Reviewerly/exHarmony huggingface dataset:
Contrastive Pre-training for Personalized Expert Finding

Expert finding could help route questions to potential suitable users to answer in Community Question Answering (CQA) platforms. Hence it is essential to learn accurate representations of experts and questions according to the question text articles. Recently the pre-training and fine-tuning paradigms are powerful for natural language understanding, which has the potential for better question modeling and expert finding. Inspired by this, we propose a CQA-domain Contrastive Pre-training framework for Expert Finding, named CPEF, which could learn more comprehensive question representations. Specifically, considering that there is semantic complementation between question titles and bodies, during the domain pre-training phase, we propose a title-body contrastive learning task to enhance question representations, which directly treats the question title and the corresponding body as positive samples of each other, instead of designing extra data-augmentation strategies. Furthermore, a personalized tuning network is proposed to inject the personalized preferences of different experts during the fine-tuning phase. Extensive experimental results on six real-world datasets demonstrate that our method could achieve superior performance for expert finding.
Multi Document Summarization Evaluation in the Presence of Damaging Content

In the Multi-document summarization (MDS) task, a summary is produced for a given set of documents. A recent line of research introduced the concept of damaging documents, denoting documents that should not be exposed to readers due to various reasons. In the presence of damaging documents, a summarizer is ideally expected to exclude damaging content in its output. Existing metrics evaluate a summary based on aspects such as relevance and consistency with the source documents. We propose to additionally measure the ability of MDS systems to properly handle damaging documents in their input set. To that end, we offer two novel metrics based on lexical similarity and language model likelihood. A set of experiments demonstrates the effectiveness of our metrics in measuring the ability of MDS systems to summarize a set of documents while eliminating damaging content from their summaries.
Dialect Transfer for Swiss German Speech Translation

This paper investigates the challenges in building Swiss German speech translation systems, specifically focusing on the impact of dialect diversity and differences between Swiss German and Standard German. Swiss German is a spoken language with no formal writing system, it comprises many diverse dialects and is a low-resource language with only around 5 million speakers. The study is guided by two key research questions: how does the inclusion and exclusion of dialects during the training of speech translation models for Swiss German impact the performance on specific dialects, and how do the differences between Swiss German and Standard German impact the performance of the systems? We show that dialect diversity and linguistic differences pose significant challenges to Swiss German speech translation, which is in line with linguistic hypotheses derived from empirical investigations.
Closed Boundary Learning for Classification Tasks with the Universum Class

The Universum class, often known as the *other* class or the*miscellaneous* class, is defined as a collection of samples that do not belong to any class of interest. It is a typical class that exists in many classification-based tasks in NLP, such as relation extraction, named entity recognition, sentiment analysis, etc. The Universum class exhibits very different properties, namely heterogeneity and lack of representativeness in training data; however, existing methods often treat the Universum class equally with the classes of interest, leading to problems such as overfitting, misclassification, and diminished model robustness. In this work, we propose a closed boundary learning method that applies closed decision boundaries to classes of interest and designates the area outside all closed boundaries in the feature space as the space of the Universum class. Specifically, we formulate closed boundaries as arbitrary shapes, propose the inter-class rule-based probability estimation for the Universum class to cater to its unique properties, and propose a boundary learning loss to adjust decision boundaries based on the balance of misclassified samples inside and outside the boundary. In adherence to the natural properties of the Universum class, our method enhances both accuracy and robustness of classification models, demonstrated by improvements on six state-of-the-art works across three different tasks. Our code is available at https://github.com/hzzhou01/Closed-Boundary-Learning.
GenCodeSearchNet: A Benchmark Test Suite for Evaluating Generalization in Programming Language Understanding

Language models can serve as a valuable tool for software developers to increase productivity. Large generative models can be used for code generation and code completion, while smaller encoder-only models are capable of performing code search tasks using natural language queries. These capabilities are heavily influenced by the quality and diversity of the available training data. Source code datasets used for training usually focus on the most popular languages and testing is mostly conducted on the same distributions, often overlooking low-resource programming languages. Motivated by the NLP generalization taxonomy proposed by Hupkes et.,al., we propose a new benchmark dataset called GenCodeSearchNet (GeCS) which builds upon existing natural language code search datasets to systemically evaluate the programming language understanding generalization capabilities of language models. As part of the full dataset, we introduce a new, manually curated subset StatCodeSearch that focuses on R, a popular but so far underrepresented programming language that is often used by researchers outside the field of computer science. For evaluation and comparison, we collect several baseline results using fine-tuned BERT-style models and GPT-style large language models in a zero-shot setting.
Accuracy is not enough: Evaluating Personalization in Summarizers

