Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection of Sickle Cell, Megaloblastic Anemia, Thalassemia and Malaria through Convolutional Neural Network

This paper presents an alternative method to diagnose Malaria and Anemia (Sickle Cell Anemia, Megaloblastic Anemia and Thalassemia) as well as to differentiate between them. First, different related high resolution images of blood samples are taken from multiple datasets. Second, Convolutional Neural Networks (CNN) technique is implemented and applied in order to process the images without the need of the standard protocol of Complete Blood Count (CBC) test. The implemented convolutional Neural Network has been designed using Python to train on a number of microscopic images. After completing the training phase, the built model has been tested on other images to classify them into normal blood cells, Malaria, Sickle cell anemia, Megaloblastic anemia or Thalassemia. Third, the diagnosis is made based on the outcomes. Finally, the accuracy of results is assessed. The total accuracy of the test is 93.4%. The suggested approach yields promising outcomes that help diagnose blood samples faster, with low cost as well as without the need of an analysis laboratory.
An Important Sampling over Graph Convolutional Network for Community Detection

According to graph theory, the complex network is transformed into graph structure data to process the convolutional neural network efficiently and conveniently. The convolution kernel is defined by extending the convolution operation on the image to the graph structure data, and the convolution layer is used for the graph. Coarse-graining and pooling operations extract features of complex networks with irregular data. When training the network with stochastic gradient descent, an important sampling method is designed to change the distribution of samples to reduce variance, thereby saving gradient calculation time. Experimental results show that this method can obtain better community discovery accuracy with lower computational complexity in social networks, citation networks, and knowledge graph datasets, reducing computational time compared with the existing graph convolutional network. In addition, the memory footprint can be extended to use in large-scale complex networks.
IOT Based Patient Monitoring System

The proposed system is used to monitor the physical parameters of the patients. This provides continuous monitoring to the patients which is an essential to give a perfect care to the patients. Some patients may need continuous care and support by the medical experts while the others may not; for those who need it, the proposed system will work to fulfill the requirement with the help of sensor assisted network and IOT cloud tools for data storage and monitoring. Processes like recording the temperature and heart beat of the patients, storing of the data, connecting with the receiver end and finally providing immediate treatment will help to avoid deaths of many patients. The proposed project analysis the size of the system, privacy concerns for the data storing and sharing and eventual treatment.
A Hybrid Convolutional Recurrent (CNN-GRU) Model for Stock Price Prediction

Stock price forecasting systems are on-demand that used for prediction in the financial world. The deep learning models are used for handling large data and making predictions more accurately than the traditional methods. This paper proposed a hybrid convolutional recurrent model architecture for stock price prediction based on exploitation of two different deep learning models properties into a single model. Hybrid Convolutional Recurrent Model consists of 1D-CNN and GRU where 1D-CNN is leveraged as feature extraction task and GRU for temporal regression. The proposed CNN-GRU Model outperform for stock price prediction. The performance evaluation and comparison analysis between the existing hybrid models and proposed hybrid model have also been done with experiments to justify.
TCP-Net: Test Case Prioritization using End-to-End Deep Neural Networks

Regression testing is facing a bottleneck due to the growing number of test cases and the wide adoption of continuous integration (CI) in software projects, which increases the frequency of running software builds, making it challenging to run all the regression test cases. Machine learning (ML) techniques can be used to save time and hardware resources without compromising quality. In this work, we introduce a novel end-to-end, self-configurable, and incremental learning deep neural network (DNN) tool for test case prioritization (TCP-Net). TCP-Net is fed with source code-related features, test case metadata, test case coverage information, and test case failure history, to learn a high dimensional correlation between source files and test cases. We experimentally show that TCP-Net can be efficiently used for test case prioritization by evaluating it on three different real-life industrial software packages.
Repairing Fragile GUI Test Cases Using Word and Layout Embedding

