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The guided pathways model, comprising 14 different practices, is a framework for comprehensive, whole-college reform undertaken by community colleges to help all students choose, enter, progress through, and complete a program of study that enables them to secure sustaining-wage employment or transfer with junior standing in a major. Since its introduction in 2015, it has been adopted by hundreds of community colleges across the United States. This paper asks whether guided pathways practices implemented at 62 community and technical colleges in three states--Tennessee, Ohio, and Washington--are associated with improvements in student outcomes during the first year of college. Specifically, using institutional survey and rich administrative data, we construct measures of adoption of guided pathways reforms to examine the association between guided pathways practices and fall-to-fall persistence, college credits earned, college math credits earned, and STEM credits earned. Our study reveals substantial variation in the adoption of guided pathways reforms across the states and across community colleges within the states over time. While we cannot establish a causal relationship between guided pathways adoption and student outcomes, we find significant positive associations between the statewide adoption of guided pathways reforms and early student outcomes in Tennessee. The observed improvements in that state are likely the result of concurrent reforms--guided pathways and others--implemented simultaneously, rather than of guided pathways reforms alone. We do not find evidence of improved student outcomes in either Ohio or Washington following the launch of statewide guided pathways initiatives. Our findings suggest that complementarities among adopted practices within and across areas of practice--rather than the adoption of individual practices or the intensity of adoption--seem to drive larger improvements in early academic success across the three states. Our study is the first of its kind to explore the potential of guided pathways reforms in contributing to improved early academic success, representing a significant descriptive contribution given that whole-college reforms in higher education are understudied.

This study used Postsecondary Student Information System (PSIS) administrative data within the Education and Labour Market Longitudinal Platform to compare enrolment and persistence in postsecondary education (PSE) among high school graduates in British Columbia with and without special needs across five cohorts from 2010/2011 to 2014/2015 before and after controlling for several sociodemographic characteristics and academic achievement. The use of integrated longitudinal administrative data from the British Columbia Ministry of Education, the PSIS and the T1 Family File and the disaggregation of the special needs categorization were two major strengths of this study. Results show that high school graduates with mental health-related or cognitive needs and those with physical or sensory needs were less likely to enrol in PSE compared with high school graduates without special needs, even after controlling for covariates. Moreover, graduates with mental health-related or cognitive needs were less likely to transition to PSE immediately and less likely to persist in PSE two years after enrolment. These findings suggest that high school graduates with special needs, particularly those with mental health-related or cognitive needs, may encounter different types of barriers in transitioning to PSE.

Using British Columbia Ministry of Education administrative school data within the Education and Labour Market Longitudinal Platform, this study compared the proportions of high school graduates among Grade 12 students with and without special needs across nine cohorts from 2010/2011 to 2018/2019 before and after controlling for several sociodemographic characteristics. Two major strengths of this study were the use of longitudinal administrative education data integrated with income tax data from the T1 Family File and the further disaggregation of the special education needs categorization. Students with special needs in all different categories (excluding those with gifted status) were less likely to have graduated across all nine cohorts compared with students without special needs, even after controlling for sociodemographic characteristics and academic achievement, suggesting that students with special needs may face other types of barriers in completing high school. Yet there was diversity among students with special needs, with the highest proportions of graduation among students with learning disabilities or those with sensory needs and the lowest among students with intellectual disabilities. A larger share of females than males graduated high school among students without special needs. However, sex differences were less consistent among students with special needs status (including students with gifted status). As expected, the proportions of graduation were significantly higher at age 19 compared with at age 18 or younger, with the differences being slightly higher among students with special needs (excluding those with gifted status; 5 to 10 percentage points) compared with those without special needs (3 to 7 percentage points). The largest age differences were observed among students with autism spectrum disorder, behavioural needs or mental illness, and those with physical needs across all nine cohorts.

