Reviewerly/exHarmony huggingface dataset:
TRACE: A Fast Transformer-based General-Purpose Lossless Compressor

Deep-learning-based compressor has received interests recently due to much improved compression ratio. However, modern approaches suffer from long execution time. To ease this problem, this paper targets on cutting down the execution time of deep-learning-based compressors. Building history-dependencies sequentially (e.g., recurrent neural networks) is responsible for long inference latency. Instead, we introduce transformer into deep learning compressors to build history-dependencies in parallel. However, existing transformer is too heavy in computation and incompatible to compression tasks.
Explainable Neural Rule Learning

Although neural networks have achieved great successes in various machine learning tasks, people can hardly know what neural networks learn from data due to their black-box nature. The lack of such explainability is one of the limitations of neural networks when applied in domains, e.g., healthcare and finance, that demand transparency and accountability. Moreover, explainability is beneficial for guiding a neural network to learn the causal patterns that can extrapolate out-of-distribution (OOD) data, which is critical in real-world applications and has surged as a hot research topic.
Fostering Engagement of Underserved Communities with Credible Health Information on Social Media

The COVID-19 pandemic has necessitated rapid top-down dissemination of reliable and actionable information. This presents unique challenges in engaging low-literate communities that live in poverty and lack access to the Internet. We describe the design and deployment of a voice-based social media platform, accessible over simple phones, for actively engaging such communities in Pakistan with reliable COVID information. We developed three strategies to overcome users’ hesitation, mistrust, and skepticism in engaging with COVID content. Users were: (1) encouraged to listen to reliable COVID advisory, (2) incentivized to share authentic content with others, and (3) prompted to critically think about COVID-related information behaviors. Using a mixed-methods evaluation, we show that users approached with all three strategies had a significantly higher engagement with COVID content compared to others. We discuss how new designs of social media can enable users to engage with and propagate authentic information.
Research on the Value Orientation of ELT Integration Based on Data Mining under the Background of Megadata

The research on the value orientation of ELT integration reflects the significance in the implementation and teaching of English courses. Based on the continuous development of metadata technology, this paper studies the value orientation of ELT integration based on DM. This paper interprets the characteristics and applications of megadata and analyzes the multiple challenges faced by ELT in the era of megadata. It also explores the value orientation of teaching integration in the era of megadata-data-driven teaching, which promotes scientific teaching decision-making, refined management, personalized learning, and teaching informationization. Depending on discussing the value of the English curriculum, this paper probes into the value orientation of English curriculum objectives, the value orientation of English curriculum content, the value orientation of students to the English curriculum, and the value orientation of ELT integration. This paper investigates and analyzes the realization basis of the integrative value orientation of ELT. Making full use of teaching resources may benefit not only the deep integration of instructional materials but also the scientific application of current scientific and technology methods to support ELT’s integrated value orientation. The essential significance of the research is to improve students’ comprehensive English literacy and comprehensively promote the realization of ELT value.
DUET: A Generic Framework for Finding Special Quadratic Elements in Data Streams

Finding special items, like heavy hitters, top-k, and persistent items, has always been a hot issue in data stream processing for web analysis. While data streams nowadays are usually high-dimensional, most prior works focus on special items according to a certain primary dimension and yield little insight into the correlations between dimensions. Therefore, we propose to find special quadratic elements to reveal close correlations. Based on the items mentioned above, we extend our problem to three applications related to heavy hitters, top-k, and persistent items, and design a generic framework DUET to process them. Besides, we analyze the error bound of our algorithm and conduct extensive experiments on four data sets. Our experimental results show that DUET can achieve 3.5 times higher throughput and three orders of magnitude lower average relative error compared with cutting-edge algorithms.
Testability of Instrumental Variables in Linear Non-Gaussian Acyclic Causal Models

This paper investigates the problem of selecting instrumental variables relative to a target causal influence X→Y from observational data generated by linear non-Gaussian acyclic causal models in the presence of unmeasured confounders. We propose a necessary condition for detecting variables that cannot serve as instrumental variables. Unlike many existing conditions for continuous variables, i.e., that at least two or more valid instrumental variables are present in the system, our condition is designed with a single instrumental variable. We then characterize the graphical implications of our condition in linear non-Gaussian acyclic causal models. Given that the existing graphical criteria for the instrument validity are not directly testable given observational data, we further show whether and how such graphical criteria can be checked by exploiting our condition. Finally, we develop a method to select the set of candidate instrumental variables given observational data. Experimental results on both synthetic and real-world data show the effectiveness of the proposed method.
Identifying Source-Language Dialects in Translation

