Reviewerly/exHarmony huggingface dataset:
A Novel Approach to Detect Unmanned Aerial Vehicle using Pix2Pix Generative Adversarial Network

The usage of Unmanned Aerial Vehicles(UAVs) are increasing day by day. UAV’s usage for terrorism activities is one of the main threats faced by every country. As per THE HINDU news media, there are 6 lakh rogue drones in India. The ineffectiveness of standard air defence systems against tiny, sluggish, low-flying UAVs has been acknowledged in the National Counter Rogue Drone Guidelines. Due to their various sizes, flying characteristics, capabilities, performance parameters, missions, and weaknesses, the growing drone threats are challenging to fight. So an efficient UAV detecting system is required to detect the UAVs and prevent these illegal activities using UAVs. So for the first, this paper proposes a Generative Adversarial Network (GAN)-based method to detect the UAVs. Pix2Pix generative adversarial network framework for this UAV detection model.
INN: An Interpretable Neural Network for AI Incubation in Manufacturing

Both artificial intelligence (AI) and domain knowledge from human experts play an important role in manufacturing decision making. Smart manufacturing emphasizes a fully automated data-driven decision-making; however, the AI incubation process involves human experts to enhance AI systems by integrating domain knowledge for modeling, data collection and annotation, and feature extraction. Such an AI incubation process not only enhances the domain knowledge discovery but also improves the interpretability and trustworthiness of AI methods. In this article, we focus on the knowledge transfer from human experts to a supervised learning problem by learning domain knowledge as interpretable features and rules, which can be used to construct rule-based systems to support manufacturing decision making, such as process modeling and quality inspection. Although many advanced statistical and machine learning methods have shown promising modeling accuracy and efficiency, rule-based systems are still highly preferred and widely adopted due to their interpretability for human experts to comprehend. However, most of the existing rule-based systems are constructed based on deterministic human-crafted rules, whose parameters, such as thresholds of decision rules, are suboptimal. Yet the machine learning methods, such as tree models or neural networks, can learn a decision rule based structure without much interpretation or agreement with domain knowledge. Therefore, the traditional machine learning models and human experts’ domain knowledge cannot be directly improved by learning from data. In this research, we propose an interpretable neural network (INN) model with a center-adjustable sigmoid activation function to efficiently optimize the rule-based systems. Using the rule-based system from domain knowledge to regulate the INN architecture not only improves the prediction accuracy with optimized parameters but also ensures the interpretability by adopting the interpretable rule-based systems from domain knowledge. The proposed INN will be effective for supervised learning problems when rule-based systems are available. The merits of the INN model are demonstrated via a simulation study and a real case study in the quality modeling of a semiconductor manufacturing process. The source code of this work is hosted here: https://github.com/XiaoyuChenUofL/Interpretable-Neural-Network .
Video super-resolution with inverse recurrent net and hybrid local fusion

Video super-resolution converts low-resolution videos to sharp high-resolution ones. In order to make better use of temporal information in video super-resolution, we design inverse recurrent net and hybrid local fusion. We concatenate the original low-resolution input sequence and its inverse sequence repeatedly. The new sequence is viewed as a combination of different stages, and is processed sequentially by using orent net. The outputs of the last two stages in opposite directions are fused to generate the final images. Our inverse recurrent net can extract more bidirectional temporal information in the input sequence, without adding parameter to the corresponding unidirectional recurrent net. We also propose a hybrid local fusion method which uses parallel fusion and cascade fusion for incorporating sliding-window-based methods into our inverse recurrent net. Extensive experimental results demonstrate the effectiveness of the proposed inverse recurrent net and hybrid local fusion, in terms of visual quality and quantitative evaluations. The code will be released at https://github.com/5ofwind.
Hybrid Contrastive Quantization for Efficient Cross-View Video Retrieval

With the recent boom of video-based social platforms (e.g., YouTube and TikTok), video retrieval using sentence queries has become an important demand and attracts increasing research attention. Despite the decent performance, existing text-video retrieval models in vision and language communities are impractical for large-scale Web search because they adopt brute-force search based on high-dimensional embeddings. To improve efficiency, Web search engines widely apply vector compression libraries (e.g., FAISS [26]) to post-process the learned embeddings. Unfortunately, separate compression from feature encoding degrades the robustness of representations and incurs performance decay. To pursue a better balance between performance and efficiency, we propose the first quantized representation learning method for cross-view video retrieval, namely Hybrid Contrastive Quantization (HCQ). Specifically, HCQ learns both coarse-grained and fine-grained quantizations with transformers, which provide complementary understandings for texts and videos and preserve comprehensive semantic information. By performing Asymmetric-Quantized Contrastive Learning (AQ-CL) across views, HCQ aligns texts and videos at coarse-grained and multiple fine-grained levels. This hybrid-grained learning strategy serves as strong supervision on the cross-view video quantization model, where contrastive learning at different levels can be mutually promoted. Extensive experiments on three Web video benchmark datasets demonstrate that HCQ achieves competitive performance with state-of-the-art non-compressed retrieval methods while showing high efficiency in storage and computation. Code and configurations are available at https://github.com/gimpong/WWW22-HCQ.
GL-LSTM Model For Multi-label Text Classification Of Cardiovascular Disease Reports

