Reviewerly/exHarmony huggingface dataset:
Text Simplification for Legal Domain: {I}nsights and Challenges

Legal documents such as contracts contain complex and domain-specific jargons, long and nested sentences, and often present with several details that may be difficult to understand for laypeople without domain expertise.In this paper, we explore the problem of text simplification (TS) in legal domain.The main challenge to this is the lack of availability of complexsimple parallel datasets for the legal domain.We investigate some of the existing datasets, methods, and metrics in the TS literature for simplifying legal texts, and perform human evaluation to analyze the gaps. 1 We present some of the challenges involved, and outline a few open questions that need to be addressed for future research in this direction.
Not Just Plain Text! Fuel Document-Level Relation Extraction with Explicit Syntax Refinement and Subsentence Modeling

Document-level relation extraction (DocRE)aims to identify semantic labels among entities within a single document.One major challenge of DocRE is to dig decisive details regarding a specific entity pair from long text.However, in many cases, only a fraction of text carries required information, even in the manually labeled supporting evidence.To better capture and exploit instructive information, we propose a novel expLicit syntAx Refinement and Subsentence mOdeliNg based framework (LARSON).By introducing extra syntactic information, LARSON can model subsentences of arbitrary granularity and efficiently screen instructive ones.Moreover, we incorporate refined syntax into text representations which further improves the performance of LAR-SON.Experimental results on three benchmark datasets (DocRED, CDR, and GDA) demonstrate that LARSON significantly outperforms existing methods.
STGN: an Implicit Regularization Method for Learning with Noisy Labels in Natural Language Processing

Noisy labels are ubiquitous in natural language processing (NLP) tasks.Existing work, namely learning with noisy labels in NLP, is often limited to dedicated tasks or specific training procedures, making it hard to be widely used.To address this issue, SGD noise has been explored to provide a more general way to alleviate the effect of noisy labels by involving benign noise in the process of stochastic gradient descent.However, previous studies exert identical perturbation for all samples, which may cause overfitting on incorrect ones or optimizing correct ones inadequately.To facilitate this, we propose a novel stochastic tailor-made gradient noise (STGN), mitigating the effect of inherent label noise by introducing tailor-made benign noise for each sample.Specifically, we investigate multiple principles to precisely and stably discriminate correct samples from incorrect ones and thus apply different intensities of perturbation to them.A detailed theoretical analysis shows that STGN has good properties, beneficial for model generalization.Experiments on three different NLP tasks demonstrate the effectiveness and versatility of STGN.Also, STGN can boost existing robust training methods.
AfriCLIRMatrix: Enabling Cross-Lingual Information Retrieval for African Languages

Language diversity in NLP is critical in enabling the development of tools for a wide range of users.However, there are limited resources for building such tools for many languages, particularly those spoken in Africa.For search, most existing datasets feature few or no African languages, directly impacting researchers' ability to build and improve information access capabilities in those languages.Motivated by this, we created AfriCLIRMatrix, a test collection for cross-lingual information retrieval research in 15 diverse African languages.In total, our dataset contains 6 million queries in English and 23 million relevance judgments automatically mined from Wikipedia inter-language links, covering many more African languages than any existing information retrieval test collection.In addition, we release BM25, dense retrieval, and sparsedense hybrid baselines to provide a starting point for the development of future systems.We hope that these efforts can spur additional work in search for African languages.
MuGER2: Multi-Granularity Evidence Retrieval and Reasoning for Hybrid Question Answering

Hybrid question answering (HQA) aims to answer questions over heterogeneous data, including tables and passages linked to table cells.The heterogeneous data can provide different granularity evidence to HQA models, e.t., column, row, cell, and link.Conventional HQA models usually retrieve coarse-or fine-grained evidence to reason the answer.Through comparison, we find that coarse-grained evidence is easier to retrieve but contributes less to the reasoner, while fine-grained evidence is the opposite.To preserve the advantage and eliminate the disadvantage of different granularity evidence, we propose MuGER 2 , a Multi-Granularity Evidence Retrieval and Reasoning approach.In evidence retrieval, a unified retriever is designed to learn the multi-granularity evidence from the heterogeneous data.In answer reasoning, an evidence selector is proposed to navigate the fine-grained evidence for the answer reader based on the learned multigranularity evidence.Experiment results on the HybridQA dataset show that MuGER 2 significantly boosts the HQA performance.Further ablation analysis verifies the effectiveness of both the retrieval and reasoning designs. 1 .
Singe Image Dehazing using Deep Neural Networks

