Reviewerly/exHarmony huggingface dataset:
Comparison of Security Frameworks for SMEs

Small to medium-sized enterprises (SMEs) represent about 90% of businesses and more than 50% of employment in the world. The amount of SME’s volume in Turkey is larger than the volume in the rest of the world. Besides, security challenges appear and become more important with the improvement of online business opportunities even for SMEs. SMEs need a security pathway to progress in a security landscape. In this paper, the IT audit of the Turkish Court of Accounts (TCA), the Information and Communication Security Guide (ICSG), ISO 27001, and NIST IR 7621 are compared. As a result, the paper aims the most useful framework for SMEs with minimal technical knowledge and cost. Plenty of studies focus on ISO 27001 and NIST IR 7621. However, there is limited focus on the IT audit of TCA and ICSG. By exemplifying TCA and ICSG, the paper is trying to compare frameworks for SMEs.
Comparison of Classifiers for Text Classification in an E-commerce Service

In this study, performance of several document vectorization techniques and classification models are compared for text classification. The work was done with a dataset consisting of unstructured and hand-labeled customer text queries collected at the e-commerce service that we offer in the logistics industry. The queries were obtained from raw texts submitted to the customer support center through written communication channels. The dataset is cleaned up using ChatGPT for text normalization and regular expressions for tagging entities in queries. Three vectorization methods, Tf-Idf, fastText and BERT, and three classification models, gradient boosting, support vector machine (SVM) and multilayer perceptron (MLP), were used for the experiments. Each vectorization method was cross-validated with each classification model, and the results were evaluated for accuracy, recall, and F1 score. Tf-Idf method performed the best when used with MLP and SVM classifiers.
Exploring Factors Influencing the Satisfaction of Adult Software Engineering Students with Teamwork in Distance Education

Using team-based software development assignments is a prevalent instructional strategy in software engineering (SE) education. Students utilize these development assignments as a vehicle to (co-)learn SE concepts, practice problem-solving, and develop soft skills. The satisfaction of SE students with their teamwork experience in team-based assignments is an important educational and motivational factor that contributes to increased participation in future team-based projects.
Data Literacy as a Fundamental Component of Artificial Intelligence Education in Schools (Doctoral Consortium)

School students actively contribute to artificial intelligence (AI) technologies as data producers. As future professionals and creators of AI technologies, they will shape the data culture in various domains. Therefore, AI literacy is proposed as a top priority for education systems worldwide. However, when it comes to education about AI in schools, there is little evidence on which data-related skills are fundamental and how to help students acquire them effectively. In this work, I propose considering data literacy as a fundamental component in AI education. The main objective of this research is to discuss this position and present a method for introducing AI through the lens of data literacy using case studies from the real world.
Common Errors in Machine Learning Projects: A Second Look

While machine learning (ML) has proved impactful in many disciplines, design decisions involved in building ML models are difficult for novices to make, and mistakes can cause harm. Prior work by Skripchuk et al. [35] identified common errors made by ML students via qualitative analysis of open-ended ML assessments, but their sample was limited to a single institution, course, and assessment setting. Our work is an extended, conceptual replication of this work to understand the common errors made by machine learning students. We use a mixed-method approach to analyze errors in 30 final project reports in an undergraduate machine learning course. The final reports describe the model-building process for a classification task, where students build models on a complex data set with numerical, categorical, ordinal and text features. Our choice to analyze project reports (rather than code) allows us to uncover design errors via how students justify their methodology. Our project task is to achieve the best test accuracy on an unseen test set; thus, as a way to validate these common errors, we identify the association between these errors and the model’s test accuracy performance. Common errors we find include those consistent with Skripchuk et al. [35], for example issues with data processing, hyperparameter tuning, and model selection. In addition, our focus on design error exposes other common errors, for example where students use certain kinds of features (e.g., bag of words representations) only with particular models (e.g., Naive Bayes). We call these latter types of errors model misconceptions, and such errors are associated with lower test accuracy. Some of these errors are also present in work by practitioners. Others reflect a difficulty by students to make correct connections between ML concepts and achieve the relational level of the SOLO taxonomy. We identify areas of opportunity to improve machine learning pedagogy, particularly related to data processing, data leakage, hyperparameters, nonsensical outputs, and disentangling data decisions from model decisions.
Removing the Notional Machine Discrepancy (Doctoral Consortium)

