Reviewerly/exHarmony huggingface dataset:
Self-Supervised Federated Adaptation for Multi-Site Brain Disease Diagnosis

The multi-site approach has attracted increasing attention in brain disease diagnosis, because it can improve the prediction performance by integrating sample information from different medical institutions. However, its training procedure requires the transmission of subject's original images or features among sites, which may cause privacy disclosure. In this paper, we propose a self-supervised federated adaptation (S2FA) framework for robust multi-site prediction, which can reduce the risk of privacy disclosure. As far as we know, it is the first work to investigate the cross-site brain disease diagnosis, which trains model on source sites and tests on target site, often occurring in clinical practice. Firstly, we implement a decentralized federated optimization strategy, by which each site communicates model parameters periodically. Secondly, we construct an auxiliary self-supervised model for target site through transferring knowledge from source sites with self-paced learning. Then, a hash mapping is proposed to encode the target feature, simultaneously reducing the risk of privacy information disclosure and alleviating data heterogeneity among sites. Finally, we achieve the cross-site prediction by weighted federated source model and auxiliary target model. Experimental results on multi-site datasets show that the proposed S2FA can accurately identify brain disease. Our codes are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/nuaayqm/S2FA</uri> .
Summarization of Elicitation Conversations to Locate Requirements-Relevant Information

[Context and motivation] Conversations around requirements, such as interviews and workshops, are a key activity of requirements elicitation, and play a significant role in the creation of requirements specifications. [Question/problem] While these conversations contain a wealth of knowledge, requirements engineers use them mainly through note-taking during the conversation and by recalling the information from their memory. There is potential for supporting practitioners by retrieving important information from the recordings of these conversations. [Principal ideas/results] Although transcriptions can be automatically generated with good accuracy, they often contain excessive text to be efficiently used for processing requirements elicitation sessions. Thus, we observed a need to transform these datasets into a useful format for requirements engineers to analyze. [Contribution] We present REConSum, a prototype that utilizes Natural Language Processing (NLP) to summarize requirements conversations. REConSum takes as input a transcribed conversation, and it filters the speaker turns by keeping only those that include a question and that are expected to contain, or to be answered with, requirements-relevant information. In addition to presenting REConSum, we experiment with different algorithms to assess the most effective combination.
Deep learning-based breast cancer detection using VGG-NiN architecture

Breast cancer is a steadily growing disease that provides elevated mortality than other cancer types for global women. The survival rate of this cancer type can be uplifted through appropriate screening and diagnosis where the role of early detection of breast cancer is significant. In this regard, the design of Computer-Aided Diagnosis (CAD) with improved reliability is always in demand for earlier detection. The work proposed a VGG-NiN architecture integrating the pretrained CNN of VGG-16 together with spatial pyramid pooling (SPP) layer and network-in-network (NiN). The above-said layers are stacked to classify the breast cancer severities with minimum parameters and to speed up the model convergence and training process. For this, the work utilized the INbreast dataset for digital mammogram inputs. The obtained experimental results reveal the robustness of the proposed method as compared with the state-of-the-art CAD techniques.
Trinity‐Yolo: High‐precision logo detection in the real world

Abstract Logo detection has a wide range of applications in the multimedia field, such as video advertising research, brand awareness monitoring and analysis, trademark infringement detection, autonomous driving and intelligent transportation. Compared with other types of images, logo images in the real world have greater diversity in appearance and more complex backgrounds. Therefore, identifying logos from images is a challenge. A strong baseline method Trinity‐Yolo, is proposed, which incorporates attention mechanism, stripe pooling and weighted boxes fusion (WBF) into the state‐of‐the‐art Yolov4 framework for large‐scale logo detection. The attention mechanism improves the feature extraction ability of the deep detection model, the stripe pooling expands the field of view of the model and the weighted boxes fusion enables the model to obtain excellent corrections when outputting the prediction boxes. Trinity‐Yolo can solve the problems of lack of training data, multi‐scale objects and inconsistent bounding‐box regression. On the dataset LogoDet‐3K, the average performance of Trinity‐Yolo is 3% higher than that of Yolov4. Compared with other deep detection models, the performance of Trinity‐Yolo is improved more. The experimental performance on other existing datasets verifies the effectiveness of this method.
Temporal Co-Attention Guided Conditional Generative Adversarial Network for Optical Image Synthesis

