Miracle-dz/UniFAR-FTUs huggingface dataset:
Modelling the impact of referral guideline changes for mild dyskaryosis on colposcopy services in England

Objectives: This model examines the effects of changing referral strategies within the established structure of NHS cervical screening driven colposcopy practice. It considers the effects of the new strategy on colposcopy workload, patient waiting times, and associated costs and health benefits. Methods: By postal survey, the current operational strategies of colposcopy services were established by questionnaire with respect to referral practices and management protocols. After first-cut piloting, and utilising published and original research, a Markovian model was constructed, and the impact of the new strategy was determined on colposcopy workload and patient waiting times for three hypothetical clinic types. Expected costs and benefits of the new policy were assessed through the adaptation of a previous ScHARR cervical screening model. Results: Clinic workload is expected to increase by between 21% and 35% within three years of the policy change, depending on clinic efficiency in other areas; the majority of this impact would be seen within the first year. It is predicted that particularly inefficient clinics would struggle to meet the existing waiting time requirements for women referred with low-grade disease, owing to the increased level of workload seen throughout the patient pathway as a result of the implementation of the new policy. The impact of the new policy can, however, be mitigated through improving the efficiency of existing clinics, by altering policies relating to surveillance of low grade disease, post-treatment follow-up, treatment policy (whether or not treatment is performed at the initial colposcopy visit), and through adherence to national guidelines. A cost-effectiveness analysis using the ScHARR liquid-based cytology model suggests that the policy change is likely to be have a cost per quality-adjusted lifeyear gained of between £1,400 and £5,500 per quality-adjusted life-year gained (excluding the costs of follow-up), which would be deemed acceptable to organisations such as the National Institute for Health and Clinical Excellence.
Approximating the Permanent by Sampling from Adaptive Partitions

Computing the permanent of a non-negative matrix is a core problem with practical applications ranging from target tracking to statistical thermodynamics. However, this problem is also #P-complete, which leaves little hope for finding an exact solution that can be computed efficiently. While the problem admits a fully polynomial randomized approximation scheme, this method has seen little use because it is both inefficient in practice and difficult to implement. We present ADAPART, a simple and efficient method for exact sampling of permutations, each associated with a weight as determined by a matrix. ADAPART uses an adaptive, iterative partitioning strategy over permutations to convert any upper bounding method for the permanent into one that satisfies a desirable `nesting' property over the partition used. These samples are then used to construct tight bounds on the permanent which hold with a high probability. Empirically, ADAPART provides significant speedups (sometimes exceeding 50x) over prior work. We also empirically observe polynomial scaling in some cases. In the context of multi-target tracking, ADAPART allows us to use the optimal proposal distribution during particle filtering, leading to orders of magnitude fewer samples and improved tracking performance.
Neural Topic Modelling

We introduce neural topic modelling an unsupervised, scalable and interpretable neural data analysis tool which can be applied across different spatial and temporal scales. The aim is an approach that can handle the everincreasing number of neurons recorded by high channel count multi-electrode arrays. Neural topic modelling is based on latent Dirichlet allocation, a method routinely used in text mining to find latent topics in texts. The spike trains are converted into “neural words” the presence or absence of discrete events (e.g. neuron 1 has a higher firing rate than usual). Neural topic modelling results in a number of topics (probability distributions over words) which best explain the given co-occurrences of neural words over time. Applied to an electrophysiological dataset of mouse visual cortex, hippocampus and thalamus neurons, neural topic modelling groups neural words into topics which exhibit common attributes such as overlapping receptive fields or proximity on the recording electrode. It recovers these relationships despite receiving no knowledge about the cortex topography or about the spatial structure of the stimuli. Choosing neural activity patterns as neural words that are relevant to the brain makes the topics interpretable by both the brain and researchers, setting neural topic modelling apart from other machine learning approaches.
A brief tour on control-flow protection

The purpose of this work is to give an overview on the topic under discussion, control-flow protection. An effort is made for the result to be more accessible by providing sufficient background in beginning and related material in the end. Most of the work was done by searching, consuming and referring to relevant research material. Additionally a control-flow integrity feature of Clang compiler is tried out and the results reported. Control-flow protection can be attacked in various ways on multiple levels and this makes it challeging to implement a trustworthy protection. For this reason it is important to understand the topic both in depth and breadth.
Neural Information Flow: Learning neural information processing systems from brain activity

