Reviewerly/exHarmony huggingface dataset:
Elastic Weight Consolidation Continual Learning Based Signal Detection in Multiple Channel MIMO System

In the multi-input multi-output (MIMO) signal detection based on deep learning, there is a wireless channel overtraining problem, that is, the signal detection network generally only remembers a single wireless channel model. An artificial neural network (ANN)-assisted receiver trained for a specific channel model is often not suitable for another channel model. In mobile communication, the wireless channel is greatly randomly affected by the wireless propagation environment. Variations in channel parameters will change the channel model, and this makes the signal detector based on deep learning use limited and poor robustness. In this paper, a continual learning method based on elastic weight consolidation(EWCCL) is proposed to solve the issue. The basic idea of this paper is to calculate the importance of each parameter to the task through the EWCCL method after the completion of one training. Then, in the next training, regular terms are added into the loss function to avoid the result of the last training being completely covered due to excessive deviation of these parameters. This method can make the neural network memorize the training results of multiple wireless channels, so it has stronger robustness to the transformation of wireless channels, and also reduces the cost of retraining. Finally, we verified this hypothesis through simulation experiments. The simulation experiment results show that the EWCCL method proposed in this paper has better detection performance and better robustness than the fine-tune method.
Recognizing and Splitting Conditional Sentences for Automation of Business Processes Management

Business Process Management (BPM) is the discipline which is responsible for management of discovering, analyzing, redesigning, monitoring, and controlling business processes.One of the most crucial tasks of BPM is discovering and modelling business processes from text documents.In this paper, we present our system that resolves an end-toend problem consisting of 1) recognizing conditional sentences from technical documents, 2) finding boundaries to extract conditional and resultant clauses from each conditional sentence, and 3) categorizing resultant clause as Action or Consequence which later helps to generate new steps in our business process model automatically.We created a new dataset and three models to solve this problem.Our best model achieved very promising results of 83.82, 87.84, and 85.75 for Precision, Recall, and F1, respectively, for extracting Condition, Action, and Consequence clauses using Exact Match metric.
Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation

Despite recent progress, state-of-the-art question answering models remain vulnerable to a variety of adversarial attacks. While dynamic adversarial data collection, in which a human annotator tries to write examples that fool a model-in-the-loop, can improve model robustness, this process is expensive which limits the scale of the collected data. In this work, we are the first to use synthetic adversarial data generation to make question answering models more robust to human adversaries. We develop a data generation pipeline that selects source passages, identifies candidate answers, generates questions, then finally filters or re-labels them to improve quality. Using this approach, we amplify a smaller human-written adversarial dataset to a much larger set of synthetic question-answer pairs. By incorporating our synthetic data, we improve the state-of-the-art on the AdversarialQA dataset by 3.7F1 and improve model generalisation on nine of the twelve MRQA datasets. We further conduct a novel human-in-the-loop evaluation and show that our models are considerably more robust to new human-written adversarial examples: crowdworkers can fool our model only 8.8% of the time on average, compared to 17.6% for a model trained without synthetic data.
Meaning deviation through a mispronunciation of some words during communicative practices in Bukavu Swahili

This study investigates the way some speakers of Bukavu Swahili in Bukavu transform the message due to a mispronunciation of some words during communication. The present study aims at understanding, how, why and when some word meanings are deviated by these speakers. The mispronunciation of some words in Bukavu Swahili creates a deviation of their meanings. This is done due to the fact that by mispronouncing a word, a different word is born which puts the listener into confusion. Thence a different message is conveyed contrary to what the speaker wanted to give. During this investigation, we noticed that deviations in Bukavu Swahili can be analysed through two different factors, namely intentional and non intentional factors. For intentional factors we identified deviations related to comic usage of Bukavu Swahili whereas non intentional factors, consisted of deviations related to the origin of the speaker and others related to word confusing through imitation. To carry out this investigation we used the interview and documentation as major methods while observation, discussion and comparison helped as major techniques.
Revisiting Runtime Dynamic Optimization for Join Queries in Big Data Management Systems