In this paper, we aim to explore the degree to which translated texts preserve linguistic features of dialectal varieties. We release a dataset of augmented annotations to the Proceedings of the European Parliament that cover dialectal speaker information, and we analyze different classes of written English covering native varieties from the British Isles. Our analyses aim to discuss the discriminatory features between the different classes and to reveal words whose usage differs between varieties of the same language. We perform classification experiments and show that automatically distinguishing between the dialectal varieties is possible with high accuracy, even after translation, and propose a new explainability method based on embedding alignments in order to reveal specific differences between dialects at the level of the vocabulary.
Fully Decentralized Multiagent Communication via Causal Inference

Many real-world tasks can be cast into multiagent (MA) reinforcement learning problems, and most algorithms in this field obey to the centralized learning and decentralized execution framework. However, enforcing centralized learning is impractical in many scenarios. Because it requires integrating the information from agents, while agents may not hope to share local information due to the issue of privacy. Thus, this article proposes a novel approach to achieve fully decentralized learning based on communication among multiple agents via reinforcement learning. Benefiting from causality analysis, an agent will choose the counterfactual that has the most significant influence on communication information of others. We find that this method can be applied in classic or complex MA scenarios and in federated learning domains, which are now attracting much attention.
Research on Cultural Translation Based on Neural Network

The translation of pragmatic meaning requires the processing of pragmatic implicit meaning, the translator’s responsibility for the readers of the target language to understand the pragmatic meaning, and the domestication and foreignization of cultural images. The way of pragmatic language equivalence is domestication translation to achieve the fluency of language expression. The creation of foreignized translation in order to foster the equitable interchange and dissemination of culture, which is precisely the aim of cultural translation, is the equivalence of the pragmatic meaning of cultural imagery. Neural network (NN) machine translation technology (MTT) simulates the human brain nervous system. Based on deep learning technology, the whole sentence is used as the basic unit of translation, which greatly improves the accuracy of machine translation. This paper selects the relevant texts of Shaanxi’s red tourism culture and uses the NN translation system to translate. It helps to improve the accuracy of the text translation of Shaanxi red tourism culture by analyzing the translation and providing a reference direction for further revision by neural translation users and promote the overseas dissemination of red culture. The part of the source language side that generates a vector representation of a dense and continuous sentence from the input corresponds to the encoder.
What’s in an Index: Extracting Domain-specific Knowledge Graphs from Textbooks

A typical index at the end of a textbook contains a manually-provided vocabulary of terms related to the content of the textbook. In this paper, we extend our previous work on extraction of knowledge models from digital textbooks. We are taking a more critical look at the content of a textbook index and present a mechanism for classifying index terms according to their domain specificity: a core domain concept, an in-domain concept, a concept from a related domain, and a concept from a foreign domain. We link the extracted models to DBpedia and leverage the aggregated linguistic and structural information from textbooks and DBpedia to construct and prune the domain-specific knowledge graphs. The evaluation experiments demonstrate (1) the ability of the approach to identify (with high accuracy) different levels of domain specificity for automatically extracted concepts, (2) its cross-domain robustness, and (3) the added value of the domain specificity information. These results clearly indicate the improved quality of the refined knowledge graphs and widen their potential applicability.
Optimization-based Model Predictive Tube Control for Autonomous Ground Vehicles with Minimal Tuning Parameters

The trajectory planning and tracking problem are critical points of intelligent vehicles concerning their safety and stability. If these parts are separated, interactions should be made between them, especially when sudden changes and disturbances appear. This paper presents a method that integrates the two parts using a cascade structure. Additionally, the proposed method deals with the interaction between the lateral and the longitudinal trajectory based on dynamical considerations. The whole problem is handled using the model predictive method based on online optimization. This system receives the path borders as input and generates the control requests for the actuators on its output. The configuration space of the system can be maximized to gain stability by handling the lateral-longitudinal parts and the trajectory planning and tracking in one complex system. The main advantage of the proposed approach is that the optimization problem in the predictive method is formulated so that the path and dynamics are considered equality and inequality constraints, and the cost function includes only a physical phenomenon to be minimized without tuning parameters. The evaluation of the proposed algorithm is presented in this paper based on simulation and real-time measurements results.
Investigating Advertisers’ Domain-changing Behaviors and Their Impacts on Ad-blocker Filter Lists

