Miracle-dz/UniFAR-FTUs huggingface dataset:
From Two Graphs to N Questions: A VQA Dataset for Compositional Reasoning on Vision and Commonsense

Visual Question Answering (VQA) is a challenging task for evaluating the ability of comprehensive understanding of the world. Existing benchmarks usually focus on the reasoning abilities either only on the vision or mainly on the knowledge with relatively simple abilities on vision. However, the ability of answering a question that requires alternatively inferring on the image content and the commonsense knowledge is crucial for an advanced VQA system. In this paper, we introduce a VQA dataset that provides more challenging and general questions about Compositional Reasoning on vIsion and Commonsense, which is named as CRIC. To create this dataset, we develop a powerful method to automatically generate compositional questions and rich annotations from both the scene graph of a given image and some external knowledge graph. Moreover, this paper presents a new compositional model that is capable of implementing various types of reasoning functions on the image content and the knowledge graph. Further, we analyze several baselines, state-of-the-art and our model on CRIC dataset. The experimental results show that the proposed task is challenging, where state-of-the-art obtains 52.26% accuracy and our model obtains 58.38%.
Concolic testing with adaptively changing search heuristics

We present Chameleon, a new approach for adaptively changing search heuristics during concolic testing. Search heuristics play a central role in concolic testing as they mitigate the path-explosion problem by focusing on particular program paths that are likely to increase code coverage as quickly as possible. A variety of techniques for search heuristics have been proposed over the past decade. However, existing approaches are limited in that they use the same search heuristics throughout the entire testing process, which is inherently insufficient to exercise various execution paths. Chameleon overcomes this limitation by adapting search heuristics on the fly via an algorithm that learns new search heuristics based on the knowledge accumulated during concolic testing. Experimental results show that the transition from the traditional non-adaptive approaches to ours greatly improves the practicality of concolic testing in terms of both code coverage and bug-finding.
Do Neural Language Representations Learn Physical Commonsense?

Humans understand language based on the rich background knowledge about how the physical world works, which in turn allows us to reason about the physical world through language. In addition to the properties of objects (e.g., boats require fuel) and their affordances, i.e., the actions that are applicable to them (e.g., boats can be driven), we can also reason about if-then inferences between what properties of objects imply the kind of actions that are applicable to them (e.g., that if we can drive something then it likely requires fuel). In this paper, we investigate the extent to which state-of-the-art neural language representations, trained on a vast amount of natural language text, demonstrate physical commonsense reasoning. While recent advancements of neural language models have demonstrated strong performance on various types of natural language inference tasks, our study based on a dataset of over 200k newly collected annotations suggests that neural language representations still only learn associations that are explicitly written down.
Cerebro: context-aware adaptive fuzzing for effective vulnerability detection

Existing greybox fuzzers mainly utilize program coverage as the goal to guide the fuzzing process. To maximize their outputs, coverage-based greybox fuzzers need to evaluate the quality of seeds properly, which involves making two decisions: 1) which is the most promising seed to fuzz next (seed prioritization), and 2) how many efforts should be made to the current seed (power scheduling). In this paper, we present our fuzzer, Cerebro, to address the above challenges. For the seed prioritization problem, we propose an online multi-objective based algorithm to balance various metrics such as code complexity, coverage, execution time, etc. To address the power scheduling problem, we introduce the concept of input potential to measure the complexity of uncovered code and propose a cost-effective algorithm to update it dynamically. Unlike previous approaches where the fuzzer evaluates an input solely based on the execution traces that it has covered, Cerebro is able to foresee the benefits of fuzzing the input by adaptively evaluating its input potential. We perform a thorough evaluation for Cerebro on 8 different real-world programs. The experiments show that Cerebro can find more vulnerabilities and achieve better coverage than state-of-the-art fuzzers such as AFL and AFLFast.
Analyzing the Convenience of Adopting a Product Line Engineering Approach: An Industrial Qualitative Evaluation

Engineering Software Product Lines may be a strategy to reduce costs and efforts for developing software and increasing business productivity. However, it cannot be considered as a "silver bullet" that applies to all types of organizations. Companies must consider pros and cons to determine sound reasons and justify its adoption. In previous work, we proposed the APPLIES evaluation framework to help decision-makers find arguments that may justify (or not) adopting a product line engineering approach. This paper presents our experience using this framework in a mid-sized software development company with more than 25 years of experience but without previous experience in product line engineering. This industrial experience, conducted as a qualitative empirical evaluation, helped us to evaluate to what extent APPLIES is practical to be used in a real environment and to gather ideas from real potential users to improve the framework.
Eagle: a team practices audit framework for agile software development

