Reviewerly/exHarmony huggingface dataset:
Virtual Reality Technology in Architectural Theory Learning: An Experiment on the Module of History of Architecture

The purpose of this study is to determine the significance of incorporating virtual reality (VR) technology as an additional learning aid in the delivery of architectural theory modules. The aim is to expand students’ horizons by utilizing VR as an immersive blended learning resource, particularly after the COVID-19 epidemic pandemic and the emergent need for new ways of delivering higher education. Empirical samples were drawn from a group of participating students in the History of Architecture module. This study adopted a mixed-methods approach comprising a quantitative inquiry (n = 60), which was complemented by a qualitative focus group discussion (n = 15). For the quantitative inquiry, three activities created virtual environments for three instances of Christian churches and cathedrals from the History of Christian Architecture: Hagia Sofia Cathedral, Notre Dame Cathedral, and Santa Maria del Fiore Cathedral. This was conducted during a 13-week teaching semester, with two groups of students voluntarily taking part in each activity: a VR group and a non-VR group. Following each activity, both groups responded to the same set of questions in order to assess the impact of using VR technologies on students’ cognitive and analytical understanding of the related building. A focus group was formed with the participating students to enhance their understanding of the results of the quantitative inquiry. The findings of the research show an increase in student learning experience and knowledge recollection when using VR in comparison to conventional teaching methods. The findings emphasize the role of digital technologies in enhancing students’ learning experiences, in particular, as a tool for sustainable approaches to teaching. This research expands knowledge and understanding about the use of VR in the History of Architecture pedagogy by examining its effectiveness as a medium of instruction. Furthermore, this study provides recommendations for adopting and incorporating new technologies in higher education for a better learning environment.
A Versatile Approach to Polygonal Object Avoidance in Indoor Environments with Hardware Schemes Using an FPGA-Based Multi-Robot

Service robots perform versatile functions in indoor environments. This study focuses on obstacle avoidance using flock-type indoor-based multi-robots. Each robot was developed with rendezvous behavior and distributed intelligence to perform obstacle avoidance. The hardware scheme-based obstacle-avoidance algorithm was developed using a bio-inspired flock approach, which was developed with three stages. Initially, the algorithm estimates polygonal obstacles and their orientations. The second stage involves performing avoidance at different orientations of obstacles using a heuristic based Bug2 algorithm. The final stage involves performing a flock rendezvous with distributed approaches and linear movements using a behavioral control mechanism. VLSI architectures were developed for multi-robot obstacle avoidance algorithms and were coded using Verilog HDL. The novel design of this article integrates the multi-robot's obstacle approaches with behavioral control and hardware scheme-based partial reconfiguration (PR) flow. The experiments were validated using FPGA-based multi-robots.
Towards a German labor market ontology: Challenges and applications

The labor market is an area with diverse data structures and multiple applications, such as matching job seekers with the right training or job. For this reason, the multilingual classification of European Skills, Competences, Qualifications and Occupations (ESCO) is a good example of the central role of ontologies in this area. However, ESCO cannot provide all the details of local labor market needs and does not provide links to other hierarchies of competences. For example, other taxonomies of occupations and skills are widely used in German-speaking countries, but they are not in a state where they are easily accessible for interoperability and reasoning. In this paper, we present a first version of a German Labor Market Ontology (GLMO) that uses ESCO as a top-level ontology for the target domain. This makes it highly interoperable and comparable to existing ontologies by providing details for the regional structures in German-speaking countries. In addition, we present a detailed evaluation of the provided data and applications, as well as an extensive discussion of future work.
A novel hybrid CNN-LSTM approach for assessing StackOverflow post quality

Abstract Maintaining the content quality on social media Q&amp;A platforms is pivotal for user attraction and retention. Automating post quality assessment offers benefits such as reduced moderator workload, amplified community impact, enhanced expert user recognition, and importance to expert feedback. While existing approaches for post quality mainly employ binary classification, they often lack optimal feature selection. Our research introduces an automated system that categorizes features into textual, readability, format, and community dimensions. This system integrates 20 features belonging to the aforementioned categories, with a hybrid convolutional neural network–long short-term memory deep learning model for multi-class classification. Evaluation against baseline models and state-of-the-art methods demonstrates our system’s superiority, achieving a remarkable 21–23% accuracy enhancement. Furthermore, our system produced better results in terms of other metrics such as precision, recall, and F 1 score.
Public Perception of ChatGPT and Transfer Learning for Tweets Sentiment Analysis Using Wolfram Mathematica

