Reviewerly/exHarmony huggingface dataset:
Paraphrasing Compound Nominalizations.

A nominalization uses a deverbal noun to describe an event associated with its underlying verb. Commonly found in academic and formal texts, nominalizations can be difficult to interpret because of ambiguous semantic relations between the deverbal noun and its arguments. Our goal is to interpret nominalizations by generating clausal paraphrases. We address compound nominalizations with both nominal and adjectival modifiers, as well as prepositional phrases. In evaluations on a number of unsupervised methods, we obtained the strongest performance by using a pre-trained contextualized language model to re-rank paraphrase candidates identified by a textual entailment model.
The THE IMPACT OF GREEN WAREHOUSING, LOGISTICS OPTIMIZATION, AND SOCIAL VALUES AND ETHICS ON THE PERFORMANCE OF MANUFACTURING FIRMS OF PAKISTAN: TRIPLE BOTTOM-LINE APPROACH

Main objective of this study is to examine the impact of logistics optimization, green warehousing, social values, and ethics on the supply chain sustianlanblity, economic, social and environmental performance by employing the theory of resource dependency theory (RDT). Using numerical evidence that can provide predictive understanding and help to learn about behaviors and attitudes, quantitative research is used in this study to quantify the problem. The findings of the study showed that green warehousing had a positive significant effect on supply chain sustainability. Moreover, it is observed that the supply chain sustianablity is effected by the social values and ethics, positively. Further, logistic optimization has a positive and significant effect on supply chain sustainability. However, supply chain sustainability has a positive but insignificant effect on financial performance. Green warehousing has a positive and significant effect on financial performance. Social value and ethics also have a positive significant effect on financial performance. Lastly, logistic optimization has positive but insignificant effect on financial performance. The study urges executives to embrace logistics optimization, green warehousing, social values, and ethics to promote the supply chain's sustainability.
Tips for Answering Unusual Questions

In this last chapter, we provide some tips for answering unusual questions. Unusual questions are questions where the question requirement is not immediately clear.
A Coarse-to-Fine Object Detection Framework for High-Resolution Images with Sparse Objects

To detect sparse small objects in high resolution images at a low cost is significantly more challenging than regular detection tasks. Compared to the overall detection accuracy, the recall rate is much less affected when using properly downsampled images for detection. Based on this fact, we propose a clustering-based coarse-to-fine object detection framework to enhance the object detection of sparse small objects. The first stage is coarse detection on a downsampled image to obtain image chips based on a clustering-baed region generation method. After that, the associated high resolution image clips are sent to a second-stage detector for fine detection. This approach reduces the number of chips for final object detection compared to regular methods, which divide the image into small tiles of the same size, and makes the best use of information in high-resolution images to increase detection accuracy. Experimental results show that our proposed approach achieves promising performance compared with other state-of-the-art detectors.
"It's our fault!": insights into users' understanding and interaction with an explanatory collaborative dialog system

Human-AI collaboration, a long standing goal in AI, refers to a partnership where a human and artificial intelligence work together towards a shared goal. Collaborative dialog allows human-AI teams to communicate and leverage strengths from both partners. To design collaborative dialog systems, it is important to understand what mental models users form about their AI-dialog partners, however, how users perceive these systems is not fully understood. In this study, we designed a novel, collaborative, communication-based puzzle game and explanatory dialog system. We created a public corpus from 117 conversations and post-surveys and used this to analyze what mental models users formed. Key takeaways include: Even when users were not engaged in the game, they perceived the AI-dialog partner as intelligent and likeable, implying they saw it as a partner separate from the game. This was further supported by users often overestimating the system’s abilities and projecting human-like attributes which led to miscommunications. We conclude that creating shared mental models between users and AI systems is important to achieving successful dialogs. We propose that our insights on mental models and miscommunication, the game, and our corpus provide useful tools for designing collaborative dialog systems.
InFoBERT: Zero-Shot Approach to Natural Language Understanding Using ContextualizedWord Embedding

Natural language understanding is an important task in modern dialogue systems.It becomes more important with the rapid extension of the dialogue systems' functionality.In this work, we present an approach to zero-shot transfer learning for the tasks of intent classification and slot-filling based on pre-trained language models.We use deep contextualized models feeding them with utterances and natural language descriptions of user intents to get text embeddings.These embeddings then used by a small neural network to produce predictions for intent and slot probabilities.This architecture achieves new state-of-the-art results in two zero-shot scenarios.One is a single language new skill adaptation and another one is a cross-lingual adaptation.
Improving Students’ Motivation to Learn English by Serving English Book with Local Wisdom