In the field of SAR-to-optical image synthesis, current methods based on conditional generative adversarial networks (CGANs) have satisfying performance under simple scenarios, but the performance drops severely under complicated scenarios. Considering that SAR images can form a robust time series due to SAR’s all-weather imaging ability, we take advantage of this and extract a temporal correlation from bi-temporal SAR images to guide the translation. To achieve this, we introduce a co-attention mechanism into the CGAN that learns the correlation between optically-available and optically-absent time points, selectively enhances the features of the former time point, and eventually guides the model to a better optical image synthesis on the latter time point. Additionally, we adopt a strategy to balance the weight of optical and SAR features to extract better features from the SAR input. With these strategies, the quality of synthesized images is notably improved in complicated scenarios. The synthesized images can increase the spatial and temporal resolution of optical imagery, greatly improving the availability of data for the applications of crop monitoring, change detection, and visual interpretation.
The search for the cognitive map

Microbial communities are found throughout the biosphere, from human guts to glaciers, from soil to activated sludge. Understanding the statistical properties of such diverse communities can pave the way to elucidate the common mechanisms ...Multiple ecological forces act together to shape the composition of microbial communities. Phyloecology approaches—which combine phylogenetic relationships between species with community ecology—have the potential to disentangle such forces but are often ...
A real-time voice cloning system with multiple algorithms for speech quality improvement

With the development of computer technology, speech synthesis techniques are becoming increasingly sophisticated. Speech cloning can be performed as a subtask of speech synthesis technology by using deep learning techniques to extract acoustic information from human voices and combine it with text to output a natural human voice. However, traditional speech cloning technology still has certain limitations; excessively large text inputs cannot be adequately processed, and the synthesized audio may include noise artifacts like breaks and unclear phrases. In this study, we add a text determination module to a synthesizer module to process words the model has not included. The original model uses fuzzy pronunciation for such words, which is not only meaningless but also affects the entire sentence. Thus, we improve the model by splitting the letters and pronouncing them separately. Finally, we also improved the preprocessing and waveform conversion modules of the synthesizer. We replace the pre-net module of the synthesizer and use an upgraded noise reduction algorithm combined with the SV2TTS framework to achieve a system with superior speech synthesis performance. Here, we focus on improving the performance of the synthesizer module to achieve higher-quality speech synthesis audio output.
Trust based Reputation Framework for Data Center Security in Cloud Computing Environment

A modern method of accessing and using computer resources on the Internet is through the usage of cloud computing. Contrary to the conventional Internet, there are certain security concerns and vulnerabilities related to confidentiality, integrity, and availability. Most of the current trust management system models do not fully account for the relevance of interactions, which hinders the accuracy of the trust ratings and needs to be fixed. In this paper, Trust based reputation framework for data center security in cloud computing environment is proposed. In this framework, each DCs reputation values are determined independently from the trust degrees of its servers. The level of each server's trust is computed based on the Interaction Efficiency (IE), Service Satisfaction Index (SSI) and Service Response Delay (SRD). During the transmission from DC to user, the user evaluates the reputation of the serving DC and ignores if the reputation is low. Simulation results using Cloudsim indicate that the proposed TRF-DCS has lower computation cost and service access delay with higher correctness of data and service success rate.
Gesture-based Control of a Bionic Arm – A Prototype Design

A bionic arm is a robotic hand that can be controlled manually or by machines. It resembles and acts like a human hand. It makes use of AI technology to make decisions and control other hand movements. The primary function of this hand is to ensure that all productions and tasks are flawless. The movement of the hand can be controlled via a wireless medium; it utilises a camera to grasp the gesture or the task that it needs to perform. It can be used for industrial applications where the periodic function is not carried out by the machine; it is specially designed to perform unique tasks. This can be used to accent the words in the understandable mode for deaf people as well; it converts written or stored content into accent. This system performs multiple tasks as per the instructions provided to it as an input. Most applications that humans cannot perform in various situations can be performed by this bionic arm.
A Competent Medical Image Steganography using Improved Optimization Algorithm with Huffman Encoding Techniques

Steganography is considered a reliable solution for avoiding third-party attacks. It is the process of embedding confidential information in another image. So that, they can be sent to the user's end with full security. When it comes to data privacy and authentication, steganography techniques are essential. This proposed medical image steganography procedure by exploiting Dual Tree-Complex Wavelet Transform based transform and image encryption procedure were proposed in this proposed work. Then an improved SSOA optimization algorithm is engaged to detect smooth edge blocks. As a result, the selection of pixels for embedding is facilitated. Embedding the Secret data into the cover image is then done using a double matrix XOR encoding. After the embedding process, the stego image is produced. The Stego output image is further compressed with Huffman Coding which produces the stego Compressed image. The stego image is compressed for fast transmission of secret data via the wireless network. Thus, the proposed method shows the best results with high payz load capacity, security, and image quality than the existing methods. Testing was performed on PSNR, MSE, IF, and SSIM metrics to verify the performance of proposed methodologies.
An Interpretation of the Challenges and Solutions for Agriculture-based Supply Chain Management using Blockchain and IoT

