Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Nonlinear Mixup with Distance-based Sample Selection

Data augmentation with mixup has shown to be effective on the NLP tasks. Although its great success, the mixup still has shortcomings. First, vanilla mixup randomly selects one sample to generate the mixup sample for a given sample. It remains unclear how to best choose the input samples for the mixup. Second, linear interpolation limits the space of synthetic data and its regularization effect. In this paper, we propose the dynamic nonlinear mixup with distance-based sample selection, which not only generates multiple sample pairs based on the distance between each sample but also enlarges the space of synthetic samples. Specifically, we compute the distance between each input data by cosine similarity and select multiple samples for a given sample. Then we use the dynamic nonlinear mixup to fuse sample pairs. It does not use a linear, scalar mixing strategy, but a nonlinear interpolation strategy, where the mixing strategy is adaptively updated for the input and label pairs. Experiments on the multiple public datasets demonstrate that dynamic nonlinear mixup outperforms state-of-the-art methods.
An Emotion-based Korean Multimodal Empathetic Dialogue System

We propose a Korean multimodal dialogue system targeting emotion-based empathetic dialogues because most research in this field has been conducted in a few languages such as English and Japanese and in certain circumstances. Our dialogue system consists of an emotion detector, an empathetic response generator, a monitoring interface, a voice activity detector, a speech recognizer, a speech synthesizer, a gesture classification, and several controllers to provide both multimodality and empathy during a conversation between a human and a machine. For comparisons across visual influence on users, our dialogue system contains two versions of the user interface, a cat face-based user interface and an avatar-based user interface. We evaluated our dialogue system by investigating the dialogues in text and the average mean opinion scores under three different visual conditions, no visual, the cat face-based, and the avatar-based expressions. The experimental results stand for the importance of adequate visual expressions according to user utterances.
CausalQA: A Benchmark for Causal Question Answering

At least 5% of questions submitted to search engines ask about cause-effect relationships in some way. To support the development of tailored approaches that can answer such questions, we construct Webis-CausalQA-22, a benchmark corpus of 1.1 million causal questions with answers. We distinguish different types of causal questions using a novel typology derived from a data-driven, manual analysis of questions from ten large question answering (QA) datasets. Using high-precision lexical rules, we extract causal questions of each type from these datasets to create our corpus. As an initial baseline, the state-of-the-art QA model UnifiedQA achieves a ROUGE-L F1 score of 0.48 on our new benchmark.
WordNet-QU: Development of a Lexical Database for Quechua Varieties

In the effort to minimize the risk of extinction of a language, linguistic resources are fundamental. Quechua, a low-resource language from South America, is a language spoken by millions but, despite several efforts in the past, still lacks the resources necessary to build high-performance computational systems. In this article, we present WordNet-QU which signifies the inclusion of Quechua in a well-known lexical database called wordnet. We propose WordNet-QU to be included as an extension to wordnet after demonstrating a manually-curated collection of multiple digital resources for lexical use in Quechua. Our work uses the synset alignment algorithm to compare Quechua to its geographically nearest high-resource language, Spanish. Altogether, we propose a total of 28,582 unique synset IDs divided according to region like so: 20510 for Southern Quechua, 5993 for Central Quechua, 1121 for Northern Quechua, and 958 for Amazonian Quechua.
The Role of Context and Uncertainty in Shallow Discourse Parsing

Discourse parsing has proven to be useful for a number of NLP tasks that require complex reasoning. However, over a decade since the advent of the Penn Discourse Treebank, predicting implicit discourse relations in text remains challenging. There are several possible reasons for this, and we hypothesize that models should be exposed to more context as it plays an important role in accurate human annotation; meanwhile adding uncertainty measures can improve model accuracy and calibration. To thoroughly investigate this phenomenon, we perform a series of experiments to determine 1) the effects of context on human judgments, and 2) the effect of quantifying uncertainty with annotator confidence ratings on model accuracy and calibration (which we measure using the Brier score (Brier et al, 1950)). We find that including annotator accuracy and confidence improves model accuracy, and incorporating confidence in the model’s temperature function can lead to models with significantly better-calibrated confidence measures. We also find some insightful qualitative results regarding human and model behavior on these datasets.
Measuring Robustness for NLP

