Reviewerly/exHarmony huggingface dataset:
Automatic Generation of Interactive Nonlinear Video for Online Apparel Shopping Navigation

We present an automatic generation pipeline of interactive nonlinear video for online apparel shopping navigation. Our approach was inspired by Google's “Messy Middle” theory, which suggests that people mentally are faced with two tasks—exploration and evaluation—before purchasing online. Given a set of apparel product presentation videos, our navigation UI organizes them to optimize users' product exploration and automatically generates interactive videos for users' product evaluation. To support automatic methods, we proposed a video clustering similarity ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\operatorname{CSIM}$</tex-math></inline-formula> ) and a camera movement similarity ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\operatorname{MSIM}$</tex-math></inline-formula> ), as well as a comparative video generation algorithm for product recommendation, presentation, and comparison. To evaluate our pipeline's effectiveness, we conducted several user studies. The results showed that our pipeline can help users complete the consumption process more efficiently, making it easier for them to understand and choose a product.
Association Between Patient Portal Activities and End-of-Life Outcomes Among Deceased Patients in the Last 12 Months of Life (FR206C)

Results.Of 35,886 outpatients included in the analysis, 9,700 (27%) had a provider launch their "Patient Values Tab."Of 10,055 inpatients included in the analysis, 4,517 (45%) had a provider launch their "Patient Values Tab." Patterns of adoption varied by provider role, with 67% of chaplains, 77% of social workers, 49% of advanced practice providers, 32% of nurses, and 27% of attending physicians launching the "Patient Values Tab" for at least one patient; this was consistent across outpatient and inpatient cohorts. Conclusion. Patterns of adoption of an innovative"Patient Values Tab" EHR feature varied across clinical contexts and provider roles.Ongoing research is exploring factors that impede/facilitate adoption at the level of the patient, provider, and clinical context to inform strategies to enhance/sustain adoption.
ICS information security evaluation method based on fuzzy analytic hierarchy process

With the increasing scale of applications and system programs and the increased processing capacity of software, in order to improve the efficiency of software, more and more information-sharing technologies are adopted. There are more and more loopholes in large-scale information-sharing, mainly because of the uncertainty of information-sharing scheduling, which leads to great uncertainty in the control flow of programs. In This paper, the theory of fuzzy cooperation evaluation is applied to the research. Through the analysis of the participants in data-sharing and their fuzzy cooperation relationships, the fuzzy cooperation evaluation model of data-sharing is constructed based on the degree of participants' participation. The benefit allocation model of data-sharing subjects is constructed by using the fuzzy Shapley value and Banzhaf-Coleman right index. It provides a new research perspective and ideas for the smooth long-term practice of data-sharing and the equilibrium of its rights and interests.
Improving Oracle Bone Characters Recognition via A CycleGAN-Based Data Augmentation Method

Oracle bone inscription is the earliest writing system in China which contains rich information about the history of Shang dynasty. Automatically recognizing oracle bone characters is of great significance since it could promote the research on history, philology and archaeology. The proposed solutions for oracle bone characters recognition are mainly based on machine learning or deep learning algorithms which rely on a large number of supervised training data. However, the existing dataset suffers from the problem of severe class imbalance. In this work, we propose a CycleGAN-based data augmentation method to overcome the limitation. Via learning the mapping between the glyph images data domain and the real samples data domain, CycleGAN could generate oracle character images of high-quality. The quality is evaluated using the quantitative measure. Totally, 185362 samples are generated which could serve as a complementary to the existing dataset. With these generated samples, the state of the art results of recognition task on OBC306 are improved greatly in terms of mean accuracy and total accuracy.
An intrusion detection model using extreme learning machine

In the information age, with the increase of data feature dimensions, the performance of Intrusion Detection System (IDS) in training time and classification accuracy is declining. A large number of researchers have conducted a lot of research on how to use machine learning algorithms for fast and accurate intrusion detection. This paper presents an IDS model based on Extreme Learning Machine (ELM). Firstly, the intrusion detection data set NSL-KDD is normalized. Then, uses the normalized data as input, and the hidden layer activation function of ELM algorithm suitable for intrusion detection is optimized by comparing the correct rate of the model under different hidden layer activation functions and the number of hidden layer nodes. Finally, the optimal number of hidden layer nodes corresponding to the optimal hidden layer activation function is obtained. Experiments show that the optimized ELM has better performance.
SURVEY PAPER ON IMAGE CAPTION GENERATOR USING NEURAL NETWORK

