Reviewerly/exHarmony huggingface dataset:
Comparing the Interpretability of Machine Learning Classifiers for Brain Tumour Survival Prediction

Background and Objective: Prediction of survival in patients diagnosed with a brain tumour is challenging because of heterogeneous tumour behaviours and treatment response. Better estimations of prognosis would support treatment planning and patient support, and advances in machine learning have informed development of clinical predictive models, but due to the lack of model interpretability, integration into clinical practice is almost non-existent. Using a real-world dataset specifically available for this study, we compare five classification models with varying degrees of interpretability for the prediction of brain tumour survival greater than one year following diagnosis.Methods: Three intrinsically interpretable ‘glass box’ classifiers (Bayesian Rule Lists, Explainable Boosting Machine, and Logistic Regression), and two ‘black box’ classifiers (Random Forest and Support Vector Machine) were trained on a novel brain tumour dataset for the prediction of one-year survival. All models were evaluated using standard performance metrics and model interpretability was quantified using both global and local explanation methods. The black box models, which do not provide means for intrinsic interpretability analysis, were evaluated globally using the Morris method and locally using LIME and SHAP.Results: The Random Forest model marginally outperformed all other algorithms achieving 78.8% balanced accuracy. Across all models, diagnosis (tumour type), Karnofsky performance score, tumour morphology and first treatment were top contributors to the prediction of one year survival. The Explainable Boosting Machine demonstrated high balanced accuracy (77.7%) on-par with the black box models, while remaining fully interpretable. Post-hoc explanation methods, LIME and SHAP, were beneficial for understanding miss-classified predictions, however the instability of these explanations highlights the need for robust gold-standard interpretability methods.Conclusion: Interpretable models are a natural choice for the domain of predictive medicine, and intrinsicially interpretable models, such as the Explainable Boosting Machine, may provide an advantage over traditional clinical assessment of brain tumour prognosis by weighting additional risk factors and stratifying patients accordingly. An agreement between model predictions and clinical knowledge is essential for establishing trust in the models decision making process, as well as trust that the model will make accurate predictions when applied to new data.
Face Mask Detection with Thermal Scanning

The coronavirus COVID-19 outbreak is spread- ing rapidly across the globe, posing a serious health threat. Coronaviruses have grown extremely common, contagious, and dangerous to the whole human population. Many precautions have been put in place to prevent the disease from spreading, including wearing masks which is among the most effective safety equipment. Wearing a facial mask in public areas, as per results of a study, greatly minimizes the probability of transmission. Body temperature, in addition to the face mask, plays an important part in staying safe.In this research, we propose a solution based on Raspberry Pi for limiting COVID- 19 development by detecting persons who are not using a face mask and tracking their temperature. The model is an automated system that focuses on real-time face surveillance of people in order to create a COVID-free workplace. This article shows how to recognise the face mask using TensorFlow, Keras, OpenCV and MobileNetV2. A contactless temperature sensor is also used to measure the temperature of the body. Furthermore, the suggested system outputs whether or not the face mask is present, as well as the individual’s body temperature. This technique would help to contain the spread of the infectious illness. It will urge individuals to wear face masks, and monitoring of temperature will ensure the workplace is secure. Any commercial mall, hotel, apartment entry or any other public place where people gather can benefit from the proposed approach.
Informative Software Defect Data Generation and Prediction: INF-SMOTE

Highly imbalanced data typically make accurate predictions difficult. Unfortunately, software defect datasets tend to have fewer defective modules than non-defective modules. Synthetic oversampling approaches, namely SMOTE, address this concern by creating new minority defective modules to balance the class distribution before a model is trained. Despite its success, these approaches come with the following shortcomings such as 1) over-generalization problem and generate near-duplicated data instances (less diverse data) due to oversampling of noisy samples, and 2) increasing the overlaps between different classes around the class boundaries. This paper introduces INF-SMOTE (Informative- Synthetic Minority Oversampling Technique), a novel and efficient synthetic oversampling approach for software defect datasets, simultaneously targeting all the shortcomings. INF-SMOTE identifies the informative minority samples that are appropriate for over-sampling. The process is in two way 1.) it identify and remove the noisy and overlapping samples from borderline minority instances based on the sampling seeds, and 2) synthetic samples are generated from the informative minority samples. Experiments were conducted on 12 releases of SDP (Software Defect Prediction) Datasets from the NASA repository. By comparing with the state-of-the-art techniques, we observe that the INF-SMOTE improves the defect prediction performance.
Genetic Algorithm

