Reviewerly/exHarmony huggingface dataset:
Dialogue-based generation of self-driving simulation scenarios using Large Language Models

Simulation is an invaluable tool for developing and evaluating controllers for self-driving cars. Current simulation frameworks are driven by highly-specialist domain specific languages, and so a natural language interface would greatly enhance usability. But there is often a gap, consisting of tacit assumptions the user is making, between a concise English utterance and the executable code that captures the user's intent. In this paper we describe a system that addresses this issue by supporting an extended multimodal interaction: the user can follow up prior instructions with refinements or revisions, in reaction to the simulations that have been generated from their utterances so far. We use Large Language Models (LLMs) to map the user's English utterances in this interaction into domain-specific code, and so we explore the extent to which LLMs capture the context sensitivity that's necessary for computing the speaker's intended message in discourse.
FMMRec: Fairness-aware Multimodal Recommendation

Recently, multimodal recommendations have gained increasing attention for effectively addressing the data sparsity problem by incorporating modality-based representations. Although multimodal recommendations excel in accuracy, the introduction of different modalities (e.g., images, text, and audio) may expose more users' sensitive information (e.g., gender and age) to recommender systems, resulting in potentially more serious unfairness issues. Despite many efforts on fairness, existing fairness-aware methods are either incompatible with multimodal scenarios, or lead to suboptimal fairness performance due to neglecting sensitive information of multimodal content. To achieve counterfactual fairness in multimodal recommendations, we propose a novel fairness-aware multimodal recommendation approach (dubbed as FMMRec) to disentangle the sensitive and non-sensitive information from modal representations and leverage the disentangled modal representations to guide fairer representation learning. Specifically, we first disentangle biased and filtered modal representations by maximizing and minimizing their sensitive attribute prediction ability respectively. With the disentangled modal representations, we mine the modality-based unfair and fair (corresponding to biased and filtered) user-user structures for enhancing explicit user representation with the biased and filtered neighbors from the corresponding structures, followed by adversarially filtering out sensitive information. Experiments on two real-world public datasets demonstrate the superiority of our FMMRec relative to the state-of-the-art baselines. Our source code is available at https://anonymous.4open.science/r/FMMRec.
Information reconciliation for discretely-modulated continuous-variable quantum key distribution

The goal of this note is to explain the reconciliation problem for continuous-variable quantum key distribution protocols with a discrete modulation. Such modulation formats are attractive since they significantly simplify experimental implementations compared to protocols with a Gaussian modulation. Previous security proofs that relied crucially on the Gaussian distribution of the input states are rendered inapplicable, and new proofs based on the entropy accumulation theorem have emerged. Unfortunately, these proofs are not compatible with existing reconciliation procedures, and necessitate a reevaluation of the reconciliation problem. We argue that this problem is nontrivial and deserves further attention. In particular, assuming it can be solved with optimal efficiency leads to overly optimistic predictions for the performance of the key distribution protocol, in particular for long distances.
Scrum team self-organization: an understanding in the light of the systemic-complex paradigm

Self-Organization of project teams is one of the six characteristics identified by Takeuchi; Nonaka (1986) that provide companies with flexibility and speed in the development of products and solutions. Schwaber (1997) incorporated this feature by creating a new approach to software development process which he called Scrum, one of the most used agile methods in the world. So, the objective of this research was to understand the self-organization in a Scrum Team (ST), in the light of the Systemic-Complex Paradigm (SCP), from the principles of Recursiveness, Dialogic and Hologrammaticity. This research involved the understanding of self-organization in a ST in light of the SCP in a large Information Technology (IT) company. A qualitative methodological approach was used through an action research. As a result, people's relationships were analyzed and the influence of the Environment (external to the team), the Whole (team), and the Parts (team people) were understood, where 31 factors were found that influence and promote self-organization on a ST. From this, the Scrum relationship with the SCP was established and management contributions were suggested for companies that have self-organizing ST or that wish to implement them.
Research on Prediction Model of Concrete Compressive Strength Based on Random Forest

Concrete compressive strength prediction is a key part of batching ratio design, the traditional concrete cube compressive strength standard test method is vulnerable to external influences, a random forest algorithm is proposed to predict the compressive strength of concrete, by optimizing the data of cement, blast furnace slag mixing, fly ash mixing, water content, high efficiency water reducer dosage, coarse aggregates, fine aggregates content, and age, etc., as raw material indicators. The optimized data set is used as the input data set, and the data set is divided to construct the model, and then the model parameters are optimized by defining the parameter network and performing the grid search cross-validation to construct the perfect models. The results show that the prediction accuracy of the prediction model based on the random forest algorithm (R²=0.91547) is much higher than that of the support vector regression model (R²=0.76802) and the decision tree model (R²=0.87539) and the error is small (RMSE=5.24087), which is of great significance for the research of compressive strength prediction model.
Dimensional Analysis Demystified