Understanding public opinion on ChatGPT is crucial for recognizing its strengths and areas of concern. By utilizing natural language processing (NLP), this study delves into tweets regarding ChatGPT to determine temporal patterns, content features, and topic modeling and perform a sentiment analysis. Analyzing a dataset of 500,000 tweets, our research shifts from conventional data science tools like Python and R to exploit Wolfram Mathematica’s robust capabilities. Additionally, with the aim of solving the problem of ignoring semantic information in the LDA model feature extraction, a synergistic methodology entwining LDA, GloVe embeddings, and K-Nearest Neighbors (KNN) clustering is proposed to categorize topics within ChatGPT-related tweets. This comprehensive strategy ensures semantic, syntactic, and topical congruence within classified groups by utilizing the strengths of probabilistic modeling, semantic embeddings, and similarity-based clustering. While built-in sentiment classifiers often fall short in accuracy, we introduce four transfer learning techniques from the Wolfram Neural Net Repository to address this gap. Two of these techniques involve transferring static word embeddings, “GloVe” and “ConceptNet”, which are further processed using an LSTM layer. The remaining techniques center on fine-tuning pre-trained models using scantily annotated data; one refines embeddings from language models (ELMo), while the other fine-tunes bidirectional encoder representations from transformers (BERT). Our experiments on the dataset underscore the effectiveness of the four methods for the sentiment analysis of tweets. This investigation augments our comprehension of user sentiment towards ChatGPT and emphasizes the continued significance of exploration in this domain. Furthermore, this work serves as a pivotal reference for scholars who are accustomed to using Wolfram Mathematica in other research domains, aiding their efforts in text analytics on social media platforms.
Organizational Model of Project-Based Learning in Undergraduate Studies

This paper presents a case of project-based learning organizational model implementation in the Engineering School of Information Technologies, Telecommunications and Control Systems (IRIT-RTF) of Ural Federal University for undergraduate educational programs. The authors analyze three stages of organizational model transformation: initiating, piloting, and scaling. Within each stage, there are discussed technologies of collecting applications for students’ projects based on a role model of competences; organizational and technical support of students’ project work in accordance with the activity classifi cation of projects; the number of supervisors and their level of training required to accompany the process. The presented case can provide the reader with practical recommendations for implementing project-based learning at any university.
End-to-End System of Engaging Students in Project Activities as a Tool to Ensure Education Quality

The paper presents the case of Peter the Great St. Petersburg Polytechnic University for organizing the end-to-end system of engaging students in project activities within higher education programmes. The project unit of such a programme consists of two parts (obligatory and variable) and combines students’ self-studying theory online, practical tutorial instruction classes, as well as a considerable amount of independent work in project teams with external experts’ participation. The digital tool for organizing, supporting, and monitoring project activities are the LMS Moodle-based portals. The authors describe the principles of organizing the system, its structure, project cycle stages, approaches to assessing students’ results and the quality of project work organization.
Vector—towards quantum key distribution with small satellites

Abstract A satellite-constellation based global quantum network could allow secure quantum communication between remote users worldwide. Such a constellation could be formed of micro- or even nanosatellites, which have the advantage of being more cost-effective than larger expensive spacecrafts. At the same time, the features of quantum communication impose a number of technical requirements that are more difficult to meet when using small satellites. Full-fledged quantum communication has been demonstrated with neither a micro- nor a nanosatellite so far. The authors took up this challenge and have developed a 6U CubeSat weighting 9.5 kg. The satellite is to be launched in 2023 and has already successfully passed all the pre-flight tests. The mission is not yet intended for fully quantum communication. Nevertheless, the authors are testing such key functional elements as polarization reference-frame synchronization and acquisition, pointing and tracking system on it. Besides that, the payload accommodates a full-duplex telecommunication system operating at a bit rate of 50 Mbit/s: an up- and a downlink at wavelengths of 808 and 850 nm. After the satellite is launched, the main goal to be achieved is to demonstrate stable connection between it and an optical ground station and carry out multiple communication sessions. In quantum communication, generating secret keys from raw measurement data implies two-way exchange of significant amount of information and therefore availability of a classical communication channel with a high bandwidth is one of the crucial things. In the following mission, which envisages an overall quantum key distribution system, we plan to use the free-space optical link for such an exchange of data, whereas the RF link will only be used for telemetry and telecommand.
Low-cost UAV detection via WiFi traffic analysis and machine learning

Abstract In recent years, unmanned aerial vehicles (UAVs) have undergoing experienced remarkable advancements. Nevertheless, the growing utilization of UAVs brings forth potential security threats to the public, particularly in private and sensitive locales. To address these emerging hazards, we introduce a low-cost, three-stage UAV detection framework for monitoring invading UAVs in vulnerable zones. This framework devised through an exhaustive investigation of the Chinese UAV market. Various scenarios were examined to evaluate the effectiveness of the framework, and it was subsequently implemented on a portable board. Experiments demonstrated that the proposed framework can detect invading UAVs at an early stage, even in stealthy mode. As such, the framework has the potential to be applied in the formulation of a portable surveillance system for a UAV-restricted region.
On Finetuning Large Language Models