In recent years, the rapid growth of electronic data and information has gotten a lot of attention to find relevant knowledge such as textual information. The goal of automatic text classification is to automatically predict textual articles classes, especially in the medical domain. However, for some applications, the used data must inherently be described by more than one label. In this research, a new scheme of medical multi-label text classification is investigated which is based on intelligent engineering features using GloVe technique and LSTM classifier. The main particularity of GloVe permits the extraction of informative features to the word level automatically and capture the global and local textual semantics. The choice of the LSTM model is motivated by the success that has been achieved by taking into account the very long-term dependencies between words. The experiment of our approach named GL-LSTM based on Ohsumed cardiovascular text dataset has produced impressive results with an overall accuracy of 0.927 compared with related works existing in the literature
An Intelligent Unsupervised Approach for Handling Context-Dependent Words in Urdu Sentiment Analysis

The characteristic of context dependency in Urdu words needs to be handled carefully while performing Urdu sentiment analysis. In this research, an already constructed Urdu sentiment lexicon of positive and negative words is further expanded by the addition of context-dependent words. These context-dependent words are used with or without conjunctions. Rules are formulated for assigning polarities to those context-dependent words that are surrounded by the positive or negative words. These rules were incorporated in the Urdu sentiment analyzer. Fusion of these rules for handling context-dependent words and the expanded Urdu sentiment lexicon resulted in increasing the accuracy of the Urdu sentiment analyzer from 83.43% to 89.03% with 0.8655 precision, 0.9053 recall, and 0.8799 F-measure, which is a statistically significant improvement.
Hate Speech Detection using Convolutional Neural Network Algorithm Based on Image

Hate speech is words behavior that can cause an attitude of violence and anarchy against other individuals or groups. The internet has become necessary in this day and age, so internet morals need to be considered. However, several parties deviate from using the internet to spread hate speech, such about race, ethnicity, and religion. Nowadays, hate speech detection systems are usually through text but hate speech detection through images tends to be rare. For that reason, this study is aimed to detect whether there is hate speech or not in the selected image. This project uses the Convolutional Neural Network (CNN) algorithm and Deep Learning method to classify the aspect of hate speech contained in an image and recognize any hate speech on the image through the existing text. After this application is developed, the machine learning system can detect some hate speech on an image that contains the text. It achieves about 95.89% accuracy and 94.43% precision. After that, the authors hoped that the authorities could reduce hate speech in the community and follow up more quickly.
Nowcasting System Based on Sky Camera Images to Predict the Solar Flux on the Receiver of a Concentrated Solar Plant

As part of the research for techniques to control the final energy reaching the receivers of central solar power plants, this work combines two contrasting methods in a novel way as a first step towards integrating such systems in solar plants. To determine the effective power reaching the receiver, the direct normal irradiance was predicted at ground level using a total sky camera, TSI-880 model. Subsequently, these DNI values were used as the inputs for a heliostat model (Fiat-Lux) to trace the sunlight’s path according to the mirror features. The predicted valuex of flux, obtained from these simulations, differ of less than 20% from the real values. This represents a significant advance in integrating different technologies to quantify the losses produced in the path from the heliostats to the central receiver, which are normally caused by the presence of atmospheric attenuation factors.
Forecasting of Power Output of a PVPS Based on Meteorological Data Using RNN Approaches

Artificial intelligence (AI) has become increasingly popular as a tool to model, identify, optimize, forecast, and control renewable energy systems. This work aimed to evaluate the capability of the artificial neural network (ANN) procedure to model and forecast solar power outputs of photovoltaic power systems (PVPSs) by using meteorological data. For this purpose, based on the literature review, important factors affecting energy generation in a PVPS were selected as inputs, and a recurrent neural network (RNN) architecture was established. After completing the trained network, the RNN capability was assessed to predict the energy output of the PVPS for days not included in the training database. The performance evaluation of the trained RNN revealed a regression value of 0.97774 for test data, whereas the RMSE and the mean actual output power for a sample day were 0.0248 MJ and 0.538 MJ, respectively. In addition to RMSE, an error histogram and regression plots obtained by MATLAB were employed to evaluate the network’s capability, and validation results represented a sufficient prediction accuracy of the trained RNN.
Group data freshness scheme for outsourced data in distributed systems

