Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation and implementation of ListeningTime; a web-based preparatory communication tool for elderly cancer patients and their healthcare providers.

Background: Effective patient-provider communication is an important condition to deliver optimal care and it supports patients in coping with their disease. The complex and emotionally loaded setting of oncology care challenges both health care providers (HCPs) and patients in reaching effective communication. ListeningTime is developed for elderly patients with cancer and their oncological HCPs to help them (better) prepare the clinical encounter and overcome communication barriers. ListeningTime is a Web-based preparatory communication tool including modeling videos and has an audio-facility to listen back to recorded encounters. Objective: This study aims to evaluate the usability, perceived usefulness, and actual use of ListeningTime, through the eyes of elderly patients with cancer and their oncological HCPs. If highly rated, the ultimate goal is to make ListeningTime publicly available. Methods: First, members of a panel of elderly cancer survivors and patients (age ≥65 years) were approached to evaluate ListeningTime through a Web-based questionnaire. The usability and perceived usefulness were assessed. Second, ListeningTime was evaluated in real-life practice through a pilot study in 3 Dutch hospitals. In these hospitals, elderly patients with cancer and their oncological HCPs were approached to evaluate ListeningTime through a similar Web-based questionnaire, measuring the perceived usefulness. In addition, we examined log files and user statistics to get insight into how the program was used. Results: A total of 30 cancer survivors or patients from the patient panel, and 17 patients and 8 HCPs from the hospitals, evaluated ListeningTime. Overall, both panel members and hospital patients were positive about the ListeningTime website, audio-facility, and video fragments. Some patients suggested improvements with respect to the actors’ performances in the video fragments and believed that ListeningTime is mainly suitable for non experienced patients. HCPs were also positive about ListeningTime; they valued the video fragments for patients and the audio-facility for patients and themselves. However, providers did not relisten their own recorded encounters. Patients did use the audio-facility to relisten their encounters. Conclusions: ListeningTime was highly rated, both by patients and their oncological HCPs. As a result, the video fragments of ListeningTime are now made publicly available for elderly patients with cancer through the Dutch website “kanker.nl. ” (aut. ref.)
Panel data in accounting and finance: theory and application

The use of models that involve longitudinal data in accounting and finance is common. However, there is often a lack of proper care regarding the criteria for adopting one model over another as well as an insufficiently detailed discussion of the possible estimators to be studied in each situation. This article presents, in conceptual and applied form, the main panel data estimators that can be used in these areas of knowledge and discusses the definition of the most consistent model to be adopted in function of the data characteristics. The models covered for short panels are the POLS with clustered robust standard errors, with between estimator, fixed effects, fixed effects with clustered robust standard errors, random effects and random effects with clustered robust standard errors. In turn, for long panels, the models discussed are fixed effects, random effects, fixed effects with AR(1) error terms, random effects with AR(1) error terms, POLS with AR(1) errors and pooled FGLS with AR(1) errors. The models are also applied to a real case, based on data from Compustat Global. At the end, the main routines for applying each of the models in Stata are presented.
Reasons for outsourcing services in medium and large companies

This study identifies the reasons firms decide to outsource activities instead of performing them internally. The study is restricted to the logistics, information technology, gate-keeping and/or reception and cleaning services as well as part of the production activities. The research was carried out in two steps. The first one consisted of interviews with four executives of large companies and the second one consisted of a survey of executives of 80 industrial companies and more than 100 direct employees. From the quantitative date we prepared contingency tables, cross-referencing the frequencies of the main factors given as leading the firms to outsource services with the corresponding activities, using Pearson’s Chisquare test. The results show that the option for outsourcing is the result of a combination of factors, mainly involving the service to be contracted out and the firm’s economic segment. Specialization of services and reduction and control of operating costs were the reasons given most often for outsourcing. This study contributes to the debate over the reasons companies outsource their activities and can serve as a reference for firms involved in the outsourcing processes.
Soft Neuro Computing GUI-based Diabetes Mellitus

