Miracle-dz/UniFAR-FTUs huggingface dataset:
Low leakage pocket junction-less DGTFET with biosensing cavity region

Low leakage current junction-less double gate tunnel field-effect transistor (JLDGTFET) with narrow band gap material pocket region of Si0.7Ge0. 3 shows increased band to band tunneling and sharp subthreshold characteristics to meet low power, high speed digital and memory applications. The proposed JLDGTFET exploits the junction-less behavior that supports reasonable values of ON/OFF currents as well as improved subthreshold parameters. First, the performance optimization of the JLDGTFET is carried out with different gate contact and oxide region materials in terms of ION/IOFF current ratio, subthreshold slope, and drain induced barrier lowering. The ON/OFF performance of the pocket Si0.7Ge0. 3 JLDGTFET with cavity region is also examined to enhance the sensing capability for biomolecules present in the atmosphere that affect the dielectric constant of air present in the cavity and hence overall performance changes. The 2D/3D Visual TCAD tool is used to simulate novel thin body pocket Si0.7Ge0. 3 JLDGTFET.
SVARs Identification Through Bounds on the Forecast Error Variance

Abstract This article identifies structural vector autoregressions (SVARs) through bound restrictions on the forecast error variance decomposition (FEVD). First, the article shows FEVD bounds correspond to quadratic inequality restrictions on the columns of the rotation matrix transforming reduced-form residuals into structural shocks. Second, the article establishes theoretical conditions such that bounds on the FEVD lead to a reduction in the width of the impulse response identified set relative to only imposing sign restrictions. Third, this article proposes a robust Bayesian approach to inference. Fourth, the article shows that elicitation of the bounds could be based on DSGE models with alternative parameterizations. Finally, an empirical application illustrates the potential usefulness of FEVD restrictions for obtaining informative inference in set-identified monetary SVARs and remove unreasonable implications of models identified through sign restrictions.
A Decision Support System for real-time track assignment at railway yards

In this thesis, we study the Real-Time Track Assignment Problem (RT-TAP), a real-time assignment problem that arises from the high percentage of stochastic arrivals of freight trains and the large quantity of last-minute parking requests at railway yards. We show that the RT-TAP is NP-Hard and provide a mixed integer program for solving the RT-TAP by minimizing the total weighted delay of trains. Because of its computational complexity, we develop a problem specific Genetic Algorithm (GA) and compare it with a First Scheduled First Served (FSFS) heuristic. Smaller instances show that there is no optimality gap between the Brute Force (BF) approach and the GA. The heuristic approaches are tested on two Real-Time simulations where we consider 74 inbound trains and 9 tracks. In order to define the effect of the input on the two models, we excluded in one of the simulations the track length i.e. all trains can be assigned to all tracks. Although we saw that the output of the GA remained the same, the FSFS heuristic was not able to show results as good as the GA. Therefore, we conclude that we developed a Decision Support System (DSS) that cannot only stabilize, but also improve the current decision-making process with regards to real-time track assignment at railway yards.
Is it Time to Revisit Erasure Coding in Data-Intensive Clusters?

Data-intensive clusters are heavily relying on distributed storage systems to accommodate the unprecedented growth of data. Hadoop distributed file system (HDFS) is the primary storage for data analytic frameworks such as Spark and Hadoop. Traditionally, HDFS operates under replication to ensure data availability and to allow locality-aware task execution of data-intensive applications. Recently, erasure coding (EC) is emerging as an alternative method to replication in storage systems due to the continuous reduction in its computation overhead. In this work, we conduct an extensive experimental study to understand the performance of data-intensive applications under replication and EC. We use representative benchmarks on the Grid'5000 testbed to evaluate how analytic workloads, data persistency, failures, the back-end storage devices, and the network configuration impact their performances. Our study sheds the light not only on the potential benefits of erasure coding in data-intensive clusters but also on the aspects that may help to realize it effectively.
Visibility Attribute Extraction and Anomaly Detection for Chinese Diagnostic Report Based on Cascade Networks

In the positron emission tomography/computed tomography (PET/CT) image diagnosis report, the semantic analysis of image findings section is an important part of the automatic diagnosis of medical image, which is an essential step for extracting keywords and abnormal sentences in the diagnostic report. To this end, this paper combines visibility attribute extraction network (VAE-Net) and bi-directional gated recurrent unit (BiGRU) into cascade networks to solve the tasks of attribute extraction and anomaly detection. First, a visibility attribute (VA) is defined to summary the vocabulary into 12 patterns based on the language characteristics in image findings. Second, a visibility attribute extraction network (VAE-Net) is developed to automatically extract VA from word embeddings, which is composed of residual convolutional neural network (residual CNN), BiGRU, and conditional random field (CRF). Finally, word embeddings and the corresponding VA are input into BiGRU and softmax to perform sentence-level anomaly detections. We evaluate the proposed method on a proprietary Chinese PET/CT diagnostic report dataset with an F1-score of 94.35% in the attribute extraction, an F1-score of 96.40% in sentence-level anomaly detection, and an F1-score of 96.77% in case-level anomaly detection. Besides, a publicity English national center for biotechnology information (NCBI) disease corpus dataset is used for externed validation with an F1-score of 95.81% in disease detection. The experimental results demonstrate the advantage of the proposed cascade networks as compared to other related methods.
Co-Designing a Real-Time Classroom Orchestration Tool to Support Teacher-AI Complementarity