Neural information flow (NIF) is a new framework for system identification in neuroscience. NIF models represent neural information processing systems as coupled brain regions that each embody neural computations. These brain regions are coupled to observed data specific to that region via linear observation models. NIF models are trained via backpropagation, directly leveraging the neural signal as the loss. Trained NIF models are accessible for in silico analyses. Using a large-scale fMRI video stimulation dataset and a feed-forward convolutional neural network-based NIF model as an example we show that, in this manner, we can estimate models that learn meaningful neural computations and representations. Our framework is general in the sense that it can be used in conjunction with any neural recording techniques. It is also scalable, providing neuroscientists with a principled approach to make sense of high-dimensional neural datasets.
3D Vision Geometry for Rolling Shutter Cameras

Many modern CMOS cameras are equipped with Rolling Shutter (RS) sensors which are considered as low cost, low consumption and fast cameras. In this acquisition mode, the pixel rows are exposed sequentially from the top to the bottom of the image. Therefore, images captured by moving RS cameras produce distortions (e.g. wobble and skew) which make the classic algorithms at best less precise, at worst unusable due to singularities or degeneracies. The goal of this thesis is to propose a general framework for modelling and solving structure from motion (SfM) with RS cameras. Our approach consists in addressing each sub-task of the SfM pipe-line (namely image correction, absolute and relative pose estimation and bundle adjustment) and proposing improvements.The first part of this manuscript presents a novel RS correction method which uses line features. Unlike existing methods, which uses iterative solutions and make Manhattan World (MW) assumption, our method R4C computes linearly the camera instantaneous-motion using few image features. Besides, the method was integrated into a RANSAC-like framework which enables us to detect curves that correspond to actual 3D straight lines and reject outlier curves making image correction more robust and fully automated.The second part revisits Bundle Adjustment (BA) for RS images. It deals with a limitation of existing RS bundle adjustment methods in case of close read-out directions among RS views which is a common configuration in many real-life applications. In contrast, we propose a novel camera-based RS projection algorithm and incorporate it into RSBA to calculate reprojection errors. We found out that this new algorithm makes SfM survive the degenerate configuration mentioned above.The third part proposes a new RS Homography matrix based on point correspondences from an RS pair. Linear solvers for the computation of this matrix are also presented. Specifically, a practical solver with 13 point correspondences is proposed. In addition, we present two essential applications in computer vision that use RS homography: plane-based RS relative pose estimation and RS image stitching. The last part of this thesis studies absolute camera pose problem (PnP) and SfM which handle RS effects by drawing analogies with non-rigid vision, namely Shape-from-Template (SfT) and Non-rigid SfM (NRSfM) respectively. Unlike all existing methods which perform 3D-2D registration after augmenting the Global Shutter (GS) projection model with the velocity parameters under various kinematic models, we propose to use local differential constraints. The proposed methods outperform stat-of-the-art and handles configurations that are critical for existing methods.
Expanding the Current Possibilities of Data Recognition and Retransmission Using Modern Aerostats

The article presents the issues of using modern aerostats to increase the capabilities of image recognition and radiolocation systems, as well as data retransmission. A feature that differentiates airships from balloons is their own propulsion and rudders, thanks to which their flight can take place after a preplanned route and long distances. Airships are divided into three classes; pressure (frameless), half-skeletal, and skeletal. The article recalls the concept of the stratospheric aerostat and also presents the aerostat of low altitudes made in this country, which is a continuator of the interwar reconnaissance system using the LECH airship. The aerostats presented in the article belong to the group of airships. Already in pre-war Poland, it was necessary to eliminate the military superiority of the western and eastern neighbours through the possibility of pre-emptive actions resulting from the possibility of recognizing the situation from the air at that time. Currently, the country is working on innovative solutions for modern aerostats. There is the possibility of implementing a stratospheric aerostat by Polish companies that are adapted to the specificity of geographical latitude, which is in the sphere of our interest in recognition and retransmission.
Partners in Crime: Multi-view Sequential Inference for Movie Understanding

Multi-view learning algorithms are powerful representation learning tools, often exploited in the context of multimodal problems. However, for problems requiring inference at the token-level of a sequence (that is, a separate prediction must be made for every time step), it is often the case that single-view systems are used, or that more than one views are fused in a simple manner. We describe an incremental neural architecture paired with a novel training objective for incremental inference. The network operates on multi-view data. We demonstrate the effectiveness of our approach on the problem of predicting perpetrators in crime drama series, for which our model significantly outperforms previous work and strong baselines. Moreover, we introduce two tasks, crime case and speaker type tagging, that contribute to movie understanding and demonstrate the effectiveness of our model on them.
A Multiobjective GI-Based Control for Effective Operation of PV Pumping System Under Abnormal Grid Conditions

