Reviewerly/exHarmony huggingface dataset:
Continuous Entailment Patterns for Lexical Inference in Context

Combining a pretrained language model (PLM) with textual patterns has been shown to help in both zero- and few-shot settings. For zero-shot performance, it makes sense to design patterns that closely resemble the text seen during self-supervised pretraining because the model has never seen anything else. Supervised training allows for more flexibility. If we allow for tokens outside the PLM’s vocabulary, patterns can be adapted more flexibly to a PLM’s idiosyncrasies. Contrasting patterns where a “token” can be any continuous vector from those where a discrete choice between vocabulary elements has to be made, we call our method CONtinous pAtterNs (CONAN). We evaluate CONAN on two established benchmarks for lexical inference in context (LIiC) a.k.a. predicate entailment, a challenging natural language understanding task with relatively small training data. In a direct comparison with discrete patterns, CONAN consistently leads to improved performance, setting a new state of the art. Our experiments give valuable insights on the kind of pattern that enhances a PLM’s performance on LIiC and raise important questions regarding our understanding of PLMs using text patterns.
Long-Short Transformer: Efficient Transformers for Language and Vision

Transformers have achieved success in both language and vision domains. However, it is prohibitively expensive to scale them to long sequences such as long documents or high-resolution images, because self-attention mechanism has quadratic time and memory complexities with respect to the input sequence length. In this paper, we propose Long-Short Transformer (Transformer-LS), an efficient self-attention mechanism for modeling long sequences with linear complexity for both language and vision tasks. It aggregates a novel long-range attention with dynamic projection to model distant correlations and a short-term attention to capture fine-grained local correlations. We propose a dual normalization strategy to account for the scale mismatch between the two attention mechanisms. Transformer-LS can be applied to both autoregressive and bidirectional models without additional complexity. Our method outperforms the state-of-the-art models on multiple tasks in language and vision domains, including the Long Range Arena benchmark, autoregressive language modeling, and ImageNet classification. For instance, Transformer-LS achieves 0.97 test BPC on enwik8 using half the number of parameters than previous method, while being faster and is able to handle 3x as long sequences compared to its full-attention version on the same hardware. On ImageNet, it can obtain the state-of-the-art results (e.g., a moderate size of 55.8M model solely trained on 224x224 ImageNet-1K can obtain Top-1 accuracy 84.1%), while being more scalable on high-resolution images. The source code and models are released at this https URL .
Traded Control of Human–Machine Systems for Sequential Decision-Making Based on Reinforcement Learning

Sequential decision-making (SDM) is a common type of decision-making problem with sequential and multistage characteristics. Among them, the learning and updating of policy are the main challenges in solving SDM problems. Unlike previous machine autonomy driven by artificial intelligence alone, we improve the control performance of SDM tasks by combining human intelligence and machine intelligence. Specifically, this article presents a paradigm of a human–machine traded control systems based on reinforcement learning methods to optimize the solution process of sequential decision problems. By designing the idea of autonomous boundary and credibility assessment, we enable humans and machines at the decision-making level of the systems to collaborate more effectively. And the arbitration in the human–machine traded control systems introduces the Bayesian neural network and the dropout mechanism to consider the uncertainty and security constraints. Finally, experiments involving machine traded control, human traded control were implemented. The preliminary experimental results of this article show that our traded control method improves decision-making performance and verifies the effectiveness for SDM problems.
Resources and Evaluations for Danish Entity Resolution

Automatic coreference resolution is understudied in Danish even though most of the Danish Dependency Treebank (Buch-Kromann, 2003) is annotated with coreference relations. This paper describes a conversion of its partial, yet well-documented, coreference relations into coreference clusters and the training and evaluation of coreference models on this data. To the best of our knowledge, these are the first publicly available, neural coreference models for Danish. We also present a new entity linking annotation on the dataset using WikiData identifiers, a named entity disambiguation (NED) dataset, and a larger automatically created NED dataset enabling wikily supervised NED models. The entity linking annotation is benchmarked using a state-of-the-art neural entity disambiguation model.
The Practice of Using Specialized Knowledge to Counteract Dissemination of Extremist Materials

