Reviewerly/exHarmony huggingface dataset:
Detection of Accurate Facial Detection using Hybrid Deep Convolutional Recurrent Neural Network

Facial Landmark discovery is an imperative issue in numerous PC vision applications about appearances. It is extremely testing as human faces in wild conditions regularly present expansive varieties fit as a fiddle because of various stances, impediments or demeanors. Profound neural systems have been connected to take in the guide from face pictures to confront shapes. To the best of our insight, Recurrent Neural Network (RNN) has not been utilized in this issue yet. In this paper, we propose a technique which uses RNN and Deep Neural Network (DNN) to take in the face shape. To start with, we design a system utilizing Convolutional Neural Network (CNN) to get the underlying Landmark estimation of appearances. At that point, we utilize feed-forward neural systems for neighborhood look where a segment based seeking technique is investigated. By utilizing LSTM- CNN-RNN, the underlying estimation is more dependable which makes the accompanying segment based pursuit doable and exact. Tests demonstrate that the worldwide system utilizing CNN-LSTM-RNN shows signs of improvement results than past systems in the two recordings and single picture. Our technique beats the cutting edge calculations particularly regarding fine estimation of Landmark spots.
Combining Knowledge Graph and Word Embeddings for Spherical Topic Modeling

Probabilistic topic models are considered as an effective framework for text analysis that uncovers the main topics in an unlabeled set of documents. However, the inferred topics by traditional topic models are often unclear and not easy to interpret because they do not account for semantic structures in language. Recently, a number of topic modeling approaches tend to leverage domain knowledge to enhance the quality of the learned topics, but they still assume a multinomial or Gaussian document likelihood in the Euclidean space, which often results in information loss and poor performance. In this article, we propose a Bayesian embedded spherical topic model (ESTM) that combines both knowledge graph and word embeddings in a non-Euclidean curved space, the hypersphere, for better topic interpretability and discriminative text representations. Extensive experimental results show that our proposed model successfully uncovers interpretable topics and learns high-quality text representations useful for common natural language processing (NLP) tasks across multiple benchmark datasets.
Design of Integrated Management System for English Teaching Resources Based on Artificial Intelligence

With the rapid development of the Internet, an extensive teaching resource sharing phenomenon appears in the field of education. However, there may be problems in the sharing of teaching resources between different campuses and the defects in resource information management. This article mainly addresses the sharing and management of school English teaching resources, and proposes the design of constructing English teaching resources information management system based on artificial intelligence technology. The design of the system is based on the design of resource storage system and the hardware level. And it will realize the software level of the system through the design and the operating environment. The experimental design and simulation test results show that the information management system is the design of English teaching information management system.
Application of Human Movement and Movement Scoring Technology in Computer Vision Feature in Sports Training

An important goal of computer vision research is to give computers human-like cognitive abilities so that the computer can recognize the environment in the visual field, understand the content of emotions, and take appropriate actions. Most of the information that people can perceive is obtained through vision. It is the human goal to make computers have the same visual functions as humans. At present, although computers do not have efficient, flexible, and intelligent human vision, with the joint efforts of researchers, people are gradually achieving their goals. The purpose of this thesis is to study the application of human movement and movement scoring technology in computer vision features in sports training. In this paper, the support vector machine (SVM) method is used to recognize the human movement in the video. After the recognition, the score is then performed, which can help sports athletes to carry out standardized training. The results of this paper show that the use of SVM method can basically achieve a recognition accuracy of more than 80% of human motion actions, so that motor actions can be scored better and more conveniently. Computer vision technology for action scoring can help athletes standardize various sports skills and improve their ability to compete by improving various sports skills.
D-BIN: A Generalized Disentangling Batch Instance Normalization for Domain Adaptation