In dusty and hazzy circumstances, pictures caputured tend to be unclear. To tackle this challenge researchers have founded different image-dehazing approaches. Lately, high quality pictures which could be used to extract maximal data from autonomous systems are in great demand. In order to minimize the haze from the picture, this research study uses various deep learning (also known as DL) architectures to identify the information recovered and extract key details from the image. The research paper focuses on utilizing deep learning techniques to remove haze from clear images.The first phase of the suggested architecture involves applying multiple pre-processing methods, including estimating the airlight, implementing contextual regularization and boundary constraints, as well as contrast enhancement techniques (CS, CLAHE, HE) and Image Fusion. The subsequent goal of this study is to pinpoint the optimal deep learning model that can produce clear images based on the resulting dehazed images. The quality of the dehazed images is evaluated by comparing them with clear images using metrics such as PSNR and SSIM. Experimental findings shows that Alex Net upon the implementation of pre-processing beats other state of the art techniques with regard to PSNR value and holds its own w.r.t SSIM values.
Review of Existing Datasets Used for Software Effort Estimation

The Software Effort Estimation (SEE) tool calculates an estimate of the amount of work that will be necessary to effectively finish the project.Managers usually want to know how hard a new project will be ahead of time so they can divide their limited resources in a fair way.In fact, it is common to use effort datasets to train a prediction model that can predict how much work a project will take.To train a good estimator, you need enough data, but most data owners don't want to share their closed source project effort data because they are worried about privacy.This means that we can only get a small amount of effort data.The purpose of this research was to evaluate the quality of 15 datasets that have been widely utilized in studies of software project estimation.The analysis shows that most of the chosen studies use artificial neural networks (ANN) as ML models, NASA as datasets, and the mean magnitude of relative error (MMRE) as a measure of accuracy.In more cases, ANN and support vector machine (SVM) have done better than other ML techniques.
Effectiveness of a Mixed Methods-Based Literacy Program in Improving Reading Comprehension, Vocabulary Mastery, and Reading Fluency Skills of Early Grade Students

Many young children still experience reading difficulties when they enter school age. This study aimed to examine the effectiveness of a mixed methods-based reading literacy program (computer-assisted reading, independent tiered reading, and group-based reading teaching) to improve reading comprehension skills, vocabulary mastery, and reading fluency among early grade students. The study used a quantitative approach with experimental research methods. A quasi-experimental design was used to achieve the research objectives by involving 350 elementary school students who were in the fourth to sixth grade range of elementary school. The sample selection in this study involved the purposive sampling technique using several criteria. The teachers involved in this study totaled 150 teachers from 8 schools who acted as program implementers at school. The reading literacy program was implemented for two semesters for five days each week. The research findings show that the mixed methods-based reading literacy program had a significant effect on all competencies (word reading efficiency, reading comprehension ability, and reading fluency) in Grade 3 students, but this increase in ability was not seen in grades 5 and 6 students. More specifically, the effect size of the intervention on oral reading fluency in Grade 3 students was greater than the effect size in Grade 5 students. Furthermore, the effect size in Grade 4 was greater than the effect size in grades 5 and 6. This is because the treatment of reading difficulties in younger students is easier and more effective than in older students. In addition, based on the results of the correlation analysis, the number of words read during reading activities using a computer had a positive relationship with word reading scores and visual word reading efficiency in the post-test phase. Therefore, mixed methods-based literacy programs have a significant impact on reading fluency, and on vocabulary mastery and reading comprehension competence and reading fluency. This research has implications in that improving reading ability in the early grades is better done by using a combination of methods, such as computer-assisted reading, independent tiered reading, and group-based reading teaching. In this way, the improvement of students’ reading skills is more optimal.
Privacy-preserving Real-world Video Anomaly Detection

Video anomaly detection is a significant problem in computer vision that aims to detect unusual or abnormal behaviors in video data that can be used to enhance public safety. Given the widespread deployment of cameras in public areas, video anomaly detection for public safety has become increasingly important in recent years. There are numerous applications, including but not limited to security, traffic monitoring, healthcare, and manufacturing, where video anomaly detection can be useful. However, anomaly detection in nature is an open-set problem that further complicates the task. Moreover, the definition of anomalous behavior may differ in various environments, adding to real-world anomaly detection challenges. On the other hand, addressing ethical issues and privacy concerns related to this task is also crucial. We aim to design an anomaly detection method that uses non-identifiable features such as pose, trajectory, and optical flow to avoid discrimination against distinct minority groups and safeguard the privacy of individuals.
Sistem Informasi Bimbingan Konseling Pada SMA 4 Bandar Lampung Menggunakan Metode Certainty Factor

