Reviewerly/exHarmony huggingface dataset:
Improving Maximum Likelihood Training for Text Generation with Density Ratio Estimation.

Auto-regressive sequence generative models trained by Maximum Likelihood Estimation suffer the exposure bias problem in practical finite sample scenarios. The crux is that the number of training samples for Maximum Likelihood Estimation is usually limited and the input data distributions are different at training and inference stages. Many method shave been proposed to solve the above problem (Yu et al., 2017; Lu et al., 2018), which relies on sampling from the non-stationary model distribution and suffers from high variance or biased estimations. In this paper, we propose{\psi}-MLE, a new training scheme for auto-regressive sequence generative models, which is effective and stable when operating at large sample space encountered in text generation. We derive our algorithm from a new perspective of self-augmentation and introduce bias correction with density ratio estimation. Extensive experimental results on synthetic data and real-world text generation tasks demonstrate that our method stably outperforms Maximum Likelihood Estimation and other state-of-the-art sequence generative models in terms of both quality and diversity.
Pre-training via Leveraging Assisting Languages for Neural Machine Translation

Sequence-to-sequence (S2S) pre-training using large monolingual data is known to improve performance for various S2S NLP tasks.However, large monolingual corpora might not always be available for the languages of interest (LOI).Thus, we propose to exploit monolingual corpora of other languages to complement the scarcity of monolingual corpora for the LOI.We utilize script mapping (Chinese to Japanese) to increase the similarity (number of cognates) between the monolingual corpora of helping languages and LOI.An empirical case study of low-resource Japanese-English neural machine translation (NMT) reveals that leveraging large Chinese and French monolingual corpora can help overcome the shortage of Japanese and English monolingual corpora, respectively, for S2S pre-training.Using only Chinese and French monolingual corpora, we were able to improve Japanese-English translation quality by up to 8.5 BLEU in lowresource scenarios.
Semi-Supervised Learning to Perceive Children's Affective States in a Tablet Tutor

Like good human tutors, intelligent tutoring systems should detect and respond to students' affective states. However, accuracy in detecting affective states automatically has been limited by the time and expense of manually labeling training data for supervised learning. To combat this limitation, we use semi-supervised learning to train an affective state detector on a sparsely labeled, culturally novel, authentic data set in the form of screen capture videos from a Swahili literacy and numeracy tablet tutor in Tanzania that shows the face of the child using it. We achieved 88% leave-1-child-out cross-validated accuracy in distinguishing pleasant, unpleasant, and neutral affective states, compared to only 61% for the best supervised learning method we tested. This work contributes toward using automated affect detection both off-line to improve the design of intelligent tutors, and at runtime to respond to student affect based on input from a user-facing tablet camera or webcam.
Digital Lusternik-Schnirelmann category of digital functions

Roughly speaking, the digital Lusternik-Schnirelmann category of digital images studies how far a digital image is away from being digitally contractible. The digital Lusternik-Schnirelmann category (digital LS category, for short) is defined in [A. Borat and T. Vergili, Digital Lusternik-Schnirelmann category, Turkish J. Math. 2018]. In this paper, we introduce the digital LS category of digital functions. We will give some basic properties and discuss how this new concept will behave if we change the adjacency relation in the domain and in the image of the digital function and discuss its relation with the digital LS category of a digital image.
#NotAWhore! A Computational Linguistic Perspective of Rape Culture and Victimization on Social Media

The recent surge in online forums and movements supporting sexual assault survivors has led to the emergence of a 'virtual bubble' where survivors can recount their stories.However, this also makes the survivors vulnerable to bullying, trolling and victim blaming.Specifically, victim blaming has been shown to have acute psychological effects on the survivors and further discourage formal reporting of such crimes.Therefore, it is important to devise computationally relevant methods to identify and prevent victim blaming to protect the victims.In our work, we discuss the drastic effects of victim blaming through a short case study and then propose a single step transferlearning based classification method to identify victim blaming language on Twitter.Finally, we compare the performance of our proposed model against various deep learning and machine learning models on a manually annotated domain-specific dataset.
Character Beliefs in Story Generation

Character beliefs play a central role in many narratives, but are often not represented in planning-based author-centered narrative generation systems, or only represented in an ad hoc way. In this paper we will discuss how actions in Dynamic Epistemic Logic that affect the story world as well as the characters' beliefs, can be used in the context of narrative generation. By using this logical foundation we can ensure that the actors' beliefs are maintained in a logically consistent way, but we will also discuss how our system supports writing these complex logical operations in a concise way. We will also show how our system, which has limited planning capabilities, can come up with simple stories that cause characters to have beliefs desired by the author, and how our approach can be integrated with other work in the field.
Think2Type: Thoughts to Text using EEG Waves