The Elementary and Secondary School Emergency Relief Fund (ESSER) was the largest onetime federal investment in K-12 schools in history, funneling almost $200 billion to states and school districts. We use novel data from Washington State to investigate the extent to which ESSER funding causally influenced spending on school personnel. We argue one cannot infer this directly from ESSER claims data because of the fungibility of school budgets. Thus, we rely on a more direct signal of district hiring decisions: public education job postings scraped from district hiring websites. To address endogeneity concerns, our preferred approach employs an instrumental variables strategy that exploits a formula mechanism used to determine Title I funding for 2020-21 (and thus ESSER allocations in 2022) based on the number of Title I formula-eligible children. We find strong, arguably causal, evidence that public school hiring increased in response to the availability of ESSER funding. Specifically, we estimate that each $1,000 in ESSER allocations caused districts to seek to hire $206 in additional staff, disproportionately teachers. These estimates suggest that roughly 12,000 new staff (including 5,100 teachers) were hired in Washington because of ESSER. In the absence of new funding, school staffing budgets will likely need to contract substantially following the sunset of ESSER.

Departmentalized instruction, in which teachers specialize in one or more core subjects and instruct multiple groups of students in a day, has become increasingly prominent in elementary schools. Using 8 years of data from Massachusetts and a difference-in-differences design, we estimate the effects of departmentalization on student achievement. We find that departmentalization has positive effects in English language arts (ELA) and science and mixed evidence of positive effects in math. These positive effects are not driven by teacher productivity improvements: Consistent with prior findings on teacher specialization, teachers are less effective when specializing in math and no more effective in ELA than when teaching self-contained classrooms. Rather, consistent with the theoretical underpinnings for specialization, departmentalized schools tend to assign teachers to their stronger subjects.

Who ends up in the teacher workforce is greatly influenced by who is admitted into teacher education programs (TEPs). To better understand how the preferences of teacher education faculty might shape admissions of STEM teacher candidates, we surveyed faculty who teach content or methods courses to STEM teacher candidates across five universities. Faculty reported that they most value information collected from individual interviews with applicants and data on the number of STEM courses taken in college and their performance in these courses, and least value data on university admissions tests, high school GPA, and teacher licensure test scores. When we investigate faculty members' revealed preferences through a conjoint analysis, we find that faculty most value applicants who have worked with students from diverse backgrounds and applicants from a marginalized racial or ethnic community, and least value whether they received high grades in math and/or science courses. Finally, we find significant variation in these perceptions across respondents in different faculty roles, who teach different courses, and from different institutions: for example, Arts and Sciences faculty tend to value TEP applicants' performance in college STEM courses relatively more than STEM education faculty, while STEM education faculty tend to value applicants' race and ethnicity relatively more than Arts and Sciences faculty.

In 1993, the Commonwealth of Massachusetts dramatically overhauled its K-12 education system and created a new school finance formula, building an educational accountability structure to ensure every child has access to a high-quality education. The Massachusetts Education Reform Act (MERA) established academic standards in core subjects, mandated assessments to measure student outcomes on those standards, and established a system for holding schools accountable when students failed to meet basic expectations. This system has helped Massachusetts' public schools become the highest performing in the country. Student outcomes in all tested subjects and across demographic groups have improved steadily over time, but disparities in achievement and attainment exist between the Commonwealth's most privileged students and their less privileged counterparts, many of whom are black or Hispanic. Without the MERA and its requirement to assess every student and publish aggregate academic outcomes, policymakers may not understand the extent of disparity or how to address it as student outcomes data are integral to understanding where Massachusetts' public schools have been, where they are going, and how they can get there. This paper illustrates the importance of the Massachusetts Education Reform Act and how it has positively impacted students over time. It explains why the current accountability system evolved as it did and why preserving the most important aspects of that system is critical if the state is going to fulfill its constitutional obligation to educate all children to a high common standard.

It is now well established that the COVID-19 pandemic had a devastating and unequal impact on student achievement. Test score declines were disproportionately large for historically marginalized students, exacerbating preexisting achievement gaps and threatening educational and economic inequality. In this paper, we use longitudinal student-level NWEA MAP Growth test data to estimate differences in test score declines for students at different points on the prepandemic test distribution. We also test the extent to which students' schools and districts accounted for these differences in declines. We find significant differences in learning loss by baseline achievement, with lower-achieving student's scores dropping 0.100 SD more in math and 0.113 SD more in reading than higher-achieving students' scores. We additionally show that the school a student attended accounts for about three-quarters of this widening gap in math achievement and about one-third in reading. The findings suggest school and district-level policies may have mattered more for learning loss than individual students' experiences within schools and districts. Such nuanced information regarding the variation in the pandemic's impacts on students is critical for policymakers and practitioners designing targeted academic interventions and for tracking disparities in academic recovery. [Additional funding for this report was provided by Kenneth C. Griffin.]