Ad blockers heavily rely on filter lists to block ad domains, which can serve advertisements and trackers. However, recent research has reported that some advertisers keep registering replica ad domains (RAD domains)—new domains that serve the same purpose as the original ones—which tend to slip through ad-blocker filter lists. Although this phenomenon might negatively affect ad blockers’ effectiveness, no study to date has thoroughly investigated its prevalence and the issues caused by RAD domains. In this work, we proposed methods to discover RAD domains and categorized their change patterns. From a crawl of 50,000 websites, we identified 1,748 unique RAD domains, 1,096 of which survived for an average of 410.5 days before they were blocked; the rest have not been blocked as of February 2021. Notably, we found that non-blocked RAD domains could extend the timespan of ad or tracker distribution by more than two years. Our analysis further revealed a taxonomy of four techniques used to create RAD domains, including two less-studied ones. Additionally, we discovered that the RAD domains affected 10.2% of the websites we crawled, and 23.7% of the RAD domains exhibiting privacy-intrusive behaviors, undermining ad blockers’ privacy protection.
Stability analysis of proportional delayed projection neural network for quadratic programming problem

At present, projection neural network (PNN) with bounded time delay has been widely used for solving convex quadratic programming problem (QPP). However, there is little research concerning PNN with unbounded time delay. In this paper, we propose the proportional delayed PNN to solve QPP with equality constraints. By utilizing homeomorphism mapping principle, we prove the proportional delayed PNN exists with unique equilibrium point which is the optimal solution of QPP. Simultaneously, delay-dependent criteria about global exponential stability (GES) and global polynomial stability (GPS) are also acquired by applying the method of variation of constants and inequality techniques. On the other hand, when proportional delay factor [Formula: see text] is equal to 1, the proportional delayed PNN becomes the one without time delay which still can be utilized for solving QPP. But in most situations, [Formula: see text] is not equal to 1, and time delay is unpredictable and may be unbounded in the actual neural network, which causes instability of system. Therefore, it is necessary to consider proportional delayed PNN. A numerical example demonstrates that, compared with the proportional delayed Lagrange neural network, the proportional delayed PNN is faster in terms of convergence rate. The possible reason is that appropriate parameters make the model converge to the equilibrium point along the direction of gradient descent.
Fair k-Center Clustering in MapReduce and Streaming Settings

Center-based clustering techniques are fundamental to many real-world applications such as data summarization and social network analysis. In this work, we study the problem of fairness aware k-center clustering over large datasets. We are given an input dataset comprising a set of n points, where each point belongs to a specific demographic group characterized by a protected attribute, such as race or gender. The goal is to identify k clusters such that all clusters have considerable representation from all groups and the maximum radius of these clusters is minimized.
A number theoretic view on binary shift registers

Abstract We describe a number theoretic view on binary shift registers. We illustrate this approach on some basic shift registers by revisiting known and obtaining new results, which we prove using tools from basic number theory, including modular arithmetic.
A Guided Topic-Noise Model for Short Texts

Researchers using social media data want to understand the discussions occurring in and about their respective fields. These domain experts often turn to topic models to help them see the entire landscape of the conversation, but unsupervised topic models often produce topic sets that miss topics experts expect or want to see. To solve this problem, we propose Guided Topic-Noise Model (GTM), a semi-supervised topic model designed with large domain-specific social media data sets in mind. The input to GTM is a set of topics that are of interest to the user and a small number of words or phrases that belong to those topics. These seed topics are used to guide the topic generation process, and can be augmented interactively, expanding the seed word list as the model provides new relevant words for different topics. GTM uses a novel initialization and a new sampling algorithm called Generalized Polya Urn (GPU) seed word sampling to produce a topic set that includes expanded seed topics, as well as new unsupervised topics. We demonstrate the robustness of GTM on open-ended responses from a public opinion survey and four domain-specific Twitter data sets.
Distributional Contrastive Embedding for Clarification-based Conversational Critiquing

