Reviewerly/exHarmony huggingface dataset:
Stationary Points of a Shallow Neural Network with Quadratic Activations and the Global Optimality of the Gradient Descent Algorithm

We consider the problem of training a shallow neural network with quadratic activation functions and the generalization power of such trained networks. Assuming that the samples are generated by a full rank matrix [Formula: see text] of the hidden network node weights, we obtain the following results. We establish that all full-rank approximately stationary solutions of the risk minimization problem are also approximate global optimums of the risk (in-sample and population). As a consequence, we establish that, when trained on polynomially many samples, the gradient descent algorithm converges to the global optimum of the risk minimization problem regardless of the width of the network when it is initialized at some value [Formula: see text], which we compute. Furthermore, the network produced by the gradient descent has a near zero generalization error. Next, we establish that initializing the gradient descent algorithm below [Formula: see text] is easily achieved when the weights of the ground truth matrix [Formula: see text] are randomly generated and the matrix is sufficiently overparameterized. Finally, we identify a simple necessary and sufficient geometric condition on the size of the training set under which any global minimizer of the empirical risk has necessarily zero generalization error. Funding: The research of E. C. Kizildag is supported by Columbia University, with the Distinguished Postdoctoral Fellowship in Statistics. Support from the National Science Foundation [Grant DMS-2015517] is gratefully acknowledged.
Effective preprocessing and feature analysis on Twitter data for Fake news detection using RWS algorithm

The tremendous headway of web empowered gadgets develops the clients dependably strong in virtual redirection affiliations. Individuals from social affairs getting moment notices with respect to news, amusement, training, business, and different themes. The development of artificial intelligence-based classification models plays an optimum role in making deeper analysis of text data. The massive growth of text-based communication impacts the social decisions also. People rely on news and updates coming over in social media and networking groups. Micro blogs such as tweeter, facebooks manipulate the news as faster as possible.&#x0D; The quality of classification of fake news and real news depends on the processing steps. The proposed articles focused on deriving a significant method for pre-processing the dataset and feature extraction of the unique data. Dataset is considered as the input data for analyzing the presence of fake news. The extraction of unique features from the data is implemented using Bags of relevant tags (BORT) extraction and Bags of relevant meta words (BORMW).
A Novel Federated Learning Framework Based on Conditional Generative Adversarial Networks for Privacy Preserving in 6G

With the rapid development of 6G networks, data transmission speed has significantly increased, making data privacy protection issues even more crucial. The federated learning (FL) is a distributed machine learning framework with privacy protection and secure encryption technology, aimed at enabling dispersed participants to collaborate on model training without disclosing private data to other participants. Nonetheless, recent research indicates that the exchange of shared gradients may lead to information disclosure, and thus FL still needs to address privacy concerns. Additionally, FL relies on a large number of diverse training data to forge efficient models, but in reality, the training data available to clients are limited, and data imbalance issues lead to over fitting in existing federated learning models. To alleviate these issues, we introduce a Novel Federated Learning Framework based on Conditional Generative Adversarial Networks (NFL-CGAN). NFL-CGAN divides the local networks of each client into private and public modules. The private module contains an extractor and a discriminator to protect privacy by retaining them locally. Conversely, the public module is shared with the server to aggregate the shared knowledge of clients, thereby improving the performance of each client local network. Comprehensive experimental analyses demonstrate that NFL-CGAN surpasses traditional FL baseline methods in data classification, showcasing its superior efficacy. Moreover, privacy assessments also verified robust and reliable privacy protection capabilities of NFL-CGAN.
Complexity for one-dimensional discrete-time quantum walk circuits

We compute the complexity for the mixed-state density operator derived from a one-dimensional discrete-time quantum walk (DTQW). The complexity is computed using a two-qubit quantum circuit obtained from canonically purifying the mixed state. We demonstrate that the Nielson complexity for the unitary evolution oscillates around a mean circuit depth of $k$. Further, the complexity of the stepwise evolution operator grows cumulatively and linearly with the steps. From a quantum circuit perspective, this implies a succession of circuits of (near) constant depth to be applied to reach the final state.
Microsoft Teams Utilization for Group Function in Maritime-Focused Mechanical Design Capstone

Evaluating the ability of students to do group work or function effectively on a team has always been a challenge.This was especially true with the transition to online learning that occurred during the pandemic in early 2020.This challenged not only the ability of the students to complete their group assignments, but also the ability of the instructors to perform ABET assessments on the ability of students to function on a team.To mitigate these issues, Microsoft Teams was implemented as a collaboration platform and a tool for additional assessments.This paper details the implementation strategy in a capstone design course and the results from Fall 2020.Results demonstrate that significant benefits can be generated in both group functionality and group interactions by incorporating Microsoft Teams.
Research on Automatic Scoring Method of Intelligent Translation System Based on TSO Optimized LSTM Networks

