Miracle-dz/UniFAR-FTUs huggingface dataset:
Eliciting information from cooperative sources about single & repeated multi-actor events

Successful investigations in forensic and security contexts depend on eliciting reliable and detailed information from sources. Although research has developed effective interviewing protocols to improve recall of witnessed events in criminal investigations, there is only a small body of research on information elicitation tools for intelligence gathering. The overarching aim of this thesis is to contribute to the development of interviewing techniques for use with cooperative sources in security settings. Specifically, we examined the effectiveness of mnemonics, reporting formats and prompts to facilitate recall for multi-actor events witnessed on both single and repeated occasions. In Experiment 1, we introduced a new mnemonic to the timeline technique. Participants witnessed a multi-actor crime-event under full or divided attention and provided an account using self-generated cues, other-generated cues or no additional cues across timeline reporting conditions. The results showed that use of the selfgenerated cues increased the reporting of correct details (cf. other-generated and no cues) under full but not under divided attention conditions. In Experiments 2 and 3, we examined the efficacy of open-ended questions to follow-up on an initial report. In Experiment 2, participants witnessed a multi-actor crime-event and used the timeline or a free recall format to provide an initial report. In Experiment 3, participants used the timeline to provide their recall of a video depicting a group planning and carrying out an attack. Before being asked follow-up questions, half of the participants were instructed to avoid guessing, to feel free to withhold an answer and to consider the precision of their answers (i.e. provide general or specific details). The results showed that follow-up questions elicited new information across conditions. However, the accuracy of the responses to the follow-up questions was not as high as the initially reported information (Expt. 2 & 3), even after participants were instructed to monitor the accuracy of their
The Effect of Co-authorship Network Density on the Performance of Postgraduate Programs

By focusing on the performance of research teams in the management area, this study analyzes the effect of the co-authorship of scientific publications on the performance of postgraduate programs (PPGs) in management. The data was collected from management researchers in Brazil. Along with this, scientific publications from 2013 to 2016 were collected to comprise the social network of each PPG. The evaluation of the PPG was used as the dependent variable. The effects of the density of the co-authorship networks on the programs’ performance was verified. The results show that the density of the links between researchers follows a curvilinear trend of effect on organizational performance; that is, both highly dense and highly dispersed networks positively influence team performance. This study contributes to the literature on networks and team performance by providing evidence that the density of a network is not linearly related to performance.
Recent Advances in Optical Mammography

Diffuse optical imaging (DOI) techniques provide a non-invasive, low cost and sensitive method for assessing the functional state of the human breast. Several studies undertaken in recent years with the objective of investigating the clinical applications of diffuse optical methods, demonstrate the potential of the technique for screening for breast cancer, associated risk factors and for monitoring the efficacy of therapeutic regimen post-diagnosis. In this review, the different diffuse optical methods and instrumentation used in the functional imaging of the breast are described and the results of the clinical studies of the healthy and diseased breast are provided. Moreover, the application of optical imaging in moni- toring neo-adjuvant chemotherapy is summarized, the role of alternate contrast mechanisms in optical imaging is outlined and lastly, the role of optical imaging in multimodal imaging - wherein optical imaging is combined with existing clinical imaging modalities providing complementary diagnostic information - is discussed.
New targets in idiopathic pulmonary fibrosis: from inflammation and immunity to remodeling and repair

ABSTRACT Introduction: Idiopathic pulmonary fibrosis (IPF) is a chronic respiratory disease whose incidence and prevalence are increasing. Recent evidence has led to a change of paradigm regarding understanding of the underlying pathophysiology. In parallel, we have witnessed the development and rise of the first anti-fibrotic drugs, namely pirfenidone and nintedanib. However, with clinical results being below expectations there is a clear need for new medications in this field. Areas covered: After covering new mechanisms involved in IPF pathogenesis, the present review discusses current clinical trials and deciphers potential new targets in light of in vitro and in vivo experimental studies. Expert Opinion: All in all, we believe that future development will require (1) a significant improvement in experimental models, (2) a proper selection and characterization of patients, allowing us to foresee which will respond to a given treatment and (3) an evaluation of combined therapies, targeting different pathways.
REACT: Scalable and High-Performance Regular Expression Pattern Matching Accelerator for In-Storage Processing

