectively. Testing the model performance on NVIDIA Jetson Xavier NX board shows a desirable balance between the inference time and the memory cost. We also show qualitatively the model robustness and efficiency across the diverse complex scenarios of these datasets. We hope this work facilitates the advancement of the UAV-based perception in such crucial industrial applications.
Multilingual Image caption Generator using Big data and Deep Learning

Automatic image captioning aims to produce a descriptive sentence about a picture. For this task, we are creating a model that will spit out an English sen- tence when an image is given as input describing the image’s subject. Scien- tists in the field of cognitive computing have paid much attention to it in recent years. The endeavor is challenging because it requires merging ideas from two distinct but related disciplines: natural language processing and computer vision. Using the integration of CNN with LSTM, we developed a model for generating image captions. The ideas behind a Convolutional Neural Network and a Long Short-Term Memory model were combined to create this model. The convolutional neural network serves as the encoder, extracting informa- tion from images. At the same time, the long short-term memory is responsible for the decoder role, coming up with words to describe the image. The problem arises when the dataset is significant, and it takes weeks for systems to have only CPU support to train the network to decrease the time it is required to train big data can be taken into accounts. After the caption generation phase, we use BLEU Scores to assess our model’s performance. Using this informa- tion, our technology can help users find a fitting description for the uploaded photo with the desired language.
Pengembangan E-Modul Model Flipped Classroom Pada Pembelajaran Untuk Meningkatkan Kemampuan Penalaran Matematis Siswa Sekolah Dasar

The purpose of this study was to find out the process of developing teaching materials for the E-Module Mathematics and to find out the increase in students' mathematical reasoning abilities using the E-Module Mathematics. This type of research is research and development of the Borg and Gall model. A total of 21 fifth grade students became subjects in this study. Data collection techniques in the form of test guidelines, interviews, and questionnaires. The data analysis technique used is descriptive statistics in the form of assessments from material experts and media experts, assessment of teacher responses and student responses, validity tests, reliability tests, normality tests, and inferential statistics, namely the T-test. The contribution of this paper is to describe the process of developing appropriate and effective teaching materials to improve the mathematical reasoning abilities of elementary school students. The results of the study are as follows: 1) the teaching materials for the Mathematics E-Module are developed from gathering information to field tests, 2) the teaching materials developed are declared fit for use with good criteria, indicated by the material expert's assessment with a percentage score of 78% and the media expert's assessment with percentage score of 82%. 3) Mathematics E-Module teaching materials are effectively used in learning, as indicated by the results of the calculation of the Paired Sample T-test obtained a significance value (2-tailed) of 0.000 (p &lt;0.05), 4) Students' mathematical reasoning abilities increased as indicated by the average acquisition -the average pretest score is 66.95 and the average posttest score after using teaching materials is 81.57.
Impact of inclusive leadership on variables affecting employee performance: A review of empirical papers

In today's world symbolized by complexity and volatility, human capital has assumed even more relevance as diverse talents and ideas rule the economies.It is in this context that inclusive leadership (IL) refers to an authentic style of leadership that encourages and leads heterogeneous groups of people towards accomplishing the objectives of the firm.An inclusive leader brings about efficiency in performance while honoring their uniqueness with empathy and an unbiased approach.This paper reviews empirical works on IL and how it impacts employee's performance directly on its own as well as through other mediating variables.40 research papers belonging to the period 2010-2021 were shortlisted and reviewed for their output.The results suggest a strong preference for social exchange theory and leader member exchange theory among researchers.Further, psychological safety and innovative work behavior emerged as the most sought-after variables explaining the different relationships pertaining to IL.On reviewing, it can be concluded that IL remains an essential component in the making and continued performance of an organization with many more variables interjecting through human interaction.
Pengaruh Kecerdasan Emosional Terhadap Motivasi Belajar Peserta Didik Sekolah Dasar Gugus III Kecamatan Caringin

