Miracle-dz/UniFAR-FTUs huggingface dataset:
Text Augmentation Using Dataset Reconstruction for Low-Resource Classification

In the deployment of real-world text classiﬁcation models, label scarcity is a common problem. As the number of classes increases, this problem becomes even more complex. One way to address this problem is by applying text augmentation methods. One of the more prominent methods involves using the text-generation capabilities of language models. We propose Text AUgmenta-tion by Dataset Reconstruction (TAU-DR), a novel method of data augmentation for text classiﬁcation. We conduct experiments on several multi-class datasets, showing that our approach improves the current state-of-the-art techniques for data augmentation.
Making Pre-trained Language Models both Task-solvers and Self-calibrators

Pre-trained language models (PLMs) serve as backbones for various real-world systems. For high-stake applications, it's equally essential to have reasonable confidence estimations in predictions. While the vanilla confidence scores of PLMs can already be effectively utilized, PLMs consistently become overconfident in their wrong predictions, which is not desirable in practice. Previous work shows that introducing an extra calibration task can mitigate this issue. The basic idea involves acquiring additional data to train models in predicting the confidence of their initial predictions. However, it only demonstrates the feasibility of this kind of method, assuming that there are abundant extra available samples for the introduced calibration task. In this work, we consider the practical scenario that we need to effectively utilize training samples to make PLMs both task-solvers and self-calibrators. Three challenges are presented, including limited training samples, data imbalance, and distribution shifts. We first conduct pilot experiments to quantify various decisive factors in the calibration task. Based on the empirical analysis results, we propose a training algorithm LM-TOAST to tackle the challenges. Experimental results show that LM-TOAST can effectively utilize the training data to make PLMs have reasonable confidence estimations while maintaining the original task performance. Further, we consider three downstream applications, namely selective classification, adversarial defense, and model cascading, to show the practical usefulness of LM-TOAST. The code will be made public at \url{https://github.com/Yangyi-Chen/LM-TOAST}.
End-to-end Aspect-based Sentiment Analysis with Combinatory Categorial Grammar

End-to-end Aspect-based Sentiment Analysis (EASA) is a natural language processing (NLP) task that involves extracting aspect terms and identifying the sentiments for them, which provides a fine-grained level of text analysis and thus requires a deep understanding of the running text. Many previous studies leverage advanced text encoders to extract context information and use syntactic information, e.g., the dependency structure of the input sentence, to improve the model performance. However, such models may reach a bottleneck since the dependency structure is not designed to provide semantic information of the text, which is also important for identifying the sentiment and thus leave room for further improvement. Considering that combinatory categorial grammar (CCG) is a formalism that expresses both syntactic and semantic information of a sentence, it has the potential to be beneficial to EASA. In this paper, we propose a novel approach to improve EASA with CCG supertags, which carry the syntactic and semantic information of the associated words and serve as the most important part of the CCG derivation. Specifically, our approach proposes a CCG su-pertag decoding process to learn the syntactic and semantic information carried by CCG su-pertags and use the information to guide the attention over the input words so as to identify important contextual information for EASA. Furthermore, a gate mechanism is used in incorporating the weighted contextual information into the backbone EASA decoding process. We evaluate our approach on three publicly available English datasets for EASA, and show that it outperforms strong baselines and achieves state-of-the-art results on all datasets. 1
Reasoning in Large Language Models Through Symbolic Math Word Problems

Large language models (LLMs) have revolutionized NLP by solving downstream tasks with little to no labeled data. Despite their versatile abilities, the larger question of their ability to reason remains ill-understood. This paper addresses reasoning in math word problems (MWPs) by studying symbolic versions of the numeric problems, since a symbolic expression is a"concise explanation"of the numeric answer. We create and use a symbolic version of the SVAMP dataset and find that GPT-3's davinci-002 model also has good zero-shot accuracy on symbolic MWPs. To evaluate the faithfulness of the model's reasoning, we go beyond accuracy and additionally evaluate the alignment between the final answer and the outputted reasoning, which correspond to numeric and symbolic answers respectively for MWPs. We explore a self-prompting approach to encourage the symbolic reasoning to align with the numeric answer, thus equipping the LLM with the ability to provide a concise and verifiable reasoning and making it more interpretable. Surprisingly, self-prompting also improves the symbolic accuracy to be higher than both the numeric and symbolic accuracies, thus providing an ensembling effect. The SVAMP_Sym dataset will be released for future research on symbolic math problems.
Towards Iterative Relational Algebra on the GPU