The increasing renewable energy penetration has encouraged solar water pumping (SWP) as an effective solution to escalating water demand in remote areas. Despite receiving wide-spread popularity, such systems exhibit an unreliable operation due to intermittent weather conditions. As the utility grid integration enables the bidirectional power flow, it has emerged as a viable solution for uninterrupted pumping. However, since most of the SWP systems are remotely installed at radial ends of the distribution network, they encounter problems associated with abnormal grid conditions, which degrade their performance. A generalized integrator (GI) is used to deal with these issues. However, convention GI structures are incapable of effectively mitigating them. Therefore, a multiresonant cascaded second-order generalized integrator (MC-SOGI) based control approach is developed in this article to deal with these issues. The proposed MC-SOGI structure provides effective dc-offset rejection along with filtering of harmonics as well as subharmonics. Therefore, the proposed control approach is capable of effectively extracting the balanced fundamental positive sequence components even under unbalanced distorted grid voltages and maintaining unity displacement power factor under all conditions. An enhanced system efficiency is achieved by employing a permanent magnet synchronous motor (PMSM) for driving the pump. The well-known vector control technique and an incremental conductance algorithm are used for controlling the speed of PMSM and optimum power harvesting, respectively. The estimation of rotor speed and position is carried out by using voltage and current templates for eliminating the encoder thereby enhancing the system reliability and reducing the cost. The system performance is investigated experimentally and the acquired results validate the system feasibility.
A taxonomy of attacks via the speech interface

—This paper investigates the security of human-computer interaction via a speech interface. The use of speech interfaces for human-computer interaction is becoming more widespread, particularly in the form of voice-controlled digital assistants. We argue that this development represents new security vulnerabilities which have yet to be comprehensively investigated and addressed. This paper presents a comprehensive review of prior work in this area to date. Based on this review, we propose a high level taxonomy of attacks via the speech interface. Our taxonomy systematises the prior work on the security of voice-controlled digital assistants, and identiﬁes new categories of potential attacks which have yet to be investigated and thus represent a focus for future research.
An overview of industrial X-ray computed tomography.

Driven by the need for quality control of complex three-dimensional engineering components, and enabled by progress in medical imaging and consumer graphics processing, X-ray computed tomography (XCT) has been increasingly used for industrial inspection in recent years. A review of the recent development of industrial XCT systems in the context of traceable dimensional metrology is presented. The market trends of industrial XCT systems are briefly reviewed. Progress towards standards and methods for XCT calibration and verification are studied in detail. The report also covers the recent development of both hardware and software and discusses the challenges regarding systematic errors. Commercial systems are summarised to establish the capability available to the industrial user. Finally, what will be required to develop XCT technology in the field of dimensional metrology is considered.
Performance Analysis and Adaptive DRX Scheme for Dual Connectivity

The 3GPP (Rel.12 and Rel.16) introduces and explores the dual connectivity (DC). It is one of the most important future technologies adopted by 5G. It enhances the throughput and mobility robustness by enabling simultaneous dual connections with macro and small-cell evolved NodeBs (eNBs). While aggregating spectrum with the dual connections, DC induces two power-saving mechanisms of discontinuous reception (DRX). This results into offset synchronizations that decrease the feasibility of saving power. Therefore, the conventional DRX mechanism is not suitable for DC. Hence, we have proposed a synchronized adaptive DRX sleep (SAS) scheme for DRX that synchronizes its short-/long-sleep durations to make it suitable for active and background traffic. Next, we have evaluated the performance of SAS scheme with semi-Markov model in terms of DRX sleep time ratio and average delays. Moreover, in-depth analytical and simulation analyses of the DRX performance with DC have also been conducted. The accuracy of our model has been verified on the basis of a wide range of parameters. The proposed SAS scheme performs more effectively than the conventional DRX scheme for DC under different traffic types.
e‐LiteSense: Self‐adaptive energy‐aware data sensing in WSN environments

