Miracle-dz/UniFAR-FTUs huggingface dataset:
Avoiding Reasoning Shortcuts: Adversarial Evaluation, Training, and Model Development for Multi-Hop QA

Multi-hop question answering requires a model to connect multiple pieces of evidence scattered in a long context to answer the question. In this paper, we show that in the multi-hop HotpotQA (Yang et al., 2018) dataset, the examples often contain reasoning shortcuts through which models can directly locate the answer by word-matching the question with a sentence in the context. We demonstrate this issue by constructing adversarial documents that create contradicting answers to the shortcut but do not affect the validity of the original answer. The performance of strong baseline models drops significantly on our adversarial test, indicating that they are indeed exploiting the shortcuts rather than performing multi-hop reasoning. After adversarial training, the baseline’s performance improves but is still limited on the adversarial test. Hence, we use a control unit that dynamically attends to the question at different reasoning hops to guide the model’s multi-hop reasoning. We show that our 2-hop model trained on the regular data is more robust to the adversaries than the baseline. After adversarial training, it not only achieves significant improvements over its counterpart trained on regular data, but also outperforms the adversarially-trained baseline significantly. Finally, we sanity-check that these improvements are not obtained by exploiting potential new shortcuts in the adversarial data, but indeed due to robust multi-hop reasoning skills of the models.
Optimal Network Coding-Based In-Network Data Storage and Data Retrieval for IoT/WSNs

The evolution of the Internet of Things (IoT) allows the development of new services and applications but triggers as well a full set of new issues to be solved. Among them, there are the problems related to the integration of the WSNs in the IoT realm including those related to data access. In this paper, we address more precisely the in-network data storage and data retrieval performed in a WSN integrated in the IoT realm. In order to develop an adequate data storage scheme for this scenario, we first design a system that integrates the Virtual Broking Coding (VBC) data storage scheme in the IoT realm. Then, we propose an algorithm called Dynamic Adaptive Virtual Broking Coding (DA-VBC) that adapts dynamically the packet redundancy level adopted in VBC to the optimal redundancy level, regarding the actual condition of the network, in order to ensure a reliable data storage and data retrieval. To do so, we model the choice of the optimal redundancy level as a Markov Decision Process (MDP) problem. Using the optimal policy found by the MDP, DA-VBC always performs with the minimum cost-benefit for the network which means allowing more packet to be retrieved without overload the energy consumption. The simulation results confirm that the dynamic adaptation of the redundancy level improves the reliability of the data storage scheme while achieving an energy consumption comparable to the solution that does not use any redundancy. Besides, they show that the optimization of the cost-benefit metric is far more efficient than optimizing only one metric (for instance the cost or the packet delivery ratio), or using a fixed redundancy level.
Software modularization in global software development

In a Global Software Development (GSD) environment, multiple teams are located in geographically dispersed locations and project management is becoming a difficult task; and the work distribution process has become an important function in GSD. Work distribution process can influence both the benefits of global software development (e.g. cost reduction, availability of people, proximity to the customer) and its risks (e.g. communication and coordination overhead). Therefore, work should be distributed after a thorough study on the different tasks and the various locations. To do this, the categorization of work should be undertaken before it is distributed. This is known as modularization which can help overcome communication and coordination problems in the development process and makes the process more stable. In this paper, we present a survey on software modularization which includes principles of modularization and applicability of the concept to GSD.
Using Derivation Trees for Informative Treebank Inter-Annotator Agreement Evaluation

This paper discusses the extension of a system developed for automatic discovery of treebank annotation inconsistencies over an entire corpus to the particular case of evaluation of inter-annotator agreement. This system makes for a more informative IAA evaluation than other systems because it pinpoints the inconsistencies and groups them by their structural types. We evaluate the system on two corpora - (1) a corpus of English web text, and (2) a corpus of Modern British English.
NUMERICAL SCATTERING SIMULATIONS FROM TIME-EVOLVING OCEAN-LIKE SURFACES AT LAND X-BAND : DOPPLER ANALYSIS AND COMPARISONS WITH A COMPOSITE SURFACE ANALYTICAL MODEL

Direct numerical simulations of sea surface scattering are used to evaluate the accuracy of a recently proposed analytical model for ocean-generated Doppler spectra. Comparisons are performed for backscattering at incidence angles varying from near-nadir to low grazing at Land X-bands. Doppler centroids predicted by the model show, as expected, large discrepancies close to nadir and grazing directions. However, even for moderate incidence angles, a disagreement of 1025% is observed, especially at X-band. At present, the reason for this deviation is not well understood. The Doppler widths, however, show good agreement across the broad range of incidence angles.
IMPROVEMENT STRATEGIES OF JOB PERFORMANCE: A PERSPECTIVE ON NURSING PROFESSION IN VIETNAM

