Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence Techniques That May Be Applied to Primary Care Data to Facilitate Earlier Diagnosis of Cancer: Systematic Review

More than 17 million people worldwide, including 360,000 people in the United Kingdom, were diagnosed with cancer in 2018. Cancer prognosis and disease burden are highly dependent on the disease stage at diagnosis. Most people diagnosed with cancer first present in primary care settings, where improved assessment of the (often vague) presenting symptoms of cancer could lead to earlier detection and improved outcomes for patients. There is accumulating evidence that artificial intelligence (AI) can assist clinicians in making better clinical decisions in some areas of health care.This study aimed to systematically review AI techniques that may facilitate earlier diagnosis of cancer and could be applied to primary care electronic health record (EHR) data. The quality of the evidence, the phase of development the AI techniques have reached, the gaps that exist in the evidence, and the potential for use in primary care were evaluated.We searched MEDLINE, Embase, SCOPUS, and Web of Science databases from January 01, 2000, to June 11, 2019, and included all studies providing evidence for the accuracy or effectiveness of applying AI techniques for the early detection of cancer, which may be applicable to primary care EHRs. We included all study designs in all settings and languages. These searches were extended through a scoping review of AI-based commercial technologies. The main outcomes assessed were measures of diagnostic accuracy for cancer.We identified 10,456 studies; 16 studies met the inclusion criteria, representing the data of 3,862,910 patients. A total of 13 studies described the initial development and testing of AI algorithms, and 3 studies described the validation of an AI algorithm in independent data sets. One study was based on prospectively collected data; only 3 studies were based on primary care data. We found no data on implementation barriers or cost-effectiveness. Risk of bias assessment highlighted a wide range of study quality. The additional scoping review of commercial AI technologies identified 21 technologies, only 1 meeting our inclusion criteria. Meta-analysis was not undertaken because of the heterogeneity of AI modalities, data set characteristics, and outcome measures.AI techniques have been applied to EHR-type data to facilitate early diagnosis of cancer, but their use in primary care settings is still at an early stage of maturity. Further evidence is needed on their performance using primary care data, implementation barriers, and cost-effectiveness before widespread adoption into routine primary care clinical practice can be recommended.
Transfer Learning for Design-Space Exploration with High-Level Synthesis

High-level synthesis (HLS) raises the level of design abstraction, expedites the process of hardware design, and enriches the set of final designs by automatically translating a behavioral specification into a hardware implementation. To obtain different implementations, HLS users can apply a variety of knobs, such as loop unrolling or function inlining, to particular code regions of the specification. The applied knob configuration significantly affects the synthesized design's performance and cost, e.g., application latency and area utilization. Hence, HLS users face the design-space exploration (DSE) problem, i.e. determine which knob configurations result in Pareto-optimal implementations in this multi-objective space. Whereas it can be costly in time and resources to run HLS flows with an enormous number of knob configurations, machine learning approaches can be employed to predict the performance and cost. Still, they require a sufficient number of sample HLS runs. To enhance the training performance and reduce the sample complexity, we propose a transfer learning approach that reuses the knowledge obtained from previously explored design spaces in exploring a new target design space. We develop a novel neural network model for mixed-sharing multi-domain transfer learning. Experimental results demonstrate that the proposed model outperforms both single-domain and hard-sharing models in predicting the performance and cost at early stages of HLS-driven DSE.
ApproxDet

Advanced video analytic systems, including scene classification and object detection, have seen widespread success in various domains such as smart cities and autonomous systems. With an evolution of heterogeneous client devices, there is incentive to move these heavy video analytics workloads from the cloud to mobile devices for low latency and real-time processing and to preserve user privacy. However, most video analytic systems are heavyweight and are trained offline with some pre-defined latency or accuracy requirements. This makes them unable to adapt at runtime in the face of three types of dynamism --- the input video characteristics change, the amount of compute resources available on the node changes due to co-located applications, and the user's latency-accuracy requirements change. In this paper we introduce ApproxDet, an adaptive video object detection framework for mobile devices to meet accuracy-latency requirements in the face of changing content and resource contention scenarios. To achieve this, we introduce a multi-branch object detection kernel, which incorporates a data-driven modeling approach on the performance metrics, and a latency SLA-driven scheduler to pick the best execution branch at runtime. We evaluate ApproxDet on a large benchmark video dataset and compare quantitatively to AdaScale and YOLOv3. We find that ApproxDet is able to adapt to a wide variety of contention and content characteristics and outshines all baselines, e.g., it achieves 52% lower latency and 11.1% higher accuracy over YOLOv3. Our software is open-sourced at https://github.com/purdue-dcsl/ApproxDet.
SP-CIDS: Secure and Private Collaborative IDS for VANETs

