Reviewerly/exHarmony huggingface dataset:
On the Role of Relevance in Natural Language Processing Tasks

Many recent Natural Language Processing (NLP) task formulations, such as question answering and fact verification, are implemented as a two-stage cascading architecture. In the first stage an IR system retrieves "relevant'' documents containing the knowledge, and in the second stage an NLP system performs reasoning to solve the task. Optimizing the IR system for retrieving relevant documents ensures that the NLP system has sufficient information to operate over. These recent NLP task formulations raise interesting and exciting challenges for IR, where the end-user of an IR system is not a human with an information need, but another system exploiting the documents retrieved by the IR system to perform reasoning and address the user information need. Among these challenges, as we will show, is that noise from the IR system, such as retrieving spurious or irrelevant documents, can negatively impact the accuracy of the downstream reasoning module. Hence, there is the need to balance maximizing relevance while minimizing noise in the IR system. This paper presents experimental results on two NLP tasks implemented as a two-stage cascading architecture. We show how spurious or irrelevant retrieved results from the first stage can induce errors in the second stage. We use these results to ground our discussion of the research challenges that the IR community should address in the context of these knowledge-intensive NLP tasks.
Evaluation of College English Textbooks Based on Computer-Aided Analysis Corpus

In order to improve college students’ perception and acceptance of college English textbooks and improve their professional English communication ability in their future careers, the application of corpus plays a guiding role in college English teaching reform and provides reliable resources for college English language research and learning. This study analyzes the college English textbooks in the corpus based on the comparison of conventional technology assistance and computer technology assistance. Through the comparative analysis of the coupling degree, corpus vocabulary, and students’ perception of teaching materials between conventional technology and computer technology, it can be seen that the evaluation of college English textbooks based on computer-aided analysis corpus is more in line with the needs of college students. College students’ learning ability and their English application ability are improved. The establishment of a corpus of college English teaching materials is of great significance to the research of college English teaching and also has a positive impact on college students’ learning English. With the help of computer technology, college English teaching materials can be closer to the diversified concept of university scholarship. Its content is more in line with the current social demand for talents, provides a better scheme for the cultivation of talents, and is conducive to the development of the country and society.
A Dual-Expert Framework for Event Argument Extraction

Event argument extraction (EAE) is an important information extraction task, which aims to identify the arguments of an event described in a given text and classify the roles played by them. A key characteristic in realistic EAE data is that the instance numbers of different roles follow an obvious long-tail distribution. However, the training and evaluation paradigms of existing EAE models either prone to neglect the performance on "tail roles'', or change the role instance distribution for model training to an unrealistic uniform distribution. Though some generic methods can alleviate the class imbalance in long-tail datasets, they usually sacrifice the performance of "head classes'' as a trade-off. To address the above issues, we propose to train our model on realistic long-tail EAE datasets, and evaluate the average performance over all roles. Inspired by the Mixture of Experts (MOE), we propose a Routing-Balanced Dual Expert Framework (RBDEF), which divides all roles into "head" and "tail" two scopes and assigns the classifications of head and tail roles to two separate experts. In inference, each encoded instance will be allocated to one of the two experts by a routing mechanism. To reduce routing errors caused by the imbalance of role instances, we design a Balanced Routing Mechanism (BRM), which transfers several head roles to the tail expert to balance the load of routing, and employs a tri-filter routing strategy to reduce the misallocation of the tail expert's instances. To enable an effective learning of tail roles with scarce instances, we devise Target-Specialized Meta Learning (TSML) to train the tail expert. Different from other meta learning algorithms that only search a generic parameter initialization equally applying to infinite tasks, TSML can adaptively adjust its search path to obtain a specialized initialization for the tail expert, thereby expanding the benefits to the learning of tail roles. In experiments, RBDEF significantly outperforms the state-of-the-art EAE models and advanced methods for long-tail data.
Coupled Splines for Sparse Curve Fitting

We formulate as an inverse problem the construction of sparse parametric continuous curve models that fit a sequence of contour points. Our prior is incorporated as a regularization term that encourages rotation invariance and sparsity. We prove that an optimal solution to the inverse problem is a closed curve with spline components. We then show how to efficiently solve the task using B-splines as basis functions. We extend our problem formulation to curves made of two distinct components with complementary smoothness properties and solve it using hybrid splines. We illustrate the performance of our model on contours of different smoothness. Our experimental results show that we can faithfully reconstruct any general contour using few parameters, even in the presence of imprecisions in the measurements.
Beyond Opinion Mining