In improving English language skills, reading serves as a supportive activity. Reading is fundamental to improve students’ vocabulary in order to facilitate their speaking and writing skills. However, one of the barriers that students face in reading contents obtained from overseas references is the level of reading difficulty which is classified as quite high that it reduces reading motivation. This study aims to develop an English reading comprehension book that contains the local values to motivate students in learning English as well as an as attempt to preserve local culture among millennial students who have massively been exposed to western lifestyles. This study used a Research and Development (R&amp;D) approach with the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) model. However, only the initial three stages out of five will be discussed. 187 participants studying at a university in West Kalimantan were invited online to take part in the needs analysis stage and initial evaluation. The research resulted in a book with an ISBN in accordance to the participants’ needs and points of view in the analysis stage. Initial evaluation reveals that the book is interesting and easier to read that it is expected to increase motivation of English learners.
MOOC of mathematics, a strategic for strengthening basic competencies and concepts in higher education.

espanolDebido a los altos indices de reprobacion en matematicas, el Departamento de Ciencias Basicas de la Universidad Santo Tomas -Bucaramanga (USTABUCA), con el apoyo de su Campus Virtual, propone el diseno de un Curso Masivo Abierto Online (MOOC) para ayudar en el desarrollo decompetencias y procesos matematicos que permitan desempenarse con exito en Calculo Diferencial. El curso se diseno en cuatro apartados: Aritmetica, Algebra, Trigonometria y Geometria, los cuales se estructuraron en lecciones con ejercicios practicos y pruebas de entrada y salida para comprobar las competencias alcanzadas en cada apartado del curso. Para determinar la efectividad del MOOC se llevo a cabo una investigacion con enfoque metodologico mixto en dos etapas: una cuantitativa con la aplicacion de cuestionarios y encuestas de satisfaccion y otra cualitativa con la aplicacion. de entrevistas a alumnos que participaron del MOOC y profesores encargados del curso de Calculo Diferencial. EnglishDue to the high rates of failure in mathematics, the Department of Basic Sciences of Santo Tomas University -Bucaramanga (USTABUCA), with the support of its Virtual Campus, proposes the design of a Massive Open Online Course (MOOC) to help in the development of mathematical competencies and processes that allow students to perform successfully in theDifferential Calculus course. The course was designed in four sections: Arithmetic, Algebra, Trigonometry and Geometry, which were structured in lessons with practical exercises and entry and exit tests to check the competencies achieved in each section of the course. To determine the effectiveness of the MOOC a research with a mixed methodological approach was carried out in two stages: a quantitative one with the application of questionnaires and satisfaction surveys and a qualitative one with the application of interviews to students who participated in the MOOC and teachers in charge of the Differential Calculus course. The results indicated the relevance of the course to improve students’ catching-up processes and the improvement
Analytical Synthesis and Implementation of Low-Frequency Precision Electronically Controlled CFOA – Based Quadrature Oscillator

This paper describes the structure and operational principle of the proposed low-frequency digitally controlled quadrature oscillator, using monolithic Current-Feedback Operational Amplifiers (CFOAs).The created oscillator is based on an all-pass filter that is synthesized by using state variable approach and includes cascade structure of two digitally controlled integrators and two adders.The oscillator employs linear digital control of the oscillation frequency within ±0.1% accuracy by controlling the time constants of the two integrators through two 1024 -position digital potentiometers.The efficiency of the circuit is demonstrated by comparing the simulation results for the electrical parameters with experimental results of sample circuits, working up to 130 kHz with Total Harmonic Distortion < 1%.The obtained results show good agreement with the theoretical hypotheses and analytical formulae.
Effects of Entry Mode on Mathematics Education Students’ Academic Performance and Retention

This study investigated the effects of entry mode on mathematics education student’s academic performance and retention of mathematics education students in Kogi State University, Anyigba. This study employed the use of ex-post facto research design. 152 subjects were sampled out of 578 population. The research questions were answered using descriptive statistics while the research hypotheses were tested at 0.05 level of significance with the use of Independent t-test statistics as well as analysis of variance and 2-way ANOVA. The result of the finding showed that students who enter the university through direct entry perform higher than their counterparts who enter the university through jamb. Based on the findings of this study, it is recommended that jamb should develop a system of continuous assessment through issuing books on their academic intelligence.
Subject and Course Guides: UIC Library ENGL 161 Research Awards: Previous Winners

Information about the UIC Library Library Research Awards for the most outstanding research papers written by English 161 students.
Input addition and deletion in reinforcement: towards protean learning