Iterative relational algebra (RA kernels in a fixed-point loop) enables bottom-up logic programming languages such as Dat-alog. Such declarative languages are attractive targets for high-performance implementations of relational data analytics in fields such as graph mining, program analysis, and social-media analytics. Language-level constructs are implemented via high-performance relational algebra primitives (e.g., projections, reorderings, and joins). Such primitives would appear a natural target for GPUs, obtaining high throughput on large datasets. However, state-of-the-art Datalog engines are still CPU-based, scaling best between 8–16 threads. While much has explored standalone RA operations on the GPU, relatively less work focuses on iterative RA, which exposes new challenges (e.g., deduplication and memory management). In this short paper, we present a GPU-based hash-join implementation, leveraging (a) a novel open-addressing-based hash table implementation, (b) operator fusing to optimize memory access and (c) two variant implementations of deduplication. To evaluate our work, we implement transitive closure using our hash-join-based CUDA library and compared its performance against cuDF (GPU-based) and Soufflé (CPU-based). We show favorable results against both, with gains up to 10 . 8 × against cuDF and 3 . 9 × against Soufflé.
FarReach: Write-back Caching in Programmable Switches

Skewed write-intensive key-value storage workloads are increasingly observed in modern data centers, yet they also incur server overloads due to load imbalance. Programmable switches provide viable solutions for realizing load-balanced caching on the I/O path, and hence implementing write-back caching in programmable switches is a natural direction to absorb frequent writes for high write performance. However, enabling in-switch write-back caching is non-trivial, as it not only is challenged by the strict programming rules and limited stateful memory of programmable switches, but also necessitates reliable protection against data loss due to switch failures. We propose FarReach, a new caching framework that supports fast, available, and reliable in-switch write-back caching. FarReach carefully co-designs both the control and data planes for cache management in programmable switches, so as to achieve high data-plane performance with lightweight control-plane management. Experiments on a Toﬁno switch testbed show that FarReach achieves a throughput gain of up to 6.6 × over a state-of-the-art in-switch caching approach under skewed write-intensive workloads.
ANALISIS CEMARAN MIKROBA PADA IKAN ASIN AIR TAWAR DI SAMARINDA

Penyakit melalui makanan marak terjadi belakangan ini, hal ini terjadi karena kontaminasi baik secara biologi ataupun kimia pada makanan. Ikan adalah salah satu jenis makanan yang besar kemungkinannya untuk mengalami kontaminasi secara biologi. Salah satu cara mencegah terjadinya kontaminasi, yaitu dengan pengawetan dengan pembuatan ikan asin. Penelitian ini dilakukan untuk mengetahui cemaran mikroba pada olahan ikan asin air tawar di Samarinda. Pengujian mikrobiologis dilakukan terhadap 3 sampel ikan asin air tawar yang diambil dari produsen ikan asin di Samarinda, meliputi pemeriksaan angka cemaran mikroba dengan metode Angka Lempeng Total (ALT) dan pemeriksaan mikrobiologis dengan Most Probable Number (MPN), serta pemeriksaan bakteri koliform dan bakteri pathogen seperti Escherichia coli dan Salmonella thyposa. Hasil penelitian menunjukkan bahwa dari 3 sampel ikan asin air tawar terdapat cemaran mikroba namun tidak melebihi batas Standar Nasional Indonesia.
Similarity-Based Content Scoring - A more Classroom-Suitable Alternative to Instance-Based Scoring?

Automatically scoring student answers is an important task that is usually solved using instance-based supervised learning. Recently, similarity-based scoring has been proposed as an alternative approach yielding similar performance. It has hypothetical advantages such as a lower need for annotated training data and better zero-shot performance, both of which are properties that would be highly beneficial when applying content scoring in a realistic classroom setting. In this paper we take a closer look at these alleged advantages by comparing different instance-based and similarity-based methods on multiple data sets in a number of learning curve experiments. We find that both the demand on data and cross-prompt performance is similar, thus not confirming the former two suggested advantages. The by default more straightforward possibility to give feed-back based on a similarity-based approach may thus tip the scales in favor of it, although future work is needed to explore this advantage in practice.
on Operating Systems Design and Implementation

Fully exploiting the computing power of an accelerator specialized for deep neural networks (DNNs) calls for the synergy between network and hardware architectures, but existing approaches partition a computational graph of DNN into multiple sub-graphs by abstracting away hardware architecture and assign resources to each sub-graph, not only producing redundant off-core data movements but also under-utilizing the hardware resources of a domain-speciﬁc architecture (DSA). This paper introduces a systematic approach for effectively scheduling DNN computational graphs on DSA platforms. By fully taking into account hardware architecture when partitioning a computational graph into coarse-grained sub-graphs, our work enables the synergy between network and hardware architectures, addressing several challenges of prior work: (1) it produces larger but fewer kernels, converting a large number of off-core data movements into on-core data exchanges; (2) it exploits the imbalanced memory usage distribution across DNN network architecture, better saturating the DSA memory hierarchy; (3) it enables across-layer instruction scheduling not studied before, further exploiting the parallelism across different specialized compute units. Results of seven DNN inference models on a DSA platform show that our work outperforms TVM and AStitch by 11.15 × and 6.16 × , respectively, and obtains throughput competitive to the vendor-crafted implementation. A case study on GPU also demonstrates that generating kernels for our sub-graphs can surpass CUTLASS with and without convolution fusion by 1.06 × and 1.23 × , respectively.
An Extensive Exploration of Back-Translation in 60 Languages