Customer reviews are vital for making purchasing decisions in the Information Age. Such reviews can be automatically summarized to provide the user with an overview of opinions. In this tutorial, we present various aspects of opinion summarization that are useful for researchers and practitioners. First, we will introduce the task and major challenges. Then, we will present existing opinion summarization solutions, both pre-neural and neural. We will discuss how summarizers can be trained in the unsupervised, few-shot, and supervised regimes. Each regime has roots in different machine learning methods, such as auto-encoding, controllable text generation, and variational inference. Finally, we will discuss resources and evaluation methods and conclude with the future directions. This three-hour tutorial will provide a comprehensive overview over major advances in opinion summarization. The listeners will be well-equipped with the knowledge that is both useful for research and practical applications.
Online DATEing

Despite more than two decades of research on temporal tagging and temporal relation extraction, usable tools for annotating text remain very basic and hard to set up from an average end-user perspective, limiting the applicability of developments to a selected group of invested researchers. In this work, we aim to increase the accessibility of temporal tagging systems by presenting an intuitive web interface, called "Online DATEing", which simplifies the interaction with existing temporal annotation frameworks. Our system integrates several approaches in a single interface and streamlines the process of importing (and tagging) groups of documents, as well as making it accessible through a programmatic API. It further enables users to interactively investigate and visualize tagged texts, and is designed with an extensible API for the inclusion of new models or data formats. A web demonstration of our tool is available at https://onlinedating.ifi.uni-heidelberg.de and public code accessible at https://github.com/satya77/Temporal_Tagger_Service.
BERT-based Dense Intra-ranking and Contextualized Late Interaction via Multi-task Learning for Long Document Retrieval

Combining query tokens and document tokens and inputting them to pre-trained transformer models like BERT, an approach known as interaction-based, has shown state-of-the-art effectiveness for information retrieval. However, the computational complexity of this approach is high due to the online self-attention computation. In contrast, dense retrieval methods in representation-based approaches are known to be efficient, however less effective. A tradeoff between the two is reached with late interaction methods like ColBERT, which attempt to benefit from both approaches: contextualized token embeddings can be pre-calculated over BERT for fine-grained effective interaction while preserving efficiency. However, despite its success in passage retrieval, it's not straightforward to use this approach for long document retrieval. In this paper, we propose a cascaded late interaction approach using a single model for long document retrieval. Fast intra-ranking by dot product is used to select relevant passages, then fine-grained interaction of pre-stored token embeddings is used to generate passage scores which are aggregated to the final document score. Multi-task learning is used to train a BERT model to optimize both a dot product and a fine-grained interaction loss functions. Our experiments reveal that the proposed approach obtains near state-of-the-art level effectiveness while being efficient on such collections as TREC 2019.
Identification and Early Warning of Financial Fraud Risk Based on Bidirectional Long-Short Term Memory Model

In modern market economy, corporate financial frauds emerge one after another, which have a huge impact on the stock market and triggered an unprecedented credit crisis. Therefore, it is particularly important to identify financial frauds. Improving the efficiency, accuracy, and coverage of fraud identification in financial reports through digital and intelligent means is one of the important links to improve credit risk control of securities companies and also for securities companies to accurately price related financial products of target companies. Traditional recognition methods based on artificial rules can cover relatively limited indicators; rule parameters are set randomly. Besides, it is difficult to make rules based on high-dimension indicators and to dig the hidden deep relationships between indicators. This paper summarizes the relevant indicators of financial fraud identification from the perspective of financial and nonfinancial characteristics. Then, the identification and early warning of financial fraud risk based on bidirectional long-short term memory model are proposed. This method uses the idea of ensemble learning, weights the probability of financial key indicators, and uses the optimal transfer probability to solve the financial fraud risk results. The results show that the industry-specific modeling can significantly improve the accuracy of the financial fraud identification model and it also can effectively help the government regulatory departments, investors, and audit departments to correctly identify the financial fraud of listed companies.
Addressing Gender-related Performance Disparities in Neural Rankers