The quality of Natural Language Processing (NLP) models is typically measured by the accuracy or error rate of a predefined test set. Because the evaluation and optimization of these measures are narrowed down to a specific domain like news and cannot be generalized to other domains like Twitter, we often observe that a system reported with human parity results generates surprising errors in real-life use scenarios. We address this weakness with a new approach that uses an NLP quality measure based on robustness. Unlike previous work that has defined robustness using Minimax to bound worst cases, we measure robustness based on the consistency of cross-domain accuracy and introduce the coefficient of variation and (epsilon, gamma)-Robustness. Our measures demonstrate higher agreements with human evaluation than accuracy scores like BLEU on ranking Machine Translation (MT) systems. Our experiments of sentiment analysis and MT tasks show that incorporating our robustness measures into learning objectives significantly enhances the final NLP prediction accuracy over various domains, such as biomedical and social media.
Are People Located in the Places They Mention in Their Tweets? A Multimodal Approach

This paper introduces the problem of determining whether people are located in the places they mention in their tweets. In particular, we investigate the role of text and images to solve this challenging problem. We present a new corpus of tweets that contain both text and images. Our analyses show that this problem is multimodal at its core: human judgments depend on whether annotators have access to the text, the image, or both. Experimental results show that a neural architecture that combines both modalities yields better results. We also conduct an error analysis to provide insights into why and when each modality is beneficial.
Accuracy meets Diversity in a News Recommender System

News recommender systems face certain challenges. These challenges arise due to evolving users’ preferences over dynamically created news articles. The diversity is necessary for a news recommender system to expose users to a variety of information. We propose a deep neural network based on a two-tower architecture that learns news representation through a news item tower and users’ representations through a query tower. We customize an augmented vector for each query and news item to introduce information interaction between the two towers. We introduce diversity in the proposed architecture by considering a category loss function that aligns items’ representation of uneven news categories. Experimental results on two news datasets reveal that our proposed architecture is more effective compared to the state-of-the-art methods and achieves a balance between accuracy and diversity.
Coordination Generation via Synchronized Text-Infilling

Generating synthetic data for supervised learning from large-scale pre-trained language models has enhanced performances across several NLP tasks, especially in low-resource scenarios. In particular, many studies of data augmentation employ masked language models to replace words with other words in a sentence. However, most of them are evaluated on sentence classification tasks and cannot immediately be applied to tasks related to the sentence structure. In this paper, we propose a simple yet effective approach to generating sentences with a coordinate structure in which the boundaries of its conjuncts are explicitly specified. For a given span in a sentence, our method embeds a mask with a coordinating conjunction in two ways (”X and [mask]”, ”[mask] and X”) and forces masked language models to fill the two blanks with an identical text. To achieve this, we introduce decoding methods for BERT and T5 models with the constraint that predictions for different masks are synchronized. Furthermore, we develop a training framework that effectively selects synthetic examples for the supervised coordination disambiguation task. We demonstrate that our method produces promising coordination instances that provide gains for the task in low-resource settings.
Quantum metrology with a single spin-3/2 defect in silicon carbide

We show that implementations for quantum sensing with exceptional sensitivity and spatial resolution can be made using spin-$\frac{3}{2}$ semiconductor defect states. We illustrate this using the silicon monovacancy deep center in hexagonal SiC based on our rigorous derivation of this defect's ground state and of its electronic and optical properties. For a single ${\mathrm{V}}_{\mathrm{Si}}^{\ensuremath{-}}$ defect, we obtain magnetic field sensitivities capable of detecting individual nuclear magnetic moments. We also show that its zero-field splitting has an exceptional strain and temperature sensitivity within the technologically desirable near-infrared window of biological systems. The concepts and sensing schemes developed here are applicable to other point defects with half spin multiplet ($S\ensuremath{\ge}\frac{3}{2}$) configurations.
Data Augmentation for Few-Shot Knowledge Graph Completion from Hierarchical Perspective

Few-shot knowledge graph completion (FKGC) has become a new research focus in the field of knowledge graphs in recent years, which aims to predict the missing links for relations that only have a few associative triples. Existing models attempt to solve the problem via learning entity and relation representations. However, the limited training data severely hinders the performance of existing models. To this end, we propose to solve the FKGC problem with the data augmentation technique. Specifically, we perform data augmentation from two perspectives, i.e., inter-task view and intra-task view. The former generates new tasks for FKGC, while the latter enriches the support or query set for an individual task. It is worth noting that the proposed framework can be applied to a number of existing FKGC models. Experimental evaluation on two public datasets indicates our model is capable of achieving substantial improvements over baselines.
Recommending the Duration of Stay in Personalized Travel Recommender Systems

The main focus of recommender systems research has been recommending fitting items to the users. However, in some domains, not only which item but also the quantity the target user should consume could be part of the recommendation. In this work, we tackle the under-researched problem of recommending the duration of stay in the domain of destination recommendation. Using two data sets, one based on hotel bookings and the other on mobility derived from geotagged Tweets, we perform extensive feature engineering with unsupervised learning to discover types of users and graph embeddings of the cities. In our experiments, we compare the performance of supervised learning algorithms with varying features to statistical baselines for predicting the duration of stay at a destination. The results underline the task’s difficulty: we obtain the best results for the hotel bookings data set using personalized mobility embeddings with CatBoost. At the same time, the simple strategy of recommending the mode duration of all users is competitive in the noisy Twitter data set.
User Story Quality Assessment Based on Multi-dimensional Perspective: A Preliminary Framework