Query Optimization remains an open problem for Big Data Management Systems. Traditional optimizers are cost-based and use statistical estimates of intermediate result cardinalities to assign costs and pick the best plan. However, such estimates tend to become less accurate because of filtering conditions caused either from undetected correlations between multiple predicates local to a single dataset, predicates with query parameters, or predicates involving user-defined functions (UDFs). Consequently, traditional query optimizers tend to ignore or miscalculate those settings, thus leading to suboptimal execution plans. Given the volume of today’s data, a suboptimal plan can quickly become very inefficient. In this work, we revisit the old idea of runtime dynamic optimization and adapt it to a shared-nothing distributed database system, AsterixDB. The optimization runs in stages (re-optimization points), starting by first executing all predicates local to a single dataset. The intermediate result created from each stage is used to re-optimize the remaining query. This re-optimization approach avoids inaccurate intermediate result cardinality estimations, thus leading to much better execution plans. While it introduces the overhead for materializing these intermediate results, our experiments show that this overhead is relatively small and it is an acceptable price to pay given the optimization benefits. In fact, our experimental evaluation shows that runtime dynamic optimization leads to much better execution plans as compared to the current default AsterixDB plans as well as to plans produced by static cost-based optimization (i.e. based on the initial dataset statistics) and other state-of-the-art approaches.
Introducing strategic drivers of innovative ideas in active small and medium-sized enterprises of different technological fields using a fuzzy cognitive map

Idea generators and investors are always interested in advancing innovative ideas and generating wealth as a result of them. One of the gaps in previous research in this field is the failure to pay attention to different criteria and strategic paths in advancing innovative ideas from various fields. This research introduced a strategy for advancing innovative ideas and identifying the strategic drivers of small and medium-sized enterprises (SMEs) in various technological fields. The indicators were derived from the literature review and expert interviews, and they were chosen using a checklist and interviews to determine their relationships. The fuzzy cognitive map in question was then created using the FCMapper software. Strategic driver indicators were identified as in advancing innovative ideas. Thus, it is recommended that idea developers consider these indicators when presenting ideas and that experts and investors consider them when evaluating innovative ideas. According to the findings, The Science and Technology Park and its ten active fields each have their own strategic paths for advancing innovative ideas.
Combiner: Full Attention Transformer with Sparse Computation Cost

Transformers provide a class of expressive architectures that are extremely effective for sequence modeling. However, the key limitation of transformers is their quadratic memory and time complexity $\mathcal{O}(L^2)$ with respect to the sequence length in attention layers, which restricts application in extremely long sequences. Most existing approaches leverage sparsity or low-rank assumptions in the attention matrix to reduce cost, but sacrifice expressiveness. Instead, we propose Combiner, which provides full attention capability in each attention head while maintaining low computation and memory complexity. The key idea is to treat the self-attention mechanism as a conditional expectation over embeddings at each location, and approximate the conditional distribution with a structured factorization. Each location can attend to all other locations, either via direct attention, or through indirect attention to abstractions, which are again conditional expectations of embeddings from corresponding local regions. We show that most sparse attention patterns used in existing sparse transformers are able to inspire the design of such factorization for full attention, resulting in the same sub-quadratic cost ($\mathcal{O}(L\log(L))$ or $\mathcal{O}(L\sqrt{L})$). Combiner is a drop-in replacement for attention layers in existing transformers and can be easily implemented in common frameworks. An experimental evaluation on both autoregressive and bidirectional sequence tasks demonstrates the effectiveness of this approach, yielding state-of-the-art results on several image and text modeling tasks.
독자변인을 적용한 영어와 국어 읽기능력 진단체계의 독서지수 상관관계 분석

The purpose of this study is to examine the correlation between reading skills for elementary school students with fluent English and Korean reading skills. The subjects of the study are 25 elementary school 3rd∼6th graders participating in the English Village reading camp. The students read storybooks 12 times a quarter, four hours a week, on variety of topics appropriate for their level. The English reading diagnostic test was measured with the Acadience Reading K-6 assessment, and the Korean was applied with the Reading Ocean, Korean reading diagnostic test. The results of the study are as follows. First, overall, Korean reading competency was higher than English reading competency in the reading index. By level of evaluation, there was a greater gap in English reading competency regardless of grade. Second, the correlation of the two diagnostic domains at different levels of evaluation showed a relatively high correlation at the G2 level. Third, the analysis of the total score showed that the higher the level of Korean reading competency, the greater the level of English reading competency. Based on the research results, the author proposes the academic and educational implications necessary for the development of an English reading diagnostic system.
Cross-Domain Label-Adaptive Stance Detection