As an increase in overloaded general hospitals in Vietnam, nurses are under intensive pressure but receive little opportunities for learning new technical skills and for developing an obvious career path. Meanwhile, nurse performance is essential to quality outcomes of healthcare services. Therefore, this study aims to investigate primary drivers of nurse performance by using the structural equation model approach to analyze a survey of 366 nurses working in four hospitals in Ho Chi Minh City and Bac Lieu province, Vietnam. The results show the facilitating effects of affective commitment and self-leadership on job performance, but not continuance commitment and communication competence. Also, the empirical evidence supports that perceived organizational support and psychological safety indirectly impact job performance through affective commitment. Job resources, however, are insignificantly related to affective commitment. The study indicated that nurses with a high degree of self-leadership and affective commitment to the organization are prone to attain high job performance. Therefore, managers should implement strategies to enhance nurses’ conception of self-leadership and affective commitment, which would be an efficient approach to improve quality healthcare services. This study also makes some explanations for the divergence between the previous literature on factors affecting nurse performance and that in Vietnamese hospital context.
RELATIONS OF FITNESS PARAMETERS AND MORPHOLOGICAL CHARACTERISTICS OF SEVEN -YEAR - OLD OBESE CHILDREN

The aim of this research was to determine the relations of fitness parameters and morphological characteristics of seven-year-old obese children. The sample of participants consisted of 103 obese children (63 girls and 40 boys), first grade students (mean age 7.04 years) of elementary schools in Nis. The following fitness parameters were measured: HR in load, VO2max (ml), HR in peace, bend forward - bend backward - throw test, hand taping, 20m dash with a standing start, Abalakov test and forward bend on the bench test. Morphological characteristics were determined by measuring 13 parameters of longitudinal and transversal dimensions of skeleton, volume and body mass, as well as subcutaneous fat tissue. Canonical correlation analysis was used to determine the relations between the set of variables representing fitness parameters and the set of variables of morphological characteristics. Based on the coefficients of canonical correlation analysis, it can be concluded that the spaces of morphological characteristics and fitness parameters are interconnected with two pairs of statistically significant canonical factors (p˂.05 and p˂.01). The general analysis of the relationships between the first pair of canonical factors suggests that subjects with higher circular dimensionalities of the body, accentuated adiposity and body mass tend to have poorer results in motoring tasks requiring body flexibility, explosive leg strength and running speed, and also have poorer results in the parameters of cardiorespiratory fitness. It can be concluded that obesity in young school age has negative implications on the fitness parameters in children, and therefore on their overall growth and development.
Edinburgh Research Explorer Differentiation and migration properties of human foetal umbilical cord perivascular cells: potential for lung repair

Mesenchymal stem cells (MSC) have been derived from different cultured human tissues, including bone marrow, adipose tissue, amniotic fluid and umbilical cord blood. Only recently it was suggested that MSC descended from perivascular cells, the latter being defined as CD146 (cid:2) neuro-glial proteoglycan (NG)2 (cid:2) platelet-derived growth factor-R (cid:3) (cid:2) ALP (cid:2) CD34 – CD45 – von Willebrand factor (vWF) – CD144 – . Herein we studied the properties of perivascular cells from a novel source, the foetal human umbilical cord (HUC) collected from pre-term newborns. By immunohistochemistry and flow cytometry we show that pre-term/foetal HUCs contain more perivascular cells than their full-term counterparts (2.5% versus0.15%). Moreover, foetal HUC perivascular cells (HUCPC) express the embryonic cell markers specific embryonic antigen-4, Runx1 and Oct-4 and can be cultured over the long term. To further confirm the MSC identity of these cultured perivascular cells, we also showed their expression at different passages of antigens that typify MSC. The multilineage differentiative capacity of HUCPC into osteogenic, adipogenic and myogenic cell lineages was demonstrated in culture. In the perspective of a therapeutic application in chronic lung disease of pre-term newborns, we demonstrated the in vitroability of HUCPC to migrate towards an alveolar type II cell line damaged with bleomycin, an anti-cancer agent with known pulmonary toxicity. The secretory profile exhibited by foetal HUCPC in the migration assay suggested a paracrine effect that could be exploited in various clinical conditions including lung disorders.
Spitzer Uncovers Active Galactic Nuclei Missed by Optical Surveys in Seven Late-Type Galaxies