Healthcare, the smart grid, and supply chain management are some of the industries where IoI(Internet of Things) technology is improving. Also, it improves the way humans communicate with each other, the atmosphere, and the people around them. Internet of things achieves this by using wearable technology, embedded sensors, and automated systems. The number, types, and sizes of IoT devices are increasing. There are issues with security, authenticity, reliability, and scalability. Because of the expected growth in IoT, IoT systems must be reliable and maintainable. Blockchain has helped solve IoT network problems. Blockchain improves trust, immutability, transparency, and release of agricultural data collection, processing, and sharing. Blockchain and IOT will streamline precision farming and transform normal farms into the Internet of Smart Farms. This study describes the IoT systems have issues, and blockchain-based solutions are suggested. Additionally, it discusses current research on the degree to which blockchain technology and Internet of Things networks. IoT integration inside the blockchain issues are also covered. This article proposes to double connect IoT and blockchain using recent computing techniques. This research work intends to make data processing and storage decentralized, secure and anonymous, and seamless and fast using blockchain technology and services.
Speech Recognition and Neural Networks based Talking Health Care Bot (THCB): Medibot

The COVID-19 pandemic has affected healthcare in several ways. Some patients were unable to make it to appointments due to curfews, transportation restrictions, and stay-at-home directives, while less urgent procedures were postponed or cancelled. Others steered clear of hospitals out of fear of contracting an infection. With the use of a conversational artificial intelligence-based program, the Talking Health Care Bot (THCB) could be useful during the pandemic by allowing patients to receive supportive care without physically visiting a hospital. Therefore, the THCB will drastically and quickly change in-person care to patient consultation through the internet. To give patients free primary healthcare and to narrow the supply-demand gap for human healthcare professionals, this work created a conversational bot based on artificial intelligence and machine learning. The study proposes a revolutionary computer program that serves as a patient's personal virtual doctor. The program was carefully created and thoroughly trained to communicate with patients as if they were real people. Based on a serverless architecture, this application predicts the disease based on the symptoms of the patients. A Talking Healthcare chatbot confronts several challenges, but the user's accent is by far the most challenging. This study has then evaluated the proposed model by using one hundred different voices and symptoms, achieving an accuracy rate of 77%.
Scaling Object Detection to the Edge with YOLOv4, TensorFlow Lite

Recently, on-device object detection has gained significant attention as it enables real-time visual data processing without the need for a connection to a remote server. However, deploying these models on edge devices poses several challenges such as limited computational resources, power constraints, and real-time performance. These challenges are faced in many of the existing methodologies such as Fast R-CNN, and Single Shot Detector. This experiment aims to investigate the scalability of on-device object detection using YOLOv4, CNNs, and TensorFlow Lite. Recent techniques for addressing these challenges include model compression and quantization and the use of hardware accelerators. The proposed objective of this experiment is to evaluate the performance and efficiency of these techniques in the context of on-device object detection and identify potential areas for improvement. The goal is to provide insights that can help guide the design of more efficient and effective edge-based object detection systems.
Automatic Attendance System based on FaceRecognition using Machine Learning

Advancements have been made in the field of face recognition technology. Controlling aperson's attendance in real time via facial recognition technology. Face recognition is the process of recognizing a person by their facial characteristics. Various computer vision algorithms, including those used for face detection, expression recognition, and video surveillance, can make use of a person's unique facial features. A face detection and recognition- based attendance monitoring system might very well rapidly and accurately locate and identify people in photographs or video footage. In addition to being laborious to maintain, the time-honored practice of physically ticking off attendees is inefficient. This research work presents the working of a cascade classifier built with machine learning to improve the face detection results. This has been done by comparing the face images in the current image to a database of previously trained faces. The acquired image contributions are searchedfor a previously registered face, and once found, the person's attendance is recorded automatically.
Embedded Sensor and IoT Technology based Substation Monitoring and Control System