While neural rankers continue to show notable performance improvements over a wide variety of information retrieval tasks, there have been recent studies that show such rankers may intensify certain stereotypical biases. In this paper, we investigate whether neural rankers introduce retrieval effectiveness (performance) disparities over queries related to different genders. We specifically study whether there are significant performance differences between male and female queries when retrieved by neural rankers. Through our empirical study over the MS MARCO collection, we find that such performance disparities are notable and that the performance disparities may be due to the difference between how queries and their relevant judgements are collected and distributed for different gendered queries. More specifically, we observe that male queries are more closely associated with their relevant documents compared to female queries and hence neural rankers are able to more easily learn associations between male queries and their relevant documents. We show that it is possible to systematically balance relevance judgment collections in order to reduce performance disparity between different gendered queries without negatively compromising overall model performance.
Virtual Machine Allocation Strategy Based on Statistical Machine Learning

At present, big data cloud computing has been widely used in many enterprises, and it serves tens of millions of users. One of the core technologies of big data cloud service is computer virtualization technology. The reasonable allocation of virtual machines on available hosts is of great significance to the performance optimization of cloud computing. We know that with the continuous development of information technology and the increasing number of computer users, different virtualization technologies and the increasing number of virtual machines in the network make the effective allocation of virtualization resources more and more difficult. In order to solve and optimize this problem, we propose a virtual machine allocation algorithm based on statistical machine learning. According to the resource requirements of each virtual machine in cloud service, the corresponding comprehensive performance analysis model is established, and the reasonable virtual machine allocation algorithm description of the host in the resource pool is realized according to the virtualization technology type or mode provided by the model. Experiments show that this method has the advantages of overall performance, load balancing, and supporting different types of virtualization.
Strype

Strype is a frame-based editor for a Python-like language that can target execution either in the browser or on the micro:bit device. Frame-based editing sits between block-based and text-based editing, featuring the easy graphical structuring of block-based editing but also the fast keyboard entry/manipulation of text-based editing. Thus frame-based editing in Strype can act as a bridge between block-based editing and text-based editing, especially if the latter involves programming in Python. In our courseware presentation we will provide a demonstration of the Strype editor. We will showcase Strype's unique features and show what it can bring to the educational community. We also hope that the presentation will permit a fruitful exchange between Strype's developers and teachers who may use it.
Application of Educational Scaffolding in Content and Language Integrated Training for Major University Disciplines

The article expands on the specifics of the educational scaffolding in content and language integrated training for specialized disciplines. It is revealed that the methods of stimulating foreign language communicative activity of students are based on the use of active learning methods in combination with interdisciplinary interaction and the use of modern digital educational technologies. The possibilities of scaffolding technology for the implementation of an individual approach to learning by building educational “footing” are presented. The value of scaffolding techniques for the formation of students’ motivation and the creation of a comfortable educational environment is emphasized. The hypothesis is put forward that two directions should be integrated into the process of professionally oriented foreign language teaching using scaffolding: linguistic and professional. The role of interdisciplinary cooperation and interdisciplinary code switching is revealed, in which students receive multilateral support and a significant competitive advantage.
(Retracted) Systematic review on ontology applications in virtual reality training domain: design components, roles, and research directions

Even though ontology and virtual reality training (VRT) are subjects that have been explored in various areas over the years, there is an absence of a systematic approach that gives an overview on how both have been utilized together. We aim to explore how ontologies have been applied in VRT technology in recent times. Therefore, the systematic literature review methodology was carried out to collect studies between 2014 and 2021 from various databases. To summarize, the main findings of this research are as follows: (1) the majority of the studies concerns two roles, i.e., either capturing and structuring knowledge or separating domain knowledge and operational knowledge; (2) all ontologies apply deficient foundational ontologies, languages, and methodologies when developing ontologies for VRT; (3) there is a general lack of capturing perdurant knowledge in ontology design; and (4) there are key design elements that are considered crucial for designing an ontology for VRT. Further directions have been provided to contribute to the body of knowledge by recommending the right design elements that could produce idealistic and ubiquitous ontologies to facilitate VRT development throughout its life cycle.
The Levene test based-leakage assessment

