Reviewerly/exHarmony huggingface dataset:
An approach for Face Detection and Face Recognition using OpenCV and Face Recognition Libraries in Python

With the extraordinary growth in images and video data sets, there is a mind-boggling want for programmed understanding and evaluation of data with the assistance of smart frameworks, since physically it is a long way off. Individuals, unlike robots, have a limited capacity to distinguish unexpected expressions. As a result, the programmed face proximity frame- work is important in face identification, appearance recognition, head-present evaluation, human-PC cooperation, and other applications. Software that uses facial recognition for face detection and identification is regarded as biometric. This study converts the mathematical aspects of a person’s face into a face print, which is then stored in a database to verify an individual’s identification. A deep learning system compares a digital image or an image taken quickly to a previously stored image(which is saved in the database). The face has a significant function in interpersonal communication for identifying oneself. Face recognition technology determines the size and placement of a human face in a digital picture. Facial recognition software has a wide range of uses in the consumer market and in the security and surveillance sectors. The COVID pandemic has brought facial recognition into greater focus lately than ever before. Face detection and recognition play a vital part in security systems that people need to interact with without making physical contact. The pattern of online exam proctoring is employing face detection and recognition. Facial recognition is used in the airline sector to enable rapid, accurate identification and verification at every stage of the passenger trip. In this research, we focused on image quality because it is the major drawback in existing algorithms and used OPEN CV, Face Recognition, and designed algorithms using libraries in python. This study discusses a method for facial recognition along with its implementation and applications.
Video Classification Using CNN and Ensemble Learning

Video classification is the task of producing an index or a label that is relevant to the given video. For this classification input can be in frames, heatmaps of respective videos, etc., depending on the application. Video classification is more than just simple image classification, it can be done using the concept of Convolutional Neural Network (CNN). Based on the application, and dataset limitations there are many extensions to this method such as CNN- Long Short-Term Memory (CNN-LSTM), Convolutional 3D network, Pose detection and LSTM, etc. In this project, we used the UCF11 (YouTube Action) dataset and classified them based on the action. A CNN model has been developed and trained with the dataset and the tested results are improved with a better approach called Ensemble Learning. Using this approach, the same CNN model having the same architecture gave better prediction results.
Language Detection Using Natural Language Processing

Natural language processing (NLP) is a method for correctly identifying text based on the provided content or topic matter. An extensive study will make it simple to interpret any language and comprehend what is being said. Despite the fact that NLP is a challenging technique, notable examples include Siri and Alexa. Natural language detection allows us to determine the language being used in a given document. A Python-written model that has been utilised in this work can be used to analyse the basic linguistics of any language. The "words" that make up sentences are the essential building blocks of knowledge and its expression. Correctly identifying them and comprehending the situation in which they are used are essential. NLP steps in to help us in this circumstance by making it easier for us to identify the linguistics used in a particular piece of information, whether it be written or vocal. NLP gives computers the ability to understand human language and respond correctly, performing language detection for us. The current paper provides a summary of developments in tongue process, including analysis, establishment, various areas of rapid advancement in natural language processing research, development tools, and techniques.
Prevention of Cyber Attacks Using Deep Learning

Today, technology has become a integral part of the day-to-day life for everyone of us. It has become very advanced, where doing bad has become more simpler than doing good. One such bad things done using technology is cyber attack. A cyber attack is the attack done by the hackers, phishers using internet as the medium. Phishing, website scamming are the most common cyber attacks committed by the cybercriminals. Phishing is the practice of stealing the sensitive, personal data from the victim machine via anonymous URL’s. These URL’s are sent to victim’s (user’s) machine via emails, SMS (smishing) and other social media. Website Scamming is the practice of making financial scams(done by hackers) through fraudulent e-commerce websites and payment gateways. The proposed work focuses on making an ideal application, which could detect if the URL is genuine or not so that many people who are exploring those fake sites and URLs, can be prevented from becoming the victims of the cyber attacks.
Automated Resume Classification System Using Ensemble Learning

One of job recruiters’ biggest challenges is selecting a suitable resume from the pool of resumes. For a single role, thousands of candidates send their resumes. Manually selecting the resume from a large number of applicants and assigning them suitable positions is time taking and not feasible. An automated system can make this process easy and efficient. This system takes candidates’ resumes in word, pdf, or any format and classifies them according to the skill set mentioned in the resume. We propose an ensemble deep-learning model to classify the resume.
QR Code with Block Chain Technology for Medical Device Ownership