This article proposes REACT, a regular expression matching accelerator, which can be embedded in a modern Solid-State Drive (SSD) and a novel data access scheduling algorithm for high matching throughput. Specifically, REACT, including our data access scheduling algorithm, increases the utilization of SSD and the degree of internal memory parallelism for pattern matching processes. While the low-level flash exhibits long latency, modern SSDs in practice achieve high I/O performance by utilizing the massive internal parallelism at the system-level. However, exploiting the parallelism is limited for pattern matching since the sub-blocks, which constitute an input data and can be placed in multiple flash pages, should be tested in a sequence to process the input correctly. This limitation can induce low utilization of the accelerator. To address this challenge, the proposed REACT simultaneously processes multiple input streams with a parallel processing architecture to maximize matching throughput by hiding the long and irregular latency. The scheduling algorithm finds a data stream which requires a sub-block in closest time and prioritizes the access request to reduce the data stall of REACT. REACT achieves maximum 22.6 percent of matching throughput improvement on a 16-channel high-performance SSD compared to the accelerator without the proposed scheduling algorithm.
Application of Software Analysis in Detecting Vulnerabilities:Testing and Security Assessment

Author(s): Alavi, Arash | Advisor(s): Gupta, Rajiv; Qian, Zhiyun | Abstract: With the increasing complexity of application software there is an acute need for software analysis approaches that are capable of identifying bugs, failures, and most importantly vulnerabilities on a large scale. In this dissertation, first we stress the necessity of having automated software analysis approaches and then propose analysis approaches for detecting vulnerabilities in software via analysis and testing in general, and security assessment in particular. We show the efficiency and effectiveness of these analysis techniques in detecting vulnerabilities. First, we study security issues in smartphone applications by studying the security discrepancies between Android apps and their website counterparts, depicting the essential need of efficient software analysis techniques to fully automate the mobile app analysis process. By a comprehensive study on 100 popular app-web pairs, we find that, with respect to various security policies, the mobile apps often have weaker or non-existent security measures compared to their website counterparts. Second, as a consequence of the former, we develop AndroidSlicer, the first novel, efficient, and effective dynamic program slicing tool for Android apps that is useful for a variety of tasks, from testing to debugging to security assessment. Our work in this domain focuses on making large scale applications of slicing practical in order to detect bugs and vulnerabilities in real-world apps. We present two new applications of the dynamic slicing technique in mobile apps: (1) detecting the "stuck'' states (missing progress indicators) in mobile apps. We present, implement, and evaluate ProgressDroid, a tool for discovering missing progress indicator bugs based on program dependencies; and (2) detecting security vulnerabilities in unique device ID generators. Finally, in the same vein of deploying analysis tools for detecting vulnerabilities, we present GAGA, an efficient genetic algorithm for graph anonymization that simultaneously delivers high anonymization and utility preservation. Experiments show that GAGA improves the defense against DA techniques by reducing the rate of successfully de-anonymized users by at least a factor of 2.7 in comparison to the baseline and at the same time, under 16 graph and application utility metrics, GAGA is overall the best at preserving utilities.
A GUIDE FOR

Members of a nursing staff collaborated to develop an admission interview guide that would be both useful and used. The outcome was a form with space to record general observations and responses to only 12 questions. Its goals were to reduce the lag between the patients' admission and the preparation of a working nursing care plan, and to assure the patient of the nurses interest in giving him the best possible care fashioned to meet his needs and wants.
Coupled Oxygen Transport in the Avascular Region of a Coronary Artery for Basal to Hyperemic Flow

The numerical investigation of coupled oxygen transport to the avascular region of the wall of coronary artery is carried out for varying wall thickness and flow rates from basal to hyperemic condition. The factors affecting the O2 transport, such as, consumption of oxygen in the avascular wall region, w, the avascular thickness, 0, supply of O2 from vasa vasorum, nonlinear O2 binding capacity of the hemoglobin and varying flow rates, are taken into account. The O2 concentration boundary layer, Ob,is observed to be of -80/lm thickness. The lowest medial partial pressure, POZ,min, decreases by -80% for a larger avascular thickness, 0, of 300 /lm when compared with that for smaller 0 of 200 /lm. INTRODUCTION O2 is supplied by radial diffusion from the vessel lumen to arteries of all species, and supplies from other sources (adventitial vessels and vasa vasorum) vary according to species and size of vessels [Crawford and Blankenhorn, 1991]. Microcathode measurements by Zemph~nyi et. al. [1989] in the iliofemoral arteries of rabbits with subintimal thickening, indicated the POZ,min to be 15 mmHg due to the proliferation of newly formed nutrient vessels in the adventitia that supplied O2 to the outer part of the avascular wall. This adaptation is an important mechanism against hypoxia, and may be an essential protective factor 10 atherogenesis [Zemplenyl et. aI. , 1989]. The purpose of this study is to calculate coupled O2 transport along a coronary artery, in particular, the variation of partial pressure of oxygen, PaZ, along the endothelium, POz.w, POZ.min, and O2 flux from the lumen, Jw,' The effect of avascular wall thickness, 0, on POZ,min is studied over a range of blood mean flow rates, Q, from basal to hyperemic flows. METHODS The analysis is for an axi-symmetric model of coronary artery of diameter de = 3 mm and an axial length of 7 cm. The thickness of avascular wall is varied from 200 to 300 J.lm. Branching and curvature effects are not taken into account The species transport equation in the lumen is: (1 + fXDc/Dt}= DbV'2C (1) and that in the avascular wall region is:
On the dynamics of user engagement in news comment media