Genetic algorithm (GA) has been found to be a vital method when integrated with the AI methods and particularly for the optimization and feature selections. Since chapter 3 explored the AI and some of its common methods, this chapter aims to continue with such essential tool like (GA). GA was introduced by John Holland (1975). It uses the Darwinian's concept of survival fitness, to tackle the required problem by producing individuals that are better. This would lead to various solutions that are possible to match and compete with each other. It is basically an optimisation that is applied to complex functions. It is noteworthy to know that GA is similar to chromosomes in individuals that are represented by linear string. This chapter gives an overview of GA and its application in Artificial Intelligence (AI).
American Sign Language Gesture Analysis Using TensorFlow and Integration in a Drive-Through

The algorithm described in this paper aims to achieve Gesture recognition in identifying American Sign Language Symbols. There are a total of 29 characters that were added to the image database for the model to classify. The Database includes 87,000 images and more than 1200 images per character. Since it is a multiclass classification problem, a CNN model is implemented. The primary objective of the algorithm is to correctly classify all the gestures that are mapped on the proposed ASL Alphabet matrix integrated with the Digital Signage. This is done using OpenCV Library which captures the real-time feed and is then converted to grayscale that is provided as an input to the Convolutional Neural Network for the classification. The CNN model uses an ADAM optimizer that mediates the learning rate by quickly reaching the global minima based on the Vector Component Precedence.
Learning 3-D Face Shape From Diverse Sources With Cross-Domain Face Synthesis

Monocular face reconstruction is a significant task in many multimedia applications. However, learning-based methods unequivocally suffer from the lack of large datasets annotated with 3-D ground truth. To tackle this problem, we proposed a novel end-to-end 3-D face reconstruction network consisting of a domain-transfer conditional GAN (cGAN) and a face reconstruction network. Our method first uses cGAN to translate the realistic face images to the specific rendered style, with a novel 2-D facial edge consistency loss function to exploit in-the-wild images. The domain-transferred images are then fed into a 3-D face reconstruction network. We further propose a novel reprojection consistency loss to restrict the 3-D face reconstruction network in a self-supervised way. Our approach can be trained with the annotated dataset, synthetic dataset, and in-the-wild images to learn a unified face model. Extensive experiments have demonstrated the effectiveness of our method.
Teaching Formal Methods to Undergraduate Students Using Maude

I have been teaching an introductory formal methods course based on Maude—first to third- and fourth-year students, and lately to second-year students—at the University of Oslo for a number of years. The first part of the course introduces functional modules in Maude and covers basic topics in term rewriting, whereas the second part of the course uses Maude to formally model and analyze a number of classic distributed systems, including: transport protocols such as the alternating bit and the sliding windows protocols, the two-phase commit protocol for distributed atomic commitment, distributed algorithms for mutual exclusion and leader election, and authentication protocols. In this invited “experience report” I briefly motivate the use of Maude for an introductory formal methods course, outline the course content, and summarize student feedback and my own impressions about the course.
Comprehensive Analysis of Heterogeneous Computing Performance of Dnns Under Typical Frameworks on Cloud and Edge Computing Platforms

In this paper, nearly 40 commonly used deep neural network(DNN) models are selected, and their cross-platform and cross-inference frameworks are deeply analyzed. The main metrics of accuracy, total model parameters and computational complexity, accuracy density, inference time, memory consumption and other related-parameters are used to measure their performance. The heterogeneous computing experiment are implemented both on Google Colab cloud computing platform and Jetson Nano embedded edge computing platform. Those obtained performance are compared with the of previous distinct two kinds of computing platforms of the workstation equipped with a NVIDIA Titan X Pascal, and an embedded system based on a NVIDIA Jetson TX1 board. In addition, in Jetson Nano embedded edge computing platform, different inference frameworks are investigated for evaluating the inference efficiency of DNN models. The regression models are established to characterize the variation of the computing performance of different DNN classification algorithms, so that the inference results of unknown models can be estimated. The methods of ANOVA were proposed to quantify the differences between models. The experimental results have important guiding significance for the better selection, deployment and application of DNN models in practice. Codes are available at this https URL https://github.com/Foreverzfy/Model-Test .
Video Descriptor Using Attention Mechanism

This paper is concerned with creation of natural language descriptions using video as an input. Auto-captioning of videos with natural language is a critical challenge of video comprehension. Compared to image, videos have specific time-based structure and various spatial information. In today’s world, data is available in different forms and is the most valuable asset. But storing the data in the form of video is not feasible as it will consume huge amount of storage space and extracting any information from such a dataset will cost more processing power. To overcome this, extracting information from the video and storing it in the form of text will allow more efficient usage of storage space. This information can be later processed according to the need of the user. With the objective of generating text from videos we proposed a system which will use a combination of Convolution Neural Network and Recurrent Neural Network followed by Attention mechanism which will provide the text outputs from image and finally the text will be summarized in such a way that it provides a clean description of complete video, the duration of videos is now limited to 1–2 min. The model is tested on Microsoft COCO dataset.
Demonstration of a Simple and Flexible Calibration Method for Structured Light System