Stance detection concerns the classification of a writer’s viewpoint towards a target. There are different task variants, e.g., stance of a tweet vs. a full article, or stance with respect to a claim vs. an (implicit) topic. Moreover, task definitions vary, which includes the label inventory, the data collection, and the annotation protocol. All these aspects hinder cross-domain studies, as they require changes to standard domain adaptation approaches. In this paper, we perform an in-depth analysis of 16 stance detection datasets, and we explore the possibility for cross-domain learning from them. Moreover, we propose an end-to-end unsupervised framework for out-of-domain prediction of unseen, user-defined labels. In particular, we combine domain adaptation techniques such as mixture of experts and domain-adversarial training with label embeddings, and we demonstrate sizable performance gains over strong baselines, both (i) in-domain, i.e., for seen targets, and (ii) out-of-domain, i.e., for unseen targets. Finally, we perform an exhaustive analysis of the cross-domain results, and we highlight the important factors influencing the model performance.
Counter-Contrastive Learning for Language GANs.

Generative Adversarial Networks (GANs) have achieved great success in image synthesis, but have proven to be difficult to generate natural language. Challenges arise from the uninformative learning signals passed from the discriminator. In other words, the poor learning signals limit the learning capacity for generating languages with rich structures and semantics. In this paper, we propose to adopt the counter-contrastive learning (CCL) method to support the generator’s training in language GANs. In contrast to standard GANs that adopt a simple binary classifier to discriminate whether a sample is real or fake, we employ a counter-contrastive learning signal that advances the training of language synthesizers by (1) pulling the language representations of generated and real samples together and (2) pushing apart representations of real samples to compete with the discriminator and thus prevent the discriminator from being overtrained. We evaluate our method on both synthetic and real benchmarks and yield competitive performance compared to previous GANs for adversarial sequence generation.
Sociolectal Analysis of Pretrained Language Models.

Using data from English cloze tests, in which subjects also self-reported their gender, age, education, and race, we examine performance differences of pretrained language models across demographic groups, defined by these (protected) attributes. We demonstrate wide performance gaps across demographic groups and show that pretrained language models systematically disfavor young non-white male speakers; i.e., not only do pretrained language models learn social biases (stereotypical associations) – pretrained language models also learn sociolectal biases, learning to speak more like some than like others. We show, however, that, with the exception of BERT models, larger pretrained language models reduce some the performance gaps between majority and minority groups.
Conceptual Design of Service-Based Geospatial Information System

At the present time, geospatial information systems are widely used for diverse spatial related decision-making processes.The power of these systems comes from the ability to relate different information in a spatial context, placing the information at a given reference system, and reaching conclusions about the relationships.Different types of such systems are designed at national, regional and local level considering the objectives of the target projects or purposes of the implementing agencies.Database structures of the systems can be extremely complex or very simple depending upon the functions of the organizations and user requirements.The aim of this study is to describe a conceptual framework and system design related to the service-based geospatial information system in Mongolia.
Making It Local: Developing a Local Usage Collection System

Presentation on the creation of a locally-based system for collecting and displaying electronic resource usage data at the UNT Libraries. The process is discussed in-depth with goals, next steps, and links to tools and documentation. It was presented at the Electronic Resources & Libraries 2020 conference held in Austin, Texas.
A Study on the Progress of Bit-Coin Crypto Currency and Its Scope & Challenges

Since the inception of Bitcoin, it has been able to manage a significant progress in short span of time. The volatile progress of Bitcoin is in a pick time now. Consideration of Bitcoin has not been welcomed yet in all the countries of the world. But, in the financial market its visibility and uses are very much seen. This is a very interesting issue, where a substitute currency emerging and running parallel without proper support of government. Considering this issue in the financial market, present study has tried to narrate the progress of Bitcoin currency. Not so many studies can be found in this area or topic. Previous studies have emphasized on the progress and volatility and on some selected factors influenced Bitcoin performance. Here, data are taken from January, 2015 to November, 2020 of different web sites as needed. Level one ARCH model is formed and it has been found that the regression coefficient of gold and crude oil price have significantly negative relationship with Bitcoin price. A considerable increasing trend is also found in the volume of Bitcoin transaction. This is showing that Bitcoin may be a significant rival against other currencies. Government should consider this early in its policy formulation. The negative impact like bubble effect may also expected if not controlled it properly in time.
Oral Communication Strategies Training: The Case of Pre-Service English Teachers in Indonesia