At this time, student delinquency, which is increasing due to not being able to adapt to various social changes, is not a disease or problem that must be avoided but must receive special attention and guidance. With the counseling guidance software as a medium of consultation for students who can accommodate students who do not want to do guidance with the BK teacher. The knowledge obtained is student problems consisting of being late for school, truancy, broken home students, inferior, online game addicts, promiscuity, lack of motivation to learn, cheating, not being smart, sleeping in class. This consultation media is built using the certainty factor method which is a parameter value to show the amount of trust. This consultation media will provide several questions related to the cause of the problem that will be selected by the student (user) to obtain the results of the consultation. The advantage of using the certainty factor method is that the user can find out the certainty value of the conclusion of the problem obtained.
Digital shearography for NDT: Determination and demonstration of the size and the depth of the smallest detectable defect

This paper presents a methodology of digital shearography for determining the size of the smallest detectable defect and the depth under different loading magnitudes for the purpose of nondestructive testing. Digital Shearography, an interferometric nondestructive testing (NDT) technique, has been proven to be a useful tool for material inspections and evaluations, especially for detecting delaminations/disbonds in composite materials and honeycomb structures. A commonly asked question in the field of NDT is about measuring sensitivity - specifically, what is the smallest detectable delamination/disbond and how deep can these be detected? Although various attempts to find the smallest detectable defect by shearography have been made, a numerical model for determining the size of the smallest detectable defect and the depth has not yet been developed. This paper did a study in this aspect, especially for NDT of delamination and disbond in polymers and honeycomb structures. First, a mechanical model based on the thin plate theory to calculate the expected bending of close-to-surface defects was proposed; the model built a relationship among the deformation caused by a defect, the size and the depth of the defect, as well as the load and the material properties. Second, the relationship between the relative deformation measured by shearography and the deformation induced by a defect was established based on the optimized shearing amount and the sensitivity of digital shearography. Based on these analyses, relationships between the size of the smallest detectable defect and the depth under different load amounts were established for different defect shapes. Finally, experimental validation based on different sizes of prefabricated defects were conducted to verify these relationships. The experimental results show that the model developed can provide useful estimation for NDT by digital shearography, especially with helping test engineers estimate the size of the smallest detectable defect and the depth with corresponding loading magnitudes.
Resource frugal optimizer for quantum machine learning

Quantum-enhanced data science, also known as quantum machine learning (QML), is of growing interest as an application of near-term quantum computers. Variational QML algorithms have the potential to solve practical problems on real hardware, particularly when involving quantum data. However, training these algorithms can be challenging and calls for tailored optimization procedures. Specifically, QML applications can require a large shot-count overhead due to the large datasets involved. In this work, we advocate for simultaneous random sampling over both the dataset as well as the measurement operators that define the loss function. We consider a highly general loss function that encompasses many QML applications, and we show how to construct an unbiased estimator of its gradient. This allows us to propose a shot-frugal gradient descent optimizer called Refoqus (REsource Frugal Optimizer for QUantum Stochastic gradient descent). Our numerics indicate that Refoqus can save several orders of magnitude in shot cost, even relative to optimizers that sample over measurement operators alone.
A Flexible Architecture Using Temporal, Spatial and Semantic Correlation-Based Algorithms for Story Segmentation of Broadcast News

In this article, we propose a novel flexible architecture, with different algorithmic procedures, for effective story segmentation of broadcast news from subtitle files. The proposed system exploits spatial and temporal distance, as well as sentence similarity, to classify different stories in news broadcasts. The computational algorithms which form the architecture mainly focus on each sentence's features (temporal distance, spatial distance, and semantic similarity), and are combined to build an overall classifier. The first algorithm in the architecture focuses on the segmentation task, detecting boundaries between news. The second and third algorithms identify high semantic correlation between pieces of text, whether they are consecutive in space or not. Video Text Track (VTT) subtitle files are used to evaluate the performance of the proposed approach, although any file format that includes temporal information could also be considered. These VTT files may contain text errors and inaccuracies, and the proposed algorithms have been designed to deal with noisy content.
Efficient Convex Optimization Requires Superlinear Memory (Extended Abstract)

