Reviewerly/exHarmony huggingface dataset:
Hybrid Image Improving and CNN (HIICNN) Stacking Ensemble Method for Traffic Sign Recognition

Traffic sign recognition techniques aim to reduce the probability of traffic accidents by increasing road and vehicle safety. These systems play an essential role in the development of autonomous vehicles. Autonomous driving is a popular field that is seeing more and more growth. In this study, a new high-performance and robust deep convolutional neural network model is proposed for traffic sign recognition. The stacking ensemble model is presented by combining the trained models by applying improvement methods on the input images. For this, first of all, by performing preprocessing on the data set, more accurate recognition was achieved by preventing adverse weather conditions and shooting errors. In addition, data augmentation was applied to increase the images in the data set due to the uneven distribution of the number of images belonging to the classes. During the model training, the learning rate was adjusted to prevent overfitting. Then, a new stacking ensemble model was created by combining the models trained with the input images that were subjected to different preprocessing. This ensemble model obtained 99.75% test accuracy on the German Traffic Sign Recognition Benchmark (GTSRB) dataset. When compared with other studies in this field in the literature, it is seen that recognition is performed with higher accuracy than these studies. Additively different approaches have been applied for model evaluation. Grad-CAM (gradient weighted class activation mapping) was used to make the model explainable. Evidential deep learning approach was applied to measure the uncertainty in classification. Results for safe monitoring are also shared with SafeML-II, which is based on measuring statistical distances. In addition to these, the migration test is applied with BTSC (Belgium Traffic Sign Classification) dataset to test the robustness of the model. With the transfer learning method of the models trained with GTSRB, the parameter weights in the feature extraction stage are preserved, and the training is carried out for the classification stage. Accordingly, with the stacking ensemble model obtained by combining the models trained with transfer learning, a high accuracy of 99.33% is achieved on the BTSC dataset. While the number of parameters the single model is 7.15 M, the number of parameters of the stacking ensemble model with additional layers is 14.34 M. However, the parameters of the models trained on a single preprocessed dataset were not trained, and transfer learning was performed. Thus, the number of trainable parameters in the ensemble model is only 39,643.
Generation and Polynomial Parsing of Graph Languages with Non-Structural Reentrancies

Abstract Graph-based semantic representations are popular in natural language processing, whereit is often convenient to model linguistic concepts as nodes and relations as edges between them.Several attempts have been made to find a generative device that is sufficiently powerful todescribe languages of semantic graphs, while at the same allowing efficient parsing. We contributeto this line of work by introducing graph extension grammar, a variant of the contextualhyperedge replacement grammars proposed by Hoffmann et al. Contextual hyperedge replacementcan generate graphs with non-structural reentrancies, a type of node-sharing that isvery common in formalisms such as abstract meaning representation, but that context-free typesof graph grammars cannot model. To provide our formalism with a way to place reentranciesin a linguistically meaningful way, we endow rules with logical formulas in counting monadicsecond-order logic. We then present a parsing algorithm and show as our main result that thisalgorithm runs in polynomial time on graph languages generated by a subclass of our grammars,the so-called local graph extension grammars.
Axioms for Constant Function Market Makers

One of the first and so far most successful applications of Decentralized Finance (DeFi), financial applications run on permissionless blockchains, are so-called Automated Market Makers (AMMs). They are used to trade cryptocurrencies algorithmically without relying on a custodian or trusted third party. The state of a typical AMM used in DeFi consists of the current inventories of the traded tokens. Trades are made such that some invariant of these inventories is kept constant. Traders who want to exchange tokens of type A for tokens of another type B, add A tokens to the inventory and in return obtain an amount of B tokens from the inventory so that the invariant is maintained. While these Constant Function Market Makers (CFMMs) proved to be very popular and reliable, the construction of invariants to define them seems in many ways ad-hoc and not founded in much theory. In this paper, we fill this gap and propose an axiomatic approach to constructing CFMMs. The approach is, as in any axiomatic theory, to formalize simple principles that are implicitly or explicitly used when constructing trading functions in practice and to check which classes of functions satisfy these principles, beyond those functions already used in practice.
The formation of a new information culture of the future: the socio-philosophical content