INTRODCTION: The study of automatic marking methods in the Department of Language Translation is conducive to the fairness and rationality of marking by examining the comprehensive level of the students' language, as well as sharing the objectivity and pressure of the marking teachers in marking the scripts.OBJECTIVES: Aiming at the current automatic scoring methods of translation systems, which have the problems of not considering the global nature of influence features and low precision.METHODS: This paper proposes an automatic scoring method for translation system based on intelligent optimization algorithm to improve the deep network. First, by analyzing the language translation scoring problem, selecting the key scoring influencing factors and analyzing the correlation and principal components; then, improving the long and short-term memory network through the triangle search optimization algorithm and constructing the automatic scoring model of the translation system; finally, the high efficiency of the proposed method is verified through the analysis of simulation experiments.RESULTS: The proposed method is effective and improves the accuracy of the scoring model.CONCLUSION: solves the problem of inefficient scoring in the automatic scoring method of the translation system.
Neural Networks Verification: Perspectives from Formal Method

Neural Networks find applications across diverse domains in Computer Science. Despite their versatility, Neural Networks often demonstrate performance inconsistencies, necessitating the evaluation of their robustness, reliability, and correctness. Traditional Formal Verification techniques, proven effective in other contexts, face challenges when applied to Neural Networks. In this work, we explore the limitations of Formal Verification methods in the context of Neural Networks and further aim to propose a principled method to improve their verification.
Data-Driven Falsification of Cyber-Physical Systems

Cyber-Physical Systems (CPS) are abundant in safety-critical domains such as healthcare, avionics, and autonomous vehicles. The formal verification of their operational safety is therefore of utmost importance. In this paper, we address the falsification problem where the focus is on searching for an unsafe execution in the system instead of proving their absence. The contribution of this paper is a framework that connects the falsification of the safety properties of CPS with the falsification of deep neural networks (DNNs). This connection is established by: (1) Constructing a DNN model of the CPS under test and (2) The application of various falsification tools of DNNs to falsify CPS. The proposed framework has the potential to exploit a repertoire of adversarial attack algorithms designed for the falsification of robustness properties of DNNs. Although the proposed technique is applicable to systems in general that can be executed/simulated, we demonstrate its effectiveness, particularly in CPS. We show that our framework implemented as a prototypical tool NNFal can detect hard-to-find counterexamples in CPS having linear as well as non-linear dynamics.
Enhancing MVC architecture pattern description using its System of Systems model

When novice engineers (fresh or recent graduates with little industry experience) join a SaaS (Software-as-a-Service) product company, they are tasked with comprehending the product, especially its behavior and dynamics. We believe that they can comprehend more effectively if they know and understand the architecture patterns used in the product.
Tutorial Report on Legacy Software Modernization: A Journey From Non-AI to Generative AI Approaches

Dealing with ageing software is a reality of the industry, and even open source software systems. This is a great opportunity for the software engineering researchers to apply the traditional techniques of program analysis to solve problems of refactoring and modernization. The generative AI advancements have opened up a whole new world of possibilities for software engineering tasks such as code generation, code translation, bug fixing among others. Industry is keen on exploring scalable solutions for refactoring, automated testing and now automatic code generation.
The emergence of case matching in discontinuous DPs

Abstract This paper explores a distinction between two phenomena that yield multiple realizations of case associated with one nominal. The first is the familiar type of nominal case concord; the second is a new phenomenon we label “case iteration.” While case concord involves the morphological realization of case on categorially distinct elements via feature sharing, case iteration arises via a separate mechanism and involves the realization of multiple instances of a functional head, which we model as D. In this sense, the case concord/case iteration distinction mirrors the agreement/clitic doubling distinction in the domain of argument-predicate matching. We argue for the existence of case iteration as a separate phenomenon primarily on the basis of novel data from Tiwa (Tibeto-Burman; India). In Tiwa, traditional case concord in continuous DPs is ruled out, but case iteration is obligatory in discontinuous DPs. We also demonstrate that this phenomenon is attested in Amahuaca (Panoan; Peru) and explore related patterns crosslinguistically.
Embedded Librarians to Support Data-management Needs of a Multidisciplinary Research Program

is the Data Services Librarian for the University of Iowa Libraries.He assists researchers with the management and sharing of research data.Brian teaches workshops and a course on data management and consults with researchers on data management plans and implementing good data practices across the data lifecycle.He curates data
The influence of training datasets on LSTM-based irradiance prediction