Visually impaired and differently abled people who can't use their hands have to depend on a helper when they wish to use a computer or a smartphone and they wish to enter any data.Depending on someone also causes privacy and security threats as all information including confidential information like passwords,etc also have to be shared so that the helper can write the data into the device.Think2Type is a BCI solution which will translate a user's active intent to text represented by morse code.BCI or 'Brain Computer Interface' is a technology that enables its users to interact with computers by means of brainactivity only, this activity being generally measured by ElectroEncephaloGraphy (EEG).Our solution helps them to be more independent as they merely have to think about the characters in the password and use active intent to enter them.The intents correspond to 'dot' and 'dash' of Morse code representation of alpha numerics.It will be converted to the corresponding text and provide them with complete confidentiality.
Validating multi-photon quantum interference with finite data

Abstract Multi-particle interference is a key resource for quantum information processing, as exemplified by Boson Sampling. Hence, given its fragile nature, an essential desideratum is a solid and reliable framework for its validation. However, while several protocols have been introduced to this end, the approach is still fragmented and fails to build a big picture for future developments. In this work, we propose an operational approach to validation that encompasses and strengthens the state of the art for these protocols. To this end, we consider the Bayesian hypothesis testing and the statistical benchmark as most favorable protocols for small- and large-scale applications, respectively. We numerically investigate their operation with finite sample size, extending previous tests to larger dimensions, and against two adversarial algorithms for classical simulation: the mean-field sampler and the metropolized independent sampler. To evidence the actual need for refined validation techniques, we show how the assessment of numerically simulated data depends on the available sample size, as well as on the internal hyper-parameters and other practically relevant constraints. Our analyses provide general insights into the challenge of validation, and can inspire the design of algorithms with a measurable quantum advantage.
Hubungan Antara Literasi Digital Dengan Prestasi Belajar Siswa Sma Negeri 6 Kota Bogor

Digital literacy is the ability to understand and use information in various forms from a variety of very broad sources that are accessed through computer devices. Learning achievement is the result of learning activities, namely the extent to which students master the material of the lesson being taught, followed by the emergence of feelings of satisfaction that he has done something well. The purpose of this study was to determine the relationship between digital literacy and student achievement in SMA Negeri 6 Bogor. Digital literacy consists of information competencies, communication competencies, content creation competencies, and security competencies. While learning achievement is measured by ranking. This study uses a quantitative approach with a correlation method. The population in this study were students of SMA Negeri 6 Bogor in class X and XI in 2018/2019 with sample criteria using probability sampling techniques. Data collection techniques in this study used questionnaires, observations, and literature. The analysis technique used is descriptive data analysis techniques and inferential analysis. The results and conclusion of this study indicate that there is a significant relationship between information competency, communication competency, content creation competency, and security competence with student learning achievement.
A Predictive Risk Model for Software Projects’ Requirement Gathering Phase

The initial stage of the software development lifecycle is the requirement gathering and analysis phase. Predicting risk at this phase is very crucial because cost and efforts can be saved while improving the quality and efficiency of the software to be developed. The datasets for software requirements risk prediction have been adopted in this paper to predict the risk levels across the software projects and to ascertain the attributes that contribute to the recognized risk in the software projects. A supervised machine learning technique was used to predict the risk across the projects using Naïve Bayes Classifier technique. The model was able to predict the risks across the projects and the performance metrics of the risk attributes were evaluated. The model predicted four (4) as Catastrophic, eleven (11) as High, eighteen (18) as Moderate, thirty-three (33) as Low and seven (7) as insignificant. The overall confusion matrix statistics on the risk levels prediction by the model had accuracy to be 98% with confidence interval (CI) of 95% and Kappa 97%.
Spatio-temporal visual analytics: a vision for 2020s

Visual analytics is a research discipline that is based on acknowledging the power and the necessity of the human vision, understanding, and reasoning in data analysis and problem solving.Visual analytics develops methods, analytical workflows, and software tools for analysing data of various types, particularly, spatio-temporal data, which can describe the processes going on in the environment, society, and economy.We briefly overview the achievements of the visual analytics research concerning spatio-temporal data analysis and discuss the major open problems.
Rception: Wide and Deep Interaction Networks for Machine Reading Comprehension (Student Abstract)