Each year, more than a million high school students nationally take college dual enrollment courses, which have been shown to increase college access and success among participants. Yet racial/ethnic and other equity gaps in dual enrollment participation are widespread. To broaden the benefits of dual enrollment, the state of Ohio passed legislation in 2017 establishing the Innovative Programs (IP) policy, allowing waivers to test-based eligibility requirements--a frequently identified barrier to equitable access--for specific high school-college partnerships providing expanded outreach and support for students underrepresented in the state's dual enrollment program. This paper describes a multiple methods study of IP we conducted to examine how these partnerships were implemented to address the needs of underrepresented students and to evaluate whether the partnerships were successful in broadening access to and success in dual enrollment, as measured by course participation, pass rates, and college matriculation after high school. We find that the IP increased participation in dual enrollment among Black and Hispanic students. And while the implementation of the policy broadened access without changing course outcomes, the impacts on college enrollment after high school were mixed. Our results underscore the importance of pairing increased access to dual enrollment with adequate financial, advising, and academic resources to promote student success in and beyond dual enrollment courses.

Graduates of special education teacher education programs can teach in a range of special education settings, raising the potential that their training can occur in very different settings than where they find their first jobs. We follow 263 completers of Moderate Disabilities programs in Massachusetts from their field placements to their early-career teaching positions and study the characteristics of their field placements and the degree to which these are aligned with their early-career teaching positions. We also assess the degree to which alignment is associated with early-career teacher turnover. We found that many of these teachers student-taught in an inclusive setting but were hired into a self-contained special education setting and vice versa, and teachers who experienced this misalignment were more likely to leave the workforce early in their careers. Teachers who student taught with a supervising practitioner without a special education license were also more likely to leave early. Findings suggest that teachers training to educate students with learning disabilities should student teach in a setting that is aligned with where they are likely to be hired, and with a supervising practitioner who is trained in special education.

Correctional education is a prevalent form of rehabilitation programming for prisoners in the United States. There is limited evidence, however, about the labor market returns to credentials received while incarcerated. Using incarceration, educational, and labor market data in Washington State, we study the labor market returns to GEDs and short-term vocational certificates earned in prison. We identify the returns to credentials by a difference-indifferences design that compares changes in earnings and employment for incarcerated persons who earn a credential to those who enroll in a program but fail to complete a GED or certificate. We estimate that GEDs increase post-incarceration earnings by about $450 per quarter and that vocational certificates increase earnings by about $250 per quarter. Degree completers have higher hourly wages, are more likely to be employed, and work more hours following release. For vocational programs, earnings increases are driven by certificates in construction and manufacturing. [The research presented presented in this report uses confidential data from the Education Research and Data Center (ERDC) located within the Washington Office of Financial Management (OFM).]

This paper presents Papilusion, an AI-generated scientific text detector developed within the DAGPap24 shared task on detecting automatically generated scientific papers. We propose an ensemble-based approach and conduct ablation studies to analyze the effect of the detector configurations on the performance. Papilusion is ranked 6th on the leaderboard, and we improve our performance after the competition ended, achieving 99.46 (+9.63) of the F1-score on the official test set., Comment: to appear in "The 4th Workshop on Scholarly Document Processing @ ACL 2024" proceedings

