Miracle-dz/UniFAR-FTUs huggingface dataset:
Adjustment Criteria for Generalizing Experimental Findings

Generalizing causal effects from a controlled experiment to settings beyond the particular study population is arguably one of the central tasks found in empirical circles. While a proper design and careful execution of the experiment would support, under mild conditions, the validity of inferences about the population in which the experiment was conducted, two challenges make the extrapolation step to different populations somewhat involved, namely, transportability and sampling selection bias. The former is concerned with disparities in the distributions and causal mechanisms between the domain (i.e., settings, population, environment) where the experiment is conducted and where the inferences are intended; the latter with distortions in the sample’s proportions due to preferential selection of units into the study. In this paper, we investigate the assumptions and machinery necessary for using covariate adjustment to correct for the biases generated by both of these problems, and generalize experimental data to infer causal effects in a new domain. We derive complete graphical conditions to determine if a set of covariates is admissible for adjustment in this new setting. Building on the graphical characterization, we develop an efficient algorithm that enumerates all possible admissible sets with polytime delay guarantee; this can be useful for when some variables are preferred over the others due to different costs or amenability to measurement.
DeepPerf: Performance Prediction for Configurable Software with Deep Sparse Neural Network

Many software systems provide users with a set of configuration options and different configurations may lead to different runtime performance of the system. As the combination of configurations could be exponential, it is difficult to exhaustively deploy and measure system performance under all possible configurations. Recently, several learning methods have been proposed to build a performance prediction model based on performance data collected from a small sample of configurations, and then use the model to predict system performance under a new configuration. In this paper, we propose a novel approach to model highly configurable software system using a deep feedforward neural network (FNN) combined with a sparsity regularization technique, e.g. the L1 regularization. Besides, we also design a practical search strategy for automatically tuning the network hyperparameters efficiently. Our method, called DeepPerf, can predict performance values of highly configurable software systems with binary and/or numeric configuration options at much higher prediction accuracy with less training data than the state-of-the art approaches. Experimental results on eleven public real-world datasets confirm the effectiveness of our approach.
Outcomes in patients treated with ticagrelor versus clopidogrel after acute myocardial infarction stratified by renal function

Objectives We aimed to analyse outcomes of ticagrelor and clopidogrel stratified by estimated glomerular filtration rate (eGFR) in a large unselected cohort of patients with acute myocardial infarction (MI). Methods We used follow-up data in MI survivors discharged on ticagrelor or clopidogrel enrolled in the Swedish Web-System for Enhancement and Development of Evidence-Based Care in Heart Disease Evaluated According to Recommended Therapies registry. The association between ticagrelor versus clopidogrel and the primary composite outcome of death, MI or stroke and the secondary outcome rehospitalisation with bleeding diagnosis at 1 year, was studied using adjusted Cox proportional hazards models, stratifying after eGFR levels. Results In total, 45 206 patients with MI discharged on clopidogrel (n=33 472) or ticagrelor (n=11 734) were included. The unadjusted 1-year event rate for the composite endpoint of death, MI or stroke was 7.0%, 18.0% and 48.0% for ticagrelor treatment and 11.0%, 33.0% and 64.0% for clopidogrel treatment in patients with eGFR>60 (n=33 668), eGFR30–60 (n=9803) and eGFR<30 (n=1735), respectively. After adjustment, ticagrelor as compared with clopidogrel was associated with a lower 1-year risk of the composite outcome (eGFR>60: HR 0.87, 95% CI 0.76 to 99, eGFR30–60: 0.82 (0.70 to 0.97), eGFR<30: 0.95 (0.69 to 1.29), P for interaction=0.55) and a higher risk of bleeding (eGFR>60: HR 1.10, 95% CI 0.90 to 1.35, eGFR30–60: 1.13 (0.84 to 1.51), eGFR<30: 1.79 (1.00 to 3.21), P for interaction=0.30) across the eGFR strata. Conclusions Treatment with ticagrelor as compared with clopidogrel in patients with MI was associated with lower risk for the composite of death, MI or stroke and a higher bleeding risk across all strata of eGFR. Of caution, bleeding events were more abundant in patients with eGFR<30.
Development and Deployment of a Large-Scale Dialog-based Intelligent Tutoring System

There are significant challenges involved in the design and implementation of a dialog-based tutoring system (DBT) ranging from domain engineering to natural language classification and eventually instantiating an adaptive, personalized dialog strategy. These issues are magnified when implementing such a system at scale and across domains. In this paper, we describe and reflect on the design, methods, decisions and assessments that led to the successful deployment of our AI driven DBT currently being used by several hundreds of college level students for practice and self-regulated study in diverse subjects like Sociology, Communications, and American Government.
Structural Test Coverage Criteria for Deep Neural Networks

