Reviewerly/exHarmony huggingface dataset:
Machine Translation Errors and L2 Learners’ Correction Strategies by Error Type and English Proficiency

To investigate L2 adolescent learners’ use of machine translation (MT), an MT error correction (EC) test was developed, based on the analysis of MT errors arising from translating the learners’ L1 of middle school EFL textbooks. Learners were also asked to report on their use of MT EC strategies on the EC task. Results indicated that mistranslated sentence and verb tense are the most difficult types of MT errors to correct. Furthermore, to resolve MT errors, guessing from context and literal translations were the two most frequently employed EC strategies. When multiple regression analysis was conducted to examine the contribution of EC strategies to the learners’ ability to correct errors, the mid proficiency learners’ reliance on literal translations and the low proficiency learners’ use of multiple EC strategies were positively associated with improved corrections of MT errors. The results of the study are discussed in light of how L2 learners need to develop competence for using MT in L2 writing.
On the Security of a Lattice-Based Multi-Stage Secret Sharing Scheme

In response to the threat posed by quantum computers, Pilaram and Eghlidos proposed the first lattice-based multi-stage secret sharing scheme which is the only post-quantum multi-stage secret sharing scheme. In this paper, we introduce an efficient attack on it and show that any adversary can easily reconstruct unrecovered secrets as long as it collects enough pseudo-secret shares. For the sake of complete, we further list two countermeasures to protect the scheme from such attack.
The sounds of silence

To understand fully the impact of the COVID-19 pandemic on concert life, the following points should be heeded: (1) The pandemic and its lockdowns arrived as the global music business had solidified around online streaming and live music as major income streams, with lockdowns wreaking havoc on the latter. (2) While music superstars are the most visible and well funded in live music, non-superstars are the ones keeping live music alive, as well as the ones who suffered particularly amidst the lockdowns. (3) The pandemic struggles of non-superstars, in turn, reverberated throughout the cities in which they once performed prior to the lockdowns, doing so financially and communally. We demonstrate these points, on the one hand, by drawing upon the insights of multiple literatures and publicly available data on the global music business. On the other hand, we track live music in three cities—Athens, Atlanta, and Taipei—showing when and where their concerts grew silent amid the pandemic, as well as for whom that silence occurred (non-superstars vs. superstars). We thus provide a ‘big-picture’ view of COVID-19’s impact on musicians and communities as it pertains to in-person concerts in an era of online streaming.
A Visual Analytics System for Improving Attention-based Traffic Forecasting Models

With deep learning (DL) outperforming conventional methods for different tasks, much effort has been devoted to utilizing DL in various domains. Researchers and developers in the traffic domain have also designed and improved DL models for forecasting tasks such as estimation of traffic speed and time of arrival. However, there exist many challenges in analyzing DL models due to the black-box property of DL models and complexity of traffic data (i.e., spatio-temporal dependencies). Collaborating with domain experts, we design a visual analytics system, AttnAnalyzer, that enables users to explore how DL models make predictions by allowing effective spatio-temporal dependency analysis. The system incorporates dynamic time warping (DTW) and Granger causality tests for computational spatio-temporal dependency analysis while providing map, table, line chart, and pixel views to assist user to perform dependency and model behavior analysis. For the evaluation, we present three case studies showing how AttnAnalyzer can effectively explore model behaviors and improve model performance in two different road networks. We also provide domain expert feedback.
Grover on sparkle quantum resource estimation for a NIST LWC call finalist

In this paper, we propose an estimation of quantum resources necessary for recovering a key using Known Plain Text Attack (KPA) model for SPARKLE family of LWC authenticated block ciphers - SCHWAEMM. The procedure is based on a general attack using Grover's search algorithm with encryption oracle over key space in superposition. The paper explains step by step how to evaluate the cost of each operation type in encryption oracle in terms of various quantum and reversible gates. The result of this paper is an implementation of the simplified version of this cipher using quantum computer and summary table which shows the depth of quantum circuit, the size of quantum register and how many gates of NCT family are required for implementing the ciphers and attacks on them.
Russian Academic Journals in New Geopolitical Conditions: Difficulties and Prospects of Development

The article presents a point of view on possible ways to further develop and support Russian academic periodicals under conditions of sanctions pressure and a significant reduction in opportunities for cooperation with Western companies, including those owning Web of Science and Scopus. The author discusses a number of negative consequences of the orientation of Russian science and scholarship mainly to these scientometric databases. It is emphasized that it is necessary to continue working in order to improve the quality level of academic publications and to develop national indexes, while not cutting ties where possible, and establishing new collaborations in this field.
Finding space to store archaeological collections

