Reviewerly/exHarmony huggingface dataset:
Semantic Slicing of Architectural Change Commits

Software architectural changes involve more than one module or component and are complex to analyze compared to local code changes. Development teams aiming to review architectural aspects (design) of a change commit consider many essential scenarios such as access rules and restrictions on usage of program entities across modules. Moreover, design review is essential when proper architectural formulations are paramount for developing and deploying a system. Untangling architectural changes, recovering semantic design, and producing design notes are the crucial tasks of the design review process. To support these tasks, we construct a lightweight tool [4] that can detect and decompose semantic slices of a commit containing architectural instances. A semantic slice consists of a description of relational information of involved modules, their classes, methods and connected modules in a change instance, which is easy to understand to a reviewer. We extract various directory and naming structures (DANS) properties from the source code for developing our tool. Utilizing the DANS properties, our tool first detects architectural change instances based on our defined metric and then decomposes the slices (based on string processing). Our preliminary investigation with ten open-source projects (developed in Java and Kotlin) reveals that the DANS properties produce highly reliable precision and recall (93-100%) for detecting and generating architectural slices. Our proposed tool will serve as the preliminary approach for the semantic design recovery and design summary generation for the project releases.
Estimating Expected Calibration Errors

Uncertainty in probabilistic classifiers predictions is a key concern when models are used to support human decision making, in broader probabilistic pipelines or when sensitive automatic decisions have to be taken. Studies have shown that most models are not intrinsically well calibrated, meaning that their decision scores are not consistent with posterior probabilities. Hence being able to calibrate these models, or enforce calibration while learning them, has regained interest in recent literature. In this context, properly assessing calibration is paramount to quantify new contributions tackling calibration. However, there is room for improvement for commonly used metrics and evaluation of calibration could benefit from deeper analyses. Thus this paper focuses on the empirical evaluation of calibration metrics in the context of classification. More specifically it evaluates different estimators of the Expected Calibration Error (ECE), amongst which legacy estimators and some novel ones, proposed in this paper. We build an empirical procedure to quantify the quality of these ECE estimators, and use it to decide which estimator should be used in practice for different settings.
Enhancing the Cloud Security using Novel Dominant Game Strategy

In cloud computing technology the security issues are the major challenges. To meet the security issues in the cloud, the data-centric networks have been developed with software defined networks for the next generation technology. A novel framework has been proposed to accomplish the challenges of cloud security. The usage of mathematical approach namely novel dominant game strategy in software defined networks solves the problem of security issues in the cloud computing technology. The framework defines an enhanced version of intrusion detection and prevention system and provides the high security for the deployment of cloud services. The algorithms were tested over 25 test beds with various numbers of nodes in the cloud simulation and arrived with the best result of 90% efficiency in network services through the cloud. When this system is compared with a lexicographic game algorithm, efficiency in utility of cloud services during the peak hour is very poor. The novel framework along with the dominant game strategy in software defined networks, overcomes the difficulties faced by the fuzzy logic and lexicographic game and thus enhances the efficiency of the network services through software defined networks.
Comparative Analysis of Image Enhancement Techniques for Ultrasonic Ovarian Cyst Images

The diagnosis of an ovarian cyst from an ultrasonic image is challenging due to inherent noises caused due to movements. The speckle noises present in the ultrasound degrades the image. Ultrasound imaging and analyzing the images for any cyst is the current method for the diagnosis of ovarian cysts. Thus an effective image preprocessing to reduce the noises and enhance the image is necessary for an accurate diagnosis of ovarian cysts. This work studies the different image filtering mechanisms namely median filter, fourier filter, butterworth filter, and daubechies wavelet filter to filter noises from ultrasonic ovarian images and analyzes the effectiveness of them for proper visualization of ovarian cysts. The different filter evaluation is done by measuring different performance of the filter metrics for image quality with respect to Mean Square Error(MSE), Signal-to-noise ratio(SNR), and Peak Signal to Noise ratio(PSNR). The wavelet filter showed a least value of MSE and SNR and PSNR are more compared to the other filters used. The wavelet filter is best among other filter in respect of MSE, SNR and PSNR.
Thermal Face Image Generator