A notional machine of a programming language is an abstraction that explains how programs in the language execute. I argue that when novices learn to program, there is a problematic discrepancy between the presented notional machine of the part of the language that they have seen and a notional machine of the whole language. This discrepancy can be removed by creating different languages that gradually change to the complete language. I am creating a Programming Education Runtime System that allows to modularly combine programming language constructs to create these gradual languages. As a next step, evaluation of the use of these gradual languages will be carried out.
Enhancing Computer Programming Education using ChatGPT- A Mini Review

This paper aims to provide insights into how ChatGPT enhances computer programming education by synthesizing existing studies using rapid review. We analysed 13 articles published in 2023, where studies focused on different aspects of basic programming education. The results indicate that 21% of these studies demonstrate that ChatGPT served as a tool for code explanation and handling complex topics. However, 36% show that ChatGPT had difficulty answering non-text-based and code-related questions, revealing reliability and accuracy issues with these tools. Another 36% of the studies showed that blindly over-reliance on ChatGPT affected critical thinking, student creativity, and problem-solving skills in programming education. 46% of the studies indicated the need to provide clear guidelines and employ plagiarism-detection tools to instruct students effectively. We suggest that educators should adopt diverse approaches to integrating ChatGPT as an educational tool while highlighting ethical considerations and model limitations.
Introduction of Artificial Intelligence Literacy and Data Literacy in Computer Science Teacher Education

The integration of artificial intelligence (AI) literacy and data literacy into school education is increasingly proposed in educational frameworks, leading to an urgent need to train computer science teachers in AI and data literacy. However, there is a lack of professional development programs for these skills. To fill this gap, we developed a training concept and evaluated it with computer science teachers from Germany, Austria, and Lithuania in three sessions. In the following, we present selected evaluation results.
A Video Quality Evaluation Method Based on Transformers

This article proposes a dual channel video quality evaluation method based on Transformers. The jitter, exposure, and compression during video capture and transmission can all affect the quality of the video. In response to the spatial effects of exposure and defocus, a network based on Swin Transformers is used to extract multi-scale spatial features. The Transformer network is used to extract spatial and temporal features from 2D and 3D perspectives, respectively. In response to factors such as jitter, a backbone network based on video spin transformers is used to extract motion information. At the same time, considering the human visual system, an improved version of the time effect module is proposed, providing a new time memory attention that can more effectively map video frame quality scores. The model proposed in this article has been tested on multiple datasets, and the results show that the method has good performance.
Application of YOLOv5m Network Based on Deep Learning in Aquaculture Detection

This article proposes an improved pig eye object detection method based on YOLOv5m, which adopts a multi head self attention mechanism on top of the original object detection attention mechanism. The multi head self attention mechanism is added to feature fusion and a new network model is established. The residual module in YOLOv5m is compared with the existing YOLOv5m through ablation experiments, and the improved model has a mAP of 0.969, with accuracy The effective recall rate is superior to other feature recognition models. This method improves the detection ability of the network model for small targets, effectively reducing the missed detection rate and false detection rate.
A Chinese Grammar Error Correction Method Based on Single Reference Task Learning

Chinese grammar error correction technology aims to automatically correct various errors such as spelling, morphology, and grammar. However, the existing CGEC data sets contain a small number of samples and contain a lot of noise, which greatly limits the error correction performance of the model. To address this problem, we proposed a simple and effective method to construct a single reference data set with a sample size close to half of the original data set. Experimental results on Seq2Edit-based and Seq2Seq-based models show that training with a single reference data set not only makes. The training time is greatly shortened and the model performance can be significantly improved. In addition, we also constructed a syntax error correction synthetic data set based on word confusion sets for model pre-training, which significantly improved the error correction performance of the Seq2Edit-based model. At the same time, compared with a single model, we have significantly improved the performance of the error correction system by rationally integrating heterogeneous grammatical error correction models, which is far higher than the error correction effect of a single model.
Underwater Image Enhancement Method Based on Illumination Correction and Color Correction