Abstract The United States has been in a serious ‘crisis’ regarding the storage and curation of archaeological collections since the 1970s when a number of federal, state, and tribal laws and regulations were issued to protect archaeological resources. As collections accumulated, repositories to curate and store them usually lacked space, trained staff, necessary funding, security, and research access. This chapter outlines the development of the curation crisis and its challenges, including costs, repository closures, ownership, orphan collections, creation and management of digital associated records, professional curation standards, and attitudes. The last section discusses the progress made to overcome these challenges through improved awareness, more use of collections, and cooperation among many stakeholders who have worked to create sustainable management practices.
E-commerce Application with Analytics for Pharmaceutical Industry

E-commerce applications play a very crucial role in the medical field these days. With so many medicines and illness medicines, it is very difficult to track the pattern of medicines to find the best medicine for the illness. There is a lot of research on medical pattern prediction there. This is because the clinic could not predict which drugs would be dispensed frequently. So, here this study object is to create a data mining program that uses the time association rule method to determine the relationships between drugs. This is accompanied by the date when the drug was released. Almost all studies use static datasets to predict medical patterns, discover knowledge, and create useful “patterns” through the “drug medicine” triad of associative learning.
Interactively Assessing Disentanglement in GANs

Abstract Generative adversarial networks (GAN) have witnessed tremendous growth in recent years, demonstrating wide applicability in many domains. However, GANs remain notoriously difficult for people to interpret, particularly for modern GANs capable of generating photo‐realistic imagery. In this work we contribute a visual analytics approach for GAN interpretability, where we focus on the analysis and visualization of GAN disentanglement. Disentanglement is concerned with the ability to control content produced by a GAN along a small number of distinct, yet semantic, factors of variation. The goal of our approach is to shed insight on GAN disentanglement, above and beyond coarse summaries, instead permitting a deeper analysis of the data distribution modeled by a GAN. Our visualization allows one to assess a single factor of variation in terms of groupings and trends in the data distribution, where our analysis seeks to relate the learned representation space of GANs with attribute‐based semantic scoring of images produced by GANs. Through use‐cases, we show that our visualization is effective in assessing disentanglement, allowing one to quickly recognize a factor of variation and its overall quality. In addition, we show how our approach can highlight potential dataset biases learned by GANs.
An Approach for Formant Synthesis of Kannada

Formant Synthesis is one of the ways of producing artificial speech using a digital computer. Artificial or synthetic speech is commonly used in Text To Speech (TTS) conversion systems and in various Human Machine Interfaces (HMI). A very good example of usage of synthetic speech is in automatic public address or announcement systems that are commonly found in railway stations, airports, bus stations, banks, malls etc., This paper presents a method of synthesizing Kannada speech utterances such as Vowels, Semi-vowels, Diphthongs, Consonants and Consonant-Vowel (CV) co-articulations that forms the building block of a fully functional Formant Synthesizer for Kannada language. A Klatt type formant synthesizer implemented using C# .NET is employed for producing these utterances in this work. PRAAT tool is used to perform analysis and obtaining the control parameters needed for generating various utterances. A total of 192 utterances including 10 vowels, 2 diphthongs and 180 CV co-articulations were synthesized and of which 176 were correctly identified in perception test giving an accuracy of 91.67% on an average. Formant synthesizer allows more prosody control of synthesized utterances over other methods of speech synthesis and hence this work can serve as a guide to researchers trying to develop a functional format synthesizer for Kannada language.
Does <i>f</i> O <sub>2</sub> influence reversible silicate melting without redox-active cations?

Microbial communities are found throughout the biosphere, from human guts to glaciers, from soil to activated sludge. Understanding the statistical properties of such diverse communities can pave the way to elucidate the common mechanisms ...Multiple ecological forces act together to shape the composition of microbial communities. Phyloecology approaches—which combine phylogenetic relationships between species with community ecology—have the potential to disentangle such forces but are often ...
Translating SysML Activity Diagrams for nuXmv Verification of an Autonomous Pancreas