Agile/XP (Extreme Programming) software teams are expected to follow a number of specific practices in each iteration, such as estimating the effort (”points”) required to complete user stories, properly using branches and pull requests to coordinate merging multiple contributors’ code, having frequent ”standups” to keep all team members in sync, and conducting retrospectives to identify areas of improvement for future iterations. We combine two observations in developing a methodology and tools to help teams monitor their performance on these practices. On the one hand, many Agile practices are increasingly supported by web-based tools whose ”data exhaust” can provide insight into how closely the teams are following the practices. On the other hand, some of the practices can be expressed in terms similar to those developed for expressing service level objectives (SLO) in software as a service; as an example, a typical SLO for an interactive Web site might be ”over any 5-minute window, 99% of requests to the main page must be delivered within 200ms” and, analogously, a potential Team Practice (TP) for an Agile/XP team might be ”over any 2-week iteration, 75% of stories should be ’1-point’ stories”. Following this similarity, we adapt a system originally developed for monitoring and visualizing service level agreement (SLA) compliance to monitor selected TPs for Agile/XP software teams. Specifically, the system consumes and analyzes the data exhaust from widely-used tools such as GitHub and Pivotal Tracker and provides team(s) and coach(es) a ”dashboard” summarizing the teams’ adherence to various practices. As a qualitative initial investigation of its usefulness, we deployed it to twenty student teams in a four-sprint software engineering project course. We find an improvement of the adherence to team practice and a positive students’ self-evaluations of their team practices when using the tool, compared to previous experiences using an Agile/XP methodology. The demo video is located at https://youtu.be/A4xwJMEQh9c and a landing page with a live demo at https://isa-group.github. io/2019-05-eagle-demo/.
Rethinking Secure Precoding via Interference Exploitation: A Smart Eavesdropper Perspective

Based on the concept of constructive interference (CI), multiuser interference (MUI) has recently been shown to be beneficial for communication secrecy. A few CI-based secure precoding algorithms have been proposed that use both the channel state information (CSI) and knowledge of the instantaneous transmit symbols. In this article, we examine the CI-based secure precoding problem with a focus on smart eavesdroppers that exploit statistical information gleaned from the precoded data for symbol detection. Moreover, the impact of correlation between the main and eavesdropper channels is taken into account. We first modify an existing CI-based precoding scheme to better utilize the destructive impact of the interference. Then, we point out the drawback of both the existing and the new modified CI-based precoders when faced with a smart eavesdropper. To address this deficiency, we provide a general principle for precoder design and then give two specific design examples. Finally, the scenario where the eavesdropper’s CSI is unavailable is studied. Numerical results show that although our modified CI-based precoder can achieve a better energy-secrecy trade-off than the existing approach, both have a limited secrecy benefit. On the contrary, the precoders developed using the new CI-design principle can achieve a much improved tradeoff and significantly degrade the eavesdropper’s performance.
MOTSD: a multi-objective test selection tool using test suite diagnosability

Performing regression testing on large software systems becomes unfeasible as it takes too long to run all the test cases every time a change is made. The main motivation of this work was to provide a faster and earlier feedback loop to the developers at OutSystems when a change is made. The developed tool, MOTSD, implements a multi-objective test selection approach in a C# code base using a test suite diagnosability metric and historical metrics as objectives and it is powered by a particle swarm optimization algorithm. We present implementation challenges, current experimental results and limitations of the tool when applied in an industrial context. Screencast demo link: https://www.youtube. com/watch? v=CYMfQTUu2BE
CloneCognition: machine learning based code clone validation tool

A code clone is a pair of similar code fragments, within or between software systems. To detect each possible clone pair from a software system while handling the complex code structures, the clone detection tools undergo a lot of generalization of the original source codes. The generalization often results in returning code fragments that are only coincidentally similar and not considered clones by users, and hence requires manual validation of the reported possible clones by users which is often both time-consuming and challenging. In this paper, we propose a machine learning based tool 'CloneCognition' (Open Source Codes: https://github.com/pseudoPixels/CloneCognition ; Video Demonstration: https://www.youtube.com/watch? v=KYQjmdr8rsw) to automate the laborious manual validation process. The tool runs on top of any code clone detection tools to facilitate the clone validation process. The tool shows promising clone classification performance with an accuracy of up to 87.4%. The tool also exhibits significant improvement in the results when compared with state-of-the-art techniques for code clone validation.
Designing Personalized Therapy Tools for People with Dementia

Non-pharmacological interventions for people with dementia benefit from the usage of personalized materials. These are not always easy to obtain. In a set of formative studies, we looked at how clinical staff and families recur to the usage of biographical elements to stimulate and foster the self-identity of people with dementia. Results showed that these stakeholders cherish biographically-rich information although it is often hard to obtain relevant materials. We present the iterative design of Scrapbook, a web platform that supports psychologists in the process of collecting meaningful information about a person, and applying it in reminiscence and cognitive stimulation sessions. A 3-phase deployment in a clinical setting allowed us to evolve the platform to respond to stakeholder requirements. Findings include a positive view of the developed features (e.g., biography-enriched games, caregiver app, group therapy) but also an underwhelming usage of the platform. We discuss the adoption, usage patterns, and feedback collected from clinicians, seeking to inform the design of personalized, i.e. biographically-rich, tools for therapy settings.
Generating effective test cases for self-driving cars from police reports