In a letter to Weierstrass Riemann asserted that the number $N_0(T)$ of zeros of $\zeta(s)$ on the critical line to height $T$ is approximately equal to the total number of zeros to this height $N(T)$. Siegel studied some posthumous papers of Riemann trying to find a proof of this. He found a function $\mathop{\mathcal R }(s)$ whose zeros are related to the zeros of the function $\zeta(s)$. Siegel concluded that Riemann's papers contained no ideas for a proof of his assertion, connected the position of the zeros of $\mathop{\mathcal R }(s)$ with the position of the zeros of $\zeta(s)$ and asked about the position of the zeros of $\mathop{\mathcal R }(s)$. This paper is a summary of several papers that we will soon upload to arXiv, in which we try to answer Siegel's question about the position of the zeros of $\mathop{\mathcal R }(s)$. The articles contain also improvements on Siegel's results and also other possible ways to prove Riemann's assertion, but without achieving this goal., Comment: 18 pages, 4 figures. Added links to the papers in arXiv

Machine learning is widely utilized across various industries. Identifying the appropriate machine learning models and datasets for specific tasks is crucial for the effective industrial application of machine learning. However, this requires expertise in both machine learning and the relevant domain, leading to a high learning cost. Therefore, research focused on extracting combinations of tasks, machine learning models, and datasets from academic papers is critically important, as it can facilitate the automatic recommendation of suitable methods. Conventional information extraction methods from academic papers have been limited to identifying machine learning models and other entities as named entities. To address this issue, this study proposes a methodology extracting tasks, machine learning methods, and dataset names from scientific papers and analyzing the relationships between these information by using LLM, embedding model, and network clustering. The proposed method's expression extraction performance, when using Llama3, achieves an F-score exceeding 0.8 across various categories, confirming its practical utility. Benchmarking results on financial domain papers have demonstrated the effectiveness of this method, providing insights into the use of the latest datasets, including those related to ESG (Environmental, Social, and Governance) data., Comment: 10 pages, 8 figures

We describe the design, construction and characterisation of paper based devices to perform spirometry, a standard test for lung function assessment. In this device the instantaneous flowrate of the incoming breath from a person gets transformed to a specific acoustic frequency. In this manner, the time course of the persons breath profile is mapped to a time varying acoustic signal. The captured acoustic signal can be converted to standard spirometry curves and relevant parameters can be extracted as we describe in this paper. We compared our device with commercially available devices and showed that these paper based devices provide similar performance. These devices have advantage of low cost and simplicity of operation compared to currently available commercial products.

Teaching Computer Science (CS) by having students write programs by hand on paper has key pedagogical advantages: It allows focused learning and requires careful thinking compared to the use of Integrated Development Environments (IDEs) with intelligent support tools or "just trying things out". The familiar environment of pens and paper also lessens the cognitive load of students with no prior experience with computers, for whom the mere basic usage of computers can be intimidating. Finally, this teaching approach opens learning opportunities to students with limited access to computers. However, a key obstacle is the current lack of teaching methods and support software for working with and running handwritten programs. Optical character recognition (OCR) of handwritten code is challenging: Minor OCR errors, perhaps due to varied handwriting styles, easily make code not run, and recognizing indentation is crucial for languages like Python but is difficult to do due to inconsistent horizontal spacing in handwriting. Our approach integrates two innovative methods. The first combines OCR with an indentation recognition module and a language model designed for post-OCR error correction without introducing hallucinations. This method, to our knowledge, surpasses all existing systems in handwritten code recognition. It reduces error from 30\% in the state of the art to 5\% with minimal hallucination of logical fixes to student programs. The second method leverages a multimodal language model to recognize handwritten programs in an end-to-end fashion. We hope this contribution can stimulate further pedagogical research and contribute to the goal of making CS education universally accessible. We release a dataset of handwritten programs and code to support future research at https://github.com/mdoumbouya/codeocr

I analyze the evolution of papers certified by the Graphics Replicability Stamp Initiative (GRSI) to be reproducible, with a specific focus on the subset of publications that address visualization-related topics. With this analysis I show that, while the number of papers is increasing overall and within the visualization field, we still have to improve quite a bit to escape the replication crisis. I base my analysis on the data published by the GRSI as well as publication data for the different venues in visualization and lists of journal papers that have been presented at visualization-focused conferences. I also analyze the differences between the involved journals as well as the percentage of reproducible papers in the different presentation venues. Furthermore, I look at the authors of the publications and, in particular, their affiliation countries to see where most reproducible papers come from. Finally, I discuss potential reasons for the low reproducibility numbers and suggest possible ways to overcome these obstacles. This paper is reproducible itself, with source code and data available from github.com/tobiasisenberg/Visualization-Reproducibility as well as a free paper copy and all supplemental materials at osf.io/mvnbj., Comment: 9 pages plus appendix; 12 figures plus 14 figures in the appendix

