Reviewerly/exHarmony huggingface dataset:
Binarized $l_{1}$-Regularization Parameters Enhanced Strip-Wise Optimization Algorithm for Efficient Neural Network Optimization

The combination of machine learning techniques with other computer science-based technologies has shown tremendous potential and exceptional possibilities to improve computer vision tasks, providing a useful alternative and workable way for image processing research. In manufacturing, machine learning can be used to improve manufacturing efficiency, reduce costs, and enhance product quality and safety. However, modern object detection techniques tend to either focus on speed in a lightweight model or accuracy in a large model. In this work, we propose a Binarized <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$l1$</tex-math></inline-formula> -Regularization Parameters Enhanced Strip-wise ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$l1$</tex-math></inline-formula> -RPES) optimization approach as a solution to this problem. This is a powerful technique that can reduce the size of the machine-learning model without compromising accuracy. The presented binarized <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$l1$</tex-math></inline-formula> -RPES pruning approach, which was influenced by earlier works such as [1], [2], and [3], focuses on strips rather than the entire filter, in comparison to existing methods such as filter pruning and weight pruning. Additionally, it includes a technique for effectively optimizing the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$l1$</tex-math></inline-formula> -regularization parameter, which enhances the optimization algorithm [1]. The enhanced method can effectively strike a balance between model size and precision. Our Binarized <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$l1$</tex-math></inline-formula> -RPES optimization algorithm effectively compresses the model into a smaller size while maintaining good accuracy, according to experimental evidence.
Intra- &amp; Extra-Source Exemplar-Based Style Synthesis for Improved Domain Generalization

Abstract The generalization with respect to domain shifts, as they frequently appear in applications such as autonomous driving, is one of the remaining big challenges for deep learning models. Therefore, we propose an exemplar-based style synthesis pipeline to improve domain generalization in semantic segmentation. Our method is based on a novel masked noise encoder for StyleGAN2 inversion. The model learns to faithfully reconstruct the image, preserving its semantic layout through noise prediction. Random masking of the estimated noise enables the style mixing capability of our model, i.e. it allows to alter the global appearance without affecting the semantic layout of an image. Using the proposed masked noise encoder to randomize style and content combinations in the training set, i.e., intra-source style augmentation ( $$\textrm{ISSA}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mtext>ISSA</mml:mtext> </mml:math> ) effectively increases the diversity of training data and reduces spurious correlation. As a result, we achieve up to $$12.4\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>12.4</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> mIoU improvements on driving-scene semantic segmentation under different types of data shifts, i.e., changing geographic locations, adverse weather conditions, and day to night. $$\textrm{ISSA}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mtext>ISSA</mml:mtext> </mml:math> is model-agnostic and straightforwardly applicable with CNNs and Transformers. It is also complementary to other domain generalization techniques, e.g., it improves the recent state-of-the-art solution RobustNet by $$3\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>3</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> mIoU in Cityscapes to Dark Zürich. In addition, we demonstrate the strong plug-n-play ability of the proposed style synthesis pipeline, which is readily usable for extra-source exemplars e.g., web-crawled images, without any retraining or fine-tuning. Moreover, we study a new use case to indicate neural network’s generalization capability by building a stylized proxy validation set. This application has significant practical sense for selecting models to be deployed in the open-world environment. Our code is available at https://github.com/boschresearch/ISSA .
IMPROVING TEXT CATEGORIZATION IN INFORMATION RETRIEVAL: AN ENHANCED KERNEL RIDGE APPROACH

Information Retrieval (IR) systems play a crucial role in identifying relevant documents from a collection based on user queries. Popular examples of IR systems include search engines like Google, which locate web documents that match given words. In library settings, IR systems are employed to search digital records containing information about books rather than the books themselves. The primary objective of this research is to automatically classify information within a table. Categorization is a fundamental requirement for text retrieval systems. This research encompasses three key steps: Pre-processing, Searching, and Classification. For the searching phase, two existing algorithms, Boyer Moore and Brute Force, are utilized, and Naïve Bayes based searching algorithm is proposed. In the classification phase, existing algorithms, Linear Regression, Random Forest Regression and Kernal Ridge Regression are employed and enhanced algorithm called Enhanced Kernal Ridge for Text Categorization (EKRTC) is used. Experimental results demonstrate that EKRTC achieves the highest accuracy.
Image Watermarking Using Data Compression