This study aims to determine the significance of the effect of emotional intelligence on the learning motivation of elementary school students in cluster III Caringin District. The approach used in this study is a quantitative approach to the type of functional correlation with simple regression analysis, with a sample of 157 students. Data was collected using interview techniques and questionnaires with instruments in the form of interview guidelines and a psychological scale to measure each variable, namely emotional intelligence, and learning motivation. Based on the results of the t-test variables on motivation to learn emotional intelligence obtained t-count &gt; t-table (5,065 &gt; 1,960), then H0 is rejected and Ha is accepted, which means there is a partial effect of emotional intelligence to motivate learners Cluster III Elementary School in the District Caringin.
Differentiable Parsing and Visual Grounding of Natural Language Instructions for Object Placement

We present a new method, PARsing And visual GrOuNding (PARAGON), for grounding natural language in object placement tasks. Natural language generally describes objects and spatial relations with compositionality and ambiguity, two major obstacles to effective language grounding. For compositionality, Paragon parses a language instruction into an object-centric graph representation to ground objects individually. For ambiguity, Paragon uses a novel particle-based graph neural network to reason about object placements with uncertainty. Essentially, Paragon integrates a parsing algorithm into a probabilistic, data-driven learning framework. It is fully differentiable and trained end-to-end from data for robustness against complex, ambiguous language input.
PatternGPT :A Pattern-Driven Framework for Large Language Model Text Generation

Large language models(LLMS)have shown excellent text generation capabilities, capable of generating fluent human-like responses for many downstream tasks. However, applying large language models to real-world critical tasks remains challenging due to their susceptibility to hallucinations and inability to directly use external knowledge. To cope with the above challenges, this paper proposes PatternGPT, a pattern-driven text generation framework for Large Language Models. Firstly, the framework utilizes the extraction capability of Large Language Models to generate rich and diversified structured and formalized patterns, which facilitates the introduction of external knowledge to do the computation, and then draws on the idea of federated learning to use multiple agents to achieve the sharing in order to obtain more diversified patterns, and finally uses judgment criteria and optimization algorithm to search for high-quality patterns to guide the generation of models. Finally, external knowledge such as judgment criteria and optimization algorithms are used to search for high-quality patterns, and the searched patterns are used to guide model generation. This framework has the advantages of generating diversified patterns, protecting data privacy, combining external knowledge, and improving the quality of generation, which provides an effective method to optimize the text generation capability of large language models, and make it better applied to the field of intelligent dialogue and content generation.
Exploring the In-context Learning Ability of Large Language Model for Biomedical Concept Linking

The biomedical field relies heavily on concept linking in various areas such as literature mining, graph alignment, information retrieval, question-answering, data, and knowledge integration. Although large language models (LLMs) have made significant strides in many natural language processing tasks, their effectiveness in biomedical concept mapping is yet to be fully explored. This research investigates a method that exploits the in-context learning (ICL) capabilities of large models for biomedical concept linking. The proposed approach adopts a two-stage retrieve-and-rank framework. Initially, biomedical concepts are embedded using language models, and then embedding similarity is utilized to retrieve the top candidates. These candidates' contextual information is subsequently incorporated into the prompt and processed by a large language model to re-rank the concepts. This approach achieved an accuracy of 90.% in BC5CDR disease entity normalization and 94.7% in chemical entity normalization, exhibiting a competitive performance relative to supervised learning methods. Further, it showed a significant improvement, with an over 20-point absolute increase in F1 score on an oncology matching dataset. Extensive qualitative assessments were conducted, and the benefits and potential shortcomings of using large language models within the biomedical domain were discussed. were discussed.
A hierarchical self‐attention‐guided deep learning framework to predict breast cancer response to chemotherapy using pre‑treatment tumor biopsies

