Introducing Handwriting into a Multimodal LATEX Formula Editor

Handwriting has been shown to be a useful input modality for math. However, math recognizers are imperfect, especially when recognizing complex expressions. Instead of improving the recognizer itself, we explore ways to best visualize the recognizer\u27s output to help the user fix recognition mistakes more efficiently. To do this, we propose changes to the visual editing operations in MathDeck, a math-aware search engine and formula editor, as well as the addition of an n-best list of results for each symbol in the recognizer\u27s output. We present two experiments to help us find good ways to help users fix errors in the recognizer, and to test whether these changes help novices input formulas more efficiently than they would if they did not have handwriting as an input modality. In the first experiment, users had the option to fix errors with an in-place drop-down menu of alternate symbols, a side symbol correction panel, or by typing the symbols themselves or dragging them from a symbol palette. In our experiment, most users preferred to fix the errors manually by typing the correct symbols or using the symbol palette. In the second experiment, participants entered formulas using handwriting and/or LaTeX. We found evidence that suggests that novices can input formulas faster when they have access to handwriting, but experts still do better when they can just type LaTeX

Teaching Chemistry to Students with Disabilities: A Manual For High Schools, Colleges, and Graduate Programs - Edition 4.1

Ever since it was first published, Teaching Chemistry to Students with Disabilities: A Manual for High Schools, Colleges, and Graduate Programs has served as a vital resource in the chemistry classroom and laboratory to students with disabilities as well as their parents, teachers, guidance counselors, and administrators. The comprehensive 4th edition was last updated in 2001, so the American Chemical Society’s (ACS) Committee on Chemists with Disabilities (CWD) thought it prudent to update such a valuable text at this time. In a changing time of technology, rapid access to information, accessibility tools for individuals with disabilities, and publishing, Edition 4.1 is being published digitally/online as an Open Access text. Having Teaching Chemistry to Students with Disabilities: A Manual for High Schools, Colleges, and Graduate Programs in this format will allow for widespread dissemination and access by maximum numbers of readers at no cost- and will allow the text to remain economically sustainable.https://scholarworks.rit.edu/ritbooks/1001/thumbnail.jp

Leveraging Formulae and Text for Improved Math Retrieval

Large collections containing millions of math formulas are available online. Retrieving math expressions from these collections is challenging. Users can use formula, formula+text, or math questions to express their math information needs. The structural complexity of formulas requires specialized processing. Despite the existence of math search systems and online community question-answering websites for math, little is known about mathematical information needs. This research first explores the characteristics of math searches using a general search engine. The findings show how math searches are different from general searches. Then, test collections for math-aware search are introduced. The ARQMath test collections have two main tasks: 1) finding answers for math questions and 2) contextual formula search. In each test collection (ARQMath-1 to -3) the same collection is used, Math Stack Exchange posts from 2010 to 2018, introducing different topics for each task. Compared to the previous test collections, ARQMath has a much larger number of diverse topics, and improved evaluation protocol. Another key role of this research is to leverage text and math information for improved math information retrieval. Three formula search models that only use the formula, with no context are introduced. The first model is an n-gram embedding model using both symbol layout tree and operator tree representations. The second model uses tree-edit distance to re-rank the results from the first model. Finally, a learning-to-rank model that leverages full-tree, sub-tree, and vector similarity scores is introduced. To use context, Math Abstract Meaning Representation (MathAMR) is introduced, which generalizes AMR trees to include math formula operations and arguments. This MathAMR is then used for contextualized formula search using a fine-tuned Sentence-BERT model. The experiments show tree-edit distance ranking achieves the current state-of-the-art results on contextual formula search task, and the MathAMR model can be beneficial for re-ranking. This research also addresses the answer retrieval task, introducing a two-step retrieval model in which similar questions are first found and then answers previously given to those similar questions are ranked. The proposed model, fine-tunes two Sentence-BERT models, one for finding similar questions and another one for ranking the answers. For Sentence-BERT model, raw text as well as MathAMR are used

Fine Art Pattern Extraction and Recognition

This is a reprint of articles from the Special Issue published online in the open access journal Journal of Imaging (ISSN 2313-433X) (available at: https://www.mdpi.com/journal/jimaging/special issues/faper2020)

Improved Study of Side-Channel Attacks Using Recurrent Neural Networks

Differential power analysis attacks are special kinds of side-channel attacks where power traces are considered as the side-channel information to launch the attack. These attacks are threatening and significant security issues for modern cryptographic devices such as smart cards, and Point of Sale (POS) machine; because after careful analysis of the power traces, the attacker can break any secured encryption algorithm and can steal sensitive information. In our work, we study differential power analysis attack using two popular neural networks: Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN). Our work seeks to answer three research questions(RQs): RQ1: Is it possible to predict the unknown cryptographic algorithm using neural network models from different datasets? RQ2: Is it possible to map the key value for the specific plaintext-ciphertext pair with or without side-band information? RQ3: Using similar hyper-parameters, can we evaluate the performance of two neural network models (CNN vs. RNN)? In answering the questions, we have worked with two different datasets: one is a physical dataset (DPA contest v1 dataset), and the other one is simulated dataset (toggle count quantity) from Verilog HDL. We have evaluated the efficiency of CNN and RNN models in predicting the unknown cryptographic algorithms of the device under attack. We have mapped to 56 bits key for a specific plaintext-ciphertext pair with and without using side-band information. Finally, we have evaluated vi our neural network models using different metrics such as accuracy, loss, baselines, epochs, speed of operation, memory space consumed, and so on. We have shown the performance comparison between RNN and CNN on different datasets. We have done three experiments and shown our results on these three experiments. The first two experiments have shown the advantages of choosing CNN over RNN while working with side-channel datasets. In the third experiment, we have compared two RNN models on the same datasets but different dimensions of the datasets