Image captioning is becoming an essential task for virtual assistants, editing software, and image indexing.Automatically generating a caption for an image is known as image captioning.It is becoming more popular as a freshly developed scientific field.The objective of image captioning requires the capture and expression of semantic information of images in natural languages.The approach for generating image captions generally consists of two general methods i.e., encoding and decoding.The RNN is used to create phrases, whereas the CNN is used to extract characteristics or features from images.When an image is given as input, the model has been trained such that it generates captions that most accurately describe the input image.For a particular image, some software tools create and provide a caption.Using CNN, it recognizes noteworthy items and their connections.A current study has shown CNN's benefits for automatic image caption synthesis and machine translation to address the issue.
Differential privacy protection algorithm for network sensitive information based on singular value decomposition

In order to reduce the risk of data privacy disclosure and improve the effect of information privacy protection, a differential privacy protection algorithm for network sensitive information based on singular value decomposition is proposed. TF-IDF method is used to extract network sensitive information text. By comparing the word frequency of network sensitive information, high word frequency word elements in network information content are collected to obtain the mining results of network sensitive information text. According to the decision tree theory, the equal difference privacy budget allocation mechanism is improved to achieve equal difference privacy budget allocation. By discarding some small singular values and corresponding spectral vectors, the data can be disturbed, and the availability of the original data can be retained, so that it can truly represent the original data set structure. According to the results of equal difference privacy budget allocation and singular value decomposition disturbance, the data of high-dimensional network graph is reduced by random projection, singular value decomposition is performed on the reduced data, and Gaussian noise is added to the singular value. Finally, the matrix to be published is generated through the inverse operation of singular value decomposition to achieve differential privacy protection of network sensitive information. The experimental results show that the privacy protection quality of this algorithm is high and the data availability is effectively improved.
NO SQL- NOT OBLIGATORY SQL (NATURAL LANGUAGE TO SQL CONVERSION)

Natural Language Processing (NLP) is a subfield of Artificial Intelligence that is used to build intelligent computers that can interact with humans, narrowing the gap between man and machine.NLP is widely used in research fields and allows users to ask questions in natural language and obtain the required results, making data access convenient and easy.However, NLP involves a lot of complexities, which can be a challenge for users who do not have knowledge about query languages like SQL.This project aims to address this issue and help users who are unfamiliar with query languages by using NLP techniques to extract data.For example, placement cell officers who work with student databases can use this technique to extract data easily.Speech recognition is an important aspect of NLP, allowing machines to recognize spoken or written words and phrases in a systemunderstandable format.Natural speech, which is widely used and convenient for users, is accepted by the software.However, analyzing or generating natural language text involves several challenges, such as tokenization, syntactic and semantic analysis, and working with dictionaries and grammars necessary for such analysis.Semantic Grammar is used to perform this translation.The system has a Graphic User Interface and an Error Handling module to ensure a smooth and user-friendly experience for the users.Overall, this project showcases the potential of NLP in simplifying complex data management tasks and making data access more convenient for users.
Defects via factorization algebras

We provide a mathematical formulation of the idea of a defect for a field theory, in terms of the factorization algebra of observables and using the BV formalism. Our approach follows a well-known ansatz identifying a defect as a boundary condition along the boundary of a blowup, but it uses recent work of Butson–Yoo and Rabinovich on boundary conditions and their associated factorization algebras to implement the ansatz. We describe how a range of natural examples of defects fits into our framework.
Entanglement witness and multipartite quantum state discrimination

Abstract We consider multipartite quantum state discrimination and show that the minimum-error discrimination by separable measurements is closely related to the concept of entanglement witness (EW). Based on the properties of EW, we establish some necessary and/or sufficient conditions on minimum-error discrimination by separable measurements. We also provide some conditions on the upper bound of the maximum success probability over all possible separable measurements. Our results are illustrated by examples of multidimensional multipartite quantum states. Finally, we provide a systematic way in terms of the EW to construct multipartite quantum state ensembles showing nonlocality in state discrimination.
Image rendering efficiency improvement based on deep autoencoder in virtual environment