Medical device quality and safety are important because they affect people's lives and health, and society has placed a high value on these factors. Healthcare supply chain has been majorly intruded for fake medical devices and technologies. Products that are counterfeited are frequently created to reach profit from the original product's lower price. Because of its high risk, a traceability system must be set up. Most businesses are making greater efforts to prevent counterfeiting. The QR code system presented in this project is application-, security-and privacy-focused. The existing system uses non-fungible token (NFT) based solution that enhances the tokenization protocols. In our paper we propose the usage of QR technology. It is easy and affordable to create a QR code for a medical device's identification. Because QR codes are simple to use. This system stores the information in decentralized storage of the Blockchain technology system to verify the legitimacy of medical equipment, ensuring that users are not dependent on outside apps. Blockchain which is defined as the distributed ledger system that is based upon apowerful cryptographic technique and is used to capture and preserve information about medical devices, making it difficult for anybody to tamper with them. Along with Blockchain, the usage of decentralized system makes the privacy of the data more effective. The paper also allows the customer end to view and verify the medical device ownership.
Continual learning with Bayesian model based on a fixed pre-trained feature extractor

Abstract Deep learning has shown its human-level performance in various applications. However, current deep learning models are characterized by catastrophic forgetting of old knowledge when learning new classes. This poses a challenge such as in intelligent diagnosis systems where initially only training data of a limited number of diseases are available. In this case, updating the intelligent system with data of new diseases would inevitably downgrade its performance on previously learned diseases. Inspired by the process of learning new knowledge in human brains, we propose a Bayesian generative model for continual learning built on a fixed pre-trained feature extractor. In this model, knowledge of each old class can be compactly represented by a collection of statistical distributions, e.g., with Gaussian mixture models, and naturally kept from forgetting in continual learning over time. Unlike existing class-incremental learning methods, the proposed approach is not sensitive to the continual learning process and can be additionally well applied to the data-incremental learning scenario. Experiments on multiple medical and natural image classification tasks reveal that the proposed approach outperforms state-of-the-art approaches that even keep some images of old classes during continual learning of new classes.
Comprehensive assessment of open source software ecosystem health

Recent surveys expose that the use of Open Source Software (OSS) is increasingly becoming a need for organizations in their development projects. However, deciding a proper OSS to be adopted or to contribute to its development is a complex and error-prone task. Analyzing the OSS ecosystem (OSSECO) health may help providing information about: (1) the OSS itself (number of commits, days after the last release, etc.); and (2) their main actors (number of contributors, partners, etc.). There exist proposals that go further and provide aggregated high-level indicators (e.g. visibility as an aggregation of number of community events, number of partners, and other metrics). Nevertheless, there is a lack of useful OSSECO analysis tools to ease the decision making on which OSSECO has the health required by a potential OSS adopter or contributor. In this work, we provide OSS-CARE (OSSeCo heAlthy monitoR and analysEr), an OSS-independent, fully automatic, and real-time framework to assess OSSECO's health. OSS-CARE supports defining the ecosystem health objectives of potential OSS adopters, OSS contributors, and even OSS managers to inspect their provided health. These objectives are defined based on a well-established model characterizing health metrics that can be potentially aggregated by using a Bayesian network technique. Moreover, the integrated monitoring and analysis components perform an automated assessment of OSSECO's health by checking the fulfillment of the required health objectives. Furthermore, the result is shown in an appealing dashboard that may ease the complex decision making of which OSS to choose.
Effective Graph-Based Modeling of Articulation Traits for Mispronunciation Detection and Diagnosis

Mispronunciation detection and diagnosis (MDD) manages to pinpoint phone-level erroneous pronunciation segmentations and provide instant and informative diagnostic feedback to L2 (second-language) learners. Among the various modeling paradigms for MDD, dictation-based neural methods have recently become a de facto standard, which identifies pronunciation errors and returns diagnostic feedback at the same time by aligning the recognized phone sequence uttered by an L2 learner to the corresponding canonical phone sequence of a given text prompt. Despite their decent efficacy, dictation-based methods have at least two downsides. First, the dictation process and alignment process are made independent of each other, often resulting in a poor diagnostic feedback. Second, prior knowledge about the articulation traits of the canonical phones in the text prompt is not fully utilized in MDD. On account of this, we propose a novel end-to-end MDD method that can streamline the dictation process and the alignment process in a non-autoregressive manner. In addition, knowledge about phone-level articulation traits are extracted with a graph convolutional network (GCN) to obtain more discriminative phonetic embeddings so as to promote the MDD performance. An extensive set of experiments conducted on the L2-ARCTIC benchmark dataset suggest the feasibility and effectiveness of our approach in relation to competitive baselines.
Fuzzy ontology datatype learning using Datil