Embedding a hidden stream of bits in a file is named Digital Watermarking. The file can be an audio, text, an image, or a video.
Methods of Obtaining Scientific Knowledge in the Theory of Upbringing

The article contains the main materials of the “round table” devoted to modern trends in the field of methodology for obtaining scientific knowledge in the theory of upbringing. The participants in a lively discussion described the modern methods of scientific research in the theory of upbringing. The authors argue that in modern post-non-classical pedagogy, the authority of qualitative methods of scientific research is growing. Qualitative methods of pedagogical research complement and refine the results obtained by quantitative methods of data collection and processing. The discussion showed that the identified trend is of particular importance for the development of the theory of upbringing, in particular, for solving topical issues of pedagogical expertise of the process of upbringing and for developing modern methods for assessing the results of upbringing. The discussion was held on April 10, 2023 at the Russian Academy of Education. The Scientific Council on Educational Problems at the Department of Philosophy and Theoretical Pedagogy of the Russian Academy of Education organized this discussion. The transcript of the discussion is published with abbreviations.
A Joint Relation Extraction Model Based on Domain N-Gram Adapter and Axial Attention for Military Domain

Domain-specific relation extraction plays an important role in constructing domain knowledge graph and further analysis. In the field of military intelligence relation extraction, there are challenges such as relation overlapping and exposure bias. Therefore, on the basis of a combination of domain N-gram adapter and axial attention, this paper presents a single-step joint relation extraction model for the field of military text analysis. Considering domain-specific language structures and patterns, the domain-specific N-gram adapter is incorporated into the pre-trained language model to improve the encoding of the proposed model. Furthermore, the axial attention mechanism is applied to capture the dependencies between token pairs and their contexts, so as to enhance the encoding representation ability of the proposed model. After that, entities and relations are jointly extracted by a relation-specific decoding method. The effectiveness of the proposed model is demonstrated through experiments on a military relation extraction dataset with F1-Score 0.6690 and CMeIE with F1-Score 0.6051, which is better than existing joint relation extraction models.
Design and Simulation of Collision Avoidance Algorithm for an Unmanned Aerial Vehicle

In a complex environment, autonomous and agile flight for an Unmanned Aerial Vehicle (UAV) is an essential problem that must be solved. This paper describes the design and simulation of an obstacle avoidance algorithm for a quadrotor UAV using low-cost ultrasonic and infrared sensors. The quadrotor operated by the control algorithm was able to fly and reach a height of 1.2 m in 5 seconds, hover for 10 seconds, then proceed towards its target obstacle and by implementing a collision avoidance strategy with the optimal input signal from the Kalman filter, avoided the target obstacle at the 50cm mark, as designed. Our research has shown that the algorithm has a low computational cost, applied data fusion technique and can be implemented using low-cost sensors and thus provides a cheap and effective navigation method for UAVs.
Attendance Monitoring System using Machine Learning Algorithms

Facial expressions are vital in practically every industry in this digital age. One of the most popular biometrics is face recognition. It can be used for security, authentication, identification, and has many additional benefits. Face recognition systems can also be utilised in schools, colleges, and offices, among other places. This program aims to build a classroom plan that uses the concept of facial recognition as a reality manual presence system. It is expensive and difficult to maintain. And there may be opportunities to become a representative to be present. This program contains four phases- database construction, face detection, facial recognition, review of attendees. After face identification, the Haar-Cascade Section and Local Binary Histogram pattern method sequence is used to detect faces
Website sebagai Sarana Promosi dan Akreditasi pada Rumah Sakit Mahyuzahra di Kecamatan Inderalaya Kabupaten Ogan Ilir

Website is a necessity in today's modern world because with a website all needs regarding information, registration, knowledge and others can be fulfilled. Mahyuzahra Hospital as one of the hospitals in Inderalaya District, Ogan Ilir Regency, South Sumatra also needs a website as a means of introducing/promoting and also efficiency in its daily operations. This need is also in line with the accreditation problems faced by hospitals. So the needs and benefits of the website are needed. The website was designed and created based on the results of discussions and input from Mahyuzahra Hospital. After the upload is complete, training is also carried out for officers who are authorized to manage the website and its operations. The training was carried out using the face-to-face method and experimenting with inputting news, pictures, doctor's names, facilities and others.
Identification of issues and Future Improvement Strategies for Emergency Welfare Support Act for Ex-prisoners