Currently deployed in a wide variety of applicational scenarios, wireless sensor networks (WSNs) are typically a resource‐constrained infrastructure. Consequently, characteristics such as WSN adaptability, low‐overhead, and low‐energy consumption are particularly relevant in dynamic and autonomous sensing environments where the measuring requirements change and human intervention is not viable. To tackle this issue, this article proposes e‐LiteSense as an adaptive, energy‐aware sensing solution for WSNs, capable of auto‐regulate how data are sensed, adjusting it to each applicational scenario. The proposed adaptive scheme is able to maintain the sensing accuracy of the physical phenomena, while reducing the overall process overhead. In this way, the adaptive algorithm relies on low‐complexity rules to establish the sensing frequency weighting the recent drifts of the physical parameter and the levels of remaining energy in the sensor. Using datasets from WSN operational scenarios, we prove e‐LiteSense effectiveness in self‐regulating data sensing accurately through a low‐overhead process where the WSN energy levels are preserved. This constitutes a step‐forward for implementing self‐adaptive energy‐aware data sensing in dynamic WSN environments.
Automatic Detection of Insecure Codes in Stack Overflow

Automatic Detection of Insecure Codes in Stack Overflow Shifu Hou As the popularity of modern social coding paradigm such as Stack Overflow grows, its potential security risks increase as well (e.g., insecure codes could be easily embedded and distributed). To address this largely overlooked issue, we bring a new insight to exploit social coding properties in addition to code content for automatic detection of insecure code snippets in Stack Overflow. To determine if the given code snippets are insecure, we not only analyze the code content, but also utilize various kinds of relations among users, badges, questions, answers, code snippets and keywords in Stack Overflow. To model the rich semantic relationships, we first introduce a structured heterogeneous information network (HIN) for representation and then use meta-path based approach to incorporate higher-level semantics to build up relatedness over code snippets. Later, we propose two different novel network embedding models named Snippet2vec and CodeHin2Vec for representation learning in HIN to automate the insecure code snippet detection in Stack Overflow. More specifically, Snippet2vec learns the low dimensional representations for the nodes (i.e., code snippets) in the HIN where both the HIN structures and semantics are maximally preserved, while CodeHin2Vec utilizes HIN to depict relatedness over code snippets to generate code-to-code sequences, based on which sequence-to-sequence (seq2seq) concept in machine translation is further leveraged to learn representations of code snippets. Accordingly, we developed systems ICSD and iTrustSO which integrate our proposed methods respectively in insecure code snippet detection in Stack Overflow. Comprehensive experiments on the data collections from Stack Overflow are conducted to validate the effectiveness of our developed systems by comparisons with the state-of-the-art baseline methods.
Investigating Process Maturity Modeling as an Advertising Process Improvement Paradigm

Summary This article examined a variant of the Capability Maturity Model integrated (CMMi) through the lens of advertising process improvement. The population and sample were taken from a national array of U.S. marketing organizations. Using ANOVA, a 0.05 significance level, and a stratification of service marketing organizations versus product marketing organizations, the study showed a statistically significant difference (F(1, 304) = 4.03; p = 0.04; ω2 = 0.00) regarding the hypothesis representing the notion that processes were potentially sporadic, chaotic, and ad hoc. This notion corresponded to the first maturity level of the examined process maturity framework. With respect to the Likert-scale data representing the first maturity level, the successive means analysis showed that both service marketing firms (M = 2.99) and product marketing firms (M = 2.74) reported neutrality regarding whether processes were deemed sporadic, chaotic, and ad hoc. Thus, the respondents perceived no evidence of the first maturity level among the queried work settings. Future studies may examine different stratifications of marketing firms (e.g., for-profit versus non-profit; domestic versus international; and so on) to better explore the proposed advertising maturity model.
Effectivenes of Using E-Module and E-Assessment

4.0 industrial revolution gives opportunity for education through learning technology. Mobile learning is the use of technology in the learning process using tablets, PCs or smartphones. Technological development in education is the use of Android which is a medium for mobile learning that is more flexible. Online based mobile learning provides opportunities for students to study anytime and anywhere. This study aims to determine the effectiveness of the use of electronic guide books and electronic-based assessment in a basic physics practicum with reflectance material on a flat mirror. The study involved 65 physics education students with 35 students in the experimental class and 30 students in the control class. This research is true experimental research with the post-test end-experimental control group design. Based on the evaluation, in very good category at the communication’s indicator and classification’s indicator for experimental class, and in very good category at the compiles the data table’s indicator, for control class.