Abstract In solar photovoltaic power generation, the prediction of solar irradiance is essential for minimizing energy costs and ensuring the provision of high-quality electricity. Deep learning models have recently gained popularity in the field, as numerous scholars have successfully employed them to predict solar irradiance. In line with this, this paper proposes three distinct methods for dividing the training dataset. Subsequently, these methods are employed in predicting solar irradiance using the LSTM-based model. Furthermore, an error analysis of the prediction results is conducted for each of the three models. The optimal training dataset division method is determined and proposed based on a comparison of the sizes of the three models using six error evaluation indexes.
Investigating the Correlation between Public Hospitals Managers' Accountability Pressure in Kerman City and Budgetary Slack with Focusing on the Role of Moral Intelligence and Information Asymmetry in 2022: A Descriptive Study

Investigating the Correlation between Public Hospitals Managers' Accountability Pressure in Kerman City and Budgetary Slack with Focusing on the Role of Moral Intelligence and Information Asymmetry in 2022: A Descriptive Study
Multi-View Scene Classification Based on Feature Integration and Evidence Decision Fusion

Leveraging multi-view remote sensing images in scene classification tasks significantly enhances the precision of such classifications. This approach, however, poses challenges due to the simultaneous use of multi-view images, which often leads to a misalignment between the visual content and semantic labels, thus complicating the classification process. In addition, as the number of image viewpoints increases, the quality problem for remote sensing images further limits the effectiveness of multi-view image classification. Traditional scene classification methods predominantly employ SoftMax deep learning techniques, which lack the capability to assess the quality of remote sensing images or to provide explicit explanations for the network’s predictive outcomes. To address these issues, this paper introduces a novel end-to-end multi-view decision fusion network specifically designed for remote sensing scene classification. The network integrates information from multi-view remote sensing images under the guidance of image credibility and uncertainty, and when the multi-view image fusion process encounters conflicts, it greatly alleviates the conflicts and provides more reasonable and credible predictions for the multi-view scene classification results. Initially, multi-scale features are extracted from the multi-view images using convolutional neural networks (CNNs). Following this, an asymptotic adaptive feature fusion module (AAFFM) is constructed to gradually integrate these multi-scale features. An adaptive spatial fusion method is then applied to assign different spatial weights to the multi-scale feature maps, thereby significantly enhancing the model’s feature discrimination capability. Finally, an evidence decision fusion module (EDFM), utilizing evidence theory and the Dirichlet distribution, is developed. This module quantitatively assesses the uncertainty in the multi-perspective image classification process. Through the fusing of multi-perspective remote sensing image information in this module, a rational explanation for the prediction results is provided. The efficacy of the proposed method was validated through experiments conducted on the AiRound and CV-BrCT datasets. The results show that our method not only improves single-view scene classification results but also advances multi-view remote sensing scene classification results by accurately characterizing the scene and mitigating the conflicting nature of the fusion process.
Annual Daily Irradiance Analysis of Clusters in Mexico by Machine Learning Algorithms

The assessment of solar resources involves the utilization of physical or satellite models for the determination of solar radiation on the Earth’s surface. However, a critical aspect of model validation necessitates comparisons against ground-truth measurements obtained from surface radiometers. Given the inherent challenges associated with establishing and maintaining solar radiation measurement networks—characterized by their expense, logistical complexities, limited station availability and the imperative consideration of climatic criteria for siting—countries endowed with substantial climatic diversity face difficulties in station placement. In this investigation, the measurements of annual solar irradiation, from meteorological stations of the National Weather Service in Mexico, were compared in different regions clustered by similarities in altitude, TL Linke, albedo and cloudiness index derived from satellite images; the main objective is to find the best ratio of annual solar irradiation in a set of clusters. Employing machine learning algorithms, this research endeavors to identify the most suitable model for predicting the ratio of annual solar irradiation and to determine the optimal number of clusters. The findings underscore the efficacy of the L-method as a robust technique for regionalization. Notably, the cloudiness index emerges as a pivotal feature, with the Random Forest algorithm yielding superior performance with a R2 score of 0.94, clustering Mexico into 17 regions.
Demo: On-Device Video Analysis with LLMs