Due to the attenuation and scattering of light propagating in water, underwater images are subject to blurring, uneven illumination, and color distortion. To cope with these problems, an underwater image enhancement method building upon illumination correction and color correction is proposed. This method makes an effective integration of illumination correction and color correction. To commence with, the underwater fish image is deblurred by dark channel prior (DCP). Subsequent to this, contingent upon the varying brightness of the images arising from the uneven illumination, the camera response model is used to perform low illumination enhancement on the low-brightness areas, and the high-brightness areas undergo exposure reduction. As such, the generated low-brightness images are fused with the de-exposed images by the principal component analysis (PCA) fusion algorithm. Finally, based on the gray world hypothesis theory, the deblurred and illumination-corrected images proceed with the color correction to generate the final underwater enhanced image. Experiments on manifold underwater images demonstrate that the proposed method exhibits good performance in objective evaluation indexes. When juxtaposed with other methods, this method can substantiate the brightness of low-illumination areas while maintaining the brightness of well-exposed areas, and effectively solve the problem of color distortion.
DCGCN-CPCT: Dual-Channel GCN Modules and CPCT for Aspect-Level Sentiment Classification

The task of Aspect-level Sentiment Classification (ALSC) is to predict the sentiment polarity of particular aspect words within a sentence. Researches conducted previously have demonstrated relatively promising experimental outcomes on using Graph Neural Networks to exploit sentence structure information on ALSC. But these methods do not do a good job of establishing the relations between the aspect word and the context well. In order to enhance the relationship between them, we propose DCGCN-CPCT, which mainly includes dual-channel Graph Convolutional Network (GCN) modules and Compound Point-Wise Convolution Transformation (CPCT). Specifically, we extract the syntactic information by constructing a dependency tree and transforming it into an adjacency matrix which is then employed in our GCN layer. In order to capture the semantic information, a self-attention matrix was leveraged to capture the interconnections among nodes, enabling the learning of features that the syntactic GCN module may fail to capture. Furthermore, we enhance the efficiency of feature concatenation from the dual-channel GCN modules through a Gate Mechanism. Finally, we leverage our CPCT layer to learn more complex features. Our method was evaluated on three public datasets. The experimental results show that our model DCGCN -CPCT outperforms the baseline models.
Harnessing Small Models and LLMs: A Key Points Extraction Approach for High-Quality Abstracts in Wireless Network Tuning Logs

Wireless network tuning logs, pivotal in maintaining and improving network health, offer invaluable insights about network issues. However, the variance in their structure and content can make extracting essential details time-consuming and challenging. This thesis presents a novel method combining the power of small models with Large Language Models (LLMs) to efficiently generate high-quality abstracts from these logs. We use small models to capture key information from a wireless network tuning log, and feed the information to a LLM for generating high quality abstract. Our method achieves a remarkable 77.18 Rouge-l score, outperforming the state-of-the-art general abstract generation model by 30 points. Not only does this method streamline the summarization process, but it also introduces a model that can be adapted to similar challenges in various fields. Notably, an abstract generation model based on our method is now serving as a functionality of a wireless network maintenance system.
A Super Pixel Segmentation Algorithm for SAR Images Based on APKSLIC

To improve the accuracy of Simple Linear Iterative Clustering (SLIC) algorithm in image segmentation, a superpixel segmentation algorithm based on adaptive parameters and Kuan filter mean estimation is proposed. This algorithm adaptively generates feature equalization parameter g based on the specific situation of the image, and then combines it with the average grayscale difference and spatial fluctuation difference constructed by Kuan filter estimation to improve the superpixel segmentation effect of SAR images. The experimental results show that compared with the other three superpixel segmentation algorithms, the proposed algorithm has a recall improvement of at least 0.7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> , an undersegmentation error rate reduction of at least 5%, and an average running time reduction of at least 15%.
Few-Shot Object Detection Based on Generalized Features

Few-shot object detection aims to rapidly detect novel classes of objects using a minimal number of annotated instances. Compared to methods such as meta-learning, few-shot object detection based on transfer learning can achieve better performance. However, when training on base-class datasets, the model gradually improves its detection performance on base classes but does not enhance its generalization. A base-class model lacking generalization tends to result in lower detection performance for novel classes during fine-tuning. To address the aforementioned issues, this paper tackles the problem from two aspects: firstly, we introduce the Confusion-Dropblock Module (CDM) to perturb the model's features, thus enhancing feature generalization. Secondly, we incorporate Deformable Convolution Modules to mitigate the impact of object shapes and sizes on detection performance, thereby improving overall detection performance. Experimental results on the COCO dataset demonstrate the effectiveness of the proposed algorithm, outperforming the compared methods.
On the Evaluation of ChatGPT-3.5 on Swahili Classification Tasks