The paper describes the directions of counteracting extremist materials dissemination in Russia and their results as seen in the Federal List of Extremist Materials of the Ministry of Justice of the Russian Federation, statistics of the State Information and Analytical Agency of the Ministry of Internal Affairs of Russia on the number of registered crimes and persons brought to criminal responsibility as well as the Judicial Department at the Supreme Court of the Russian Federation on the state of criminal record and activities of the federal courts of general jurisdiction and justices of the peace. The study aims to develop recommendations to optimize the judicial and investigative use of specialized knowledge to counteract the dissemination of extremist materials. The research tasks are: examining the shortcomings of preliminary investigation, analyzing the current judicial practice of administrative proceedings, and formulating proposals for the best use of specialized knowledge to identify the signs of hatred and enmity in the disseminated products. The study reveals the problems of the investigative practice related to the definition of the forensic examination subject for various categories of objects in terms of their material carriers. The author also notes cases of unjustified expansion of the subject of expert examination of extremist materials. Furthermore, the article identifies the shortcomings and positive experience in judicial practice on publicly available administrative cases on recognition materials as extremist and prosecution persons for inciting hatred or hostility or for posting on the Internet materials included in the Federal List of Extremist Materials. Finally, forensic recommendations on using specialized knowledge in the analysis of disseminated extremist materials are offered.
Application of Artificial Intelligence in Constructing Personalized and Accurate Student Feedback Teaching System

In view of the lack of real-time emotional interaction between teachers and students in the current e-learning system, according to the characteristics of the teaching environment, seven kinds of basic emotions which often appear in teaching are selected, and the OCC emotion model is simplified according to this. In this paper, a personalized intelligent emotional e-learning teaching system is designed based on the principles of intelligence and emotion. The system has four emotion related modules: emotion recognition module, emotion analysis module, feedback module and evaluation module. The system can identify each student's current emotional state, and according to their current emotional state and cognitive state, give corresponding personalized positive feedback that can promote students' learning. On the basis of e-learning system, a personalized and accurate student feedback teaching application system is constructed. The system includes online personalized learning recommendation and offline classroom quality two-way evaluation. In the on-line system, the performance prediction and online learning behavior analysis based on deep learning are designed, and the learning emotion classification is realized by combining with image processing technology. In the offline system, through training target detection model, face detection model and face segmentation model, and combined with the online system, online learning behavior feature extraction, offline performance prediction, learning law analysis and personalized learning recommendation are realized. At the same time, the evaluation and feedback of teaching quality and students' learning behavior are realized through offline classroom information data. The experimental results show that the system not only has a convenient and fast access to information, but also can reduce a lot of time cost. It caters to the new learning and teaching method which combines offline and offline, and can effectively improve the teaching efficiency of teachers and the learning efficiency of students.
Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification.

Traditional hand-crafted linguistically-informed features have often been used for distinguishing between translated and original non-translated texts. By contrast, to date, neural architectures without manual feature engineering have been less explored for this task. In this work, we (i) compare the traditional feature-engineering-based approach to the feature-learning-based one and (ii) analyse the neural architectures in order to investigate how well the hand-crafted features explain the variance in the neural models’ predictions. We use pre-trained neural word embeddings, as well as several end-to-end neural architectures in both monolingual and multilingual settings and compare them to feature-engineering-based SVM classifiers. We show that (i) neural architectures outperform other approaches by more than 20 accuracy points, with the BERT-based model performing the best in both the monolingual and multilingual settings; (ii) while many individual hand-crafted translationese features correlate with neural model predictions, feature importance analysis shows that the most important features for neural and classical architectures differ; and (iii) our multilingual experiments provide empirical evidence for translationese universals across languages.
Temporal Adaptation of BERT and Performance on Downstream Document Classification: Insights from Social Media

Language use differs between domains and even within a domain, language use changes over time. For pre-trained language models like BERT, domain adaptation through continued pre-training has been shown to improve performance on in-domain downstream tasks. In this article, we investigate whether temporal adaptation can bring additional benefits. For this purpose, we introduce a corpus of social media comments sampled over three years. It contains unlabelled data for adaptation and evaluation on an upstream masked language modelling task as well as labelled data for fine-tuning and evaluation on a downstream document classification task. We find that temporality matters for both tasks: temporal adaptation improves upstream and temporal fine-tuning downstream task performance. Time-specific models generally perform better on past than on future test sets, which matches evidence on the bursty usage of topical words. However, adapting BERT to time and domain does not improve performance on the downstream task over only adapting to domain. Token-level analysis shows that temporal adaptation captures event-driven changes in language use in the downstream task, but not those changes that are actually relevant to task performance. Based on our findings, we discuss when temporal adaptation may be more effective.
Comprehensive Teaching Materials Based on Cognitive Conflict Strategies to Reduce Misconception of Calories for Junior High School Students