Technical Language Supervision for Intelligent Fault Diagnosis in Process Industry

In the process industry, condition monitoring systems with automated fault diagnosis methods assist human experts and thereby improve maintenance efficiency, process sustainability, and workplace safety. Improving the automated fault diagnosis methods using data and machine learning-based models is a central aspect of intelligent fault diagnosis (IFD). A major challenge in IFD is to develop realistic datasets with accurate labels needed to train and validate models, and to transfer models trained with labeled lab data to heterogeneous process industry environments. However, fault descriptions and work-orders written by domain experts are increasingly digitised in modern condition monitoring systems, for example in the context of rotating equipment monitoring. Thus, domain-specific knowledge about fault characteristics and severities exists as technical language annotations in industrial datasets. Furthermore, recent advances in natural language processing enable weakly supervised model optimisation using natural language annotations, most notably in the form of natural language supervision (NLS). This creates a timely opportunity to develop technical language supervision (TLS) solutions for IFD systems grounded in industrial data, for example as a complement to pre-training with lab data to address problems like overfitting and inaccurate out-of-sample generalisation. We surveyed the literature and identify a considerable improvement in the maturity of NLS over the last two years, facilitating applications beyond natural language; a rapid development of weak supervision methods; and transfer learning as a current trend in IFD which can benefit from these developments. Finally we describe a general framework for TLS and implement a TLS case study based on Sentence-BERT and contrastive learning based zero-shot inference on annotated industry data

Word Importance Modeling to Enhance Captions Generated by Automatic Speech Recognition for Deaf and Hard of Hearing Users

People who are deaf or hard-of-hearing (DHH) benefit from sign-language interpreting or live-captioning (with a human transcriptionist), to access spoken information. However, such services are not legally required, affordable, nor available in many settings, e.g., impromptu small-group meetings in the workplace or online video content that has not been professionally captioned. As Automatic Speech Recognition (ASR) systems improve in accuracy and speed, it is natural to investigate the use of these systems to assist DHH users in a variety of tasks. But, ASR systems are still not perfect, especially in realistic conversational settings, leading to the issue of trust and acceptance of these systems from the DHH community. To overcome these challenges, our work focuses on: (1) building metrics for accurately evaluating the quality of automatic captioning systems, and (2) designing interventions for improving the usability of captions for DHH users. The first part of this dissertation describes our research on methods for identifying words that are important for understanding the meaning of a conversational turn within transcripts of spoken dialogue. Such knowledge about the relative importance of words in spoken messages can be used in evaluating ASR systems (in part 2 of this dissertation) or creating new applications for DHH users of captioned video (in part 3 of this dissertation). We found that models which consider both the acoustic properties of spoken words as well as text-based features (e.g., pre-trained word embeddings) are more effective at predicting the semantic importance of a word than models that utilize only one of these types of features. The second part of this dissertation describes studies to understand DHH users\u27 perception of the quality of ASR-generated captions; the goal of this work was to validate the design of automatic metrics for evaluating captions in real-time applications for these users. Such a metric could facilitate comparison of various ASR systems, for determining the suitability of specific ASR systems for supporting communication for DHH users. We designed experimental studies to elicit feedback on the quality of captions from DHH users, and we developed and evaluated automatic metrics for predicting the usability of automatically generated captions for these users. We found that metrics that consider the importance of each word in a text are more effective at predicting the usability of imperfect text captions than the traditional Word Error Rate (WER) metric. The final part of this dissertation describes research on importance-based highlighting of words in captions, as a way to enhance the usability of captions for DHH users. Similar to highlighting in static texts (e.g., textbooks or electronic documents), highlighting in captions involves changing the appearance of some texts in caption to enable readers to attend to the most important bits of information quickly. Despite the known benefits of highlighting in static texts, research on the usefulness of highlighting in captions for DHH users is largely unexplored. For this reason, we conducted experimental studies with DHH participants to understand the benefits of importance-based highlighting in captions, and their preference on different design configurations for highlighting in captions. We found that DHH users subjectively preferred highlighting in captions, and they reported higher readability and understandability scores and lower task-load scores when viewing videos with captions containing highlighting compared to the videos without highlighting. Further, in partial contrast to recommendations in prior research on highlighting in static texts (which had not been based on experimental studies with DHH users), we found that DHH participants preferred boldface, word-level, non-repeating highlighting in captions

Evolution of Natural Language Processing Technology: Not Just Language Processing Towards General Purpose AI

Since the invention of computers, communication through natural language (actual human language) has been a dream technology. However, natural language is extremely difficult to mathematically formulate, making it difficult to realize as an algorithm without considering programming. While there have been numerous technological developments, one cannot say that any results allowing free utilization have been achieved thus far. In the case of language learning in humans, for instance when learning one's mother tongue or foreign language, one must admit that this process is similar to the adage "practice makes perfect" in principle, even though the learning method is significant up to a point. Deep learning has played a central role in contemporary AI technology in recent years. When applied to natural language processing (NLP), this produced unprecedented results. Achievements exceeding the initial predictions have been reported from the results of learning vast amounts of textual data using deep learning. For instance, four arithmetic operations could be performed without explicit learning, thereby enabling the explanation of complex images and the generation of images from corresponding explanatory texts. It is an accurate example of the learner embodying the concept of "practice makes perfect" by using vast amounts of textual data. This report provides a technological explanation of how cutting-edge NLP has made it possible to realize the "practice makes perfect" principle. Additionally, examples of how this can be applied to business are provided. We reported in June 2022 in Japanese on the NLP movement from late 2021 to early 2022. We would like to summarize this as a memorandum since this is just the initial movement leading to the current large language models (LLMs).Comment: 40 page