Miracle-dz/UniFAR-FTUs huggingface dataset:
Automating Claim Construction in Patent Applications: The CMUmine Dataset

Intellectual Property (IP) in the form of issued patents is a critical and very desirable element of innovation in high-tech. In this position paper, we explore the possibility of automating the legal task of Claim Construction in patent applications via Natural Language Processing (NLP) and Machine Learning (ML). To this end, we first create a large dataset known as CMUmine™and then demonstrate that, using NLP and ML techniques the Claim Construction in patent applications, a crucial legal task currently performed by IP attorneys, can be automated. To the best of our knowledge, this is the first public patent application dataset. Our results look very promising in automating the patent application process.
“It seemed like an annoying woman”: On the Perception and Ethical Considerations of Affective Language in Text-Based Conversational Agents

Previous research has found that task-oriented conversational agents are perceived more positively by users when they provide information in an empathetic manner compared to a plain, emotionless information exchange. However, users’ perception and ethical considerations related to a dialog systems’ response language style have received comparatively little attention in the field of human-computer interaction. To bridge this gap, we explored these ethical implications through a scenario-based user study. 127 participants interacted with one of three variants of an affective, task-oriented conversational agent, each variant providing responses in a different language style. After the interaction, participants filled out a survey about their feelings during the experiment and their perception of various aspects of the chatbot. Based on statistical and qualitative analysis of the responses, we found language style played an important role in how human-like participants perceived a dialog agent as well as how likable. Language style also had a direct effect on how users perceived the use of personal pronouns ‘I’ and ‘You’ and how they projected gender onto the chatbot. Finally, we identify and discuss ethical implications. In particular we focus on what factors/stereotypes influenced participants’ impressions of gender, and what trade-offs a more human-like chatbot brings.
The FairyNet Corpus - Character Networks for German Fairy Tales

This paper presents a data set of German fairy tales, manually annotated with character networks which were obtained with high inter rater agreement. The release of this corpus provides an opportunity of training and comparing different algorithms for the extraction of character networks, which so far was barely possible due to heterogeneous interests of previous researchers. We demonstrate the usefulness of our data set by providing baseline experiments for the automatic extraction of character networks, applying a rule-based pipeline as well as a neural approach, and find the neural approach outperforming the rule-approach in most evaluation settings.
A Corpus for Multilingual Analysis of Online Terms of Service

We present the first annotated corpus for multilingual analysis of potentially unfair clauses in online Terms of Service. The data set comprises a total of 100 contracts, obtained from 25 documents annotated in four different languages: English, German, Italian, and Polish. For each contract, potentially unfair clauses for the consumer are annotated, for nine different unfairness categories. We show how a simple yet efficient annotation projection technique based on sentence embeddings could be used to automatically transfer annotations across languages.
Detecting Depression in Thai Blog Posts: a Dataset and a Baseline

We present the first openly available corpus for detecting depression in Thai. Our corpus is compiled by expert verified cases of depression in several online blogs. We experiment with two different LSTM based models and two different BERT based models. We achieve a 77.53% accuracy with a Thai BERT model in detecting depression. This establishes a good baseline for future researcher on the same corpus. Furthermore, we identify a need for Thai embeddings that have been trained on a more varied corpus than Wikipedia. Our corpus, code and trained models have been released openly on Zenodo.
Sequence Mixup for Zero-Shot Cross-Lingual Part-Of-Speech Tagging

There have been efforts in cross-lingual transfer learning for various tasks. We present an approach utilizing an interpolative data augmentation method, Mixup, to improve the generalizability of models for part-of-speech tagging trained on a source language, improving its performance on unseen target languages. Through experiments on ten languages with diverse structures and language roots, we put forward its applicability for downstream zero-shot cross-lingual tasks.
Learning Cross-lingual Representations for Event Coreference Resolution with Multi-view Alignment and Optimal Transport

We study a new problem of cross-lingual transfer learning for event coreference resolution (ECR) where models trained on data from a source language are adapted for evaluations in different target languages. We introduce the first baseline model for this task based on XLM-RoBERTa, a state-of-the-art multilingual pre-trained language model. We also explore language adversarial neural networks (LANN) that present language discriminators to distinguish texts from the source and target languages to improve the language generalization for ECR. In addition, we introduce two novel mechanisms to further enhance the general representation learning of LANN, featuring: (i) multi-view alignment to penalize cross coreference-label alignment of examples in the source and target languages, and (ii) optimal transport to select close examples in the source and target languages to provide better training signals for the language discriminators. Finally, we perform extensive experiments for cross-lingual ECR from English to Spanish and Chinese to demonstrate the effectiveness of the proposed methods.
Polar Embedding