Smartphone vendors apply both device and brand-specific customisations to the underlying operating systems, resulting in a wide range of device configurations. It is crucial that all of the device variations provide compatibility with the default version of the underlying operating system, such as Android. To ensure that widely and commonly used apps run on each of these device variations without any problem, vendors depend on automated GUI level testing of widely and commonly used apps: the failure of a GUI test script that emulates a routine usage of these apps would raise an alarm that a recent change made to a specific device variation may have caused a regression fault. These GUI level compatibility smoke tests are unique in the sense that they are GUI level automated test scripts that are written outside the software development life cycle of the target apps: they are written and maintained by the engineers of the smartphone vendors, and not the app developers. As such, these test scripts are extra vulnerable to the fragility of GUI test scripts, which are already known to be fragile when maintained by app developers. This paper introduces a repair technique for View Identification Failures (VIFs) in those smoke tests so that the smartphone vendors can quickly update their GUI test scripts when they break due to changed view ids. Our technique matches view ids between old and new versions of the target app based on various similarity metrics such as the semantic embedding similarity between ids and GUI labels, and layout similarity based on node embeddings of the GUI layout tree. We evaluate the proposed technique using 512 VIFs collected from real-world Android mobile apps. The proposed technique can repair 72 % of the 512 studied VIFs with only one attempt, compared to 28 % repaired using lexical distance-based matching.
A Sensor-Based Data Analytics for Patient Monitoring in Connected Healthcare Applications

Nowadays, Remote sensor networks have been broadly utilized in medical services applications, like emergency clinic and home patient checking. Compared to wired organizations, Remote clinical sensor networks are more vulnerable to spying, modification, manipulation, and playback attacks. To get remote clinical sensor organizations, a lot of work has been done. The current methods can protect patient information during transmission, but they can't preclude the director of the patient data set from uncovering sensitive patient information during an attack. In this research, we offer a practical method for preventing internal attacks by storing patient data on multiple information servers. The main goal of this work is to properly disseminate patient information across several information servers while using the Paillier and elgamal cryptosystems to conduct measurement investigations on the data without jeopardizing the patients' safety. Remote clinical sensor networks definitely lead to an improved knowledge of the nature of care without affecting patients' comfort. However, there are numerous potential security risks to the patient's sensitive physiological information transferred over public networks and stored in back-end frameworks. Snooping creates a security risk to the privacy of patient information.
FAUSTA: Scaling Dynamic Analysis with Traffic Generation at WhatsApp

We introduce Fausta, an algorithmic traffic gener-ation platform that enables analysis and testing at scale. Fausta has been deployed at Meta to analyze and test the WhatsApp plat-form infrastructure since September 2020, enabling WhatsApp developers to deploy reliable code changes to a code base of millions of lines of code, supporting over 2 billion users who rely on WhatsApp for their daily communications. Fausta covers expected and unexpected program behaviors in a privacy-safe controlled environment to support multiple use cases such as reliability testing, privacy analysis and performance regression detection. It currently supports three different algorithmic input generation strategies, each of which construct realistic backend server traffic that closely simulates production data, without replaying any real user data. Fausta has been deployed and closely integrated into the WhatsApp continuous integration process, catching bugs in development before they hit production. We report on the development and deployment of Fausta's reliability use case between September 2020 and August 2021. During this period it has found 1,876 unique reliability issues, with a fix rate of 74%, indicating a high degree of true positive fault revelation. We also report on the distribution of fault types revealed by Fausta, and the correlation between coverage and faults found. Overall, we do find evidence that higher coverage is correlated with fault revelation.
Performance Assessment of Routing Protocols in an Internet of Things by communicating in a Wireless Sensor Networks

Protocols have an important role on the overall performance of Wireless Sensor Networks, including throughput and power consumption, postponement and transmission rate. This research study has a portion of suggested computations that focus on network performance concerns in the proposed sensor network. The directing convention monitors the energy and draws out the steadiness time of organization as well as organization life time. As a result of this convention, sensor nodes will no longer establish pairs with one another and share information about what is being detected. Additionally, the Cluster Head selection technique has been improved and CHs are selected based on the remaining power of the sensor nodes. The reenactment has been done on MATLAB for assessing the exhibition of these conventions on various boundaries thinking about two situations. In all scenarios, the suggested convention outperforms alternative steering conventions, according to the findings of the simulations.
Deep Residual Learning based Attendance Monitoring System

In the field of personal identification Face recognition technology is more trustworthy than other technologies. In the proposed work, we elaborated on the use of face recognition in the monitoring of attendance of a class without the intervention of humans. Face recognition gives fruitful results compared to previous existing techniques like a fingerprint, iris, and RFID techniques. Using the capabilities of the dlib framework, this project provides a method for detecting and identifying the faces of students present in a classroom in real-time. Our trained network uses the CNN or HOG method to identify and locate students' faces in the captured frames. The facial features are extracted from those images using Deep Residual Networks called ResNets. And distinguishes between feature vectors using the KNN classifier. This network has an accuracy of 99.83% on the Labelled Faces in the Wild benchmark. And finally, we have shown some performance metrics and plots by comparing them with other existing methods in face recognition. Hence this method is cost-efficient and requires little maintenance.
AcoFuzz: Adaptive Energy Allocation for Greybox Fuzzing