This exploration delves into the world of dimensional analysis, a fundamental tool in mathematics and physics. Dimensions, such as length, width, height, and time, are the basic attributes that define our physical reality. The powers of these dimensions play a pivotal role in understanding how various physical quantities are interrelated. This study introduces the concept of dimensional transitions, both from lower to higher dimensions and vice versa, shedding light on how new dimensions and powers are introduced, presenting mathematical challenges, and deepening our comprehension of the physical world. The study showcases mathematical equations central to quantum mechanics and quantum gravity, elucidating the intricate relationship between energy, length scales, and fundamental constants. Of particular interest is the interplay of dimensions and powers in equations that encompass spatial and temporal dimensions, emphasizing the influence of the fourth dimension, often associated with time. This analysis demonstrates that by following this method, dimensional analysis becomes a powerful tool for exploring the relationships between dimensions in the physical world and their role in mathematical and physical equations. In summary, this work unravels the complexities of dimensional analysis and its applications, offering insights into how it contributes to our understanding of the fundamental laws of physics.
Critical Minerals for a Carbon-Neutral Future

The Earth Mapping Resources Initiative is filling data gaps and fostering more holistic understanding of critical mineral resources in the United States using a novel mineral systems framework.
A ChatGPT-Generated Taxonomy of Risky Real-Life Situations

With the recent emergence of large language models (e.g., ChatGPT) trained on enormous amounts of information regarding human affairs it is now possible to easily explore naturalistic environments by digital means. Here, this technology was put to to use by having ChatGPT 4.0 identify everyday decisions that can involve risk. Risk was defined according to four different and commonly used definitions of risk (Hansson, 2023); (i) the cause of an unwanted event that may or may not occur, (ii) an unwanted event that may or may not occur, (iii) the probability of an unwanted event that may or may not occur, and (iv) the statistical expectation value of an unwanted event that may or may not occur. After, several conversation reiterations over 1,000 decisions were identified, and after redundancy actions (vectorization, word embedding techniques, and manual review) over 350 situations remained .
A New Improved Prediction of Software Defects Using Machine Learning-based Boosting Techniques with NASA Dataset

Predicting when and where bugs will appear in software may assist improve quality and save on software testing expenses. Predicting bugs in individual modules of software by utilizing machine learning methods. There are, however, two major problems with the software defect prediction dataset: Social stratification (there are many fewer faulty modules than non-defective ones), and noisy characteristics (a result of irrelevant features) that make accurate predictions difficult. The performance of the machine learning model will suffer greatly if these two issues arise. Overfitting will occur, and biassed classification findings will be the end consequence. In this research, we suggest using machine learning approaches to enhance the usefulness of the CatBoost and Gradient Boost classifiers while predicting software flaws. Both the Random Over Sampler and Mutual info classification methods address the class imbalance and feature selection issues inherent in software fault prediction. Eleven datasets from NASA's data repository, "Promise," were utilised in this study. Using 10-fold cross-validation, we classified these 11 datasets and found that our suggested technique outperformed the baseline by a significant margin. The proposed methods have been evaluated based on their abilities to anticipate software defects using the most important indices available: Accuracy, Precision, Recall, F1 score, ROC values, RMSE, MSE, and MAE parameters. For all 11 datasets evaluated, the suggested methods outperform baseline classifiers by a significant margin. We tested our model to other methods of flaw identification and found that it outperformed them all. The computational detection rate of the suggested model is higher than that of conventional models, as shown by the experiments..
Comparision of Different Classifiers for Prediction of Breast Cancer

The cell formed in the breast are known as breast cancer. It occurs mainly in women and it may occur rarely in men also. It is considered as the most common ailment that can lead to large number of death in females every year. In spite of the factuality that cancer is treatable and can be relieve if treated at its early stages; many patients are screened for cancer only at a very late stage. Data mining technique such as classifications provides an efficient technique to classify data, where these methods are commonly used for diagnostic decision making. The Machine learning techniques propound various methods such as statistical and probabilistic methods which allow system to learn from past experiences to distinguish and identify patterns from a standard dataset. The research work presents a review of machine learning techniques which can be used in breast cancer disease detection by applying algorithms on breast cancer Wisconsin data set. Algorithms such as Navies Bayes, Random Forest, Support Vector Machine, Adaboost and Decision Trees were used. The result outcome shows that Random Forest performs better than other techniques.
Handwritten Devanagari Text Recognition using Single Classifier Approach with VSPCA Scheme