Vehicular Ad hoc NETworks (VANETs) serve as the backbone of Intelligent Transportation Systems (ITS), providing passengers with safety and comfort. However, VANETs are vulnerable to major threats that affect data privacy and network services either from an individual or distributed attacker. In this paper, a Secure and Private-Collaborative Intrusion Detection System (SP-CIDS) is proposed to detect network attacks and to mitigate security concerns. In SP-CIDS, a Distributed Machine Learning (DML) model based on the Alternating Direction Method of Multipliers (ADMM) is used, which leverages the potential of vehicle-to-vehicle collaboration in the learning process to improve the storage efficiency, accuracy, and scalability of the IDS. However, there are significant data privacy concerns possible in such collaboration, where a CIDS can act as a malicious system that has access to the intermediate stages of the learning process. Additionally, the SP-CIDS system uses Differential Privacy (DP) technique to address the aforementioned data privacy risk associated with the DML-based CIDS. The SP-CIDS system is evaluated with logistic regression, naïve bayes, and ensemble classifiers. Simulation results substantiate that a private ensemble classifier secures the training data with DP and also achieves 96.94% accuracy.
Sequential convex programming method using adaptive mesh refinement for entry trajectory planning problem

Sequential convex programming method is one of the potential approaches to make trajectory generation onboard. However, the contradiction between the solution accuracy and the computational efficiency restricts its application. To overcome the difficulty, a novel sequential convex programming method based on customized adaptive mesh refinement is proposed in this paper. The main contribution includes two aspects. Firstly, a customized adaptive mesh refinement method is developed. The mesh points are adjusted adaptively based on the linearization error after each iteration, and thus the number of mesh points is changed. Secondly, an unconventional convergence condition is proposed, which can make the sequential convex programming method converges to a feasible solution of the original problem with fewer iterations. Taking the entry trajectory planning problem as an example, the simulation results show that the proposed method can decrease the number of mesh points while ensuring the feasibility of converged solution. Due to the fewer iterations, the computational time to solve the original problem is decreased. As a result, a good balance between solution accuracy and computational efficiency is achieved.
Complex ISAR target recognition using deep adaptive learning

Target recognition in real-world environment is a most important and challenging computer vision task. In this paper, a real-world complex ISAR target recognition method is proposed on a computationally limited platform using deep adaptive learning (DAL). Particularly, adaptive multimodal mechanism (AMM) is presented to efficiently handle the complex multimodal recognition problem, which substantially improves CNNs' sampling and transformation capability and significantly increases output feature maps' resolutions. Moreover, feature learning, attribute prediction, relation mining, image matching, image understanding, image summarization, and label expansion are considered in a unified framework. Extensive qualitative and quantitative experiments are performed, and the results show the proposed method outperforms the several state-of-the-art methods.
Assessing the overhead of post-quantum cryptography in TLS 1.3 and SSH

The advances in quantum computing present a threat to public key primitives due to their ability to solve hard cryptographic problems in polynomial time. To address this threat to critical Internet security protocols like the Transfer Layer Security (TLS), and Secure Shell (SSH), the National Institute of Standards and Technology (NIST) is currently working on the new generation of quantum-resistant key encapsulation and authentication schemes. In this paper, we evaluate protocol handshake performance when both post-quantum key exchange and authentication are integrated into TLS and SSH. Our experiments consider realistic network conditions and reveal that the introduced handshake latency ranges between 1-300% for TLS and 0.5-50% for SSH depending on the post-quantum algorithms used. In addition, we examine how the initial TCP window size affects post-quantum TLS and SSH performance, and show that even a small size increase can reduce the observed post-quantum slowdown by 50%. Finally, we discuss alternatives that can encourage the early adoption of post-quantum cryptography with minimum protocol performance degradation.
Ensuring social acceptability of technological tracking in the COVID-19 context