This study proposes alternatives for improving the delivery system of emergency welfare support for ex-prisoners, particularly suggesting ways to integrate the emergency support system with the rehabilitative welfare system. The aim is to propose alternatives for ex-prisoners' independence and recidivism prevention. Based on existing research data, this study explores the current status, trends, and operational issues of emergency welfare support. It identifies issues and key points of contention in emergency welfare support for ex-prisoners. Drawing from this analysis, the study aims to present policy alternatives and development strategies to enhance the effectiveness and efficiency of the emergency welfare support system for ex-prisoners.&#x0D; The study results reveal a continuous and sharp increase in the number of cases and the amount of support provided through the emergency welfare support. Among these, the number of support cases for ex-prisoners has drastically risen from 1,135 cases in 2014 to 49,513 cases in 2020. Given the nature of the emergency support system, the entire process, from application-related investigations to decisions, disbursement management, and post-assessment, is carried out by dedicated personnel in a one-stop delivery system. This has led to burdens on frontline local government agencies due to heavy workloads and stresses from public complaints. Notably, many challenges have been raised concerning support for ex-prisoners.&#x0D; Consequently, this study aims to discuss integrated community services by proposing a delivery system and case management system linkage and improvement approach with the Korea Rehabilitation Agency responsible for ex-prisoners' recidivism prevention and social integration.
PVT panel efficiency enhancement with cooling methods and sun tracking

Solar energy is the most abundant and easily available renewable source of electricity. However, the installed solar panels may not always provide the optimal amount of energy because of the changing position of the sun from east to west. This study focused on extending the research and development of a model for a dual-axis solar panel tracking system with a cooling mechanism. The primary objective of this mechanism is to program the Arduino board in a way that ensures continuous alignment of the panel direction and Light-Dependent Resistor (LDR) sensors with the sun's rays. Direct Current (DC) motors were employed to adjust the panel direction accordingly. Sustained exposure to sunlight can lead to reduced efficiency when the panel is heated. To counteract overheating, an improved cooling system was implemented utilizing water and a pump arrangement to enhance the productivity of the panel. Water is sprayed or sprinkled using various Pulse Code Modulation (PCM) techniques, effectively maintaining the solar panel temperature at an optimal level. This mechanism can be enhanced by integrating a Real-Time Clock (RTC) to accurately track the direction of the sun. This ensures that the panel remains properly positioned even in the event of a power outage, enabling the utilization of solar energy for electrical purposes. This research focuses on developing an efficient solar panel tracking system with a cooling mechanism to maximize the energy output and address temperature-related efficiency issues. The integration of advanced features such as RTC further enhances the reliability and usability of the system.
Enhancing Surveillance and Face Recognition with YOLO-Based Object Detection

This object detection for surveillance systems is used to locate, watch over, and classify items. The ability to separate all of the instances in an image or video according to their classifications and locations is provided by intelligent video surveillance. Identification of any entity and its correlation to the real world are classes of instances in a picture. It could be a specific component of any instance in an image that can be uniquely identified, such as a car on a road or a phone on a desk, or it could be a live or non-living item. If a tiger is chasing a boar or a boar is chasing a tiger, for example, their respective positions in the image will have a significant impact on how one interprets the situation. Along with the identification of an entity's class, the position of an instance in an image or video is also an important factor. The method for intelligent surveillance systems that includes object detection and recognition is suggested in this research.
An Approach of Code Summary Generation Using Multi-Feature Fusion Based on Transformer

The automatic generation of code comments is an important research in program understanding. Code summary describes the function and purpose of the code. It helps developers comprehend the program, and reduces the cost of software maintenance. Many previous approaches only use token sequence of source code as input or only rely on learning code structure information from Abstract Syntax Tree (AST), which will result in extracting source code information and obtaining richer semantic information ineffectually. We propose an approach of code summary generation using Multi-Feature Fusion based on Transformer (MFFT). The Multi-way Tree-LSTM is used to process the AST structure to obtain the semantic information of the code. To extract more code information, we also obtain code token features and combine them with a fusion method to generate a high-quality code summary. The experimental results show that our MFFT method has a good performance improvement.
FocusCap: Object-Focused Image Captioning with CLIP-Guided Language Model