In this research paper we used individual classifier approach for Handwritten Devanagari text recognition. We experimented different categorical classifiers namely Random Forest Classifier (RFC), Support Vector Machine (SVM), K Nearest Neighbor Classifier (KNN), Logistic Regression Classifier (LogRegr), Decision Tree Classifier (DTree). Seven different feature sets are used namely Eccentricity, Euler Number, Horizontal Histogram, Vertical Histogram, HOG Features, LBP Features, and Statistical Features. The experimentation is carried out on 9434 different characters whose features are extracted from 220 handwritten image documents from PHDIndic_11 dataset. We deduced and implemented a unique scheme namely VSPCA scheme. VSPCA is Vectorization, Scaling, and Principal Component Analysis carried out on all feature sets before being given for model training. We obtained varied accuracies using all these five classifiers on all these six feature sets in which 99.52% highest accuracy is observed.
A Review on Software Quality Forensics: Techniques, Challenges, and Limitations

Software quality forensics plays a vibrant role related to software quality, security, and integrity. The paper aims to derive a software quality forensics model through existing software quality models and their factors. The papers explore quality models, factors, approaches, tools, techniques, and standards regarding software quality investigation and confine the research area for software quality integrity breach forensics. The explore the deviations of quality attributes, standards, factors, and artifacts, it leads to further investigation of root-cause followed by digital evidence procedure for alleged software quality issues. Therefore, there is a need for a software quality forensics model and dedicated standards to fulfill the digital evidence procedure validation, satisfiable, and prosecution in the court of law in the context of alleged or illegal activity investigation quality of software. The paper has derived the techniques, challenges, and limitations of software quality forensics based on the review of research questions.
fairret: a Framework for Differentiable Fairness Regularization Terms

Current tools for machine learning fairness only admit a limited range of fairness definitions and have seen little integration with automatic differentiation libraries, despite the central role these libraries play in modern machine learning pipelines. We introduce a framework of fairness regularization terms (fairrets) which quantify bias as modular objectives that are easily integrated in automatic differentiation pipelines. By employing a general definition of fairness in terms of linear-fractional statistics, a wide class of fairrets can be computed efficiently. Experiments show the behavior of their gradients and their utility in enforcing fairness with minimal loss of predictive power compared to baselines. Our contribution includes a PyTorch implementation of the fairret framework.
Adaptive Uncertainty Estimation via High-Dimensional Testing on Latent Representations

Uncertainty estimation aims to evaluate the confidence of a trained deep neural network. However, existing uncertainty estimation approaches rely on low-dimensional distributional assumptions and thus suffer from the high dimensionality of latent features. Existing approaches tend to focus on uncertainty on discrete classification probabilities, which leads to poor generalizability to uncertainty estimation for other tasks. Moreover, most of the literature requires seeing the out-of-distribution (OOD) data in the training for better estimation of uncertainty, which limits the uncertainty estimation performance in practice because the OOD data are typically unseen. To overcome these limitations, we propose a new framework using data-adaptive high-dimensional hypothesis testing for uncertainty estimation, which leverages the statistical properties of the feature representations. Our method directly operates on latent representations and thus does not require retraining the feature encoder under a modified objective. The test statistic relaxes the feature distribution assumptions to high dimensionality, and it is more discriminative to uncertainties in the latent representations. We demonstrate that encoding features with Bayesian neural networks can enhance testing performance and lead to more accurate uncertainty estimation. We further introduce a family-wise testing procedure to determine the optimal threshold of OOD detection, which minimizes the false discovery rate (FDR). Extensive experiments validate the satisfactory performance of our framework on uncertainty estimation and task-specific prediction over a variety of competitors. The experiments on the OOD detection task also show satisfactory performance of our method when the OOD data are unseen in the training. Codes are available at https://github.com/HKU-MedAI/bnn_uncertainty.
The Influence of Product Quality and Price on Consumer Satisfaction in the Jdm Project Printing Business

This study aims to determine the effect of product quality and price on customer satisfaction in the JDM PROJECT printing business. This study used a quantitative method with a total sample of 96 respondents using incidental sampling technique. The data collection method uses an online questionnaire. This study uses multiple linear regression analysis with data processing tools in the form of the IBM SPSS 21 program. The results show that product quality partially affects consumer satisfaction (Sig 0.000 &lt;0.05, and t count 3.056 &gt; t table 1.989) and price partially affect consumer satisfaction (sig 0.000 &lt;0.05 and t count 6.192 &gt; t table 1.989). Product quality and price simultaneously affect consumer satisfaction in the JDM PROJECT printing business (sig 0.000 &lt;0.05 and f count 22.075 &gt; f table 3.09).
Place and time in architecture of university campuses

