ReviewRanker: A Semi-Supervised Learning Based Approach for Code Review Quality Estimation

Code review is considered a key process in the software industry for minimizing bugs and improving code quality. Inspection of review process effectiveness and continuous improvement can boost development productivity. Such inspection is a time-consuming and human-bias-prone task. We propose a semi-supervised learning based system ReviewRanker which is aimed at assigning each code review a confidence score which is expected to resonate with the quality of the review. Our proposed method is trained based on simple and and well defined labels provided by developers. The labeling task requires little to no effort from the developers and has an indirect relation to the end goal (assignment of review confidence score). ReviewRanker is expected to improve industry-wide code review quality inspection through reducing human bias and effort required for such task. The system has the potential of minimizing the back-and-forth cycle existing in the development and review process. Usable code and dataset for this research can be found at: https://github.com/saifarnab/code_revie

Structural modeling of tissue-specific mitochondrial alanyl-tRNA synthetase (AARS2) defects predicts differential effects on aminoacylation.

Peer reviewe

A reliability test system for educational purposes-basic results

A set of basic reliability indices at the generation and composite generation and transmission levels for a small reliability test system are presented. The test system and the results presented have evolved from reliability research and teaching programs. The indices presented are for fundamental reliability applications which should be covered in a power system reliability teaching program. The RBTS test system and the basic indices provide a valuable reference for faculty and students engaged in reliability teaching and researc

Deep-Learning Frameworks For The Classification And Segmentation Of Alzheimer\u27s Disease Cell Types

Neuroimaging experts in biotech industries can benefit from using cutting-edge artificial intelligence techniques for Alzheimerâ??s disease (AD) and dementia stage prediction, despite the fact that it may be difficult to anticipate the precise stage of AD. AD is one cause of dementia with very limited to no treatment available. Cell-type classification studies are essential for developing novel drugs for this lethal and common disease. Neuronal cell segmentation is the process of identifying and separating individual neurons in an image, typically in order to study their properties or analyze their organization in the nervous system. This is significant because neurological problems and diseases can only be treated effectively when the structure and function of neurons are understood. Therefore, the goal of this research is to develop a cutting-edge, advanced deep-learning algorithm for addressing this problem. We developed Convolution Neural Networks (CNNs) and Graph Convolution Networks (GCNs) based methods for classifying different AD stages. Four separate models were developed: CNNs built from scratch, VGG-16 with additional convolutional layers, GCNs, and a novel CNN-GCN model for classifying the AD stages. We employed DenseNet, ResNet, MobileNet, InceptionNet, and EfficientNet-B7 transfer learning models trained on ImageNet to classify AD cell types. We then implemented the proposed modified EfficientNet-B7 model for multi-class classification of cell types of AD along with a binary classification of cell types individually. We proposed an image segmentation method based on CNNs and graph attention networks (GATs) for segmenting biomedical images. We achieved an overall accuracy of 43.83%, 71.17%, 99.06%, and 100% by applying CNNs, VGG16 with additional convolutional layers, GCNs, and the CNN-GCN model, respectively, and the CNN-GCN model showed excellent performance in classifying different stages of dementia. By performing 5-fold cross-validation on the multi-class cell-types dataset, we achieved a training accuracy of 80% and a validation accuracy of 63% for the modified EfficientNet-B7 model. By implementing our proposed U-GAT algorithm for image segmentation, we obtained the highest accuracy of 86.5% and the lowest loss of 0.30 compared to the benchmarking algorithms CNNs, U-Net, and GATs, respectively. Understanding the stages of AD will assist biotech industry researchers in uncovering molecular markers and pathways connected with each stage. This approach can be used or extended to improve accuracy for detecting the impact of various cell types on novel drug developments in AD

FAST ADEQUACY ASSESSMENT OF COMPOSITE POWER SYSTEMS

Quantitative adequacy assessment of a composite power system is generally done using a contingency enumeration approach. Exhaustive adequacy analysis includes the evaluation of· contingencies, the classification of these contingencies according to selected failure criteria· and the accumulation of adequacy indices. Various. contingency enumeration approaches are available to analyze the adequacy of a composite power system depending upon the failure criteria and the intent behind the studies. Adequacy indices are calculated using DC and AC load flow methods in this thesis using two selected test systems. The differences in the indices resulting from using these load flow methods are discussed. The inclusion of high level outage contingencies can not be ignored in the calculation of representative adequacy indices in relatively large systems. This requirement, however, significantly increases the computation time. One of the most widely used approaches to reduce the computation time is to rank the outage contingencies using fast techniques and then investigate these ranked contingencies using an AC load flow method. This procedure is then terminated by. an appropriate stopping criterion. In this thesis, adequacy indices are evaluated using different ranking methods. The computation times and the differences in the adequacy indices as compared with a base case AC solution are discussed. A new technique designated as the selection method is introduced to reduce computation time. This method can be used to examine both the continuity and the quality of power supply at major load centers for any type of contingency. The method is well suited for large power networks because of the reduced computation time and. storage requirements. In this thesis, the application of the method and its comparison with the base case and with various ranking methods are illustrated using two test systems

A novel deep learning graph attention network for Alzheimer’s disease image segmentation