The article deals with the socio-philosophical perspectives of the transformation and creation of information culture process. The actual context of this phenomenon and prognostic abstractions are highlighted. The research goal of the presented work is to systematise the content of information culture from the ontological position of the social reality of our time. The research process is focused on a comprehensive study of the intersections of culture and information as fundamental categories in the context of their merger in the relevant concept. In addition, the transformational impact of the new culture on ethical frameworks and epistemological reality is analysed. The research methodology is formed on the basis of a dialectical approach with an encapsulating intention, in order to expand the subject matter and cover related constructs. The results made possible the concretisation and operationalisation of the elements of the studied process. The dyadic definitions of information culture as a contextual and instrumental reality are presented. Given its recursive nature of connection with epistemological reality, manipulation of values and ideas is considered as promising. Selectivity is defined as a cross-cutting process of constructing an ethical norm, and the collective nature of the norm is considered through the evolutionary significance of the information system. The implications are the prospects of applying the conceptual framework for interventions aimed at reconstructing public information standards, innovations in the educational process and management. The objectification of the process of creating a new culture provides the necessary method for its cognition and direction. The value transformation, in turn, is the basis for regulating the social reality.
Attendance System using Face Recognition

Abstract—In the current educational system, student’s frequent attendance in class is very important for performance evaluation and the quality control. Most institutions still use insecure, time-consuming traditional methods like calling people by the name or having them sign documents. Face recognition technology based on high-definition monitor video and other information technologies is used to solve the problem of the recognizing face for taking attendance. A facial recognition attendance system uses facial technology to automatically record attendance while identifying and verifying a person based on their facial features. This research focusing on a face recognition-based attendance system with getting a less false- positive rate using a threshold to confidence i.e., Euclidean distance value while detecting unknown persons and save their images. Compare to other Euclidean distance-based algorithms like Eigenfaces and Fisher faces, Local Binary Pattern Histogram (LBPH) algorithm is better. We used Haar cascade for face detection because of their robustness and LBPH algorithm for face recognition. It is robust against monotonic grayscale transformations. Scenarios such as face recognition rate, false- positive rate for that and false-positive rate with and without using a threshold in detecting unknown persons are considered to evaluate our system. We got face recognition rate of students is 77% and its false-positive rate is 28%. This system is recognizing students even when students are wearing glasses or grown a beard. Face Recognition of unknown persons is nearly 60% for both with and without applying threshold value. Its false-positive rate is 14% and 30% with and without applying threshold respectively. Keywords—Face recognition, face detection, machine learning, Haar Cascade Algorithm, Local Binary Pattens Histograms (LBPH).
Evaluation of hybrid deep learning and optimization method for 3D human pose and shape reconstruction in simulated depth images

In this paper, we address the problem of capturing both the shape and the pose of a human character using a single depth sensor. Some previous works proposed to fit a parametric generic human template into the depth image, while others developed deep learning (DL) approaches to find the correspondence between depth pixels and vertices of the template. We designed a hybrid approach, combining the advantages of both methods, and conducted extensive experiments on the SURREAL Varol et al. (2017), DFAUST datasets Bogo etal (2017) and a subset of AMASS Mahmood et al. (2019). Results show that this hybrid approach enables us to enhance pose and shape estimation compared to using DL or model fitting separately. We also evaluated the ability of the DL-based dense correspondence method to segment also the background — not only the body parts. We also evaluated 4 different methods to perform the model fitting based on a dense correspondence, where the number of available 3D points differs from the number of corresponding template vertices. These two results enabled us to better understand how to combine DL and model fitting, and the potential limits of this approach to deal with real-depth images. Future works could explore the potential of taking temporal information into account, which has proven to increase the accuracy of pose and shape reconstruction based on a unique depth or RGB image.
Real-Time Vehicle Accident Recognition from Traffic Video Surveillance using YOLOV8 and OpenCV

The automatic detection of traffic accidents is a significant topic in traffic monitoring systems. It can reduce irresponsible driving behavior, improve emergency response, improve traffic management, and encourage safer driving practices. Computer vision can be a promising technique for automatic accident detection because it provides a reliable, automated, and speedy accident detection system that can improve emergency response times and ultimately save lives. This paper proposed an ensemble model that uses the YOLOv8 approach for efficient and precise event detection. The model framework's robustness is evaluated using YouTube video sequences with various lighting circumstances. The proposed model has been trained using the open-source dataset Crash Car Detection Dataset, and its produced precision, recall, and mAP are 93.8% and 98%, 96.1%, respectively, which is a significant improvement above the prior precision, recall, and mAP figures of 91.3%, 87.6%, and 93.8%. The effectiveness of the proposed approach in real-time traffic surveillance applications is proved by experimental results using actual traffic video data.
Agile Methodology for the Standardization of Engineering Requirements Using Large Language Models