University campuses are multifunctional complexes comprising academic, residential, cultural and leisure facilities and public spaces. They fully reflect modern trends in shaping the architectural environment of cities. It is in universities that distance and network learning technologies have been tested in recent years, which inevitably affects the concepts of ‘place’ and ‘time’ in their organisation. In this article, the ‘artificial’ architectural environment is understood as an environment which is created simultaneously under the influence of realistic requirements for the organization of spatial environment and virtual commitments to creating a common information space.
Automation of Open VSwitch-Based Virtual Network Configuration Using Ansible on Proxmox Virtual Environment

Proxmox has a feature that can build a private network in it. Each host on a private network on Proxmox generally shares physical resources, including network connections using a virtual network, one of which is a VLAN. The Proxmox server supports Open VSwitch as a virtual switch. Open virtual switch, an alternative virtual switch quite popular among cloud developers, can be a solution for managing traffic between Virtual Machins and external communications. The method used in this study is the Network Development Life Cycle (NDLC). After applying the automation engine using Ansible, it overcame the problem, namely speeding up configuration and reducing human error or errors in configuring virtual networks. The automation system can speed up the virtual network management process compared to the manual method based on 5 (five) experiments, namely when the manufacturing operation has an average time of 08 minutes and 42 seconds faster. Whereas when the addition operation has an average faster time of 08 minutes 17 seconds. On the other hand, when the deletion operation has an average time of 42 seconds faster.
Streamlining Medical Services: A Virtual Chatbot for Stomach- related Illnesses

The stomach is an important part of the digestive system as it stores and breaks down food for the body's energy. In Indonesia, some people engage in dense work activities but eat late, which can lead to stomach problems. The younger generation often consumes high-fat and junk foods, increasing their risk of stomach-related. Raising public awareness through social media platforms about the various stomach illnesses, including symptoms, risks, and treatments, is important. To achieve this, a chatbot called "A.L.T.H.A" has been proposed to provide information on diseases related to the stomach, utilizing Google Dialogflow and third-party applications, it’s programmed to understand different intents, actions, parameters, and entities, and respond appropriately to user queries. The chatbot was tested on 10 end-users and successfully provided relevant information on different stomach-related issues, including welcome, fallback, main, also closing intents.
Implementation of an Additional Course on Creative Thai Dramatic Art Skills and Appreciation of the Values of Thai Culture for Grade 7 Student Under the Secondary Educational Service Area Office Kalasin

Thailand has many cultural heritages that have been inherited from the ancestors, including language, traditions, architecture, art and culture, music, as well as dramatic arts that have been evident since the Sukhothai period and have been handed down to the present. This study aimed to investigate the implementation outcomes of an additional course on creative Thai dramatic art skills and appreciation of the values of Thai culture for grade 7 students under the Secondary Educational Service Area Office Kalasin. The sample was forty-two grade 7 students of Anukoolnaree School in the first semester of the academic year 2022, selected by cluster random sampling (classroom). The research instruments were a Thai dramatic art knowledge test, an evaluation form on basic Thai dramatic art ability, an evaluation form on a creative Thai dramatic art designing ability, and an appreciation of the values of Thai culture questionnaire. The data were analyzed by mean, standard deviation, and t-test (Dependent Samples). The results showed that the post-test mean score of Thai dramatic art knowledge was higher than the pre-test mean score, the basic Thai dramatic art ability and the creative Thai dramatic art designing ability were at the highest levels, and appreciation of the values of Thai culture was significantly higher at the .05 level. Furthermore, students’ overall satisfaction with the course was at the highest level.
Sistem Pendukung Keputusan Dengan Menerapkan Metode Multi-Attribute Utility Theory Pada Pemilihan Content Writer

Content Writer is an important profession in the process of creating interesting content. The definition of Content Writer is a professional writer whose job is to write concepts of interesting content which will eventually be produced in online media. The income that is large enough for this profession results in many people being interested in working in that field. Therefore it will be difficult for those who open jobs in the field of Content Writers to choose who should be chosen. To select Content Writer candidates, it is better to use a system so that with this system it can help interested parties so that the results of the selection are not subjective or impartial. A good system to use is a decision support system. decision support system (DSS) is the development of an information system using the same way of working as an information system, namely a computer-based system. This SPK can be used in conjunction with supporting methods such as the MOORA method, the PSI method and so on. In this study the method used is the DEATH method. The MAUT method or the Multi-Attribute Utility Theory (MAUT) method is a method that prioritizes quality as a comparison so that the quality of each criterion will be considered as the first choice. Based on the calculation results from the MAUT method, a result of 0.6701 is obtained with the alternative A3 On behalf of Aldi as the best alternative that deserves to be accepted as a content writer.