Reviewerly/exHarmony huggingface dataset:
Prediction of Breast Cancer Survival by Machine Learning Methods: An Application of Multiple Imputation

The low breast cancer survival rates in less developed countries are critical. The machine learning techniques predict cancers survival with high accuracy. Missing data are the most important limitation for using the highest potential of these techniques to predict cancers survival. Multiple imputation (MI) was implemented and analyzed in detail to impute the missing data of a breast cancer dataset.The dataset was from The Omid Treatment and Research Center Urmia, Iran between Jan 2006 and Dec 2012 and had information from 856 women. The algorithms such as C5 and repeated incremental pruning to produce error reduction were applied on the imputed versions of the original dataset and the non-imputed dataset to predict and extract clinical rules, respectively.The findings showed the performance of C5 in all the evaluation criteria including accuracy (84.42%), sensitivity (92.21%), specificity (64%), Kappa statistic (59.06%), and the area under the receiver operator characteristic (ROC) curve (0.84), was improved after imputation.The dataset of the present study met the requirements for using the multiple imputation method. The extracted rules after the application of MI were more comprehensive and contained knowledge that is more clinical. However, the clinical value of the extracted rules after filling in the missing data did not noticeably increase.
Nonlinear All-Optical Diffractive Deep Neural Network with 10.6 μm Wavelength for Image Classification

A photonic artificial intelligence chip is based on an optical neural network (ONN), low power consumption, low delay, and strong antiinterference ability. The all-optical diffractive deep neural network has recently demonstrated its inference capabilities on the image classification task. However, the size of the physical model does not have miniaturization and integration, and the optical nonlinearity is not incorporated into the diffraction neural network. By introducing the nonlinear characteristics of the network, complex tasks can be completed with high accuracy. In this study, a nonlinear all-optical diffraction deep neural network (N-D2NN) model based on 10.6 μm wavelength is constructed by combining the ONN and complex-valued neural networks with the nonlinear activation function introduced into the structure. To be specific, the improved activation function of the rectified linear unit (ReLU), i.e., Leaky-ReLU, parametric ReLU (PReLU), and randomized ReLU (RReLU), is selected as the activation function of the N-D2NN model. Through numerical simulation, it is proved that the N-D2NN model based on 10.6 μm wavelength has excellent representation ability, which enables them to perform classification learning tasks of the MNIST handwritten digital dataset and Fashion-MNIST dataset well, respectively. The results show that the N-D2NN model with the RReLU activation function has the highest classification accuracy of 97.86% and 89.28%, respectively. These results provide a theoretical basis for the preparation of miniaturized and integrated N-D2NN model photonic artificial intelligence chips.
SDLV: Verification of Steering Angle Safety for Self-Driving Cars

Abstract Self-driving cars over the last decade have achieved significant progress like driving millions of miles without any human intervention. However, behavioral safety in applying deep-neural-network-based (DNN based) systems for self-driving cars could not be guaranteed. Several real-world accidents involving self-driving cars have already happened, some of which have led to fatal collisions. In this paper, we present a novel and automated technique for verifying steering angle safety for self-driving cars. The technique is based on deep learning verification (DLV), which is an automated verification framework for safety of image classification neural networks. We extend DLV by leveraging neuron coverage and slack relationship to solve the judgement problem of predicted behaviors, and thus, to achieve verification of steering angle safety for self-driving cars. We evaluate our technique on the NVIDIA’s end-to-end self-driving architecture, which is a crucial ingredient in many modern self-driving cars. Experimental results show that our technique can successfully find adversarial misclassifications (i.e., incorrect steering decisions) within given regions if they exist. Therefore, we can achieve safety verification (if no misclassification is found for all DNN layers, in which case the network can be said to be stable or reliable w.r.t. steering decisions) or falsification (in which case the adversarial examples can be used to fine-tune the network).
Learning to refer informatively by amortizing pragmatic reasoning

A hallmark of human language is the ability to effectively and efficiently convey contextually relevant information. One theory for how humans reason about language is presented in the Rational Speech Acts (RSA) framework, which captures pragmatic phenomena via a process of recursive social reasoning (Goodman & Frank, 2016). However, RSA represents ideal reasoning in an unconstrained setting. We explore the idea that speakers might learn to amortize the cost of RSA computation over time by directly optimizing for successful communication with an internal listener model. In simulations with grounded neural speakers and listeners across two communication game datasets representing synthetic and human-generated data, we find that our amortized model is able to quickly generate language that is effective and concise across a range of contexts, without the need for explicit pragmatic reasoning.
Ready,<tt>Set</tt>, Verify! Applying<tt>hs-to-coq</tt>to real-world Haskell code