Neuronal cell segmentation identifies and separates individual neurons in an image, typically to study their properties or analyze their organization in the nervous system. This is significant because neurological problems and diseases can only be treated effectively when the structure and function of neurons are understood. The proposed method is based on convolutional neural networks (CNNs) and graph attention networks (GATs) for segmenting biomedical images. A contracting path built upon a couple of convolution layers and max pooling is included in the architecture to capture context. After that, the GATs are applied to the captured context. In GATs, each node in the graph is associated with a vector of hidden features, and the model calculates attention coefficients between pairs of nodes. These attention coefficients are learned during training and can be used to weigh the contribution of each node’s features to the representation of the graph. An expanding path that utilizes the outputs generated by GATs paves the way for exact segmentation. The dataset comprises 606 microscopic images, mainly categorized into different cell types (astrocytes, cortex, and SHSY5Y). By implementing our proposed U-GAT algorithm, we obtained the highest accuracy of 86.5% and an F1 score of 0.719 compared to the CNN, U-Net, SegResNet, SegNet VGG16, and GAT benchmarking algorithms. This proposed method could help researchers in the biotech industry develop novel drugs since a more accurate deep-learning method is essential for segmenting complex images like neuronal images

New Convolutional Neural Network and Graph Convolutional Network-Based Architecture for AI Applications in Alzheimer’s Disease and Dementia-Stage Classification

Neuroimaging experts in biotech industries can benefit from using cutting-edge artificial intelligence techniques for Alzheimer’s disease (AD)- and dementia-stage prediction, even though it is difficult to anticipate the precise stage of dementia and AD. Therefore, we propose a cutting-edge, computer-assisted method based on an advanced deep learning algorithm to differentiate between people with varying degrees of dementia, including healthy, very mild dementia, mild dementia, and moderate dementia classes. In this paper, four separate models were developed for classifying different dementia stages: convolutional neural networks (CNNs) built from scratch, pre-trained VGG16 with additional convolutional layers, graph convolutional networks (GCNs), and CNN-GCN models. The CNNs were implemented, and then the flattened layer output was fed to the GCN classifier, resulting in the proposed CNN-GCN architecture. A total of 6400 whole-brain magnetic resonance imaging scans were obtained from the Alzheimer’s Disease Neuroimaging Initiative database to train and evaluate the proposed methods. We applied the 5-fold cross-validation (CV) technique for all the models. We presented the results from the best fold out of the five folds in assessing the performance of the models developed in this study. Hence, for the best fold of the 5-fold CV, the above-mentioned models achieved an overall accuracy of 43.83%, 71.17%, 99.06%, and 100%, respectively. The CNN-GCN model, in particular, demonstrates excellent performance in classifying different stages of dementia. Understanding the stages of dementia can assist biotech industry researchers in uncovering molecular markers and pathways connected with each stage

FAST ADEQUACY ASSESSMENT OF COMPOSITE POWER SYSTEMS

Quantitative adequacy assessment of a composite power system is generally done using a contingency enumeration approach. Exhaustive ade­quacy analysis includes the evaluation of·contingencies, the classification of these contingencies according to selected failure criteria and the accumulation of adequacy indices. Various. contingency enumeration approaches are available to analyze the adequacy of a composite power system depending upon the failure criteria and the intent behind the studies. Adequacy indices are calculated using DC and AC load flow methods in this thesis: using two selected test· systems. The differences in the indices resulting from using these load flow methods are discussed. The inclusion of high level outage contingencies can not be ignored in the calculation of representative adequacy indices in relatively large systems. This requirement, however, significantly increases the computation time. One of the most widely used approaches to reduce the computation time is to rank the outage contingencies using fast techniques and then investigate these ranked contingencies using an AC load flow method. This procedure is then terminated by.an appropriate stopping criterion. In this thesis, adequacy indices are evaluated using different ranking methods. The computation times and the:differences in the adequacy indices as compared with a base case AC solution are discussed. A new technique designated as the selection method is introduced to reduce computation time. This method can be used to examine both the continuity and the quality of power supply at major load centers for any type of contingency. The method is wen suited for large power networks be­cause of the reduced computation time and. storage requirements. In this thesis, the application of the method and its comparison with the base cue and with various ranking methods are illustrated using two test systems

A SECURITY BASED APPROACH TO COMPOSITE POWER SYSTEM RELIABILITY EVALUATION

There is considerable interest in the application of probability methods to composite system reliability evaluation. The problem is extremely complex because of the need to include detailed modelling of both generation and transmission facilities and to consider multiple levels of component failures. Quantitative adequacy assessment of a composite power system is generally done using a contingency enumeration approach which includes the evaluation of contingencies, the classification of these contingencies according to selected failure criteria and the accumulation of adequacy indices. There are several network solution methods presently available depending upon the failure criteria and the intent behind the studies. In this thesis, adequacy indices are calculated using three methods, namely network flow, dc and ac load flow and the importance of utilizing an ac load flow method in composite system reliability analysis is clearly illustrated. The computation time increases tremendously when an ac load flow method is used specifically for a large system where the inclusion of higher level outages cannot be ignored in the calculation of representative indices. In order to reduce the computation time when using ac load flow without sacrificing much accuracy, different approximate methods can be utilized. Adequacy indices are calculated and presented in this thesis using three ranking methods and a new technique designated as the selection method. The CPU times and the accuracy of the methods as compared to the reference values obtained with an exhaustive ac load flow solution are discussed. The presently available techniques for quantitative reliability evaluation of composite power systems are in the adequacy domain. The most significant quantitative indices in this regard are those which relate to load curtailments and many utilities have concerns in interpreting the expected load curtailment indices. A framework for incorporating the steady-state security considerations in the reliability evaluation of composite power systems is examined and extended in this thesis. The system operating states are quantified using the contingency enumeration method for three different constraint sets. The indices are also calculated by combining the contingency enumeration method and a Monte Carlo simulation approach through the use of hybrid methods to quantify the various system operating states and the results are compared with the analytical values. This thesis presents a new risk index designated as the Composite System Operating State Risk (CSOSR). This index is defined and its utilization in system expansion and unit commitment in composite generation and transmission systems is illustrated. The concepts presented in this thesis are illustrated numerically using two basic test systems