Abstract Background Pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) has demonstrated a strong correlation to improved survival in breast cancer (BC) patients. However, pCR rates to NAC are less than 30%, depending on the BC subtype. Early prediction of NAC response would facilitate therapeutic modifications for individual patients, potentially improving overall treatment outcomes and patient survival. Purpose This study, for the first time, proposes a hierarchical self‐attention‐guided deep learning framework to predict NAC response in breast cancer patients using digital histopathological images of pre‐treatment biopsy specimens. Methods Digitized hematoxylin and eosin‐stained slides of BC core needle biopsies were obtained from 207 patients treated with NAC, followed by surgery. The response to NAC for each patient was determined using the standard clinical and pathological criteria after surgery. The digital pathology images were processed through the proposed hierarchical framework consisting of patch‐level and tumor‐level processing modules followed by a patient‐level response prediction component. A combination of convolutional layers and transformer self‐attention blocks were utilized in the patch‐level processing architecture to generate optimized feature maps. The feature maps were analyzed through two vision transformer architectures adapted for the tumor‐level processing and the patient‐level response prediction components. The feature map sequences for these transformer architectures were defined based on the patch positions within the tumor beds and the bed positions within the biopsy slide, respectively. A five‐fold cross‐validation at the patient level was applied on the training set (144 patients with 9430 annotated tumor beds and 1,559,784 patches) to train the models and optimize the hyperparameters. An unseen independent test set (63 patients with 3574 annotated tumor beds and 173,637 patches) was used to evaluate the framework. Results The obtained results on the test set showed an AUC of 0.89 and an F1‐score of 90% for predicting pCR to NAC a priori by the proposed hierarchical framework. Similar frameworks with the patch‐level, patch‐level + tumor‐level, and patch‐level + patient‐level processing components resulted in AUCs of 0.79, 0.81, and 0.84 and F1‐scores of 86%, 87%, and 89%, respectively. Conclusions The results demonstrate a high potential of the proposed hierarchical deep‐learning methodology for analyzing digital pathology images of pre‐treatment tumor biopsies to predict the pathological response of breast cancer to NAC.
A quantum computing approach for minimum loss problems in electrical distribution networks

Abstract This paper presents an application of a novel quadratic unconstrained binary optimization (QUBO) formulation to the minimum loss problem in distribution networks. The proposed QUBO formulation was conceived to be employed in quantum annealing—a quantum computing paradigm useful for solving combinatorial optimization problems. Quantum annealing is expected to provide better and/or faster solutions to optimization problems when compared to the ones provided by classical computers. With the problem at stake, better solutions result in lower energy losses, and faster solutions contribute to the same outcome given the future need for frequent reconfiguration of distribution networks to accommodate highly volatile demand, as anticipated by recent low-carbon solutions. The paper presents the results obtained through a hybrid quantum-classical solver for a standard 33-node test network and compares them with the ones obtained from classical solvers. Our main conclusion is that quantum annealing has potential to show advantage in the near future in terms of solution quality and time-to-solution, as quantum annealers and hybrid solvers continue to improve their performance.
Digital Reading Attitudes of Pre-Service Teachers

The study explored the attitudes of pre-service teachers of two different groups majoring in numerical and verbal fields towards digital reading in terms of different variables. By examining variables such as daily digital reading time, daily print reading time, number of books read from digital devices, the level of digital reading attitudes of pre-service teachers in numerical and verbal fields were examined and the findings were discussed. Selected using the convenient sampling method among the non-random sampling methods, the study sample consisted of a total of 100 participants, 56 of whom were majoring in Mathematics Education and 42 of whom were majoring in Social Studies Education at a state university in Central Anatolia. The "Attitude Scale Towards Digital Reading for Pre-Service Teachers" developed by Yurdakal and Susar Kırmızı (2021) was utilized as the data collection tool. The study revealed that while the digital reading attitudes of pre-service teachers showed a statistically significant difference according to their daily reading duration from printed sources, reading books from any kind of digital devices, digital reading competencies and the number of books they read in a year, there was no significant difference according to the program they were in, daily digital reading duration and electronic text that can be read in an internet-free environment. In line with the study results, suggestions were provided for educators regarding digital reading, which are believed to improve the quality of education.
Prior knowledge about events depicted in scenes decreases oculomotor exploration