Heat is a material that is close to everyday life. However, most students still have misconceptions about this material. Thus, teachers need to develop teaching materials that can reduce the misconception of heat material. One alternative is to use teaching materials with cognitive conflict strategies. The research aims to develop teaching materials that are able to reduce students' misconceptions about heat material. The development model used is the 4D model which consists of four stages, namely defining, designing, developing, and distributing. The teaching materials developed include lesson plans, textbooks, student worksheets, and tests. The subjects of the study were seventh-grade students in Lamongan regency which consisted of 10 state junior high schools and private junior high schools. The research instrument was a feasibility test sheet for teaching materials and a questionnaire to see students’ responses. Data collection techniques used the feasibility sheet of teaching materials and questionnaires. Therefore, the result shows that cognitive conflict-based teaching materials are appropriate to be used classroom with a score between 3.0 and 3.9 and a reliability coefficient value of more than 90%.  and can reduce misconceptions with a percentage reduction in misunderstandings from 10 junior high schools in the Lamongan regency ranging from 30.3 to 47.8 percent.
The Impact of Corporate Governance Mechnanisms on Financial Reporting Quality in Yemen

The goal of this study was to look at the impact of corporate governance mechanisms (Board of Directors, audit committee, and internal audit) as well as international financial reporting standards (IFRS) on financial reporting quality. In order to gather relevant information about Yemen's financial reporting quality, the researcher conducted a survey and distributed it via application. In addition, 290 Yemeni private sector workers participated in this study. Smart PLS software was used to conduct structural equation modelling prior to using SPSS (SEM). Corporate governance can be measured in a variety of ways, and the audit committee is no exception. The audit committee's size is determined by its independence as well as the expertise of its members. The study's findings show that the audit committee, internal audit, and international financial reporting standards all have a significant impact on financial reporting quality. The board of directors, however, has no influence on the quality of financial reporting. International financial reporting standards, as well as the outcomes of corporate governance (Audit Committee and Internal Audit), all contribute to improved financial reporting quality.
Not So Fast, Classifier – Accuracy and Entropy Reduction in Incremental Intent Classification

Incremental intent classification requires the assignment of intent labels to partial utterances.However, partial utterances do not necessarily contain enough information to be mapped to the intent class of their complete utterance (correctly and with a certain degree of confidence).Using the final interpretation as the ground truth to measure a classifier's accuracy during intent classification of partial utterances is thus problematic.We release in-CLINC, a dataset of partial and full utterances with human annotations of plausible intent labels for different portions of each utterance, as an upper (human) baseline for incremental intent classification.We analyse the incremental annotations and propose entropy reduction as a measure of human annotators' convergence on an interpretation (i.e.intent label).We argue that, when the annotators do not converge to one or a few possible interpretations and yet the classifier already identifies the final intent class early on, it is a sign of overfitting that can be ascribed to artefacts in the dataset.
Partitioning and Replication Technique Survey on Security of Data

Re-appropriating information to be another definitive control will be done in conveyed framework, climb to security concern. This information exchange off happen due to ambushes by various sorts of clients and hubs inside the cloud framework. Henceforth, high security endeavors to build up wellbeing are required to make sure about data inside the distributed storage. On the opposite side, the given security methodology ought to similarly consider the headway of the data recuperation time. Paper will be, In this paper we be proposes Division and Replication of Data in the Cloud stockpiling for Optimal Performance and Security (DROPS) that will be collectivel1y approaches the security and execution issues. In the DROPS technique, according to client input document parceling to areas, and recreate the isolated data on the distributed storage hubs. Every one of the hub store only a little byte part of that document data record that ensures that even in case of a productive ambush, no significant information is reveal to the aggressor. Additional reasoning like, the hubs taking care of the segment is disengaged with partition by the strategy for outlines T-concealing to confine an aggressor of hypothesizing the regions of the segment. At that point, the DROPS System doesn’t rely on client cryptographic framework will be the data security; thusly reducing the game plan of computationally exorbitant methodologies. We show the exhibition that the probability to discover and deal most of the hubs taking care of the area of a single chronicle is to the extraordinary degree low. We in like manner break down the run of the DROPS framework with various plans. The more raised measure of security with an execution overhead was watches.
Redundancy Is Not Necessarily Detrimental in Classification Problems