This work addresses two image-to-image translation tasks. The first task is to convert a visible face image into a thermal face image (V2T) and the second task is to convert a thermal face image into another thermal face image with a given target temperature (T2T). We propose to use conditional generative adversarial networks to solve the two tasks. We train our models using Carl and SpeakingFaces Datasets, and use SSIM to measure the performance of our models. The SSIM of the generated thermal images reach 0.82 and 0.84 for the V2T and T2T tasks respectively.
Edge-assisted Collaborative Image Recognition for Mobile Augmented Reality

Mobile Augmented Reality (AR), which overlays digital content on the real-world scenes surrounding a user, is bringing immersive interactive experiences where the real and virtual worlds are tightly coupled. To enable seamless and precise AR experiences, an image recognition system that can accurately recognize the object in the camera view with low system latency is required. However, due to the pervasiveness and severity of image distortions, an effective and robust image recognition solution for “in the wild” mobile AR is still elusive. In this article, we present CollabAR, an edge-assisted system that provides distortion-tolerant image recognition for mobile AR with imperceptible system latency . CollabAR incorporates both distortion-tolerant and collaborative image recognition modules in its design. The former enables distortion-adaptive image recognition to improve the robustness against image distortions, while the latter exploits the spatial-temporal correlation among mobile AR users to improve recognition accuracy. Moreover, as it is difficult to collect a large-scale image distortion dataset, we propose a Cycle-Consistent Generative Adversarial Network-based data augmentation method to synthesize realistic image distortion. Our evaluation demonstrates that CollabAR achieves over 85% recognition accuracy for “in the wild” images with severe distortions, while reducing the end-to-end system latency to as low as 18.2 ms.
Transferable Condition Monitoring for Linear Guidance Systems Using Anomaly Detection

Condition monitoring is essential for the OEE of machine tools. Existing solutions are customized to specific settings. However, linear guidance systems commonly used in machine tools are exposed to varying process conditions. Thus, this contribution proposes a concept for a transferable condition monitoring system, which enables a static system to be applied to different settings. The solution is composed of a combination of data preparation methods, feature generation and an anomaly detection model. The system is demonstrated on two test beds with different linear guidance systems. The selected isolation forest for anomaly detection is trained on a series of experiments from one test bed before transferring the condition monitoring to the other test bed.
NEWRON: A New Generalization of the Artificial Neuron to Enhance the Interpretability of Neural Networks

In this work, we formulate Newron:a generalization of the McCulloch-Pitts neuron structure. This new framework aims to explore additional desirable properties of artificial neurons. We show that some specializations of Newronallow the network to be interpretable without affecting their expressiveness. We can understand the rules governing the task by just inspecting the models produced by our Newnon-based networks. Extensive experiments show that the quality of the generated models is better than traditional interpretable models and in line or better than standard neural networks.
Integrating Quantum Simulation for Quantum-Enhanced Classical Network Emulation

We describe a method of investigating the near-term potential of quantum communication technology for communication networks from the perspective of current networks. For this, we integrate an instance of the quantum network simulator QuNetSim at the link layer into the communication network emulator ComNetsEmu. This novel augmented version of ComNetsEmu is thereby enabled to run arbitrary quantum protocols between any directly connected pair of network hosts. To give an example of the proposed method, we implement the link layer method of generating and storing entanglement while idle, to accelerate data transmission at later times using superdense coding.
LocalNorm: Robust Image Classification Through Dynamically Regularized Normalization

While modern convolutional neural networks achieve outstanding accuracy on many image classification tasks, they are, once trained, much more sensitive to image degradation compared to humans. Much of this sensitivity is caused by the resultant shift in data distribution. As we show, dynamically recalculating summary statistics for normalization over batches at test-time improves network robustness, but at the expense of accuracy. Here, we describe a variant of Batch Normalization, LocalNorm, that regularizes the normalization layer in the spirit of Dropout during training, while dynamically adapting to the local image intensity and contrast at test-time. We show that the resulting deep neural networks are much more resistant to noise-induced image degradation, while achieving the same or slightly better accuracy on non-degraded classical benchmarks and where calculating single image summary statistics at test-time suffices. In computational terms, LocalNorm adds negligible training cost and little or no cost at inference time, and can be applied to pre-trained networks in a straightforward manner.
How to Catch a Phish with Statistics

Abstract Rakesh M. Verma and David J. Marchette show how text analysis can be used to spot scam emails and prevent so-called “phishing” attacks
Demonstrating Exception Handling in JaCaMo