Data freshness ensures accessing recent data that could help in achieving high business values and providing effective customer service. Group data freshness is a challenging aspect in a distributed outsourced environment, as stale data among different entities may mislead the business goal of the system. Generally, three-party data outsourcing model is found in practice: users, data owner, and cloud service provider. The users require to register with the data owner to access data files directly from the cloud service provider. A scheme verifying the freshness of whole outsourced data of its readers is called the group data freshness auditing scheme (GDFAS). Existing GDFASs focus on a probabilistic guarantee and require high computational cost at the data owner. In this paper, an efficient group data freshness auditing scheme is proposed, where the data owner does auditing in a distributed system with the help of the system users. As the data owner is not directly involved in their user's data access, it needs mechanisms such as auditing data through an additional third-party to ensure the data is fresh. However, the third-party data storage service provider may not be fully trusted by a data owner. In such context, auditing data with respect to its freshness property without involving additional third-party storage service is challenging, but would be more effective in terms of the system's performance and efficacy. The proposed GDFAS provides real-time data freshness verification using Merkle hash trees. In comparison to the existing scheme, it takes less computational cost at the data owner without involving any third party and less communication cost between the data owner and the service provider. The proposed GDFAS is implemented on the AWS cloud and the auditing cost at the data owner is experimentally evaluated. The proposed GDFAS is analyzed and compared with the relevant existing scheme and is found that the proposed GDFAS outperforms other schemes with respect to its security and efficiency.
Real time Indian sign language recognition system

Hand gestures are one of the nonverbal communication modalities used in sign language. It is most often used by deaf and/or dumb individuals who have hearing or speech impairments to communicate with other deaf and/or dumb people or with normal people. Many manufacturers across the world have created various sign language systems; however they are neither adaptable nor cost- effective for end users. To address this, a model has been developed that provides a system prototype that can detect sign language automatically, allowing deaf and dumb individuals to transfer the message more successfully with normal people. The static visuals were processed with a convolutional neural network and the feature extraction approach, and each sign was trained with ten samples. Image processing methods are used to determine the fingertip location of static pictures and convert them to text. The suggested technique can recognize the signer's pictures that are taken in real time during the testing phase. The results demonstrate that the suggested Sign Language Recognition System is capable of accurately recognizing pictures.
Affirmative Ant Colony Optimization Based Support Vector Machine for Sentiment Classification

Sentiment analysis is part of contextual text mining, which detects, extracts and supports an organization in understanding their brand or service in social sentiment while monitoring the reviews provided by customers in online shops. The rise of online shopping and digitalization is practically achieved, and the quality of products is tough for users to judge. There is no model to find out about the same or unlike a set of people with similar sentiment analysis concerning online product evaluations. In this paper optimization-based classification algorithm is proposed namely, Affirmative Ant Colony Optimization Based Support Vector Machine (AACOSVM) to classify sentiments provided by customers in online shopping. This paper provides a new Ant Colony Optimization method via providing a novel pheromone model for support vector machine optimization parameters in two steps. The first one is statute of state transition, and the second step is statute of state updates. They aim to allow the ants to use the fake pheromone path to pick parameters and to motivate ants to create subsets having the least classification mistakes. The proposed work includes product review datasets from Amazon to assess the performance of the AACOSVM against existing classifiers, namely, Entropy-Based Classifier (EBC) and Enhanced Feature Attention Network (EFAN). Various review datasets are accessible at Amazon for various items. This research effort has identified a dataset from DVDs, books, kitchen appliances and electronics from the many multiple available review datasets. It utilizes the natural foraging behavior of ants towards searching for food to identify and classify the sentiments present in the product reviews. AACOSVM is evaluated using two standard data mining performance metrics, namely F-Measure and Classification Accuracy. Results indicate that the proposed classification algorithm AACOSVM achieves better F-Measure and Classification Accuracy than the EBC and EFAN classifiers.
Evaluation of Functional Magnetic Resonance Imaging under Artificial Intelligence Algorithm on Plan-Do-Check-Action Home Nursing for Patients with Diabetic Nephropathy