We report the discovery using Spitzer's high-resolution spectrograph of seven active galactic nuclei (AGNs) in a sample of 32 late-type galaxies that show no definitive signatures of AGNs in their optical spectra. These observations suggest that the AGN detection rate in late-type galaxies is possibly 4 times larger than what optical spectroscopic observations alone suggest. We demonstrate using photoionization models with an input AGN and an extreme UV-bright starburst ionizing radiation field that the observed mid-infrared line spectrum, which includes the high-ionization [Ne V] 14 μm and/or 24 μm lines, cannot be replicated unless an AGN contribution, in some cases as little as 10% of the total galaxy luminosity, is included. These models show that when the fraction of the total luminosity due to the AGN is low, optical diagnostics are insensitive to the presence of the AGN. In this regime of parameter space, the mid-infrared diagnostics offer a powerful tool for uncovering AGNs missed by optical spectroscopy. The AGN bolometric luminosities in our sample range from ~3 × 1041 to ~2 × 1043 ergs s−1, which, based on the Eddington limit, corresponds to a lower mass limit for the black hole that ranges from ~3 × 103 to as high as ~1.5 × 105 M☉. These lower mass limits, however, do not put a strain on the well-known relationship between the black hole mass and the host galaxy’s stellar velocity dispersion established in predominantly early-type galaxies. Our findings add to the growing evidence that black holes do form and grow in low-bulge environments and that they are significantly more common than optical studies indicate.
Boosting Supervision with Self-Supervision for Few-shot Learning

We present a technique to improve the transferability of deep representations learned on small labeled datasets by introducing self-supervised tasks as auxiliary loss functions. While recent approaches for self-supervised learning have shown the benefits of training on large unlabeled datasets, we find improvements in generalization even on small datasets and when combined with strong supervision. Learning representations with self-supervised losses reduces the relative error rate of a state-of-the-art meta-learner by 5-25% on several few-shot learning benchmarks, as well as off-the-shelf deep networks on standard classification tasks when training from scratch. We find the benefits of self-supervision increase with the difficulty of the task. Our approach utilizes the images within the dataset to construct self-supervised losses and hence is an effective way of learning transferable representations without relying on any external training data.
A Methodology and an Open Software Infrastructure for Constraint-Driven Synthesis of On-Chip Communications

An SoC is modeled as a set of pre-designed cores characterized by their area and interfaces and a set of end-to-end communication requirements on the channels connecting them. These requirements are the input specification to a synthesis process that produces an on-chip communication (OCC) by assembling components such as interfaces, routers, buses and links, from a target library. Models for functionality, cost, and performance of each element are captured in the library together with their composition rules. Our contributions is two pronged: 1) A methodology for the design of OCC based on: • a mathematical framework to model communication at different levels of abstraction from the point-to-point input specification to the library elements and the final implementation; • the formulation of an optimization problem and a heuristic synthesis algorithms to solve it: given a set of communication requirements on the throughput and latency of the channels it returns a communication implementation that satisfies them while minimizing power consumption. 2) A publicly available software package that includes alternative synthesis algorithms, various communication libraries, supporting tools for simulation and visualization, and a collection of SoC benchmarks.
Cyberbullying in WhatsApp Classroom Groups among Children and Adolescents: Exposure and Victimization

Social networks are an essential part of school-aged children's social lives. WhatsApp application is perceived as a social network which enjoys enormous popularity among children and adolescents. Nonetheless, alongside the increasing popularity of WhatsApp, increases cyberbullying, defined as an aggressive online behavior aimed to harm another person through internet and technological communication means. The present study is a pilot research aimed to learn about the extent, characteristics and expressions of cyberbullying that children and adolescents experience in WhatsApp groups in their classes, either as witnesses, or as victims. Additionally, the study aims to offer a classification of cyberbullying in WhatsApp groups. Data was collected from Israeli students who learn in 4 th to 12 th grades ( N = 1111). The participants completed a questionnaire regarding cyberbullying in their class WhatsApp groups. Findings indicated that the vast majority of participants are members in at least one WhatsApp group in their classes, to which it`s important for them to belong. Alongside, most participants experience cyberbullying in their WhatsApp class-groups, either as victims or as witnesses and over half of them experience more than one expression of cyberbullying simultaneously. Insults are the most common expression of cyberbullying. After that swearword, forced removal from a group and posting offensive photos. Witnessing WhatsApp cyberbullying is more common than personal victimization. In addition, findings indicated of differences in cyberbullying expressions according to the level of educational institution (elementary school, middle school, and high school). Implications regarding schools` and parents` roles, including developing educational policy and school curriculum, are discussed.
A Game System for Cognitive Rehabilitation