Hierarchical relationships are invaluable information for many natural language processing (NLP) tasks. Distributional representation has become a fundamental approach for encoding word relationships, particularly embeddings in hyperbolic space showed great performance in representing hierarchies by taking advantage of their spatial properties. However, most machine learning systems do not suppose to use in such complex non-Euclidean geometries. To achieve hierarchy representations in commonly used Euclidean space, we propose Polar Embedding that learns word embeddings with the polar coordinate system. Utilizing characteristics of polar coordinates, the hierarchy of words is expressed with two independent variables: radius (generality) and angles (similarity), and their variables are optimized separately. Polar embedding shows word hierarchies explicitly and allows us to use beneficial resources such as word frequencies or word generality annotations for computing radiuses. We introduce an optimization method for learning angles in limited ranges of polar coordinates, which combining a loss function controlling gradient and distribution uniformization. Experimental results on hypernymy datasets indicate that our approach outperforms other embeddings in low-dimensional Euclidean space and competitively performs even with hyperbolic embeddings, which possess a geometric advantage.
Exploring Metaphoric Paraphrase Generation

Metaphor generation is a difficult task, and has seen tremendous improvement with the advent of deep pretrained models. We focus here on the specific task of metaphoric paraphrase generation, in which we provide a literal sentence and generate a metaphoric sentence which paraphrases that input. We compare naive, “free” generation models with those that exploit forms of control over the generation process, adding additional information based on conceptual metaphor theory. We evaluate two methods for generating paired training data, which is then used to train T5 models for free and controlled generation. We use crowdsourcing to evaluate the results, showing that free models tend to generate more fluent paraphrases, while controlled models are better at generating novel metaphors. We then analyze evaluation metrics, showing that different metrics are necessary to capture different aspects of metaphoric paraphrasing. We release our data and models, as well as our annotated results in order to facilitate development of better evaluation metrics.
Argumentation Mining in Scientific Literature for Sustainable Development

Science, technology and innovation (STI) policies have evolved in the past decade. We are now progressing towards policies that are more aligned with sustainable development through integrating social, economic and environmental dimensions. In this new policy environment, the need to keep track of innovation from its conception in Science and Research has emerged. Argumentation mining, an interdisciplinary NLP field, gives rise to the required technologies. In this study, we present the first STI-driven multidisciplinary corpus of scientific abstracts annotated for argumentative units (AUs) on the sustainable development goals (SDGs) set by the United Nations (UN). AUs are the sentences conveying the Claim(s) reported in the author’s original research and the Evidence provided for support. We also present a set of strong, BERT-based neural baselines achieving an f1-score of 70.0 for Claim and 62.4 for Evidence identification evaluated with 10-fold cross-validation. To demonstrate the effectiveness of our models, we experiment with different test sets showing comparable performance across various SDG policy domains. Our dataset and models are publicly available for research purposes.
Bayesian Argumentation-Scheme Networks: A Probabilistic Model of Argument Validity Facilitated by Argumentation Schemes

We propose a methodology for representing the reasoning structure of arguments using Bayesian networks and predicate logic facilitated by argumentation schemes. We express the meaning of text segments using predicate logic and map the boolean values of predicate logic expressions to nodes in a Bayesian network. The reasoning structure among text segments is described with a directed acyclic graph. While our formalism is highly expressive and capable of describing the informal logic of human arguments, it is too open-ended to actually build a network for an argument. It is not at all obvious which segment of argumentative text should be considered as a node in a Bayesian network, and how to decide the dependencies among nodes. To alleviate the difficulty, we provide abstract network fragments, called idioms, which represent typical argument justification patterns derived from argumentation schemes. The network construction process is decomposed into idiom selection, idiom instantiation, and idiom combination. We define 17 idioms in total by referring to argumentation schemes as well as analyzing actual arguments and fitting idioms to them. We also create a dataset consisting of pairs of an argumentative text and a corresponding Bayesian network. Our dataset contains about 2,400 pairs, which is large in the research area of argumentation schemes.
Test Harder than You Train: Probing with Extrapolation Splits

Previous work on probing word representations for linguistic knowledge has focused on interpolation tasks. In this paper, we instead analyse probes in an extrapolation setting, where the inputs at test time are deliberately chosen to be ‘harder’ than the training examples. We argue that such an analysis can shed further light on the open question whether probes actually decode linguistic knowledge, or merely learn the diagnostic task from shallow features. To quantify the hardness of an example, we consider scoring functions based on linguistic, statistical, and learning-related criteria, all of which are applicable to a broad range of NLP tasks. We discuss the relative merits of these criteria in the context of two syntactic probing tasks, part-of-speech tagging and syntactic dependency labelling. From our theoretical and experimental analysis, we conclude that distance-based and hard statistical criteria show the clearest differences between interpolation and extrapolation settings, while at the same time being transparent, intuitive, and easy to control.
TESTS FOR CREATIVE ABILITY IN MACHINE DESIGN