Text summarization models are evaluated in terms of their accuracy and quality using various measures such as ROUGE, BLEU, METEOR, BERTScore, PYRAMID, readability, and several other recently proposed ones. The central objective of all accuracy measures is to evaluate the model’s ability to capture saliency accurately. Since saliency is subjective w.r.t the readers’ preferences, there cannot be a fit-all summary for a given document. This means that in many use-cases, summarization models need to be personalized w.r.t user-profiles. However, to our knowledge, there is no measure to evaluate the degree-of-personalization of a summarization model. In this paper, we first establish that existing accuracy measures cannot evaluate the degree of personalization of any summarization model, and then propose a novel measure, called EGISES, for automatically computing the same. Using the PENS dataset released by Microsoft Research, we analyze the degree of personalization of ten different state-of-the-art summarization models (both extractive and abstractive), five of which are explicitly trained for personalized summarization, and the remaining are appropriated to exhibit personalization. We conclude by proposing a generalized accuracy measure, called P-Accuracy, for designing accuracy measures that should also take personalization into account and demonstrate the robustness and reliability of the measure through meta-evaluation.
Shifted PAUQ: Distribution shift in text-to-SQL

Semantic parsing plays a pivotal role in advancing the accessibility of human-computer interaction on a large scale. Spider, a widely recognized dataset for text2SQL, contains a wide range of natural language (NL) questions in English and corresponding SQL queries. Original splits of Spider and its adapted to Russian language and improved version, PAUQ, assume independence and identical distribution of training and testing data (i.i.d split). In this work, we propose a target length split and multilingual i.i.d split to measure compositionality and cross-language generalization. We present experimental results of popular text2SQL models on original, multilingual, and target length splits. We also construct a context-free grammar for the evaluation of compositionality in text2SQL in an out-of-distribution setting. We make the splits publicly available on HuggingFace hub via https://huggingface.co/datasets/composite/pauq
VivesDebate-Speech: A Corpus of Spoken Argumentation to Leverage Audio Features for Argument Mining

In this paper, we describe VivesDebate-Speech, a corpus of spoken argumentation created to leverage audio features for argument mining tasks. The creation of this corpus represents an important contribution to the intersection of speech processing and argument mining communities, and one of the most complete publicly available resources in this topic. Moreover, we have performed a set of first-of-their-kind experiments which show an improvement when integrating audio features into the argument mining pipeline. The provided results can be used as a baseline for future research.
VARCO-MT: NCSOFT’s WMT’23 Terminology Shared Task Submission

A lack of consistency in terminology translation undermines quality of translation from even the best performing neural machine translation (NMT) models, especially in narrow domains like literature, medicine, and video game jargon. Dictionaries containing terminologies and their translations are often used to improve consistency but are difficult to construct and incorporate. We accompany our submissions to the WMT ‘23 Terminology Shared Task with a description of our experimental setup and procedure where we propose a framework of terminology-aware machine translation. Our framework comprises of an automatic terminology extraction process that constructs terminology-aware machine translation data in low-supervision settings and two model architectures with terminology constraints. Our models outperform baseline models by 21.51%p and 19.36%p in terminology recall respectively on the Chinese to English WMT’23 Terminology Shared Task test data.
Quantification of Active Visual Attention using RGB camera

Active visual attention (AVA) is the cognitive ability that helps to focus on important visual information while responding to a stimulus and is important for human-behavior and psychophysiological research. Existing eye-trackers/camera-based methods are either expensive or impose privacy issues as face videos are recorded for analysis. Proposed approach using blink-rate variability (BRV), is inexpensive, easy to implement, efficient and handles privacy issues, making it amenable to real-time applications. Our solution uses laptop camera/webcams and a single blink feature, namely BRV. First, we estimated participant’s head pose to check camera alignment and detect if he is looking at the screen. Next, subject-specific threshold is computed using eye aspect ratio (EAR) to detect blinks from which BRV signal is constructed. Only EAR values are saved, and participant’s face video is NOT saved or transmitted. Finally, a novel AVA score is computed. Results shows that the proposed score is robust across participants, ambient light conditions and occlusions like spectacles.
Constant Field of View Display Size Effects on First-Person Aiming Time

Under constant display field of view, FPS game aiming performance improves with display size, resulting in 3% faster aiming time comparing 13 and 26 inches diagonal.
X-RIM: Extreme Recurrent Independent Mechanisms for Noise-resistant and Interpretable Stroke Risk Prediction