Brain injury such as traumatic brain injury (TBI) and stroke is the major cause of long-term disabilities in many countries. The increasing rate of brain damaged victims and the heterogeneity of impairments decrease rehabilitation effectiveness and competence resulting in higher cost of rehabilitation treatment. On the other hand, traditional rehabilitation exercises are boring, thus leading patients to neglect the prescribed exercises required for recovery. Therefore, we propose game-based approach to address these problems. This paper presents a rehabilitation gaming system (RGS) for cognitive rehabilitation. The RGS is developed based on a proposed conceptual framework which has also been presented in this paper.
Sparse Brain Network Recovery Under Compressed Sensing

Partial correlation is a useful connectivity measure for brain networks, especially, when it is needed to remove the confounding effects in highly correlated networks. Since it is difficult to estimate the exact partial correlation under the small-n large-p situation, a sparseness constraint is generally introduced. In this paper, we consider the sparse linear regression model with a l1-norm penalty, also known as the least absolute shrinkage and selection operator (LASSO), for estimating sparse brain connectivity. LASSO is a well-known decoding algorithm in the compressed sensing (CS). The CS theory states that LASSO can reconstruct the exact sparse signal even from a small set of noisy measurements. We briefly show that the penalized linear regression for partial correlation estimation is related to CS. It opens a new possibility that the proposed framework can be used for a sparse brain network recovery. As an illustration, we construct sparse brain networks of 97 regions of interest (ROIs) obtained from FDG-PET imaging data for the autism spectrum disorder (ASD) children and the pediatric control (PedCon) subjects. As validation, we check the network reproducibilities by leave-one-out cross validation and compare the clustered structures derived from the brain networks of ASD and PedCon.
Femtosecond demagnetization and hot-hole relaxation in ferromagneticGa1−xMnxAs

We have studied ultrafast photoinduced demagnetization in GaMnAs via two-color time-resolved magneto-optical Kerr spectroscopy. Below-bandgap midinfrared pump pulses strongly excite the valence band, while near-infrared probe pulses reveal sub-picosecond demagnetization that is followed by an ultrafast ($\sim$1 ps) partial recovery of the Kerr signal. Through comparison with InMnAs, we attribute the signal recovery to an ultrafast energy relaxation of holes. We propose that the dynamical polarization of holes through $p$-$d$ scattering is the source of the observed probe signal. These results support the physical picture of femtosecond demagnetization proposed earlier for InMnAs, identifying the critical roles of both energy and spin relaxation of hot holes.
Evaluation of Talking Parents, Healthy Teens, a new worksite based parenting programme to promote parent-adolescent communication about sexual health: randomised controlled trial

Objective To evaluate a worksite based parenting programme—Talking Parents, Healthy Teens—designed to help parents learn to address sexual health with their adolescent children. Design Randomised controlled trial (April 2002-December 2005). Setting 13 worksites in southern California. Participants 569 parents completed baseline surveys at work, gave permission for confidential surveys to be posted to their adolescent children, and were randomised to intervention or control groups. Parents and adolescents completed follow-up surveys at one week, three months, and nine months after the programme. Intervention Talking Parents, Healthy Teens consists of eight weekly one hour sessions at worksites for parents of adolescent children in 6th-10th grade (about ages 11-16 years). Main outcome measures Parent-adolescent communication about a list of sexual topics; whether parent taught adolescent how to use a condom; ability to communicate with parent/adolescent about sex; openness of parent-adolescent communication about sex. Results Differences between intervention and control groups were significant for the mean number of new sexual topics that parents and adolescents reported discussing between baseline and each follow-up (P<0.001 for each); intervention parents were less likely than controls to discuss no new topics (8% v 29%, 95% confidence interval for difference 16% to 24%) and more likely to discuss seven or more new topics (38% v 8%, 19% to 41%) at nine months. Some differences increased after completion of the programme: at one week after the programme, 18% of adolescents in the intervention group and 3% in the control group (6% to 30%) said that their parents had reviewed how to use a condom since baseline (P<0.001); this grew to 29% v 5% (13% to 36%) at nine months (P<0.001). Compared with controls at nine months, parents and adolescents in the intervention group reported greater ability to communicate with each other about sex (P<0.001) and more openness in communication about sex (P<0.001). Conclusions A worksite based programme can have substantial effects on communication between parents and adolescents about sexual health. Trial registration Clinical Trials NCT00465010.
Stock Market Forecasting Algorithm Based on Improved Neural Network

This paper further analyzes the problems in stock short market forecasting and compares multiple stock price forecasting algorithms. It also discusses the feasibility of BP neural network, PCA method and Genetic Algorithm in short market forecasting. For the defects of traditional BP algorithm which often traps into local minima, in forecasting accuracy, we optimize the BP neural network and establish a GA-BP algorithm based forecasting model. The experiments adopt the Shanghai index data to make simulation and provide corresponding error analysis. The results show that the GABP model proposed in this paper has certain improvement in stock price forecasting accuracy.