User stories are widely adopted in agile development. Generally, user stories are written by users or customers to describe their needs for the to-be software system. User stories are written in structured natural languages, which may suffer from ambiguity and inconsistency. These user story defects make it difficult for agile development teams to understand requirements and make development plans. User story quality assessment has been investigated for years to mitigate this problem. Most of the current works focus on grammatical defect checking of a user story, but lack semantic defect checking between user stories. In this paper, we propose a multi-dimensional user story quality assessment framework, which is an ongoing study. Specifically, our proposal considers three dimensions, including completeness, testability, and consistency. 11 quality criteria are used to observe user story quality. Our method combines typical NLP techniques and a novel iStar modeling-based analysis to discover defects in user stories from the three dimensions.
Evaluation of iStar 2.0 Models using Linear Programming

Goal models are an effective mechanism for elicitation and analysis in early Requirements Engineering, improving communication with stakeholders. However, in real scenarios, goal models become a complex network of actors and evaluating them can be difficult. Some tools assess the level of achievement of actors and intentional elements, which alleviate this limitation. However, using tools to evaluate models has some limitations, like the difficulty of integrating goal models with other models, the training required to use these tools, and the impossibility of assessing models when tools become outdated. In this work, we propose a formalization of iStar 2.0 models in the form of linear constraints that makes it possible to evaluate and optimize models using linear programming. This formalization allows the evaluation of goal models on existing tools and facilitates integration with other approaches.
Towards Goal-based Generation of Reinforcement Learning Domain Simulations

Reinforcement learning (RL) is a much studied branch of machine learning and one in which substantial progress has taken place over the past few years. In RL, intelligent agents repeatedly interact with their environment and learn from the consequences of their actions. A key to effective RL is often the presence of a simulated environment that mimics the one that the agent is supposed to be optimized against. Such simulators allow for great numbers of training iterations in a cost-effective manner and without affecting the real environment. A systematic modeling and design process that allows efficient development of maintainable and comprehensible simulators can, hence, be beneficial for effective RL. We propose an approach for model-driven generation of RL environment simulations with discrete action spaces using goal models. The proposal utilizes earlier work for model-driven development of decision-theoretic action theories, whereby the standard iStar 2.0 notation is extended to include preconditions, stochastic effects, and reward modeling. Models in the extended notation can be translated into a formal specification for model-based reasoning based on Markov Decision Processes (MDPs). To also allow for model-free RL we introduce a module that queries the action-theoretic, stochastic action and reward structure aspects of the generated formal specification in order to guide episodic simulations of the modeled domain. The module is wrapped by a popular framework for building RL training and testing environments, making it accessible by popular RL agent frameworks.
Declarative Smart Contract Testing by Domain Experts

We present a novel approach to testing Answer Set Programs (ASPs) in the context of a system designed to enable a domain expert to write, test and deploy legal smart contracts. Common practice is to use compiled imperative languages to write smart contracts which limits what can be achieved, and provides a clear opportunity for an approach that empowers domain experts. Our system supports the construction of declarative smart contracts by domain experts with the help of a smart user interface that communicates visually and verbally using domain expert level concepts. It captures the ontology and legal logic of a legal document in a model automatically constructed as an ASP program. This paper discusses a complementary approach to testing, achieved by structuring ASP rules and splitting testing into model validation and program verification. Holding ontology information about the application domain allows the approach to be highly automated, so that we achieve automatic discovery of all hypothetical scenarios and exhaustive testing for each rule. Our approach places the domain expert in a tight learning loop where the behaviour of each rule scenario can be understood from the visual and verbal feedback, and rule corrections can be made immediately as required.
BALANCING THE COSMIC ENERGY BUDGET: THE COSMIC X-RAY BACKGROUND, BLAZARS, AND THE COMPTON THICK ACTIVE GALACTIC NUCLEUS FRACTION