Stroke is a leading cause of mortality and long-term disability worldwide. An accurate stroke risk prediction is crucial for its early detection and prevention. Using deep learning to exploit patients’ time-series electronic health records (EHRs) has been shown as a promising and efficient solution for such a prediction. Although time-series data could be more informative than a single cross-section in time, real-world time-series EHRs usually have a significantly high missing rate due to irregular patient visits. This could undermine sequential data’s benefits unless a proper deep-learning model design is adopted. Furthermore, deep models have long been challenged for their interpretability, which is especially crucial for medical applications. In this study, we propose an extreme design based on the concept of recurrent independent mechanisms (RIM), termed extreme RIM (X-RIM). With no need for imputation, X-RIM utilizes the information of each input feature’s temporal records through independent recurrent modules. Experiments on real-world data from the National Taiwan University Hospital showed that, in terms of the area under the precision-recall curve (AUPRC), the area under the receiver-operating characteristics curve (AUROC), and Youden Index, X-RIM (AUPRC: 0.210; AUROC: 0.764; Youden: 0.373) outperformed the classic risk score CHA <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> DS <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> -VASc (AUPRC: 0.103; AUROC: 0.650; Youden: 0.223) and other benchmarks in stroke risk prediction. Additional experiments also indicate that individual feature contributions to a prediction could be evaluated intuitively under X-RIM’s independent structure to enhance interpretability.
High Efficiency Image Correction for Object Detection Improvement in Low-Power Underwater Drones

In this paper, we propose a highly efficient underwater image correction method that leverages structural similarity evaluation along with real-time performance from a traditional FUnIEGAN algorithm. The proposed approach is designed for implementation on an embedded GPU module (Jetson Xavier NX), ensuring its practicality and versatility. Furthermore, our method incorporates algorithmic enhancements to significantly enhance color correction performance. To optimize computational resources, we employ automatic mixed precision (AMP), effectively reducing redundancy and enabling rapid processing. As a result, our method demonstrates improved object detection outcomes, achieving an impressive increase in frames per second (FPS) from approximately 55 FPS to about 77 FPS.
Museum Collection Search Improvement based on Knowledge Graph: Semantic Mapping from Cataloging Metadata to the CIDOC-CRM

This paper presents a methodology for semantic mapping from collection cataloging metadata to the Conceptual Reference Model by the International Committee for Documentation (CIDOC-CRM) and further creating an extension ontology called Ancient Chinese Artifacts Conceptual Reference Model (CRM-ACA). The approach is designed to suit the characteristics of ancient Chinese artifacts and can be effectively applied to other cultural heritage collections. By leveraging the semantic relationships based on a knowledge graph, the approach offers a search experience that is more nuanced and sophisticated for users. The benefits of this approach underscore the value of adopting standardized ontology as a basis for schema design, enabling potential information exchange and integration between heterogeneous digital resources.
Aligning Documentation and Q&amp;A Forum through Constrained Decoding with Weak Supervision

Stack Overflow (SO) is a widely used question-and-answer (Q&A) forum dedicated to software development. It plays a supplementary role to official documentation (DOC for short) by offering practical examples and resolving uncertainties. However, the process of simultaneously consulting both the documentation and SO posts can be challenging and time-consuming due to their disconnected nature. In this study, we propose DOSA, a novel approach to automatically align SO and DOC, which inject domain-specific knowledge about the DOC structure into large language models (LLMs) through weak supervision and constrained decoding, thereby enhancing knowledge retrieval and streamlining task completion during the software development procedure. Our preliminary experiments find that DOSA outperforms various widely-used baselines, showing the promise of using generative retrieval models to perform low-resource software engineering tasks.
Towards a Universal Python: Translating the Natural Modality of Python into Other Human Languages

The Python programming language plays a large role in computer science today, both in industry and education. While the pseudo-code nature of its keywords and built-in functions/modules makes programming easy to learn for English speakers, non-English speakers do not have this advantage. Our goal is to further the democratization of computer science, allowing anyone to code in their native language, anywhere. This paper describes our vision for realizing this goal by automatically translating Python (keywords, error messages, identifiers) into other human languages, leveraging recent developments in machine translation and language technologies in general. As a first step, we introduce a preliminary multi-lingual Python tool that enables a user to code, translate, and execute Python in 5 additional languages, as well as a roadmap for the future development of our automated framework.
Video Reconstruction with Multimodal Information