Model Based Systems Engineering (MBSE) provides a single platform capable of defining complex, multidisciplinary systems, but commonly-used tools such as Systems Modeling Language (SysML) lack the ability to formally validate and verify these systems. Symbolic model checking operates on system models of similar levels of abstraction to SysML, providing a push-button technique for ensuring the possible behavior set always obeys temporal requirements, e.g., for safe operation. We propose a translation method from SysML activity diagrams to the popular symbolic model checker nuXmv to enable their formal verification in four main steps: main module definition, submodule definition, activity diagram organization, and activity diagram translation. We apply this process to the Autonomous Artificial Pancreas System (AAPS) as a trade study. We then verify and validate the AAPS nuXmv model against a set of specifications derived from the AAPS safety requirements.
A Novel Tropical Geometry-Based Interpretable Machine Learning Method: Pilot Application to Delivery of Advanced Heart Failure Therapies

A model's interpretability is essential to many practical applications such as clinical decision support systems. In this article, a novel interpretable machine learning method is presented, which can model the relationship between input variables and responses in humanly understandable rules. The method is built by applying tropical geometry to fuzzy inference systems, wherein variable encoding functions and salient rules can be discovered by supervised learning. Experiments using synthetic datasets were conducted to demonstrate the performance and capacity of the proposed algorithm in classification and rule discovery. Furthermore, we present a pilot application in identifying heart failure patients that are eligible for advanced therapies as proof of principle. From our results on this particular application, the proposed network achieves the highest F1 score. The network is capable of learning rules that can be interpreted and used by clinical providers. In addition, existing fuzzy domain knowledge can be easily transferred into the network and facilitate model training. In our application, with the existing knowledge, the F1 score was improved by over 5%. The characteristics of the proposed network make it promising in applications requiring model reliability and justification.
The Semantics and Pragmatics of Argumentation

Arguments have always played a central role within logic and philosophy. This chapter overviews recent work on the semantics and pragmatics of argumentative discourse, with particular attention to work on the semantics of argument connectives such as ‘therefore’ in discourse coherence theory and in dynamic semantics, as well as on modal analyses of ‘therefore’. A dynamic analysis of the semantics of ‘therefore’ is described, that captures both uses of ‘therefore’ in categorical arguments as well as its uses in suppositional and complex arguments. In the final section, I overview some issues that arise on the pragmatics of arguments, such as how we are to characterize the distinctive utterance force of arguments versus explanations.
Blockchain and the Sports Tech Dilemma

Technology has begun to fundamentally change the sport industry. With the upsurge in the use of artificial intelligence, machine learning, virtual reality, and augmented reality, the sport industry has begun to use more technology to enhance the consumer experience. Along with the rise in these other technologies, is the use of blockchain technology. Blockchain technology is driven by three key pillars: (1) distributed computation, (2) public key cryptography, and (3) decentralized consensus. Blockchain is changing the sport landscape by supporting the use of non-fungible tokens for creative sport content, cryptocurrencies for purchases, and platforms that better protect consumer information and enhance their engagement opportunities with athletes, teams, and leagues. In this chapter, we discuss how blockchain technologies can provide opportunities and risks to the sport industry.
High-speed secure optical hashing for transformational computing (Conference Presentation)

In recent years, heterogeneous machine learning accelerators have become of significant interest to science, engineering, and industry. At the same time, demand for data security has increased significantly, especially in the looming post- quantum encryption era. From a hardware processing point of view, both are challenged by electronic capacitive interconnect delay and energy, and, in the case of heterogeneous systems such as electronic-photonic accelerators, by parasitic domain crossings. With analog optical AI accelerators having demonstrated high throughout potential (TOPS to even POPS) and high operation efficiency (TOPS/W), they have not demonstrated the ability to perform AI classification task on encrypted data. Here, we present an optical hashing and compression scheme that is based on SWIFFT - a post-quantum hashing family of algorithms. High degree optical hardware-to-algorithm homomorphism allows to optimally harvest well- understood potential of free-space processing: innate parallelism, low latency tensor by-element multiplication and Fourier transform. The algorithm can provide several orders of magnitude increase in processing speed by replacing slow high-resolution CMOS cameras with ultra-fast and signal-triggered CMOS detector arrays. Additionally, the information acquired in this way will require much lower transmission throughput, less in silico processing power, storage, and will be pre-hashed facilitating cheap optical information security. This technology has the potential to allow heterogeneous convolutional 4f classifiers to get closer in performance to their fully electronic counterparts
Design and Application of Intelligent Management Platform Based on Big Data