Abstract Good tools can bring mechanical verification to programs written in mainstream functional languages. We use hs-to-coq to translate significant portions of Haskell’s containers library into Coq, and verify it against specifications that we derive from a variety of sources including type class laws, the library’s test suite, and interfaces from Coq’s standard library. Our work shows that it is feasible to verify mature, widely used, highly optimized, and unmodified Haskell code. We also learn more about the theory of weight-balanced trees, extend hs-to-coq to handle partiality, and – since we found no bugs – attest to the superb quality of well-tested functional code.
A computer tool to identify best matches for pottery fragments

Archaeologists working with pottery spend a considerable amount of time on a fundamental task – providing precise descriptions of pottery fragments. This study presents a survey of existing computational solutions to identify the best matches for a given fragment, based on its shape. Four methods (ICP, DCT, RDP, and RTC) are compared, using a pottery dataset from Graufesenque (southern France), dated to the Roman Period. The first three methods produced successful and very similar results for rim fragments (within the five best candidates for 95% of the dataset). The ICP algorithm produced the best overall results for rim fragments, and can also be used for non-rim fragments. A practical computer application, including all the above methods, was developed in R programming language, with an easy-to-use graphical interface, and is now made freely available to the archaeological community for future studies, and further development.
Multi-level Metric Learning for Few-Shot Image Recognition

Few-shot learning devotes to training a model on a few samples. Most of these approaches learn a model based on a pixel-level or global-level feature representation. However, using global features may lose local information, and using pixel-level features may lose the contextual semantics of the image. Moreover, such works can only measure their relations on a single level, which is not comprehensive and effective. And if query images can simultaneously be well classified via three distinct level similarity metrics, the query images within a class can be more tightly distributed in a smaller feature space, generating more discriminative feature maps. Motivated by this, we propose a novel Part-level Embedding Adaptation with Graph (PEAG) method to generate task-specific features. Moreover, a Multi-level Metric Learning (MML) method is proposed, which not only calculates the part-level similarity but also considers the similarity of pixel-level and global-level metrics. Extensive experiments on popular few-shot image recognition datasets prove the effectiveness of our method compared with the state-of-the-art methods. Our code is available at: https://github.com/chenhaoxing/M2L .
Australia’s ‘COVIDSafe’ law for contact tracing: an experiment in surveillance and trust

Australia’s ‘COVIDSafe’ law for contact tracing: an experiment in surveillance and trust Graham Greenleaf, Graham Greenleaf Graham Greenleaf, Professor of Law & Information Systems, Faculty of Law, UNSW, Sydney, Australia. Email: graham@austlii.edu.au Search for other works by this author on: Oxford Academic Google Scholar Katharine Kemp Katharine Kemp Search for other works by this author on: Oxford Academic Google Scholar International Data Privacy Law, Volume 11, Issue 3, August 2021, Pages 257–275, https://doi.org/10.1093/idpl/ipab009 Published: 13 March 2021
RETRACTED ARTICLE: Multi-parameter optimization for load balancing with effective task scheduling and resource sharing

Cloud Computing is turning into an undeniably appreciated worldview that conveys superior figuring assets over the Internet to take care of the complex logical issues, yet at the same time, it has different moves that should be routed to execute logical work processes. The current research primarily centered around limiting completing time (makespan) or minimization of expense while meeting the nature of organization prerequisites. Be that as it may, a large portion of them do not think about fundamental normal for cloud and serious issues, for example, virtual machines (VMs) execution variety and procurement delay. Task scheduling for load balancing is one of the basic systems in the distributed computing condition. It is required for dispensing undertakings to be the best possible assets and streamlining the general framework execution. In recent research, the most popular scheduling algorithms named particle swarm optimization (PSO) algorithm is utilized to maximize resource utilization. In any case, the PSO scheduling algorithm performance gets degraded when the task number is critical. In this paper, we propose a meta-heuristic practical, multi-objective scheduling based particle swarm optimization (MOSPSO) that limits the execution cost of the work process while complying with the time constraint in cloud computing condition. Multi-objective scheduling based on particle swarm optimization (MOSPSO) is proposed under this research to give optimal allocation for a large number of tasks. This is accomplished by part of the submitted tasks into groups in a dynamic manner. The assets use state is considered in every creation for groups. In the wake of getting an imperfect answer for each group, the proposed method annexes all the problematic answers for clumps into the last assignment map. At last, MOSPSO attempts to adjust the loads over the last assignment map. The proposed calculation is contrasted and distinctive booking calculations in particular particle swarm optimization (PSO) and improved particle swarm optimization (IPSO). The results of analyses demonstrate the effectiveness of the proposed algorithm as in terms of makespan, degree of load balance, and total execution time.
Challenges in Cryptography