The visual input that the eyes receive usually contains temporally continuous information about unfolding events. Therefore, humans can accumulate knowledge about their current environment. Typical studies on scene perception, however, involve presenting multiple unrelated images and thereby render this accumulation unnecessary. Our study, instead, facilitated it and explored its effects. Specifically, we investigated how recently-accumulated prior knowledge affects gaze behavior. Participants viewed sequences of static film frames that contained several 'context frames' followed by a 'critical frame'. The context frames showed either events from which the situation depicted in the critical frame naturally followed, or events unrelated to this situation. Therefore, participants viewed identical critical frames while possessing prior knowledge that was either relevant or irrelevant to the frames' content. In the former case, participants' gaze behavior was slightly more exploratory, as revealed by seven gaze characteristics we analyzed. This result demonstrates that recently-gained prior knowledge reduces exploratory eye movements.
Worksheets Dilemma in Early Childhood Education: Why and How Much Do We Use?

The study was structured to determine the opinions of preschool teachers and preservice teachers on the use of worksheets within the scope of literacy preparation practices and was conducted with 30 preschool teachers and 30 preservice preschool teachers. Under the guidance of the research questions, it was determined that there were differences in the use and reasons for worksheets by teachers and preservice teachers. At the end of the descriptive analysis, 4 themes emerged to discuss the worksheets' positive and negative aspects in early childhood education. These themes are conceptualized as effects on academic skills, effects on social life skills, effects on motor skills, and effects on parents. The findings also revealed that teachers use writing sheets more, while preservice teachers avoid book studies and worksheets.
Quantum Machine Learning for Optical and SAR Classification

We present in this paper a method to compare scene classification accuracy of C-band Synthetic aperture radar (SAR) and optical images utilizing both classical and quantum computing algorithms. This REU study uses data from the Sentinel satellite. The dataset contains (i) synthetic aperture radar images collected from the Sentinel-1 satellite and (ii) optical images for the same area as the SAR images collected from the Sentinel-2 satellite. We examine classical neural networks to classify four classes of images. We then explore Quantum Convolutional Neural Networks and deep learning techniques in terms of their training and classification performance. A hybrid Quantum-classical model that is trained on the Sentinel1-2 dataset is proposed, and its performance is then compared against the classical model in terms of classification accuracy.
Ensemble Clustering with Attentional Representation

Ensemble clustering has emerged as a powerful framework for analyzing heterogeneous and complex data. Despite the abundance of existing schemes, co-association matrix-based methods remain the mainstream approach. However, focusing solely on pairwise correlations falls short of fully capturing the intricate cluster relationships. Moreover, despite its potential, ensemble clustering has yet to effectively leverage the powerful representation capabilities of neural networks. To address these limitations, we propose a deep ensemble clustering method called Ensemble Clustering with Attentional Representation (ECAR). Our method considers the results of base partition as groups with related information to explore higher-order fusion information. ECAR captures the importance of each sample's association with its related group by employing an attentional network, and encodes this information into a low-dimensional representation. The attentional network is trained by jointly optimizing the clustering loss from soft assignments learned from the embeddings and the reconstruction loss from the weighted graph generated from ensemble clustering. During training, the weights of base partitions are adaptively refined to promote diversity and consistency while reducing the impact of low-quality and redundant base partitions. Extensive experimental results on real-world datasets demonstrate the substantial improvement of our method over existing baseline ensemble clustering methods and deep clustering methods.
Evaluating prediction model performance

This article highlights important performance metrics to consider when evaluating models developed for supervised classification or regression tasks using clinical data. When evaluating model performance, we detail the basics of confusion matrices, receiver operating characteristic curves, F1 scores, precision-recall curves, mean squared error, and other considerations. In this era, defined by the rapid proliferation of advanced prediction models, familiarity with various performance metrics beyond the area under the receiver operating characteristic curves and the nuances of evaluating model value upon implementation is essential to ensure effective resource allocation and optimal patient care delivery.