Involving stakeholders throughout the creation of new educational technologies can help ensure their usefulness and usability in real-world contexts. However, given the complexity of learning analytics (LA) systems, it can be challenging to meaningfully involve non-technical stakeholders throughout their design and development. This article reports on the iterative co-design, development, and classroom evaluation of Konscia, a wearable, real-time awareness tool for teachers working in AI-enhanced K-12 classrooms. In the process, we argue that the co-design of LA systems requires new kinds of prototyping methods. We introduce one of our own prototyping methods, REs, to address unique challenges of co-prototyping LA tools. This work presents the first end-to-end demonstration of how non-technical stakeholders can participate throughout the whole design process for a complex LA system—from early generative phases to the selection and tuning of analytics to evaluation in real-world contexts. We conclude by providing methodological recommendations for future LA co-design efforts.
Multimodel Approach to Robust Identification of Multiple-Input Single-Output Nonlinear Time-Delay Systems

The robust multimodel solution for multiple-input single-output nonlinear time-delay systems identification with polluted outputs is derived in this article. First, all the local autoregressive exogenous models are preidentified at the working points; then, the global system model is built by interpolating the local models with a smoothing strategy. The outliers and input time-delays which often increase the nonideality of process data are both considered. To cope with the outliers, the Laplace distribution is reutilized to describe the output measurement process and the negative impact resulted from each outlier imposed on parameter estimation can be suppressed through automatically assigned small weight. The parameter estimation procedure is realized with the expectation–maximization algorithm and the joint posterior probability of all input delays is also maximized to calculate the unknown input time-delays. With the verifications on a numerical example and the continuous fermentation process, the validity of the proposed approach is proved.
Who are Tweeting Research Articles and Why

The purpose of this paper is to understand the profiles of users and their motivations in sharing research articles on Twitter. The goal is to contribute to the understanding of Twitter as a new altmetric measure for assessing impact of research articles. In this paper, we extended the previous study of tweet motivations by finding out the profiles of twitter users. In particular, we examined six characteristics of users: gender, geographic distribution, academic, non-academic, individual, and organization. Out of several, we would like to highlight here three key findings. First, a great majority of users (86%) were from North America and Europe indicating the possibility that, if in general, tweets for research articles are mainly in English, Twitter as an alternative metric has a Western bias. Second, several previous altmetrics studies suggested that tweets, and altmetrics in general, do not indicate scholarly impact due to their low correlation with citation counts. This study provides further details in this aspect by revealing that most tweets (77%) were by individual users, 67% of whom were nonacademic. Therefore, tweets mostly reflect impact of research articles on the general public, rather than on academia. Finally, analysis from profiles and motivations showed that the majority of tweets (from 42% to 57%) in all user types highlighted the summary or findings of the article indicating that tweets are a new way of communicating research findings.
Design and Implementation of Smart Multi-Touch Interface Using Special Purpose CORDIC Processor

This paper presents a smart and embedded multi-touch system for consumer electronics. The infrared (IR) camera is utilized to capture the coordinate of the IR blob from IR stylus or human finger in an emitted IR field. This system allows the consumer to easily convert large surface, projector screen, or conference table into multi-touch interface. The keystone distortion of IR camera is compensated by using homography. To improve the user experience, the calibration of homography is automatically triggered by accelerometer sensor. The special purpose (SP) processor with the parallel COordinate Rotation Digital Computer (CORDIC) architecture computes the observation-to-track association, and the computation result updates the motion status of each tracked trajectory. These motion data support the multi-touch tracking system to evaluate the sliding window of each tracked trajectory for reducing the tracking error. Additionally, the motion data are reused by the adaptive trajectory estimation (TE) system to smooth the touch trajectory and mitigate the measurement noise. This research presents multiple autonomous functions to provide a user-friendly multi-touch interface for consumer electronics. These include the automatic calibration system, the multi-touch tracking architecture with motion estimation system, and the adaptive TE system.
Multimodal Convolutional Neural Networks to Detect Fetal Compromise During Labor and Delivery