Counterfeit products cause severe harm to public safety and health by penetrating untrusted supply chains. Numerous anti-counterfeiting techniques have been proposed, among which the use of inherent, unclonable irregularities of paper surfaces has shown considerable potential as a high-performance economical solution. Prior works do not consider supply chains cohesively, either focusing on creating or improving unclonable identifiers or on securing digital records of products. This work aims to systematically unify these two separate but connected research areas by comprehensively analyzing the needs of supply chains. We construct a generalized paper-based authentication framework and identify important shortcomings and promising ideas in the existing literature. Next, we do a stage-wise security analysis of our consolidated framework by drawing inspiration from works in signal processing, cryptography, and biometric systems. Finally, we examine key representative scenarios that illustrate the range of practical and technical challenges in real-world supply chains, and we outline the best practices to guide future research.

The impact of research papers, typically measured in terms of citation counts, depends on several factors, including the reputation of the authors, journals, and institutions, in addition to the quality of the scientific work. In this paper, we present an approach that combines natural language processing and machine learning to predict the impact of papers in a specific journal. Our focus is on the text, which should correlate with impact and the topics covered in the research. We employed a dataset of over 40,000 articles from ACS Applied Materials and Interfaces spanning from 2012 to 2022. The data was processed using various text embedding techniques and classified with supervised machine learning algorithms. Papers were categorized into the top 20% most cited within the journal, using both yearly and cumulative citation counts as metrics. Our analysis reveals that the method employing generative pre-trained transformers (GPT) was the most efficient for embedding, while the random forest algorithm exhibited the best predictive power among the machine learning algorithms. An optimized accuracy of 80\% in predicting whether a paper was among the top 20% most cited was achieved for the cumulative citation count when abstracts were processed. This accuracy is noteworthy, considering that author, institution, and early citation pattern information were not taken into account. The accuracy increased only slightly when the full texts of the papers were processed. Also significant is the finding that a simpler embedding technique, term frequency-inverse document frequency (TFIDF), yielded performance close to that of GPT. Since TFIDF captures the topics of the paper we infer that, apart from considering author and institution biases, citation counts for the considered journal may be predicted by identifying topics and "reading" the abstract of a paper.

Most published Information Systems research are of the behavioral science research (BSR) category rather than the design science research (DSR) category. This is due in part to the BSR orientation of many IS doctoral programs, which often do not involve much technical courses. This includes IS doctoral programs that train Information and Communication Technologies for Development (ICT4D) researchers. Without such technical knowledge many doctoral and postdoctoral researchers will not feel confident in engaging in DSR research. Given the importance of designing artifacts that are appropriate for a given context, an important question is how can ICT4D and other IS researchers increase their IS technical content knowledge and intimacy with the DSR process. In this paper we present, a process for reviewing DSR papers that has as its objectives: enhancing technical content knowledge, increasing knowledge and understanding of approaches to designing and evaluating IS/IT artifacts, and facilitating the identification of new DSR opportunities. This process has been applied for more than a decade at a USA research university., Comment: 44 pages, 9 Figures

Identifying significant references within the complex interrelations of a citation knowledge graph is challenging, which encompasses connections through citations, authorship, keywords, and other relational attributes. The Paper Source Tracing (PST) task seeks to automate the identification of pivotal references for given scholarly articles utilizing advanced data mining techniques. In the KDD CUP OAG-Challenge PST track, we design a recommendation-based framework tailored for the PST task. This framework employs the Neural Collaborative Filtering (NCF) model to generate final predictions. To process the textual attributes of the papers and extract input features for the model, we utilize SciBERT, a pre-trained language model. According to the experimental results, our method achieved a score of 0.37814 on the Mean Average Precision (MAP) metric, outperforming baseline models and ranking 11th among all participating teams. The source code is publicly available at https://github.com/MyLove-XAB/KDDCupFinal., Comment: KDD CUP 2024 OAG-Challenges, Paper Source Tracing, Technical Report of Team AoboSama @ KDD CUP 2024. August 25--29, 2024. Barcelona, Spain