The calibration of the system’s geometric parameters is always an important step in 3D topography measurement based on fringe projection profilometry. However, the traditional calibration method needs to be realized by the implementation of an electric guide rail, which exhibits certain problems, such as complicated operation and inconvenient carrying. Along these lines, in this work, a new simple and flexible calibration method is introduced. For the proposed method, a round calibration board only needs to be moved arbitrarily many times within the depth of field of the camera and projector. By using the existing camera calibration method, each calibration plate position and pose can be derived, while the phase of every pixel and the height data, as well as the phase of every pixel-height mapping relationship, were established. In addition, the rational function fitting method was used to accurately estimate the phase distribution of the black area of the calibration plate, avoiding thus the influence of the phase artifacts caused by high-contrast calibration targets. The minimum root mean square error (RMSE) of the experimental results was 0.0735 mm. Compared with the traditional method, our approach is more flexible and convenient, and has no loss of reliability.
DyD2: Dynamic Double anomaly Detection Application to on-board space radiation faults

Anomaly detection is a crucial aspect of embedded applications. However, limited computational power, evolving environments, and lack of training data are difficulties that can limit anomaly detection algorithms. One class classification algorithms are often used for this task to circumvent the need of anomalous data in the training set. This paper presents a new machine learning algorithm for anomaly detection called Dynamic Double anomaly Detection DyD2that is suited to evolving environments and on-board requirements. The contributions made by DyD2 are thoroughly presented and an experimental evaluation is set up to compare DyD2 to state-of-the-art algorithms.
Securing TEEs With Verifiable Execution Contracts

Recent works have demonstrated that trusted execution environments, such as Intel Software Guard Extensions, are vulnerable to various attacks from the privileged software, including side-channel attacks. Existing solutions, such as T-SGX, Déjà Vu, Cloak and Varys, detect side-channel attacks at runtime. But they are limited by design, because false detection is unavoidable in these detection methods and therefore any security policy developed atop these mechanisms has to tolerate some malicious operations to achieve practical false positive detection rates. In this article, we propose the concept of verifiable execution contracts, which request the privileged software to provide a benign execution environment for enclaves within which launching attacks becomes very difficult, if not impossible. Since the privileged software is untrusted, we design methods for verifying that the execution contracts are observed. With the proposed verifiable execution contracts, we analyzed how existing attacks could be mitigated.
Tumbling Robot Control Using Reinforcement Learning: An Adaptive Control Policy That Transfers Well to the Real World

Tumbling robots are simple platforms that are able to traverse large obstacles relative to their size, at the cost of being difficult to control. Existing control methods apply only a subset of possible robot motions and make the assumption of flat terrain. Reinforcement learning (RL) allows for the development of sophisticated control schemes that can adapt to diverse environments. By utilizing domain randomization while training in simulation, a robust control policy can be learned that transfers well to the real world. In this article, we implement autonomous set point navigation on a tumbling robot prototype and evaluate it on flat, uneven, and valley–hill terrain. Our results demonstrate that RL-based control policies can generalize well to challenging environments that were not encountered during training. The flexibility of our system demonstrates the viability of nontraditional robots for navigational tasks.
Real-Time Multi-task Network for Autonomous Driving

End-to-end Network has become increasingly important in multi-tasking, especially a driving perception system in autonomous driving. This work systematically introduces an end-to-end perception network for multi-tasking and proposes several key optimizations to improve accuracy. First, we propose efficient segmentation head and box/class prediction networks based on weighted bidirectional feature network. Second, we propose automatically customized anchor for each level in the weighted bidirectional feature network. Third, we propose an efficient training loss function. Based on these optimizations, we develope an end-to-end perception network to perform multi-tasking, including traffic object detection, drivable area segmentation and lane detection simultaneously which achieves better accuracy than prior art. In particular, our network design achieves the state-of-the art 77 mAP@.5 on BDD100K Dataset, outperforms lane detection with 0.293 mIOU on 12.83 parameters and 15.6 FLOPs. The network can perform visual perception tasks in real-time and thus is a practical and accurate solution to the multi-tasking problem.
A Voice Assisted Chatbot Framework for Real-Time Implementation in Medical Care