Health is a vital aspect of life. According to this, we need to improve public health due to an unhealthy lifestyle which may lead to the occurrence of various kinds of diseases. Diabetes Mellitus (DM) is one negative impacts left by an unhealthy lifestyle which is caused by high blood sugar levels. Numbers of diabetes patients get late treatments due to delayed identification on them. In fact, if a diagnosis report conducted earlier, the treatment can be given earlier as well, and bad conditions may be avoided. Therefore, a system identifying diabetes is needed so that the disease can be detected as fast, accurate, and early as possible. To tackle this issue, we aimed to design soft computing system for early detection of diabetes mellitus by using neural network and binary sigmoid activation function. In a nutshell, The accuracy level of the DM detection was 92.44%.
Algorithm 3 Global Live Privilege Analysis Algorithm

To help programmers write programs that follow Saltzer and Schroeder’s Principle of Least Privilege, modern operating systems divide the power of the administrative user into separate privileges which applications can enable on demand and permanently discard when no longer needed. However, using such privileges requires tedious temporal reasoning of program behavior. This paper describes a compiler, named AutoPriv, that helps programmers use privileges more easily. AutoPriv uses wholeprogram analysis during link-time optimization to determine where programs use privileges; it then transforms programs to remove unnecessary privileges during their execution. We tested AutoPriv on several privileged open-source programs that typically run as root. Our results show that AutoPriv increases optimization time by 19% on average but that transformed programs exhibit practically no overhead.
Detecting cognitive impairments by agreeing on interpretations of linguistic features

Linguistic features have shown promising applications for detecting various cognitive impairments. To improve detection accuracies, increasing the amount of data or the number of linguistic features have been two applicable approaches. However, acquiring additional clinical data can be expensive, and hand-crafting features is burdensome. In this paper, we take a third approach, proposing Consensus Networks (CNs), a framework to classify after reaching agreements between modalities. We divide linguistic features into non-overlapping subsets according to their modalities, and let neural networks learn low-dimensional representations that agree with each other. These representations are passed into a classifier network. All neural networks are optimized iteratively. In this paper, we also present two methods that improve the performance of CNs. We then present ablation studies to illustrate the effectiveness of modality division. To understand further what happens in CNs, we visualize the representations during training. Overall, using all of the 413 linguistic features, our models significantly outperform traditional classifiers, which are used by the state-of-the-art papers.
Induction of immune response in broiler chickens immunized with recombinant FliC and challenged by Salmonella Typhimurium

Baptista A.A. S. , Donato T.C. , Souza, E.E. , Goncalves G.A.M., Garcia, K.C. O.D. , Ro- drigues J.C. Z. , Sequeira J.L. & Andreatti Filho R.L. 2013. Induction of immune response in broiler chickens immunized with recombinant FliC and challenged by Salmonella
Selecting Discriminative Binary Patterns for a Local Feature

Abstract The local descriptors based on a binary pattern feature have state-of-the-art distinctiveness. However, their high dimensionality resists them from matching faster and being used in a low-end device. In this paper we propose an efficient and feasible learning method to select discriminative binary patterns for constructing a compact local descriptor. In the selection, a searching tree with Branch&Bound is used instead of the exhaustive enumeration, in order to avoid tremendous computation in training. New local descriptors are constructed based on the selected patterns. The efficiency of selecting binary patterns has been confirmed by the evaluation of these new local descriptors’ performance in experiments of image matching and object recognition.
Browser history re: visited