Image captioning refers to generating a corresponding natural language caption for a given image. This task is usually thought to be a large amount of supervised training to align the two modalities of image and text, which can be suffered from expensive occupation of time and resources. Recently, unsupervised methods based on multi-modal models for image captioning have attracted more attentions. However, the generated captions are often limited by the structure of the multi-modal model and cannot accurately capture essential information within the images. To address these limitations, we propose a method named FocusCap, which combines the CLIP multi-modal model with a pre-trained language model, for image captioning in an unsupervised way. FocusCap uses CLIP to calculate the similarity between image and text, as well as between objects and text. The calculated similarity score is used as visual information to control the language model generation process. FocusCap leverages the multi-modal feature information extracted in CLIP to guide the language model for text generation, thereby simplifying the costly process of large-scale training for image-text feature extraction and modality alignment. Experimental results show that our proposed FocusCap outperforms existing zero-shot methods on the Microsoft COCO and Flickr30K datasets.
Similarity Based Answer Evaluation in Academic Questions Using Natural Language Processing Techniques

Every organization, both educational and non-educational, administers conducting tests to check the. Along with objective questions, the question papers used to assess a student’s performance include descriptive questions. In contrast to competitive exams, which include objective or multiple-choice questions, school exams typically include descriptive or subjective questions. Machines can quickly and readily assess the objective responses, which is highly helpful for saving time and money. However, because there is no automatic mechanism or computer to analyze student responses, schools and institutions have difficulties when analyzing descriptive questions. Due to the time and effort required by manual review, it is the end result since the evaluator's feelings have an effect on the evaluation's quality, which in turn has an effect on the student's performance score, there is also a potential of bias. Finding comparable words and phrases is the first step in assessing similarity. The project includes a Natural Language Processing (NLP), concepts, Deep learning as well as machine learning to demonstrate the development of autonomous subjective response assessment algorithms. In order to evaluate results in real-time datasets, this work analyses a variety of machine learning classifiers and deep learning concepts.
Active learning with label quality control

Training deep neural networks requires a large number of labeled samples, which are typically provided by crowdsourced workers or professionals at a high cost. To obtain qualified labels, samples need to be relabeled for inspection to control the quality of the labels, which further increases the cost. Active learning methods aim to select the most valuable samples for labeling to reduce labeling costs. We designed a practical active learning method that adaptively allocates labeling resources to the most valuable unlabeled samples and the most likely mislabeled labeled samples, thus significantly reducing the overall labeling cost. We prove that the probability of our proposed method labeling more than one sample from any redundant sample set in the same batch is less than 1/k, where k is the number of the k-fold experiment used in the method, thus significantly reducing the labeling resources wasted on redundant samples. Our proposed method achieves the best level of results on benchmark datasets, and it performs well in an industrial application of automatic optical inspection.
Development and Validation of a Mobile Application for Predicting the Risk of Anastomotic Leakage in Patients Operated for the Treatment of Colorectal Cancer

Background: The scientific literature presents some tools for predicting the risk of anastomotic leakage in patients operated on for the treatment of colorectal cancer. However, a mobile application for this purpose has not been built yet.&#x0D; Aim: To build and validate a mobile application capable of predicting the risk of anastomotic leakage in patients operated for the treatment of colorectal cancer.&#x0D; Methods: An application containing a prognostic index for urgent colectomy and an index for elective colectomy was built (Leak Index). This application was validated by judges in the area of ​​general surgery and coloproctology using the SUS table (Scale Usability System).&#x0D; Results: Regarding the general evaluation, it is observed that the judges agree regarding the usability of the application, with a general S-CVI of 0.90 being registered, above the criterion of 0.80, which corresponds to 90.01% of general agreement of the evaluators. Raters mostly agree on ease of use (I-CVI = 0.95) and that people will quickly learn to use it (I-CVI = 0.955).&#x0D; Conclusion: An application with excellent usability rates was built to predict the risk of anastomotic leakage in patients who underwent colectomy for the treatment of colorectal cancer.
A Survey on the Current Status of Research Data Management in Mecklenburg-Vorpommern

High quality research data management (RDM) is essential to support state of the art comprehensible and reproducible research processes and, thus, foster the sustainable production of novel and trustworthy research findings. While there are lots of national and international initiatives supporting researchers in all respects of RDM, the local infrastructures provide the foundation for these concepts. In this contribution, we present preliminary results of a study that collects the requirements of researchers on these local infrastructures in all seven higher education research institutions in Mecklenburg-Vorpommern (MV).