In recent years, coverage-based greybox fuzzing (CGF) has become one of the most important techniques to discover security bugs. The existing fuzzers score the seeds, and then allocate the energy to the seeds according to the scoring results, but most seeds obtain the same energy, and then repeatedly select the same seeds for fuzzing. These strategies have been proved to be inefficient. Our experimental observations show that various seeds have diverse efficiency, and the efficiency of test cases changes increase with execution time. In this paper, we propose a novel yet lightweight energy allocation strategy, called AcoFuzz, to improve fuzzing efficiency. AcoFuzz has one following distinct advantage: Dynamically allocate energy for seeds to cope with their efficiency variation. Extensive experiments based on real-world programs and the LAVA-M dataset have been conducted to evaluate the path discovery and vulnerability detection ability of AcoFuzz, which substantially outperforms 3 state-of-the-art fuzzers.
Distinguishing Reviews Through Sentiment Analysis Using Machine Learning Techniques

Opinion Mining is a technique of automated extraction of information from the opinion of people about any certain subject or problem. The feasibility of Opinion mining and Sentiment Analysis tool is to “process a bunch of indexed lists for a given thing, creating a rundown of item credits (quality, highlights and so forth) and accumulating opinion”. Yet, with the progression of time additional fascinating applications and improvements appeared around here and presently its fundamental objective is to make PC ready to perceive and produce feelings like human. This paper will attempt to zero in on the essential meanings of Opinion Mining, investigation and Sentiment Analysis is the name given to this new field of study. In recent years, scientists have come up with a number of solutions to the problem. Information Retrieval (IR) and Natural Language Processing (NLP) interact in the subject of Opinion Mining and Sentiment Analysis, which has a few distinct trains, such as message mining and Information Extraction. Sentiment Analysis may be performed using a variety of NLP tools, all of which this paper aimed to demonstrate, from basic definitions to a broad range of applications. Recently, it's been a really active exploring zone. In fact, it has made its way into everything from board science to software engineering of etymological assets needed for Opinion Mining, scarcely any AI methods based on their utilization and significance for the examination, assessment of Sentiment arrangements and its different applications. They are attempting to get assessment data to investigate and sum up the suppositions communicated naturally with PCs. Opinion Mining
Insect visuomotor delay adjustments in group flight support swarm cohesion

Flying insects routinely demonstrate coordinated flight in crowded assemblies despite strict communication and processing constraints. This study experimentally records multiple flying insects tracking a moving visual stimulus. System identification techniques are used to robustly identify the tracking dynamics, including a visuomotor delay. The population delay distributions are quantified for solo and group behaviors. An interconnected visual swarm model incorporating heterogeneous delays is developed, and bifurcation analysis and swarm simulation are applied to assess swarm stability under the delays. The experiment recorded 450 insect trajectories and quantified visual tracking delay variation. Solitary tasks showed a 30ms average delay and standard deviation of 50ms, while group behaviors show a 15ms average and 8ms standard deviation. Analysis and simulation indicate that the delay adjustments during group flight support swarm formation and center stability, and are robust to measurement noise. These results quantify the role of visuomotor delay heterogeneity in flying insects and their role in supporting swarm cohesion through implicit communication.
QuNex—An integrative platform for reproducible neuroimaging analytics

Introduction Neuroimaging technology has experienced explosive growth and transformed the study of neural mechanisms across health and disease. However, given the diversity of sophisticated tools for handling neuroimaging data, the field faces challenges in method integration, particularly across multiple modalities and species. Specifically, researchers often have to rely on siloed approaches which limit reproducibility, with idiosyncratic data organization and limited software interoperability. Methods To address these challenges, we have developed Quantitative Neuroimaging Environment & Toolbox (QuNex), a platform for consistent end-to-end processing and analytics. QuNex provides several novel functionalities for neuroimaging analyses, including a “turnkey” command for the reproducible deployment of custom workflows, from onboarding raw data to generating analytic features. Results The platform enables interoperable integration of multi-modal, community-developed neuroimaging software through an extension framework with a software development kit (SDK) for seamless integration of community tools. Critically, it supports high-throughput, parallel processing in high-performance compute environments, either locally or in the cloud. Notably, QuNex has successfully processed over 10,000 scans across neuroimaging consortia, including multiple clinical datasets. Moreover, QuNex enables integration of human and non-human workflows via a cohesive translational platform. Discussion Collectively, this effort stands to significantly impact neuroimaging method integration across acquisition approaches, pipelines, datasets, computational environments, and species. Building on this platform will enable more rapid, scalable, and reproducible impact of neuroimaging technology across health and disease.
GPU-Accelerated All-atom Particle-Mesh Ewald Continuous Constant pH Molecular Dynamics in Amber