Most of models for machine reading comprehension (MRC) usually focus on recurrent neural networks (RNNs) and attention mechanism, though convolutional neural networks (CNNs) are also involved for time efficiency. However, little attention has been paid to leverage CNNs and RNNs in MRC. For a deeper understanding, humans sometimes need local information for short phrases, sometimes need global context for long passages. In this paper, we propose a novel architecture, i.e., Rception, to capture and leverage both local deep information and global wide context. It fuses different kinds of networks and hyper-parameters horizontally rather than simply stacking them layer by layer vertically. Experiments on the Stanford Question Answering Dataset (SQuAD) show that our proposed architecture achieves good performance.
Automated Generation of Conversational Non Player Characters

An integral part of social believability in role playing games is believability of non-player characters (NPC). In this paper we argue for the importance of believability in NPCs, even those that are completely outside of any pre-written quest or plot. We present NPCAgency, a system designed to generate many conversational NPCs as packaged narrative assets that can be shared and imported into various projects to increase story-world immersion. We believe such a system can help solve two problems. First, the authorial burden of the game designer is lessened, allowing renderings of large numbers of NPCs, each with their own unique background and conversation topics, all conforming to the norms of a predefined “universe”. Second, the immersive aspect of the game is heightened as the player can engage complex characters with lengthy dialogue affordances. We demonstrate the concept by generating fifty characters with attributes drawn from “Game of Thrones” (GOT) / “A Song of Ice and Fire” universe, and exporting them as Inform 7 code, a popular declarative interactive fiction (IF) programming language and authoring tool. A user study of thirty-seven Inform 7 programmers demonstrates that a 62% majority find the tool useful enough to consider for their own work. Further 70% said they would use the system to create “Game of Thrones” background characters for their own projects.
Effectively Aligning and Filtering Parallel Corpora under Sparse Data Conditions

Parallel corpora are key to developing good machine translation systems.However, abundant parallel data are hard to come by, especially for languages with a low number of speakers.When rich morphology exacerbates the data sparsity problem, it is imperative to have accurate alignment and filtering methods that can help make the most of what is available by maximising the number of correctly translated segments in a corpus and minimising noise by removing incorrect translations and segments containing extraneous data.This paper sets out a research plan for improving alignment and filtering methods for parallel texts in low-resource settings.We propose an effective unsupervised alignment method to tackle the alignment problem.Moreover, we propose a strategy to supplement state-of-theart models with automatically extracted information using basic NLP tools to effectively handle rich morphology.
Achieving forward unforgeability in keyword-field-free conjunctive search

It is commonsense that the data should be encrypted by searchable encryption before being outsourced to the cloud for its confidentiality and availability. Most of existing studies focus on forward privacy, we initiate the study of forward unforgeability, which defines the time periods when files can be uploaded while the forward privacy defines the time periods when files can be queried. For example in log-files management, we should block the attackers from revising the logs of previous time periods. We find that forward unforgeability is helpful in dealing with the problem. However, it is still challenging to achieve forward unforgeability in case of searchable encryption. In this paper, we formalize the key exposure problem in keyword-field-free conjunctive keyword search (KFF-CKS) and define the forward unforgeability of KFF-CKS formally. Then we design a general framework where any forward unforgeable KFF-CKS scheme can be applied there for practical use. After that, we construct a novel KFF-CKS scheme with forward unforgeability (FU-KFF-CKS) under this framework. Furthermore, we formally prove the security properties of our FU-KFF-CKS under random oracle model and compare our construction with previous ones in a theoretical view. Finally, we conduct extensive experiments and the results validate the effectiveness and efficiency of the proposed FU-KFF-CKS.
Quantized Frank-Wolfe: Faster Optimization, Lower Communication, and Projection Free

How can we efficiently mitigate the overhead of gradient communications in distributed optimization? This problem is at the heart of training scalable machine learning models and has been mainly studied in the unconstrained setting. In this paper, we propose Quantized-Frank-Wolfe (QFW), the first projection-free and communication-efficient algorithm for solving constrained optimization problems at scale. We consider both convex and non-convex objective functions, expressed as a finite-sum or more generally a stochastic optimization problem, and provide strong theoretical guarantees on the convergence rate of QFW. This is accomplished by proposing novel quantization schemes that efficiently compress gradients while controlling the noise variance introduced during this process. Finally, we empirically validate the efficiency of QFW in terms of communication and the quality of returned solution against natural baselines.
Towards an Accessible Interface for Story World Building