Deep Neural Networks (DNNs) have a wide range of applications, and software employing them must be thoroughly tested, especially in safety-critical domains. However, traditional software test coverage metrics cannot be applied directly to DNNs. In this paper, inspired by the MC/DC coverage criterion, we propose four novel test criteria that are tailored to structural features of DNNs and their semantics. We validate the criteria by demonstrating that the generated test inputs, guided by our coverage criteria, are able to capture undesirable behaviours in DNNs. Test cases are generated using both a symbolic approach and a gradient-based heuristic. Our experiments are conducted on state-of-the-art DNNs, obtained using the MNIST and ImageNet datasets.
Water Toxicity Monitoring using Optical Oxygen Sensing and Respirometry

Approximately one third of available freshwater is currently used for agricultural, industrial or domestic purposes. This results in contamination of the water with a wide range of pollutants originating from ~300 million tons of compounds used in industrial and consumer products, ~140 million tons of fertilizers, several million tons of pesticides, 0.4 million tons from oil and gasoline spillages (1). To tackle the emerging threat of contamination and depletion of freshwater stocks, large initiatives such as the EU Water Framework Directive (WFD) (2) have been established. The WFD is concerned with “scope of water protection to include all waters, to set clear objectives in order that a “good status” be achieved. ” Successful realization of such projects, and of the other environmental monitoring tasks, is linked to the availability of techniques for detailed toxicological assessment, screening and monitoring of large number of chemical and environmental samples, plus validation and wide deployment of such techniques. Conventional toxicity tests with higher animal models such as rodents or primates based on the determination of lethal doses of toxicants (3) have limited use, due to their ethical constrains, low speed and high costs. Other systems include bioluminescent test for the presence of toxic compounds using freeze dried luminescent bacteria Vibrio fischeri (formerly called Photobacterium phosphoreum (4)) found in the marine environments (5) and functioning via an endogenous flavin monooxygenase enzyme luciferase. V. fischeri provided the basis for several commercial kits such as Microtox® (Azur Environmental, Carlsbad, CA), Mutatox® (with dark mutant of V.fischeri) (6), Deltatox® (portable, without temperature control), which have been extensively validated (7, 8) and accepted as a standard method by International Standard Organization (ISO) (9). Although providing good sensitivity, short assay time and simplicity, these tests are limited to just one strain of simple prokaryotic test organism and to samples that do not interfere with luminescent measurements. Samples that are turbid, absorb light or quench luminescent reaction can interfere the assay and cause measurement problems and invalid results. The need to find alternatives to expensive, space, time and labour consuming toxicity tests using aquatic and terrestrial species has led to the development of alternative methods. Thus, ethical (10) and regulatory issues (11) are favouring the use of animal models such as bacteria (12), small vertebrates (zebrafish Danio rerio) (13), invertebrates (the fruit fly Drosophila melanogaster (14), and brine shrimp Artemia salina (15). Daphnids, particularly D.
UAV and Ground Image-Based Phenotyping: A Proof of Concept with Durum Wheat

Climate change is one of the primary culprits behind the restraint in the increase of cereal crop yields. In order to address its effects, effort has been focused on understanding the interaction between genotypic performance and the environment. Recent advances in unmanned aerial vehicles (UAV) have enabled the assembly of imaging sensors into precision aerial phenotyping platforms, so that a large number of plots can be screened effectively and rapidly. However, ground evaluations may still be an alternative in terms of cost and resolution. We compared the performance of red–green–blue (RGB), multispectral, and thermal data of individual plots captured from the ground and taken from a UAV, to assess genotypic differences in yield. Our results showed that crop vigor, together with the quantity and duration of green biomass that contributed to grain filling, were critical phenotypic traits for the selection of germplasm that is better adapted to present and future Mediterranean conditions. In this sense, the use of RGB images is presented as a powerful and low-cost approach for assessing crop performance. For example, broad sense heritability for some RGB indices was clearly higher than that of grain yield in the support irrigation (four times), rainfed (by 50%), and late planting (10%). Moreover, there wasn’t any significant effect from platform proximity (distance between the sensor and crop canopy) on the vegetation indexes, and both ground and aerial measurements performed similarly in assessing yield.
Optimizing Regression Testing with Functional Flow Block Reliability Diagram