Abstract : This study concerns primarily the identification of creative machine design ability at the industrial level. Prediction of creative ability at the time individuals begin specialized engineering courses in college is also considered. Six special ability tests and a personality test were constructed in an effort to identify creative machine design ability. Two experimental test batteries were assembled, each consisting of 3 special ability tests and a personality test. Tests comprising Battery A were Mechanical Ingeuity-Power Source Apparatus (PSA); Three-Dimensional Space Relations Test (3-D); Figure Matrices Test (FM); and Personal Inventory (PI). Tests in Battery B included Mechanical Ingenuity-Design A Machine Test (DM), Number Series Test (NS), and Unstructured Test for Creativity in Machine Design (Unstr.). The Personal History Form was included in both batteries. Sixteen companies, contributing a total of 136 engineers, cooperated in supplying personnel for testing. Battery A was administered to 70 men, and Battery B was given to 66 men. The results of the measures of association obtained among discriminating items on the several tests were restricted, both by item reliability, and by the fact that all items were correlated with the criterion. Further item construction was postponed until more information about present items could be collected.
Atmospheric Transport Modeling and Data Fusion in Support of Comprehensive Nuclear-Test-Ban Treaty Verification

Abstract : Waveform signals can be processed to distinguish between explosions and natural events and can help locating explosion centers fast and with high accuracy. However, they cannot be used to demonstrate a possible nuclear nature for the event. This final proof can only be provided by detection of airborne radioactivity downwind of the event. To support the verification of the Comprehensive Nuclear-Test-Ban Treaty (CTBT) based on particulate radionuclide and noble gas monitoring in combination with seismic explosion location results, the Provisional Technical Secretariat (PTS) has developed the Atmospheric Transport Modeling (ATM) software. The software receives as input weather analyses from two world-leading centers (European Centre for Medium-Range Weather Forecasts and US Center for Environmental Prediction) in near-real-time and with high resolution. The PTS then computes, 14 days backward in time, source-receptor sensitivity (SRS) fields for all radionuclide samples taken at its operating and non-operating (future) sites. The diagnostic Lagrangian particle diffusion model FLeXpaRT is utilized as transport code. The code simulates transport and diffusion in inverse (backtracking) mode. The simulated adjoint tracer is released at the exact location of the monitoring site from the end to the start time of the air sampling, and then re-sampled every three hours at the surface level (0-30 m) on a global grid up to 14 days backward. The resulting SRS fields (equivalent to concentration fields in forward simulations) are stored in a database and are available ahead of the measurement results. In case of qualified events, the PTS will have the opportunity to request SRS fields from all Regional Specialized Meteorological Centres (RSMCs) of the World Meteorological Organisation (WMO).
Inviting the World into the Online Classroom: Teaching a Gaming in Libraries Course Via YouTube

This article discusses an online graduate-level course that was taught primarily using videos posted publ icly to YouTube and the Internet Archive. Videos were created specifically for this course by the instructor and by library professionals and posted each day for a 30-day period. All videos were posted publicly, and the American Library Association hosted an open discussion forum about each video. Formal enrollment in the course was low, but hundreds of people watched the videos. Post-course surveys indicate that viewers were a mix of librarians and library students along with hobby gamers and members of the gaming industry. This case study demonstrates the impact that a library school course can have on the field when the content is made freely available and distributed through social media networks.
Roll Printed Electronics: Development and Scaling of Gravure Printing Techniques

To realize the potential cost savings promised by printed electronics, high-speed, large-volume manufacturing methods must be established. Rotary printing techniques such as those used in the graphic arts are ideal candidates. However, very little research has been done on utilizing these techniques for printed electronics because digital processes such ink-jet printing have offered researchers a low-cost flexible solution for demonstrating the printability of their materials, despite the fact that these processes may be difficult to scale for large-volume manufacturing. In this thesis, gravure, a printing process which offers the highest resolution, highest speed, and largest volume production in the graphic arts is demonstrated as a viable technique for printed electronics. In order to make laboratory-scale research with this technique possible, a custom table-top gravure printing press was designed. This press allows for small amounts of ink to be utilized during a print and enables multi-layer prints with a registration accuracy not seen in conventional gravure printing presses and suitable for printed electronics. With this press, printing processes to deposit functional materials for printed circuits are investigated with a focus on developing process modules to manufacture fully printed organic thin film transistors. Considerable effort is made to establish processes to deposit metallic lines with feature sizes below 20 μm and a total surface roughness below 20 nm, uniform thin films of polymer dielectrics with thicknesses as low as 70 nm, and high performance polymer semiconductors. These processes are then integrated to manufacture capacitors suitable for integrated circuit components and organic thin film transistors with operating frequencies as high as 18 kHz.
Psychological problems associated with coping strategies: a web-based study in Peruvian population during the COVID-19 pandemic