With the second wave of the COVID-19 pandemic, more and more countries are implementing large-scale technology-based tracking measures to monitor and prevent the propagation of the virus across the population. Yet, despite important efforts made by governments in terms of development and media campaigns, these technology-based tracking applications are globally failing to get the adhesion of the population. Indeed, the social acceptability of these technologies is problematic, since these applications are perceived as presenting a risk on individual liberties, resulting in very low levels of trust from the population. The aim of this paper is thus to analyze how technology-based tracking in the context of a pandemic can answer the needs and requirements of social accountability. We will first describe what kind of technological means can be used in a pandemic management context, from Internet control to implanted devices. We will then present some of the key factors that can impact the social acceptability of technological tracking, as well as some of the long-term challenges associated with massive technological tracking of the population. Finally, we will propose an operational framework to conceptualize social acceptability enforcement and to optimize population adhesion to these measures via specific practical actions that can be undertaken by governments, policymakers, decision leaders, and application developers to ensure that technological tracking is not only socially accepted, but also socially acceptable.
Capturing schools' digital capacity: Psychometric analyses of the SELFIE self-reflection tool

Results from self-reflection tools for schools' digital capacity can lead to evidence-based decisions within the school community and/or the development of an action plan for a better integration of digital technologies. Thus, it is important that the information derived from self-reflection tools is complete, accurate, and relevant. However, usually self-reflection tools do not show evidence of the quality of the information provided. In this paper, we focus on SELFIE, a new, comprehensive, and customisable self-reflection tool for schools' digital capacity, and we analyse the quality of the information that it provides. In particular, we look at discrimination and difficulty item parameters (using item response theory), we analyse the reliability (using Cronbach's alpha and Omega) and the construct validity (using confirmatory factor analysis) of its core items. We find support for the tool quality and conclude that schools using SELFIE are provided with accurate information on their digital capacity. Additionally, we discuss ideas for further improving the tool and future research work. The innovative design of the SELFIE tool and the psychometric analyses of its core items are a novelty in the field of schools' digital capacity and can provide insights for the development of self-reflection tools for school communities.
An end-to-end breast tumour classification model using context-based patch modelling – A BiLSTM approach for image classification

Researchers working on computational analysis of Whole Slide Images (WSIs) in histopathology have primarily resorted to patch-based modelling due to large resolution of each WSI. The large resolution makes WSIs infeasible to be fed directly into the machine learning models due to computational constraints. However, due to patch-based analysis, most of the current methods fail to exploit the underlying spatial relationship among the patches. In our work, we have tried to integrate this relationship along with feature-based correlation among the extracted patches from the particular tumorous region. The tumour regions extracted from WSI have arbitrary dimensions having the range 20,570 to 195 pixels across width and 17,290 to 226 pixels across height. For the given task of classification, we have used BiLSTMs to model both forward and backward contextual relationship. Also, using RNN based model, the limitation of sequence size is eliminated which allows the modelling of variable size images within a deep learning model. We have also incorporated the effect of spatial continuity by exploring different scanning techniques used to sample patches. To establish the efficiency of our approach, we trained and tested our model on two datasets, microscopy images and WSI tumour regions. Both datasets were published by ICIAR BACH Challenge 2018. Finally, we compared our results with top 5 teams who participated in the BACH challenge and achieved the top accuracy of 90% for microscopy image dataset. For WSI tumour region dataset, we compared the classification results with state of the art deep learning networks such as ResNet, DenseNet, and InceptionV3 using maximum voting technique. We achieved the highest performance accuracy of 84%. We found out that BiLSTMs with CNN features have performed much better in modelling patches into an end-to-end Image classification network. Additionally, the variable dimensions of WSI tumour regions were used for classification without the need for resizing. This suggests that our method is independent of tumour image size and can process large dimensional images without losing the resolution details.
Synthesizing light field from a single image with variable MPI and two network fusion

We propose a learning-based approach to synthesize a light field with a small baseline from a single image. We synthesize the novel view images by first using a convolutional neural network (CNN) to promote the input image into a layered representation of the scene. We extend the multiplane image (MPI) representation by allowing the disparity of the layers to be inferred from the input image. We show that, compared to the original MPI representation, our representation models the scenes more accurately. Moreover, we propose to handle the visible and occluded regions separately through two parallel networks. The synthesized images using these two networks are then combined through a soft visibility mask to generate the final results. To effectively train the networks, we introduce a large-scale light field dataset of over 2,000 unique scenes containing a wide range of objects. We demonstrate that our approach synthesizes high-quality light fields on a variety of scenes, better than the state-of-the-art methods.
Trust calibration of automated security IT artifacts: A multi-domain study of phishing-website detection tools

Phishing websites become a critical cybersecurity threat affecting individuals and organizations. Phishing-website detection tools are designed to protect users against such sites. Nevertheless, detection tools face serious user trust and suboptimal performance issues which require trust calibration to align trust with the tool's capabilities. We employ the theoretical framework of automation trust and reliance as a kernel theory to develop the trust calibration model for phishing-website detection tools. We test the model using a controlled lab experiment. The results of our analysis show that users' trust in detection tools can be calibrated by trust calibrators. Moreover, users' calibrated trust has significant consequences, including users' tool reliance, use, and performance against phishing websites.
WabiQA: A Wikipedia-Based Thai Question-Answering System