Current models for multi-agent organizations offer effective abstractions to build distributed systems, but lack a systematic way to address exceptions. This demonstration presents an exception handling mechanism in the context of the JaCaMo framework, based on the notions of responsibility and feedback. The mechanism is seamlessly integrated within organizational concepts, such as goals and norms.
Self Supervised Contrastive Learning on Multiple Breast Modalities Boosts Classification Performance

Medical imaging classification tasks require models that can provide high accuracy results. Training these models requires large annotated datasets. Such datasets are not openly available, are very costly, and annotations require professional knowledge in the medical domain. In the medical field specifically, datasets can also be inherently small. Self-supervised methods allow the construction of models that learn image representations on large unlabeled image sets; these models can then be fine-tuned on smaller datasets for various tasks. With breast cancer being a leading cause of death among women worldwide, precise lesion classification is crucial for detecting malignant cases. Through a set of experiments on 30K unlabeled mammography (MG) and ultrasound (US) breast images, we demonstrate a practical way to use self-supervised contrastive learning to improve breast cancer classification. Contrastive learning is a machine learning technique that teaches the model which data points are similar or different by using representations that force similar elements to be equal and dissimilar elements to be different. Our goal is to show the advantages of using self-supervised pre-training on a large unlabeled set, compared to training small sets from scratch. We compare training from scratch on small labeled MG and US datasets to using self-supervised contrastive methods and supervised pre-training. Our results demonstrate that the improvement in biopsy classification using self-supervision is consistent on both modalities. We show how to use self-supervised methods on medical data and propose a novel method of training contrastive learning on MG, which results in higher specificity classification.
A Real-time Face Recognition Smart Attendance System with Haar Cascade Classifiers

Taking attendance of students during class time is one of the foremost tasks for teachers and it's pretty much complicated at the same time. The manual attendance system takes an enormous amount of time over the number of students and has a prospect of being a proxy. Day by day it's getting intimidating because the number of students is increasing. A previous couple of year's automated biometric systems like a fingerprint, QR-code technology is using in smart attendance systems. However, time makes the difference here, at present face recognition technology is using to identify the student's participation in the classroom. So, we proposed a smart attendance system to take attendance by detecting and recognizing the face. Our main motivation is to make the attendance system easier, less time-consuming, and also protect from being proxy. The old manual-based attendance system was an arduous process and had a chance of proxy but we will be able to ameliorate the situation. This system can make a crystal clear concept to the machine whether it's a legal attendance or proxy. This system is a more secure and hassle-free system. In this system, all the student's data are recording in the cloud database with time and class schedule. So that teachers can easily evaluate the students in marking on attending. Also, help the teacher to inform individuals' parents whether or not their son/ daughter is irregular in the class. In this system, we used an effective machine learning-based object detection method Haar Cascade classifiers proposed by Paul Viola and Michael Jones for classifying extracting images and recognizing LBPH to this system.
Intelligent Agent Based Case Base Reasoning Systems Build Knowledge Representation in COVID-19 Analysis of Recovery of Infectious Patients

Coronavirus, also known as COVID-19, has been identified as a pandemic by the World Health Organization (WHO). More than 6,750,521 cases were registered at the end of this study, causing the deaths of over 395,779 people, which are growing around the globe every day. This very infectious respiratory disorder is at risk for the development both of symptoms and of asymptoms, which lead to a growing number of contractions and deaths. There is therefore a need to step up the method of early detection and treatment of this disease worldwide. The CBR model is an effective paradigm that allows the use of experience from previous specific cases, specific problems or patient cases to address new cases. It can be used to categorize new cases and even in their early stages by using the extremely rich COVID-19 database. Any simple case logic defined as a case-set case-basis file is analyzed using a Clinical Artificial Intelligence technique. The following methodology is used for this analysis. The formalized case is therefore analyzed in a CCBR model in order to define the case positively or negatively to COVID-19 for a short period of time. This means that the CBR model uses formalized features to calculate the similarity of the new case to other cases taken from the data base of the CBR model. The proposed system has been applied with a variety of records from the Italian Medical Society and Intervention Radiology (SIRM). The results showed that the proposed approach leverages spatial and time-specific infection sites even at their earliest stages to detect cases within minutes before 14 days. In this paper, we have used CCBRS techniques for the development of Clinical Knowledge Base (CKBS) for the detection of infectious patients and for rapid recovery according to diagnostic medical model. 99.5% accuracy, 0.3% sensitivity, and 0.2% specificities were achieved by the proposed scheme. Research have shown that the proposed model can assist physicians in effectively diagnosing and isolating patients, decreasing infection, and misdiagnosed rates in certain regions of the world.
TMCOSS: Thresholded Multi-Criteria Online Subset Selection for Data-Efficient Autonomous Driving