Aiming at the problem that traditional image rendering methods are time-consuming and complex and cannot meet the application scenarios of modern design. In this paper, the depth self-encoder and capsule network in artificial intelligence (AI) technology are used to study the automatic rendering of images. Firstly, Scharr filter is used to reconstruct the image in Stack Capsule Autoencoder (SCAE) model to enhance the accuracy of image target detection and reduce the loss of reconstructed image. Then the loss function of the model is improved. Finally, in the Modified National Institute of Standards and Technology (MNIST) data set, Canadian Institute for Advanced Research-10 (CIFAR-10) data set, the performance of the improved model is tested on the Canadian Institute for Advanced Research-100 (CIFAR-100) data set and the data set made by the author. The test results show that the accuracy of the improved model is higher than that of the traditional K-means, Autoencoder network (AE) unsupervised algorithm and the improved depth clustering unsupervised model. The stacked capsule self-encoder using Scharr filter can make the classification effect of the model more accurate. The improved algorithm in this paper provides some references for improving the efficiency of image rendering in virtual environment.
PowerMat: context-aware recommender system without user item rating values that solves the cold-start problem

Recommender systems serves as an important technical asset in many modern companies. With the increasing demand for higher precision of the technology, more and more research and investment has been allocated to the field. One important sub-field of recommender systems that has been stagnating is context-aware recommender systems. Due to the difficulty of collecting input dataset, the amount of research on context-aware recommender systems is much less than other sub-fields of recommender systems. In this paper, we propose a new algorithm named PowerMat to tackle the context-aware recommendation problem. We build our theory on matrix factorization and Zipf’s law, and also more recent research work such as DotMat. We prove by experiments that our method achieves superior results to the classic matrix factorization algorithm and other context-aware recommender systems such as MovieMat+. In addition, by theoretical analysis, we show that our algorithm solves the cold-start problem for context-aware recommendation.
Process-aware part retrieval for cyber manufacturing using unsupervised deep learning

Cyber manufacturing service, which connects end users with manufacturers over the internet, is significantly hampered by the lack of an automated part retrieval method. The state-of-the-art is focused on automatic shape retrieval, which does not consider manufacturing process requirements, such as material properties. This paper proposes a manufacturing process-aware part retrieval method using deep unsupervised learning that considers both part shape and material properties. Part retrieval results show that the proposed method yields 93.0% process and function class label matching precision, which outperforms the shape-only part retrieval model and supervised learning models trained with process, function, or both labels.
PHP web-based mathematics learning application: An analysis of student responses

This study aims to analyze and describe students' responses to PHP web-based mathematics learning applications. The research method used is quantitative descriptive. The research sample were 63 students of class X at SMK Al-Ihya Selajambe, Kuningan Jawa Barat. The research instrument uses a student response questionnaire. Data analysis used is quantitative descriptive analysis in the form of percentages. The results of the data in the form of percentages are described using response interpretation criteria. Based on the results of the study, PHP web-based mathematics learning applications gets a percentage of responses 87,04 % for the layout aspect which was in the very good response interpretation criteria, 85,71 % for the display aspect which was in the very good response interpretation criteria, and 88,94 % for the function aspect which was in the very good response interpretation criteria. Overall, the PHP web-based mathematics learning application gets a percentage of responses 87,70% which was in the very good response interpretation criteria. It can be concluded that PHP web-based mathematics learning applications get a very good response from students to use in learning.
DOCUMENT VERIFICATION AND VALIDATION USING BLOCKCHAIN

According to statistics from the Indian Ministry of Education, there are roughly one million graduates each year.Some of them will continue their education at high schools or university institutions while others will be prepared to find employment.The students' many great performance records, transcripts of grades, diplomas, etc., will serve as a crucial point of reference for admission to new institutions or jobs when they have completed their studies.Only the names of the schools and the students are included when schools create different prizes or diplomas.Events that result in the graduation document being forged are frequently recognised since there is no effective anti-forge system.The blockchain-based digital document system would be suggested as a solution to the issue of document forgery.In this system, issuing digital documents is done in the manner listed below.Create an electronic version of a paper document and add it to the database, along with any relevant data.In the meantime, determine the electronic file's hash value.The hash value should then be added to the block in the chain system.To attach to the paper document, the system will generate a connected QR-code and an inquiry string code.It will enable the demand unit to check the validity of the paper document using website searches or mobile phone scanning.The technology not only increases the legitimacy of various paper-based documents, but also significantly lowers the chance of document loss because to the blockchain's changeable qualities.
DESIGN AND IMPLEMENTATION OF FIRE FIGHTING ROBOT USING SURVEILLANCE CAMERA