Cryptography as a field of study is exciting because it brings together beautiful mathematics and many cutting-edge areas of computer science and engineering to find solutions that touch all aspects of life in a digital era. In a single day at a cryptography conference, one can hear talks on election security and legislation on regulating encryption, new mathematical constructions that might yield more efficient encryption schemes, how to wirelessly hack a Tesla and drive away without the key, how Google secures communication between its data centers, and how to build a more private blockchain.
A Quantum Walk Model of Financial Options

Financial markets are often modeled using a random walk, for example in the binomial option pricing model. This paper presents an alternative approach to option pricing based on a quantum walk model. The quantum walk, which incorporates superposition states and allows for effects such as interference, was originally developed in physics, but has also seen application in areas such as cognitive psychology, where it is used to model dynamic decision-making processes. It is shown here that the quantum walk model captures key aspects of investor behavior, while the collapsed state captures the observed behavior of markets. The resulting option price model agrees quite closely with the classical random walk model, but when coupled with a model of supply and demand helps to explain some observed anomalies. The method also has the advantage that it can be run directly on a quantum device. The aim of this paper is to initiate a discussion about how non-classical models that are native to quantum computers can be applied in finance.
Anomaly detection for the individual analysis of brain PET images

Purpose: In clinical practice, positron emission tomography (PET) images are mostly analyzed visually, but the sensitivity and specificity of this approach greatly depend on the observer's experience. Quantitative analysis of PET images would alleviate this problem by helping define an objective limit between normal and pathological findings. We present an anomaly detection framework for the individual analysis of PET images. Approach: We created subject-specific abnormality maps that summarize the pathology's topographical distribution in the brain by comparing the subject's PET image to a model of healthy PET appearance that is specific to the subject under investigation. This model was generated from demographically and morphologically matched PET scans from a control dataset. Results: We generated abnormality maps for healthy controls, patients at different stages of Alzheimer's disease and with different frontotemporal dementia syndromes. We showed that no anomalies were detected for the healthy controls and that the anomalies detected from the patients with dementia coincided with the regions where abnormal uptake was expected. We also validated the proposed framework using the abnormality maps as inputs of a classifier and obtained higher classification accuracies than when using the PET images themselves as inputs. Conclusions: The proposed method was able to automatically locate and characterize the areas characteristic of dementia from PET images. The abnormality maps are expected to (i) help clinicians in their diagnosis by highlighting, in a data-driven fashion, the pathological areas, and (ii) improve the interpretability of subsequent analyses, such as computer-aided diagnosis or spatiotemporal modeling.
Brain Tumour Segmentation Using Probabilistic U-Net

We describe our approach towards the segmentation task of the BRATS 2020 challenge. We use the Probabilistic UNet to explore the effect of sampling different segmentation maps, which may be useful to experts when the opinions of different experts vary. We use 2D segmentation models and approach the problem in a slice-by-slice manner. To explore the possibility of designing robust models, we use self attention in the UNet, and the prior and posterior networks, and explore the effect of varying the number of attention blocks on the quality of the segmentation. Our model achieves Dice scores of 0.81898 on Whole Tumour, 0.71681 on Tumour Core, and 0.68893 on Enhancing Tumour on the Validation data, and 0.7988 on Whole Tumour, 0.7771 on Tumour Core, and 0.7249 on Enhancing Tumour on the Testing data. Our code is available at https://github.com/rahulkulhalli/BRATS2020 .
Detection of Advanced Web Bots by Combining Web Logs with Mouse Behavioural Biometrics

Web bots vary in sophistication based on their purpose, ranging from simple automated scripts to advanced web bots that have a browser fingerprint, support the main browser functionalities, and exhibit a humanlike behaviour. Advanced web bots are especially appealing to malicious web bot creators, due to their browserlike fingerprint and humanlike behaviour that reduce their detectability. This work proposes a web bot detection framework that comprises two detection modules: (i) a detection module that utilises web logs, and (ii) a detection module that leverages mouse movements. The framework combines the results of each module in a novel way to capture the different temporal characteristics of the web logs and the mouse movements, as well as the spatial characteristics of the mouse movements. We assess its effectiveness on web bots of two levels of evasiveness: (a) moderate web bots that have a browser fingerprint and (b) advanced web bots that have a browser fingerprint and also exhibit a humanlike behaviour. We show that combining web logs with visitors’ mouse movements is more effective and robust toward detecting advanced web bots that try to evade detection, as opposed to using only one of those approaches.
Hierarchical clustering with discrete latent variable models and the integrated classification likelihood