The gold standard to assess whether a baby is at risk of oxygen deprivation during childbirth, is monitoring continuously the fetal heart rate with cardiotocography (CTG). The aim is to identify babies that could benefit from an emergency operative delivery (e.g., Cesarean section), in order to prevent death or permanent brain injury. The long, dynamic and complex CTG patterns are poorly understood and known to have high false positive and false negative rates. Visual interpretation by clinicians is challenging and reliable accurate fetal monitoring in labor remains an enormous unmet medical need. In this work, we applied deep learning methods to achieve data-driven automated CTG evaluation. Multimodal Convolutional Neural Network (MCNN) and Stacked MCNN models were used to analyze the largest available database of routinely collected CTG and linked clinical data (comprising more than 35000 births). We also assessed in detail the impact of the signal quality on the MCNN performance. On a large hold-out testing set from Oxford ( $n= 4429$ births), MCNN improved the prediction of cord acidemia at birth when compared with Clinical Practice and previous computerized approaches. On two external datasets, MCNN demonstrated better performance compared to current feature extraction-based methods. Our group is the first to apply deep learning for the analysis of CTG. We conclude that MCNN hold potential for the prediction of cord acidemia at birth and further work is warranted. Despite the advances, our deep learning models are currently not suitable for the detection of severe fetal injury in the absence of cord acidemia – a heterogeneous, small, and poorly understood group. We suggest that the most promising way forward are hybrid approaches to CTG interpretation in labor, in which different diagnostic models can estimate the risk for different types of fetal compromise, incorporating clinical knowledge with data-driven analyses.
Piecewise visual, linearly connected metrics on boundaries of relatively hyperbolic groups

Suppose a finitely generated group [Formula: see text] is hyperbolic relative to [Formula: see text] a set of proper finitely generated subgroups of [Formula: see text]. Established results in the literature imply that a “visual” metric on [Formula: see text] is “linearly connected” if and only if the boundary [Formula: see text] has no cut point. Our goal is to produce linearly connected metrics on [Formula: see text] that are “piecewise” visual when [Formula: see text] contains cut points. Our main theorem is connected to graph of groups decompositions of relatively hyperbolic groups [Formula: see text] by work of B. Bowditch. We describe piecewise visual linearly connected metrics on connected boundaries of relatively hyperbolic groups. Our metric on [Formula: see text] agrees with the visual metric on limit sets of vertex groups and is in this sense piecewise visual.
K-Nearest Neighbor Approximation Via the Friend-of-a-Friend Principle

Suppose $V$ is an $n$-element set where for each $x \in V$, the elements of $V \setminus \{x\}$ are ranked by their similarity to $x$. The $K$-nearest neighbor graph %$G:=(V, E)$ is a directed graph including an arc from each $x$ to the $K$ points of $V \setminus \{x\}$ most similar to $x$. Constructive approximation to this graph using far fewer than $n^2$ comparisons is important for the analysis of large high-dimensional data sets. \emph{$K$-Nearest Neighbor Descent} is a parameter-free heuristic where a sequence of graph approximations is constructed, in which second neighbors in one approximation are proposed as neighbors in the next. We provide a rigorous justification for $O( n \log{n} )$ complexity of a similar algorithm, using range queries, when applied to a homogeneous Poisson process in suitable dimension, but show that the basic algorithm fails to achieve subquadratic complexity on sets whose similarity rankings arise from a "generic" linear order on the $\binom{n}{2}$ inter-point distances in a metric space.
Influence of virtual objects' shadows and lighting coherence on distance perception in optical see‐through augmented reality

This paper focuses on how virtual objects' shadows as well as differences in alignment between virtual and real lighting influence distance perception in optical see‐through (OST) augmented reality (AR). Four hypotheses are proposed: (H1) Participants underestimate distances in OST AR; (H2) Virtual objects' shadows improve distance judgment accuracy in OST AR; (H3) Shadows with different realism levels have different influence on distance perception in OST AR; (H4) Different levels of lighting misalignment between real and virtual lights have different influence on distance perception in OST AR scenes. Two experiments were designed with an OST head mounted display (HMD), the Microsoft HoloLens. Participants had to match the position of a virtual object displayed in the OST‐HMD with a real target. Distance judgment accuracy was recorded under the different shadows and lighting conditions. The results validate hypotheses H2 and H4 but surprisingly showed no impact of the shape of virtual shadows on distance judgment accuracy thus rejecting hypothesis H3. Regarding hypothesis H1, we detected a trend toward underestimation; given the high variance of the data, more experiments are needed to confirm this result. Moreover, the study also reveals that perceived distance errors and completion time of trials increase along with targets' distance.
Working Together in Learning Analytics Towards the Co-Creation of Value