Autonomous driving carries the promise to drastically reduce the number of car accidents; however, recently reported fatal crashes involving self-driving cars show that such an important goal is not yet achieved. This calls for better testing of the software controlling self-driving cars, which is difficult because it requires producing challenging driving scenarios. To better test self-driving car soft- ware, we propose to specifically test car crash scenarios, which are critical par excellence. Since real car crashes are difficult to test in field operation, we recreate them as physically accurate simulations in an environment that can be used for testing self-driving car software. To cope with the scarcity of sensory data collected during real car crashes which does not enable a full reproduction, we extract the information to recreate real car crashes from the police reports which document them. Our extensive evaluation, consisting of a user study involving 34 participants and a quantitative analysis of the quality of the generated tests, shows that we can generate accurate simulations of car crashes in a matter of minutes. Compared to tests which implement non critical driving scenarios, our tests effectively stressed the test subject in different ways and exposed several shortcomings in its implementation.
It's All About the Message: Visual Experience is a Precursor to Accurate Auditory Interaction

Anecdotal evidence suggests there is a disjoint between the interaction experiences of sighted and visually disabled web users. However, we propose the converse and suggest that this disjoint is created by the lack of understanding of the interplay between the two domains. Current research shows that there is one single locus of attention at a given time in the context of web interaction, and therefore sighted users form a serialisation of the things they look at and pay attention - an exemplar of which can be seen in eye movement sequences of users. We also suggest that web designers have a narrative in mind to be experienced by users, and they create a visual sequence they wish their audience to perceive for supporting this narrative. However, this sequence is typically lost when we move from visual presentations to auditory ones. Current audio interactions centre around page linearisation based on the sequence of the underlying source code. This linearisation typically falls short of the kind of comprehensive interaction which can be expected in the visual domain. In this paper, we use an eye tracking dataset to illustrate that the linearisation of web page component based on the underlying source code differs from what is experienced by sighted users. We then show that the web experience of visually disabled users can be improved by re-ordering the most commonly used web page components based on the order in which they are used. We also suggest that it is critical to conduct formative experimentation with sighted users to establish a visual narrative and serialisation, thereby informing the design of the auditory conversation.
DOES ANTENATAL CORTICOSTEROID THERAPY IMPROVE NEONATAL OUTCOMES IN LATE‐PRETERM BIRTH?

Abstract Background Infants who are born at 340/7 to 366/7 weeks of gestation (late preterm) are at greater risk for respiratory and other neonatal morbidities. The objective of this study was to examine the effects of administration of antenatal corticosteroids (ACSs) to women at risk for late preterm delivery on the incidence of neonatal outcomes. Methods This was a prospective cohort study of singleton gestations at risk of imminent delivery between 340/7 and 366/7 weeks. Neonatal outcomes were compared between mothers who received ACS and those who did not. Primary outcome was the rate of composite respiratory morbidity defined as the need for treatment within 72 h of life (continuous positive airway pressure or high flow nasal cannula for least 2 h or supplemental oxygen with a fraction of inspired oxygen of at least 0.30 for at least four continuous hours or mechanical ventilation). Results During the 3-year study period, 595 subjects were included in this study, comprising 234 subjects that received ACS and 361 that did not. Administration of ACS significantly reduced the rates of composite respiratory morbidity (adjusted odds ratio (aOR) 0.63, 95% confidence interval (CI) 0.40–0.99), the use of CPAP or HFNC for at least 2 h (aOR 0.57, 95% CI 0.35–0.94) and transient tachypnea of newborn (aOR 0.48, 95% CI 0.28–0.82). Neonatal hypoglycemia was more significantly increased in the ACS group compared with controls (aOR 1.64, 95% CI 1.04–2.59). We found no significant between-group differences in the rate of respiratory distress syndrome, surfactant use, need for resuscitation, jaundice requiring phototherapy, admission to neonatal intensive care or special care nursery and duration of hospitalization. Conclusion Administration of ACS during the late preterm period decreased neonatal respiratory complications, however, increased the rate of hypoglycemia.
Real-time tracking of production control : Requirements and solutions

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Does Glucagon‐like peptide‐1 induce Diuresis and Natriuresis by Modulating Afferent Renal Nerve Activity?