Back-translation is a data augmentation technique that has been shown to improve model quality through the creation of synthetic training bitext. Early studies showed the promise of the technique and follow on studies have produced additional refinements. We have undertaken a broad investigation using back-translation to train models from 60 languages into English; the majority of these languages are considered moderate-or low-resource languages. We observed consistent gains, though compared to prior work we saw conspicuous gains in quite a number of lower-resourced languages. We analyzed differences in translations between baseline and back-translation models, and observed many indications of improved translation quality. Translation of both rare and common terms is improved, and these improvements occur despite the less natural synthetic source-language text used in training.
Translation Pass-Through for Near-Native Paging Performance in VMs

Virtual machines (VMs) are used for consolidation, isolation, and provisioning in the cloud, but applications with large working sets are impacted by the overheads of memory address translation in VMs. Existing translation approaches incur non-trivial overheads: (i) nested paging has a worst-case latency that increases with page table depth; and (ii) par-avirtualized and shadow paging suffer from high hypervisor intervention costs when updating guest page tables. We describe translation pass-through (TPT), a new memory virtualization mechanism that achieves near-native performance. TPT enables VMs to control virtual memory translation from guest-virtual to host-physical addresses using one-dimensional page tables. At the same time, inter-VM isolation is enforced by the host by exploiting new hardware support for physical memory tagging in commodity CPUs. We prototype TPT by modifying the KVM/QEMU hyper-visor and enlightening the Linux guest. We evaluate it by emulating the memory tagging mechanism of AMD CPUs. Our conservative performance estimates show that TPT achieves native performance for real-world data center applications, with speedups of up to 2.4 × and 1.4 × over nested and shadow paging, respectively.
Open-World Factually Consistent Question Generation

Question generation methods based on pre-trained language models often suffer from factual inconsistencies and incorrect entities and are not answerable from the input paragraph. Domain shift – where the test data is from a different domain than the training data - further exacerbates the problem of hallucination. This is a critical issue for any natural language application doing question generation. In this work, we propose an effective data processing technique based on de-lexicalization for consistent question generation across domains. Unlike existing approaches for remedying hallucination, the proposed approach does not filter training data and is generic across question-generation models. Experimental results across six benchmark datasets show that our model is robust to domain shift and produces entity-level factually consistent questions without significant impact on traditional metrics.
A Bias-Corrected Method of Moments Approach to Estimation of Dynamic Short-T Panels

This paper contributes to the GMM literature by introducing the idea of self-instrumenting target variables instead of searching for instruments that are uncorrelated with the errors, in cases where the correlation between the target variables and the errors can be derived. The advantage of the proposed approach lies in the fact that, by construction, the instruments have maximum correlation with the target variables and the problem of weak instrument is thus avoided. The proposed approach can be applied to estimation of a variety of models such as spatial and dynamic panel data models. In this paper we focus on the latter and consider both univariate and multivariate panel data models with short time dimension. Simple Bias-corrected Methods of Moments (BMM) estimators are proposed and shown to be consistent and asymptotically normal, under very general conditions on the initialization of the processes, individual-specific effects, and error variances allowing for heteroscedasticity over time as well as cross-sectionally. Monte Carlo evidence document BMM? s good small sample performance across different experimental designs and sample sizes, including in the case of experiments where the system GMM estimators are inconsistent. We also find that the proposed estimator does not suffer size distortions and has satisfactory power performance as compared to other estimators.
A Short-Term Negative Eddy Feedback on Midlatitude Jet Variability due to Planetary Wave Reflection