Background:&#x0D; Many researches on pre-service English teachers have been carried out in various aspects, from teaching techniques to psychology. However, research on oral communication strategies of pre-service English teachers is still little done. This study delved into the effect of oral communication strategies training on pre-service English teachersâ€™ speaking performance. In addition, it revealed the relationship between gender and types of oral communication strategies.&#x0D; Methodology: &#x0D; This study involved 27 students who joined in Speaking 3 course. The OCSI as the instrument in this study consisted of eight categories of strategies for coping with speaking problems and seven types for dealing with listening problems&#x0D; Findings: &#x0D; Results from descriptive statistics showed that before the training, message reduction mainly was used while social affective and attempt to think in English was the least often employed strategy for resolving speaking difficulties. Meanwhile, after the training, a Non-verbal strategy while speaking was frequently used by the students. Message abandonment became the least used strategy after training was implemented. In dealing with listening problems, before the training, Non-Verbal strategies while listening was mostly used while the least used strategy was getting the gist. After the training, Word oriented became the most often employed technique Meanwhile, strategies for preserving fluency were the least frequently adopted.&#x0D; Conclusion: &#x0D; The MANOVA results of data analysis revealed that female students significantly performed better in speaking English than male students. However, there was no significant difference in Oral Communication Strategy used between male and female students.
SMACE: A New Method for the Interpretability of Composite Decision Systems

Interpretability is a pressing issue for decision systems. Many post hoc methods have been proposed to explain the predictions of a single machine learning model. However, business processes and decision systems are rarely centered around a unique model. These systems combine multiple models that produce key predictions, and then apply rules to generate the final decision. To explain such decisions, we propose the Semi-Model-Agnostic Contextual Explainer (SMACE), a new interpretability method that combines a geometric approach for decision rules with existing interpretability methods for machine learning models to generate an intuitive feature ranking tailored to the end user. We show that established model-agnostic approaches produce poor results on tabular data in this setting, in particular giving the same importance to several features, whereas SMACE can rank them in a meaningful way.
Google Lit Trips in the Classroom

This free online application combines Google Earth and Google Street View as well as presentation slides focussed on literary works
EVALUATING ENGLISH TRAINING PROGRAM FOR THE LOCAL PEOPLE OF KAMPUNG NG INGGRIS KEBUMEN BASED ON CIPP

Evaluating a program is an important process by which program administrators cansee the program effectiveness and make right decision about the program. This studywas to evaluate the effectiveness of the English training program for the local people ofKampungInggrisKebumen (KIK) at Jatijajar village, Ayah, Kebumen. The evaluationwas conducted qualitatively by using CIPP evaluation model which focused onevaluating contexts, inputs, processes, and products of the program. The researchinvolved the students, the tutors, and the chief of the program. Data were obtainedthrough observation, in depth interview, and analysis of the documents gained. Thedata were analyzed through four steps, namely: 1) Data collecting, 2) Data reducing,3) Data serving; and, 4) Concluding or verifying. Data sources triangulation was usedfor data validating. The research discovered that the program was ineffective due tofour main factors. First, the context of teaching-learning was less supportive to theprogram success. Second, the inputs were in lack of quality. Third, the teachinglearningprocess did not show good teaching practices. Fourth, the course products didnot reflect the program goals.This proves Dunkin’s and Biddle’s theory (1974) thatcontext, presage, process, and product variables are interrelated to affect the successof teaching-learning for a course program.
“It doesn’t look good for a date”: Transforming Critiques into Preferences for Conversational Recommendation Systems

Conversations aimed at determining good recommendations are iterative in nature. People often express their preferences in terms of a critique of the current recommendation (e.g., “It doesn’t look good for a date”), requiring some degree of common sense for a preference to be inferred. In this work, we present a method for transforming a user critique into a positive preference (e.g., “I prefer more romantic”) in order to retrieve reviews pertaining to potentially better recommendations (e.g., “Perfect for a romantic dinner”). We leverage a large neural language model (LM) in a few-shot setting to perform critique-to-preference transformation, and we test two methods for retrieving recommendations: one that matches embeddings, and another that fine-tunes an LM for the task. We instantiate this approach in the restaurant domain and evaluate it using a new dataset of restaurant critiques. In an ablation study, we show that utilizing critique-to-preference transformation improves recommendations, and that there are at least three general cases that explain this improved performance.
Curves on Frobenius classical surfaces in $\mathbb{P}^{3}$ over finite fields

We give an upper bound on the number of rational points on an irreducible curve $C$ of degree $\delta $ defined over a finite field $\mathbb F_q$ lying on a Frobenius classical surface $S$ embedded in $\mathbb P^3$. This leads us to investigate arithmetic