Reviewerly/exHarmony huggingface dataset:
M&amp;M3D: Multi-Dataset Training and Efficient Network for Multi-view 3D Object Detection

In this research, I proposed a network structure for multi-view 3D object detection using camera-only data and a Bird's-Eye-View map. My work is based on a current key challenge domain adaptation and visual data transfer. Although many excellent camera-only 3D object detection has been continuously proposed, many research work risk dramatic performance drop when the networks are trained on the source domain but tested on a different target domain. Then I found it is very surprising that predictions on bounding boxes and classes are still replied to on 2D networks. Based on the domain gap assumption on various 3D datasets, I found they still shared a similar data extraction on the same BEV map size and camera data transfer. Therefore, to analyze the domain gap influence on the current method and to make good use of 3D space information among the dataset and the real world, I proposed a transfer learning method and Transformer construction to study the 3D object detection on NuScenes-mini and Lyft. Through multi-dataset training and a detection head from the Transformer, the network demonstrated good data migration performance and efficient detection performance by using 3D anchor query and 3D positional information. Relying on only a small amount of source data and the existing large model pre-training weights, the efficient network manages to achieve competitive results on the new target domain. Moreover, my study utilizes 3D information as available semantic information and 2D multi-view image features blending into the visual-language transfer design. In the final 3D anchor box prediction and object classification, my network achieved good results on standard metrics of 3D object detection, which differs from dataset-specific models on each training domain without any fine-tuning.
NeuroWrite: Predictive Handwritten Digit Classification using Deep Neural Networks

The rapid evolution of deep neural networks has revolutionized the field of machine learning, enabling remarkable advancements in various domains. In this article, we introduce NeuroWrite, a unique method for predicting the categorization of handwritten digits using deep neural networks. Our model exhibits outstanding accuracy in identifying and categorising handwritten digits by utilising the strength of convolutional neural networks (CNNs) and recurrent neural networks (RNNs).In this article, we give a thorough examination of the data preparation methods, network design, and training methods used in NeuroWrite. By implementing state-of-the-art techniques, we showcase how NeuroWrite can achieve high classification accuracy and robust generalization on handwritten digit datasets, such as MNIST. Furthermore, we explore the model's potential for real-world applications, including digit recognition in digitized documents, signature verification, and automated postal code recognition. NeuroWrite is a useful tool for computer vision and pattern recognition because of its performance and adaptability.The architecture, training procedure, and evaluation metrics of NeuroWrite are covered in detail in this study, illustrating how it can improve a number of applications that call for handwritten digit classification. The outcomes show that NeuroWrite is a promising method for raising the bar for deep neural network-based handwritten digit recognition.
A Survey on Coin Selection Algorithms in UTXO-based Blockchains

Coin selection algorithms are a fundamental component of blockchain technology. In this paper, we present a comprehensive review of the existing coin selection algorithms utilized in unspent transaction output (UTXO)-based blockchains. We provide a list of the desired objectives and categorize existing algorithms into three types: primitive, basic, and advanced algorithms. This allows for a structured understanding of their functionalities and limitations. We also evaluate the performance of existing coin selection algorithms. The aim of this paper is to provide system researchers and developers with a concrete view of the current design landscape.
Learning to Adapt CLIP for Few-Shot Monocular Depth Estimation

Pre-trained Vision-Language Models (VLMs), such as CLIP, have shown enhanced performance across a range of tasks that involve the integration of visual and linguistic modalities. When CLIP is used for depth estimation tasks, the patches, divided from the input images, can be combined with a series of semantic descriptions of the depth information to obtain similarity results. The coarse estimation of depth is then achieved by weighting and summing the depth values, called depth bins, corresponding to the predefined semantic descriptions. The zero-shot approach circumvents the computational and time-intensive nature of traditional fully-supervised depth estimation methods. However, this method, utilizing fixed depth bins, may not effectively generalize as images from different scenes may exhibit distinct depth distributions. To address this challenge, we propose a few-shot-based method which learns to adapt the VLMs for monocular depth estimation to balance training costs and generalization capabilities. Specifically, it assigns different depth bins for different scenes, which can be selected by the model during inference. Additionally, we incorporate learnable prompts to preprocess the input text to convert the easily human-understood text into easily model-understood vectors and further enhance the performance. With only one image per scene for training, our extensive experiment results on the NYU V2 and KITTI dataset demonstrate that our method outperforms the previous state-of-the-art method by up to 10.6\% in terms of MARE.
Optimizing Image Classification: Automated Deep Learning Architecture Crafting with Network and Learning Hyperparameter Tuning