A three-level quasigeostrophic model on the sphere is used to identify the physical nature of the negative planetary wave feedback on midlatitude jet variability. A first approach consists of studying the nonlinear evolution of normal-mode disturbances in a baroclinic westerly zonal jet. For a low-zonal-wavenumber disturbance , successive acceleration and deceleration of the jet occur as a result of reflection of the wave on either side of the jet. The planetary wave deposits momentum in opposite ways during its poleward or equatorward propagation. In contrast, a high-zonal-wavenumber disturbance is not reflected but absorbed within the subtropical critical layer. It thus only induces poleward momentum fluxes, which accelerate the jet and shift it slightly poleward. A long-term simulation forced by a relaxation toward a zonally symmetric temperature profile is then analyzed. Planetary waves are shown to be baroclinically excited. When they propagate equatorward, they induce an acceleration of the jet together with a slight poleward shift. About two-thirds of the planetary waves are absorbed by the subtropical critical layer, which allows the accelerated poleward-shifted jet to persist for a while. For the remaining third, the potential vorticity equatorward of the jet is so well homogenized that a reflection occurs. It is followed by an abrupt jet deceleration during the subsequent poleward propagation. The reflection of planetary waves on the poleward side of the jet is more systematic because of the quasi-permanent presence of a turning latitude there. This negative planetary wave feedback is shown to act more on pulses of the jet than on its latitudinal shifts.
AI4SeaIce: Task Separation and Multistage Inference CNNs for Automatic Sea Ice Concentration Charting

We investigate how different Convolutional Neural Network (CNN) U-Net models specialised in addressing partial labelling tasks related to mapping Sea Ice Concentration (SIC) can improve performance. We use Sentinel-1 SAR images and human-labelled ice charts as the reference to train models that benefit from advantages gained from different model optimisation objectives by utilising a multistage inference scheme. We find our multistage model inference approach that apply a classification (CrossEntropy or Earth Mover’s Distance squared) optimised model to separate open water, intermediate SIC and 5 fully covered ice in conjunction with a regression (Mean Square Error or Binary CrossEntropy) optimised model, that assigns specific intermediate classes, to perform the best. To evaluate the models we introduce several specific metrics illustrating the performance in key areas, such as the separation of macro classes, intermediate class, and an accuracy metric better encap-sulating uncertainties in the reference data. We achieve R 2 -score of ∼ 93%, similar to state-of-the-art in the literature (Kucik and Stokholm, 2023). However, our models exhibit significantly better open water and 100% SIC detections. The multistage 10 synergises high open water and fully covered sea ice accuracies achieved with classification optimised objectives with good intermediate class performance obtained by regressional loss functions. In addition, our findings indicate that the number of classes that the intermediate concentrations are compressed into does not influence the result significantly but rather it is the loss function used to optimise the model which assigns the specific intermediate
An integrated and homogenized global surface solar radiation dataset and its reconstruction based on an artificial intelligence approach

Surface solar radiation (SSR) is an essential factor in the flow of surface energy, enabling accurate capturing of long-term climate change and understanding the energy balance of Earth's atmosphere system. However, the long-term trend estimation of SSR is subjected to significant uncertainties due to the temporal inhomogeneity and the uneven spatial distribution of the in-situ observations. This paper 20 develops an observational integrated and homogenized global-terrestrial (except for Antarctica)) stational SSR dataset (SSRIH station ) by integrating all available SSR observations, including the existing homogenized SSR results. The series are then interpolated in order to obtain a 5° × 5° resolution gridded dataset (SSRIH grid ). On this basis, we further reconstruct a long-term (1955-2018) global land (except for Antarctica) SSR anomalies dataset with a 5° × 2.5° resolution (SSRIH 20CR ) by training improved partial 25 convolutional neural network deep learning methods based on the reanalysis 20CRv3. Based on this, we analysed the global land (except for Antarctica) /regional scale SSR trends and spatiotemporal variations: the reconstruction results reflect the distribution of SSR anomalies and have high reliability in filling and reconstructing the missing values. At the global land (except for Antarctica) scale, the decreasing trend of the SSRIH 20CR (-1.276±0.205 W/m 2 per decade) is slightly smaller than the trend of the SSRIH grid (- 30 1.776±0.230 W/m 2 per decade) from 1955 to 1991. The trend of SSRIH 20CR (0.697±0.359 W/m 2 per decade) from 1991 to 2018 is also marginally lower than that of the SSRIH grid (0.851±0.410 W/m 2 per decade). At the regional scale, the difference between the SSRIH 20CR and SSRIH grid is more significant in years and areas with insufficient coverage. Asia, Africa, Europe and North America cause the global dimming of the SSRIH 20CR , while Europe and North America drive the global brightening of the
News Consumption and Behavior of Young Adults and the Issue of Fake News

This study aimed to understand young adults’ attitudes concerning news and news resources they consumed, and how they encounter the fake news phenomenon. A qualitative approach was used with semi-structured interviews with 41 young adults (aged 20-30) in Tehran, Iran. Findings revealed that about half of the participants favored social media, and a smaller group used traditional media and only a few maintained that traditional and modern media should be used together. News quality was considered to be lower on social media than in traditional news sources. Furthermore, young adults usually followed the news related to the issues which had impact on their daily life, and they typically tended to share news. To detect fake news, they checked several media to compare the information; and profiteering and attracting audiences’ attention were the most important reasons for the existence of fake news. This is the first qualitative study for understanding news consumption behavior of young adults in a politicized society.