In order to use computational intelligence for automated narrative synthesis, domain knowledge of the story world must be defined, a task which is currently confined to experts. This paper discusses the benefits and tradeoffs between agent-centric and event-centric approaches towards authoring the domain knowledge of story worlds. In an effort to democratize story world creation, we present an accessible graphical platform for content creators and even end users to create their own story worlds, populate it with smart characters and objects, and define narrative events that can be used by existing tools for automated narrative synthesis. We demonstrate the potential of our system by authoring a simple bank robbery story world, and integrate it with existing solutions for event-centric planning to synthesize example digital stories.
The Use of Voca-Lens to Enhance the Students Vocabulary Repertoire

Education has shifted to the digital era. The primary pupils whom known as the alpha generation, grow up with technology, were no longer passive learners. Information and communications technology (ICT) is used as teaching tool in which claims to help in dissemination of learning contents. English language educators play the role to bridge the gap between ICT and English language teaching in order to optimize its benefits in education field. This current study which employs mixed method triangulation design model is intended to (1) enhance the repertoire of vocabulary and (2) reading motivation among primary pupils. A total of 56 respondents were chosen from 3 schools in Song and Mukah. The quantitative data was collected from pre and post tests of reading comprehension achievement test to answer first research question. Quantitative data from semantic differential scale questionnaire which comprises of 20 items was analysed to answer the second research question. It was also justified by qualitative data from semi structured interview. The positive findings from the three instruments showed the effectiveness of Voca- Lens. The innovation of Voca- Lens could be further utilized in the lesson if the availability of devices and internet connection were enhanced.
Evaluating Compositionality of Sentence Representation Models

We evaluate the compositionality of generalpurpose sentence encoders by proposing two metrics to quantify compositional understanding capability of sentence encoders.We introduce a novel metric, Polarity Sensitivity Scoring (PSS), which utilizes sentiment perturbations as a proxy for measuring compositionality.We then compare results from PSS with those obtained via our proposed extension of a metric called Tree Reconstruction Error (TRE) (Andreas, 2019) where compositionality is evaluated by measuring how well a true representation-producing model can be approximated by a model that explicitly combines representations of its primitives.
Topic Balancing with Additive Regularization of Topic Models

This article proposes a new approach for building topic models on unbalanced collections in topic modelling, based on the existing methods and our experiments with such methods.Real-world data collections contain topics in various proportions, and often documents of the relatively small theme become distributed all over the larger topics instead of being grouped into one topic.To address this issue, we design a new regularizer for Θ and Φ matrices in probabilistic Latent Semantic Analysis (pLSA) model.We make sure this regularizer increases the quality of topic models, trained on unbalanced collections.Besides, we conceptually support this regularizer by our experiments.
Financial development, economic growth and welfare: evidence from emerging countries

Purpose-This study investigates how financial development has affected the economic growth and welfare for four emerging economies, namely Brazil, Turkey, Hungary, and, Poland over the period 2000-2013.Methodology-In this research, we employed a panel dataset for the four countries using five different indicators for financial development separately.Which are (financial development index, domestic credit to the private sector, stock exchange market capitalization, and Lerner Index).Findings-The results confirmed that financial development has a positive impact on economic growth, and there is a difference between Turkey, Brazil, Poland, and Hungary in terms of the impact size of financial development on economic growth.The highest and most energetic effect was on Turkey then on Poland, then on Hungary, and finally on Brazil Conclusion-This study found that GDP growth is highly correlated with financial development.Thus, countries may undertake different steps to increase the efficiency of their financial sectors and therefore enhance their economic growth.
Symbolic Plan Recognition in Interactive Narrative Environments

Interactive narratives suffer from the narrative paradox: the tension that exists between providing a coherent narrative experience and allowing a player free reign over what she can manipulate in the environment. Knowing what actions a player in such an environment intends to carry out would help in managing the narrative paradox, since it would allow us to anticipate potential threats to the intended narrative experience and potentially mediate or eliminate them. The process of observing player actions and attempting to come up with an explanation for those actions (i.e. the plan that the player is trying to carry out) is the problem of plan recognition. We adopt the framing of narratives as plans and leverage recent advances that cast plan recognition as planning to develop a symbolic plan recognition system as a proof-of-concept model of a player's reasoning in an interactive narrative environment. In this paper we outline the system architecture, report on performance metrics that demonstrate adequate performance for non-trivial domains, and discuss the implications of treating players as plan recognizers.