We present four new history sniffing attacks. Our attacks fit into two classical categories—visited-link attacks and cache-based attacks—but abuse new, modern browser features (e.g., the CSS Paint API and JavaScript bytecode cache) that do not account for privacy when handling cross-origin URL data. We evaluate the attacks against four major browsers (Chrome, Firefox, Edge, and IE) and several security-focused browsers (ChromeZero, Brave, FuzzyFox, DeterFox, and the Tor Browser). Two of our attacks are effective against all but the Tor Browser, whereas the other two target features specific to Chromium-derived browsers. Moreover, one of our visited-link attacks (CVE2018-6137) can exfiltrate history at a rate of 3,000 URLs per second, an exfiltration rate that previously led browser vendors to break backwards compatibility in favor of privacy. We hope that this work will lead browser vendors to further reconsider the design of browser features that handle privacy-sensitive data.
High Throughput Phenotyping of Sorghum for the Study of Growth Rate, Water Use Efficiency, and Chemical Composition

ii ACKNOWLEDGMENTS . iv TABLE OF CONTENTS . v LIST OF FIGURES . viii LIST OF TABLES . xi CHAPTER 1 . 1 1.1 PHENOTYPING FOR PLANT BREEDING . 1 1.2 HIGH THROUGHPUT PLANT PHENOTYPING . 3 1.3 DIGITAL IMAGING MODULES . 3 1.3. 1 Phenotyping using RGB images . 5 1.3. 2 Phenotyping using hyperspectral images . 6 1.4 HIGH THROUGHPUT CHEMICAL PHENOTYPING OF SORGHUM . 8 1.4. 1 Cell wall characterization . 9 1.4. 2 Nutrient analysis . 10 1.5 OBJECTIVES OF THE STUDY . 12 CHAPTER 2 . 13 2.1 EXPERIMENT DESIGN . 13
Visual awareness for a humanoid soccer robot

Imaging technology plays an enormously important role in real-time robotics. Using sophisticated vision algorithms, these autonomous humanoid soccer robots are able to precisely identify their surroundings using extremely limited computing resources. In this project, a number of new student-led innovations helped catapult an Australian university to the top level of international RoboCup soccer. Comparison to Leaders in the Field / Student Contribution: The RoboCup Standard Platform League attracts universities and research groups from all around the world, with 25 teams qualifying for the 2012 World Cup. The UNSW team (rUNSWift) placed third overall, and scored more goals during the competition than any other team. The rUNSWift RoboCup entry and the innovations described in this report are entirely student work. Related Publications: 1. Peter Anderson, Yongki Yusmanthia, Bernhard Hengst, and Arcot Sowmya. Robot Localisation Using Natural Landmarks. In Proceedings of the RoboCup International Symposium 2012, Mexico City, Mexico, 18-24 June, 2012. (Nominated for best paper). 2. Sean Harris, Peter Anderson, Belinda Teh, Youssef Hunter, Roger Liu, Bernhard Hengst, Ritwik Roy, Sam Li, Carl Chatfield. Robocup Standard Platform League rUNSWift 2012 Innovations. Accepted into Australasian Conference on Robotics and Automation (ACRA) 2012. 3. Peter Anderson and Bernhard Hengst. Fast Visual Odometry for a Humanoid Robot. Submitted to the IEEE International Conference on Robotics and Automation (ICRA) 2013. 4. Peter Anderson, Youssef Hunter and Bernhard Hengst. An ICP Inspired Unified Sensor Model with Unknown Data Association. Submitted to the IEEE International Conference on Robotics and Automation (ICRA) 2013. Visual awareness for a humanoid soccer robot 1
Simple Language Independent Sequence Labelling for the Annotation of Disabilities in Medical Texts

In this paper we describe our participation to the DIANN 2018 shared task at IberEval 2018 for the annotation of disabilities in the medical domain. We use IXA pipes to model the annotation of disabilities as a sequence labelling task. Our system consists of a combination of clustering features implemented on top of a simple set of shallow local features. We show how leveraging distributional features obtained from large in-domain unlabelled data helps to easily develop a robust system for the medical domain. The system and models generated in this work are available for public use and to facilitate reproducibility of results.
Plant Recognition by Inception Networks with Test-time Class Prior Estimation

The paper describes an automatic system for recognition of 10,000 plant species from one or more images. The system finished 1st in the ExpertLifeCLEF 2018 plant identification challenge with 88.4% accuracy and performed better than 5 of the 9 participating plant identification experts. The system is based on the Inception-ResNet-v2 and Inception-v4 Convolutional Neural Network (CNN) architectures. Performance improvements were achieved by: adjusting the CNN predictions according to the estimated change of the class prior probabilities, replacing network parameters with their running averages, and test-time data augmentation.
Seizure-Related Cortical Volume Alterations in Alzheimer’s Disease: A Preliminary Study