Finding a set of nested partitions of a dataset is useful to uncover relevant structure at different scales, and is often dealt with a data-dependent methodology. In this paper, we introduce a general two-step methodology for model-based hierarchical clustering. Considering the integrated classification likelihood criterion as an objective function, this work applies to every discrete latent variable models (DLVMs) where this quantity is tractable. The first step of the methodology involves maximizing the criterion with respect to the partition. Addressing the known problem of sub-optimal local maxima found by greedy hill climbing heuristics, we introduce a new hybrid algorithm based on a genetic algorithm efficiently exploring the space of solutions. The resulting algorithm carefully combines and merges different solutions, and allows the joint inference of the number $K$ of clusters as well as the clusters themselves. Starting from this natural partition, the second step of the methodology is based on a bottom-up greedy procedure to extract a hierarchy of clusters. In a Bayesian context, this is achieved by considering the Dirichlet cluster proportion prior parameter $\alpha$ as a regularization term controlling the granularity of the clustering. A new approximation of the criterion is derived as a log-linear function of $\alpha$, enabling a simple functional form of the merge decision criterion. This second step allows the exploration of the clustering at coarser scales. The proposed approach is compared with existing strategies on simulated as well as real settings, and its results are shown to be particularly relevant. A reference implementation of this work is available in the R package greed accompanying the paper.
A systematic literature review on image information needs and behaviors

Purpose With ready access to search engines and social media platforms, the way people find image information has evolved and diversified in the past two decades. The purpose of this paper is to provide an overview of the literature on image information needs and behaviors. Design/methodology/approach Following an eight-step procedure for conducting systematic literature reviews, the paper presents an analysis of peer-reviewed work on image information needs and behaviors, with publications ranging from the years 1997 to 2019. Findings Application of the inclusion criteria led to 69 peer-reviewed works. These works were synthesized according to the following categories: research methods, users targeted, image types, identified needs, search behaviors and search obstacles. The reviewed studies show that people seek and use images for multiple reasons, including entertainment, illustration, aesthetic appreciation, knowledge construction, engagement, inspiration and social interactions. The reviewed studies also report that common strategies for image searches include keyword searches with short queries, browsing, specialization and reformulation. Observed trends suggest common deployment of query analysis, survey questionnaires and undergraduate participant pools to research image information needs and behavior. Originality/value At this point, after more than two decades of image information needs research, a holistic systematic review of the literature was long overdue. The way users find image information has evolved and diversified due to technological developments in image retrieval. By synthesizing this burgeoning field into specific foci, this systematic literature review provides a foundation for future empirical investigation. With this foundation set, the paper then pinpoints key research gaps to investigate, particularly the influence of user expertise, a need for more diverse population samples, a dearth of qualitative data, new search features and information and visual literacies instruction.
On-Device Lumbar-Pelvic Movement Detection Using Dual-IMU: A DNN-Based Approach

Lumbar-pelvic movements (LPMs) are generally performed in the clinical setting to identify limitations in a range of movements. Continuous monitoring of these movements can provide real-time feedback to both patients and medical experts with the potential of identifying activities that may precipitate symptoms of low back pain (LBP) as well as improving therapy by providing a personalised approach. Recent advances in mobile computing technology and wearable sensors have paved the way for developing mobile physical activity monitoring applications with more advanced and complex algorithms, such as deep neural network (DNN) based models. However, there is a lack of prior studies that focus on real-time LPMs detection with multimodal sensory data. Meanwhile, most research in the area of body movement detection do not consider the potential transition logic of the constituent low-level body movements (e.g., LPMs) within their corresponding high-level physical activity. This information could significantly increase accuracy of detection results. Further, current studies mainly perform deep learning-based movement detection in the cloud (or a backend server) that could increase network bandwidth and response time. To address these limitations, this paper proposes a novel LPMs detection approach using an enhanced and adapted hybrid DNN model, which includes a convolutional neural network followed by a long-short term memory recurrent network (CNN-LSTM), and performs detection locally on the mobile device. The results of a comparative evaluation of the proposed model and baseline models are described. We also introduce a set of domain-specific pre-defined rules, based on the transition logic information, to reconstruct the detection outputs to further improve the detection accuracy.
Robust Vision-Based Cheat Detection in Competitive Gaming

Game publishers and anti-cheat companies have been unsuccessful in blocking cheating in online gaming. We propose a novel, vision-based approach that captures the frame buffer's final state and detects illicit overlays. To this aim, we train and evaluate a DNN detector on a new dataset, collected using two first-person shooter games and three cheating software. We study the advantages and disadvantages of different DNN architectures operating on a local or global scale. We use output confidence analysis to avoid unreliable detections and inform when network retraining is required. In an ablation study, we show how to use Interval Bound Propagation (IBP) to build a detector that is also resistant to potential adversarial attacks and study IBP's interaction with confidence analysis. Our results show that robust and effective anti-cheating through machine learning is practically feasible and can be used to guarantee fair play in online gaming.