Reinforcement Learning (RL) agents are commonly thought of as adaptive decision procedures. They work on input/output data streams called “states”, “actions” and “rewards”. Most current research about RL adaptiveness to changes works under the assumption that the streams signatures (i.e. arity and types of inputs and outputs) remain the same throughout the agent lifetime. As a consequence, natural situations where the signatures vary (e.g. when new data streams become available, or when others become obsolete) are not studied. In this paper, we relax this assumption and consider that signature changes define a new learning situation called Protean Learning (PL). When they occur, traditional RL agents become undefined, so they need to restart learning. Can better methods be developed under the PL view? To investigate this, we first construct a stream-oriented formalism to properly define PL and signature changes. Then, we run experiments in an idealized PL situation where input addition and deletion occur during the learning process. Results show that a simple PL-oriented method enables graceful adaptation of these arity changes, and is more efficient than restarting the process.
Deduplication in large web corpora

Our paper tries to find answers to some questions related to deduplication process in large-scale web-crawled corpora. An experiment based on eight corpora from the Aranea family is introduced, and first results are presented.
Perturbation CheckLists for Evaluating NLG Evaluation Metrics

Natural Language Generation (NLG) evaluation is a multifaceted task requiring assessment of multiple desirable criteria, e.g., fluency, coherency, coverage, relevance, adequacy, overall quality, etc. Across existing datasets for 6 NLG tasks, we observe that the human evaluation scores on these multiple criteria are often not correlated. For example, there is a very low correlation between human scores on fluency and data coverage for the task of structured data to text generation. This suggests that the current recipe of proposing new automatic evaluation metrics for NLG by showing that they correlate well with scores assigned by humans for a single criteria (overall quality) alone is inadequate. Indeed, our extensive study involving 25 automatic evaluation metrics across 6 different tasks and 18 different evaluation criteria shows that there is no single metric which correlates well with human scores on all desirable criteria, for most NLG tasks. Given this situation, we propose CheckLists for better design and evaluation of automatic metrics. We design templates which target a specific criteria (e.g., coverage) and perturb the output such that the quality gets affected only along this specific criteria (e.g., the coverage drops). We show that existing evaluation metrics are not robust against even such simple perturbations and disagree with scores assigned by humans to the perturbed output. The proposed templates thus allow for a fine-grained assessment of automatic evaluation metrics exposing their limitations and will facilitate better design, analysis and evaluation of such metrics. Our templates and code are available at https://iitmnlp.github.io/EvalEval/
Revealing and Protecting Labels in Distributed Training

Distributed learning paradigms such as federated learning often involve transmission of model updates, or gradients, over a network, thereby avoiding transmission of private data. However, it is possible for sensitive information about the training data to be revealed from such gradients. Prior works have demonstrated that labels can be revealed analytically from the last layer of certain models (e.g., ResNet), or they can be reconstructed jointly with model inputs by using Gradients Matching [Zhu et al'19] with additional knowledge about the current state of the model. In this work, we propose a method to discover the set of labels of training samples from only the gradient of the last layer and the id to label mapping. Our method is applicable to a wide variety of model architectures across multiple domains. We demonstrate the effectiveness of our method for model training in two domains - image classification, and automatic speech recognition. Furthermore, we show that existing reconstruction techniques improve their efficacy when used in conjunction with our method. Conversely, we demonstrate that gradient quantization and sparsification can significantly reduce the success of the attack.
Blindness to Modality Helps Entailment Graph Mining

Understanding linguistic modality is widely seen as important for downstream tasks such as Question Answering and Knowledge Graph Population. Entailment Graph learning might also be expected to benefit from attention to modality. We build Entailment Graphs using a news corpus filtered with a modality parser, and show that stripping modal modifiers from predicates in fact increases performance. This suggests that for some tasks, the pragmatics of modal modification of predicates allows them to contribute as evidence of entailment.
The Impact of Text Normalization on Multiword Expressions Discovery in Persian

This paper evaluates normalization procedures of Persian text for a downstream NLP task -multiword expressions (MWEs) discovery.We discuss the challenges the Persian language poses for NLP and evaluate open-source tools that try to address these difficulties.The best-performing tool is later used in the main task -MWEs discovery.In order to discover MWEs, we use association measures and a subpart of the MirasText corpus.The results show that an F-score is 26% higher in the case of normalized input data.
On the Role of Corpus Ordering in Language Modeling