Abstract A recent paper by Häffner et al. (2023, Political Analysis 31, 481–499) introduces an interpretable deep learning approach for domain-specific dictionary creation, where it is claimed that the dictionary-based approach outperforms finetuned language models in predictive accuracy while retaining interpretability. We show that the dictionary-based approach’s reported superiority over large language models, BERT specifically, is due to the fact that most of the parameters in the language models are excluded from finetuning. In this letter, we first discuss the architecture of BERT models, then explain the limitations of finetuning only the top classification layer, and lastly we report results where finetuned language models outperform the newly proposed dictionary-based approach by 27% in terms of $R^2$ and 46% in terms of mean squared error once we allow these parameters to learn during finetuning. Researchers interested in large language models, text classification, and text regression should find our results useful. Our code and data are publicly available.
A Comparison of Latent Semantic Analysis and Latent Dirichlet Allocation in Educational Measurement

Topic models are mathematical and statistical models used to analyze textual data. The objective of topic models is to gain information about the latent semantic space of a set of related textual data. The semantic space of a set of textual data contains the relationship between documents and words and how they are used. Topic models are becoming more common in educational measurement research as a method for analyzing students’ responses to constructed-response items. Two popular topic models are latent semantic analysis (LSA) and latent Dirichlet allocation (LDA). LSA uses linear algebra techniques, whereas LDA uses an assumed statistical model and generative process. In educational measurement, LSA is often used in algorithmic scoring of essays due to its high reliability and agreement with human raters. LDA is often used as a supplemental analysis to gain additional information about students, such as their thinking and reasoning. This article reviews and compares the LSA and LDA topic models. This article also introduces a methodology for comparing the semantic spaces obtained by the two models and uses a simulation study to investigate their similarities.
Unraveling User Perceptions and Biases: A Comparative Study of ML and DL Models for Exploring Twitter Sentiments Towards ChatGPT

ChatGPT is a powerful chatbot that used to generate human-like text. Chat GPT, developed by OpenAI, can answer many questions honestly, like a personal teacher who knows almost everything. It can perform various tasks such as question and answer, solving mathematical equations, writing text, debugging, and translating between languages. Compared to traditional chatbots, ChatGPT has been adopted by many users since its introduction. Some users feel that ChatGPT will override most content creation professions. This study analyzed users’ feelings about ChatGPT by analyzing tweets shared about ChatGPT. For this purpose, a hybrid Deep Learning (DL) model was developed using Convolutional Neural Networks (CNN) and Bi- Long Short-Term Memory (Bi-LSTM) models. The study has been compared with a number of DL and Machine Learning algorithms, LSTM, Bi-LSTM, CNN, Gated Recurrent Unit, Random Forests and Support Vector Machines. As demonstrated by the experimental outcomes, the FastText-trained CNN-Bi-LSTM model outperformed the other models in terms of accuracy, reaching 96.59%.
Expanding long-term thinking through life-centred design

In this exploratory paper, we draw from a series of participatory design experiments to reflect on the impact of life-centred design approaches on long-term thinking. We conceptualise long-term thinking according to three components: 1) Thinking through diverse perspectives, 2) enhancing futures imagination, and 3) challenging anticipatory assumptions. Long-term thinking is increasingly recognised as invaluable in urban regeneration projects, however, to date, knowledge on how to nurture long-term thinking remains sparse.
Integrated photonic convolution acceleration core for wearable devices

With the advancement of deep learning and neural networks, the computational demands for applications in wearable devices have grown exponentially. However, wearable devices also have strict requirements for long battery life, low power consumption, and compact size. In this work, we propose a scalable optoelectronic computing system based on an integrated optical convolution acceleration core. This system enables high-precision computation at the speed of light, achieving 7-bit accuracy while maintaining extremely low power consumption. It also demonstrates peak throughput of 3.2 TOPS (tera operations per second) in parallel processing. We have successfully demonstrated image convolution and the typical application of an interactive first-person perspective gesture recognition application based on depth information. The system achieves a comparable recognition accuracy to traditional electronic computation in all blind tests.
Mineral prospecting mapping with conditional generative adversarial network augmented data