A chatbot is one of the trending technologies used in many technologies. It is a fast-growing technology that combines hardware and software techniques into a single platform. Many websites take advantage of this technology to communicate directly with their visitors. The chatbot plays a crucial role in a clear-cut conversation with the website. This article provides a comprehensive study of the application-specific implementation of chatbots in real-time applications developed in the past years. The authors propose a framework and implement a medical care specific application using a robotic skull model with IoT, the software programming using embedded C. It leads to opening up numerous possibilities for other technologies to participate. Moreover, the chatbot design is programmed to answer all the queries about first aid prescriptions and offer a reminder for users. As a result, the proposed implementation leads to a framework for integrating intelligent conversational systems with the Android operating system.
DL-CapsNet: A Deep and Light Capsule Network

Capsule Network (CapsNet) is among the promising classifiers and a possible successor of the classifiers built based on Convolutional Neural Network (CNN). CapsNet is more accurate than CNNs in detecting images with overlapping categories and those with applied affine transformations. In this work, we propose a deep variant of CapsNet consisting of several capsule layers. In addition, we design the Capsule Summarization layer to reduce the complexity by reducing the number of parameters. DL-CapsNet, while being highly accurate, employs a small number of parameters and delivers faster training and inference. DL-CapsNet can process complex datasets with a high number of categories.
Fabricated fractals as unique fingerprints for data and image encryption

With advancements in telecommunication technology, data or information transfer has become the backbone of daily transactions.
The $$\langle $$Im|Possibility$$\rangle $$ of Quantum Annealing for Maximum Likelihood Estimation

This paper provides a casual exploration of quantum ideas and quantum computing to solve maximum likelihood problems common in statistical and econometric estimations. A brief description of quantum ideas, and in particular, the quantum bit and its properties, is followed by how quantum annealing, specifically conducted by the D-Wave quantum computer, takes advantage of “quantum tunneling” to solve optimization problems that may typically be handled by simulated annealing. Although there is now established theoretical grounds in favor of quantum annealing over simulated annealing, there remain several limitations of its usefulness and useability in MLE, namely, improving its accuracy by formulating the problem more fully for quantum computers and/or reducing hardware noise which may mask the true solution. Currently, the process of embedding quantum maximum likelihood into the quantum machine reduces the scale of problems available and as of now non-artificial problems in which quantum annealing outperforms simulated annealing are hard to find.
High Security Object Integrity and Manipulation of Conceal Information by Hiding Partition Technique

In the current period, because of the far reaching accessibility of the Internet, it is incredibly simple for individuals to convey and share interactive media substance with one another. Nonetheless, simultaneously, secure exchange of individual and protected material has turned into a basic issue. Thusly, secure method for information move are the most dire need of the time. Steganography is the science and specialty of shielding the restricted information from an unapproved access. The steganographic approaches hide restricted information into a cover record of type sound, video, text or potentially image. The genuine test in steganography is to accomplish high strength and limit without dealing on the subtlety of the cover document. In this article, a proficient steganography strategy is proposed for the exchange of privileged information in computerized images utilizing number hypothesis. For this reason, the proposed technique addresses the cover image utilizing the Fibonacci grouping. The portrayal of an image in the Fibonacci succession permits expanding the bit planes from 8-bit to 12-bit planes. The test aftereffects of the proposed strategy in examination with other existing steganographic strategies display that our technique accomplishes high installing of privileged information as well as gives top caliber of stego images as far as pinnacle signal-to-clamor proportion (PSNR). Besides, the power of the technique is likewise assessed within the sight of salt and pepper clamor assault on the cover images.
TOUS: A New Technique for Imbalanced Data Classification

Imbalanced data is an issue attracting much concern in classification. With imbalanced input, classification models often suffer from poor performance on the minority class. Moreover, the methods become inefficient if there are noise and overlapping samples in training sets. Although number of papers separately studied either imbalance, noise or overlapping issues, only few works addressed all of them. Besides, these method implementations were so far not accessible. These are the reasons of the new proposed algorithm called TOUS, which combines Tomek-link, random under and over sampling methods. TOUS generates a list of balanced training data sets with different numbers of samples. They are then the input data sets for parallel classifier ensembles. To clarify the performance of TOUS, we build two classifier ensembles, called LLE (Lasso-Logistic ensemble) and DTE (Decision tree ensemble), respectively based on Lasso-Logistic regression and Decision Tree. Our empirical results over six different data sets showed that TOUS helped LLE and DTE to outperform several popular methods for balancing data. Moreover, the execution time of LLE and DTE were relatively less than other tested methods.