The secret information is split into several parts(multivariate) in the high-order mask. The test vector leakage assessment (TVLA) relied on Welch's t-test(T-test), the analysis of variance (ANOVA) and normalized inter-class variance (NICV) relied on Fisher-test(F-test) are preliminary assessment for univariate. It is necessary to combine several univariates when we perform the leakage detection on the high-order mask. We find that the combined traces are mostly the non-normal distribution. While T-test and F-test are applied to the normal distribution in statistics. In order to evaluate the effect of T-test, NICV, ANOVA on the non-normal distribution, we introduce a metric observed power (OP) in statistics. And we find the are below the threshold 0.8, which indicates an unreliable result. To solve the problem, we propose the Levene-test based-leakage assessment (LTBLA) in this work. The LTBLA follows the same procedure as TVLA with Levene-test and add the identification of distribution in TVLA. There is the leakage if the P-value is below the threshold 10−5. The experiments show that in order to obtain the P-value that is below the threshold, the number of traces 6.5×103 are acquired by Levene-test, which is 1/2 of ANOVA and 1/5 of T-test. For the skewed distribution, the number of traces 5.3×103 are needed, while the sample size of ANOVA is around1.5×104. More surprisingly, the P-values of T-test is around 10−1∼ 10−2, if the number of traces is 8×104. Because Hotelling's T2-test is the testing without combing for the multivariate. We also compare Levene-test with Hotelling's T2-test. For the normal traces, the number of traces 0.93×103 are needed by Levene-test, while the sample size of Hotelling's T2-test is about 0.67×103. For the skewed distribution, the sample size of Levene-test is about 0.4×103 less than Hotelling's T2-test. For the normal distribution, the small difference among the three tests in the sample size. For the skewed distribution the difference of sample size between Levene-test and T-test or ANOVA is more than two orders of magnitude. T-test and ANOVA have the risk of missed detection under the same sample complexity. LTBLA follows the same procedure as TVLA with Levene-test for the skewed distribution to avoid the unreliable results.
Movie Recommendation System Using Machine Learning Algorithms

Movies are one kind of enjoyment, however the challenge is locating the specified resources among the lots of movies discharged annually. To search out a show what users are seeking out through this technology is incredibly troublesome. However, recommendation structures return abundant handier in these things. Searching is the first step depending on which the movies are recommended to the user. To search a movie the user may use any of the parameter like name of the actor, director, genres etc. Searching based on one of the parameter and recommending movies based on the parameter is how the current recommendation system works. The movies are recommended with the help of filtering technique i,e collaborative filtering which recommends the movies based on user preference and ratings of the movies. This can be further enhanced by taking multiple parameters (2 in this case) for searching the movies based on which the recommendation can be done. With the help of multiple parameters (2 in this case) the user can be more specific in what he wants to search so that it helps in more accurate recommendations of the movies.
User Interface Design Patterns for Infotainment Systems Based on Driver Distraction: A Colombian Case Study

In this paper, we present a set of nine user interface design patterns for in-vehicle infotainment systems centered on the content, interaction mode, style, shortcuts, and notifications of the system, which are built based on both theoretical support and an empirical pattern finding process. This empirical process consists in an exploratory test made with 10 users from an age range from 18 to 29 and with a minimum of 2 years of driving experience, who were asked to perform a set of 13 secondary tasks in the vehicle to identify recurrent problems, behavioral patterns, and best practices. The output of this exploratory test was used as the main data source for the design patterns consolidation. It is worth mentioning that the proposal of these patterns arose as an initiative to begin to consider factors that can affect the driver’s experience in the Latin American context. The set of design patterns has been validated via a content validation (as an exploratory study) by a panel of Latin American experts in the area of HCI and UX through the development of a prototype using the patterns as a tool for the redesign proposal of the infotainment systems tested during the empirical finding process.
An explanation of <i>or</i>-deletions and other paradoxical disjunctive inferences

Some inferences of the sort: A or B; therefore A, which are invalid in standard logics, are sensible in life: You can enter now or later; therefore, you can enter now. That these "or-deletions" follow necessarily or only possibly is a by-product of a theory of mental models. Its semantics for "or" refers to conjunctions of possibilities holding in default of knowledge to the contrary. It predicts new sorts of or-deletion, such as: He likes to drink red wine or white wine. So, he likes to drink red wine. and: You are permitted to do only one of the following: You can enter now. You can enter later. Therefore, you are permitted to enter now. They are invalid in standard logics, and neither previous pragmatic nor semantic theories predicted them. Four experiments corroborated their occurrence.
Blockchain Technology in Healthcare: An Analysis of Strengths, Weaknesses, Opportunities, and Threats

SUMMARY The dawn of the crypto age has highlighted the potential of blockchain technology in an array of functions for multiple industries. Blockchain technology disrupts existing architecture by offering an efficient and decentralized data management platform. It provides a potential solution to current health information technology problems. The strengths of blockchain are interoperability, data accuracy, security, and transparency. Its weaknesses include a lack of standardization, accessibility, ownership, and change management. Applications in healthcare include revenue cycle management, physician credentialing, electronic health records, and supply chain management, although potential government regulation and internal attacks pose threats to its wider adoption.