Model based testing techniques are widely used to generate tests from models and their specifications. Regression Testing is critical in evolving software systems. It implies that Software undergoes continuous integration and subsequent testing of modules and components to develop a final release of the software. Recent research on test automation have revealed challenges in testing such as enormous test cases generated that are either redundant or irrelevant tests. This prevents the critical parts of code from testing and leave faults uncovered. Manual testing also slows the process and makes exhaustive testing for quality difficult. This paper proposes an approach to generate regression tests from model synthesized from UML interaction diagrams and also prioritize tests. The algorithm generate test suites from the proposed intermediate model. The reliability analysis is performed on blocks and the results govern the prioritization of tests.
Graph Embedding Based Familial Analysis of Android Malware using Unsupervised Learning

The rapid growth of Android malware has posed severe security threats to smartphone users. On the basis of the familial trait of Android malware observed by previous work, the familial analysis is a promising way to help analysts better focus on the commonalities of malware samples within the same families, thus reducing the analytical workload and accelerating malware analysis. The majority of existing approaches rely on supervised learning and face three main challenges, i.e., low accuracy, low efficiency, and the lack of labeled dataset. To address these challenges, we first construct a fine-grained behavior model by abstracting the program semantics into a set of subgraphs. Then, we propose SRA, a novel feature that depicts the similarity relationships between the Structural Roles of sensitive API call nodes in subgraphs. An SRA is obtained based on graph embedding techniques and represented as a vector, thus we can effectively reduce the high complexity of graph matching. After that, instead of training a classifier with labeled samples, we construct malware link network based on SRAs and apply community detection algorithms on it to group the unlabeled samples into groups. We implement these ideas in a system called GefDroid that performs Graph embedding based familial analysis of AnDroid malware using unsupervised learning. Moreover, we conduct extensive experiments to evaluate GefDroid on three datasets with ground truth. The results show that GefDroid can achieve high agreements (0.707-0.883 in term of NMI) between the clustering results and the ground truth. Furthermore, GefDroid requires only linear run-time overhead and takes around 8.6s to analyze a sample on average, which is considerably faster than the previous work.
Safety-relevant V2X Beaconing in Realistic and Scalable Heterogeneous Radio Propagation Fading Channels

Performance evaluations for heterogeneous communication technologies in the area of V2X safety applications for either improvement, comparison or combination purposes are in general focusing on the realistic representation of the upper communication stack layers, but therefore - often for the sake of simplicity - reducing the radio propagation channel to a maximum range model. The impact and hence the importance to model the environment dependent propagation effects in a representative manner has already been stressed in the literature several times - but separately for ad-hoc or cellular systems and not under the consideration of V2X safety-beaconing applications. By combining a realistic heterogeneous radio propagation channel model with a state-of-the-art V2X communication stack, a representative performance comparison of safety-relevant beaconing applications for 802.11p single-hop broadcast (SHB) and LTE Geocast can be conducted. Our simulation results show that the effects caused by the radio propagation channel cannot be neglected as they significantly impact key communication performance metrics such as channel gain, packet error ratio (PER) and channel load, where we primarily focus on the latter one to give further research directions for an efficient dissemination of safety-relevant V2X beacons.
How to Avoid Sentences Spelling Boring? Towards a Neural Approach to Unsupervised Metaphor Generation

Metaphor generation attempts to replicate human creativity with language, which is an attractive but challengeable text generation task. Previous efforts mainly focus on template-based or rule-based methods and result in a lack of linguistic subtlety. In order to create novel metaphors, we propose a neural approach to metaphor generation and explore the shared inferential structure of a metaphorical usage and a literal usage of a verb. Our approach does not require any manually annotated metaphors for training. We extract the metaphorically used verbs with their metaphorical senses in an unsupervised way and train a neural language model from wiki corpus. Then we generate metaphors conveying the assigned metaphorical senses with an improved decoding algorithm. Automatic metrics and human evaluations demonstrate that our approach can generate metaphors with good readability and creativity.
Submodular Cost Submodular Cover with an Approximate Oracle

In this work, we study the Submodular Cost Submodular Cover problem, which is to minimize the submodular cost required to ensure that the submodular benefit function exceeds a given threshold. Existing approximation ratios for the greedy algorithm assume a value oracle to the benefit function. However, access to a value oracle is not a realistic assumption for many applications of this problem, where the benefit function is difficult to compute. We present two incomparable approximation ratios for this problem with an approximate value oracle and demonstrate that the ratios take on empirically relevant values through a case study with the Influence Threshold problem in online social networks.
Phylogenic Multi-Lingual Dependency Parsing