The increased complexity of modern systems is calling for an integrated and comprehensive approach to system design and development and, in particular, a shift toward Model-Based Systems Engineering (MBSE) approaches for system design. The requirements that serve as the foundation for these intricate systems are still primarily expressed in Natural Language (NL), which can contain ambiguities and inconsistencies and suffer from a lack of structure that hinders their direct translation into models. The colossal developments in the field of Natural Language Processing (NLP), in general, and Large Language Models (LLMs), in particular, can serve as an enabler for the conversion of NL requirements into machine-readable requirements. Doing so is expected to facilitate their standardization and use in a model-based environment. This paper discusses a two-fold strategy for converting NL requirements into machine-readable requirements using language models. The first approach involves creating a requirements table by extracting information from free-form NL requirements. The second approach consists of an agile methodology that facilitates the identification of boilerplate templates for different types of requirements based on observed linguistic patterns. For this study, three different LLMs are utilized. Two of these models are fine-tuned versions of Bidirectional Encoder Representations from Transformers (BERTs), specifically, aeroBERT-NER and aeroBERT-Classifier, which are trained on annotated aerospace corpora. Another LLM, called flair/chunk-english, is utilized to identify sentence chunks present in NL requirements. All three language models are utilized together to achieve the standardization of requirements. The effectiveness of the methodologies is demonstrated through the semi-automated creation of boilerplates for requirements from Parts 23 and 25 of Title 14 Code of Federal Regulations (CFRs).
Pengembangan Media Pembelajaran Comic Book IPA untuk Meningkatkan Keterampilan Berpikir Kritis dan Karakter Peduli Lingkungan

This research is motivated by the low critical thinking skills and environmental care character of students, one of which is the lack of supporting media for interesting learning textbooks with lots of pictures and content that can make it easier for students to understand the material. This study aims to examine and develop a valid, practical and effective science comic book media to improve critical thinking skills and students' environmental care character. This study uses research and development methods, which means that research aims to produce certain products that can be used in the learning process, in the form of science comic book media. The sources of this research are students, teachers, and experts. The data in this study were obtained through data on the needs of students and teachers, which were obtained through a need’s questionnaire; IPA comic book media assessment data, which was obtained through a science comic book media readability questionnaire; and media application data obtained through test instruments, journals, and observation sheets. The data analysis technique used was the research prerequisite test including the normality test, as well as the media effectiveness test including the paired t test and the N-Gain test.The results showed that, (1) the assessment of 1 media expert validator and 2 material expert validators obtained an average percentage score of 95% and 92% with a very valid category; (2) the results of the media practicality test based on the teacher's response obtained an average percentage score of 96% in the very practical category, and based on the student's response the average percentage score was 95% in the very practical category; (3) The results of the T test on the pre-test and post-test of critical thinking skills obtained a Sig score. (2-tailed) of 0.002, while the results of the T-test on the pre-test and post-test of environmental care characters obtained a Sig score. (2-tailed) of 0.000 (4) the results of the N-Gain pre-test and post-test critical thinking skills obtained a score of 0.63 in the "moderate" category, while the environmental care character obtained a score of 0.42 with the category "currently".
Multi-UAV Roundup Inspired by Hierarchical Cognition Consistency Learning Based on an Interaction Mechanism

This paper is concerned with the problem of multi-UAV roundup inspired by hierarchical cognition consistency learning based on an interaction mechanism. First, a dynamic communication model is constructed to address the interactions among multiple agents. This model includes a simplification of the communication graph relationships and a quantification of information efficiency. Then, a hierarchical cognition consistency learning method is proposed to improve the efficiency and success rate of roundup. At the same time, an opponent graph reasoning network is proposed to address the prediction of targets. Compared with existing multi-agent reinforcement learning (MARL) methods, the method developed in this paper possesses the distinctive feature that target assignment and target prediction are carried out simultaneously. Finally, to verify the effectiveness of the proposed method, we present extensive experiments conducted in the scenario of multi-target roundup. The experimental results show that the proposed architecture outperforms the conventional approach with respect to the roundup success rate and verify the validity of the proposed model.
IoT for smart English education: AI-based personalised learning resource recommendation algorithm