We present a new on-device pipeline that efficiently summarizes lecture videos and provides relevant answers directly from a smartphone. We utilize widely accessible tools like OCR and Vosk speech-to-text, coupled with powerful large language models (LLMs), to identify crucial sentences and generate summaries. By harnessing the capabilities of LLMs and the computational power of mobile devices, we fine-tune and quantize BERT and GPT-2 to achieve efficient lecture video summarization and question answering on consumer-grade smartphones like the Pixel 8 Pro. Notably, this approach eliminates the need for cloud APIs, ensuring enhanced user privacy and minimal mobile data usage.
Salted Inference: Enhancing Privacy while Maintaining Efficiency of Split Inference in Mobile Computing

In split inference, a deep neural network (DNN) is partitioned to run the early part of the DNN at the edge and the later part of the DNN in the cloud. This meets two key requirements for on-device machine learning: input privacy and computation efficiency. Still, an open question in split inference is output privacy, given that the outputs of the DNN are observable in the cloud. While encrypted computing can protect output privacy too, homomorphic encryption requires substantial computation and communication resources from both edge and cloud devices.
OII: An Orientation Information Integrating Network for Oriented Object Detection in Remote Sensing Images

Oriented object detection for remote sensing images poses formidable challenges due to arbitrary orientation, diverse scales, and densely distributed targets (e.g., across terrain). Current investigations in remote sensing object detection have primarily focused on improving the representation of oriented bounding boxes yet have neglected the significant orientation information of targets in remote sensing contexts. Recent investigations point out that the inclusion and fusion of orientation information yields substantial benefits in training an accurate oriented object system. In this paper, we propose a simple but effective orientation information integrating (OII) network comprising two main parts: the orientation information highlighting (OIH) module and orientation feature fusion (OFF) module. The OIH module extracts orientation features from those produced by the backbone by modeling the frequency information of spatial features. Given that low-frequency components in an image capture its primary content, and high-frequency components contribute to its intricate details and edges, the transformation from the spatial domain to the frequency domain can effectively emphasize the orientation information of images. Subsequently, our OFF module employs a combination of a CNN attention mechanism and self-attention to derive weights for orientation features and original features. These derived weights are adopted to adaptively enhance the original features, resulting in integrated features that contain enriched orientation information. Given the inherent limitation of the original spatial attention weights in explicitly capturing orientation nuances, the incorporation of the introduced orientation weights serves as a pivotal tool to accentuate and delineate orientation information related to targets. Without unnecessary embellishments, our OII network achieves competitive detection accuracy on two prevalent remote sensing-oriented object detection datasets: DOTA (80.82 mAP) and HRSC2016 (98.32 mAP).
Construction and Implementation of Application-Oriented Undergraduate Teaching and Teaching Evaluation System Based on Multiple Information Fusion

At present, many schools implement teaching quality evaluation systems with student performance and practical activities as the core data of evaluation. Based on the introduction of the training objectives and discipline nature of the information management and information system specialty, this paper analyzes the construction principles of the multi-disciplinary curriculum system of the specialty and puts forward relevant countermeasures for the construction of the multi-disciplinary education system. This paper introduces the definition of practical teaching evaluation system and teaching evaluation algorithm. Among them, naive Bayesian algorithm can improve the sample accuracy by 95%, so it is necessary to build an application-oriented undergraduate practice teaching evaluation system.
Upper gastrointestinal haemorrhage patients' survival: A causal inference and prediction study

Abstract Background Upper gastrointestinal (GI) bleeding is a common medical emergency. This study aimed to develop models to predict critically ill patients with upper GI bleeding in‐hospital and 30‐day survival, identify the correlation factor and infer the causality. Methods A total of 2898 patients with upper GI bleeding were included from the Medical Information Mart for Intensive Care‐IV and eICU‐Collaborative Research Database, respectively. To identify the most critical factors contributing to the prognostic model, we used SHAP (SHapley Additive exPlanations) for machine learning interpretability. We performed causal inference using inverse probability weighting for survival‐associated prognostic factors. Results The optimal model using the light GBM (gradient boosting algorithm) algorithm achieved an AUC of .93 for in‐hospital survival, .81 for 30‐day survival in internal testing and .87 for in‐hospital survival in external testing. Important factors for in‐hospital survival, according to SHAP, were SOFA (Sequential organ failure assessment score), GCS (Glasgow coma scale) motor score and length of stay in ICU (Intensive critical care). In contrast, essential factors for 30‐day survival were SOFA, length of stay in ICU, total bilirubin and GCS verbal score. Our model showed improved performance compared to SOFA alone. Conclusions Our interpretable machine learning model for predicting in‐hospital and 30‐day mortality in critically ill patients with upper gastrointestinal bleeding showed excellent accuracy and high generalizability. This model can assist clinicians in managing these patients to improve the discrimination of high‐risk patients.