The value of technology lies not only with the service or functionality of the tool, but also with its subsequent value to the people who use it. New learning analytics (LA) software and platforms for capturing data and improving student learning are frequently introduced; however, they suffer from issues of adoption and continued usage by stakeholders. Scholars have previously suggested that it is not enough to introduce stakeholders (e.g., teachers and students) to LA technologies; they must also be a part of the LA creation and design process. In this paper, we will continue the ongoing work to clarify and compare different approaches of human-centred design through an overview of participatory frameworks in LA (co-design, co-creation). We will also present a case study using an LA tool used and developed within several Australian universities that was utilized over six years as an example of how LA designers can co-create dynamic platforms with teachers. Implications of participatory design frameworks in LA will also be presented through a discussion of the costs, challenges, and benefits of adopting human-centred design.
Twitter, Incivility and “Everyday” Gendered Othering: An Analysis of Tweets Sent to UK Members of Parliament

Recently, widely reported studies assessed messages sent to UK female MPs online and concluded that they suffer high levels of abuse. However, these studies tended to focus on the most high-profile MPs and the worst instances of abuse or did not include male MPs in their study for comparison. This study aims to assess more subtle forms of incivility and othering and the experiences of less prominent MPs online. It takes a mixed-methods approach to analyzing 117,802 tweets sent to MPs over a 14-day period for evidence of incivility. Firstly, models assessing the factors associated with receiving incivility on Twitter are presented, and furthermore, an in-depth thematic analysis of gendered tweets is conducted. The findings suggest that for the receipt of certain types of incivility, there is little difference between female and male MPs. However, female MPs were more likely to receive generally uncivil tweets, tweets with stereotypes about their identity, and tweets questioning their position as politicians than male MPs. Qualitatively, in terms of gendered othering, we found several instances of tweets containing misogynistic abuse, tweets demonizing, and objectifying female MPs, as well as tweets feminizing male MPs.
Implementation of an Additional Factor for Secure Authentication in Online Transactions

ABSTRACT To eradicate financial fraud, governments encourage the digitization of financial transactions, which is also reinforced by the digital economy paradigm. Recently, there has been an exponential increase in the number of e-transactions, and the incidence of cyber crimes related to online transaction fraud has also been increasing. To prevent online transaction fraud, the stakeholders of financial-transaction-related companies have implemented various secured authentication and authorization practices at all levels. In this paper, an additional factor for secure authentication for online transactions has been proposed. A third authentication factor, in addition to Personal Identification Number (PIN) and one time password (OTP), has been proposed, which is based on the global positioning system (GPS) location of the user who initiates the transaction. The strategy is to approve / decline the transaction based on a specified distance constraint between the transaction device and the user’s mobile device; this distance is used as an additional authentication factor (third factor) to verify the online transaction. The main objective of this study is to prevent fraudsters from performing online transactions from devices that do not belong to the user and are not currently in the possession of the user. The simulation results show that a high detection rate, i.e., 98.55%, is obtained using the proposed method.
Single Performance And Cluster Evaluator (space) for clustering algorithms comparison

Clustering is renowned data mining technique used in exploratory Data Analysis which is based on unsupervised learning. Many clustering algorithms have been developed so for, and are applied in variety of disciplines based on nature of problem . The selection of suitable algorithm is necessary for accuracy, nature and complexity of data and practical representation of results.In this paper, we present SPACE algorithm which stands for“Single Performance And Cluster Evaluator”. This paper also reviews the clustering algorithms and provides foundation to the data analyst to further explore the power of clustering. SPACE algorithm is novel technique which provides solid analysis and visualizes the results efficiently.
Recall and recognition tasks within facial composite production

Witnesses are routinely interviewed as part of police investigations in order to obtain a description of the offender. This description can take the form of a verbal, written statement or a visual statement that can consist of a facial composite. This thesis investigated the construction of facial composites and explored a variety of techniques aimed at improving the accuracy of the likeness produced. A survey of E-FIT operators in the UK was used to identify a variety of techniques which had the potential to affect the accuracy of the composite produced. The three most promising techniques were the use of an initial interview prior to composite construction, working through a list of facial descriptors with the witness prior to construction and instructing the witness to image the face of the offender during construction. The utility of these techniques was tested in two experiments conducted using trained police personnel. The results of these experiments showed that neither prior interviewing or use of facial descriptors appeared to affect the accuracy of the composites produced but that composites produced by witnesses instructed to image were identified less often than when no such instruction was employed. The negative effect of imaging during composite construction was explored in two further experiments by separating the instruction to image from seeing a facial composite, which revealed that imaging a face negatively affected recognition performance but that seeing a composite of a face had no effect on recognition. The results are considered in light of previous psychological research and theory and their potential impact on police procedures.