Background and Purpose Alzheimer’s disease (AD) leads to cognitive dysfunction and neuronal loss, both of which can be exacerbated by seizures. For the treatment and diagnosis of AD, it is imperative to identify the cortical characteristics of comorbidities of AD such as seizures. The present study investigated the alterations in cortical volumes in patients with comorbid AD and seizures. Methods In this retrospective study, magnetic resonance T1-weighted brain images were collected from six patients with early AD or amnestic mild cognitive impairment without seizures (AD-No Seizure, age: 66.17 ± 4.92 years) and six individuals with seizures (AD-Seizure, age: 80.33 ± 4.63 years). The gray matter volumes estimated from the T1 images were compared between the groups using nuisance variables (e.g., age). Subsequently, a correlation analysis was performed to investigate the relationship between cortical structure and global cognitive function. Results AD-Seizure group showed volumetric alterations compared with AD-No Seizure group. In the volumetrically altered regions, correlation analysis revealed that the AD-Seizure group showed a positive correlation between the mini-mental state examination (MMSE) score and cortical volume, with smaller volumes than the AD-No Seizure group in the right parahippocampal gyrus, left angular gyrus, and middle temporal gyrus. The AD-No Seizure group showed negative correlations with MMSE score in the volume of right inferior frontal gyrus and cerebellar culmen and a positive correlation with the volume of the left middle frontal gyrus. Conclusions Our findings revealed that smaller temporal region volumes are predictive of cognitive dysfunction in AD patients with seizures. Given that these temporal areas overlap with regions showing abnormal brain activities in AD patients with seizures, these results suggest synergistic effects of AD and seizures on cortical volume and cognitive function.
Social Community-Aware Content Placement in Wireless Device-to-Device Communication Networks

In this paper, a novel framework for optimizing the caching of popular user content at the level of wireless user equipments (UEs) is proposed. The goal is to improve content offloading over wireless device-to-device (D2D) communication links. In the considered network, users belong to different social communities while their UEs form a single multi-hop D2D network. The proposed framework allows us to exploit the multi-community social context of users for improving the local offloading of cached content in a multi-hop D2D network. To model the collaborative effect of a set of UEs on content offloading, a cooperative game between the UEs is formulated. For this game, it is shown that the Shapley value (SV) of each UE effectively captures the impact of this UE on the overall content offloading process. To capture the presence of multiple social communities that connect the UEs, a hypergraph model is proposed. Two line graphs, an influence-weighted graph, and a connectivity-weighted graph, are developed for analyzing the proposed hypergaph model. Using the developed line graphs along with the SV of the cooperative game, a precise offloading power metric is derived for each UE within a multi-community, multi-hop D2D network. Then, UEs with high offloading power are chosen as the optimal locations for caching the popular content. Simulation results show that, on the average, the proposed cache placement framework achieves 12, 19, and 21 percent improvements in terms of the number of UEs that received offloaded popular content compared to the schemes based on betweenness, degree, and closeness centrality, respectively.
Cognitive Functions among Recently Detoxified Patients with Alcohol Dependence and Their Association with Motivational State to Quit