Nature language processing (NLP) has entered a new era with the creation of large language models (LLMs). In this study, we assess ChatGPT's performance in Swahili, a language with limited resources, and compare the results with a language model trained specifically for Swahlihi, i.e., SwahBERT (Martin et al., 2022), and a language model trained with various languages, i.e., mBERT, across three classification tasks. We observe that Chat- GPT underperforms SwahBERT and mBERT in news classification and emotion classification. However, it achieves comparable performance to SwahBERT in sentiment classification. Our results imply that ChatGPT can still be improved in situations with limited resources.
A Personalized English Course Recommendation Strategy Combining Users' English Learning Interest and Relationship

Most of the existing English course recommendation models are monotonous when solving user representations, and they only simply use the correlation between English learning interests, which makes user representations inaccurate and the recommended courses hardly meet the needs of both teachers and students. Based on this, a personalized course recommendation model combining users' interest and relationship in English learning is proposed. Firstly, a user intention separation model combining user-English course interaction and relationship is constructed. It uses graph neural network to collect each user's user-English course interaction and relationship information, and then solves user intention and English course representation. Secondly, according to the direct and indirect trust relationship between users, the network walking algorithm is used to calculate the course weight of users. Finally, the representation of teaching and students is weighted linearly to two-way preference by weight, and then the representation of English courses is calculated by inner product, and the list of English course teaching is recommended to students for target users. This method fully considers the influence of English learning interest and relationship on students' learning interest, and uses relational network to alleviate the problem of data sparsity, which improves the performance of the model. The experimental results show that compared with several classical models, the new model is better in both Recall and NDCG.
News Text Generation Method Integrating Pointer-Generator Network with Bidirectional Auto-Regressive Transformer

Text generation technology in natural language processing has gained prominence, notably in automating comprehensible text production for journalism, a research hotspot. Generating fluid, readable news content, which coherently integrates key news elements, poses a significant challenge. This technology's potential lies in reducing journalists' repetitive work, enhancing creative contributions. Common text generation models, however, often suffer from large parameter sizes and produce repetitive or inaccurate content. Addressing these challenges in news text generation, we present a lightweight model parameterization method, incorporating the BART (Bidirectional and Auto-Regressive Transformers) model with a pointer-generator network. Experimental results demonstrate that our model, using fewer parameters compared to baseline models, achieves a Rouge-L score of 57.6% on real-world news datasets, thereby demonstrating enhanced performance.
Generalized Self-Adaption Network for Domain Adaptation

The purpose of domain adaptation is to transfer knowledge learned in the labeled source domain to unlabeled but related target domains without requiring a large number of target domain labels. The latest method of domain adaptation mainly adjusts the distribution of the source and target domains to adapt to the target domain. Despite significant success, when there are significant distribution differences between the source and target domains, the adaptability in the target domain still decreases, which may be due to insufficient exploration of domain specific features leading to domain offset issues. To address this issue, we propose the GSA-NET framework in this article. We designed an adversarial dropout method to improve the generalization performance of the model in the target domain by pushing the decision boundary away from the feature dense region. At the same time, in order to effectively match the distribution of advanced features across domains, we deploy feature adaptation modules before the classification layer, which can significantly reduce domain differences. Our experiments on two cross domain benchmarks, Office Home and Office-31, have shown that the proposed method is significantly superior to existing methods.
A Small-Object-Detection Model Based on Improved YOLOv8 for UAV Aerial Images

Aiming at the problems of high model complexity and poor detection effect of UAV object detection algorithm, a small-object-detection model based on improved YOLOv8 is proposed. First, the Diverse Branch Block generalized module component is used in the Backbone network, which can improve the performance of the convolutional neural network in the model training phase and does not affect the inference time in the inference phase. The number of parameters of the model is reduced without decreasing the detection accuracy. Secondly, a kind of Asymptotic Feature Pyramid Network is introduced in the Neck network, which improves the small-object detection ability of the model. Finally, the Wise-IoU loss function is integrated to improve the overall performance of the detector. Experimental results on VisDrone, show that the improved model improves the mean average precision (MAP) by 9.5% and reduces the number of parameters by 37% compared to the baseline YOLOv8-s model. Overall, the improved algorithm has a large improvement in detection effect and can be applied and deployed to real-world scenarios.