This study aimed to evaluate the effect of functional magnetic resonance imaging (fMRI) under the fuzzy C-means (FCM) clustering algorithm on plan-do-check-action (PDCA) home nursing for patients with diabetic nephropathy (DN). As the characteristics of fMRI image data were combined, the FCM algorithm was improved and applied into the clustering processing of fMRI activation regions of patients. 64 patients with DN were chosen as the research objects and were divided into the research group with PDCA home nursing and the control group with routine home nursing. The patients were randomly divided into the research group (n = 32) and the control group (n = 32). The curative effect, nursing satisfaction, and quality of life of patients after nursing were compared. The results showed that the coverage of fMRI activation points was significantly higher as being detected by the FCM algorithm, and the running time was shortened by 33.6 min. After nursing, the total effective rates in the research group and the control group were 87.5% vs. 34.4% in 3 months, 93.8% vs. 68.8% in 6 months, and 96.9% vs. 75.0% in 12 months, respectively; those in the research group were significantly higher than those in the control group (P < 0.05). The nursing satisfaction score (91.3 ± 4.5 vs. 80.9 ± 5.2) and nursing service quality score (89.7 ± 6.6 vs. 80.3 ± 7.1) in the research group were also significantly higher than those in the control group (P < 0.05). Meanwhile, the scores of each item after nursing in the research group were significantly higher than those in the control group (P < 0.05). The improved FCM algorithm detected the activation regions in the fMRI images more effectively, which could provide help for diagnosis and reduce error and misdiagnosis. At the same time, the PDCA home nursing also offered great help to the recovery of patients with DN, which was more superior for the curative effect of hospitalization, the promotion of recovery, and the improvement of patients' quality of life.
Research on College Students’ Physique Testing Platform Based on Big Data Analysis

With the progress of the times, people’s living standards have significantly improved, gradually put physical health in the first place, college students as the country’s future talent reserve, its physical health development is particularly important. But for a long time, the physique level of college students has failed to reach the standard, and universities are exploring ways to improve the physique level of college students. In view of the existing deficiencies in the monitoring platform of student’s physique, we fully utilize the advanced technology of computer and Internet development and follow the basic principles of economical practicality, scalability, user-friendliness and real-time information exchange to establish monitoring and service platform for college students’ physique. With the aid of the thinking mode of big data and related technology, build the physical health of college students in China big data analytics platform framework, physical health of large numbers of college students in China are put forward According to the analysis platform construction principle, data source, data handling, data platform and application platform, etc., in order to guide the physical health of college students practice in our country, to promote the reform of physical health education for students in colleges and universities. By using big data analysis technology, 65,535 physical health records of all students in a university from 2017 to 2019 were recorded as data sources, and algorithm based on distance was applied to cluster analysis of two groups of data classified by male and female gender, and a series of data were processed, converted, and modeled.
Cybersecurity decision support model to designing information technology security system based on risk analysis and cybersecurity framework

The proposed work was a recommendation model for designing cyber security decision support, in building an information technology security system based on risk analysis and the ISO/IEC 27001 cybersecurity framework. The proposed model aimed to obtain the best security system in mitigating security threats. This paper contributed to strategic policymakers in designing cyber security decision support recommendations to determine the best steps in designing information technology security systems. The model built can map the priority value of threat mitigation based on the relative threat score against the relative evaluation score of the implementation of ISO/IEC 27001 compliance. The mitigation priority value is the key in determining priority recommendations for building an information technology security system based on the ISO/IEC 27001 framework. Furthermore, the results implementation of information technology security system recommendations is tested by carrying out security attacks directly on the system being built. The work ends by conducting a statistical evaluation of the system built based on the recommendations of the information technology security system. The results achieved indicate an increase in the average value of the evaluation of ISO/IEC 27001 compliance from 36.27 to 82.37 with the p-value of Paired T-Test being 0.002138 < 0.05, meaning that there is a significant influence between threats to information technology security systems that implement and do not apply the recommendations of information technology security systems to the ISO/IEC 27001 compliance evaluation index value. Furthermore, based on 12 types of threat samples, it shows a decrease in the average threat criticality level from 8.75 to 4.00 with the p-value of the chi-square test being 0.0006605 < 0.05 and Fisher Test's p-value is 0.000008284 < 0.05, meaning that there is an association relationship between threats to information technology security systems that apply recommendations and do not apply recommendations to the criticality level of information technology security threats. While the results of the evaluation of the relationship between the implementation of security system recommendations on cybersecurity attack mitigation showed an increase in the effectiveness of cyber-attack mitigation from an average rating of 18.32 to 40.74 with the p-value of the chi-square test being 0.000005221 < 0.05 and the Fisher Test being 0.00000005658 < 0.05 means that there is an association relationship between systems that implement and do not implement recommendations based on ISO/IEC 27001 for cybersecurity attack mitigation.