ABSTRACT:BACKGROUND: The Coronavirus disease has quickly spread to all corners across Latin-American countries. Its exponential capacity has overwhelmed even the most resilient health systems (1). The already significant impact of the Coronavirus (COVID-19) on the global scale is worsened by psychological responses that may aggravate the health crisis. OBJECTIVE: This study is aimed at collecting demographic information, prevalence of psychological morbidities and associated coping styles during the COVID-19 pandemic on Peruvian population. Methods: This cross-sectional study was conducted through online survey using snowball sampling techniques after the state of emergency was declared in Perú. The General Health Questionnaire (GHQ -28) identify somatic symptom, anxiety/ insomnia, social misfunctioning and depression prevalence and the Coping Strategy Questionnaire (COPE-28) maps strategies addressing recent stress. RESULTS: We collected data from 434 self-selected participants within 18-68 years old (ME =33.87), by analysing the socio-economic information in which the majority of the respondents were women (61.3%), aged between 18 and 28 (41.7%), from well-educated groups (>=85.0 %), Peruvian (94.2%), working citizen (57.4%) and single (71.20 %). Nearly 40.8% rated psychological problems, expressing fear of being infected by coronavirus (71.43%). Regression analysis shows that female gender is associated to higher somatic (p<0.001*, C.I: -2.75 to -.99) and anxiety/insomnia symptoms (p=0.00*, C.I: -2.98 to 0.84). Depression and social dysfunction are experienced at any age. Educational status is protective against developing psychological disorders (p<0.05). While active responses (Acceptance and Social support) are scarcely used by individuals with psychological problems; Passive strategies are commonly reported (Denial, Self-Distraction, Self-Blame, Disconnection, and Emotional discharge). CONCLUSION: These findings provide a better understanding of psychological disorders resulting from the current pandemic in Peruvian population. This evidence informs the need to strengthen mental health programs, especially in less privileged groups after the Covid-19 pandemic.
ELECTRICAL SWITCHING PROPERTIES OF TERNARY AND LAYERED CHALCOGENIDE PHASE-CHANGE MEMORY DEVICES

The following individuals read and discussed the thesis submitted by student Martin Jared Barclay, and they evaluated his presentation and response to questions during the final oral examination. They found that the student passed the final oral examination. v ACKNOWLEDGEMENTS I would like to express my most sincere gratitude for the individuals that follow. They have all leant an invaluable hand in finishing this thesis. First, I would thank my advisor Dr. Kristy A. Campbell for her willingness to see this thesis through to the end. She has spent many hours training, mentoring, and instructing me; I could not have accomplished this thesis without her help. I am also indebted to many others at Micron Technology, such as John Smythe and Du Li, for all of their support during this project and for expediting the needed x-section TEM images on the Ternary and Layered devices. I would also like to thank all of those involved in the design, fabrication, and processing of the Ternary and Layered devices at Boise State University, specifically, Morgan Davis for the common bottom electrode Ternary samples and Pulok Pattanayak for the isolated bottom electrode Ternary devices. Thank you to the members of my committee, Dr. Maria Mitkova and Dr. R. Jacob Baker, for their participation in making this thesis possible. Finally, I would like to take a moment to thank my wife, Jacqueline M. Barclay, for her love and support through this entire process. She is the driving force in my life; I could not have asked for a better companion. Development since 2004. He has worked in a number of processing areas, such as Physical Vapor Deposition (PVD), Chemical Vapor Deposition (CVD), and is currently a Process Engineer in Chemical Mechanical Planarization (CMP). He received his Bachelors Degree at Boise State University in 2007. Since this time he has continued his education, pursuing a Master of Science Degree in Electrical Engineering while continuing to work full time at Micron Technology. His emphasis of study has been in organic and glass-based devices. vii ABSTRACT Due to the increasing demand for products which use Non-Volatile Memory (NVM) and the near realization of the scaling limits of Flash [1, 2], a large research effort is underway. This effort is to develop new forms of NVM capable of replacing Flash [3]. At the forefront of this research is Phase-Change Random Access Memory (PCRAM). Chalcogenide based PCRAM is one …
RHIC Injector Complex Online Model Status and Plans

An online modeling system is being developed for the RHIC injector complex, which consists of the Booster, the AGS and the transfer lines connecting the Booster to the AGS and the AGS to RHIC. Historically the injectors have been operated using static values from design specifications or offline model runs, but tighter beam optics constraints required by polarized proton operations (e.g, accelerating with near-integer tunes) have necessitated a more dynamic system. An online model server for the AGS has been implemented using MAD-X [1] as the model engine, with plans to extend the system to the Booster and the injector transfer lines and to add the option of calculating optics using the Polymorphic Tracking Code (PTC [2]) as the model engine.