Pattern recognition is significantly challenging in real-world scenarios by the variability of visual statistics. Therefore, most existing algorithms relying on the independent identically distributed assumption of training and test data suffer from the poor generalization capability of inference on unseen testing datasets. Although numerous studies, including domain discriminator or domain-invariant feature learning, are proposed to alleviate this problem, the data-driven property and lack of interpretation of their principle throw researchers and developers off. Consequently, this dilemma incurs us to rethink the essence of networks’ generalization. An observation that visual patterns cannot be discriminative after style transfer inspires us to take careful consideration of the importance of style features and content features. Does the style information related to the domain bias? How to effectively disentangle content and style features across domains? In this article, we first investigate the effect of feature normalization on domain adaptation. Based on it, we propose a novel normalization module to adaptively leverage the propagated information through each channel and batch of features called disentangling batch instance normalization (D-BIN). In this module, we explicitly explore domain-specific and domaininvariant feature disentanglement. We maneuver contrastive learning to encourage images with the same semantics from different domains to have similar content representations while having dissimilar style representations. Furthermore, we construct both self-form and dual-form regularizers for preserving the mutual information (MI) between feature representations of the normalization layer in order to compensate for the loss of discriminative information and effectively match the distributions across domains. D-BIN and the constrained term can be simply plugged into state-of-the-art (SOTA) networks to improve their performance. In the end, experiments, including domain adaptation and generalization, conducted on different datasets have proven their effectiveness.
Distributed H$\infty$-Constraint Robust Estimator for Multi-Sensor Networked Hybrid Uncertain Systems

Due to the complex communication channel environment, uncertainties, including random measurement delays, missing measurements and uncertain measurement noise variances, often exist in multi-sensor networked hybrid uncertain systems. These uncertainties will lead to poor accuracy and robustness for traditional information fusion methods. Hence, this paper presents a distributed robust estimator based on the H <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\infty$</tex-math></inline-formula> constraint to solve the mentioned issues in sensor networked systems. The nonlinear pseudomeasurement model is first constructed with Bernoulli distributed random variables to describe the mentioned uncertainties in the communication channels. Then, a sufficient condition with a local optimal upper bound on estimation error covariance is derived rigorously with respect to the robustness definition, which is based on the H <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\infty$</tex-math></inline-formula> constraint inequality to improve the robustness of state estimation. Furthermore, the proposed estimator is proven to be stable through boundedness analysis for the upper bound matrix. The numerical simulation and the target tracking experiment are finally applied to evaluate the effectiveness of the novel fusion estimator.
MapReduced Based A New Stream Cipher Technique for Data Encryption

Nowadays, data keeps increasing; this in turn makes big data one of the hot topics in the modern era of technology. The biggest challenge, however, is big data security and cryptography is one of the most secure techniques. In this proposed model, we use this technique to secure data via a proposed new stream cipher technique to process more than one block by dividing the total size of the block into two parts, and swapping them then, combine and apply XOR operation with key and make some of mathematical operation. This operation is of fifteen rounds which make it very difficult for attacks to guess the plaintext.
Bad Smells of Gang of Four Design Patterns: A Decade Systematic Literature Review

Gang of Four (GoF) design patterns are widely approved solutions for recurring software design problems, and their benefits to software quality are extensively studied. However, the occurrence of bad smells in design patterns increases the crisis of degenerating design patterns’ structure and behavior. Their occurrences are detrimental to the benefits of design patterns and they influence software sustainability by increasing maintenance costs and energy consumption. Despite the destructive roles of bad smells in such designs, there are an absence of studies systematically reviewing bad smells of GoF design patterns. This study systematically reviews a 10-year state of the art sample, identifying 16 studies investigating this phenomenon. Following a thorough evaluation of the full contents, we observed that the occurrence of bad smells have been investigated in proportion to four granularity levels of analysis: Design level, category level, pattern level, and role level. We identified 28 bad smells, categorized under code smells and grime symptoms, and emphasized their relationship with GoF pattern types and categories. The utilization of design pattern bad smell detection approaches and datasets were also discussed. Consequently, we observed that the research phenomenon is growing intensively, with a prominent focus of studies analyzing code smell occurrences rather than grime occurrences, at various granularity levels. Finally, we uncovered research gaps and areas with significant potentials for future research.
Distributed Ledger for Cybersecurity: Issues and Challenges in Railways

The railway is a complex technical system of systems in a multi-stakeholder environment. The implementation of digital technologies is essential for achieving operational excellence and addressing stakeholders’ needs and requirements in relation to the railways. Digitalization is highly dependent on an appropriate digital infrastructure provided through proper information logistics, whereas cybersecurity is critical for the overall security and safety of the railway systems. However, it is important to understand the various issues and challenges presented by governance, business, and technical requirements. Hence, this paper is the first link in the chain to explore, understand, and address such requirements. The purpose of this paper is to identify aspects of distributed ledgers and to provide a taxonomy of issues and challenges to develop a secure and resilient data sharing framework for railway stakeholders.
Sentiment Analysis and Image Classification in Social Networks with Zero-Shot Deep Learning: Applications in Tourism