The development of a fire fighting robot with cameras is a promising solution for the increasing demand for effective and efficient firefighting tools.The robot is equipped with cameras that provide live footage of the fire scene, allowing firefighters to better assess the situation and make informed decisions.The robot is also equipped with water sprayers that can be remotely controlled to extinguish the fire.This technology has the potential to significantly reduce the risk of injury and death among firefighters, as well as the damage caused by fires.In conclusion, fire fighting robots equipped with cameras provide a range of advantages and applications for firefighting and emergency response situations.These robots can enter hazardous environments and provide real-time information to firefighters, enhancing their safety and ability to make informed decisions.
Neural Networks Map the Ebb and Flow of Tiny Ponds

Ponds play an outsized role in carbon emissions, but their size makes them hard to track. Enter machine learning.
Reverse survival model (RSM): a pipeline for explaining predictions of deep survival models

The aim of survival analysis in healthcare is to estimate the probability of occurrence of an event, such as a patient’s death in an intensive care unit (ICU). Recent developments in deep neural networks (DNNs) for survival analysis show the superiority of these models in comparison with other well-known models in survival analysis applications. Ensuring the reliability and explainability of deep survival models deployed in healthcare is a necessity. Since DNN models often behave like a black box, their predictions might not be easily trusted by clinicians, especially when predictions are contrary to a physician’s opinion. A deep survival model that explains and justifies its decision-making process could potentially gain the trust of clinicians. In this research, we propose the reverse survival model (RSM) framework that provides detailed insights into the decision-making process of survival models. For each patient of interest, RSM can extract similar patients from a dataset and rank them based on the most relevant features that deep survival models rely on for their predictions. RSM acts as an add-on to a deep survival model and offers three functionalities: 1) Finding the most relevant clinical measurements for the probability density functions (PDFs) of events. 2) Categorizing patients into disjoint clusters based on the similarity of their survival PDFs. 3) Ranking similar patients based on the similarity of survival outcomes and relevant clinical measurements. The explainability of deep survival models is rarely addressed in the literature. Therefore, the RSM pipeline is a unique approach to explaining the predictions of deep survival models. We validated the RSM pipeline by testing it on a synthetic dataset and MIMIC-IV, a dataset of intensive care unit (ICU) clinical observations. Our experiments showed that given a deep survival model and a patient of interest, RSM can successfully detect similar patient records from historical data and rank them based on the similarities between their survival PDFs and the most relevant patient observations.
Mitigating Data Stalls in Deep Learning with Multi-times Data Loading Rule

With the growth of AI data scale, most deep learning jobs separate data storage and computation tasks. Therefore, I/O optimization has gradually become an important issue for training optimization of deep learning. Recent studies focus on I/O optimization for those deep learning methods with one-time data loading rule, where each data is used only once per epoch. However, these methods cannot deal with some jobs with multi-times data loading rule (e.g., meta-learning). By analyzing the characteristic of multi-times data loading, we design a simple, intuitive and effective cache replacement strategy called steady cache strategy. This strategy utilizes a cache to mitigate data stalls and converts the data placement problem to a 0/1 knapsack problem. To our best knowledge, we are the first to mitigate data stalls in AI jobs with multi-times data loading rule and our method is suitable for multi-job scenario. Our experiments demonstrate that the steady cache strategy achieves great improvement over the LRU strategy.
Factors Supporting the Digitalization of Business: A Descriptive Study

Digitalization has become a buzzword in the business world, and its importance cannot be overstated in the current era of technology-driven change.As businesses strive to remain competitive, digitalization is seen as a key factor that can drive innovation, increase efficiency, and improve customer experiences.Technological infrastructure was identified as a critical factor that supports digitalization, including access to reliable and fast internet, hardware, and software.Leadership support was also identified as a key factor, as managers and business owners who prioritize digitalization tend to invest in technology and provide training to employees.Overall, this study provides insights into the factors that support digitalization in business, highlighting the importance of technological infrastructure, leadership support, employee skills, and customer demands.The findings of this study can be useful for businesses that are considering digital transformation, as they can help identify areas of focus and investment.By embracing digitalization and leveraging the factors that support it, businesses can increase their competitiveness, improve operational efficiency, and enhance customer experiences.
DiCausal: Exploiting Domain Knowledge for Interactive Causal Discovery

We propose an interactive causal discovery system called DiCausal, which allows users to apply their simple domain knowledge of how variables are generated and interactively edit the graph during the causal discovery process without incurring too much burden. A novel form of domain knowledge representation and an adapted feature engineering method are introduced in DiCausal. Two existing causal discovery algorithms are adapted for verification. Experiment proves that such a way of incorporating domain knowledge into the discovery algorithms can achieve better results than pure data-driven methods.