Semantic Web technologies (especially, ontologies) are very important nowadays in intelligent applications to represent the important knowledge in an application domain. Shortly after the birth of ontologies, the inability to adequately handle knowledge affected by imprecision or vagueness, which is inherent to many real-world scenarios, was observed. Fuzzy ontologies were proposed and developed as a solution. However, the problem of building fuzzy ontologies has received little attention in the literature. In fact, there are few examples of publicly available fuzzy ontologies, suggesting that new techniques helping users to build fuzzy ontologies are needed. In this paper, we develop advanced strategies to learn fuzzy datatypes for fuzzy ontologies. Our approach uses real-world data and unsupervised clustering algorithms to compute the fuzzy datatype definitions in an automatic way. Our learning algorithm is implemented in the Datil system, a tool supporting different input formats and devices, including a mobile version. Our approach could be orthogonal to other existing approaches learning fuzzy subclass axioms.
MVAM: Multi-variant Attacks on Memory for IoT Trust Computing

The growth of the Internet of Things (IoT) and the availability of low-cost cloud services have led to an increase in the sensory and data processing needs of IoT systems. TrustZone is a hardware-based security solution designed for ARM processors in IoT handheld systems. It provides memory isolation to protect trusted application data from potential exploitation by malicious actors. This paper examines the vulnerabilities of the TrustZone extension of ARM Cortex-M processors and develops a threat model to carry out these attacks. After performing multi-variety attacks from different angles, it is found that TrustZone is susceptible to buffer overflow attacks that can compromise the security of other trusted apps. The vulnerabilities in TrustZone are attributed to the absence of validation for input parameters in the entry function and the failure to perform boundary-checking of memory allocation. The vulnerability known as Achilles’ Heel can be found in any aspect of the TrustZone security system, making it susceptible to MOFlow attacks because the framework does not have adequate automatic safeguards in place. The proposed attacks were successfully tested on two recent ARM Cortex-M23 and M33 processors. Finally, a trust model is proposed to address these vulnerabilities.
Ensemble and continual federated learning for classification tasks

Abstract Federated learning is the state-of-the-art paradigm for training a learning model collaboratively across multiple distributed devices while ensuring data privacy. Under this framework, different algorithms have been developed in recent years and have been successfully applied to real use cases. The vast majority of work in federated learning assumes static datasets and relies on the use of deep neural networks. However, in real-world problems, it is common to have a continual data stream, which may be non-stationary, leading to phenomena such as concept drift. Besides, there are many multi-device applications where other, non-deep strategies are more suitable, due to their simplicity, explainability, or generalizability, among other reasons. In this paper we present Ensemble and Continual Federated Learning, a federated architecture based on ensemble techniques for solving continual classification tasks. We propose the global federated model to be an ensemble, consisting of several independent learners, which are locally trained. Thus, we enable a flexible aggregation of heterogeneous client models, which may differ in size, structure, or even algorithmic family. This ensemble-based approach, together with drift detection and adaptation mechanisms, also allows for continual adaptation in situations where data distribution changes over time. In order to test our proposal and illustrate how it works, we have evaluated it in different tasks related to human activity recognition using smartphones.
Short-Term PV Power Prediction Based on FCM-ISSA-LSTM

Accurate prediction of short-term photovoltaic output power is an important technical measure to improve the grid-connected capacity of intermittent photovoltaic power generation. A short-term PV power prediction method is proposed in this paper based on FCM-ISSA-LSTM as to improve the obvious problems of low accuracy and poor applicability. Fuzzy-C-Means clustering method (FCM) is firstly used to identify and classify the input data of three types of typical weather for all-weather historical data, and then improved sparrow search algorithm (ISSA) is presented to optimize the structure and parameters of long short-term memory (LSTM) neural network in the training process for higher optimization efficiency and accuracy. The simulation results show that the proposed method has relatively better prediction accuracy and efficiency in short-term photovoltaic power prediction.
Action identification with fusion of BERT and 3DCNN for smart home systems