Social media is one of the data sources that could provide more information or potential knowledge in almost any field of application. One of the main challenges of machine learning and big data is to solve the difficulty involved in the identification, classification, and, in general, the processing of all this data to extract useful information for a specific field. In this work, we propose a methodology for the detection of tourist places of interest through the combined use of images and text from social networks. For that purpose, we will be assisted by pre-trained neural networks for image classification and sentiment analysis. The result is frequency information of types of places according to a tourism-specific taxonomy combined with user sentiment indicators, which is potentially relevant information for tourism analysts.
The Research on Optimization Strategy of College English Teaching Mode Based on 5G Technology and Artificial Intelligence Technology

In order to overcome the limitations of the current college English teaching mode in China, and to expand the service scope of 5G technology and artificial intelligence technology, this paper proposes an optimization strategy of English teaching mode in colleges and universities based on 5G technology and artificial intelligence technology. This optimization strategy breaks the traditional college English teaching mode in China, and introduces 5G technology and artificial intelligence technology, which greatly improves the intelligence of English classroom in colleges and universities. At the same time, the optimization strategy can also make 5G technology and artificial intelligence technology skillfully combine, which can improve the quality of English teaching in colleges and universities from many aspects, and comprehensively and systematically realize the teaching concept of "teach students according to their aptitude". The results show that the optimization strategy can not only overcome the limitations of English model in China in colleges and universities , but also improve the service scope of 5G technology and artificial intelligence technology, so as to improve students' English listening, speaking, reading and writing ability in multiple dimensions in colleges and universities.
Application and Effectiveness of Big Data and Artificial Intelligence in the Construction of Nursing Sensitivity Quality Indicators

In order to explore the quality management efficiency of applying big data and artificial intelligence in nursing quality index, a method of building a nursing management platform integrating nursing indicators and nursing events is proposed. Based on the investigation of the application demand of nursing information system, the method achieves timely data sharing and transmission through WLAN technology and realizes nursing management monitoring, nursing quality index enquiry, and automatic statistical analysis under the vertical management mode of nursing. The results showed that 77 people (73%) thought the time decreased, 19 people (18%) thought the time was the same, and 9 people (7%) thought the time increased. In terms of intelligent application and big data of nursing information management system, there is a significant difference in nursing management efficiency before and after using nursing management information system (P &lt; 0.001). The nursing management control platform is designed and applied, and the nursing quality control method and actual management process are improved, which is very good for strengthening nursing quality management. The overall optimization of the quality control process is realized, which helps to mobilize the initiative and enthusiasm of nursing staff and continuously improve the effectiveness of nursing management and nursing efficiency.
Conversational Multi-Hop Reasoning with Neural Commonsense Knowledge and Symbolic Logic Rules

One of the challenges faced by conversational agents is their inability to identify unstated presumptions of their users' commands, a task trivial for humans due to their common sense.In this paper, we propose a zeroshot commonsense reasoning system for conversational agents in an attempt to achieve this.Our reasoner uncovers unstated presumptions from user commands satisfying a general template of if-(state ), then-(action ), because-(goal ).Our reasoner uses a state-ofthe-art transformer-based generative commonsense knowledge base (KB) as its source of background knowledge for reasoning.We propose a novel and iterative knowledge query mechanism to extract multi-hop reasoning chains from the neural KB which uses symbolic logic rules to significantly reduce the search space.Similar to any KBs gathered to date, our commonsense KB is prone to missing knowledge.Therefore, we propose to conversationally elicit the missing knowledge from human users with our novel dynamic question generation strategy, which generates and presents contextualized queries to human users.We evaluate the model with a user study with human users that achieves a 35% higher success rate compared to SOTA.
Sentiment Analysis Based on Reviews Using Machine Learning Techniques