In feature selection, redundancy is one of the major concerns since the removal of redundancy in data is connected with dimensionality reduction. Despite the evidence of such a connection, few works present theoretical studies regarding redundancy. In this work, we analyze the effect of redundant features on the performance of classification models. We can summarize the contribution of this work as follows: (i) develop a theoretical framework to analyze feature construction and selection, (ii) show that certain properly defined features are redundant but make the data linearly separable, and (iii) propose a formal criterion to validate feature construction methods. The results of experiments suggest that a large number of redundant features can reduce the classification error. The results imply that it is not enough to analyze features solely using criteria that measure the amount of information provided by such features.
Text and Non-text Frame Classification in Video

The feature discussed in the previous chapter may not be robust for text frames classification because those features are sensitive to background, image quality, and poor resolution.
PAUSE: Positive and Annealed Unlabeled Sentence Embedding

Sentence embedding refers to a set of effective and versatile techniques for converting raw text into numerical vector representations that can be used in a wide range of natural language processing (NLP) applications. The majority of these techniques are either supervised or unsupervised. Compared to the unsupervised methods, the supervised ones make less assumptions about optimization objectives and usually achieve better results. However, the training requires a large amount of labeled sentence pairs, which is not available in many industrial scenarios. To that end, we propose a generic and end-to-end approach – PAUSE (Positive and Annealed Unlabeled Sentence Embedding), capable of learning high-quality sentence embeddings from a partially labeled dataset. We experimentally show that PAUSE achieves, and sometimes surpasses, state-of-the-art results using only a small fraction of labeled sentence pairs on various benchmark tasks. When applied to a real industrial use case where labeled samples are scarce, PAUSE encourages us to extend our dataset without the burden of extensive manual annotation work.
Corrected CBOW Performs as well as Skip-gram

Mikolov et al. (2013a) observed that continuous bag-of-words (CBOW) word embeddings tend to underperform Skip-gram (SG) embeddings, and this finding has been reported in subsequent works. We find that these observations are driven not by fundamental differences in their training objectives, but more likely on faulty negative sampling CBOW implementations in popular libraries such as the official implementation, word2vec.c, and Gensim. We show that after correcting a bug in the CBOW gradient update, one can learn CBOW word embeddings that are fully competitive with SG on various intrinsic and extrinsic tasks, while being many times faster to train.
Recent Innovations in Solid-State and Molecular Qubits for Quantum Information Applications

ADVERTISEMENT RETURN TO ISSUEEditorialNEXTRecent Innovations in Solid-State and Molecular Qubits for Quantum Information ApplicationsRobert H. LavroffRobert H. LavroffDepartment of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California 90095-1569, United StatesMore by Robert H. Lavroffhttps://orcid.org/0000-0002-6133-5766, Doran L. PenningtonDoran L. PenningtonDepartment of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California 90095-1569, United StatesMore by Doran L. Pennington, Ash Sueh HuaAsh Sueh HuaDepartment of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California 90095-1569, United StatesMore by Ash Sueh Hua, Barry Yangtao LiBarry Yangtao LiDepartment of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California 90095-1569, United StatesMore by Barry Yangtao Li, Jillian A. WilliamsJillian A. WilliamsDepartment of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California 90095-1569, United StatesMore by Jillian A. Williams, and Anastassia N. Alexandrova*Anastassia N. AlexandrovaDepartment of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California 90095-1569, United States*Email: [email protected]More by Anastassia N. Alexandrovahttps://orcid.org/0000-0002-3003-1911Cite this: J. Phys. Chem. A 2021, 125, 44, 9567–9570Publication Date (Web):November 11, 2021Publication History Published online11 November 2021Published inissue 11 November 2021https://doi.org/10.1021/acs.jpca.1c08677Copyright © Published 2021 by American Chemical SocietyRIGHTS & PERMISSIONSArticle Views376Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (2 MB) Get e-AlertsSUBJECTS:Algorithms,Theoretical calculations,Defects,Quantum mechanics,Molecules Get e-Alerts