Every utility now requires substation automation to raise and improve its efficiency and the calibre of the electricity supplied. Surgery for medical purposes is of utmost importance. IoT has generally been heavily utilized to link cutting-edge medical equipment and provide individuals with smart, efficient healthcare. Automating substations has become necessary for every service organization as the complexity of the distribution network has risen. To enhance the nature of force, it is necessary to identify what type of urgency has occurred. Furthermore, assuming that the security, checking, and control of a power framework are insufficient, consequently, it needs a testing framework capable of recognizing, screening, and grouping the current imperatives on electrical lines. The goal of this framework is to get remote electrical limitations such as voltage, flow, and recurrence and transmit these continuous characteristics, as well as temperature, to the power station over the network. This technology is also intended to protect electrical hardware through a transfer. This transfer is triggered when the electrical boundaries reach the predefined thresholds. This framework is capable of regularly renewing the continuous electrical boundaries. This framework may be set to sound an alarm if the hand-off fails or if the voltage or current exceeds the maximum value.
A Hyperledger Fabric-based System Framework for Healthcare Data Management

A blockchain is an ever-growing list of records that are linked to each other in a distributed network. These linked records called ledgers are immutable in nature providing resistance to change. Blockchain provides a secure way of processing the data in a distributed environment. It was widely involved in crypto currencies in the earlier days and however its application in bit coin motivated and inspired other applications to adapt its concepts. Its application in healthcare requires blockchain to be highly secure, provide a more trusted environment than the traditional blockchain, that is by design should be an enterprise level blockchain by restricting access to the public. Hyperledger Fabric caters to all these requirements in providing a secure and distributed environment for healthcare systems. In healthcare there are a lot of fields where Hyperledger Fabric can be adopted, but the focus here is given to management of patient's medical records. Traditionally the medical records are either stored centrally in a database that is accessible to only the hospitals owning it, this creates a several of problems for patients. The aim is to consider the records are handled, how the patient will interact in the real world and design a system using hyper ledger Fabric to tackle major problems using smart contract.
Neural Machine Translation using Adam Optimised Generative Adversarial Network

Natural Language Processing is among the emerging fields in machine learning and deep learning. Neural machine translation is a subfield of Natural Language Processing that focuses on language translation. In this paper, the different methods of Neural Machine Translation (NMT) are discussed along with their architectures. It starts from traditional NMT techniques that give poor performance when it encounters long sentences and when there are problems related to vocabulary. Attention-based NMT can provide better performance for long sentences, but the problem of vocabulary remains the same. This can get solved by Attention-based NMT along with sub-word segmentation. Moreover, some of the essential models developed in recent times are discussed. An Adam-based Bi-directional GAN is employed in this work to optimize the training process and to stabilize the GANs. The model is evaluated based on BLEU scores and is compared with the existing models.
Securing Sharable Electronic Health Records on Cloud Storage

Due to the potential of data breaches and the resultant compromise of patient's sensitive data, medical organizations find it difficult to secure the data stored on cloud storage. Thus, storing and sharing data on cloud presents numerous security issues related to authentication, identity management, access control, trust management etc. Electronic Health Records (EHR) is of the paramount importance in the domain of research as the researchers use these data/reports to analyze and find new diseases. EHR data stored on cloud contain sensitive information about the patient and cannot be disclosed to unauthorized users. Thus, sharing sensitive information is still remains unexplored. Therefore, this article proposes a novel framework to store and share the data in a secured manner using Attribute Based Encryption (ABE). Storing and sharing EHR data on the cloud allows the research community to analyze various patient reports, which intern helps pharmacy and other healthcare-companies to expand their business.
The Hustlee Credit Card Fraud Detection using Machine Learning

It is extremely crucial for financial institutions to actually acknowledge the fraudulent purchases. Clients are really not charged for products they did not order. Such problems can be solved by Digital Marketing, and also its relevance, as well as Functional Learning, can indeed be overshadowed. The said initiative aims to showcase the prediction large dataset used during machine learning identifying fraud. Credit thing wrong of order to detect data theft entails designing past debt card data and transactions from accounts that appear to be illegitimate. The whole framework then serves to evaluate if either the work being actually achieved is innovative or untruthful. The proposed research objective is to reduce the number of types of fraudulent fraud while finding 100% dishonest employment. A standard sample separation for detecting credit card fraud. Designers are starting to focus on examining and prioritizing data sets, while also submitting the several bewildering locating algorithms, including this Small Form Major consideration and Data on credit card transactions were tailored by the PCA's isolation forest technique. This study examines the effectiveness of logistic regression, decision tree, XGBoost, Naive Bayes and random forest for detecting credit card fraud. Dataset, which consists of credit card transactions made by European cardholders has been collected from Kaggle.