With vast information that has been digitized and made available online, manually finding the answer to a question can be tedious. While search engines have emerged to facilitate information needs, users would have to manually read through the retrieved articles to locate the answer to a specific question. Therefore, the ability to automatically understand users’ natural language questions and find the correct answers could prove crucial in information retrieval. Indeed, such automatic question-answering solutions have been extensively studied by the natural language processing (NLP) research communities. However, most of the development targets questions and information sources composed in high-resource languages such as English and Chinese. In this paper, we propose WabiQA, a novel system for automatically answering questions in the Thai language using the Thai Wikipedia articles as the knowledge source. Specifically, the proposed method first retrieves the Wikipedia article that is most likely to contain the answer. Then, a bi-directional LSTM model is used to read the article and locate candidate answers, which are ranked by confidence levels and returned to the user. WabiQA won the first prize award from Thailand’s National Software Contest 2019 under category “Question-Answering Program from Thai Wikipedia,” with 83.5%, 34.80%, and 45.96%, and outperforming the next best competitors’ systems by 19.99, 24.26, and 33.10 percentage points in terms of [email protected], EM, and F1 respectively. Furthermore, we also develop a prototype mobile application that aims to facilitate Thai users with visual impairment using voice-to-speech technology and an intelligent question-answer categorization. The findings of this research not only expand the horizon of the possibility to develop intelligent NLP applications for the Thai language using only available existing Thai NLP tools, resources, and deep learning technologies, but also shed light on the possibility to apply such techniques to develop many intelligent NLP tasks for the Thai and other low-resource languages such as reading assessment, writing assistance, and entity linking.
Accumulated Polar Feature-Based Deep Learning for Efficient and Lightweight Automatic Modulation Classification With Channel Compensation Mechanism

In next-generation communications, massive machine-type communications (mMTC) induce severe burden on base stations. To address such an issue, automatic modulation classification (AMC) can help to reduce signaling overhead by blindly recognizing the modulation types without handshaking. Thus, it plays an important role in future intelligent modems. The emerging deep learning (DL) technique stores intelligence in the network, resulting in superior performance over traditional approaches. However, DL-based approaches suffer from heavy training overhead, memory overhead, and computational complexity, which severely hinder practical applications for resource-limited scenarios, such as Internet-of-Things (IoT) networks and unmanned aerial vehicle (UAV)-aided systems. Furthermore, the overhead of online retraining under time-varying fading channels has not been studied in the prior arts. In this work, an accumulated polar feature-based DL with a channel compensation mechanism is proposed to cope with the aforementioned issues. Firstly, the simulation results show that learning features from the polar domain with historical data information can approach near-optimal performance while reducing training overhead by 99.8 times. Secondly, the proposed neural network-based channel estimator (NN-CE) can learn the channel response and compensate for the distorted channel with 13% improvement. Moreover, in applying this lightweight NN-CE in a time-varying fading channel, two efficient mechanisms of online retraining are proposed, which can reduce transmission overhead and retraining overhead by 90% and 76%, respectively. Finally, the performance of the proposed approach is evaluated and compared with prior arts on a public dataset to demonstrate its great efficiency and lightness. The lightweight and efficient learning features of the proposed mechanism will be very attractive for future resource-constrained/aware IoT and Vehicle-to-Everything (V2X) applications.
Achieving data security and privacy across healthcare applications using cyber security mechanisms

Purpose Currently, in the health-care sector, information security and privacy are increasingly important issues. The improvement in information security is highlighted in adopting digital patient records based on regulation, providers’ consolidation, and the growing need to exchange information among patients, providers, and payers. Design/methodology/approach Big data on health care are likely to improve patient outcomes, predict epidemic outbreaks, gain valuable insights, prevent diseases, reduce health-care costs and improve analysis of the quality of life. Findings In this paper, the big data analytics-based cybersecurity framework has been proposed for security and privacy across health-care applications. It is vital to identify the limitations of existing solutions for future research to ensure a trustworthy big data environment. Furthermore, electronic health records (EHR) could potentially be shared by various users to increase the quality of health-care services. This leads to significant issues of privacy that need to be addressed to implement the EHR. Originality/value This framework combines several technical mechanisms and environmental controls and is shown to be enough to adequately pay attention to common threats to network security.