Many news outlets allow users to contribute comments on topics about daily world events. News articles are the seeds that spring users' interest to contribute content, that is, comments. A news outlet may allow users to contribute comments on all their articles or a selected number of them. The topic of an article may lead to an apathetic user commenting activity (several tens of comments) or to a spontaneous fervent one (several thousands of comments). This environment creates a social dynamic that is little studied. The social dynamics around articles have the potential to reveal interesting facets of the user population at a news outlet. In this paper, we report the salient findings about these social media from 15 months worth of data collected from 17 news outlets comprising of over 38,000 news articles and about 21 million user comments. Analysis of the data reveals interesting insights such as there is an uneven relationship between news outlets and their user populations across outlets. Such observations and others have not been revealed, to our knowledge. We believe our analysis in this paper can contribute to news predictive analytics (e.g., user reaction to a news article or predicting the volume of comments posted to an article).
RGBT Salient Object Detection: Benchmark and A Novel Cooperative Ranking Approach

Despite significant progress, image saliency detection still remains a challenging task in complex scenes and environments. Integrating multiple different but complementary cues, like RGB and Thermal infrared (RGBT), may be an effective way for boosting saliency detection performance. This work contributes a RGBT image dataset, which includes 821 spatially aligned RGBT image pairs and their ground truth annotations for saliency detection purpose. Moreover, 11 challenges are annotated on these image pairs for performing the challenge-sensitive analysis and 3 kinds of baseline methods are implemented to provide a comprehensive comparison platform. With this benchmark, we propose a novel approach based on a cooperative ranking algorithm for RGBT saliency detection. In particular, we introduce a weight for each modality to describe the reliability and a $\ell _{1}$ -based cross-modal consistency in a unified ranking model, and design an efficient solver to iteratively optimize several subproblems with closed-form solutions. Extensive experiments against baseline methods demonstrate the effectiveness of the proposed approach on both our introduced dataset and a public dataset.
Practical Foldover‐Free Volumetric Mapping Construction

In this paper, we present a practically robust method for computing foldover‐free volumetric mappings with hard linear constraints. Central to this approach is a projection algorithm that monotonically and efficiently decreases the distance from the mapping to the bounded conformal distortion mapping space. After projection, the conformal distortion of the updated mapping tends to be below the given bound, thereby significantly reducing foldovers. Since it is non‐trivial to define an optimal bound, we introduce a practical conformal distortion bound generation scheme to facilitate subsequent projections. By iteratively generating conformal distortion bounds and trying to project mappings into bounded conformal distortion spaces monotonically, our algorithm achieves high‐quality foldover‐free volumetric mappings with strong practical robustness and high efficiency. Compared with existing methods, our method computes mesh‐based and meshless volumetric mappings with no prescribed conformal distortion bounds. We demonstrate the efficacy and efficiency of our method through a variety of geometric processing tasks.
Early Evidence of Digital Labor in Accounting: Innovation With Robotic Process Automation

Abstract Robotic Process Automation (RPA) is an emerging technology that enables the automation of rules-based business processes and tasks through the use of software bots. Drawing upon the theory of Task-Technology Fit (TTF) and Technology-to-Performance Chain (TPC) (Goodhue and Thompson 1995) and research on expert systems (Messier and Hansen 1987; Sutton 1990), this study explores emerging themes surrounding bot implementation for accounting and finance tasks. We collect and analyze interview data from adopters of RPA and document task suitability, task-technology fit, implementation issues, and resulting performance outcomes. We find that securing technical capability is only a part of RPA implementation process. Organizations engage in standardization and optimization of processes, develop scorecard-like tools to rank tasks, adjust governance structures to include digital employees, and redefine internal controls. Organizations benefit from automating only certain processes, those that are structured, repeated, rules-based, and with digital inputs. Along with cost savings, organizations experience improved process documentation, lower error rates, more accurate measurement of process performance, and better report quality.
Human-animal relationships in the Norwegian dairy goat industry: attitudes and empathy towards goats (Part I)