Constant pH molecular dynamics (MD) simulations sample protonation states on the fly according to the conformational environment and user specified pH condition; however, the accuracy of most current applications is limited due to the use of implicit-solvent models or hybrid solvent scheme. Here we report the first GPU-accelerated implementation, parameterization, and validation of the all-atom continuous constant pH MD (CpHMD) method with particle-mesh Ewald (PME) electrostatics in the Am-ber22 pmemd.cuda engine. The titration parameters for Asp, Glu, His, Cys, and Lys were derived for the CHARMM c22 and Amber ff14sb and ff19sb force fields. We then evaluated the PME-CpHMD method using the asynchronous pH replicaexchange titration simulations with the c22 force field for six benchmark proteins, including BBL, hen egg white lysozyme (HEWL), staphylococcal nuclease (SNase), thioredoxin, ribonuclease A (RNaseA), and human muscle creatine kinase (HMCK). The root-mean-square deviation from the experimental pKa’s of Asp, Glu, His, and Cys is 0.76 pH units, and the Pearson’s correlation coefficient for the pKa shifts with respect to model values is 0.80. We demonstrated that a finite-size correction or much enlarged simulation box size can remove a systematic error of the calculated pKa’s and improve agreement with experiment. Importantly, the simulations captured the relevant biology in several challenging cases, e.g., the titration order of the catalytic dyad Glu35/Asp52 in HEWL and the coupled residues Asp19/Asp21 in SNase, the large pKa upshift of the deeply buried catalytic Asp26 in thioredoxin, and the large pKa downshift of the deeply buried catalytic Cys283 in HMCK. We anticipate that PME-CpHMD will offer proper pH control to improve the accuracies of MD simulations and enable mechanistic studies of proton-coupled dynamical processes that are ubiquitous in biology but remain poorly understood due to the lack of experimental tools and limitation of current MD simulations.
Increasing information gain in animal research by improving statistical model accuracy

Reduction of the numbers of laboratory animals is one of the three pillars of ethical animal research. Equivalently, information gain per animal should be maximized. A road towards this goal that is barely taken in current animal research is the more accurate statistical modeling of experiments. Here we show for a typical experiment (“open field test”) with outcomes that are non-normally distributed count data, how this can be implemented and what information gain is achieved. We contrast the state of the art – the use of confidence intervals based on null-hypothesis significance testing (NHST) –, with a Bayesian approach with the same underlying normal model, and a Bayesian approach with a more accurate negative binomial model. We find that the more accurate model leads to a marked improvement of knowledge gained with the experiment, especially for small sample sizes. As experimental data that violate assumptions of simple, conventional models are frequent, our findings have wider implications.
Protective Human Anti-Poxvirus Monoclonal Antibodies Are Generated from Rare Memory B Cells Isolated by Multicolor Antigen Tetramers

Smallpox, an epidemic disease caused by Orthopoxvirus variola, was eradicated worldwide through immunization. The immunization against smallpox was discontinued in 1980. However, incidences of monkeypox virus infection in humans have occurred sporadically, and there is also great fear that engineered forms of poxvirus could be used as biological weapons. Therefore, monoclonal antibodies against poxvirus are in urgent need for the detection and treatment of poxvirus infection. The vaccinia virus extracellular envelope protein A33 is a potential candidate for subunit vaccine. We used multi-fluorescence-labeled tetrameric A33 antigen to identify rare poxvirus specific memory B cells from PBMC of volunteers with vaccinia virus immunization more than 40 years ago. Despite extremely low frequencies of the poxvirus specific memory B cells, we successfully sorted A33 tetramer-labeled single memory B cells and reconstructed antibodies with single-cell RT-PCR of the B-cell receptor. Among the monoclonal antibodies, one clone H2 exhibited high specificity and affinity with A33. H2 efficiently inhibited viral infection and spread in cells. Passive immunotherapy of H2 in mice protected mice from lethal infection when administered either prophylactically or therapeutically. These results suggest the potential of the anti-A33 human antibody-based detection and therapeutics for poxvirus infection.