Seeking answers to questions within long scientific research articles is a crucial area of study that aids readers in quickly addressing their inquiries. However, existing question-answering (QA) datasets based on scientific papers are limited in scale and focus solely on textual content. To address this limitation, we introduce SPIQA (Scientific Paper Image Question Answering), the first large-scale QA dataset specifically designed to interpret complex figures and tables within the context of scientific research articles across various domains of computer science. Leveraging the breadth of expertise and ability of multimodal large language models (MLLMs) to understand figures, we employ automatic and manual curation to create the dataset. We craft an information-seeking task involving multiple images that cover a wide variety of plots, charts, tables, schematic diagrams, and result visualizations. SPIQA comprises 270K questions divided into training, validation, and three different evaluation splits. Through extensive experiments with 12 prominent foundational models, we evaluate the ability of current multimodal systems to comprehend the nuanced aspects of research articles. Additionally, we propose a Chain-of-Thought (CoT) evaluation strategy with in-context retrieval that allows fine-grained, step-by-step assessment and improves model performance. We further explore the upper bounds of performance enhancement with additional textual information, highlighting its promising potential for future research and the dataset's impact on revolutionizing how we interact with scientific literature., Comment: preprint

Occlusal contacts are the locations at which the occluding surfaces of the maxilla and the mandible posterior teeth meet. Occlusal contact detection is a vital tool for restoring the loss of masticatory function and is a mandatory assessment in the field of dentistry, with particular importance in prosthodontics and restorative dentistry. The most common method for occlusal contact detection is articulating paper. However, this method can indicate significant medically false positive and medically false negative contact areas, leaving the identification of true occlusal indications to clinicians. To address this, we propose a multiclass Vision Transformer and Fully Convolutional Network ensemble semantic segmentation model with a combination hierarchical loss function, which we name as Hierarchical Fully Convolutional Branch Transformer (H-FCBFormer). We also propose a method of generating medically true positive semantic segmentation masks derived from expert annotated articulating paper masks and gold standard masks. The proposed model outperforms other machine learning methods evaluated at detecting medically true positive contacts and performs better than dentists in terms of accurately identifying object-wise occlusal contact areas while taking significantly less time to identify them. Code is available at https://github.com/Banksylel/H-FCBFormer., Comment: 15 pages, 5 figures, 2 tables, 5 equations, peer reviewed and accepted to Medical Imaging Understanding and Analysis (MIUA 2024)

In recent years, the rapid increase in scientific papers has overwhelmed traditional review mechanisms, resulting in varying quality of publications. Although existing methods have explored the capabilities of Large Language Models (LLMs) for automated scientific reviewing, their generated contents are often generic or partial. To address the issues above, we introduce an automated paper reviewing framework SEA. It comprises of three modules: Standardization, Evaluation, and Analysis, which are represented by models SEA-S, SEA-E, and SEA-A, respectively. Initially, SEA-S distills data standardization capabilities of GPT-4 for integrating multiple reviews for a paper. Then, SEA-E utilizes standardized data for fine-tuning, enabling it to generate constructive reviews. Finally, SEA-A introduces a new evaluation metric called mismatch score to assess the consistency between paper contents and reviews. Moreover, we design a self-correction strategy to enhance the consistency. Extensive experimental results on datasets collected from eight venues show that SEA can generate valuable insights for authors to improve their papers.