Languages evolve and diverge over time. Their evolutionary history is often depicted in the shape of a phylogenetic tree. Assuming parsing models are representations of their languages grammars, their evolution should follow a structure similar to that of the phylogenetic tree. In this paper, drawing inspiration from multi-task learning, we make use of the phylogenetic tree to guide the learning of multi-lingual dependency parsers leveraging languages structural similarities. Experiments on data from the Universal Dependency project show that phylogenetic training is beneficial to low resourced languages and to well furnished languages families. As a side product of phylogenetic training, our model is able to perform zero-shot parsing of previously unseen languages.
Exploiting Noisy Data in Distant Supervision Relation Classification

Distant supervision has obtained great progress on relation classification task. However, it still suffers from noisy labeling problem. Different from previous works that underutilize noisy data which inherently characterize the property of classification, in this paper, we propose RCEND, a novel framework to enhance Relation Classification by Exploiting Noisy Data. First, an instance discriminator with reinforcement learning is designed to split the noisy data into correctly labeled data and incorrectly labeled data. Second, we learn a robust relation classifier in semi-supervised learning way, whereby the correctly and incorrectly labeled data are treated as labeled and unlabeled data respectively. The experimental results show that our method outperforms the state-of-the-art models.
Online Distilling from Checkpoints for Neural Machine Translation

Current predominant neural machine translation (NMT) models often have a deep structure with large amounts of parameters, making these models hard to train and easily suffering from over-fitting. A common practice is to utilize a validation set to evaluate the training process and select the best checkpoint. Average and ensemble techniques on checkpoints can lead to further performance improvement. However, as these methods do not affect the training process, the system performance is restricted to the checkpoints generated in original training procedure. In contrast, we propose an online knowledge distillation method. Our method on-the-fly generates a teacher model from checkpoints, guiding the training process to obtain better performance. Experiments on several datasets and language pairs show steady improvement over a strong self-attention-based baseline system. We also provide analysis on data-limited setting against over-fitting. Furthermore, our method leads to an improvement in a machine reading experiment as well.
Sequential Attention with Keyword Mask Model for Community-based Question Answering

In Community-based Question Answering system(CQA), Answer Selection(AS) is a critical task, which focuses on finding a suitable answer within a list of candidate answers. For neural network models, the key issue is how to model the representations of QA text pairs and calculate the interactions between them. We propose a Sequential Attention with Keyword Mask model(SAKM) for CQA to imitate human reading behavior. Question and answer text regard each other as context within keyword-mask attention when encoding the representations, and repeat multiple times(hops) in a sequential style. So the QA pairs capture features and information from both question text and answer text, interacting and improving vector representations iteratively through hops. The flexibility of the model allows to extract meaningful keywords from the sentences and enhance diverse mutual information. We perform on answer selection tasks and multi-level answer ranking tasks. Experiment results demonstrate the superiority of our proposed model on community-based QA datasets.
Sequential Facility Location: Approximate Submodularity and Greedy Algorithm

We develop and analyze a novel utility function and a fast optimization algorithm for subset selection in sequential data that incorporates the dynamic model of data. We propose a cardinalityconstrained sequential facility location function that finds a fixed number of representatives, where the sequence of representatives is compatible with the dynamic model and well encodes the data. As maximizing this new objective function is NPhard, we develop a fast greedy algorithm based on submodular maximization. Unlike the conventional facility location, the computation of the marginal gain in our case cannot be done by operations on each item independently. We exploit the sequential structure of the problem and develop an efficient dynamic programming-based algorithm that computes the marginal gain exactly. We investigate conditions on the dynamic model, under which our utility function is (ε-approximately) submodualr, hence, the greedy algorithm comes with performance guarantees. By experiments on synthetic data and the problem of procedure learning from instructional videos, we show that our framework significantly improves the computational time, achieves better objective function values and obtains more coherent summaries.
CPD: Crowd-based Pothole Detection

Potholes and other damages of the road surface constitute a problem being as old as roads are. Still, potholes are widespread and affect the driving comfort of passengers as well as road safety. If one knew about the exact locations of potholes, it would be possible to repair them selectively or at least to warn drivers about them up to their repair. However, both scenarios require their detection and localization. For this purpose, we propose a crowd-based approach that enables as many of the vehicles already driving on our roads as possible to detect potholes and report them to a centralized back-end application. Whereas each single vehicle provides only limited and imprecise information, it is possible to determine these information more precisely when collecting them at a large scale. These more exact information may, for example, be used to warn following vehicles about potholes lying ahead to increase overall safety and comfort. In this work, this idea is examined and an offline executable version of the desired system is implemented. Additionally, the approach is evaluated with a large database of real-world sensor readings from a testing fleet and therefore its feasibility is proved. Our investigation shows that the suggested CPD approach is promising to bring customers a benefit by an improved driving comfort and higher road safety.