This study introduces ETLBOCBL-CNN, an automated approach for optimizing convolutional neural network (CNN) architectures to address classification tasks of varying complexities. ETLBOCBL-CNN employs an effective encoding scheme to optimize network and learning hyperparameters, enabling the discovery of innovative CNN structures. To enhance the search process, it incorporates a competency-based learning concept inspired by mixed-ability classrooms during the teacher phase. This categorizes learners into competency-based groups, guiding each learner’s search process by utilizing the knowledge of the predominant peers, the teacher solution, and the population mean. This approach fosters diversity within the population and promotes the discovery of innovative network architectures. During the learner phase, ETLBOCBL-CNN integrates a stochastic peer interaction scheme that encourages collaborative learning among learners, enhancing the optimization of CNN architectures. To preserve valuable network information and promote long-term population quality improvement, ETLBOCBL-CNN introduces a tri-criterion selection scheme that considers fitness, diversity, and learners’ improvement rates. The performance of ETLBOCBL-CNN is evaluated on nine different image datasets and compared to state-of-the-art methods. Notably, ELTLBOCBL-CNN achieves outstanding accuracies on various datasets, including MNIST (99.72%), MNIST-RD (96.67%), MNIST-RB (98.28%), MNIST-BI (97.22%), MNST-RD + BI (83.45%), Rectangles (99.99%), Rectangles-I (97.41%), Convex (98.35%), and MNIST-Fashion (93.70%). These results highlight the remarkable classification accuracy of ETLBOCBL-CNN, underscoring its potential for advancing smart device infrastructure development.
Selective Concept Models: Permitting Stakeholder Customisation at Test-Time

Concept-based models perform prediction using a set of concepts that are interpretable to stakeholders. However, such models often involve a fixed, large number of concepts, which may place a substantial cognitive load on stakeholders. We propose Selective COncept Models (SCOMs) which make predictions using only a subset of concepts and can be customised by stakeholders at test-time according to their preferences. We show that SCOMs only require a fraction of the total concepts to achieve optimal accuracy on multiple real-world datasets. Further, we collect and release a new dataset, CUB-Sel, consisting of human concept set selections for 900 bird images from the popular CUB dataset. Using CUB-Sel, we show that humans have unique individual preferences for the choice of concepts they prefer to reason about, and struggle to identify the most theoretically informative concepts. The customisation and concept selection provided by SCOM improves the efficiency of interpretation and intervention for stakeholders.
Feature Channel Adaptive Enhancement for Fine-Grained Visual Classification

Fine-grained classification poses greater challenges compared to basic-level image classification due to the visually similar sub-species. To distinguish between confusing species, we introduce a novel framework based on feature channel adaptive enhancement and attention erasure. On one hand, a lightweight module employing both channel attention and spatial attention is designed, adaptively enhancing the feature expression of important areas and obtaining more discriminative feature vectors. On the other hand, we incorporate attention erasure methods that compel the network to concentrate on less prominent areas, thereby enhancing the network’s robustness. Our method can be seamlessly integrated into various backbone networks. Finally, an evaluation of our approach is conducted across diverse public datasets, accompanied by a comprehensive comparative analysis against state-of-the-art methodologies. The experimental findings substantiate the efficacy and viability of our method in real-world scenarios, exemplifying noteworthy breakthroughs in intricate fine-grained classification endeavors.
The concept of 4K+. Soft skills development among students and employees of higher education organizations (based on the experience of a volunteer center)

Introduction. Soft skills are a factor that positively influences the employment demand of higher education institutions graduates. Formation of soft skills can take place while students obtain higher education, and the optimal environment for this is volunteer activity, including at major international events. Effective development of soft skills results from motivation, educational level, appropriate conditions (participation in the abovementioned events), and from mentoring by teachers and employees of an educational organization, having extensive knowledge, authority and their own soft skills, allowing them to give impetus to the students. In turn, soft skills development in teachers and staff is possible through their motivation and the appropriate conditions, such as improvement of training and professional development programs. Materials and Methods. The authors, using their knowledge and expertise, employed traditional theoretical (analysis and generalization) and private (expert assessments, observation and description) research methods. Results. Successful soft skills development in students occurs during volunteering at major international events and is based on motivation, necessary conditions, and successful mentoring (leadership). To effectively develop soft skills, it is necessary to improve education programs and advanced training programs, as well as create other necessary conditions. Conclusion. It is advisable to supplement education programs and advanced training programs for teachers and staff of higher education organizations with current innovations in the field of working with the young generation and of organizing the volunteer movement, as well as creating practical conditions for teachers to develop their own soft skills. Creating a volunteer register in higher education organizations when integrating information about volunteer experience into presentation papers of recruitment agencies is one of the technical conditions for soft skills development among students.
Determinants of Women Entrepreneurs’ Business Performance: Evidence from Micro and Small-Scale Enterprises in Arba Minch Town, Southern Ethiopia