Training vision-based Autonomous driving models is a challenging problem with enormous practical implications. One of the main challenges is the requirement of storage and processing of vast volumes of (possibly redundant) driving video data. In this paper, we study the problem of data-efficient training of autonomous driving systems. We argue that in the context of an edge-device deployment, multi-criteria online video frame subset selection is an appropriate technique for developing such frameworks. We study existing convex optimization based solutions and show that they are unable to provide solution with high weightage to loss of selected video frames. We design a novel multi-criteria online subset selection algorithm, TMCOSS, which uses a thresholded concave function of selection variables. Extensive experiments using driving simulator CARLA show that we are able to drop 80% of the frames, while succeeding to complete 100% of the episodes. We also show that TMCOSS improves performance on the crucial affordance "Relative Angle" during turns, on inclusion of bucket-specific relative angle loss (BL), leading to selection of more frames in those parts. TMCOSS also achieves an 80% reduction in number of training video frames, on real-world videos from the standard BDD and Cityscapes datasets, for the tasks of drivable area segmentation, and semantic segmentation.
Improving the Classification Performance of Dendrite Morphological Neurons

Dendrite morphological neurons (DMNs) are neural models for pattern classification, where dendrites are represented by a geometric shape enclosing patterns of the same class. This study evaluates the impact of three dendrite geometries-namely, box, ellipse, and sphere-on pattern classification. In addition, we propose using smooth maximum and minimum functions to reduce the coarseness of decision boundaries generated by typical DMNs, and a softmax layer is attached at the DMN output to provide posterior probabilities from weighted dendrites responses. To adjust the number of dendrites per class automatically, a tuning algorithm based on an incremental-decremental procedure is introduced. The classification performance assessment is conducted on nine synthetic and 49 real-world datasets. Meanwhile, 12 DMN variants are evaluated in terms of accuracy and model complexity. The DMN reaches its highest potential by combining spherical dendrites with smooth activation functions and a learnable softmax layer. It attained the highest accuracy, uses the simplest geometric shape, is insensitive to variables with zero variance, and its structural complexity diminishes by using the smooth maximum function. Furthermore, this DMN configuration performed competitively or even better than other well-established classifiers in terms of accuracy, such as support vector machine, multilayer perceptron, radial basis function network, k -nearest neighbors, and random forest. Thus, the proposed DMN is an attractive alternative for pattern classification in real-world problems.
Face Recognition Authenticated Voice Assistant System for the disabled

Most of the visually challenged, handicapped, and elderly persons are in social isolation, voice assistant system could help them to overcome their difficulties and work independently instead of depending on others. In this work, a system in python is built to encourage blind, elder, and handicapped people to keep them engaged and work independently. Face Recognition system developed with OpenCV is used as the authentication mechanism. Authenticated users can use the virtual assistant system where instead of the conventional keyboard and mouse, voice commands are used to get their job done. This system takes voice command as input request through a microphone using speech recognition module and according to the request it does either web automation with selenium, get any information from a Wikipedia article through web scrapping, perform machine reading comprehension with the BERT model, request for the current time using time module, or control inbuilt LED of Arduino board through serial communication. The contents in the screen are converted into speech through the text to speech library. Response to the request by the user is given in the form of speech.
Face Matching in Indian Citizens using CNN

Image processing is a way of performing certain image operations in order to get the desired output from the image. There are numerous applications that use the processing of images. There are several techniques in image processing, algorithms that can be used to achieve expected performance for different applications. One of the most trending studies is facial recognition and classification using image processing. Because everything in this era is digitalized, face recognition is a vital part of it. While the subject is well studied, there are still many difficulties faced, such as lighting changes, different noises or other variables. In this paper we are concentrating on face matching of Indian citizens i.e. to accurately recognize if it is the same person having considered the pictures of them at different ages. We have collected data sets of Indian citizens, pictures of each person from young age to recent ones. Vggnet is used for feature extraction and prediction. Objective for the proposed method is to get good accuracy rate and also to match the pictures correctly providing proper matching rate. The motivation for this project is to help in legal system especially in crime branch to track down guilty and also to group a huge data into that particular person's file.