In our daily life, people’s opinions and experiences are important sources of information. To measure the feeling of people’s opinions the term used that is called sentiment analysis. The text is the main method of communicating on the Internet in modern digital time. Sentiment analysis captures the user’s views, moods, and their opinion related to the specific services provided by the business organization in a real-time. This research focuses on Roman Urdu reviews. It has three basic classes: negative, positive, and neutral where reviews are classified. The proposed method is Analysis of different machine learning algorithms with different datasets has made and a comparison shows, SVM performs the best result on used data sets, a clear result in the form of accuracy, precision, recall, and f1 score shows the results against the specific techniques against the dataset.
Jointly Learning to Repair Code and Generate Commit Message

We propose a novel task of jointly repairing program codes and generating commit messages.Code repair and commit message generation are two essential and related tasks for software development.However, existing work usually performs the two tasks independently.We construct a multilingual triple dataset including buggy code, fixed code, and commit messages for this novel task.We provide the cascaded models as baseline, which are enhanced with different training approaches, including the teacher-student method, the multi-task method, and the backtranslation method.To deal with the error propagation problem of the cascaded method, the joint model is proposed that can both repair the code and generate the commit message in a unified framework.Experimental results show that the enhanced cascaded model with teacher-student method and multitask-learning method achieves the best score on different metrics of automated code repair, and the joint model behaves better than the cascaded model on commit message generation.
Audience Evaluation and Analysis of Symphony Performance Effects Based on the Genetic Neural Network Algorithm for the Multilayer Perceptron (GA-MLP-NN)

Traditional symphony performances need to obtain a large amount of data in terms of effect evaluation to ensure the authenticity and stability of the data. In the process of processing the audience evaluation data, there are problems such as large calculation dimensions and low data relevance. Based on this, this article studies the audience evaluation model of teaching quality based on the multilayer perceptron genetic neural network algorithm for the data processing link in the evaluation of the symphony performance effect. Multilayer perceptrons are combined to collect data on the audience's evaluation information; genetic neural network algorithm is used for comprehensive analysis to realize multivariate analysis and objective evaluation of all vocal data of the symphony performance process and effects according to different characteristics and expressions of the audience evaluation. Changes are analyzed and evaluated accurately. The experimental results show that the performance evaluation model of symphony performance based on the multilayer perceptron genetic neural network algorithm can be quantitatively evaluated in real time and is at least higher in accuracy than the results obtained by the mainstream evaluation method of data postprocessing with optimized iterative algorithms as the core 23.1%, its scope of application is also wider, and it has important practical significance in real-time quantitative evaluation of the effect of symphony performance.
Symbolic Brittleness in Sequence Models: On Systematic Generalization in Symbolic Mathematics

Neural sequence models trained with maximum likelihood estimation have led to breakthroughs in many tasks, where success is defined by the gap between training and test performance. However, their ability to achieve stronger forms of generalization remains unclear. We consider the problem of symbolic mathematical integration, as it requires generalizing systematically beyond the training set. We develop a methodology for evaluating generalization that takes advantage of the problem domain's structure and access to a verifier. Despite promising in-distribution performance of sequence-to-sequence models in this domain, we demonstrate challenges in achieving robustness, compositionality, and out-of-distribution generalization, through both carefully constructed manual test suites and a genetic algorithm that automatically finds large collections of failures in a controllable manner. Our investigation highlights the difficulty of generalizing well with the predominant modeling and learning approach, and the importance of evaluating beyond the test set, across different aspects of generalization.
Evaluating Temporal Bias in Time Series Event Detection Methods

The detection of events in time series is an important task in several areas of knowledge where operations monitoring is essential. Experts often have to deal with choosing the most appropriate event detection method for a time series, which can be a complex task. There is a demand for benchmarking different methods in order to guide this choice. For this, standard classification accuracy metrics are usually adopted. However, they are insufficient for a qualitative analysis of the tendency of a method to precede or delay event detections. Such analysis is interesting for applications in which tolerance for "close" detections is important rather than focusing only on accurate ones. In this context, this paper proposes a more comprehensive event detection benchmark process, including an analysis of temporal bias of detection methods. For that, metrics based on the time distance between event detections and identified events (detection delay) are adopted. Computational experiments were conducted using real-world and synthetic datasets from Yahoo Labs and resources from the Harbinger framework for event detection. Adopting the proposed detection delay-based metrics helped obtain a complete overview of the performance and general behavior of detection methods.