Women’s business performance is influenced by individual, legal, economic, and technological factors. Thus, the purpose of this study was to identify determinants of women’s business performance with reference to MSEs in Arba Minch town. To achieve this objective, the researcher used an explanatory research design with a quantitative research approach, testing six hypotheses. Primary data were collected from 281 women entrepreneurs who were selected using stratified and simple random sampling techniques through structured questionnaires. Both descriptive and inferential statistics were used to analyze the data using SPSS version 21.0. According to the findings of the study, the descriptive results indicate that variables such as access to technology, access to land premises, communication skills, and tax amount have moderate/medium means because the mean score of the variables ranges from 2.60 to 3.39 (average value). In other words, the two remaining variables, access to finance and lack of training, have low means because the mean score of the variables ranges from 1.80 to 2.59 (low value of mean). Based on the Pearson correlation coefficient analysis of the study, lack of training and an increase in tax amount have a negative and significant relationship with the business performance of women entrepreneurs, whereas the rest of the variables, such as access to finance, access to land premises, access to technology, and communication skills, have a positive and significant relationship with the women’s business performance of the MSEs in Arba Minch Town. According to the multiple regression analysis of the study, access to land was the best predictor of women’s business performance, and lack of training was the least predictor of women’s business performance.
Improving Open-Domain Answer Sentence Selection by Distributed Clients with Privacy Preservation

Open-domain answer sentence selection (OD-AS2), as a practical branch of open-domain question answering (OD-QA), aims to respond to a query by a potential answer sentence from a large-scale collection. A dense retrieval model plays a significant role across different solution paradigms, while its success depends heavily on sufficient labeled positive QA pairs and diverse hard negative sampling in contrastive learning. However, it is hard to satisfy such dependencies in a privacy-preserving distributed scenario, where in each client, fewer in-domain pairs and a relatively small collection cannot support effective dense retriever training. To alleviate this, we propose a brand-new learning framework for Privacy-preserving Distributed OD-AS2, dubbed PDD-AS2. Built upon federated learning, it consists of a client-customized query encoding for better personalization and a cross-client negative sampling for learning effectiveness. To evaluate our learning framework, we first construct a new OD-AS2 dataset, called Fed-NewsQA, based on NewsQA to simulate distributed clients with different genre/domain data. Experiment results show that our learning framework can outperform its baselines and exhibit its personalization ability.
A Low-Cost, Moderately Fast System for Online Motion Tracking in Laparoscopic Surgery Training

The paper outlines the design, implementation, and preliminary testing of a motion tracking system for laparoscopic surgery Training. After system outline, the choice of hardware components and the design experimental setup for system development are discussed. Geometric equations for system calibration and 3D motion tracking are presented, along with their numerical solution scheme and software implementation. Calibration results and first experimental data regarding system performances are then provided.
Functional Drift Filtering of IMU for Long Term Wholebody Motion Capturing

Motion capturing using IMU sensors has advantages over other methods. It will extend the field of applications if the problem of sensor drift is solved. There are several approaches to drift compensation. Namely, compensating individual sensors like the idea of Madgwick filter, compensating by closs check among sensors, and compensation adopting skeletal and behavioural knowledge of human motion, which is the focus of present work. In this paper, we report the functional drift filtering for long term wholebody motion capturing by feedback compensation based on the knowledge of skeletal and behavioral features of human motion. We proposed two kinds of feedback compensation. One is a weak feedback to an anatomical neutral position. The other is a rather strong feedback to recover from functionally unpermitted position. Our experimental investigation illustrates the feature and advantage of the IMU motion capturing using the proposed method.
Entrepreneurial Training Needs: An Imperative for Business Performance among Women Entrepreneurs in South-Eastern Nigeria

This study assesses the impact of entrepreneurial training needs among women entrepreneurs in Southeast Nigeria. The study employed exploratory research design and primary data was collected using copies of structured questionnaire from 360 sample respondents. The data collected was analysed using spearman’s rank correlation and multiple regression analysis. The research findings revealed that bookkeeping training needs and Credit recovery training needs emerged as a significant factor in enhancing the performance of women entrepreneurs. On the other hand, access to finance training needs had a highly significant negative impact on the performance of women entrepreneurs. Based on the findings, the study recommends that Government agencies, non-governmental organizations (NGOs), and other relevant stakeholders should collaborate to provide comprehensive and accessible bookkeeping, access to finance and credit recovery training to women entrepreneurs. This will enable them to develop effective financial management skills, make informed decisions, and improve overall business performance.
A Study of Faculty's Perceptions on the Educational Use of AI Chatbots: Focusing on K University