At energies ≳2 keV, active galactic nuclei (AGNs) are the source of the cosmic X-ray background (CXB). For AGN population synthesis models to replicate the peak region of the CXB (∼30 keV), a highly obscured and therefore nearly invisible class of AGN, known as Compton thick (CT) AGN, must be assumed to contribute nearly a third of the CXB. In order to constrain the CT fraction of AGNs and the CT number density we consider several hard X-ray AGN luminosity functions and the contribution of blazars to the CXB. Following the unified scheme, the radio AGN luminosity function is relativistically beamed to create a radio blazar luminosity function. An average blazar spectral energy density model is created to transform radio luminosity to X-ray luminosity. We find the blazar contribution to the CXB to be 12% in the 0.5–2 keV band, 7.4% in the 2–10 keV band, 8.9% in the 15–55 keV band, and 100% in the MeV region. When blazars are included in CXB synthesis models, CT AGNs are predicted to be roughly one-third of obscured AGNs, in contrast to the prediction of one half if blazars are not considered. Our model implies a BL Lac X-ray duty cycle of ∼13%, consistent with the concept of intermittent jet activity in low power radio galaxies.
RUBEN: A Rule Engine Benchmarking Framework

Knowledge graphs have become an essential technology for powering intelligent applications. Enriching the knowledge within knowledge graphs based on use case-specific requirements can be achieved using inference rules. Applying rules on knowledge graphs requires performant and scalable rule engines. Analyzing rule engines based on test cases covering various characteristics is crucial for identifying the optimal rule engine for a given use case. To this end, we present RUBEN: A Rule Engine Benchmarking Framework providing interfaces to benchmark rule engines based on given test cases. Besides a description of RUBEN’s interfaces, we present a selection of test cases adopted from the OpenRuleBench , and an evaluation of four rule engines. In the future, we aim to benchmark existing rule engines regularly and encourage the community to propose new test cases and include other rule engines. ,
This paper is included in the Proceedings of the 16th USENIX Symposium on Operating Systems Design and Implementation. Microsecond-scale Preemption for Concurrent GPU-accelerated DNN Inferences

Many intelligent applications like autonomous driving and virtual reality require running both latency-critical and best-effort DNN inference tasks to achieve both real time and work conserving on GPU. However, commodity GPUs lack efﬁcient preemptive scheduling support and state-of-the-art approaches either have to monopolize GPU or let the real-time tasks to wait for best-effort tasks to complete, which causes low utilization or high latency, or both. This paper presents R EEF , the ﬁrst GPU-accelerated DNN inference serving system that enables microsecond-scale kernel preemption and controlled concurrent execution in GPU scheduling. R EEF is novel in two ways. First, based on the observation that DNN inference kernels as mostly idempotent, R EEF devises a reset-based preemption scheme that launches a real-time kernel on the GPU by proactively killing and restoring best-effort kernels at microsecond-scale. Second, since DNN inference kernels have varied parallelism and predictable latency, R EEF proposes a dynamic kernel padding mechanism that dynamically pads the real-time kernel with appropriate best-effort kernels to fully utilize the GPU with negligible overhead. Evaluation using a new DNN inference serving benchmark (DISB) with diverse workloads and a real-world trace on an AMD GPU shows that R EEF only incurs less than 2% overhead in the end-to-end latency for real-time tasks but increases the overall throughput by up to 7.7 × , compared to dedicating the GPU to real-time tasks. To demonstrate
zIO: Accelerating IO-Intensive Applications with Transparent Zero-Copy IO

WepresentzIO,atransparentzero-copyIOmechanismforun-modifiedIO-intensiveapplications.zIOtracksIOdatathroughtheapplication,eliminatingcopiesthatareunnecessarywhilemaintainingdataconsistency.Applicationsoftenmodifyonlyapartofthedatatheypro-cess.zIOleveragesthisinsightandinterposesonIOstackandstandardlibrarymemorycopycallstotrackIOdataandeliminateunnecessarycopies. Instead,intermediatedatalo-cationsareunmapped,allowingzIOtointerceptandresolve any access via page faults to maintain data consistency. To avoid harming application performance in situations where data tracking overhead is high, zIO’s tracking policy decides on a per IO basis when to eliminate copies. Further, we demonstrate how to use zIO to achieve optimistic network receiver persistence for applications storing data from the network in non-volatile memory (NVM). By mapping socket receive buffers in NVM and leveraging kernel-bypass IO, we can rely on zIO to transparently eliminate all copies from the network, through the application, to storage. We implement zIO as a user-space library. On top of kernel IO stacks, zIO eliminates application-level IO copies. We also integrate zIO with kernel-bypass IO stacks, where it can additionally eliminate copies incurred by the IO stack APIs and enable optimistic network receiver persistence. We evaluate zIO with IO-intensive applications, such as Redis, Icecast, and MongoDB.zIOimprovesapplicationthroughputbyupto1. 8 × with Linux and by up to 2.5 × with kernel-bypass IO stacks and optimistic network receiver persistence. Compared to common uses of zero-copy IO stack APIs, such as memory mapped files, zIO can improve performance by up to 17% due to reduced TLB shootdown overhead.