Cardiovascular outcome studies of Glucagon‐like peptide‐1 receptor (GLP‐1R) agonist suggest that GLP‐1R agonist reduces cardiovascular events and also prevents progression of chronic kidney disease. It has been postulated that GLP‐1 causes diuresis and natriuresis by acting directly on the afferent arterioles or proximal tubules in the kidney. In addition to these direct effects on the end effectors, GLP‐1 may also regulate diuretic and natriuretic effects via the afferent and/or efferent renal nerves. The present study was designed to explore the possible underlying mechanisms for the diuretic and natriuretic actions of GLP‐1 via renal nerves. Immunohistochemistry for GLP‐1R and calcitonin‐gene‐related peptide (CGRP), which is the dominant neurotransmitters for renal afferents, revealed that GLP‐1R was expressed in pelvic wall being adjacent to afferent renal nerves in rats. There was a relatively sparse expression of GLP‐1R in all areas of the cortex of the kidney. GLP‐1 (3μM) infused into left renal pelvis increased ipsilateral afferent renal nerve activity (ARNA) in rats (ΔARNA increased by 40.8% of basal value). This represented 76.6% of the maximum activation of renal afferent nerves as gauged by injection of capsaicin (transient receptor potential V1 receptor agonist, 100μM) into the renal pelvis. Intravenous infusion of GLP‐1 (1μg/kg/min) for 30 min increased renal urine flow and sodium excretion by 7‐fold in rats. These effects were enhanced by total renal denervation (RDN) with acute surgical cutting of the renal nerves (Urine flow rate 86.9±8.2 vs. 59.6±3.3 μl/min/gkw, Sodium excretion 12.6±3.0 vs. 8.6±0.6 μEq/min/gkw, p<0.05, n=9–10). Selective afferent renal nerve denervation (A‐RDN) was performed by bilateral perivascular application of capsaicin (33 mM) on the renal nerves. Ten days after A‐RDN, there was an enhanced diuretic and natriuretic response to GLP‐1 (Urine flow rate 80.0±6.9 vs. 59.6±3.3 μl/min/gkw, Sodium excretion 12.3±1.0 vs. 8.6±0.6 μEq/min/gkw, p<0.05, n=6–10). Urine flow rate and sodium excretion responses to GLP‐1 were not significantly different between RDN and A‐RDN groups. The mean arterial pressure (MAP) and heart rate (HR) were not significantly changed during infusion of GLP‐1 (ΔMAP 3±2 mmHg, ΔHR 7±6, p=ns, n=8). However, the changes of MAP and HR to GLP‐1 were significantly increased by RDN (ΔMAP 9±2 mmHg, ΔHR 23±9, p<0.05, n=8) and A‐RDN (ΔMAP 11±3 mmHg, ΔHR 26±6 bpm, p<0.05, n=4). These changes in MAP and HR to GLP‐1 were not significantly different between the two groups, RDN and A‐RDN. These results indicate that the diuretic and natriuretic effects of GLP‐1 are partly governed via afferent renal nerve activation, by GLP‐1 acting on sensory nerves within the pelvis of the kidney.
Manufacturing and Characterization of Functionalized Aliphatic Polyester from Poly(lactic acid) with Halloysite Nanotubes

This work reports the potential of poly(lactic acid)—PLA composites with different halloysite nanotube (HNTs) loading (3, 6 and 9 wt%) for further uses in advanced applications as HNTs could be used as carriers for active compounds for medicine, packaging and other sectors. This work focuses on the effect of HNTs on mechanical, thermal, thermomechanical and degradation of PLA composites with HNTs. These composites can be manufactured by conventional extrusion-compounding followed by injection molding. The obtained results indicate a slight decrease in tensile and flexural strength as well as in elongation at break, both properties related to material cohesion. On the contrary, the stiffness increases with the HNTs content. The tensile strength and modulus change from 64.6 MPa/2.1 GPa (neat PLA) to 57.7/2.3 GPa MPa for the composite with 9 wt% HNTs. The elongation at break decreases from 6.1% (neat PLA) down to a half for composites with 9 wt% HNTs. Regarding flexural properties, the flexural strength and modulus change from 116.1 MPa and 3.6 GPa respectively for neat PLA to values of 107.6 MPa and 3.9 GPa for the composite with 9 wt% HNTs. HNTs do not affect the glass transition temperature with invariable values of about 64 °C, or the melt peak temperature, while they move the cold crystallization process towards lower values, from 112.4 °C for neat PLA down to 105.4 °C for the composite containing 9 wt% HNTs. The water uptake has been assessed to study the influence of HNTs on the water saturation. HNTs contribute to increased hydrophilicity with a change in the asymptotic water uptake from 0.95% (neat PLA) up to 1.67% (PLA with 9 wt % HNTs) and the effect of HNTs on disintegration in controlled compost soil has been carried out to see the influence of HNTs on this process, which is a slight delay on it. These PLA-HNT composites show good balanced properties and could represent an interesting solution to develop active materials.