Minimizing a convex function with access to a first order oracle---that returns the function evaluation and (sub)gradient at a query point---is a canonical optimization problem and a fundamental primitive in machine learning. Gradient-based methods are the most popular approaches used for solving the problem, owing to their simplicity and computational efficiency. These methods, however, do not achieve the information-theoretically optimal query complexity for minimizing the underlying function to small error, which are achieved by more expensive techniques based on cutting-plane methods. Is it possible to achieve the information-theoretically query complexity without using these more complex and computationally expensive methods? In this work, we use memory as a lens to understand this, and show that is is not possible to achieve optimal query complexity without using significantly more memory than that used by gradient descent.
SemFORMS: Automatic Generation of Semantic Transforms By Mining Data Science Code

Careful choice of feature transformations in a dataset can help predictive model performance, data understanding and data exploration. However, finding useful features is a challenge, and while recent Automated Machine Learning (AutoML) systems provide some limited automation for feature engineering or data exploration, it is still mostly done by humans. We demonstrate a system called SemFORMS (Semantic Transforms), which attempts to mine useful expressions for a dataset from access to a repository of code that may target the same dataset/similar dataset. In many enterprises, numerous data scientists often work on the same or similar datasets, but are largely unaware of each other's work. SemFORMS finds appropriate code from such a repository, and normalizes the code to be an actionable transform that can prepended into any AutoML pipeline. We demonstrate SemFORMS operating over example datasets from the OpenML benchmarks where it sometimes leads to significant improvements in AutoML performance.
ICDA: Illumination-Coupled Domain Adaptation Framework for Unsupervised Nighttime Semantic Segmentation

The performance of nighttime semantic segmentation has been significantly improved thanks to recent unsupervised methods. However, these methods still suffer from complex domain gaps, i.e., the challenging illumination gap and the inherent dataset gap. In this paper, we propose the illumination-coupled domain adaptation framework(ICDA) to effectively avoid the illumination gap and mitigate the dataset gap by coupling daytime and nighttime images as a whole with semantic relevance. Specifically, we first design a new composite enhancement method(CEM) that considers not only illumination but also spatial consistency to construct the source and target domain pairs, which provides the basic adaptation unit for our ICDA. Next, to avoid the illumination gap, we devise the Deformable Attention Relevance(DAR) module to capture the semantic relevance inside each domain pair, which can couple the daytime and nighttime images at the feature level and adaptively guide the predictions of nighttime images. Besides, to mitigate the dataset gap and acquire domain-invariant semantic relevance, we propose the Prototype-based Class Alignment(PCA) module, which improves the usage of category information and performs fine-grained alignment. Extensive experiments show that our method reduces the complex domain gaps and achieves state-of-the-art performance for nighttime semantic segmentation. Our code is available at https://github.com/chenghaoDong666/ICDA.
More for Less: Safe Policy Improvement with Stronger Performance Guarantees

In an offline reinforcement learning setting, the safe policy improvement (SPI) problem aims to improve the performance of a behavior policy according to which sample data has been generated. State-of-the-art approaches to SPI require a high number of samples to provide practical probabilistic guarantees on the improved policy's performance. We present a novel approach to the SPI problem that provides the means to require less data for such guarantees. Specifically, to prove the correctness of these guarantees, we devise implicit transformations on the data set and the underlying environment model that serve as theoretical foundations to derive tighter improvement bounds for SPI. Our empirical evaluation, using the well-established SPI with baseline bootstrapping (SPIBB) algorithm, on standard benchmarks shows that our method indeed significantly reduces the sample complexity of the SPIBB algorithm.
Digital Twin Tracking Dataset (DTTD): A New RGB+Depth 3D Dataset for Longer-Range Object Tracking Applications

Digital twin is a problem of augmenting real objects with their digital counterparts. It can underpin a wide range of applications in augmented reality (AR), autonomy, and UI/UX. A critical component in a good digital-twin system is real-time, accurate 3D object tracking. Most existing works solve 3D object tracking through the lens of robotic grasping, employ older generations of depth sensors, and measure performance metrics that may not apply to other digital-twin applications such as in AR. In this work, we create a novel RGB-D dataset, called Digital Twin Tracking Dataset (DTTD), to enable further research of the problem and extend potential solutions towards longer ranges and mm localization accuracy. To reduce point cloud noise from the input source, we select the latest Microsoft Azure Kinect as the state-of-the-art time-of-flight (ToF) camera. In total, 103 scenes of 10 common off-the-shelf objects with rich textures are recorded, with each frame annotated with a per-pixel semantic segmentation and ground-truth object poses provided by a commercial motion capturing system. Through extensive experiments with model-level and dataset-level analysis, we demonstrate that DTTD can help researchers develop future object tracking methods and analyze new challenges. The dataset, data generation, annotation, and model evaluation pipeline are made publicly available as open source code at: https://github.com/augcog/DTTDv1.