Context: Cognitive impairments are common among patients with alcohol dependence. It may involve frontal executive dysfunction, global cognitive impairments, or both. Motivation to quit alcohol involves recognition of alcohol use as a problem. This ability may be construed as a cognitive symptom. Aims: The aim is to study the frequency of cognitive dysfunction among patients with alcohol dependence and to study the association between cognitive dysfunction and the motivation to quit alcohol. Materials and Methods: Fifty-six adult males with alcohol dependence (International Classification of Diseases-10) who had completed a course of detoxification and who did not have active withdrawal symptoms or acute medical illnesses were recruited for this study. Their cognitive functions were tested using the Montreal Cognitive Assessment (MoCA) and Frontal Assessment Battery (FAB). Their motivation levels were assessed using the Stages of Change Readiness and Treatment Eagerness Scale. Clinical details were collected using a semi-structured pro forma. Results: Global cognitive impairment (MoCA < 26) was seen in 81% and frontal executive dysfunction (FAB < 12) in 16% of patients. Higher MoCA and FAB scores correlated with better education, while lower FAB scores correlated with higher age. The 14 patients (25%) with good motivation did not differ in age, education, years of dependence, or MoCA or FAB scores from poorly motivated patients. FAB scores, but not MoCA, were associated with poor motivation. All nine patients with FAB < 12 were poorly motivated to quit alcohol; likelihood score = 5.731, P = 0.017. Conclusions: Four-fifths of patients with alcohol dependence had global cognitive impairments after the detoxification period. One-sixth had frontal executive dysfunction. Cognitive functions were not significantly correlated with the duration of dependence. Presence of frontal executive dysfunction was associated with almost six times likelihood that the patient will be poorly motivated to quit alcohol.
MAMTRA-MED at Biomedical Abbreviation Recognition and Resolution - IberEval 2018

Biomedical literature, Electronic Health Records and clinical notes frequently contain abbreviations and acronyms, most of them are highly ambiguous. Correct interpretation of abbreviations and acronyms often represents a challenge, and this is the objective of the BARR2 track at IberEval 2018 Workshop. This paper presents our participation in the track. We propose ﬁve systems that deal with the detection of explicit occurrences of abbreviations-deﬁnitions pairs in diﬀerent ways (sub-track1), and three systems to resolve abbreviations and acronyms regardless of whether its deﬁnition is mentioned within the actual document (sub-track2). The proposals for detecting relations between ab-breviations and their deﬁnitions have obtained good results, while the proposals for abbreviation resolution, the more complex of the two tracks, has great room for improvement.
Remodeling neuronal ER–PM junctions is a conserved nonconducting function of Kv2 plasma membrane ion channels

The endoplasmic reticulum (ER) and plasma membrane (PM) form junctions crucial to ion and lipid signaling and homeostasis. The Kv2.1 ion channel is localized at ER–PM junctions in brain neurons and is unique among PM proteins in its ability to remodel these specialized membrane contact sites. Here, we show that this function is conserved between Kv2.1 and Kv2.2, which differ in their biophysical properties, modulation, and cellular expression. Kv2.2 ER–PM junctions are present at sites deficient in the actin cytoskeleton, and disruption of the actin cytoskeleton affects their spatial organization. Kv2.2-containing ER–PM junctions overlap with those formed by canonical ER–PM tethers. The ability of Kv2 channels to remodel ER–PM junctions is unchanged by point mutations that eliminate their ion conduction but eliminated by point mutations within the Kv2-specific proximal restriction and clustering (PRC) domain that do not impact their ion channel function. The highly conserved PRC domain is sufficient to transfer the ER–PM junction–remodeling function to another PM protein. Last, brain neurons in Kv2 double-knockout mice have altered ER–PM junctions. Together, these findings demonstrate a conserved in vivo function for Kv2 family members in remodeling neuronal ER–PM junctions that is distinct from their canonical role as ion-conducting channels shaping neuronal excitability.
Complexity Measures and POS n-grams for Author Identification in Several Languages: SINAI at PAN@CLEF 2018

This paper presents our approach and results for the 2018 PAN Author Identification Task. In this task, we are given a set of documents (known fanfics) by a small number (up to 20) of candidate authors. All documents are in the same language that may be English, French, Italian, Polish, or Spanish. The task consists in developing a system to identify the authors of another set of documents (unknown fanfics). We have used two strategies to solve this task. The first strategy has consisted in using several measures of the complexity of the fanfics texts for each candidate. In the second strategy, we analyzed the fanfics of each candidate by applying a Part-Of-Speech Tagger and a n-gram based vector space model.