The development of smart English education provides talent support for national economic and social development, solves the difficult problems in current education and enhances national competence. At present, Internet of Things (IoT) is constant maturing of continuous development of information technology, providing a solid technical foundation for the development of smart English education. IoT mainly senses and collects data through multi-sensors, uses a variety of communication technologies to establish a network to realise the connection between things and things. Data collection through IoT is an important source of big data in English education. IoT is the basis for smart English education, learning, management and effective collection of educational data. Artificial intelligence analyses the learning resources collected by multi-sensors to make recommendation decisions. In this study, an attention-based deep collaborative learning resource recommendation model is proposed by integrating the attention mechanism into the learning resource recommendation algorithm.
Third-Party Library Dependency for Large-Scale SCA in the C/C++ Ecosystem: How Far Are We?

Existing software composition analysis (SCA) techniques for the C/C++ ecosystem tend to identify the reused components through feature matching between target software project and collected third-party libraries (TPLs). However, feature duplication caused by internal code clone can cause inaccurate SCA results. To mitigate this issue, Centris, a state-of-the-art SCA technique for the C/C++ ecosystem, was proposed to adopt function-level code clone detection to derive the TPL dependencies for eliminating the redundant features before performing SCA tasks. Although Centris has been shown effective in the original paper, the accuracy of the derived TPL dependencies is not evaluated. Additionally, the dataset to evaluate the impact of TPL dependency on SCA is limited. To further investigate the efficacy and limitations of Centris, we first construct two large-scale ground-truth datasets for evaluating the accuracy of deriving TPL dependency and SCA results respectively. Then we extensively evaluate Centris where the evaluation results suggest that the accuracy of TPL dependencies derived by Centris may not well generalize to our evaluation dataset. We further infer the key factors that degrade the performance can be the inaccurate function birth time and the threshold-based recall. In addition, the impact on SCA from the TPL dependencies derived by Centris can be somewhat limited. Inspired by our findings, we propose TPLite with function-level origin TPL detection and graph-based dependency recall to enhance the accuracy of TPL reuse detection in the C/C++ ecosystem. Our evaluation results indicate that TPLite effectively increases the precision from 35.71% to 88.33% and the recall from 49.44% to 62.65% of deriving TPL dependencies compared with Centris. Moreover, TPLite increases the precision from 21.08% to 75.90% and the recall from 57.62% to 64.17% compared with the SOTA academic SCA tool B2SFinder and even outperforms the well-adopted commercial SCA tool BDBA, i.e., increasing the precision from 72.46% to 75.90% and the recall from 58.55% to 64.17%.
Library and Information Science contributions to university libraries management

Librarianship and Information Science are two fields that have intrinsic relation between each other, mainly in Brazil. However, in spite of the convergences, these areas arose in different historical periods and with objectives to meet very specific demands. Currently, it is noticed that Librarianship is more focused on issues related to social responsibility and democratization of education, while Information Science focuses its efforts on research in order to understand information as a phenomenon in different contexts. Among these, this article is interesting, in the strategic and professional information, usually within organizations. Between these two approaches is the university library, which occupies a prominent role in the area of education as well as strategic planning of a country. Therefore, the purpose of this article is to analyze the contributions of the two fields for the management of university libraries today. Through the literature review, it is concluded that the two areas provide complementary inputs that allow the managers of these units to create an environment and services that meet the needs of their users in a more satisfactorily way. Librarianship brings both technical and humanistic contributions to university libraries, while Information Science contributes to issues related to the instability of today's society, which demands constant redefinition of the services provided.
Banking Chatbots: How Artificial Intelligence Helps the Banks

Abstract When we speak of solutions that can be designed, developed, and implemented to solve a determined problem in a specific domain, human intelligence shows that it cannot be kept within regular bounds. In addition, it consistently manages to produce innovative elements that increase efficiency, applicability, and abstraction, with the drawback of increasing the overall underlying complexity. As technology advanced and time passed, in every system there was a visible need to automate manual, repetitive, and mundane tasks to help save time and let humans focus on more important things. A practical example of such a system is the banking system. In this paper, our aim is to present how Artificial Intelligence (AI) helps this field through the use of chatbots. In this sense, we present and analyse five examples of such applications that are already used in the international banking system, highlighting the main benefits they bring. Finally, we present some conclusions regarding the chatbot systems analysed, acknowledging that the future will increasingly belong to IT applications of this kind.