The paper referred to in the title is Allard/Almgren 1981 [AA81]. Several months ago Francesco Maggi emailed me saying that the inequalities 5.3(4),(5) of \cite{AA81} were wrong. In fact, as he pointed out, their incorrectness is immediately apparent if one takes $Z=0$ there. Maggi and his coauthor wanted to use these inequalities in their paper \cite{MN}. They were able to obtain a version of these inequalities which suffice for the carrying out the work in \cite{MN}. I started writing this paper in order to provide a version of these inequalities as needed in [AA81]. In thinking about this material I began to realize there were other problems with the paper. As a result I ended up {\em completely rewriting 5.1-5.4 on pages 243-248 of \cite{AA81}}; this rewrite is the contents of this paper. In addition to many annoying misprints many of the needed definitions and proofs in 5.1-5.4 are incomplete or absent. For example, it is not said where $z$ in 5.1(2) comes from; this omission completely surprised me since I remember doing a lot of work to come up with $z$ when \cite{AA81} was being written. Also, much of the necessary material about the reach of a submanifold as in \cite{FE2} is not provided in \cite{AA81}. The table of contents can serve as an index. In particular one sees there where the constants $\epsilon_1$ through $\epsilon_6$ are introduced. This material is extremely technical. One way to navigate this paper would be to start looking at Proposition 9.5 and work backwards.

Testing in production-like test environments is an essential part of quality assurance processes in many industries. Provisioning of such test environments, for information-intensive services, involves setting up databases that are rich-enough to enable simulating a wide variety of user scenarios. While production data is perhaps the gold-standard here, many organizations, particularly within the public sectors, are not allowed to use production data for testing purposes due to privacy concerns. The alternatives are to use anonymized data, or synthetically generated data. In this paper, we elaborate on these alternatives and compare them in an industrial context. Further we focus on synthetic data generation and investigate the use of recurrent neural networks for this purpose. In our preliminary experiments, we were able to generate representative and highly accurate data using a recurrent neural network. These results open new research questions that we discuss here, and plan to investigate in our future research., Comment: This paper was published in the proceedings of RAISE@ICSE in 2019

Human-computer interaction scholars are increasingly touching on topics related to politics or democracy. As these concepts are ambiguous, an examination of concepts' invoked meanings aids in the self-reflection of our research efforts. We conduct a thematic analysis of all papers with the word `politics' in abstract, title or keywords ($n$=378) and likewise 152 papers with the word `democracy.' We observe that these words are increasingly being used in human-computer interaction, both in absolute and relative terms. At the same time, we show that researchers invoke these words with diverse levels of analysis in mind: the early research focused on mezzo-level (i.e., small groups), but more recently the work has begun to include macro-level analysis (i.e., society and politics as played in the public sphere). After the increasing focus on the macro-level, we see a transition towards more normative and activist research, in some areas it replaces observational and empirical research. These differences indicate semantic differences, which -- in the worst case -- may limit scientific progress. We bring these differences visible to help in further exchanges of ideas and human-computer interaction community to explore how it orients itself to politics and democracy.

A draft of a paper by Mandelbaum, "The dynamic complementarity problem", was circulated in 1987, but has never been published. We give an exposition of two important results from that paper which are not readily accessible in the literature. The first is an example of a Skorokhod problem in two dimensions in the quadrant for which there is not uniqueness. The second is a proof of uniqueness for the Skorokhod problem in two dimensions in the quadrant in a critical case.

This work is motivated by two key trends. On one hand, large language models (LLMs) have shown remarkable versatility in various generative tasks such as writing, drawing, and question answering, significantly reducing the time required for many routine tasks. On the other hand, researchers, whose work is not only time-consuming but also highly expertise-demanding, face increasing challenges as they have to spend more time reading, writing, and reviewing papers. This raises the question: how can LLMs potentially assist researchers in alleviating their heavy workload? This study focuses on the topic of LLMs assist NLP Researchers, particularly examining the effectiveness of LLM in assisting paper (meta-)reviewing and its recognizability. To address this, we constructed the ReviewCritique dataset, which includes two types of information: (i) NLP papers (initial submissions rather than camera-ready) with both human-written and LLM-generated reviews, and (ii) each review comes with "deficiency" labels and corresponding explanations for individual segments, annotated by experts. Using ReviewCritique, this study explores two threads of research questions: (i) "LLMs as Reviewers", how do reviews generated by LLMs compare with those written by humans in terms of quality and distinguishability? (ii) "LLMs as Metareviewers", how effectively can LLMs identify potential issues, such as Deficient or unprofessional review segments, within individual paper reviews? To our knowledge, this is the first work to provide such a comprehensive analysis.