Action identification for smart home ecosystem has experienced a rising interest among relevant fields. Nevertheless, thoroughly understanding context and movement in video, which is the key component for an effective action identification system and a visual-based smart home ecosystem, remains a challenge. Recently, 3DCNN architectures including SlowFast, have proved to be the proper solution for this issue due to ability to filter spatiotemporal features in video. Additionally, the growth in Natural Language Processing, particularly BERT, has shown the potential of utilizing the Attention Mechanism for extracting temporal relationship of movement throughout a video. We introduce a novel fusing approach aiming to fully exploit the temporal relationship within spatiotemporal features produced by 3DCNN and enhance the efficiency of action identification system for smart home surveillance. The proposed method consists of two models: SlowFast with two distinct ResNet-50 backbones for spatiotemporal features extraction and BERT for aggregating temporal relationship. These models are sequentially stacked using two varieties of fusing techniques: early-ensemble and late-ensemble, constructing a robust unified system with the capability to compete against state-of-the-art modalities. This paper also contribute a new Kinetics51 dataset for our experiment on these mentioned models. The proposed method provides positively high top-1 accuracy for the following datasets: specifically, HMDB-51 dataset, which contains 51 variety of actions, achieves 78.05% using 10-crop testing; Kinetics51, our derived dataset from Kinetics400, achieves 82.53% using 10-crop testing. The performance on challenging action identification datasets HMDB51 and our Kinetics51 has shown the robustness of the mentioned fusing technique applied on two proposed models.
Image super-resolution with multi-scale fractal residual attention network

Deep neural networks can significantly improve the quality of super-resolution. However, previous work has made insufficient use of low-resolution scale features and channel-wise information, hence hindering the representational ability of CNNs. To address these issues, a multi-scale fractal residual attention network (MFRAN) is proposed. Specifically, MFRAN consists of fractal residual blocks (FRBs), dual-enhanced channel attention (DECA), and dilated residual attention blocks (DRABs). Among them, FRB applies multi-scale extension rule to continuously expand into a fractal structure that detects multi-scale features; DRAB constructs a combined dilated convolution to learn a generalizable and expressive feature space with a larger receptive field; DECA employs one-dimensional convolution to achieve cross-channel information interaction, and enhance the flow of information between groups by channel shuffling. Then, we integrate horizontal feature representations via local residual and feature fusion. Extensive quantitative and qualitative evaluations of benchmark datasets show that our proposed approach outperforms state-of-the-art methods in terms of quantitative metrics and visual results.
FEPVNet: A Network with Adaptive Strategies for Cross-Scale Mapping of Photovoltaic Panels from Multi-Source Images

The world is transitioning to renewable energy, with photovoltaic (PV) solar power being one of the most promising energy sources. Large-scale PV mapping provides the most up-to-date and accurate PV geospatial information, which is crucial for planning and constructing PV power plants, optimizing energy structure, and assessing the ecological impact of PVs. However, previous methods of PV extraction relied on simple models and single data sources, which could not accurately obtain PV geospatial information. Therefore, we propose the Filter-Embedded Network (FEPVNet), which embeds high-pass and low-pass filters and Polarized Self-Attention (PSA) into a High-Resolution Network (HRNet) to improve its noise resistance and adaptive feature extraction capabilities, ultimately enhancing the accuracy of PV extraction. We also introduce three data migration strategies by combining Sentinel-2, Google-14, and Google-16 images in varying proportions and transferring the FEPVNet trained on Sentinel-2 images to Gaofen-2 images, which improves the generalization performance of models trained on a single data source for extracting PVs in images of different scales. Our model improvement experiments demonstrate that the Intersection over Union (IoU) of FEPVNet in segmenting China PVs in Sentinel-2 images reaches 88.68%, a 2.37% increase compared to the HRNet. Furthermore, we use FEPVNet and the optimal migration strategy to extract photovoltaics across scales, achieving a precision of 94.37%. In summary, this study proposes the FEPVNet model with adaptive strategies for extracting PVs from multiple image sources, with significant potential for application in large-scale PV mapping.
Improved Convolutional Neural Network YOLOv5 for Underwater Target Detection Based on Autonomous Underwater Helicopter

To detect a desired underwater target quickly and precisely, a real-time sonar-based target detection system mounted on an autonomous underwater helicopter (AUH) using an improved convolutional neural network (CNN) is proposed in this paper. YOLOv5 is introduced as the basic CNN network because of its strength, lightweight and fast speed. Due to the turbidity and weak illumination of an undesirable underwater environment, some attention mechanisms are added, and the structure of YOLOv5 is optimized to improve the performance of the detector for sonar images with a 1–3% increment of mAP which can be up to 80.2% with an average speed of 0.025 s (40 FPS) in the embedded device. It has been verified both in the school tank and outdoor open water that the whole detection system mounted on AUH performs well and meets the requirements of real time and light weight using limited hardware.