Big data technology has greatly promoted the construction of intelligent administrative management and improved the decision-making ability continuously. Data mining also lays a solid foundation for the construction of administrative management platform and reflects the potential value of data. In this study, an intelligent management platform based on big data is designed and implemented. First, the problems of the intellectualization of administrative management are discussed, and the big data platform and functional framework of administrative management are introduced. Second, in order to apply data mining to administrative management, the object of data mining in administrative management is defined, and a data mining system is designed. Finally, the application of machine learning methods such as cluster analysis in administrative management is analyzed in detail. The research results show that the application of intelligent management platform based on big data can promote the construction of intelligent administration and lay a good foundation for the development of more perfect intelligent administrative management.
Spatial–Temporal Synchronous Graph Transformer network (STSGT) for COVID-19 forecasting

COVID-19 has become a matter of serious concern over the last few years. It has adversely affected numerous people around the globe and has led to the loss of billions of dollars of business capital. In this paper, we propose a novel Spatial–Temporal Synchronous Graph Transformer network (STSGT) to capture the complex spatial and temporal dependency of the COVID-19 time series data and forecast the future status of an evolving pandemic. The layers of STSGT combine the graph convolution network (GCN) with the self-attention mechanism of transformers on a synchronous spatial–temporal graph to capture the dynamically changing pattern of the COVID time series. The spatial–temporal synchronous graph simultaneously captures the spatial and temporal dependencies between the vertices of the graph at a given and subsequent time-steps, which helps capture the heterogeneity in the time series and improve the forecasting accuracy. Our extensive experiments on two publicly available real-world COVID-19 time series datasets demonstrate that STSGT significantly outperforms state-of-the-art algorithms that were designed for spatial–temporal forecasting tasks. Specifically, on average over a 12-day horizon, we observe a potential improvement of 12.19% and 3.42% in Mean Absolute Error (MAE) over the next best algorithm while forecasting the daily infected and death cases respectively for the 50 states of US and Washington, D.C. Additionally, STSGT also outperformed others when forecasting the daily infected cases at the state level, e.g., for all the counties in the State of Michigan. The code and models are publicly available at https://github.com/soumbane/STSGT.
Fifty Shades of Pink: Understanding Color in e-commerce using Knowledge Graphs

The color of the products is one of the most prevalent aspects in many e-commerce domains, and it is one of the decisive purchasing factors. Besides having thousands of color variations and shades, many brands continuously develop proprietary colors and color names to attract more customers. This often leads to color ambiguity (textual and visual), and vocabulary mismatch between buyers and sellers. Therefore, it is crucial for any e-commerce search engine to correctly identify the buyer's color intent and match it to the corresponding product listings. To address this challenge, in this work, we introduce a color query expansion approach using color Knowledge Graphs. We use Knowledge Graphs to unambiguously identify all the colors based on their properties, and the relationships to other colors, which allows us to perform semantic query expansion. Similar expansion concepts could be applied to domains outside of color.
Ensemble Learning Methods for Dirty Data

Neural network ensemble is a collaborative learning paradigm that utilizes multiple neural networks to solve a complex learning problem. Constructing predictive models with high generalization performance is an important and yet most challenging goal for robust intelligence systems in the presence of dirty data. Given a target learning task, popular approaches have been dedicated to find the top performing model. However, it is difficult in general to estimate the best model when available data is finite, possibly dirty, and insufficient for the problem. In this keynote, I will give an overview of a diversity-centric ensemble learning framework developed at Georgia Tech, including methodologies and algorithms for measuring, enforcing, and combining multiple neural networks by improving generalization performance of the overall system and maximizing ensemble utility and resilience to dirty data.
Learning about the meanings of ambiguous words: evidence from a word-meaning priming paradigm with short narratives

Fluent language comprehension requires people to rapidly activate and integrate context-appropriate word meanings. This process is challenging for meanings of ambiguous words that are comparatively lower in frequency (e.g., the "bird" meaning of "crane"). Priming experiments have shown that recent experience makes such subordinate (less frequent) word meanings more readily available at the next encounter. These experiments used lists of unconnected sentences in which each ambiguity was disambiguated locally by neighbouring words. In natural language, however, disambiguation may occur via more distant contextual cues, embedded in longer, connected communicative contexts. In the present experiment, participants (N = 51) listened to 3-sentence narratives that ended in an ambiguous prime. Cues to disambiguation were relatively distant from the prime; the first sentence of each narrative established a situational context congruent with the subordinate meaning of the prime, but the remainder of the narrative did not provide disambiguating information. Following a short delay, primed subordinate meanings were more readily available (compared with an unprimed control), as assessed by responses in a word association task related to the primed meaning. This work confirms that listeners reliably disambiguate spoken ambiguous words on the basis of cues from wider narrative contexts, and that they retain information about the outcome of these disambiguation processes to inform subsequent encounters of the same word form.