Language models pretrained on vast corpora of unstructured text using self-supervised learning framework are used in numerous natural language understanding and generation tasks. Many studies show that language acquisition in humans follows a rather structured simple-to-complex pattern and guided by this intuition, curriculum learning, which enables training of computational models in a meaningful order, such as processing easy samples before hard ones, has been shown to potentially reduce training time. The question remains whether curriculum learning can benefit pretraining of language models. In this work, we perform comprehensive experiments involving multiple curricula strategies varying the criteria for complexity and the training schedules. Empirical results of training transformer language models on English corpus and evaluating it intrinsically as well as after fine-tuning across eight tasks from the GLUE benchmark, show consistent improvement gains over conventional vanilla training. Interestingly, in our experiments, when evaluated on one epoch, the best model following a document-level hard-to-easy curriculum, outperforms the vanilla model by 1.7 points (average GLUE score) and it takes the vanilla model twice as many training steps to reach comparable performance.
Attention Weights in Transformer NMT Fail Aligning Words Between Sequences but Largely Explain Model Predictions

This work proposes an extensive analysis of the Transformer architecture in the Neural Machine Translation (NMT) setting. Focusing on the encoder-decoder attention mechanism, we prove that attention weights systematically make alignment errors by relying mainly on uninformative tokens from the source sequence. However, we observe that NMT models assign attention to these tokens to regulate the contribution in the prediction of the two contexts, the source and the prefix of the target sequence. We provide evidence about the influence of wrong alignments on the model behavior, demonstrating that the encoder-decoder attention mechanism is well suited as an interpretability method for NMT. Finally, based on our analysis, we propose methods that largely reduce the word alignment error rate compared to standard induced alignments from attention weights.
Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution Methods

Despite the widespread use of Knowledge Graph Embeddings (KGE), little is known about the security vulnerabilities that might disrupt their intended behaviour. We study data poisoning attacks against KGE models for link prediction. These attacks craft adversarial additions or deletions at training time to cause model failure at test time. To select adversarial deletions, we propose to use the model-agnostic instance attribution methods from Interpretable Machine Learning, which identify the training instances that are most influential to a neural model’s predictions on test instances. We use these influential triples as adversarial deletions. We further propose a heuristic method to replace one of the two entities in each influential triple to generate adversarial additions. Our experiments show that the proposed strategies outperform the state-of-art data poisoning attacks on KGE models and improve the MRR degradation due to the attacks by up to 62% over the baselines.
Implementation of Analytical Hierarchy Process (AHP) in Accounting Software Selection Preferences in Corporate Financial Management; Case at PT Tribudhi Pelita Indonesia, Jakarta

The existing problem  in the object of research is that the accounting system is not optimal in its financial management. So this study aims to analyze the Preference for Selection of Accounting Software in Corporate Financial Management; Case at PT Tribudhi Pelita Indonesia, Jakarta. By involving Finance Managers and Finance Staff as users of accounting software. The three accounting software that will be compared are; Accurate Software (Y1), Zahir Accounting Software (Y2) and MYOB (Y3). While the assessment indicators refer to Mutiara (2016) research, in her research on Information System Quality (X1), Information Result Quality (X2), and Perceived Usefulness (X3). Technical analysis using the Analytical Hierarchy Process (AHP). The finding of this research are; 1) the accounting software considered the best by PT Tribudhi Pelita Indonesia's financial managers are Accurate Software (Y1), Zahir Accounting Software (Y2) and MYOB (Y3), 2) The quality of the software (X1) criteria, Zahir is considered the best. 3) The criteria for quality of the result (X2) Accurate are considered the best and also Perceived Usefulness (X3) Accurate are also considered the best.
Sparse Quadratic Optimisation over the Stiefel Manifold with Application to Permutation Synchronisation.

We address the non-convex optimisation problem of finding a sparse matrix on the Stiefel manifold (matrices with mutually orthogonal columns of unit length) that maximises (or minimises) a quadratic objective function. Optimisation problems on the Stiefel manifold occur for example in spectral relaxations of various combinatorial problems, such as graph matching, clustering, or permutation synchronisation. Although sparsity is a desirable property in such settings, it is mostly neglected in spectral formulations since existing solvers, e.g. based on eigenvalue decomposition, are unable to account for sparsity while at the same time maintaining global optimality guarantees. We fill this gap and propose a simple yet effective sparsity-promoting modification of the Orthogonal Iteration algorithm for finding the dominant eigenspace of a matrix. By doing so, we can guarantee that our method finds a Stiefel matrix that is globally optimal with respect to the quadratic objective function, while in addition being sparse. As a motivating application we consider the task of permutation synchronisation, which can be understood as a constrained clustering problem that has particular relevance for matching multiple images or 3D shapes in computer vision, computer graphics, and beyond. We demonstrate that the proposed approach outperforms previous methods in this domain.