Video reconstruction refers to generate videos through the high-level representations (edge map, labels and so on), while the reconstruction quality is always unsatisfactory due to sparse high-level representations, especially on video data. In order to improve the video reconstruction quality, we proposed a novel approach that generates realistic video from its multimodal information including structure features and color features. To extract color features, we mainly apply the k-means algorithm to segment labels and the structure features are extracted by an edge detection network. Video generation is regarded as learning the mapping from multimodal representations to the original videos. So, a conditional GAN is applied with a learning objective that models the temporal video dynamics. We use a spatio-temporal generator with attention to model the inter-frame dynamics and video consistency is improved in this way. Moreover, we use a multiscale discriminator to improve the improve the intra-frame quality of the video. Experimental results on Cityscapes, Apolloscape datasets demonstrate that our proposed approach performs better in both traditional and generative evaluating indicators.
Real-time Live-Video Streaming in Delay-Critical Application: Remote-Controlled Moving Platform

Recent advancement in multimedia and communication network technology have made interactive multimedia and tele-operation applications possible. Teledriving, teleoperation, and video-based remote-controlling require real-time live-video streaming and are delay critical in principle. Supporting such applications over wireless networks for mobile users can pose fundamental challenges in maintaining video quality and service latency requirements. This paper investigates the factors affecting the end-to-end delay in a video-based, remotely controlled moving platform application. It involves the real-time acquisition of environmental information (visually) and the delay-sensitive video streaming to remote operators over wireless networks. This paper presents an innovative experimental testbed developed using a remote-controlled toy truck, off-the-shelf cameras, and wireless fidelity (Wi-Fi) network. It achieves ultra-low end-to-end latency and helped us in performing the delay, network, and video quality evaluations. Extending the experimental study, we also propose a real-time live-media streaming control (RTSC) algorithm that maximizes the video quality by selecting the best streaming (network, video, and camera) configuration while meeting the delay and network availability constraints. RTSC improves the live-streaming video quality by about 33% while meeting the ultra-low latency (< 200 milliseconds) requirement under constrained network availability conditions.
Automatic Simplification of Legal Texts in Portuguese Using Machine Learning

Texts produced by the Brazilian judiciary have a complex and technical vocabulary, with elaborate use of the Portuguese language and many legal terms difficult to be understood, generating a barrier in communication between the judiciary and the population. In this sense, the Automatic Text Simplification (ATS), activity of the Natural Language Processing (NLP) area, can be applied to improve the readability of these types of text using specialized algorithms, and promote scalability in simplifying them, in view of the great demand in the courts. In this context, this article presents an evaluation of four methods of state of the art in text simplification, evaluated according to readability metrics, to improve the quality of existing texts in the judicial summaries, dataset containing 100 summaries of the Federal Regional Court of the 5th Region (TRF5) and another 100 of the Federal Supreme Court (STF). The methods MUSS(EN), MUSS(PT), Transformers and NMT + Attention were tested, and the results of the simplifications exceeded the FRE readability index of the original texts, making them more readable.
archeoViz: an R package for the Visualisation, Exploration, and Web Communication of Archaeological Spatial Data

Plutniak, S., (2023). archeoViz: an R package for the Visualisation, Exploration, and Web Communication of Archaeological Spatial Data. Journal of Open Source Software, 8(92), 5811, https://doi.org/10.21105/joss.05811
Implementing Project-Based Learning: the Experience of a Military Medical Educational Organization

Introduction. The implementation of project-based learning into programs of advanced professional education is one of the most relevant areas of development in the modern education system. Project-based learning significantly increases the efficiency of the educational process and the interest of students in the subjects studied and contributes to the development of their leadership qualities. The acquired skills can be successfully applied to professional activities.&#x0D; The article examines the successful implementation of project-based learning using the example of a military medical educational organization and determines positive aspects that can be used in other education sectors.&#x0D; The purpose of the study was to identify the advantages and disadvantages of project-based learning in comparison to traditional forms of education.&#x0D; Materials and methods. In the study, methods of sociological research were used. Students engaged in traditional and project-based learning were asked to fill out questionnaires. Then, a comparative content analysis was performed. Statistical processing and analysis of the data obtained were carried out.&#x0D; Results and discussion. The study of students' satisfaction with traditional and project-based learning showed that the second group had better motivation, higher interest in the subject, and a higher level of academic performance. The average academic performance score in the project-based groups was 4.8, while in the group of traditional education it was only 4.2.&#x0D; Conclusion. The implementation of project-based learning in academic departments is aimed at increasing students’ professional competencies, acquiring and improving additional professional skills, and creating and implementing innovative schemes that can be used later in a medical organization as independent projects.&#x0D; Project-based learning is more effective than traditional learning in terms of critical thinking, communication and organizational skills, and teamwork experience. The practice of analytical, strategic, and planning skills as a team is an important skill difficult to acquire in traditional training.