Mineral Prospectivity Mapping (MPM) plays a pivotal role in identifying geo-anomalies that are indicative of potential mineralization, drawing upon various geological, geophysical, geochemical, and remote sensing data. With the increasing availability of such data in recent years, data-driven MPM methods have proven effective in discovering new mineral deposits, especially when utilizing machine learning techniques to unveil complex relationships between exploration data and mineral occurrences. Deep learning, specifically Convolutional Neural Networks (CNN), has demonstrated its superiority in this regard. However, these models encounter challenges due to the limited and imbalanced nature of geological exploration data. In this study, we address these challenges by proposing the adoption of a Conditional Generative Adversarial Network (cGAN) strategy for data augmentation. Additionally, we employ the sliding window algorithm for comparative analysis, and CNNs are utilized to assess the effectiveness of various data augmentation strategies. The results indicate that the cGAN-based data augmentation strategy exhibits higher resistance to overfitting, a critical concern in MPM applications. Furthermore, this research successfully delineated a prospectivity map for gold deposits in the Hezuo-Meiwu district, Gansu, China, providing valuable insights for future exploration efforts.
The Evolution of Language Models: From N-Grams to LLMs, and Beyond

In the last couple of decades language models (and artificial intelligence technologies) have had significant improvements. Along with computer vision and image processing models, large language models (LLMs) are expected to have big impacts on how AI technologies will evolve. As such, it is important to analyze how language models have advanced since their inception; and more importantly how they will grow in the future.In this article we provide a very brief overview of language models, starting from statistical and rule-based models, all the way to nowadays transformer-based multimodal large language models with billions of parameters. We also discuss the shortcomings of the existing models and the aspects that the current models need to be improved upon.
YOLO v7-CS: A YOLO v7-Based Model for Lightweight Bayberry Target Detection Count

In order to estimate bayberry yield, a lightweight bayberry target detection count model, YOLOv7-CS, based on YOLOv7, was proposed to address the issues of slow detection and recognition speed, as well as low recognition rate, of high-density bayberry targets under complex backgrounds. In this study, 8990 bayberry images were used for experiments. The training set, validation set, and test set were randomly recreated in a ratio of 8:1:1. The new network was developed with SPD-Conv detection head modules to extract features at various scales, to better capture small and indistinct bayberry targets. To improve accuracy and achieve a lightweight design, a CNxP module that replaces the backbone’s ELAN structure is proposed. We propose a global attention mechanism (GAM) in the intermediate layers of the network, to enhance cross-dimensional interactions, and a new pyramid pooling module called SPPFCSPC, to extend the field of perception and improve boundary detection accuracy. Finally, we combine the Wise-IoU function to enhance the network’s ability to identify overlapping and occluded objects. Compared with the SSD, Faster-RCNN, DSSD, and YOLOv7X target detection algorithms, YOLOv7-CS increases mAP 0.5 by 35.52%, 56.74%, 12.36%, and 7.05%. Compared with basic YOLOv7, mAP 0.5 increased from 5.43% to 90.21%, while mAP 0.95 increased from 13.2% to 54.67%. This parameter is reduced by 17.3 m. Ablation experiments further show that the designed module improves the accuracy of bayberry detection, reduces parameter counts, and makes bayberry image detection more accurate and effective.
Machine learning-based models for the prediction of breast cancer recurrence risk

Breast cancer is the most common malignancy diagnosed in women worldwide. The prevalence and incidence of breast cancer is increasing every year; therefore, early diagnosis along with suitable relapse detection is an important strategy for prognosis improvement. This study aimed to compare different machine algorithms to select the best model for predicting breast cancer recurrence. The prediction model was developed by using eleven different machine learning (ML) algorithms, including logistic regression (LR), random forest (RF), support vector classification (SVC), extreme gradient boosting (XGBoost), gradient boosting decision tree (GBDT), decision tree, multilayer perceptron (MLP), linear discriminant analysis (LDA), adaptive boosting (AdaBoost), Gaussian naive Bayes (GaussianNB), and light gradient boosting machine (LightGBM), to predict breast cancer recurrence. The area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and F1 score were used to evaluate the performance of the prognostic model. Based on performance, the optimal ML was selected, and feature importance was ranked by Shapley Additive Explanation (SHAP) values. Compared to the other 10 algorithms, the results showed that the AdaBoost algorithm had the best prediction performance for successfully predicting breast cancer recurrence and was adopted in the establishment of the prediction model. Moreover, CA125, CEA, Fbg, and tumor diameter were found to be the most important features in our dataset to predict breast cancer recurrence. More importantly, our study is the first to use the SHAP method to improve the interpretability of clinicians to predict the recurrence model of breast cancer based on the AdaBoost algorithm. The AdaBoost algorithm offers a clinical decision support model and successfully identifies the recurrence of breast cancer.