The purpose of this study is to analyze faculty's perceptions of AI chatbots' educational use. For this purpose, an online survey was conducted targeting faculty at K University in Seoul, and a total of 126 responses were analyzed. We derived keywords through both thematic analyses and word cloud analyses and used MANOVA to validate distinctions in AI chatbots comprehension, usage intention, and training requirements. The research findings are as follows: Firstly, 85.7% of professors expressed their intention to use AI chatbots in their teaching. Secondly, faculty anticipated benefits, including gaining experience with new technologies, individualization, and diversification of classes in the educational use of AI chatbots. Thirdly, they had concerns about ethical issues and reduced creativity caused by the use of AI chatbots. Lastly, there were differences in AI chatbot comprehension and usage intention depending on the faculty's gender and major field, and in usage intention and training needs depending on faculty status, but there were no significant differences depending on age groups.
Evaluasi Perangkat Lunak Electronic Record Management System (ERMS) ARTERI Menggunakan ISO 16175-2:2011

The aim of the study is to evaluate ERMS software of ARTERI that used the functional requirements of ISO 16175-2: 2011. The researcher chose aspects of search, retrieval, and rendering (disseminate) to evaluate its systems. The study uses a qualitative approach with a review of literature studies. Data were collected by observation and documentation. The findings show ARTERI only meets 10 of 49 functional requirements. Fullfilled functional requirement of ARTERI, includes: 1) Provide a flexible range of functions for locating, accessing, and retrieving records and/or metadata; 2) Allow all records in each level to be searchable; 3) Allow the user to set up a single search request; 4) Never allow a search function to reveal to a user any information; 5) Have integrated search facilities for all levels; 6) Allows users to save and re-use queries; 7) Allows users who are viewing or working with a record; 8) Allows record metadata to be searched; 9) Allows administrators to take a copy of records and reaction; and 10) Store in the metadata any change made. ARTERI needs to be developed, for instance, adding features in any field of content search, automatic search, boolean operators, and improving the user interface for search. Furthermore, the rendering, downloading, and printing features also need to be improved. Finally, the study is limited to certain functional requirements, standards, and applications. Future study needs to consider aspects that have not been discussed or comparisons with certain standards or applications
Validity Verification Research of Extracurricular Education Items for the University Life Adaptation of Foreign Students: Focusing on Foreign Students after They Have Entered Their University Major

This research is focused on creating and validating extracurricular educational items to assist foreign students enrolled in Korean universities in adapting to their university life. An education program was formed through expert evaluations during the item selection process. Initially, 66 items were selected across four categories: university life, off-campus life, interpersonal relationships, and event activities. These items underwent two stages of verification where various indicators like convergence degree, agreement degree, content validity ratio (CVR), and expert opinions were considered for refinement. Consequently, three types and 25 educational items were identified that could assist foreign students in adapting to university life. These items were further detailed into learning objectives, content, and activities for practical application in education settings. The results of this study provide essential extracurricular education content to help foreign students adapt better to Korean university life while also aiding their understanding of Korean culture and society.
Intentions to Use "SIMPAN UGM" as a Cloud-Based Records Repository Service at Universitas Gadjah Mada

Abstract University students, lecturers, and staff have entered the digital age. All administrative activities, including recordkeeping, should be carried out digitally, that can be stored both online and offline. For this reason, Universitas Gadjah Mada (UGM) provides a free cloud repository, namely “Simpan UGM” for the UGM community. However, many students, lecturers, and staff do not use it. The purpose of this study was to determine the factors that influence the intention to use Simpan UGM at UGM. This study uses a mixed-method sequential explanatory design with three important stages, namely the quantitative, qualitative, and interpretation phases. Samples were determined randomly to students, lecturers, and staff related to records management, namely students and lecturers of the “Pengelolaan Arsip dan Rekaman Informasi Study Program” (PARI) as well as staff from members of the UGM Records Manager and Archivists Forum (Forsipagama). Quantitative data were analyzed with PLS-SEM, while qualitative data were analyzed with Miles and Huberman's interactive model. The results of the study show that there are many factors that have a positive effect, although not all of them are significant, on the intention to use Simpan UGM as a records cloud repository at UGM.