The quality of human-animal relationships in the livestock industries has been increasingly recognised as an important determinant of animal welfare. Attitudes and empathy are multi-dimensional traits that may be associated with the stockpersons’ behaviour. The aim of this study was to determine the dimensionality of the goat-oriented attitudes and empathy of stockpeople in the Norwegian dairy goat industry. We also explored how empathic and attitudinal dimensions are interrelated, and how the demographic background variables may predict empathy and attitudes. A total of 260 dairy goat farmers participated in the study, by the means of either postal or internet-based questionnaire formats. Multi-item rating scales were developed specifically for the assessment of attitudes and empathy towards goats, and Principal Component Factor Analysis was conducted to determine the dimensionality of the farmers’ goat-oriented attitudes and empathy. Subsequently, linear and ordinal regression analyses were performed to explore the interrelationships. The analyses revealed dimensions of empathy that can be recognised from studies of human-oriented empathy, and attitude dimensions that can be interpreted similarly as dimensions described in studies from other livestock industries. Our results show that different dimensions of attitudes and empathy were associated with different demographic variables, and that each empathy dimension was associated with a different attitude factor.
Optimal Stochastic Feedback in Asymmetric Dynamic Contests

Contests, in which contestants compete at their own expenses for prizes offered by a contest holder, have become the foundational primitive of many theories of competition. Recently, the focus in contest research has turned to the role of in-contest performance feedback. The extant literature on feedback has focused on specific ad-hoc policies and hence failed to more broadly characterize optimal feedback policies. In this note we solve a general formulation of a contest involving feedback, and thus characterize the optimal feedback policy in a very wide class of (stochastic) feedback policies. We find that, in many settings where informative feedback is useful, feedback is optimal when it is both truthful and fully informative.
Recent advances in unmanned aerial vehicles real-time trajectory planning

The growing interest in unmanned aerial vehicles (UAV) can be attributed to many factors, particularly the potential for them to operate beyond visual line of sight and at increasingly higher levels of autonomy. Trajectory planning defines the extent to which a vehicle is “autonomous”, therefore, to operate at high levels of autonomy, UAV will require real-time embedded trajectory planning that respects a minimum of flyability. This paper presents a review of recent advances in UAV trajectory planning and seeks to clarify the flyability, the real-time, and the embedded aspects, which are not adequately considered by previous survey papers. This work, which specifically focuses on Class II (≥150 and ≤600 kg) and Class III (>600 kg) fixed-wing UAV, analyses 60 papers from the last decade that mention some real-time achievement with respect to UAV trajectory planning. From this analysis, we highlight some challenges and some suggested orientations for future works.
Monte-Carlo Tree Search for Artificial General Intelligence in Games

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What is Monitored and by Whom in Online Collaborative Learning?: Analysis of Monitoring Tools in Learner Dashboard

This study aims to draw implications for designing online tools to support monitoring in collaborative learning. For this purpose, eighteen research papers exploring learner dashboard and group awareness tools were analyzed. Driving questions for the analysis were 'who monitors whose learning' and 'which learning information is monitored. ' The analytical frameworks used for this study included the followings: three modes of co-regulation in terms of who regulates whose learning (self-regulation in collaborative learning, other-regulation, and socially-shared regulation) and four categories of dashboard information in terms of which information is monitored (information about preparation, participation, interaction, and achievement). As a result, five design implications for learner dashboard supporting monitoring were drawn. a) Monitoring tools for collaborative learning should support not a single but multiple targets: learner oneself, peer learners, and the group as a whole. b) When supporting the monitoring of oneself, information about self and peers should be illustrated together so that they can be compared. c) Information on collaborative learning achievement should be provided in terms of the content of knowledge acquired rather than test scores. d) In addition to the information about the interaction between learners, the interaction between learners and learning materials can also be provided. e) There should be differences in a manner when providing the same information to the individual and group level.
Implementation via transfers with identical but unknown distributions

We consider mechanism design environments in which agents commonly know that types are identically distributed across agents, but without assuming that the actual distribution is common knowledge, nor that it is known to the designer (common knowledge of identicality). Under these assumptions, we explore problems of partial and full implementation, as well as robustness. First, we characterize the transfers which are incentive compatible under the assumption of common knowledge of identicality, and provide necessary and sufficient conditions for partial implementation. Second, we characterize the conditions under which full implementation is possible via direct mechanisms, as well as the transfer schemes which achieve full implementation whenever it is possible. Finally, we study the robustness properties of the implementing transfers with respect to misspecifications of agents’preferences and with respect to lower orders beliefs in rationality.