Managing uncertainty in preferences is core to creating the next generation of conversational recommender systems (CRS). However, an often-overlooked element of conversational interaction is the role of clarification. Users are notoriously noisy at revealing their preferences, and a common error is being unnecessarily specific, e.g., suggesting ”chicken fingers” when a restaurant with a ”kids menu” was the intended preference. Correcting such errors requires reasoning about the level of generality and specificity of preferences and verifying that the user has expressed the correct level of generality. To this end, we propose a novel clarification-based conversational critiquing framework that allows the system to clarify user preferences as it accepts critiques. To support clarification, we propose the use of distributional embeddings that can capture the specificity and generality of concepts through distributional coverage while facilitating state-of-the-art embedding-based recommendation methods. Specifically, we incorporate Distributional Contrastive Embeddings of critiqueable keyphrases with user preference embeddings in a Variational Autoencoder recommendation framework that we term DCE-VAE. Our experiments show that our proposed DCE-VAE is (1) competitive in terms of general performance in comparison to state-of-the-art recommenders and (2) supports effective clarification-based critiquing in comparison to alternative clarification baselines. In summary, this work adds a new dimension of clarification to enhance the well-known critiquing framework along with a novel data-driven distributional embedding for clarification suggestions that significantly improves the efficacy of user interaction with critiquing-based CRSs.
Scheduling Virtual Conferences Fairly: Achieving Equitable Participant and Speaker Satisfaction

Recently, almost all conferences have moved to virtual mode due to the pandemic-induced restrictions on travel and social gathering. Contrary to in-person conferences, virtual conferences face the challenge of efficiently scheduling talks, accounting for the availability of participants from different timezones and their interests in attending different talks. A natural objective for conference organizers is to maximize efficiency, e.g., total expected audience participation across all talks. However, we show that optimizing for efficiency alone can result in an unfair virtual conference schedule, where individual utilities for participants and speakers can be highly unequal. To address this, we formally define fairness notions for participants and speakers, and derive suitable objectives to account for them. As the efficiency and fairness objectives can be in conflict with each other, we propose a joint optimization framework that allows conference organizers to design schedules that balance (i.e., allow trade-offs) among efficiency, participant fairness and speaker fairness objectives. While the optimization problem can be solved using integer programming to schedule smaller conferences, we provide two scalable techniques to cater to bigger conferences. Extensive evaluations over multiple real-world datasets show the efficacy and flexibility of our proposed approaches.
Measuring the Privacy vs. Compatibility Trade-off in Preventing Third-Party Stateful Tracking

Despite active privacy research on sophisticated web tracking techniques (e.g., fingerprinting, cache collusion, bounce tracking, CNAME cloaking), most tracking on the web is basic “stateful” tracking enabled by classical browser storage policies sharing per-site storage across all HTTP contexts. Alternative, privacy-preserving storage policies, especially for third-party contexts, have been proposed and even deployed, but these can break websites that presume traditional, non-partitioned storage. Such breakage discourages privacy-preserving experimentation, cementing the dismal status quo. Our work measures the privacy vs. compatibility trade-offs of representative third-party storage policies to enable design of browsers that are both compatible and privacy respecting. Our contributions include web-scale measurements of page behaviors under multiple third-party storage policies inspired by production browsers. We define metrics for measuring aggregate effects on web privacy and compatibility, including a novel system for quantitatively estimating aggregate website breakage under different policies. We find that making third-party storage partitioned by first-party, and lifetimes by site-session achieves the best privacy and compatibility trade-off. We provide complete measurement datasets and storage policy implementations.
Students Understanding of Earthquakes and Tsunamis in High Risk Areas

Population growth and spread have increased human exposure to natural hazards and potential disasters affecting people’s quality of life. This situation is especially manifested in marginalized or vulnerable areas. Moreover, within such vulnerable areas, children are especially affected, and are, at the same time, considered to be agents of change. However, children’s voices have been scarcely considered for disaster risk reduction planning, and science education has not widely addressed these ideas. This study explores the understanding of earthquakes and tsunamis by children living in high-risk areas of Chile during a learning unit and according to their geographical zone. The study was part of a context-based science education learning unit. One hundred and two students from four schools used explanations to draw and write the causes of the risk situation, revealing their understanding of each phenomenon. The results show most students attributed earthquakes to the Tectonic Plates Theory while holding ancient scientific ideas about tsunami causes, for example, some explanations were based on air pressures. More sophisticated reasoning was found at the end of the learning unit, advancing their understanding of hazards promoted by the science education of the learning unit. The relevance of context-based science education for disaster risk reduction is discussed, especially for the cases of children with animistic - transferring human needs and attributes to non-animated entities- or fatalist understandings of the phenomena. The scientific understanding perhaps promotes empowerment and action-based choices for safety. Furthermore, the argument for policy curriculum design in primary science education for mitigating disasters is discussed.